Same Winners, Different Success Rates: Evaluating How LLM Agents Recover from Failures
cs.AI, cs.LG, cs.MA
Submitted: 2026-09-28
Updated: 2026-09-28
Sources
- Do Repetitions Matter? Strengthening Reliability in LLM Evaluations
- Optimal Decision Rules when Payoffs are Partially Identified
- WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models
- Set-Valued Policy Learning
- Auditing Provenance Sensitivity in LLM Agent Action Selection
- AgentBench: Evaluating LLMs as Agents
- AgentAbstain: Do LLM Agents Know When Not to Act?
- Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
- Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning
- Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures
- Safeguarding LLM Agents from Misalignment through Provenance Analysis
- Optimal Best-Arm Identification under Fixed Confidence with Multiple Optima
- CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems
- Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems
- Qwen3 Technical Report
- When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents
- ClaimReceipt: Verifying Evidence Sufficiency and Coverage in Agent Evaluations
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems