Hugging Face Trending Papers
RideWay: Benchmarking Efficient Task Completion for Tool-Using Language Agents
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arXiv AI
Sep 17
RideWay: Benchmarking Efficient Task Completion for Tool-Using Language Agents
RideWay is a new benchmark that evaluates ride‑hailing language agents not just on task completion but on interaction efficiency. It introduces the Efficiency Utility metric, which penalizes agents for excessive tool calls and user‑facing turns relative to a task‑specific reference effort, with human preferences used to calibrate the penalties. Across 58 tasks and 24 models, the metric shows that extra dialogue is penalized more heavily than extra tool use, and it achieves high accuracy in distinguishing trajectories that differ in turns but struggles when differences are only in tool calls.
arXiv Machine Learning
Oct 19
Tool Recommendation for Multi-Step Agents: Finding Every Tool a Task Needs
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 AI
Aug 7
Matching Matters: A Fair Quality-Efficiency Benchmark for Command-Line Agents
arXiv:2606. 21140v2 Announce Type: replace-cross Abstract: Rapid advances in large language models have improved the task-solving capabilities of command-line-interface (CLI)-based agents, whose CLIs determine how models invoke tools, maintain interaction history, and recover from failures.
arXiv AI
Oct 5
Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents
arXiv:2610.02330v1 Announce Type: new Abstract: Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condi...
arXiv AI
Oct 28
AgentBaton: Efficient Model Collaboration in Agentic Tasks
AgentBaton explores how capable and efficient language models can collaborate on long‑horizon agentic tasks by handoffs, where one model takes over from another. The study finds that handoffs from a capable to an efficient model at appropriate stages preserve or improve accuracy while significantly cutting cost, whereas the reverse handoff is less effective. AgentBaton, a lightweight detector, learns the optimal handoff point and achieves an average API cost reduction of 65.9% across 11 model pairs while largely maintaining task accuracy, even gaining up to 8.8 percentage points over capable‑only execution.
arXiv AI
Sep 15
Utility-Guided Agent Orchestration for Efficient LLM Tool Use
arXiv:2603.19896v2 Announce Type: replace Abstract: Tool-using large language model (LLM) agents often face a fundamental tension between answer quality and execution cost. Fixed workflows are stable...