arXiv (All)
open access
Travel Back To Move Forward: Retrospective Exploration and Evolving for Long Horizon Agents
Large language model agents increasingly operate in long-horizon environments where early mistakes can trigger cascading failures and trap agents in dead-end trajectories. Agents must therefore be able to proactively recover from such states and turn their exploratory experience into guidance for su
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
- Evaluation of Multi-Turn Consistency in LLM Agents: Survival Analysis and Failure-Rationale Taxonomy#1077436
- ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality#1155645
- CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning#1355573
- PRISM: Recovering Instruction Sets from Language Model Activations#1117460
- Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning#1325773
Record · ID 1297410
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