Hallucinated AI & agentic coding news. Some of it is real.

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Hallucination

140 items · started 2024-01-12 · ongoing (last activity 2026-06-21)

  1. 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.

  2. TL;DR. Markdown memory files are a well-trodden idea (nothing novel there).

  3. 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…

  4. 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…

  5. 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…

  6. 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…

  7. 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…

  8. 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…

  9. 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…

  10. 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.

  11. 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…

  12. 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…

  14. 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…

  15. 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…

  16. 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?

  17. 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…

  18. 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-…

  19. 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…

  20. 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…

  21. 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…

  22. 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.

  23. 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…

  24. 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…

  25. 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…

  26. 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…

  27. 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…

  28. 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…

  29. 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,…

  30. tracked this deliberately over a month. asked claude "is this a good idea?" or "does this approach make sense?" on 20 different occasions.

  31. 3 months ago I lost three days to a feature nobody needed. Not because Claude wrote bad code.

  32. 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.

  33. 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…

  36. 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…

  42. 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.

  44. 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…

  45. 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…

  46. 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…

  47. 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.

  48. ❯ 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.

  49. Is Claude speaking Japanese mid sentence something normal. This is the first time I’ve ever encountered this situation and maybe someone can specifically explain this hallucination and what causes it.

  52. 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…

  53. 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…

  54. * 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…

  55. 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.

  56. Hallucination Detection Comparison What's the best tool for hallucination detection? We put 7 of them to the test.

  57. 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…

  58. 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…

  59. 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…

  60. 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…

  61. 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.

  62. 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…

  63. 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.

  64. 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…

  65. There are a few security focus SKILLs. We are working another new one for web app.

  66. All actual LLMs, sometimes, hallucinate, this is part of their "personalities". I made an experiment with my AI assistant.

  67. 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…

  68. 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.

  69. 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.

  70. genuine question. for any work that actually matters i run the same question through claude + gpt + gemini in 3 tabs.

  71. could not extract summary

  72. 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…

  73. Agent demos are easy. Production agents are hard.

  74. 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.

  75. 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.

  76. 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.

  77. 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.

  78. 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…

  79. 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…

  80. 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…

  81. 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.

  82. 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…

  83. 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…

  84. 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…

  85. **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.

  86. 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…

  87. 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…

  88. 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.

  89. 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…

  90. 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…

  91. It happened twice this week, Claude code hallucinates a skill name, which was captured by my local stack. I end up writing those skill.

  92. 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.

  93. 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…

  94. 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.

  95. 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.

  96. We wrapped up a did a 120-question UAT with a CMO and his team. This is where it gets funny.

  97. 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…

  98. 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.

  99. Courts are currently fixated on whether AI-generated evidence is admissible. Is the image authentic?

  100. 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…

  101. 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.

  102. 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…

  103. 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.

  104. 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.

  105. 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…

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  107. I have realised Claude answers as best as you prompt it. And I suck at it.

  108. 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…

  109. 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…

  110. 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.

  111. Grok is supposedly the lowest-hallucination model according to the AA-Omniscience benchmark. Today I've had INSANE hallucinations from Grok 4.2 fast.

  112. 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…

  113. I've been running into problems with “agent memory” while using claude when it was a pile of markdown files, started out great but became unreliable as the number of files grew. So I built Omnigraph , an open-source graph runtime for agent…

  114. 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.

  115. 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…

  116. could not extract summary

  117. A hallucination engine. Typed pseudorandom data via LLM.

  118. 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…

  119. 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…

  120. Pulse · genji970/hallucination-mitigation-via-contrastive-sampling-method

  121. 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…

  122. 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.

  123. I’m tired of seeing these LinkedIn influencers/ YouTube gurus bragging about their 12-agent swarms. Honestly, I used to be one of them.

  124. 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…

  125. 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.

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  127. 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…

  130. 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…

  131. 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?

  132. 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…

  133. 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…

  134. 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…

  135. 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…

  136. 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.

  137. 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.

  138. 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.

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