arXiv AI
Lookahead-R: Budget-Aware Tool Retrieval via Execution-Centric Planning
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arXiv AI
Aug 26
PeakBench: Benchmarking Resource-Aware Tool Invocation in LLM Agents
PeakBench is a new benchmark designed to evaluate how large language model agents invoke multiple tools while respecting resource constraints and parallel execution. It provides executable multi‑tool workflows with dependency annotations and measured resource profiles, and introduces a two‑part evaluation framework that separates logical planning from physical scheduling. The study shows that strong logical planning alone does not guarantee safe or efficient execution, and that providing resource information can reduce overflows and improve utilization.
arXiv AI
Aug 13
VAKRA: Evaluating Multi-Hop Reasoning Across APIs and Retrieval Under Tool-Use Policies
arXiv:2608. 12282v1 Announce Type: new Abstract: Agents deployed in enterprise settings must reason across structured APIs and document collections, yet existing benchmarks evaluate these capabilities in isolation.
arXiv Machine Learning
Sep 22
Toollery: Scaling LLM Agents to Thousands of Skills and Tools
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.
arXiv AI
Sep 3
Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives
The paper introduces Tool Primitives, a design that replaces rigid API schemas with natural language interfaces for tool calling, enabling seamless inter-tool communication. It builds ToolFace, a repository of over 25,000 functions that LLMs can dynamically retrieve, and HEART, a harness engineering framework that orchestrates tool use with planning, routing, and verification. Experiments show HEART outperforms fine‑tuned models and leading commercial LLMs while cutting API costs by up to 85%.
arXiv AI
Jun 12
ToolSense: A Diagnostic Framework for Auditing Parametric Tool Knowledge in LLMs
arXiv:2606. 12451v1 Announce Type: new Abstract: Large language models deployed as agents over large tool catalogs face a critical tool-retrieval bottleneck.
arXiv AI
Aug 18
Budget-Aware Tool Use Enables Effective Agent Scaling
arXiv:2511. 17006v2 Announce Type: replace Abstract: 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.