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Source note3 min read

Retry, Switch, or Abstain? Learning Strategy-Aware Tool-Use Policies via Controlled Error Injection

Source

arXiv

arxiv.org/abs/2608.11977v1

Read the source

Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Chaoran Chen, Vy Nguyen, Ziji Zhang, Abhinav Gullapalli, Ziyi Wang, Yuxuan Lu, Dakuo Wang, Jing Huang, Zhou Yu, Jin Lai
  • Venue: arXiv: cs.AI
  • Published: 2026-08-12
  • Collected: 2026-08-13
  • URL: http://arxiv.org/abs/2608.11977v1
  • PDF: https://arxiv.org/pdf/2608.11977v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The paper presents a framework called BENCH2ROBUST, which converts reliable tool-use benchmarks into controlled stochastic environments with failure scenarios. The authors test two interventions to improve the robustness of tool-using LLM agents: Bayesian Tool Memory (BTM) for structured runtime recovery context and curriculum-controlled reinforcement learning. The results suggest that combining environment-specific recovery knowledge with learned recovery behavior can significantly improve an agent's robustness.

What the Document Says

  • The paper presents a framework called BENCH2ROBUST, which transforms failure-free tool-use benchmarks into controlled stochastic environments.
  • The transformed environments contain scenarios where tools can fail transiently, persistently, or silently, requiring retrying, switching to alternatives, or recognizing that no viable path remains.
  • Two interventions are studied: structured runtime recovery context through Bayesian Tool Memory (BTM), and curriculum-controlled reinforcement learning.
  • The authors test these interventions on 7 models from 4 families and two multi-turn benchmark families.
  • Tool failures produce a near-universal robustness gap across the tested models.
  • On held-out Retail tasks, BTM improves robustness by up to 16.8 percentage points without retraining.
  • RL learns complementary recovery behavior that remains beneficial without inference-time BTM.

Key Concepts Mentioned

  • tool-using LLM agents
  • BENCH2ROBUST framework
  • Bayesian Tool Memory (BTM)
  • reinforcement learning
  • robustness
  • tool failures
  • multi-turn benchmark families

Claims Made by the Document

  • Robust recovery requires more than repeated retries.
  • BTM improves robustness without retraining.
  • RL learns complementary recovery behavior.

Evidence Used

  • The paper presents a framework that converts failure-free tool-use benchmarks into controlled stochastic environments with scenario-controlled solvability.
  • On held-out Retail tasks, BTM improves robustness by up to 16.8 percentage points without retraining.
  • RL learns complementary recovery behavior that remains beneficial without inference-time BTM.

Methods / Approach

  • BENCH2ROBUST framework
  • Bayesian Tool Memory (BTM)
  • curriculum-controlled reinforcement learning

Limitations Stated by the Authors

  • The results are based on tests across 7 models from 4 families and two multi-turn benchmark families.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The findings may not be universally applicable to all tool-using LLM agents.
  • The results might vary with different types of tasks or environments.

Open Questions

  • How does the combination of BTM and RL affect other types of tasks or environments?
  • What is the optimal balance between environment-specific recovery knowledge and learned recovery behavior?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper does not provide information on the specific types of tasks or environments used in the tests.
  • The results suggest that a combination of environment-specific recovery knowledge and learned recovery behavior can improve robustness, but further research is needed to determine the optimal balance.

Raw Abstract / Excerpt

Tool-using LLM agents are commonly trained and evaluated in environments where tool calls succeed reliably, yet deployed tools can fail transiently, persistently, or silently. Robust recovery therefore requires more than repeated retries: an agent may need to retry the same path, switch to an alternative, or recognize that no viable path remains. We present BENCH2ROBUST, a framework that converts failure-free tool-use benchmarks into controlled stochastic environments with scenario-controlled solvability, where episodes explicitly require retrying, switching, or stopping after available paths are exhausted. We use BENCH2ROBUST to study two complementary interventions: structured runtime recovery context through Bayesian Tool Memory (BTM), and curriculum-controlled reinforcement learning. Across 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 improves robustness by up to 16.8 percentage points without retraining, while RL learns complementary recovery behavior that remains beneficial without inference-time BTM. Combining the two reaches 40.8-45.5% under injection while preserving failure-free performance. These results suggest that robust tool use benefits from combining environment-specific recovery knowledge with learned recovery behavior.