Insights Generator: Systematic Corpus-Level Trace Diagnostics for LLM Agents
cs.AI, cs.LG, cs.SE
Submitted: 2026-05-20
Updated: 2026-10-06
Terminology
Sources
- AgentRx: Diagnosing AI Agent Failures from Execution Trajectories
- Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities
- Why Do Multi-Agent LLM Systems Fail?
- DENIAHL: In-Context Features Influence LLM Needle-In-A-Haystack Abilities
- TRAIL: Trace Reasoning and Agentic Issue Localization
- RULER: What's the Real Context Size of Your Long-Context Language Models?
- Automated Design of Agentic Systems
- Meta-Harness: End-to-End Optimization of Model Harnesses
- DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems
- Demystifying the Lifecycle of Failures in Platform-Orchestrated Agentic Workflows
- SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation
- Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data
- Wink: Recovering from Misbehaviors in Coding Agents
- Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
- APEX-Agents
- AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems?
- Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems
- Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
- Where LLM Agents Fail and How They can Learn From Failures
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- 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