arXiv (All)
open access
DENSE: Distilling Agent Trajectories into Evidence-Grounded Shortcut Trees for Self-Refinement
Online agent deployments produce abundant execution traces, while task-specific verification and expert annotation are costly to scale. We study how to distill these traces into reusable feedback without post-hoc outcome labels, drawing on their evidence of local progress, recovery, and unfinished r
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments#1149480
- E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch#1169651
- E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch#1142936
- When Upstream Messages Override Correct Answers: A Controlled Study of Multi-Agent LLM Collaboration#1132056
- TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration#14052
Record · ID 1035827
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive