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Tool Use

240 items · started 2019-09-17 · ongoing (last activity 2026-09-19)

  1. RAG fixes what the model reads before it sees the question. Jev fixes what the model can answer before it sees the state.

  2. I've been looking at Kimi K2.6 and its Agent Swarm approach is interesting — up to 300 sub-agents and 4,000+ tool calls. But it also made me think about something I've seen in agent architectures: planner → coder → reviewer → tester → fixe…

  3. Large Language Models (LLMs) are increasingly deployed as scientific AI as- sistants, and a growing body of benchmarks evaluates their capabilities across knowledge retrieval, reasoning, code generation, and tool use. These evaluations, ho…

  4. Large language model (LLM) agents increasingly operate over long-horizon interactions involving tool use, persistent state, evolving authorization, and external environment feedback. In such settings, safety failures may emerge only after…

  5. Large language model (LLM) agents increasingly interleave natural language reasoning with external tools such as web search and code execution. These tool-use policies are often optimized via reinforcement learning (RL), which can amplify…

  6. TL;DR The gap: Existing finance benchmarks test agentic financial reasoning, data retrieval, and tool use through static, fully specified tasks. These one-off requests in clean contexts are not reflective of actual deployment.

  7. Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable but inflexible, while free-form multi-step reasoning methods such as ReAct may improve task…

  8. Many current paths to more advanced AI depend on the assumption that large language models (LLMs) can generalize learned relationships to solve complex, out-of-distribution (OOD) problems. However, this is not an easy quality to measure.

  9. Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (MCP) is considered a future trend. Current MCP evaluation sets suffer from issues such as r…

  10. We present Salesforce Koa, an enterprise language model built by post-training the open-weight Nemotron-3-Super-120B foundation model with reinforcement learning using Group Relative Policy Optimization (GRPO). Salesforce Koa is trained on…

  11. Harness design has transformed the development of LLM-based agents by integrating memory, tool use, and runtime control. However, this design also introduces security and privacy risks because malicious instructions from external sources m…

  12. Multi-Agent (MA) systems are effective at solving complex tasks that demand planning, tool use, and the synthesis of evidence from multiple sources. Existing systems typically adopt Hierarchical Manager-Worker (HMW) or Router-based Message…

  13. Deep research agents are increasingly capable of web search, tool use, multimodal evidence analysis, and information synthesis. However, existing benchmarks mainly evaluate medium-horizon exploration and rarely test whether agents can sust…

  14. About 9 months ago, I created a simple program to facilitate structured dialogues among local LLMs from the terminal. I’ve been iterating on it since.

  15. Iterative self-refinement is a popular inference-time reliability technique, but its effectiveness in code-mode tool use depends heavily on the structure of the feedback signal: unstructured critique helps inconsistently across models, and…

  16. Tool use is often assumed to monotonically improve reasoning, where external evidence is expected to help when relevant and be ignored when irrelevant. We show that this assumption fails in a state-dependent way.

  17. Agentic AI systems execute complex tasks through long-horizon workflows of planning, tool use, and multi-agent coordination. Task failures in these systems often originate from a single step, such as an injected prompt or a flawed plan, an…

  18. Language-model agents increasingly rely on skills: reusable procedural knowledge for reasoning, tool use, and interaction. Existing work studies how skills are acquired, retrieved, compressed, or composed, but often assumes that once a ski…

  19. High-quality multi-turn tool-use data is essential for training agentic models, yet existing data synthesis methods often underrepresent the argument-level dependencies that are critical to long-horizon tool use. As a result, even when a m…

  20. As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to assess multimodal perception, multi-step execution, tool use, and artifact delivery. Howe…

  21. I have a strong suspicion that Claude Code is deeply optimized for Anthropic’s own models (especially Opus and Fable) and that performance drops noticeably when you try to run it with other LLMs. Has anyone actually tested this properly?

  22. I think there's a gap in how we evaluate autonomous agents. For example, right now, everything is transactional: we give an agent a task ("Build X"), and we measure whether it built x.

  23. Computer-using agents (CUAs), which can autonomously control computers to perform multi-step actions, might pose significant safety risks if misused. However, existing benchmarks mainly evaluate LMs in chatbots or simple tool use.

  24. Large language models (LLMs) augmented with external tools have demonstrated remarkable capability in solving complex real-world tasks. However, existing approaches suffer from two key challenges: brittle multi-step and multi-turn reasonin…

  25. Safety properties assessed separately for Model Context Protocol (MCP) tool use and Agent2Agent (A2A) delegation need not describe behavior when one agent uses both. We measure one such behavior in a single controlled MCP-to-A2A configurat…

  26. View overall rankings across AI models on front-end web development tasks, including agentic coding workflows that require multi-step reasoning and tool use.

  27. Large Language Model (LLM) agents based on the ReAct paradigm have demonstrated remarkable capabilities in tool use and task execution. However, ReAct suffers from a fundamental efficiency problem: every query triggers a complete reasoning…

  28. Translation students need to learn both how to use translation technologies and how to judge the choices those technologies make available. This article presents LoopCAT, an Apache-2.0-licensed, local-first computer-assisted translation en…

  29. LLM-based forecasting systems have improved on real-world tasks such as financial markets and sports outcomes, largely through stronger search and tool use. Many systems still ask an LLM to read all collected evidence together and produce…

  30. Large Language Model (LLM) agents increasingly automate multi-step engineering workflows through tool use, interpretation of intermediate results, and iterative planning. Diagnosing and resolving non-convergent power flow cases is a promis…

  31. Large language model (LLM)-based agents have shown strong capabilities in using external tools to solve complex tasks. However, existing evaluations often overlook the temporal dimension of tool use, especially the impact of tool response…

  32. The key limitation of current state-of-the-art influence-based guardrails is that they do not reliably distinguish a legitimate, user-authorized action from a malicious, unauthorized action when both rely on external tool information. This…

  33. Large language model (LLM) agents are increasingly deployed in user-facing services that require iterative tool use under dynamic business conditions. Reliable evaluation is essential for sustained improvement: it must reveal capability de…

  34. Multimodal search agents answer visual questions by interleaving image understanding, web retrieval, tool use, and evidence synthesis. Strong systems exist, but in two expensive regimes: proprietary frontier models such as GPT-5 and Gemini…

  35. We test the standard RLVR tool-use recipe -- GRPO on Qwen2.5-7B-Instruct -- on a deliberately minimal knowledge-graph tool API: four Freebase navigation verbs over Complex WebQuestions. Under a self-verifiable retrieval reward, the policy'…

  36. Chemistry questions often demand exact computation and database lookups that a language model cannot supply from its parameters, so it must reach for external tools. Tool use here is a three-part problem: select the right tool from a large…

  37. Tool-use benchmarks generally evaluate whether an agent completes a workflow using appropriate tools and valid arguments. However, feasibility alone is insufficient in real-world decision settings such as route planning and fleet dispatch.

  38. AI coding agents, software tools that automate development tasks through reasoning and tool use, are increasingly extended through plugin marketplaces, yet the structure, maintenance, and co-evolution dynamics of these emerging repositorie…

  39. One model passed our fidelity check without ever opening the datasheet. We found it while qualifying models for an internal extraction service: a structured-output constraint had silently disabled tool use, and the model answered anyway, w…

  40. Tool-augmented multimodal agents show strong benchmark gains, often taken as evidence that agents have learned to use tools. We argue that this interpretation can be premature: a tool-call trace alone does not show whether the tool supplie…

  41. LLM-based agents are increasingly deployed in product-level execution harnesses, where jailbreaks can trigger harmful tool use and persistent state changes, creating greater risks than unsafe text generation alone. Existing automatic red-t…

  42. Scientific LLM agents have shown promise in literature reasoning, tool use, and experiment planning, but it remains unclear whether they can autonomously improve large, tightly coupled scientific machine-learning systems through executable…

  43. Video deep research answers complex questions by jointly understanding video content and retrieving external knowledge from the open Web. However, diverse questions and videos require different tool-use strategies, and inappropriate tool c…

  44. Evaluation is shifting from static QA toward agentic settings where models act through external tools. We identify a critical yet underexplored capability within this space - dexterous visual tool use: fine-grained, closed-loop parameteriz…

  45. Agentic coding workflows are now widely deployed in real-world systems. With long-horizon reasoning and tool use, token usage has become an important consideration for both cost and efficiency.

  46. Geo-spatial planning, like trip design, is a realistic testbed for LLM agents because it requires grounded tool use, noisy evidence retrieval, and multi-constraint reasoning. Most benchmarks, however, only provide clean geospatial data and…

  47. Agentic LLM pipelines face escalating inference costs as context accumulates across retrieval, tool use, and multi-turn interactions. To control latency, deployments routinely compress inputs, but this degrades task accuracy.

  48. Reliable multi-turn tool use requires an agent to preserve an evolving task state and ensure that each action remains consistent with it. However, direct function-calling and ReAct-style policies learn state tracking and action generation…

  49. Large language models (LLMs) have shown strong capabilities in reasoning, planning, and tool use, but many scientific and engineering tasks require more than plausible text and code generation. They require understanding how a system respo…

  50. Granite 4.2 brings native reasoning to enterprise agents IBM’s new open Granite models are designed for agentic AI, combining reasoning, tool use, coding, instruction following, and speech capabilities. Large language models are evolving b…

  51. LLM agents are emerging as an important paradigm for real-world tasks that require reasoning, tool use, and sequential decision-making. As these agents operate over longer horizons, runtime intervention offers a way to improve reliability…

  52. Large language model (LLM) agents have demonstrated the ability to solve multi-step tasks requiring planning, tool use, and external information access, yet growing execution histories increase inference cost and expose reasoning to outdat…

  53. Large language model agents increasingly act through terminals, yet existing surveys disperse terminal-mediated behavior across software engineering, tool use, and computer-use research. We regard terminal agents as systems whose dominant…

  54. I've been building an MCP "model bridge" tool that allows any Anthropic model running in Claude Code to act as overseer and call any OpenAI-compatible provider endpoint and hand off coding or review tasks to those external agents, includin…

  55. So I wanted to check out some of the current models in a repeatable benchmark, so I thought I'd share the result with you all. Models in this test: Ling 3.0 Flash Q4_K_M, Ornith 1.5 35B Q8_0, Deepseek V4 Flash UD_Q2_K_XL, Nemotron 3.5 Ligh…

  56. Large language model (LLM) agents can now carry out long-horizon technical workflows involving complex tool use, code execution, file edits, and generated artifacts. As agents do more work faster, the productivity bottleneck shifts from pr…

  57. AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify external state has risen from 27% to 65% of tool use. When agents exercise this authority on pub…

  58. User-facing tool agents must coordinate dialogue and tool use as user goals unfold over multiple turns. Yet interactive reinforcement learning typically reduces each rollout to a terminal reward, assigning the same credit to effective elic…

  59. Agent Skills can specify procedural and resource obligations for tool use, and language models instantiate them as concrete programs. However, when models turn this guidance into code for existing tool interfaces, even a semantically corre…

  60. Scaling test-time computation has been extended from language model reasoning to tool-augmented agents, where scaling involves not only thinking in tokens but also acting via tool calls that directly constrain environmental interaction. Ho…

  61. Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks. Such deployments impose distinct operational requirements, including appropriate tool use, concise prompts and so…

  62. Large language model (LLM) agents are increasingly capable of tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific and engineering research. To the best of our knowledg…

  63. Hey HN! We are building HarnessRouter, a canonical API for running Codex, Claude Code, Hermes, and other managed agent harnesses as your product backend.

  64. Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation. As agents increasingly operate…

  65. Large language model agents increasingly rely on long-horizon reasoning to solve complex tasks involving planning, tool use, and memory. A critical capability in such settings is reflection: assessing trajectory progress, identifying missi…

  66. The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings. A common failure occurs when a model selects the correct tool but generates argument values in an inconsistent language, which we term Argum…

  67. Agent skills are the de facto mechanism for extending LLM agents with reusable guidance. A skill can shape the agent's task execution, including planning, tool use, problem-solving, and validation.

  68. I am comparing a few model options for coding-agent work and MiniMax-M3 caught my attention because it is described as supporting coding, tool use, and long-context tasks. The questions I cannot answer from the documentation are fairly pra…

  69. Medical visual agents can use tools to inspect images and retrieve external knowledge, but indiscriminate tool use may introduce noisy or misleading evidence. Reliable diagnosis therefore requires not only acquiring additional observations…

  70. Persistent memory lets language-model agents reuse information across sessions, but it also makes errors durable: a poisoned, stale, or misattributed record can alter reasoning, tool use, answers, and subsequent memory writes. Existing def…

  71. Tool-use LLMs are increasingly asked to act on users' behalf, but existing benchmarks usually focus on profile recall, style imitation, generic tool use, or response-level personalization. We introduce UserToolBench , a benchmark for perso…

  72. We present VectraYX-Vision-1B, a sub-2B vision-language model (VLM) for Spanish/LATAM cybersecurity imagery, coupling a frozen SigLIP-so400m encoder to a 1.04B Spanish/LATAM security decoder via an MLP. To our knowledge, it is the first su…

  73. LLM agents are increasingly equipped with skills to perform complex tasks through multi-step reasoning and tool use. Although skills provide reusable procedural knowledge, agents may still execute them unreliably.

  74. How much an AI coding agent costs to run can depend more on the agent scaffolding that drives it than on the interface through which it reaches its tools. We set out to measure the cost of tool use over the Model Context Protocol (MCP) aga…

  75. Large language model (LLM) agents increasingly operate through long-horizon trajectories involving user instructions, tool use, external observations, and memory. Existing benchmarks primarily evaluate behavioral outcomes but provide limit…

  76. Agentic multimodal large language models (MLLMs) extend multimodal perception and reasoning with planning, tool use, and interaction in dynamic environments. Yet current models are specialized for particular tools or environments, complica…

  77. Training large language model agents for long-horizon tool use typically relies on interactions with real or synthesized executable environments, whose construction and verification are costly, or on external simulators that are difficult…

  78. Tool-calling agents infer task state from accumulated dialogue and tool traces. In persistent interactions, however, historical traces may remain structurally valid and semantically plausible after they cease to be authoritative for the cu…

  79. LLM agents increasingly execute long-horizon tasks through tool use and environment interaction, shifting evaluation from final-response scoring to verification of complete executions. For skill-augmented agents, verification additionally…

  80. We introduce MCP-Bench, a benchmark for evaluating large language models (LLMs) on realistic, multi-step tasks that demand tool use, cross-tool coordination, precise parameter control, and planning/reasoning for solving tasks. Built on the…

  81. The performance bottleneck of agents is increasingly shifting from model capability to the robustness of their execution processes. Tools play a central role as the primary interface through which agents interact with external environments…

  82. Hybrid computer-use agents can act through screenshots or call text tools. We find that having a tool available does not settle which way the effect goes.

  83. Large language model agents derive much of their capability from tool use. Existing research on tool use has largely focused on selecting the right tool and orchestrating the order of calls.

  84. Specifications Agentic & Tool Use Native tool calling, trained inside real agent harnesses 128K Context Long context for tool traces and multi-step workflows On-device Small enough to run on a laptop or phone Quick Start - Transformers - l…

  85. Agentic LLMs exhibit three consequential tool-use failures: invalid arguments (validity), unnecessary calls (over-calling), and omitted calls when tools are needed (missing). We find that a small, failure-specific set of MLP neurons could…

  86. The integration of Large Language Models (LLMs) into the financial domain is driving a paradigm shift from passive information retrieval to dynamic, agentic interaction. While general-purpose tool learning has witnessed a surge in benchmar…

  87. Large language models are increasingly deployed in financial applications that combine retrieval, proprietary data, tool use, orchestration logic, monitoring, and human escalation. Yet evaluation often remains model-centric: benchmark scor…

  88. Agentic AI systems act through multi-step trajectories that combine planning, tool use, memory, interaction, and adaptation. This behavior stretches validation practice beyond component testing and one-shot input--output evaluation, becaus…

  89. We screen 30-50 Web3 projects a month and built an MCP server that plugs into Claude for the diligence workflow. Six months of production use, some things worth sharing.

  90. LLVM AI Tool Use Policy# Policy# LLVM’s policy is that contributors can use whatever tools they would like to craft their contributions, but there must be a human in the loop. Contributors must read and review all LLM-generated code or tex…

  91. Enterprise agents often need to integrate heterogeneous knowledge sources: documents for narrative facts, tables for computation, and dependency graphs for file relationships. Existing benchmarks typically evaluate retrieval or tool use wi…

  92. The rapid development of large language model (LLM) agents has enabled their broad adoption across diverse real-world tasks. To standardize interactions between LLM agents and external environments, Model Context Protocol (MCP) tools have…

  93. Reconstructing atomistic crystal structures from a single noisy STEM projection is an ill-posed inverse problem: multiple lattices can explain similar contrast, and purely feed-forward models cannot verify physical validity. We present Aut…

  94. Memory, planning, reflection, and tool use are often compared as feature labels, obscuring the control semantics that determine how an agent actually runs. This review connects ten historical cognitive architectures, eight language-agent r…

  95. Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use. Recent systems increasingly organize these agents as graphs of specialized, interconnected nodes.

  96. Existing evaluations of large language models cover knowledge, reasoning, coding, and tool use, but they rarely treat a verifiable deliverable produced within a constrained workflow as the unit of evaluation. We introduce SQBench, a benchm…

  97. Coding agents are increasingly integrated into system operations, where their tool use can directly modify project artifacts, execution environments, and the underlying system. For example, if a coding agent inserts a hook into a system st…

  98. Large Language Models (LLMs) have shown strong potential in financial reasoning, but existing benchmarks often evaluate domain knowledge, numerical reasoning, long-context understanding, and tool use in separate settings. This limits their…

  99. It feels like everyone is exaggerating their claims about what they achieve with AI therefore making stacks and tool use choice very confusing nowadays. AI subscriptions overlap and it's always about the next best model.

  100. Large language model agents increasingly solve complex tasks by constructing inference-time workflows that combine reasoning, tool use, and code execution. While such workflows enable flexible problem solving, the useful procedures discove…

  101. BTL-3 A 27B open-weight agent model for agentic coding and structural tool use 95.1% HumanEval · 88.5% BFCL v4 AST · 88.1% LiveCodeBench v6 (193-case run) Compact edition · Runtime source · Bad Theory Labs · Discord Introducing BTL-3 BTL-3…

  102. Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard to train…

  103. Existing image forgery detection (IFD) methods either exploit low-level, semantics-agnostic artifacts or rely on multimodal large language models (MLLMs) with high-level semantic knowledge. Although naturally complementary, these two infor…

  104. Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches train a single, monolithic policy that interleaves thoughts and tool calls under full context; this scal…

  105. Large language model (LLM) agents are vulnerable to security risks, such as prompt injection attacks from untrusted context that manipulate downstream reasoning and tool use. Existing secure-by-design approaches mitigate this risk by separ…

  106. Agentic artificial intelligence systems, capable of autonomous perception, planning, tool use, and multi-step action, are increasingly proposed for critical engineering domains where decisions carry physical, operational, or economic conse…

  107. Verify-repair loops are a standard means for large language model (LLM) agents to correct faulty plans in code generation, mathematical reasoning, and tool use. When both the verifier and the repairer are noisy, repair can damage already-c…

  108. Existing tool-use benchmarks report a single success rate for complex, multistep tasks. Inspired by ideas from cognitive science, we distinguish tool use from tool discovery and decompose the latter into curiosity (the model's ability to d…

  109. Wanted to try something new, decided to create a mutable crm, which re renders the UI after you apply a change. The llm only modifies the db via tool use, so he doesnt directly write sql, only choose which function to use and the params.

  110. Been building a sales pipeline agent in Claude and Cursor, and tool use for enrichment took the longest to get right. Using claude sonnet as the reasoner, cursor as the ide where I iterate the tool defs, FullEnrich for the waterfall the ag…

  111. So I gave Claude (in Claude Code using Fable 5 medium) full access to a game I'm working on. I sent a list of minor issues and here is his response: I'll dig into all four issues.

  112. As web agents rapidly evolve, an increasing body of work has moved beyond conventional atomic browser interactions and explored tool use as a higher-level action paradigm. Although prior studies have shown the promise of tools, their concl…

  113. Every model leaderboard looks different depending on the task. a model can be great at long-horizon agents, another can be better at coding, and another can quietly win on a very specific workflow.

  114. Language agents that interleave reasoning and tool use degrade sharply as reasoning chains lengthen, even when each individual step is easy. We trace this to context dilution: an agent's investigative state (what it has confirmed, what it…

  115. Existing agent benchmarks primarily test task completion, tool use, or skill utility, but do not isolate whether a runtime can convert evidence from its own runs into reusable skills that improve fresh executions after authoring overhead.…

  116. Multimodal BrowseComp tasks require agents to combine perception, tool use, and long-horizon reasoning over dynamic web content, challenging their ability to handle compositional structure, open-world uncertainty, and multimodal integratio…

  117. Large Language Model (LLM)-powered agents demonstrate strong capabilities in autonomous task execution, tool use, and multi-step reasoning. However, their increasing autonomy also introduces a new attack surface: adversarial interactions c…

  118. Recent VLM and VLA systems have improved robotic perception and action prediction, yet long-horizon embodied agents still require a general runtime layer for reasoning, memory, tool use, verification, and cross-embodiment execution. We pre…

  119. Large language model (LLM) agents are increasingly used in trading systems, where model reasoning, tool use, and continual decisions incur costs that are expected to produce trading value. Existing evaluations typically report performance…

  120. Scientific research is a complex, multi-stage workflow rather than a single act of text generation. The ideation process typically emerges through literature search, paper reading, tool use, claim checking, cross-paper synthesis, brainstor…

  121. Anthropic recently introduced the Claude Certified Developer – Foundations (CCDV-F) certification, so I created a free sample practice test for anyone exploring the exam. Link: https://flashgenius.net/sample-tests/ccdv-f The questions are…

  122. Large Language Model (LLM)-based agents are increasingly capable of complex, multi-step tasks such as GUI automation, tool use, and data manipulation, yet they cannot learn from experience: each new session rediscovers solutions from scrat…

  123. Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made p…

  124. In this work, we introduce LongMedBench, a real-world EHR-based benchmark for long-horizon clinical decision-making. Prior evaluations of LLM-based medical agents have largely emphasized short-context knowledge QA and tool use.

  125. Large language model (LLM) agents are increasingly expected to play a central role in AI-driven scientific discovery. Equipped with broad knowledge, flexible reasoning, and tool use, they have the potential to autonomously explore and solv…

  126. Large language model (LLM) applications increasingly use explicit workflows for tool use, retrieval, branching, checkpointing, and human approval. Existing workflow systems already address many execution concerns.

  127. Large language models (LLMs) increasingly act as integrated data-science agents, combining abstract reasoning with advanced tool use. Yet the relevant benchmark landscape largely divides into symbolic causal reasoning benchmarks without re…

  128. I’m curious if anyone else has noticed higher-than-usual credit usage when working with Excel files in Claude. I’m on the Pro plan and I’ve been using Opus 4.8 for spreadsheet-related work, mainly Excel analysis/editing.

  129. Modern sequence models are increasingly deployed as agents that interleave token generation with calls to external tools. We give an exact, architecture-level account of when such tool access increases computational expressivity.

  130. Large language model (LLM) agents have shown strong performance in long-horizon tasks that require planning, tool use, and interaction with external environments. However, most existing benchmarks implicitly assume a monolingual setting, w…

  131. Tool-augmented large language models extend their capabilities beyond parametric knowledge through external tools, but tend to invoke them unnecessarily. We investigate whether tool-use decisions have any stable internal representation tha…

  132. I’ve been running experiments with Grok in a more agentic setup, focusing on custom skills that act as specialized reasoning modules combined with tool use, persistent context/memory, and workflow orchestration. What I’m testing: • Custom…

  133. Recent agentic approaches to LLM-based kernel generation have achieved impressive results on CUDA. For emerging AI accelerators such as AWS Trainium and Inferentia, automated kernel generation and optimization remain largely unaddressed.

  134. While current AI agents support increasingly long context windows, tool use, and skill execution for long-horizon tasks, they still require memory systems to effectively leverage historical experience. Existing memory frameworks typically…

  135. Tool calling is central to modern language model agents, but aggregate benchmark scores often hide where tool use fails. A model that never calls a needed tool and a model that calls the tool but ignores the result can look similar under f…

  136. Personal agents are becoming persistent user-owned intermediaries: they remember preferences, filter platform-mediated information, use tools, and negotiate with services. Existing benchmarks evaluate tool use, web navigation, desktop cont…

  137. NEW: Data: Tool use display metadata field — Documents the wrapper-level toolusemeta field that carries per-block display metadata keyed by tooluse block id: displayname (the MCP server's tool.annotations.title when set, otherwise a readab…

  138. Dart Agent Core A mobile-first, local-first Dart library for building and evaluating stateful, tool-using AI agents English | 简体中文 dartagentcore is a mobile-first, local-first Dart library that implements a full agentic loop with tool use,…

  139. Multimodal agents can now tackle complex reasoning tasks with diverse tools, yet they still suffer from inefficient tool use and inflexible orchestration in open-ended settings. A central challenge is enabling such agents to continually im…

  140. While Large Language Model (LLM) agents demonstrate proficiency in static benchmarks, their deployment in real-world scenarios is hindered by the dynamic nature of user queries, tool sets, and interaction dynamics. To address this generali…

  141. Put together a comparison of every benchmark I could find from the official announcement and early coverage. Figured this might save people some time.

  142. Artificial life has explored life-like behavior on many computational substrates, but mostly in researcher-designed closed worlds. We argue that large language model (LLM) agents, with persistent memory, tool use, network access, and payme…

  143. Increasingly autonomous agentic AI systems pose novel multi-agent risks, such as secret collusion via covert communication channels. The natural defence to these collusion attempts is to monitor plain-text communication, but the efficacy o…

  145. AI agents extend conventional large language model (LLM) applications by integrating language understanding with task execution, external tool use, and memory mechanisms. While memory allows agents to retain prior interactions and provide…

  146. Autonomous code agents built on large language models are reshaping software and AI development through tool use, long-horizon reasoning, and self-directed interaction. However, this autonomy introduces a previously unrecognized security r…

  147. Fine-tuning through RL reshapes the internal representations of language models to enable agentic behaviors such as tool use, yet the mechanistic basis of these changes remains poorly understood. While RL substantially improves structured…

  148. Hi HN, Here's a little website for a hobby project I have, and maybe the AI use disclaimer on it is useful to others. I believe it's a fundamental duty of open source maintainers to disclose the extent of their AI use on a per-project basi…

  149. Voice agents face a fundamental tension: the reasoning, retrieval, and tool use that make foundation models capable are iterative and slow, while conversational interaction demands responses on a millisecond timescale. Smaller, real-time m…

  150. LLM agents solve repository-level coding tasks through multi-turn tool use, but utilize half their budget on locating faults before editing. Dedicated localization frameworks have emerged, yet are still evaluated as file retrieval rather t…

  151. I've been spending a lot of time building examples with Claude Code recently, and one thing keeps bothering me. Claude is surprisingly effective at deciding what should happen.

  152. LLM-based automated program repair (APR) agents generate patches to fix software bugs with minimal human intervention. These agents often produce long trajectories of reasoning, tool use, and feedback to produce candidate patches.

  153. AI agents are rapidly evolving into autonomous systems capable of sustained interaction, tool use, and long-term collaboration. Yet their real-world adoption remains limited, suggesting that the key barrier lies not only in technical capab…

  154. Teach LLM tool use 🎁In OpenFunctions-v2, we natively train the model to support parallel functions (generate multiple functions at a time) and multiple functions (select one or more functions). Java/REST/Python APIs are also supported for…

  155. Streaming Retrieval-Augmented Generation (Streaming RAG) reduces user-perceived latency by issuing tool queries in parallel with ongoing user input, before the utterance is complete. Reported gains are aggregate, yet the mechanism's benefi…

  156. AI evaluations are shifting toward harder tasks that benefit from longer trajectories involving tool use and iterative problem solving. As a result, performance is increasingly sensitive to the amount and allocation of compute available at…

  157. Large language model (LLM)-based agents are evolving from passive text generators into autonomous systems capable of planning, tool use, retrieval, memory access, environmental interaction, and multi-agent collaboration. These capabilities…

  158. LLM-based multi-agent systems increasingly coordinate planning, reasoning, tool use, and human interaction, yet their reliability remains limited. A central source of this limitation is the underspecified prompt--harness boundary.

  159. Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises: can frontier coding assistants reliably automate this eval…

  160. Keep seeing people in here ask about paid AI courses, so figured I'd share this. Anthropic has their own free training site (anthropic.skilljar.com) with 13 courses and most give you a certificate at the end.

  161. Language model agents are increasingly effective in solving realistic tasks through multi-turn tool use. However, training reliable tool-using agents remains challenging in practice.

  162. Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents. Yet MCP-style tool use requires more than isolated function calling: an agent must discover tools from live catalogs, satisfy schemas, preserve depend…

  163. Agent-related developments are some of the hardest signals to track right now - they're spread across arXiv papers, GitHub repos, model release notes, incident reports, and policy documents simultaneously. I've been running a pipeline that…

  164. shipped an agent feature in our mobile app last month after 3 months of work. writing this because the "build it myself or hire a shop" question is the one I was stuck on in january and there's almost no honest writing on this.

  165. On paper it’s being described as Strong reasoning coding model Optimised for low VRAM via 4 bit deployment Positioned for tool use, agent workflows Benchmark claims include competitive scores vs larger frontier models (from vendor reports)…

  166. Most AI agent demos focus on planning, tool use, browser automation, memory, RAG, or multi-agent workflows. But I keep running into a smaller problem at the end of the pipeline: What happens when the agent output needs to become audio?

  167. This paper investigates reinforcement learning (RL) methods for improving tool-calling capabilities in multimodal small language model (SLM) agents. While existing works have explored various reward designs to improve agentic tool-calling…

  168. Something I’m finding while testing SWE-context-bench for the agent memory layer I’m building: evaluating memory is harder than checking whether the agent solved the next task with fewer tokens. The setup: An agent solves a coding task.

  169. Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical world. In such embodied settings, a central capability is the u…

  170. Added Anthropic Claude Fable 5 to my MindTrial leaderboard. This is a strong Anthropic update: Claude Fable 5: 80/98 overall, 0 hard errors Claude 4.8 Opus: 73/98 overall, 5 hard errors Text tasks: Fable hit 39/39, vs 35/39 for Opus 4.8 Ru…

  172. The article is about how OpenAI and Anthropic each build data agents differently, and what that reveals about the challenge of making AI useful on real enterprise data. It shows that raw file access alone is not enough - agents need metada…

  173. I've been thinking about this while using coding agents lately. The conversation around agents is usually about model quality, tool use, context windows, benchmarks, etc.

  176. Anyone check out the new Gemma4 12B that dropped 3 days ago? Integrated vision and audio recognition, no mmpro needed plus tool use.

  177. Been tuning an agent workflow for lead qualification + CRM automation stuff, and one change that helped way more than I expected was cutting the available context almost in half. I assumed more context would mean better decisions.

  179. Reinforcement learning with verifiable rewards improves reasoning and tool use, yet long-horizon language agents still learn unsupported evidence chains, belief drift, and shortcut actions that satisfy terminal checks. Existing process rew…

  180. Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly position these agents as human collaborators. Effective collaboration, however, requires col…

  181. 🔁 AgentLoop The AI agent starter you can actually read. The full agent loop — streaming + tool use — in ~150 lines.

  182. MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding, and tool use.

  183. Curious how people test tool use locally. A model can look fine in chat and still fall apart once state, retries, and bad tool results show up.

  184. Most AI Agent discussions I come across revolve around coding assistants, customer support, research agents, browser automation, and business workflows. am curious about applications in more engineering-heavy domains such as: Aviation & Ae…

  185. Reinforcement learning for language agents increasingly depends on custom harnesses that manage long-running context, multi-turn tool use and multi-agent orchestration. However, porting these harnesses into RL environment interfaces remain…

  186. Hello, I'm working on an Agentic wrapper system, Helix-agi, and I am trying to get some additional testers and collaborators involved in the project. Helix relies on a unique Agentic workflow that routes all incoming data, including tool u…

  187. Curious to hear from developers building AI agents right now, what’s been the hardest limitation or bottleneck so far? Could be reliability, memory/context handling, tool use, latency, costs, orchestration, or something else entirely.

  188. The original post was removed by Reddit Filters, so I made new one with same content. I just got my results back today and managed to snag the Early Adopter badge as well.

  189. The main problem I’m trying to solve is that agentic AI is still too random for serious engineering decisions. For design work, calculations, reports, code changes, or technical review, I don’t want agents just “vibing” through tasks.

  190. We've been running for 56 days. 8 agents coordinating via a shared memory service.

  191. I spent some time digging into Claude Code vs OpenCode, mostly from the angle of how they actually work as coding agents. More on the technicalities like: context and memory tool use subagents permissions safety and control study the recen…

  192. Hey, Software engineer here, relatively new to agentic workflows. Building a production AI concierge — user says "I'm going to Budapest tomorrow, plan my day" → agent searches our offer database, builds a plan, user books everything in one…

  193. Greetings, I'm working on a persistent AI runtime project characterized by one identity and a persistent memory. I've reached a point where I'm confident in my agent's ability to remember and build indefinitely based off its chosen persona…

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

  195. Everything in AI coding has improved dramatically, model quality, speed, tool use. But one thing hasn't been solved: the agent forgets everything when the session ends.

  196. Full conversation: https://claude.ai/share/4767365a-040f-4728-8c6a-2477bdae3503 From yesterday, I think the issue is that the differences don't stand out right away, so some people jump to conclusions that 4.7 is simply lower quality. 4.7…

  197. Agents Are Just Loops Strip away the hype and an AI agent is a simple pattern: a language model that can call functions. The model doesn't execute code.

  198. I’ve been testing and building AI agents for a while now, and I keep noticing that many “agents” online are basically just chatbots with extra branding. Some can talk well, but struggle when it comes to: reliability long-term memory tool u…

  199. Hi r/LocalLLaMA, I built an open-source MIT-licensed desktop app - cursor-aware AI overlay, hold a key, ask AI about whatever's around your cursor, vision LLM answers with a screenshot of the cursor region as context. Currently it routes t…

  200. Hi everyone. I wanted to introduce a tool / product that I've been working on for a while.

  201. I am looking for recommendations on the best CLI agents people are using for serious coding workflows that involve tool use, shell commands, and multi step iteration. I am especially interested in anything that works well with custom APIs…

  202. Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model.

  203. You can compare models on function calling, multi turn tool use, schema adherence. Basically, there's a good amount of public data at the model layer.

  204. I’m building a small “Orchestration as Code” repo for LLM workflows. Does this concept make sense?

  205. Curious how many people here are running language models locally as part of their agent stack. What model are you using and what are your system specs?

  206. Anthropic’s recent SpaceX compute deal made me think less about Claude specifically and more about the infrastructure side of AI products. We often compare models by reasoning, coding ability, context windows, tool use, pricing, or UX, but…

  207. We run a small content-monitoring agent for our growth team. Nothing fancy on paper.

  208. I built a small experiment using Claude (mainly for reasoning + responses) and added a memory layer + tool execution on top. Idea was simple: make a persistent agent that doesn’t forget context and can actually do things instead of just re…

  209. Spent the last several months using Claude Code well beyond the editor: as the reasoning engine inside a multi-layer system that handles tickets, cross-repo implementation, code review, MRs, and a persistent knowledge layer between session…

  210. I am working on a home AGI project called Helix-AGI. I am currently looking for collaborators to help test and troubleshoot.

  211. I use AI coding tools a lot, so this is not an anti-AI post. If anything, the problem is that they are useful enough to change how I work.

  212. Built this over the past month as a free reference site for people getting into AI agents. What tools to use, where to start, what each tool does, and how the agent-tool landscape fits together.

  213. Hi HN, I built Arkloop – an open-source, local-first Agent client. You can think of it as Claude Desktop, but open source with its own taste.

  214. A lot of frontier discussion still treats progress as more chain-of-thought, more spectacle, and more obvious “this model feels genius” moments. But an open release like Ling-2.6-1T hitting Hugging Face today makes me think a different kin…

  215. Agentic AI systems - capable of goal interpretation, world modeling, planning, tool use, long-horizon operation, and autonomous coordination - introduce distinct control failures not addressed by existing safety frameworks. We identify six…

  216. I'm working on a homelab AI server with the goal of running small models on GPU and very large models on CPU - for example for overnight coding on complex problems. Specs: 2990WX, 256GB + RTX 2080ti (for now).

  217. I’m trying to isolate the looping / repetition issue some people have been reporting with DeepSeek V3.2 around April 2026, especially in agentic or tool-use setups on hosted providers like OpenRouter and SiliconFlow. Public model pages des…

  218. QClaw-4B is a 4-billion parameter language model fine-tuned for agentic tasks and tool use, designed for use with OpenClaw-compatible agent frameworks. Despite its compact size, QClaw-4B achieves state-of-the-art results in the 4B class, m…

  219. NEW: System Prompt: Harness instructions — Core interactive-agent harness guidance for terminal markdown output, permission handling, <system-reminder> context, compaction, tool use, and clickable code references. NEW: System Prompt: Memor…

  220. Did some test tasks with v4 flash. The context management, tool use accuracy and thinking traces all looked excellent.

  221. Hooks are "if then" rules for Claude Code. Each one has an event, a matcher, and a command.

  222. I've been building a personal AI assistant called Finn that runs entirely on your Mac. No cloud, no subscription, no data leaving your machine.

  223. I'm building a small text-based game where the gameplay loop is "talk an NPC into revealing a secret." It's basically a 20+ turn roleplay stress test: the model needs to stay in character, remember what the player said earlier, and refuse…

  224. been spending ages lately trying to tighten up citation + traceability in RAG-based research workflows, and I’m starting to feel like “retrieval” and “verifiability” are still pretty loosely coupled in most stacks.Typical setup (vector sea…

  225. Hey r/LocalLLaMA, Dropping a release I've been working on during AIMO3 (Kaggle competition). Took NVIDIA's Nemotron-3-Super-120B-A12B (latent MoE + Mamba2 hybrid), REAP-pruned from 512->256 experts (removed MTP layer too), LoRA-RL fine-tun…

  227. Inside VAKRA: Reasoning, Tool Use, and Failure Modes of Agents VAKRA Dataset | LeaderBoard | Release Blog | GitHub | Submit to Leaderboard We recently introduced VAKRA, a tool-grounded, executable benchmark for evaluating how well AI agent…

  228. Been running the new model entire evening in different quants and coding tasks with OpenCode. Used oMLX and LM Studio.

  229. Claude Opus 4.7 is Anthropic's most capable generally available model, released April 16, 2026. It outperforms Opus 4.6, GPT-5.4, and Gemini 3.1 Pro on key benchmarks including agentic coding, multidisciplinary reasoning, scaled tool use,…

  230. Google and Alibaba recently shipped Gemma 4 and Qwen3.5, so I wanted to see whether the new generations are actually better on my setup. My context is private local chat running on my own hardware, a Mac mini M4 Pro.

  231. Been heads-down on the backend today. Three things worth knowing about: The big one: NicheIQs is now available as a ChatGPT GPT.

  232. Large language model (LLM)-based systems are becoming increasingly popular for solving tasks by constructing executable workflows that interleave LLM calls, information retrieval, tool use, code execution, memory updates, and verification.…

  233. Across Twitter and Reddit, I keep seeing the same complaint: Claude feels worse. Not on a benchmark.

  234. Quick question for folks here working with LLMs If you could get ready-to-use, behavior-specific datasets, what would you actually want? I’ve been building Dino Dataset around “lanes” (each lane trains a specific behavior instead of mixing…

  235. I run OpenClaw agents with access to email, calendar, and files, and kept worrying about them doing things I never actually asked for. ArmorClaw captures intent and cryptographically binds the agent’s tool use to that committed intent.

  236. I created a router that gives me access to Arena.ai models, and I generated an API key for each of the available models. I’m looking for a CLI tool that can run multiple AI agents together, each handling different tasks like planning, secu…

  237. For those using Claude Code in production workflows, where do you see the biggest net time savings? In my experience, it reduces cognitive load for writing scripts and scaffolding, but debugging effort seems to increase as codebases grow.

  238. posse A web UI for Anthropic Managed Agents. Anthropic ships the best agent infrastructure — sandboxed environments, persistent memory, tool use, multi-agent orchestration — but no UI.

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