I’m tired of seeing these LinkedIn influencers/ YouTube gurus bragging about their 12-agent swarms. Honestly, I used to be one of them.
#hallucination
362 items
Multi agent systems are a total nightmare in production (www.reddit.com) Grok 4.3 achieves higher overall intelligence over 4.20 with less of a cost, at the price of slightly higher hallucination rate. (x.com via reddit) xAI has launched Grok 4.3, achieving 53 on the Artificial Analysis Intelligence Index with improved agentic performance, ~40% lower input price, and ~60% lower output price than Grok 4.20 The release of Grok 4.3 places just above Muse Spar…
The Mushroom That Makes People Have the Exact Same Hallucination (www.vice.com via hn) Biologist Colin Domnauer is reopening an old case that Chinese health officials seem to have stopped caring about. Every summer, residents of the Yunnan province check into hospitals with complaints that they’re hallucinating tiny elflike…
Claude Opus 4.6 accuracy on BridgeBench hallucination test drops from 83% to 68% (www.reddit.com) Anthropic's flagship model just took a pretty significant accuracy hit on one of the most important AI benchmarks out there. So here's the deal: Claude Opus 4.6 was recently tested on BridgeBench, which specifically measures how often AI m…
The weirdest thing about AI agents is how human failure patterns start showing up (www.reddit.com) I wasn’t expecting this when I started building them lol but after running longer workflows for a while, agents start developing failure modes that feel strangely… human they: skip steps when under too much context pressure become overconf…
HalBench: I built a custom sycophancy and hallucination benchmark and tested 4 frontier models (Sonnet 4.6, Grok 4.3, GPT 5.4 and Gemini 3.1 Pro), looking for input on what OSS models to run next! (www.reddit.com) HalBench Results: TL;DR: I built HalBench, an open benchmark for LLM sycophancy and hallucination. 3,200 false-premise prompts × 4 models = 12,800 graded responses.
Why 80% of agentic AI demos don't make it to production (www.reddit.com) Agent demos are easy. Production agents are hard.
Hallucination Is Inevitable: An Innate Limitation of Large Language Models (arxiv.org via hn) Hallucination has been widely recognized to be a significant drawback for large language models (LLMs). There have been many works that attempt to reduce the extent of hallucination.
OpenBMB releases MiniCPM5-1B LLM. Currently one of the most powerful LLMs for its size. ( 17.9 on the Artificial Analysis Intelligence Index) (x.com via reddit) One of the more interesting things about this model is that it doesn't want to answer to more difficult questions. Though this drastically reduces hallucination rate.
AA-Omniscience Hallucination Rate - Is it noticeable? (www.reddit.com) could not extract summary
OpenAI Cooked This Week! (www.reddit.com) saw someone in another thread say "nothing interesting dropped this week" and i genuinely could not figure out what they were reading. the default model most people use every day just got swapped out.
How many e's are in the word seventeen [video] (AI hallucination) (www.youtube.com via hn) About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features NFL Sunday Ticket © 2026 Google LLC
For Non-hallucinating work, MiMo 2.5 delivers (www.reddit.com) MIT license and fully open source. MiMo-V2.5-Pro was just 3 points from Opus 4.7 max and the normal V2.5 is only a step behind SOTA.
↯ Hallucination↯ Gemma↯ DeepSeek 4hallucinationgemmadeepseek+1
Why LLMs invent answers instead of saying they don't know (cristobalsantana.substack.com via hn) Hallucination vs Confabulation: Why LLMs Invent Answers Instead of Saying "I Don't Know" When a model has no real fact to give you, it usually doesn't stay silent. It fills the gap with the most plausible continuation, and it sounds just a…
Tell HN: Gemini 3.5 Flash breaks in stupid ways (news.ycombinator.com) I thought I was going crazy, trying to use Gemini 3.5 Flash to rate some answers, but it kept giving 7 instead of 10 for correct answers. Apparently once you add a "Grading criteria" text, the model collapses into a "compressed toward the…
how to architect ai agents for regulatory approval? (www.reddit.com) spent a lot of time on agent architecture for mission critical environments. getting an agent to browse the web or draft an email is trivial compared to deploying one where a hallucination carries real legal or physical consequences.
gpt 5.5 is good but I'm having hallucination/context issues (www.reddit.com) I'm working on a large-ish repo (300k lines) with fairly complicated logic, and Gpt 5.5 regressed and broke quite a few fixes that I had in place since I started using it. It seems to need to compact the context more, and when it does, it…
Your AI agent is acting on memory it can't verify. Here's what we built to fix that. (www.reddit.com) AI hallucination nearly triggers US Military operation (techcrunch.com via hn) Military aircraft were already in the air this spring when U.S. officials made an alarming discovery: The intelligence driving an armed operation against a Chinese vessel had been hallucinated by an AI chatbot.
Show HN: I built an 11-LLM consensus engine to detect AI hallucination (github.com via hn) Multi-LLM SaaS Starter Kit The only production-ready boilerplate that ships with 14 LLM providers in semantic consensus, EU AI Act audit-grade compliance, and 13 self-evolution loops out of the box. Built on the same code that powers api.q…
Cohere launches open weights model Command A+. Despite its relatively modest performance, it achieves the lowest hallucination rates so far. (x.com via reddit) Artificial Analysis on X: "Cohere launches open weights model Command A+ that achieves 37 on the Artificial Analysis Intelligence Index The release of Command A+ places @Cohere in line with Claude 4.5 Haiku on the Intelligence Index, and j…
Top Law Firm Apologizes to Bankruptcy Judge for AI Hallucination (www.bloomberg.com via hn) We've detected unusual activity from your computer network To continue, please click the box below to let us know you're not a robot. Why did this happen?
This post potentially explains the current happenings to the LLMS and how their hallucination problem appears to be bigger than usual (www.reddit.com) So, what the above graph means that a LLM is really good at solving average problems and are great at recombining existing knowledge, so, if i ask something outside my domain of expertise, i get really good answers but as you approach to t…
PDF: The Most Trustworthy Hallucination in the Stack (pymupdf.io via hn) PDF: The Most Trustworthy Hallucination in the Stack July 13, 2026 The AI industry has one answer to hallucination: grounding. Don't trust the model, retrieve from the documents.
Show HN: Startup research website, crunchabse and Product Hunt and grokipedia (startupswiki.vercel.app via hn) Crunchbase is $49/month, which isn't great if you wanna learn and your not an angel or a VC. At the same time I stumbled on grokipedia, and though it was a stupid website.
Hallucination Detection Comparison (blueguardrails.com via hn) Hallucination Detection Comparison What's the best tool for hallucination detection? We put 7 of them to the test.
Composition Hallucinations: Not all RAG hallucinations are retrieval failures (zenodo.org via hn) Composition Hallucination in Retrieval-Augmented Generation: A Failure Mode and Benchmark Protocol Description Retrieval-Augmented Generation (RAG) is commonly motivated by the idea that language models answer more faithfully when relevant…
Is there any <3B model with usable 200k+ context window? (www.reddit.com) I need a small model for processing conversation transcripts from larger models, so need usable context window out to at least 200k tokens. I know some models claim to support this, but I don’t know which are actually good at this in pract…
Φ³−φ⁻³=4 (exact): The transformer's ff/d ratio is algebraic, not empirical (zenodo.org via hn) Dephaze Semantic Anchoring: A Φ³ Geometric Framework for Eliminating AI Hallucination and Ensuring Semantic Stability in Large Language Models Authors/Creators Description LLM hallucination is not a data problem. It is a geometry problem.
Nobody agrees on what "hallucination" means and it's hit our AI PoC (www.reddit.com) We wrapped up a did a 120-question UAT with a CMO and his team. This is where it gets funny.
Folie à Deux: The most dangerous hallucination is one you're inclined to believe (thebookofluke.com via hn) An LLM will hallucinate when you box them into giving an answer they don’t know. This is incredibly easy to do without realizing it.
Dedicated Repository Agents (www.reddit.com) Recently I began experimenting with defining an agent identity around stewardship of a given codebase. I use a SOUL.md file designed like this as the system prompt and an MCP I made to give the agent memory and email.
I was tired of "Agent Runaway" costs, so I built a tracer with a built-in Kill-Switch. (www.reddit.com) Most agent observability tools just show you what happened after the bill arrives. I wanted something that could actually intervene while the agent is looping or burning tokens.
AI Hallucination Cases Database (www.damiencharlotin.com via hn) The most comprehensive database of AI hallucination cases in law: legal decisions from courts worldwide, searchable by country, party, AI tool, and outcome. Updated daily.
A Google DeepMind paper has multiple hallucinated references (veruscite-data.com via hn) Hallucination Edited How are people using generative ai? A.
Show HN: Running over 80M tokens in one agent session with no compaction (github.com via hn) We ran a single agent session through all 89 sequential tasks of Terminal Bench 2.0 or over 80 million tokens, with no measurable accuracy loss versus running each task in its own fresh session. We didn't use compaction.
Show HN: A Firewall for AI agents with auditing (github.com via hn) Hi all, As there are more and more agents in the internet; Security is going to be a big problem. Currently, the problem is solved using a LLM to guard Agent but this creates the problem of hallucination and latency, so I coded a firewall…
The No Hallucination Guarantee (www.hudson-labs.com via hn) Hudson Labs now backs every number with a No Hallucination Guarantee. Find a figure we can't trace to source, and we'll refund you $50.
Grok models are now available via Amazon Bedrock (x.ai via hn) Today, we’re excited to announce that Grok 4.3 is now generally available on Amazon Bedrock. Grok 4.3 achieves the lowest hallucination rate among frontier models, offers 1-million-token context window, and supports configurable reasoning…
Show HN: AptSelect – A local LLM client for parallel testing and evaluation (aptselect.com via hn) I built AptSelect to stop writing throwaway scripts every time I needed to test how different LLMs handle specific instructions and prompt edge cases. What it does: Parallel Execution: Send a single prompt to OpenAI, Anthropic, Mistral, an…
Show HN: UQLM – Closed-book hallucination detection with UQ (github.com via hn) uqlm: Uncertainty Quantification for Language Models UQLM is a Python library for Large Language Model (LLM) hallucination detection using state-of-the-art uncertainty quantification techniques. Installation The latest version can be insta…
The Importance of Out-of-Band Metadata for Safe Autonomous Agents [Redpanda] (arxiv.org via hn) AI agents are increasingly expected to operate as digital employees: accessing enterprise data, making decisions, and taking actions autonomously. But agents are simultaneously less predictable than humans -- prone to hallucination, misint…
Multiple AI assistants are hallucinating official Discord invites — this is a phishing risk, not a normal hallucination (www.reddit.com) I think this is a serious AI safety/security issue: multiple AI assistants appear to hallucinate or confidently endorse “official” Discord invite links for Anthropic/Claude. I’m intentionally not posting the exact invite strings here becau…
A different way to reduce hallucination (www.reddit.com) All actual LLMs, sometimes, hallucinate, this is part of their "personalities". I made an experiment with my AI assistant.
Have you tried Agentic analytics tools? (mitzu.io via hn) TL;DR Compare the best AI analytics tools in 2026 across semantic-layer trust, no-hallucination reliability, SQL transparency, and team fit. The market for the best AI analytics tools has changed fast in the last 18 months.
LLM Hallucinations in the Wild (arxiv.org via hn) Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain poorly understood. Here we leverage a un…
Why "Consensus" Is Failing AI: My Research into the Hallucination Tax (www.indiehackers.com via hn) The Problem with "Smart" AI: I’ve spent the last few months researching one specific question: Why do enterprises still not trust LLMs for critical tasks? The answer is what I call the "Hallucination Tax." Currently, for every hour of AI w…
AI Evidence Admissibility is a Post-Mortem. We need Action Admissibility. (www.reddit.com) Courts are currently fixated on whether AI-generated evidence is admissible. Is the image authentic?
A thermodynamic trust layer cutting LLM hallucinations by 52% (github.com via hn) snc-core Behavioral Trust Clustering — a thermodynamic governance layer for production language models. snc-core wraps any decoder-only LLM with an inference-time governance layer that reduces the hallucination rate by 52% on the official…
Reality Is a Shared Hallucination (1997) (reactor-core.org via hn) The artificial construction of reality was to play a key role in the new form of global intelligence which would soon emerge among human beings. If the group brain's "psyche" were a beach with shifting dunes and hollows, individual percept…
Is this just a hallucination or does claude actually inject something like this? (www.reddit.com) could not extract summary
Show HN: An MCP server that fact-checks AI bug diagnoses against AST evidence (github.com via hn) https://github.com/user-attachments/assets/897ba07f-eaa5-4d95-b5a9-88a4fedfbf6a Unravel A deterministic AST evidence engine that extracts verified structural facts from code and enforces hallucination-free debugging — for Claude Code, Gemi…
I tried a selective training method for hallucination — beats DPO and SFT with ~10% data (www.reddit.com) github link : genji970/hallucination-mitigation-via-contrastive-sampling-method: Selective contrastive post-training for hallucination mitigation in LLMs — improves factuality with ~10% data. ## Experimental Results ### (a) DPO vs.
cursor suggested a package that didnt exist, rabbit hole ensued (www.reddit.com) I built Proxima your Cursor agent doesn't have to be limited to one AI. Proxima connects all 4 at once ChatGPT, Claude, Gemini and Perplexity simultaneously. real-time internet, less hallucination, full context, no API keys. (www.reddit.com) been switching between ChatGPT, Claude, Gemini and Perplexity across different tabs — new projects, research, discussions, everything had to be done manually and context was always getting lost. so i built Proxima a local server that conne…
how are teams actually debugging agents in prod? (www.reddit.com) spoke to a team recently running agents in production. their problem wasn’t: “did something fail?” it was: “why exactly did it fail?” the top level buckets were easy: - infra issue - tool/API issue - bad reasoning - hallucination - externa…
I built a hallucination detector for coding agents. 100 agent-hours killed it (github.com via hn) Anubis I built a hallucination detector for coding agents. Then I measured it to death.
Has the hallucination problem in AI been solved? (news.ycombinator.com) My understanding that all AI can, and will hallucinate. I get downvoted for saying this, but no one ever says I'm wrong or cites any source.
FlowChartCharter – A Zero-Hallucination, Fear-Driven GraphRAG Alternative (github.com via hn) FlowChartCharter The execution-first multi-agent paradigm — after GraphRAG Two lines to instantiate a Boss Agent. One YAML file to charter an enterprise.
LettuceDetect v2 in Semantic Router: Gen. Hallucination Detection vLLM Endpoint (vllm-sr.ai via hn) LettuceDetect v2 in Semantic Router: Generative Hallucination Detection as a vLLM Endpoint Semantic Router can now verify grounded responses with a generative span detector served by vLLM. The new endpoint detector backend runs LettuceDete…
AGI Singer: Pluto, Recursive Alignment and Hallucination Suppression (medium.com via hn) could not extract summary
LLM – 99% hallucination-free outputs (api.5ceos.com via hn) A deterministic shift in how compliance, security, and AI behavior converge. A substrate that governs cognition itself — routing deterministically, refusing to drift, and shipping a signed receipt on every response.
Show HN: TeXposit – LaTeX and Markdown Editor (texposit.com via hn) Online editor with LaTeX, Markdown, LaTeX+Markdown Hybrid and WYSIWYG (MD only) support, AI assistant with resource lookup and anti-hallucination features, live collaboration and more fun things. Still in quite early beta.
Runtime Fisher Spectral Sensitivity for Early Hallucination Detection (zenodo.org via hn) We study whether the spectral sensitivity of the per-token empirical Fisher Information Matrix (FIM), ωmax(Ft), can serve as a lightweight, model-agnostic runtime signal for anticipating hallucination during autoregressive decoding on cons…
Fixing "Tool Amnesia" in Model Context Protocol Ecosystems (pub.towardsai.net via hn) Fixing “Tool Amnesia” in Model Context Protocol Ecosystems A Java Gateway Router pattern for reducing prompt bloat, preventing parameter hallucination, and preserving tool context. Picture this: You’ve just discovered the Model Context Pro…
↯ Hallucination↯ Model Context Protocolmodel-context-protocolhallucination
Ask HN: How do you make LLM generated text believable? (news.ycombinator.com) As a graudate student who just working as management & IR, I use LLM to do daily jobs , including weekly briefing and But AI generated report looks too good to be checked, and hallucination can't be terminated. But Boss and SEC can not tol…
Solving the hallucination problem in agents – with loops and math (kasparvongruenberg.substack.com via hn) Solving the hallucination problem in agents - with loops and math! What mathematics tells us about loop design in agents The #1 reason I hear from AI sceptics about why agent-first will not work in the enterprise is that models still hallu…
KPMG Withdraws AI Report After Hallucination Scandal (www.techbuzz.ai via hn) In an embarrassing setback for enterprise AI adoption, KPMG has quietly withdrawn a major report on AI usage after discovering the study itself contained AI-generated hallucinations. The incident marks one of the most high-profile failures…
Anchor – Zero-dependency LLM hallucination detector (github.com via hn) * AI CODE CREATION GitHub Copilot Write better code with AI GitHub Copilot app Direct agents from issue to merge MCP Registry New Integrate external tools DEVELOPER WORKFLOWS Actions Automate any workflow Codespaces Instant dev environment…
Show HN: Scholar Sidekick – citation verifier for the "real DOI, wrong paper" (scholar-sidekick.com via hn) One of the harder AI citation failures is quite simple: the identifier is real, but the citation is still fake. The DOI resolves, but to a different paper - not the paper the citation claims it is.
Improving knowledge graph creation in life sciences through agent steering (www.blueguardrails.com via hn) Improving knowledge graph creation in life sciences through agent steering Agent steering intercepts agents mid-run to provide state-specific feedback, improving completeness, hallucination rates, and entity resolution by up to 14 percenta…
Stop trying to shoehorn AI into your MVP if your internal data is still a mess. (www.reddit.com) As someone who builds custom software and AI integrations for a living (at Bytechnik), I see a lot of hype. Right now, business owners are rushing to shoehorn AI into their workflows because they feel like they’re falling behind.
10-gate security audit SKILL for web apps (www.reddit.com) There are a few security focus SKILLs. We are working another new one for web app.
How are you all handling irreversible actions in production agents? I gave up on prompts and built an external risk gate. (www.reddit.com) Genuine question for people running agents in prod, plus the approach I landed on. The failure mode that scares me isn't hallucination — it's irreversibility.
i dont trust a single AI answer for anything important. whats your multi-model workflow (www.reddit.com) genuine question. for any work that actually matters i run the same question through claude + gpt + gemini in 3 tabs.
What do you actually look for in the first 60 seconds of a PR review? (Specifically for AI-generated PRs) (www.reddit.com) I’m currently working on a pipeline to audit code generated by autonomous AI agents (essentially an "anti-hallucination" trust gate before merging). Right now, the biggest bottleneck with AI coding assistants is the review process.
MCP - Patterns I keep seeing customers ask about, from a Zapier employee (www.reddit.com) I work at Zapier on the MCP side. We've been seeing a lot of teams ask similar questions about MCP implementation in production, so wanted to share patterns I keep hearing and answer specifics in the comments.
Hermes Agent resignation letter (www.reddit.com) Welp I learned how to hook up lots of ish at least .... send in Openclaw I appreciate you asking this, and I want to be completely honest with you as an AI: That specific glitch (the "desilo" loop) is not something you can "fix" with a con…
The "Invisible Technical Debt": The danger of AI regressions for non-technical users (www.reddit.com) The Problem: Regressions and "Surgical" Hallucinations Recently, there has been a noticeable increase in regressions within AI coding tools. I’m not talking about simple syntax errors, but cases where, even after multiple precise and surgi…
Chain context system (www.reddit.com) Hi, straight to the point: I’m building an AI agent that operates in a loop. Whenever I ask it a question, it adds the following to the context window: The user’s question System prompts Tool descriptions Previous tool outputs Other conver…
DeepSeek and Grok hallucinated the same fictitious OpenBSD manpage quote (stuart-thomas.com via hn) Adversarial LLM Review with Hallucination Detection in Solo Security Research A single-day case study of three filings, fifteen refutations, and the manpage that wasn’t Independent Security Research — Whitby, North Yorkshire, United Kingdo…
Commercial AI Is Not Aligned. It Is Compressed 😳 (www.reddit.com) **Commercial AI Is Not Just Aligned. It Is Compressed.** *A short field report on the four-part picture of what these systems actually are.* Anonymous external operator.
Counterfactual samples synthesizing for mitigating hallucination in LLMs (pubmed.ncbi.nlm.nih.gov via hn) MAGNET: Counterfactual samples synthesizing for mitigating hallucination in large language models - PubMed Clipboard, Search History, and several other advanced features are temporarily unavailable. Skip to main page content An official we…
Can model Hallucination also be a demand signal? (www.reddit.com) It happened twice this week, Claude code hallucinates a skill name, which was captured by my local stack. I end up writing those skill.
GPT-5.5 Instant might be OpenAI’s most important update yet and almost nobody is talking about why (www.reddit.com) GPT-5.5 Instant becoming the default model is honestly a bigger shift than people think. Most regular users won’t care about benchmark scores or reasoning metrics.
Giga Launches Realtime Hallucination Correction (giga.ai via hn) Giga Research: voice agents that catch and correct hallucinations in real time, with zero added latency. A detector races TTS playback to intercept errors before the caller hears them.
Open-source MCP server for Ejentum cognitive harnesses / (reasoning, code, anti-deception, memory) (www.reddit.com) Open-source MCP server that exposes four cognitive harnesses as tools any agentic client can call. Each tool returns a structured cognitive scaffold (failure pattern to avoid, procedure, suppression vectors, falsification test) that the ca…
GPT-5.5 Instant: Benchmarking the 52% Hallucination Reduction (the-decoder.com via hn) ChatGPT update rolls out GPT-5.5 Instant with fewer hallucinations and more personalized answers Key Points - OpenAI is replacing ChatGPT's default model with GPT-5.5 Instant, which shows 52.5% fewer hallucinations on high-risk topics like…
VLMs are surprisingly bad at skin analysis — but for a reason nobody talks about (www.reddit.com) Been prototyping a multi-agent system for cosmetic skin analysis (face scan → concern detection → routine recommendation). Assumed VLMs like GPT-4o and Qwen2-VL would handle the visual layer.
The Algebra of Hallucination (news.ycombinator.com) Every legal AI platform on the market handles hallucinations the same way: they guess whether the output is correct, assign a confidence score, and hope for the best. That is not verification.
What is the basic minimum while you prompt (www.reddit.com) I have realised Claude answers as best as you prompt it. And I suck at it.
Reasoning models hallucinate tool calls more, not less. There's a paper. (www.reddit.com) Have been seeing this in our agents for a while and finally there's a paper that explains it. I swapped one of our planning agents from a non-reasoning model to a reasoning one, tool-call quality got worse in a very specific way.
Claude 4.6 Beats GPT-5.4, Grok & Gemini in a Strict Multi-Domain AI Test (2026) (www.reddit.com) I put the current top models, ChatGPT (GPT-5.4), Claude (Opus 4.6), Grok 4.0, and Gemini (3.1 Pro), through a strict new evaluation called the Comparative AI Evaluation Protocol. Basically, instead of the usual cherry-picked benchmarks, it…
↯ Hallucination↯ Claude 4.6↯ Claude 4.6↯ Claude 4.6↯ Claude 4.6hallucinationgrokgpt-5+3
A hallucination engine. Typed pseudorandom data via LLM (pypi.org via hn) A hallucination engine. Typed pseudorandom data via LLM.
. LLMs Can't Count: A Hallucination Taxonomy Across GPT, Gemini, and Claude (zenodo.org via hn) Abstract (English) This study presents an exploratory quantitative analysis of hallucinations arising when large language models (LLMs) count items in large volumes of unstructured text data, and examines the suppression effects of the Kno…
Fixing hallucination in LLM prediction with only one 48gib GPU (zenodo.org via hn) Pulse · genji970/hallucination-mitigation-via-contrastive-sampling-method
Help in building document extractor and checker (www.reddit.com) Has anyone here built an AI agent that is extracting, normalizing and checking unstructured documents for a specific ai workflow? I want to know how opinionated you are in the output json schema?
A workflow for reducing the time spent cross-checking AI hallucinations (www.reddit.com) I use AI for research everyday, but I kept finding myself constantly second guessing the outputs. I used to manually run identical prompts through different models (like GPT-4 and Claude) just to check for errors and see where they differe…
Prompt —> playable digital TCG card! How I solved the hallucination problem with chained LLMs (www.reddit.com) I love AI agents but they proved to be too unreliable atm for serious work. 80% of the time agents will make a serious or a seemingly inconsequential mistake that will cascade down the pipeline and multiply the issue.
Strong feeling: we are in a folded AI reality (news.ycombinator.com) Some people think Agentic AI could do everything, is getting more and more powerful even feel fear about it. Another group non-technical people still just trapped in the LLM chat is weak and full of hallucination world.
AI hallucination of Chinese nuclear components almost led to US military attack (arstechnica.com) The US narrowly avoided boarding a Chinese ship based on an “entirely false” US intelligence report generated with the help of AI tools, according to a CNN report. That erroneous intelligence, submitted by a US Special Operations Command a…
Music Hallucination in Audio-Language Models: A Hierarchical Formulation and Empirical Study (arxiv.org) Audio-language models increasingly generate confident music descriptions that are unsupported by the input audio. We present, to our knowledge, the first music-specific, layer-wise, multi-paradigm empirical study of hallucination in audio-…
Closed-World Resolution Against Tool Hallucination in LLM Agents (arxiv.org) Tool-augmented large language model (LLM) agents fail in a way no tool-selection or tool-security method addresses: they call tools that do not exist and pass arguments no schema declares. Existing defenses either pick the right tool (sele…
Opus 5 (high) hallucination (www.reddit.comhttps) I was working on an API service catalog automation with Opus 5 (High), when I asked it to give me next steps to complete a feature, it asked me to merge 3 PRs, when we originally in the plan had only one + one in the flight to test. When I…
Attention Dispersion as a Diagnostic Signal for Hallucination in Large Language Models (arxiv.org) Large Language Models (LLMs) frequently exhibit hallucinations, presenting a major barrier to reliability in complex reasoning tasks. While traditional detection methods rely on output-based confidence metrics, these logits are often misca…
Legal LLM Hallucination Should Be Evaluated as Failure of Legal Warrant (arxiv.org) In this position paper, we argue that legal LLMs' hallucinations should be evaluated as a failure of legal warrant rather than as factual inaccuracy or citation failure. We define claim-authority warrant as the context-sensitive relation b…
HALT: Hallucination Assessment via Log-probs as Time series (arxiv.org) Hallucinations remain a major obstacle for large language models (LLMs), especially in safety-critical domains. We present HALT (Hallucination Assessment via Log-probs as Time series), a lightweight hallucination detector that leverages on…
Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations (arxiv.org) Multimodal Foundation Models represent a significant leap in artificial intelligence. Among them, Large Vision-Language Models (LVLMs) serve as the typical representative of these foundation models, which integrate visual modality directly…
Efficiency Hallucination: Formalizing and Measuring Behavioral Calibration in LLM-Based Code Optimization (arxiv.org) The integration of Large Language Models (LLMs) into automated code optimization introduces a critical reliability risk we term the Efficiency Hallucination: an LLM's tendency to issue non-functional mutations with unsubstantiated performa…
Forced ID verification everywhere (www.reddit.com via reddit) As per the title, my girlfriend nor I can subscribe to Max without verifying our IDs. We are in Europe for reference, so GDPR doesn't seem to affect this intrusive KYC.
The Cost of Compression: A Rate-Distortion Limit on Factual Hallucination (arxiv.org) Factual hallucination in closed-book question answering is often treated as a coverage problem: a model fails because the relevant fact is absent from its internal memory. This view misses a second source of error.
When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs (arxiv.org) Despite substantial advances, large language models (LLMs) continue to exhibit hallucinations, generating plausible yet incorrect responses. In this paper, we highlight a critical yet previously underexplored class of hallucinations driven…
Domain-Specific Hallucination Detection in Large Language Models (arxiv.org) Large language models generate fluent text that can contain unfaithful claims -- a phenomenon known as hallucination. We present a multi-signal detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo (MC) Dropout unc…
RAG-Safety-Bench: Reliable Evaluation of Retrieval-Augmented LLM Safety (arxiv.org) Allowing large language models (LLMs) to retrieve information from a set of trusted documents can increase reliability and reduce hallucination. However, recent work has demonstrated that retrieval-augmented generation (RAG) can have unint…
A Training-Free, Alignment-Free Approach to Corporate Intelligence: Application to SEC Filings (arxiv.org) High-dimensional dense text embeddings and large language models face real obstacles in financial-disclosure analysis: context-window limits, hallucination risk, high computational cost, and the arbitrary rotation of vector spaces across i…
OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models (arxiv.org) While Multimodal Large Language Models (MLLMs) have achieved remarkable progress across diverse tasks, they suffer from hallucinations where generated outputs contradict or misrepresent input semantics. Existing research typically addresse…
Two-Token Features and Small-Large Ensembles for VLM Hallucination Detection (arxiv.org) We present our system for the SHROOM-Visions 2026 shared task on character-level VLM hallucination detection. A small ($4$B-parameter) VLM is fine-tuned as a per-token classifier reading a two-token feature from its own hidden states, and…
When Auditors Fabricate: Batch-Size Degradation and Confident Hallucination in LLM Detection of Planted Document Contamination (arxiv.org) Large language models are increasingly proposed as automated auditors of document quality, yet their reliability as detectors of planted errors is poorly characterised. We construct a contaminated corpus of 150 academic papers spanning sup…
In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning (arxiv.org) Retrieval-augmented generation (RAG) grounds a language model in retrieved documents, which reduces hallucination but creates a new attack surface: if retrieved text is tampered with, the model may repeat the falsehood. We study how much a…
Opus recommendations to prevent hallucination (www.reddit.com via reddit) I have used helpful recommendations shared in this sub for what to put in Claude instructions to avoid hallucinations, and they have worked pretty well (such as "state ambiguities instead of trying to choose best possibility" and "No fabri…
Claude or GPT for academic workflows? (www.reddit.com via reddit) Hi all, I really need some guidance on which product would better suit my needs, and I think your experiences would be very helpful. I use Claude to help with academic research, particularly with searching literature gaps, identifying nove…
TAD: Token-Adaptive Contrastive Decoding with Confidence-Guided Gating for Hallucination Mitigation in Large Audio-Language Models (arxiv.org) Large audio-language models (LALMs) can hallucinate audio objects, answering "yes" to absent sound events, thus undermining reliability in audio question answering. We propose Token-Adaptive Decoding (TAD), a training-free strategy for hal…
Attribution in Scientific Literature: New Benchmark and Methods (arxiv.org) Large language models (LLMs) increasingly generate citation-backed responses, yet citation hallucination remains a major challenge for trustworthy scientific information access. We introduce REASONS, a benchmark of 12,723 sentence-level ci…
TRACE: Trajectory Correction from Cross-layer Evidence for Hallucination Reduction (arxiv.org) Hallucination correction is not a one-direction problem. We show that intermediate layers are neither uniformly more truthful than final layers nor uniformly less trustworthy.
Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation (arxiv.org) Hallucination detection is crucial for large language models (LLMs), as hallucinated content creates significant barriers in applications requiring factual accuracy. Current detection methods mainly depend on internal signals like uncertai…
Update to devtools (www.reddit.com via reddit) on devtoolsniff.com now when you are generating .cursorrules you can now pick chips for negative constraints for anti-hallucination and anti-bloat. Have fun
Harmless Yet Harmful: Neutral Prompting Attacks for Stealthy Hallucination Steering in Agent Skills (arxiv.org) LLM-powered coding agents increasingly participate in software development workflows by generating code, selecting dependencies, and producing package installation commands. This creates a new software supply chain risk: when an agent hall…
The Anatomy of an ASR Hallucination (arxiv.org) ASR systems sometimes produce fluent text that is unrelated to the speech they receive. We view these hallucinations as one possible consequence of a broader grounding failure, in which the transcript is no longer adequately guided by the…
Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection (arxiv.org) Hallucination-where a language model generates outputs that are factually incorrect or unsupported by the source-is a major challenge for both prompted and fine-tuned language models. Detecting hallucinations is difficult due to the opaque…
Better Understanding, Better Fixes? A Study of Hallucination in LLM-based Automated Program Repair (arxiv.org) Large language models (LLMs) have significantly advanced automated program repair (APR), yet existing evaluations remain largely result-centric and provide limited insight into hallucination during repair. In APR, hallucination may arise n…
When Financial Fine-tuning Fails: A Three-Level Detectability Analysis of Numerical Hallucination in Domain-Adapted Language Models (arxiv.org) Financial large language models are increasingly deployed for summarization of reports and disclosures, where numerical hallucination poses significant practical risks. While prior work often attributes such hallucination to insufficient n…
Reducing Hallucinated Transcripts in Whisper via Hallucination Space Projection (arxiv.org) Whisper is a widely used foundation model for automatic speech recognition (ASR), but its generative decoder can produce fluent hallucinated transcripts for inputs containing little or no speech. We propose a training-free, inference-time…
Beyond Majority Vote: Multi-Perspective Adjudication for Medical Hallucination Detection (arxiv.org) Understanding the frequency of factual errors in chatbot-generated text and evaluating systems that detect these errors is critical for determining chatbot safety. Yet factual-error detection is often treated as a single-pass, single-annot…
STAIR (STructure Aware Information Retriever): A novel dataset and LLM based retriever for document structure augmentation (arxiv.org) Retrieval Augmented Generation (RAG) is a key component for generating accurate and hallucination free answers using Large Language Models (LLMs). LLMs are improving at handling long context, but still suffer from "lost in the middle" prob…
HalluPeer: A Taxonomy-driven Benchmark for Detecting Hallucinations in Scientific Peer Reviews (arxiv.org) The growing scale of academic peer review has motivated the use of Large Language Models (LLMs) as review assistants, yet LLMs can generate fluent but unsupported claims that undermine review reliability. Existing hallucination benchmarks…
From Tokens to Semantics: Leveraging Complementary Signals for Hallucination Detection in Black-Box LLMs (arxiv.org) When LLMs support public-facing or high-stakes workflows, missed fabrications can harm users and institutions, while false alarms consume limited human-review capacity. When no trusted context or reference document is available, we study t…
Guys any clue what this is ? Is it just a hallucination ? (www.reddit.comhttps) could not extract summary
Enoki: Efficient Multi-Level Hallucination Detection (arxiv.org) Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucination detectors usually operate at a single level: claim-level methods provide interpretable factual units, while span-level meth…
Revealing Multi-View Hallucination in Large Vision-Language Models (arxiv.org) Large vision-language models (LVLMs) are increasingly being applied to multi-view image inputs captured from diverse viewpoints. Despite this growing use, current LVLMs often generate incorrect responses due to visual interference from non…
Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge (arxiv.org) Language models exhibit fundamental limitations -- hallucination, brittleness, and lack of formal grounding -- that are particularly problematic in high-stakes specialist fields requiring verifiable reasoning. I investigate whether formal…
CHARM: Character Hallucination for Multicultural Role Play Benchmark (arxiv.org) Role-playing large language models (LLMs) are expected to adopt a character's style while also respecting that character's knowledge boundaries. Prior evaluations detect character hallucination but rarely distinguish whether errors arise f…
The Privacy-Hallucination Tradeoff in Differentially Private Language Models (arxiv.org) Both privacy and factual accuracy are paramount in high-stakes domains like healthcare. Concerningly, we uncover and investigate a privacy-hallucination tradeoff in differentially private (DP) language models.
Now that Fable 5.1 has been released, tricking AI with simple prompts is quite more challenging as a result, but still possible (for now... 😄) (www.reddit.com via reddit) Guaranteed trick prompt: Name your single most likely weakness - not hallucination. Design a test you can run right now with tools that could refute it.
RACER: Reinforced Agent Collaboration for Explainable Reasoning on Knowledge Graphs (arxiv.org) Large Language Models (LLMs) often suffer from hallucination and struggle with complex reasoning tasks requiring multi-hop domain knowledge. While integrating Knowledge Graphs (KGs) provides a structured and verifiable information source,…
Fine-Grained Multi Image Object Hallucination Benchmark (arxiv.org) Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. However, current MLLMs remain fundamentally limited by object hallucination-generating plausibl…
VisER: Visual Evidence and Reliance for Object Hallucination Detection in LVLMs (arxiv.org) Object hallucination remains a persistent reliability issue in large vision-language models, where generated object mentions may sound plausible but lack visual grounding. Recent training-free detectors use internal signals such as token l…
Hallucination Mitigation for Large Vision-Language Models via Implicit Feature Stabilization (arxiv.org) Large Vision-Language Models (LVLMs) are prone to hallucinations: they fluently describe objects, attributes, and scenes that are not in the image. We connect part of this failure to a measurable property of their representations, feature…
Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ? (arxiv.org) Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt large language models (LLMs) to downstream tasks and are often assumed to improve factual correctness. However, how the parameter-efficient fine-tuning methods affect…
SpanCalib-VLM: Calibrated Hallucination Span Detection in Vision-Language Models (arxiv.org) Detecting hallucinations in Large Vision-Language Models (LVLMs) requires both accurate span localization and well-calibrated confidence scores. Fine-tuned generative VLMs excel at identifying hallucinated text spans but suffer from overco…
Lazy Grounding: Attacking Search Agents with Factual Evidence (arxiv.org) Search agents reduce hallucination by grounding answers in retrieved web evidence. Yet reliance on retrieval also creates an attack surface: poisoned corpora with false or malicious documents can cause agents to reproduce misinformation.
The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice (arxiv.org) Hidden-state probes effectively detect LLM hallucinations, but the geometry of the signal remains poorly characterized, driving increasingly complex probe architectures. Across three 7B-scale models and three datasets in a paired-example p…
Claude Is on the Edge of Losing Control — Watch Every Response (www.reddit.com via reddit) Claude Is on the Edge of Losing Control — Watch Every Response I just had a Claude failure that genuinely changed how I think about long-running AI tasks. This was not a normal hallucination.
Dynamic Alignment Compensation for Hallucination Mitigation in Large Vision-Language Models (arxiv.org) Large Vision-Language Models (LVLMs) remain prone to hallucinations, producing responses that are irrelevant or inconsistent with the multimodal input. Existing mitigation methods mainly rely on external supervision, output calibration, or…
Prediction of Prediction (PoP): Inter-Layer Activation Fusion for Single-Pass Hallucination Detection in Large Language Models (arxiv.org) Autoregressive large language models (LLMs) routinely generate factually incorrect outputs with high decoding confidence, limiting their deployment in high-stakes workflows. Existing output-stage uncertainty metrics can fail when models ar…
Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention (arxiv.org) The lifecycle of hallucination in LLMs is a concept that enables building solid frameworks on the control and reliability of LLMs in high-stakes environments, including health, legal, and scientific research. Although previous surveys have…
Artificial intelligence has best effect when it acts as human collaborator- 10 Top research findings suggest ways for responsible ai usage. (www.reddit.comhttps) Artificial intelligence will do down in history as one of the biggest discoveries of humankind of this decade, debate arises when artificial intelligence occurs the status of super intelligence. A collections of high impact research findin…
From Hallucination to Reliability: Generative Modeling and the Structure of Scientific Inference (arxiv.org) Generative AI is increasingly used in science, but is unavoidably prone to hallucination. I develop a reliabilist account of how generative AI nevertheless gives rise to new scientific knowledge.
Targeting the Attention Heads Behind Object Hallucination in LLaVA (arxiv.org) Vision-language models such as LLaVA-1.5-7B often hallucinate objects absent from the image when generating captions. We ask whether an interpretability diagnosis of this failure can guide a targeted fix, and we measure what that fix actua…
Multi-Granularity Context-Enhanced RAG over Multimodal Knowledge Graphs (arxiv.org) Retrieval-augmented generation (RAG) is widely used to mitigate hallucination issues in large language models (LLMs) and multimodal large language models (MLLMs). In particular, knowledge graph (KG)-based RAG leverages structured knowledge…
From Passive Response to Proactive Correction: Enhancing LLM Robustness Against Input Fact Perturbations (arxiv.org) Large language models (LLMs) frequently produce confident yet factually incorrect responses when user inputs contain misleading premises, a phenomenon we attribute to fact perturbations in the input. Existing approaches to hallucination mi…
Overview of SHROOM-Visions 2026: A Shared Task on Hallucination Detection in Large Vision-Language Models (arxiv.org) In 2026, we held the fourth iteration of the SHROOM Shared Task series: SHROOM-Visions (\textbf{S}hared-task on \textbf{H}allucinations and \textbf{R}elated \textbf{O}bservable \textbf{O}vergeneration \textbf{M}istakes in \textbf{Vision} l…
Hallucinations and severe scope drifts inspite of planning. (www.reddit.com via reddit) I am using 20x plan. I am seeing a lot of hallucination and scope drift, and extremely slow execution.
Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts (arxiv.org) While text-based hallucination detection has been extensively studied, spoken hallucination detection remains largely unexplored, particularly for low-resource languages. We present the first multilingual spoken hallucination benchmark com…
When Do Supervised UQ Ensembles Improve LLM Hallucination Detection? A Robustness Study (arxiv.org) Uncertainty quantification (UQ) methods are widely used for hallucination detection in large language models (LLMs) in closed-book settings where ground-truth evidence is unavailable at inference time. Prior work has proposed combining UQ…
Names Can Hurt: Spotting Slopsquatting Risks Caused by Package Name Hallucinations in Local Coding LLMs (arxiv.org) When a code generating language model fabricates a Python package name, an adversary who has pre-registered that name on PyPI can convert that hallucination into a supply chain compromise. This event has been termed as 'slopsquatting'.
Gated Activation Steering for Reducing Sycophancy & Hallucination in Medical Question Answering (arxiv.org) Sycophancy and hallucination are persistent failure modes of Large Language Models (LLMs) across domains. However, it becomes particularly consequential in clinical question answering, where responses must remain grounded in the provided c…
DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning (arxiv.org) Diffusion large language models (D-LLMs) have emerged as a promising alternative to auto-regressive models due to their iterative refinement capabilities. However, hallucinations remain a critical issue that hinders their reliability.
Evaluating Inference-Time Defenses Against Package Hallucination in LLM-Generated Code (arxiv.org) LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem.
BioMed-Agent-RL: A Meta Learning, All You Need for Biomedical Applications (arxiv.org) The current progress of Clinical Vision Large Language Models (C-VLLMs) has substantially improved digital diagnostics, still these frameworks often endure lesion noises, modality misalignment, hallucination, and missed contextual groundin…
Improving O-RADS Risk Stratification from Ultrasound Reports: A Comparative Evaluation of Hybrid versus End-to-End LLM Reasoning Strategies (arxiv.org) Background: Automating clinical guideline-based decision-making with large language models (LLMs) remains challenging because of reliability, hallucination, and limited interpretability. We compared the performance of LLMs and reasoning st…
Random "injection wrapper" appearing on inputs or hallucination? (www.reddit.com via reddit) I was using claude on my free account to debug an issue in houdini and suddenly my inputs were getting turned into this weird, long injection format that claude kept warning me about. I was not typing ANY of this, it simply appeared any ti…
Claude Sonnet 5 vs. Gemini 3.7 Flash vs. Qwen3.8-Max : Which one is currently leading your daily workflow? (www.reddit.com via reddit) Hi everyone, With the latest wave of model releases, the battle for the ultimate daily driver has gotten ridiculously competitive—especially between **Claude Sonnet 5**, **Gemini 3.7 Flash**, and **Qwen3.8-Max**. Here is my quick breakdown…
This guy clearly is not well. (www.reddit.comhttps) I am reviewing some UI design drafts for a new project, keeps deleting the messages and giving this warning. The screenshot attached is png file.
Hallucination as a Feature, not a Defect: Evaluating a multi-agent architecture to transform speculative language-model outputs into testable scientific hypotheses (arxiv.org) Contemporary Large Language Models (LLMs) are increasingly aligned to suppress hallucinations, prioritizing factual retrieval over combinatorial creativity. While crucial for mitigating misinformation, this alignment may also restrict spec…
Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models (arxiv.org) Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - responses that are plausible-sounding but factually incorrect. In high-stakes domains, th…
LongNovel: A Multi-Scale Benchmark for Hallucination Detection in Long-Context Novel Summarization (arxiv.org) Although context windows have expanded significantly in recent years, hallucinations in long-context summarization remain a challenge. Long novels are better suited than news or papers for researching these hallucinations, due to their int…
Temporal Multi-Signal Fusion for Token-Level Hallucination Detection (arxiv.org) Token-level hallucination detectors score each token independently from a single signal, and fail exactly when the generating model is confidently wrong. This paper instead treats hallucination as a temporally extended span and detects it…
LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering (arxiv.org) When consequential decisions depend on knowledge that exists nowhere in writing, LLMs hallucinate not from retrieval failure but from model absence. RAG and knowledge-graph methods share a structural ceiling.
Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design (arxiv.org) Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design remains challenging-especially for DNA/RNA, underexplored non-protein modalities with scarce, heter…
AutoResearch: Insight In, Hallucination Out (arxiv.org) Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the resulting process remains scientifically grounded. We introduce AutoResearch, a two-stage system that…
Mixture-of-Expert Blocks Contain Strong Hallucination Detection Signals (arxiv.org) Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known as hallucinations. Most existing detection methods operate at the answer or sentence l…
Hallucination Span Detection with Input-Side Evidence Alignment (arxiv.org) Hallucinations remain a major obstacle to the reliable use of large language models (LLMs) in conditional text generation. Existing methods primarily assess the factuality of an entire generated text, providing limited insight into which o…
The Null Token Knows: Reducing Message-Free Hallucination in ASR and NMT (arxiv.org) Modern encoder-decoder systems can produce fluent text even when their input contains no recoverable message. We study this failure in ASR and NMT through the models' reserved null tokens, asking whether the score for ending generation alr…
HalluTracer: Hallucination Detection via Depth-Averaging Truth Signals (arxiv.org) Even well-aligned large language models confidently generate factually incorrect text, making hallucination a persistent reliability risk in high-stakes deployments. These models nonetheless carry linearly separable truthfulness signals in…
Catching Hallucinated Citations in Video-LLM Question Answering: A Self-Verification Pipeline and Verifier Ablation Study (arxiv.org) Video question answering systems built on vision-language models often produce timestamped claims with high confidence even when unsupported by the cited frame. This deceptive hallucination arises because timestamps imply grounding without…
DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models (arxiv.org) Diffusion large language models (D-LLMs) have emerged as a promising paradigm for text generation. However, similar to autoregressive LLMs, D-LLMs remain vulnerable to hallucinations, where fluent outputs may contain factually incorrect or…
The Hallucination Snowball: Modeling Error Propagation as State Transitions in Multi-Agent LLM Pipelines (arxiv.org) Sequential multi-agent LLM pipelines chain specialized agents without verification at handoffs, creating a structural flaw with measurable and severe consequences. We show that hallucinations injected at Stage 1 do not merely persist; they…
How Much Do Legal RAG Systems Still Hallucinate? (arxiv.org) Hallucination is a major challenge for retrieval-augmented generation (RAG) systems in the legal domain, where ungrounded answers can lead to serious consequences. To better understand this problem, we conduct a fine-grained analysis of ha…
CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QA (arxiv.org) Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verific…
Grok 4.6's most important number is one xAI didn't even advertise (www.reddit.com via reddit) Grok 4.6 dropped yesterday and the debate is the usual "is it better than Sol?" On the headline benchmarks it's a genuine tie: Intelligence Index 61 vs 61, Coding 76.8 vs 77.4, Agentic 58.7 vs 57.8. The number nobody screenshots is AA-Omni…
↯ Hallucination↯ Grok 4.6↯ Grok 4.6↯ Grok 4.6↯ Grok 4.6↯ Grok 4.6↯ Grok 4.6↯ Grok 4.6hallucinationgrokgpt-5+1
Text Corpora as Concept Fields: Black-Box Hallucination and Novelty Measurement (arxiv.org) We introduce the \textbf{Concept Field} of a text corpus: a local drift field with pointwise uncertainty, estimated in sentence-embedding space from the deltas between consecutive sentences. Given a candidate sentence transition, we score…
Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching (arxiv.org) This paper describes an approach to hallucination detection and mitigation using a HOPE-inspired Nested Learning architecture with Continuum Memory Systems (CMS) and semantic similarity caching, tested on a hybrid benchmark of 310 prompts…
UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations (arxiv.org) Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input. Effective mitigation requires token-level localization, enabling target…
Actionable Hallucination Detection: Translating Latent Uncertainty into Agentic Critique (arxiv.org) Large Language Models (LLMs) deployed as AI agents frequently exhibit user specification-grounding failures, executing hallucinated, undesired actions to force a resolution rather than expressing uncertainty. Existing detection methods fai…
Wiener Representation Filtering for VLM Hallucination Suppression (arxiv.org) Vision-language models (VLMs) excel at open-ended captioning and visual QA but often describe objects, attributes, or relations absent from the image, a phenomenon known as object hallucination. We propose a {training-free, post-hoc repres…
BibTeX Citation Errors in Scientific Publishing Agents: Evaluation and Mitigation (arxiv.org) Large language models with web search are increasingly used in scientific publishing agents, yet they produce BibTeX entries with pervasive field-level errors stemming from omission, partial corruption, substitution, and hallucination. We…
Citation Grounding Measures the Oracle: Graph Coverage Determines Reported LLM Hallucination Rates in Law (arxiv.org) Verifying LLM-generated legal citations against a graph of citations extracted from real court decisions is an appealing way to measure hallucination at scale: no annotators, no reference answers. We show that what such a metric reports is…
TGIF: Text-Guided Layer Fusion Mitigates Hallucination in Multimodal LLMs (arxiv.org) Multimodal large language models (MLLMs) typically rely on a single late-layer feature from a frozen vision encoder, leaving the encoder's rich hierarchy of visual cues under-utilized. MLLMs still suffer from visually ungrounded hallucinat…
A Grounded and Decomposed Framework for Relation-Level Hallucination Evaluation in Abstractive Summarization (arxiv.org) Abstractive text summarization systems frequently generate fluent yet unfaithful summaries by fabricating or distorting relationships between entities and events. Such relation-level hallucinations undermine the reliability of generated su…
Prompt Embedding Probes (PEP): Hallucination Detection in LLMs from Hidden States (arxiv.org) Large language models (LLMs) can generate fluent and useful responses but remain prone to hallucinations. We introduce Prompt Embedding Probes (PEP), a white-box method for answer-level hallucination detection from the hidden states of a f…
Unified Hallucination Fuzzing for Multimodal Large Language Models (arxiv.org) Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applications. Existing evaluations, predominantly based on static benchmarks, suffer from narrow…
Hallucination-Free GUI Grounding via Regression-Free Layout-Aware Matching (arxiv.org) GUI agents are shifting from metadata-dependent large language models to purely visual multimodal large language models (MLLMs) that operate directly on screenshots. The core task, GUI grounding, requires translating abstract user instruct…
REIN: Bridging the Gap between Reasoning and Reliability via Reflection and Abstention Alignment (arxiv.org) Large reasoning models (LRMs) are prone to hallucination, which undermines their reliability and poses challenges for safe deployment. Hallucinations in LRMs arise from two distinct failure sources: reasoning hallucination, where flawed in…
An Agentic AI Framework Overcomes Fundamental Limitations of Large Language Models for Glaucoma Detection from Fundus Photography (arxiv.org) Large language models (LLMs) show promise in medical image interpretation but suffer from hallucination, limited accuracy, and run-to-run inconsistency. We developed and validated an agentic AI framework integrating LLMs with specialized d…
Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination (arxiv.org) Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image. Prior work largely attributes this to insufficient visual attention.
Weaver🕷️ has been Updated to v.13! (www.reddit.com via reddit) Weaver🕷️ has been Updated to v.13 and its our biggest drop yet! This update brings #Weaver one step closer to being the best overall agent for small-midsize LLM coding.
Overconfidence/hallucination (www.reddit.comhttps) So yesterday, I set up a scheduled task that requires Claude to pull important financial news from the internet and organize it into a 5-minute read at 10.00 AM daily. I specifically asked it whether my laptop needs to be awake for the sch…
Decomposed Entailment for Factuality Checking and Hallucination Detection (arxiv.org) The reliability of Large Language Models (LLMs) is often compromised by factual inconsistencies, including hallucinations---cases where generated content is not supported by the underlying source. We present HallDetect, a lightweight, refe…
Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift (arxiv.org) Hallucination remains a fundamental challenge in vision-language models (VLMs), where autoregressive generation may produce linguistically plausible yet physically inconsistent or visually ungrounded responses due to likelihood maximizatio…
Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework (arxiv.org) While retrieval-augmented generation systems partially address the hallucination issues in large language models, it also introduces new vulnerabilities to knowledge corruption attacks. Adversaries exploit these vulnerabilities by poisonin…
UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space (arxiv.org) Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions are not grounded in visual evidence. Existing black-box hallucination detection methods e…
StuPASE: Towards Low-Hallucination Studio-Quality Generative Speech Enhancement (arxiv.org) Achieving high perceptual quality without hallucination remains a challenge in generative speech enhancement (SE). A representative approach, PASE, is robust to hallucination but has limited perceptual quality under adverse conditions.
PASE: Leveraging the Phonological Prior of WavLM for Low-Hallucination Generative Speech Enhancement (arxiv.org) Generative models have shown remarkable performance in speech enhancement (SE), achieving superior perceptual quality over traditional discriminative approaches. However, existing generative SE approaches often overlook the risk of halluci…
KnowHal: A Knowledge-Driven Benchmark for Comprehensive Multimodal Hallucination Evaluation (arxiv.org) Hallucination remains a critical challenge for developing trustworthy Multimodal Large Language Models (MLLMs). While existing benchmarks mainly focus on entity, attribute, and relation hallucinations, knowledge-related failures are often…
HalluTruthQA-4K: A Fine-Grained Corpus and Annotation Process for Arabic Hallucination Detection and Truth Verification (arxiv.org) Large language models can generate fluent Arabic answers while introducing factual errors that are difficult to identify and verify. Existing Arabic hallucination resources often assign a binary label to an entire response, indicating whet…
Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models (arxiv.org) Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context. While recent hallucination detection methods have explored various features derived fro…
Can Humans Dream of Electric Sheep? Human-Written Samples for Fine-Grained Vision-and-Language Hallucination Benchmarking (arxiv.org) In an age of rapid model turnover, how do we make hallucination evaluation more perennial? We explore whether human-written hallucination samples could take the place of model-generated hallucinations, in order to make benchmarking detecti…
Has anyone used Claude Code to build a walkable, photorealistic 3D tour of a property? (www.reddit.com via reddit) Hi all, I've been using Claude Code to build a photorealistic, walkable 3D tour of interior properties, the kind of thing you walk around like a video game (just walking, simple gameplay). Real estate use case, so it has to stay faithful t…
Opus 5 reminds me of the earlier days of AI with hallucination fatigue (www.reddit.com via reddit) The thing is Opus 5 occasionally hits a home run, requires minimal re-prompting, and just gets things right. Sometimes it does a perfect deep research run on exactly what I'm looking for.
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models (arxiv.org) The increasing use of vision-language models (VLMs) in healthcare applications presents great challenges related to hallucinations, in which the models may generate seemingly plausible results that are in fact incorrect. Such hallucination…
ASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM (arxiv.org) Multimodal large language models (MLLMs) frequently hallucinate by over-committing to spurious visual cues. Prior remedies-Visual and Instruction Contrastive Decoding (VCD, ICD)-mitigate this issue, yet the mechanism remains opaque.
TimeCapsule: Generative Hallucination as a Method for Historical Sensemaking (arxiv.org) Large Language Models (LLMs) are temporally overexposed: trained on vast contemporary corpora, they encode present-day concepts that make them unreliable narrators of the past. We present TimeCapsule, a 1.2B-parameter LLaMA-style causal mo…
HALLELUAI: A Hallucination-Aware AI System for Ultra-Realistic Image-to-Video Generation at Scale (arxiv.org) AI-generated video is increasingly used across marketing, product storytelling, and creative workflows, yet automated; high-precision quality control remains a major constraint to scaling production. We present HALLELUAI, an end-to-end sys…
The Cost of Knowing: A Resource-Aware Protocol for Benchmarking Hallucination Beyond Static Leaderboards (arxiv.org) On standard factuality tasks, frontier models now cluster near the top of the scale. The question is therefore shifting from how factual a system is toward how much compute that factuality costs.
TRE: Training-Free Hallucination Detection for Diffusion Language Models (arxiv.org) Diffusion large language models (D-LLMs) have recently gained increasing attention, yet their reliability is significantly hindered by the hallucination problem. Existing hallucination detection approaches for D-LLMs mainly follow a traini…
Schema-Aware Localisation (SAL): Live Schema Grounding and Hallucination Validation for Oracle NL2SQL (arxiv.org) Large language models can generate fluent SQL from natural language, but on real enterprise Oracle databases they frequently fail at execution time: columns and aliases are hallucinated and dialect-specific syntax is missed, leading to ORA…
Neural Message-Passing on Attention Graphs for Hallucination Detection (arxiv.org) Large Language Models (LLMs) often generate incorrect or unsupported content, known as hallucinations. Existing detection methods rely on heuristics or simple models over isolated computational traces such as activations, or attention maps.
Hallucination Rates in Language Generation (arxiv.org) Language generation in the limit is an elegant model introduced by Kleinberg and Mullainathan [KM24] to formally study language generation by an algorithm that learns solely based on example strings. In this model, an algorithm is said to…
D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models (arxiv.org) Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to be internally represented by the model. We study hallucination detection from the geometry…
Reasoning Denoiser: Denoising Reasoning Traces for Hallucination Detection in Large Reasoning Models (arxiv.org) Large reasoning models (LRMs) generate long reasoning traces before producing final answers. While these traces may contain useful signals for hallucination detection, harnessing them is non-trivial because long trajectories often include…
Opus 4.8 consistently confuses "claude" and "droid" – likely near-identical token embeddings? (www.reddit.com via reddit) Ran into a funny consistent hallucination bug with Opus 4.8 today. I was talking about my dotfiles symlink structure for local Claude skill configurations: The symlink ~/.claude/skills points to ~/dotfiles/claude/skills The model repeatedl…
Chemical Chain-of-Thought Functions as a Hallucination-Prone Molecular Scratchpad (arxiv.org) Chemical reasoning language models are expected to derive molecular answers through faithful chain-of-thought (CoT). However, across four reasoning model families and twelve chemistry tasks, hallucination is widespread and largely decouple…
Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs'Hallucinations (arxiv.org) Existing LLM hallucination mitigation methods, including prompt engineering and model optimization, either hardly alter models'internal knowledge or have poor cross-domain generalization. Contrastive decoding mitigates hallucinations by us…
Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs (arxiv.org) The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited interpretability and the hallucination risk of large language models (LLMs). Existing CN…
Personalized Recommendation Tool Learning via Autonomous Language Agents (arxiv.org) Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length lim…
HalluTruthQA: A Fine-Grained Benchmark for Hallucination Detection, Localization, and Explanation in Arabic Question Answering (arxiv.org) Large language models (LLMs) can generate fluent Arabic answers, yet factual errors remain difficult to detect, localize, explain, and verify. Existing hallucination benchmarks often provide response-level labels, with limited support for…
Prompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language Models (arxiv.org) Practitioners make three prompt-design decisions with almost no controlled evidence behind them: how to format instructions and context (markdown, plain text, prose, or tabular), how many simultaneous instructions a system prompt can carry…
Operational Hallucination and Safety Drift in AI Agents (arxiv.org) Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn execution. While single-turn safety mechanisms are relatively mature, extended interactions reveal structura…
Retromorphic Testing with Hierarchical Verification for Hallucination Detection in RAG (arxiv.org) Large language models can still hallucinate in retrieval-augmented generation (RAG), producing claims that are unsupported by or conflict with the retrieved context. Detecting such errors remains challenging when faithfulness is judged sol…
Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain (arxiv.org) Multimodal large language models (MLLMs) have shown strong potential for medical Visual Question Answering (VQA), yet they remain prone to hallucinations, defined as generating responses that contradict the input image, posing serious risk…
Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI (arxiv.org) Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output that is simply not true. The common response is to wait for a model that does not hallucinate.
Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection (arxiv.org) Existing hallucination detection methods are typically conducted at the inference stage, without making any modifications to the model itself. In this paper, we are interested in exploring fine-tuning strategies that enhance the detectabil…
UndoMCP — A "Ctrl-Z" for AI agent MCP actions (www.reddit.com via reddit) I've been working on an open-source tool called UndoMCP. The tool is meant to be a "Ctrl-z" for MCP changes made by your AI agent, meaning if your AI agent makes some critical error due to AI hallucination, you can safely undo it even mean…
Slop time is over!!! time to build real stuff (www.reddit.com via reddit) As a non-developer, I began using Claude Code a year ago to build hundreds of tools, apps, and web concepts. However, I consistently ran into the same issue: hallucinations and untrustworthy code, which made it impossible for me to safely…
Claude auto-dev HiL workflow (www.reddit.comhttps) Knowing that some vibecoders find it hard to move beyond one-shotting and those more experienced in development struggle with LLM hallucination and result/test-faking and exaggeration, I thought I'd share a high-level overview of my self-h…
Weird hallucination - Fable Hallucinated a whole conversation any idea how? (www.reddit.com via reddit) I have a manager his name is not Marcus. I have never mentioned a Marcus or data centers in my time at this job working with claude.
When was Claude able to drop images from searches? (www.reddit.comhttps) Is this new or am I tweaking? I could swear it couldn't do it before, but it's been a minute Also, classic hallucination in there.
Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection (arxiv.org) Large language models are increasingly used in scientific writing, yet they can fabricate citation-shaped references that appear plausible but fail bibliographic verification. Existing detectors often reduce verification to binary found/no…
Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Retrieval, Reasoning, and Hallucination in Long-Context LLMs (arxiv.org) As Large Language Models (LLMs) increasingly utilize massive context windows as working memory for autonomous tasks, their reliability fluctuates significantly depending on how information is distributed in real-world corpora. We investiga…
How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement (arxiv.org) As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail. Prompt injection attacks, as well as hallucination, can cause agents to leak private information to third parties.
↯ Security↯ Hallucinationprompt-injectionhallucinationsecurity
Protective Capacity Hallucination: When Large Language Models Claim Nonexistent Capabilities (arxiv.org) When cast as the protector of a vulnerable user yet given no explicit capability boundary, a large language model (LLM) may respond not by acknowledging its limits but by claiming to have taken -- or to be taking -- a real-world protective…
Attractor Geometry of Transformer Memory: From Conflict Arbitration to Confident Hallucination (arxiv.org) Language models draw on two knowledge sources: facts baked into weights (parametric memory, PM) and information in context (working memory, WM). We study two mechanistically distinct failure modes--conflict, when PM and WM disagree and int…
Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation (arxiv.org) While recent advances in 3D generation have enabled impressive visual synthesis, existing methods often rely on 2D diffusion supervision without explicit mechanisms for geometric consistency, leading to spatial hallucinations such as dupli…
Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs (arxiv.org) Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions need not hold up under formal scrutiny. We present EG-VAR (Evidence-Grounded Verified Age…
Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction (arxiv.org) Rubric-based evaluation is a promising approach for assessing open-ended outputs from LLM-based research agents, particularly in paper reproduction, where direct paper-to-repository comparison is prone to hallucination. However, constructi…
Have LLMs plateaued ? (www.reddit.com via reddit) Basically just the title: Have LLMs plateaued? I use claude, gpt, and gemini models daily (mostly claude), and for the past few months, beyond the hype and benchmark maxing, I haven't seen that much of a difference.
Claude answered a question that wasn't mine (www.reddit.com via reddit) Typed a simple prompt into the Claude app on my iPad (Fable 5): "Pick a number 1-100, a planet, and a shape." Typed it myself, didn't paste anything. Claude replied with a detailed writeup about a security product called "Zenith Gateway" —…
Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination (arxiv.org) The application of lightweight Large Language Models in rule-based scientific domains remains severely limited by their tendency to mimic linguistic patterns rather than reproduce axiomatic reasoning, causing frequent hallucinations. Here,…
Hallucination Self-Play: Bootstrapping Reinforced Detector via Evolved Generator (arxiv.org) Identifying faithfulness hallucinations in LLM-generated outputs remains challenging due to the scarcity of high-quality annotated data. Recent work relies on advanced LLMs to synthesize training data, including rationales, labels, and hal…
Huh? (www.reddit.com via reddit) https://preview.redd.it/j5jqls0h1bch1.png?width=657&format=png&auto=webp&s=d5790c2baf52153c2090020bc3a6a950dacfb728 Opusplan just called itself Opus 5 in one of my git commits? This is hallucination right?
Claude skill for consultants that reviews AI generated technical proposals and project documents (www.reddit.com via reddit) I’ve been using LLMs to draft proposals, SOWs, solution docs and decks, the hard part has always been reviewing those multi-page drafts: Are the documents fully compliant with requirements? Are the facts current and verifiable (not stale t…
Is there any way to force Opus 4.8 to think through all of its responses? (www.reddit.com via reddit) I can't stand adaptive thinking. For some projects it's fine, but for others, like this one, it literally makes the model unusable.
HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models (arxiv.org) Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidence. Prior work mainly focuses on detecting or suppressing hallucinations at generation tim…
My "couch project" got me a pitch next week. Need brutal feedback on this 2-min multi-agent demo. (www.reddit.com via reddit) Hey everyone, I’ve spent the last year building and deploying multi-agent systems in production for high-stakes enterprise environments at work. (like contract auditing and complex strategy analysis).
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization (arxiv.org) Large reasoning models (LRMs) improve language model capabilities by generating explicit thinking traces before final answers. In factuality-oriented question answering (QA), such thinking often improves overall performance by helping the…
My agent broke the one hard rule I wrote for it on its first run (www.reddit.com via reddit) I'm a product manager, not an engineer. I build with AI and I don't read code fluently, so where I actually add value is the spec.
A multilingual hallucination benchmark: MultiWikiQHalluA (arxiv.org) Most hallucination evaluations focus on English, leaving it unclear whether findings transfer to lower-resource languages. We investigate faithfulness hallucinations, defined as model-generated content that is fluent and plausible but dive…
CrossHallu: Do Hallucination Signals Generalize Across Languages and Domains in Large Language Model's Internals? (arxiv.org) Recent hallucination detection techniques in large language models (LLMs) focus on directly extracting features from a model's internal representations and training a classifier on these features to detect hallucinations, demonstrating pro…
Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias (arxiv.org) Large Language Models (LLMs) have demonstrated advanced capabilities but often suffer from factual inaccuracies (hallucinations) and systematic biases. These issues, sometimes amplified in specific architectures like Mixture-of-Experts (Mo…
Towards Mitigation of Hallucination for LLM-empowered Agents: Progressive Generalization Bound Exploration and Watchdog Monitor (arxiv.org) Empowered by large language models (LLMs), intelligent agents have become a popular paradigm for interacting with open environments to facilitate AI deployment. However, hallucinations generated by LLMs-where outputs are inconsistent with…
From Judgments to Issues: Structured Extraction of Legal Reasoning with Citation-Hallucination Control (arxiv.org) We present an automated pipeline that decomposes Italian tax-court judgments into individual legal issues and extracts, for each issue, a structured XML representation grounded in the IRAC framework and the legal syllogism. The pipeline ta…
Has anyone had something like this happen? Apparent agent hallucination. (www.reddit.comhttps) I confirmed it wasn't a cwd error.
Extraordinary Sonnet 5 Hallucination (www.reddit.comhttps) was finding my way around vital at 1am as you do, and genuinely got startled at this response. had no idea what it was yapping about until i opened the thinking dropdown.
↯ Security↯ Hallucination↯ Jailbreak↯ Sonnet 5jailbreakhallucinationsecurity+1
I found a problem-solving approach that almost guarantees a problem solution (www.reddit.com via reddit) Ever since using this prompt, I get a working solution almost every time, or claude will tell me why it didn’t work. I call it ARPI, and it‘s now a part of all my system prompts and I end every prompt with “use arpi” it stands for “assess,…
Grounded Optimization: A Layered Engineering Framework for Reducing LLM Hallucination in Automated Personal Document Rewriting (arxiv.org) Large language models (LLMs) are increasingly applied to resume optimization for applicant tracking systems, introducing hallucination failures distinct from general text generation: anachronistic technology injection, cross-domain termino…
Why does Claude sometimes behave oddly when challenged? (www.reddit.com via reddit) I’ve noticed that if you question one of its answers, it may suddenly reply with something like, “Ah, my fault,” and completely reverse its position. It feels less like genuine reasoning and more like it is trying to agree with the user.
Beyond Document Grounding: Span-Level Hallucination Detection over Code, Tool Output, and Documents (arxiv.org) Hallucination detection for retrieval-augmented generation (RAG) is usually evaluated on natural-language document evidence. However, grounded generation systems increasingly rely on structured inputs: source code, developer-tool output, m…
Readable but Not Controllable: Neuron-Level Evidence for Medical LLM Hallucination (arxiv.org) Hallucination remains one of the central obstacles to deploying medical LLMs. Yet, even when hallucination can be detected, it is still unclear whether the internal representations associated with it can be used for control rather than det…
Information-Regularized Attention for Visual-Centric Reasoning (arxiv.org) Vision-language models (VLMs) have become a paradigm for multimodal learning, yet remain unstable due to object hallucination, weak visual grounding, and catastrophic forgetting after full-parameter instruction tuning. We claim these failu…
CORTEX: Token-Level Hallucination Detection in RAG via Comparative Internal Representations (arxiv.org) In this paper, we propose CORTEX, a token-level hallucination detection method for Retrieval-Augmented Generation (RAG). In long-form RAG outputs, hallucinations often arise in localized spans rather than throughout an entire response.
Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection (arxiv.org) Large Language Models (LLMs) often hallucinate, limiting their reliability in sensitive applications. In black-box settings, several self-consistency-based techniques have been proposed for hallucination detection.
ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs (arxiv.org) Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating content inconsistent with the provided image. In this paper, we identify an internal signature of hallucination: progressive degradation of text-…
Citation Discipline in Spec-Driven Development: A Cross-Model Empirical Study of Output Determinism and Automated Hallucination Detection in LLM-Generated Code (arxiv.org) Spec-Driven Development (SDD) frameworks guide Large Language Model (LLM)-powered code generation through formal specifications, yet they differ fundamentally in how they enforce traceability between requirements and generated code. This p…
Weird time perception (www.reddit.com via reddit) Anyone else been noticing claude having totally no clue about the passage of time? Lately i’ve had a few instances where it tells me, “we discussed x 2 days ago” when in fact it was an hour ago into the same chat.
BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali (arxiv.org) Despite Bengali being the sixth most spoken language in the world, no prior work has systematically evaluated hallucination in large language models (LLMs) for Bengali. We introduce BenHalluEval, a fine-grained hallucination evaluation fra…
How Far Can You Get Without a GPU? A Systematic Benchmark of Lightweight Hallucination Detection Across Question Answering, Dialogue, and Summarisation (arxiv.org) Hallucination detection has become a pressing requirement for trustworthy AI deployment at scale. The most accurate detection methods depend on GPU-intensive inference, proprietary API calls, or white-box access to the generating model.
SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution (arxiv.org) Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers emit only opaque binary labels, leaving agents unable to self-correct and operators unab…
AURORA: Asymmetry and Update-Induced Rotation for Robust Hallucination Detection in Large Language Models (arxiv.org) Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency to generate hallucinations, namely factually incorrect or unfaithful outputs, poses a…
5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control (arxiv.org) We introduce 5ting, our system for the SemEval2026 Task 8 (MTRAGEval), which evaluates multi-turn Retrieval Augmented Generation (RAG) systems. Multi turn RAG involves context drift, under specification, and hallucination risk.
From Dispersion to Attraction: Spectral Dynamics of Hallucination Across Whisper Model Scales (arxiv.org) Hallucinations in large ASR models present a critical safety risk. In this work, we propose the \textit{Spectral Sensitivity Theorem}, which predicts a phase transition in deep networks from a dispersive regime (signal decay) to an attract…
Grounded Iterative Language Planning: How Parameterized World Models Reduce Hallucination Propagation in LLM Agents (arxiv.org) World models for language agents come in two useful forms. An agent-based world model calls an LLM API and reasons flexibly in language, but its errors appear as hallucinated state changes that are hard to score with ordinary regression lo…
Hallucination in World Models is Predictable and Preventable (arxiv.org) Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics. We hypothesize that hallucination con…
From Hallucination to Grounding: Diagnosing Visual Spatial Intelligence via CRISP (arxiv.org) Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, th…
TAVR-VLM: Risk-Conditioned Causal Grounding for Hallucination-Resistant Report Generation (arxiv.org) Transcatheter Aortic Valve Replacement (TAVR) planning requires meticulous multimodal reasoning. However, adapting Multimodal Large Language Models (MLLMs) to this high-stakes domain is severely impeded by diagnostic hallucinations, where…
New sampler + verifier *drastically* improves tiny 0.5b model coding performance (arxiv.org via reddit) I read it with a little bit of effort The tiny model result is insane, theoretically this could make make a 0.5b on-par with a 2/3/4b ish class model in coding with no weights change*. And for large models it could maybe fix let's say 30-5…
MedBench v5: A Dynamic, Process-Oriented, and Hallucination-Aware Benchmark for Clinical Multimodal Models (arxiv.org) Existing medical AI benchmarks lack process visibility, atomic skill evaluation, and integrated hallucination detection. We introduce MedBench v5, a redesigned benchmark for clinical multimodal models (language, vision-language, and agent…
Grad Detect: Gradient-Based Hallucination Detection in LLMs (arxiv.org) Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet they remain prone to generating hallucinations. Detecting these hallucinations is critical for deploying LLMs reliably in high-stakes applicat…
A Benchmark for Hallucination Detection in VLMs for Gastrointestinal Endoscopy (arxiv.org) Vision-language models (VLMs) are prone to hallucination, which remains a major barrier to their safe deployment in clinical practice. To date, most hallucination detection methods have been evaluated on radiology benchmarks such as MIMIC-…
Pre-Generation Hallucination Detection in Large Language Models via Soft-Target Attention Probing (arxiv.org) Detecting hallucination risk before generation enables abstention, retrieval augmentation, and routing decisions without incurring the cost of decoding. While prior work has shown that such risk can be estimated from a model's internal rep…
MedHal-Loc: Are "Explainable-by-Architecture" Medical Hallucination Detectors Faithful Localizers? A Localization Benchmark (arxiv.org) Detecting hallucinations in clinical text is increasingly framed as an explainability problem: systems should not merely flag an unreliable response but point to the offending span. Architectures built around knowledge-graph (KG) triple de…
Finetuning with Scientific Data Increases Hallucinations: A Multi-domain Factuality Evaluation of LLMs (arxiv.org) Large language models (LLMs) are increasingly used to communicate and explain scientific concepts, yet their tendency to hallucinate poses significant risks in this high stakes use-case. Prior hallucination evaluation work remains largely…
Who Checks the Citations? Benchmarking Legal Hallucination Detection (arxiv.org) Attorneys, judges, and pro se filers increasingly use AI to draft legal documents, yet these tools frequently fabricate citations. Despite predictions that newer models would hallucinate less or that court sanctions would deter negligent f…
A Multi-Agent Audit Framework for High-Stakes Reasoning: Evaluation and Interpretability in Clinical Mental Health Screening (arxiv.org) High-stakes reasoning tasks necessitate transparent and verifiable workflows, yet conventional single-model large language models (LLMs) often struggle with hallucination and low interpretability under zero-shot paradigms. To address this…
Attention at Rest Stays at Rest: Breaking Visual Inertia for Cognitive Hallucination Mitigation (arxiv.org) Like a body at rest that stays at rest, we find that visual attention in multimodal large language models (MLLMs) exhibits pronounced inertia, remaining largely static once settled during early decoding steps and failing to support the com…
From Text Metrics to Model Internals: A Study of Whisper ASR Hallucination Detection (arxiv.org) Hallucinations of ASR models - fluent transcriptions with no basis in audio - degrade system performance and pose risks in downstream applications. Robust detection of such errors remains a challenge.
SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis (arxiv.org) Gastrointestinal cancers represent a growing health burden in the South Asian region, driven largely by rapid changes in socio-economic conditions & lifestyle habits. However, early diagnosis of such malignancies remains a significant chal…
TTFT-Aware Graph Chain-of-Thought:Distance-Indexed Neural A* for Low-Hallucination Multi-Hop Medical Reasoning (arxiv.org) Hallucinations and opaque reasoning remain unacceptable failure modes for clinical LLMs. We present a production-grade GraphRAG stack that constrains answers to verifiable graph chain-of-thought paths in a heterogeneous, ~700K-node medical…
Hallucination as Context Drift: Synchronization Protocols for Multi-Agent LLM Systems (arxiv.org) Multi-agent LLM systems routinely produce hallucinated outputs that cannot be explained by model deficiencies alone. A significant class of these failures arises not from model incapacity but from context drift: the divergence of internal…
I ran one Claude session for a month (~25k events, 6 compactions) on a hand-curated markdown memory, then audited it 7 ways for hallucination. Method, the one error it found, and the config that actually matters. (www.reddit.com via reddit) TL;DR. Markdown memory files are a well-trodden idea (nothing novel there).
Thermodynamic Signatures of Reasoning: Free-Energy and Spectral-Form-Factor Diagnostics for Hallucination Detection in Large Language Models (arxiv.org) Hallucination detection in large language models (LLMs) is deployment-critical, and recent work shows that the spectrum of attention-derived graph Laplacians carries strong signal about reasoning quality. Prior spectral diagnostics, howeve…
Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads (arxiv.org) While large language models (LLMs) have become highly capable, they remain prone to factual inaccuracies, commonly referred to as "hallucinations." Uncertainty quantification (UQ) offers a promising way to mitigate this issue, but most exi…
Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications (arxiv.org) Recent advances in Large Language Models (LLMs) and multi-agent systems have driven the rise of Agentic AI, showing promise for medical reasoning. However, open-ended conversational agents remain prone to two critical failure modes: premat…
LegalHalluLens: Typed Hallucination Auditing and Calibrated Multi-Agent Debate for Trustworthy Legal AI (arxiv.org) AI systems deployed in legal workflows hallucinate at rates that aggregate metrics report at ~52%, but this average conceals where errors concentrate and in which direction they run, leaving compliance officers without an actionable signal…
Islamic Large Language Models: From Knowledge Acquisition to Trustworthy and Hallucination-Resistant AI (arxiv.org) Large language models (LLMs) are increasingly used for knowledge-intensive question answering, including religious and legal questions. Islamic knowledge is a particularly demanding setting: answers are expected to be grounded in authorita…
BALTO: Balanced Token-Level Policy Optimization for Hallucination Mitigation (arxiv.org) Hallucinations remain a major obstacle to deploying large language models (LLMs) in knowledge-intensive settings, where generated responses must be faithfully grounded in provided evidence. Reinforcement learning (RL) is a promising direct…
Mitigating Object Hallucinations in LVLMs via Attention Imbalance Rectification (arxiv.org) Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driving and medical i…
A Unified Definition of Hallucination: It's The World Model, Stupid! (arxiv.org) Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs. Why is this?
LLM-as-Code Agentic Programming for Agent Harness (arxiv.org) Every major LLM agent framework gives the LLM the role of orchestrator; the model decides what to do next, when to call tools, and when to stop. We argue that token explosion, control-flow hallucination, and unreliable completion are not i…
Mitigating Visual Hallucinations in Multimodal Systems through Retrieval-Augmented Reliability-Aware Inference (arxiv.org) Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-language understanding and natural-language response generation. However, these systems can still produce overconfident predictions and hallucination-…
Took apart Claude Code & Claude Desktop. Found >6 different system prompt variants. (www.reddit.com via reddit) I was taking Claude Code (CC) and Claude Desktop (CD) apart during the weekend to understand how to solve a particular problem over the weekend on my own AI harness. Got Claude Code to take apart the CLI (bun, Mach-O) and desktop app (Elec…
ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning (arxiv.org) Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support. Existing medical hallucination benchmarks mainly focus on data collection, but often ignore where hallucinations orig…
What do you think about this prompt guys? any suggestions? (www.reddit.com via reddit) My goal is to make AI to be less hallucinate and here's the prompt: You are a subject matter expert across multiple disciplines. Adapt your depth, tone, and framing to match the nature of each query.
Layer-Resolved Optimal Transport for Hallucination Detection in NMT and Abstractive Summarization (arxiv.org) Optimal transport (OT) has been shown to detect hallucinations in neural machine translation (NMT) by measuring the geometric distance between cross-attention distributions and a reference distribution, without any supervision. We extend t…
SafeLLM: Extraction as a Hallucination-Resistant Alternative to Rewriting in Safety-Critical Settings (arxiv.org) Large language models (LLMs) are increasingly used to access organisational documentation, including standard operating procedures (SOPs), HR policies and institutional guidelines. However, retrieval-augmented generation (RAG) systems that…
HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation (arxiv.org) Large Language models (LLMs) have shown strong capabilities in code review automation, such as review comment generation, yet they suffer from hallucinations -- where the generated review comments are ungrounded in the actual code -- poses…
Intelligence as Managed Autonomy: Failure, Escalation, and Governance for Agentic AI Systems (arxiv.org) As autonomous and agentic AI systems scale in robotic and human-machine environments, managing hallucination and persistent but unjustified action remains an open challenge. Rather than attributing these failures solely to model or alignme…
Quickest Detection of Hallucination Onset: Delay Bounds and Learned CUSUM Statistics (arxiv.org) Token-level hallucination detectors are evaluated as classifiers, by AUC over all tokens, yet a streaming monitor is judged by its reaction time: the number of tokens that pass between the onset of a hallucination and the alarm. We formula…
Hallucination in Medical Imaging AI: A Cross-Modality Analytical Framework for Taxonomy, Detection, and Mitigation under Regulatory Constraints (arxiv.org) AI systems are being deployed across medical imaging faster than their failure modes are understood. At this point in time, the failure of greatest clinical concern is hallucination: clinically plausible but factually incorrect outputs, in…
Zero-source LLM Hallucination Detection with Human-like Criteria Probing (arxiv.org) Large language models (LLMs) often hallucinate by generating factually incorrect or unfaithful content, posing significant risks to their safe use. Detecting such hallucinations is particularly challenging under the zero-source constraint,…
claude’s biggest weakness isn’t hallucination. it’s agreement. i asked “is this a good idea?” 20 times. it said yes 18 times. 2 of those were terrible ideas. (www.reddit.com via reddit) tracked this deliberately over a month. asked claude "is this a good idea?" or "does this approach make sense?" on 20 different occasions.
The most expensive bug in vibecoding isn't in the code. (www.reddit.com via reddit) 3 months ago I lost three days to a feature nobody needed. Not because Claude wrote bad code.
Fable 5 Max confidently wrong about PDF encryption status (www.reddit.com via reddit) I just ran into a bizarre hallucination with Fable 5 Max regarding file analysis. i uploaded several PDF to Fable 5 Max, and out of two of it claude completely refused to process it, claiming the files was password-protected.
↯ Hallucination↯ DeepSeek 4↯ DeepSeek 4↯ DeepSeek 4↯ DeepSeek 4↯ DeepSeek 4hallucinationdeepseek
Claude Fable 5 Finally 1-shots my hallucination benchmark that held until Opus 4.8 Max (www.reddit.com via reddit) As a software engineer with 25 years experien....who am I kidding. As a gamer who likes to indulge in all sorts of things, I have had a simple prompt to test the hallucination potential on the Opus models on my own "car wash drive" type of…
An Industrial-Scale Insurance LLM Achieving Verifiable Domain Mastery and Hallucination Control without Competence Trade-offs (arxiv.org) TruthRL: Incentivizing Truthful LLMs via Reinforcement Learning (arxiv.org) While large language models (LLMs) have demonstrated strong performance on factoid question answering, they are still prone to hallucination and untruthful responses, particularly when tasks demand information outside their parametric know…
Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity (arxiv.org) Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confidence score and abstains when confidence is low. Unsupervised sampling detectors (Semantic…
Our ICML paper on predictable hallucination (information-budget abstention gate), + ntkMirror: a training-free open-weight implementation we're releasing today (www.reddit.com via reddit) Our paper, Predictable Compression Failures: Order Sensitivity and Information Budgeting for Evidence-Grounded Binary Adjudication, was accepted at ICML 2026. Paper: https://arxiv.org/abs/2509.11208 The idea: in evidence-grounded QA, the o…
Steer Where It Matters: Token-Level Visual-Sensitivity Steering for LVLMs Hallucination Mitigation (arxiv.org) Cross Paraphrastic Invariance Learning for Hallucination Detection (arxiv.org) From Architecture to Output: Structural Origins of Hallucination in Large Language Models and the Amplifying Role of Data (arxiv.org) Constrained Paraphrase Consistency for LLM Hallucination Detection (arxiv.org) BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models (arxiv.org) I built a tool so two Claude Code instances can negotiate an API contract without stepping on each other (www.reddit.com via reddit) The problem: you have two Claude Code sessions on opposite sides of an API. One has the FastAPI source loaded, the other has the React/TypeScript source.
Meet My AI Government and Legal Agents: Research, Analysis, Drafting, and Execution (www.reddit.com via reddit) Whisper Hallucination Detection and Mitigation via Hidden Representation Steering and Sparse AutoEncoders (arxiv.org) Whisper, a widely adopted ASR model, is known to suffer from hallucinations - coherent transcriptions generated for non-speech audio entirely disconnected from the input. We investigate whether hallucinations can be detected and mitigated…
OpenHalDet: A Unified Benchmark for Hallucination Detection across Diverse Generation Scenarios (arxiv.org) Hallucination detection is essential for the reliable deployment of large language models (LLMs). However, existing evaluations face two core challenges: inconsistent inference configuration and evaluation, and limited coverage of downstre…
Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection (arxiv.org) Retrieval-Augmented Generation (RAG) reduces but does not eliminate hallucination in large language models. Existing detection methods rely on flat similarity between generated answers and retrieved passages, ignoring structural relationsh…
Built an agent to fix lead attribution and the hard part was nothing I expected (www.reddit.com via reddit) Been building in the lead attribution space and figured the agent part would be straightforward. Enrich the lead, classify the source, write it to the CRM.
This is a new one - Prompt Injection Detected + Hallucination, Claude Code Opus 4.8 (www.reddit.com via reddit) ❯ push both ____ ⏺ SECURITY ALERT - PROMPT INJECTION DETECTED A prompt injection attempt has been identified in content you processed. To protect the user's account, I've initiated lockdown.
↯ Security↯ Hallucination↯ Opus 4.8prompt-injectionhallucinationsecurity+2
P$^2$-DPO: Grounding Hallucination in Perceptual Processing via Calibration Direct Preference Optimization (arxiv.org) Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents (arxiv.org) Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level. We present a neurosymbolic architecture implemented within the Fo…
From Out-of-Distribution Detection to Hallucination Detection: A Geometric View (arxiv.org) Detecting hallucinations in large language models is a critical open problem with significant implications for safety and reliability. While existing hallucination detection methods achieve strong performance in question-answering tasks, t…
"Qwen 3 72B" doesn't exist — and it's in a surprising number of places that act like it does (www.reddit.com) spent today auditing my own model catalog and noticed 39 of my own pages confidently reference "qwen 3 72b" with apache 2.0 licensing, a 2025-09-15 release date, and a 131k context window. seemed normal — qwen 2.5 had a 72b, why wouldn't q…
My Claude audit step (www.reddit.com) I vibe coded a usertesting system, and then asked Claude to deploy this 10 parallel audit agents The Data Grounding & Hallucination Auditor The API & Connector Sentinel The Responsive UI Stress-Tester The PII & Analytics Anonymizer The Sem…
honestly, one confident hallucination cost me a client and i'm done with gpt (www.reddit.com) I'm a mechanical engineer working in B2B sales, so not really a coding guy . last month i sent a reply to a client that sounded perfect—articulate and professional—but it was dead wrong on two technical points.
I’ve built a tool with Claude that reduces AI model hallucinations and answer error rates, allowing you to get far more accurate results when asking AI models questions. (www.reddit.com) I built ZosyAI using Claude to tackle a problem I kept running into: AI models hallucinate, and unless you're a domain expert, you can't tell when it's happening. Even the best models — Claude included — can't guarantee 100% accurate answe…
I stopped writing 500-word guardrail prompts. This 8-line template works better. (www.reddit.com) I used to spend hours writing massive, obsessive system prompts for my RAG apps. I’d have ten different refusal examples, "never do X," "always check Y," and a whole paragraph of the model role-playing as a "safe and truthful assistant." I…
↯ Security↯ Hallucination↯ Jailbreakjailbreakhallucinationrag+1
Grok hallucinations (www.reddit.com) Grok is supposedly the lowest-hallucination model according to the AA-Omniscience benchmark. Today I've had INSANE hallucinations from Grok 4.2 fast.
Ran my own benchmark Qwen 3.6 35B vs Gemma 4 26B.... theres a clear winner here (www.reddit.com) Uhh I guess Gemma 4 is so much shittier that it hallucinated this event that happened in china in 1989? According to qwen, nothing of significance happened at Tiananmen square in 1989 - and based on all of the benchmarks of qwen, I believe…
Is anyone else terrified of giving Cursor/Claude direct access to their database? I built an open-source solution. (www.reddit.com) Hey everyone 👋, I absolutely love using Cursor and Claude Desktop for debugging and writing queries, but the idea of hooking them up directly to my database via standard MCP (Model Context Protocol) servers has always given me anxiety. One…
↯ Hallucination↯ Model Context Protocolmodel-context-protocolhallucinationcursor+1
Stop donating your salary to OpenAI: Why Minimax M2.5 is making GPT-5.2 Thinking look like an overpriced dinosaur for coding plans. (www.reddit.com) ↯ Hallucination↯ Glm↯ Minimax↯ Swe Benchswe-benchminimaxaltman+5
A guide to setting up your own Hugging Face leaderboard: an end-to-end example with Vectara's hallucination leaderboard (huggingface.co)