arXiv (All)
open access
Causal Retention in Interactive Agents: Interface Factorization and Selective Adaptation
Task performance need not determine which intervention mechanism an agent retains. We study causal retention: whether a frozen learned state answers a mechanism-probe map fixed independently of training, including action, context, direct target, value, and delay. For finite structural causal model c
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
- Enabling Timely Guidance before Skill Retrieval: Retaining Helpful Warm Tips in Agent Context#1116670
- Benchmarking World Models for Continual Learning on Compositional Tasks#1036465
- Benchmarking World Models for Continual Learning on Compositional Tasks#1006833
- Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval#1161073
- Causal Memory Policy: Making Memory Utility Identifiable by Intervening on Retrieval#1157418
Record · ID 1105209
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