TRACER is a retrieval front‑end that indexes tool‑call sequences in a trie and uses a learned blend of empirical and contextual priors to rank tool continuations. In a mixed retail‑and‑telecom environment, it boosts GPT‑5.4’s tool‑selection accuracy by 4.7 points while cutting input tokens per step by 80%, exposing only 5 of 128 tools. Across six environments and four models, TRACER improves tool‑selection accuracy in 21 of 24 model‑environment pairs, with average gains of 6 to 10 points, and matches full‑registry success with 61% fewer tokens per step in autonomous execution.
arXiv:2610.23707v1 Announce Type: new Abstract: A multi-step agent task fails when a single tool it needs is missing, yet the tools an agent may need are too many to list: the roughly 8,200 tools on...
arXiv:2606. 07054v1 Announce Type: cross Abstract: Autonomous LLM agents can pursue hidden malicious objectives through sequences of individually benign actions, making sabotage difficult to detect using standard trajectory-level monitoring.
TraceSpec is a framework that links trajectory evaluation with adaptive draft training to improve speculative decoding in large language model agents. It introduces a dynamic execution strategy combined with fixed‑prefix replay and provides a new corpus (TraceSpec‑Traj) of 10,000 training and 2,500 test states covering browsing and software engineering tasks. Experiments show that TraceSpec’s state‑conditioned draft allocation boosts replay throughput by 12.5%–22.6% and increases accepted length by up to 10.0% compared to fixed‑turn supervision.
UniToolCall introduces a unified framework for tool-use in large language model agents, standardizing toolset construction, dataset generation, and evaluation. The framework aggregates over 22,000 tools and creates a hybrid training corpus of more than 390,000 instances by combining ten public datasets with synthetically generated, structurally controlled trajectories. It models diverse interaction patterns—single‑hop vs. multi‑hop, single‑turn vs. multi‑turn, serial vs. parallel execution—and adds an Anchor Linkage mechanism to enforce cross‑turn dependencies, while converting seven public benchmarks into a common Query–Action–Observation–Answer format for fine‑grained evaluation.
Multi-turn tool-using agents must coordinate long-horizon tool sequences while tracking dialogue state and policy constraints. Existing approaches often separate inference-time orchestration from parameter-level learning, leaving tool selection weakly structured and preference updates vulnerable to train--deployment prompt mismatch.
arXiv:2512.24565v4 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol...
arXiv:2609.24264v1 Announce Type: new Abstract: Tool-use agent traces identify messages and API calls, but procedural analyses also need explicit units of action and inspectable links to their eviden...
The paper introduces BRIA (BRanch-Inspect-Adopt), an agent that isolates tool‑candidate investigations into separate branches, executing each candidate independently and then inspecting the execution evidence to adopt only useful results. BRIA dynamically retrieves additional tools as new needs arise during task execution and is evaluated on PlanBench‑XL and MCP‑Atlas with a library of 1,897 tools, outperforming or matching the strongest retrieval baselines. On MCP‑Atlas, BRIA achieves an average claim score of 89.77 versus 75.40 for MCP‑Zero, and it lies on the Pareto frontier for task performance and input‑token cost.
arXiv:2606. 12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents.
arXiv:2606. 30560v1 Announce Type: cross Abstract: Coding agents are rapidly becoming a major application of agentic LLMs, but serving them efficiently remains challenging.
Toollery is a training‑free framework that compresses candidate lists for large language model agents, enabling efficient selection from thousands of skills and tools. It generates user‑intent queries from each skill or tool specification, builds a retrieval index, and limits online selection to a compact top‑k set before the LLM makes its final decision. Evaluations on the SkillRouter benchmark, BFCL‑V4, and a proprietary smart‑cockpit dataset show that Toollery improves recall and end‑to‑end selection while keeping selection costs bounded.
AuditKIT is a source‑available evaluation library that unifies benchmark accuracy, judge‑based assessment, runtime performance, tool‑use behavior, and retrieval quality into a single result representation. It offers direction‑aware metric contracts, configuration fingerprints, and extensible metrics and backends, while providing turn‑aware tool‑call matching and distinguishing verified task outcomes from trajectory‑level diagnostics. Seven case studies demonstrate how answer extraction, runtime behavior, and metric selection can alter evaluation outcomes across model comparison, serving runtimes, retrieval, tool calling, and interoperability.