BENCH2ROBUST: A Framework for Robust Tool-Use in LLM Agents
A recent paper on arXiv (2608.11977) presents BENCH2ROBUST, a framework aimed at transforming benchmarks for tool use that are free of failures into controlled stochastic settings. This framework mandates that agents must either retry, switch, or cease operations when they run out of available paths, thereby tackling the robustness issues that arise when tools fail, whether transiently, persistently, or silently. The research investigates two approaches: structured runtime recovery context using Bayesian Tool Memory (BTM) and curriculum-driven reinforcement learning. Across seven models from four families and two multi-turn benchmark categories, tool failures reveal a nearly universal robustness gap. BTM demonstrates encouraging outcomes on held-out Retail tasks. The authors of the study have made it accessible on arXiv.
Key facts
- arXiv paper 2608.11977 introduces BENCH2ROBUST framework.
- BENCH2ROBUST converts failure-free benchmarks into stochastic environments.
- Episodes require retrying, switching, or stopping after paths are exhausted.
- Two interventions studied: Bayesian Tool Memory (BTM) and curriculum-controlled RL.
- Evaluated on 7 models from 4 families and two multi-turn benchmark families.
- Tool failures produce a near-universal robustness gap.
- On held-out Retail tasks, BTM shows promising results.
- Paper is announced as new on arXiv.
Entities
Institutions
- arXiv