Search: “tool-use”

Knowing When to Ask for Help: Bayesian Self-Escalation in Hierarchical LLM Agents

arXiv:2608.24087v1 Announce Type: cross Abstract: Current LLM agent systems decide delegation before reasoning begins (a router picks a model) or after a response is complete (a verifier scores it and

26 Aug 2026

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PeakBench: Benchmarking Resource-Aware Tool Invocation in LLM Agents

DGX agent

arXiv:2608.24509v1 Announce Type: new Abstract: LLM agents increasingly solve tasks by invoking multiple tools, where parallel execution is essential for low latency but difficult to manage safely. Ex

model-releasesarxiv-cs-ai

26 Aug 2026

PROOF-Gen: From Optimized Data to Better Distillation

arXiv:2608.23911v1 Announce Type: new Abstract: Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Po

26 Aug 2026

ATP-Bench: Towards Agentic Tool Planning for MLLM Interleaved Generation

arXiv:2603.29902v2 Announce Type: replace Abstract: Interleaved text-and-image generation represents a significant frontier for Multimodal Large Language Models (MLLMs), offering a more intuitive way

EarthVerse: Benchmarking Scientific Agents Across Dynamic Earth Systems and Natural Hazards

arXiv:2608.23525v1 Announce Type: new Abstract: Earth-system analysis reconstructs changing physical processes from observations that differ in source, scale, timing, and modality. Natural hazards mak

Gated Decoupled Compositional Bandits: A Unified Theory of Contextual Bandits with Supervised-Calibrated Action Scaling and Pre-Execution Gating

model-releasesarxiv-cs-lg

arXiv:2608.21993v1 Announce Type: new Abstract: We introduce Gated Decoupled Compositional Bandits (GDCB), a family of contextual bandit algorithms with three structural innovations that jointly fall

Safety Training May Persist Through Helpfulness Optimization in LLM Agents

model-releasesarxiv-cs-cl

arXiv:2603.02229v2 Announce Type: replace-cross Abstract: Safety post-training has been studied extensively in single-step 'chat' settings where safety typically refers to refusing harmful requests. W

Think with Structured Grounding: Perceptual Reinforcement Learning for Chart and Visual-Tabular Understanding

arXiv:2608.22429v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this relianc

TRACE: A Self-Evolving Skill Bank for Consistent, Limit-Aware LLM Agents

arXiv:2608.22793v1 Announce Type: cross Abstract: Reliable deployment of LLM agents in user-facing products depends not on raw task-solving ability but on consistency and limit-awareness: behaving the

Ansari: A Retrieval-Grounded Islamic AI Assistant -- Architecture, Deployment, and Lessons from 140,000 Conversations

arXiv:2608.20390v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) are increasingly used to answer religious questions, but for Islamic content they carry two serious risks

Beyond End-to-End Success: Diagnosing Failures in Long-Horizon Security LLM Agents

arXiv:2608.20563v1 Announce Type: cross Abstract: Long-horizon security LLM agents must carry information and decisions across many dependent interactions, where later actions often depend on services

Don't Solve, Just Compare: Tiny Advisors for Runtime Intervention in LLM Agents

arXiv:2608.21027v1 Announce Type: new Abstract: LLM agents are emerging as an important paradigm for real-world tasks that require reasoning, tool use, and sequential decision-making. As these agents

MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents

arXiv:2607.11818v2 Announce Type: replace-cross Abstract: We introduce MM-ToolSandBox, a benchmark and evaluation framework for visually grounded tool-calling agents. The framework provides a stateful

VortexChat: An agentic framework for autonomous multi-objective integrated photonic design

arXiv:2608.20688v1 Announce Type: new Abstract: The advancement of modern integrated photonics is frequently bottlenecked by device design workflows that rely heavily on manual simulation and expert i

Calibrated Triage, Not Autonomy: Confidence Estimation for Medical Vision-Language Models

arXiv:2606.15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, r

agentsarxiv-cs-cl

MidTool: Mid-training Data Synthesis for Agentic Tool Use

arXiv:2608.20314v1 Announce Type: new Abstract: Mid-training is increasingly recognized as a critical stage for shaping the capabilities of large language models. Recent work has shown that targeted m

One Success Isn't Reliability: Thinkingbox, a Sandbox and Benchmark for Agents in Stateful Business Workflows

model-releasesarxiv-cs-cl

arXiv:2608.19741v1 Announce Type: new Abstract: Recent agent benchmarks increasingly ground evaluation in executable environments, from code repair to web navigation, app APIs, and function calling. Y

Local Ai

SPyCE: Skill-Policy Co-evolution for Multimodal Agents

arXiv:2607.13854v2 Announce Type: replace Abstract: Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps. Existing reinforcement learning

local-aiarxiv-cs-cl

ComponentBench: Diagnosing Component-Level Failures in Computer-Use Agents

arXiv:2608.18307v1 Announce Type: new Abstract: Current evaluation of computer-use agents is split between long-horizon workflow benchmarks and atomic GUI-grounding tests. This leaves an under-instrum

Looped Language Models Improve Compositional Tool Calling

arXiv:2608.18171v1 Announce Type: new Abstract: Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We stu

RTPO: Reverse-Turn Policy Optimization for Stabilizing Agentic RL Training

arXiv:2608.18682v1 Announce Type: new Abstract: Training multi-turn agentic workflows with reinforcement learning (RL) enables large language models to perform complex reasoning, use external tools, a

Self-Improvement of Large Language Models: A Technical Overview and Future Outlook

arXiv:2603.25681v2 Announce Type: replace Abstract: As large language models (LLMs) continue to advance, improving them solely through human supervision is becoming increasingly costly and limited in

agentsarxiv-cs-cl

SPADE: Self-Play in Adaptive Synthetic Executable Environments

arXiv:2608.19197v1 Announce Type: cross Abstract: Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing training environ

What is Missing from AI Post-Training AI: An Empirical Analysis

arXiv:2608.19072v1 Announce Type: new Abstract: Large language model (LLM) agents can now post-train an LLM end-to-end. They can write code, launch training, evaluate checkpoints, and improve downstre

Cross-View Correspondence Is a Measurement Intervention: Two-Sided Validation for Agent Evaluation and Credit Assignment

arXiv:2608.17713v1 Announce Type: new Abstract: Agent evaluations and trace-based learning often compare outputs across transformed views through a post-response correspondence treated as neutral prep

agentsarxiv-cs-lg

LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents

arXiv:2608.17393v1 Announce Type: new Abstract: Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository contexts, and execut

PDDL-ART: Autonomous Symbolic Abstraction From Demonstration For Long-Horizon Robotic Manipulation Using Vision-Language Models

arXiv:2608.17146v1 Announce Type: new Abstract: Symbolic planning with PDDL offers a principled framework for long-horizon robot manipulation, but constructing accurate PDDL domain and problem descrip

agentsarxiv-cs-ro

Token Optimization and Context Window Management in Multi-Agent AI Workflows

arXiv:2608.17188v1 Announce Type: cross Abstract: Multi-agent AI workflows are limited not only by model quality but by token cost, latency, and context-window quality. This paper presents a practitio

When AI Designs AI: Innovation or Imitation?

arXiv:2608.17471v1 Announce Type: new Abstract: Recent advances in LLM agents have made them increasingly capable of designing methods for complex AI tasks. This raises two central questions about age

Handoff-H1: An Orchestrated Vision-Agent System for Material Quantity Takeoff from Construction Blueprints

arXiv:2608.15032v1 Announce Type: cross Abstract: Converting a set of architectural blueprints into a complete material quantity takeoff requires visual perception across drawing sheets, dimensional a

Kozuchi Agent: A Language-Agnostic Open-Weight Agent for Software Repair

arXiv:2608.15579v1 Announce Type: cross Abstract: Industrial software-engineering teams increasingly need LLM agents that turn bug reports into correct patches, yet benchmark-scale operation adds long

Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning

arXiv:2608.16831v1 Announce Type: new Abstract: Generative pretraining established reusable task representations; later work on language-based task conditioning and in-context learning showed that a f

What Does Context Compression Cost an Agent? Interaction Costs Unrevealed by Task-Completion Metrics

arXiv:2608.16370v1 Announce Type: new Abstract: Task completion is the standard metric for evaluating context compression, yet it is incomplete: compression can increase an agent's interaction cost by

Knowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations

arXiv:2608.14425v1 Announce Type: new Abstract: LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise. We introduce optstop, a

17 Aug 2026

MACS: A Hybrid Multi-Agent Framework for Reliable Conversational E-Commerce Recommendation

arXiv:2608.14068v1 Announce Type: cross Abstract: Conversational recommendation for e-commerce is increasingly mediated by large language models (LLMs), yet many real-world deployments operate under a

17 Aug 2026

Never the Number: Structural Abstention for AI Systems Whose Answers Are Consumed as Fact

arXiv:2608.13926v1 Announce Type: new Abstract: Large language models have made natural language interfaces to databases (NLIDB) newly credible, but LLM text-to-SQL systems fail in a way that matters

17 Aug 2026

AaLLM: An End-to-End Analog Circuit Design Framework from Topology Generation to Sizing Using Large Language Models

arXiv:2608.13472v1 Announce Type: cross Abstract: Analog circuit design is a time-consuming, iterative process in a nonlinear and high-dimensional design space that relies heavily on expert intuition.

AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design

arXiv:2608.13560v1 Announce Type: cross Abstract: Transforming multimodal sources into condensed and structured media outputs can be fundamentally conceptualized as a long-horizon agentic process cent

Beyond Outcome Rewards: Step-Level Self-Distilled Policy Optimization for Deep Search Agents

arXiv:2608.12764v1 Announce Type: cross Abstract: Deep search agents operate over trajectories spanning dozens of steps, yet standard reinforcement learning provides only a single outcome reward per t

QuoteBench: How Matched Scores Can Hide Command-Path Failures

arXiv:2608.13547v1 Announce Type: new Abstract: LLM coding agents issue Bash commands through interfaces that may serialize, wrap, and reparse model output. Matched execution scores alone cannot disti

Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliance

arXiv:2608.12323v1 Announce Type: cross Abstract: Specifying a penalty can paradoxically convert a legal obligation into a cost-benefit calculation that favors violation. We demonstrate that this enfo

Beyond Single-Turn Confidence: Trajectory-Adapted Uncertainty Quantification for LLM Agents

arXiv:2608.11552v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) methods for language models are typically evaluated on single-turn outputs, where uncertainty is attached to one gener

13 Aug 2026

GCPO: Diagnosing and Constraining Subspace Geometry in Rollout RL for LLMs

arXiv:2608.11674v1 Announce Type: cross Abstract: On-policy rollout methods such as GRPO are central to post-training of large language models, yet they frequently suffer from training instabilities,

13 Aug 2026

From Faulty Memories to Corrected Actions: Dependency-Guided Rollback Repair for Memory-Augmented Agents

arXiv:2608.10502v1 Announce Type: new Abstract: Persistent memory lets language-model agents reuse information across sessions, but it also makes errors durable: a poisoned, stale, or misattributed re

GeoForge: Non-Parametric Self-Evolving Agents for Earth-Observation Reasoning

arXiv:2608.10494v1 Announce Type: new Abstract: Earth observation (EO) agents construct scientifically valid tool workflows and ground their conclusions in current geospatial evidence. This is challen

MEGA: Self-Evolving Agent Optimization Infrastructure via Wisdom Graph

arXiv:2608.10504v1 Announce Type: new Abstract: As coding agents increasingly handle implementation, the central challenge shifts from building individual agents to building an infrastructure that sys

When Agent Automation Becomes Profitable: Quantifying and Insuring Autonomous AI Risk through Trace-Economic Underwriting

arXiv:2606.16465v2 Announce Type: replace Abstract: AI agents can now take irreversible actions in operational systems, but agent-caused losses are still not clearly assigned, priced, or transferred.

Compositional Threat Analysis of Latent Compromise in LLM Agent Systems: The Order 66 Scenario

arXiv:2608.08131v1 Announce Type: cross Abstract: In the fictional Order 66, catastrophe does not arise from a powerful command alone: a trusted population is preconditioned, a short directive activat