arXiv (All)
open access
StateTape: Action-Conditioned Evidence Lifecycle Modeling for Long-Horizon Coding Agents
Despite the recent success of coding agents built on large language models, it remains challenging to run them over long horizons, since every observation is appended to the context and the context grows with each one. History-based maintenance is a common remedy, which masks or summarizes old obser
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
- Model Retirement Creates Reproducibility Risk in Biomedical AI Publications#927966
- Chronicle: Cut-Point Replay for Regression Testing of LLM Agents#1034432
- Chronicle: Cut-Point Replay for Regression Testing of LLM Agents#978466
- LayerRoute: Input-Conditioned Adaptive Layer Skipping via LoRA Fine-Tuning for Agentic Language Models#974446
- PACEvolve: Enabling Progress-Aware Consistent Evolution#999712
Record · ID 1131161
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