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The paper introduces WER, a multi-phase framework that trains a Skill Optimizer using reinforcement learning from execution feedback to improve tool-using agents, achieving significant performance gains on benchmarks like BFCL v4 and τ2-bench.
New research from Microsoft Research measures cross-lingual policy retention of tool-using agents by comparing action traces rather than final answers. After removing five confounds, four frontier models retain 71–73% of their action policy across languages, with code and 2.38M rollouts released.
This paper introduces CAGE, a certification method that verifies whether tool-using LLM agents remain authorized under typed-return uncertainty, including binding faults and numerical drift. It proves that separate channel certification is insufficient and provides exact and learned-gate variants for practical authorization.
This paper argues that commercial search APIs for tool-using agents should be evaluated as decision surfaces — the pre-fetch evidence state that determines agent behavior — rather than solely by answer accuracy. Using a fixed agent and three providers, it shows that while accuracy is similar, the evidence economies and contradiction ratios differ sharply, making provider choice a retrieval-budget and policy decision.
A practical guide for building structured agent trajectory datasets for training tool-using agents, emphasizing the importance of designing trajectories with six key parts and treating them as data assets rather than logs.
This paper introduces an action-graded severity rubric (L0 to L6) for evaluating AI agent attacks, going beyond binary attack-success rate. The scale scores tool-call trajectories based on harmfulness, revealing blind spots in existing defenses.
A personal reflection on mental models that help make tool-using AI agents less chaotic and more manageable.
An open-source lab framework for running controlled experiments on tool-using agents, allowing variation of tool names, personas, and history to measure effects.
ReGRPO introduces a reflection-augmented policy optimization framework for tool-using vision-language agents, leveraging structured failure observations and joint optimization of reflection tokens and actions to improve recovery from tool failures, achieving state-of-the-art results on GTA and GAIA benchmarks.
This paper introduces SENTINEL, a failure-driven reinforcement learning framework for training tool-using language model agents. It uses a Controller-Proposer-Solver loop to generate targeted training tasks from failed trajectories, improving performance on benchmarks.
This paper evaluates context engineering configurations for LLM agents in enterprise tool-use workflows, showing that summarization with selective pruning achieves 91.6% accuracy while reducing token usage by over 60% compared to full-context baselines.
A Python learning repo that reverse-engineers Claude Code-style agent architecture through 23 incremental sessions, covering planning, subagents, context management, and more.
TOBench is a new benchmark for evaluating AI agents on real-world, task-oriented tool use with multimodal inputs and closed-loop verification. Experiments show top models like Qwen 3.5 Plus achieve only 41% success, far below the 94% human benchmark, highlighting a significant gap.
RadAgent is a tool-using AI agent that generates chest CT reports through interpretable step-by-step reasoning, improving clinical accuracy by 36.4% relative and achieving 37% faithfulness—a capability absent in existing 3D vision-language models. The system provides fully inspectable reasoning traces allowing clinicians to validate and refine diagnostic outputs.