Loud Failures, Quiet Failures: Fault Detection and Recovery in Tool-Using Language Model Agents
cs.AI
Submitted: 2026-10-07
Updated: 2026-10-07
Code: https://github.com/obadaKraishan/brittle-agents
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
- AI Agents That Matter
- Why Do Multi-Agent LLM Systems Fail?
- OpenAI o1 System Card
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- Qwen3 Technical Report
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
- Reasoning Models Don't Always Say What They Think
- The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
- PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases
- ReliabilityBench: Evaluating LLM Agent Reliability Under Production-Like Stress Conditions
- When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents
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