Cognitive Extensions for Dual-Process Language Agents: Memory and Self-Reflection in Interactive Environments
Researchers extend the SwiftSage dual-process agent with adaptive memory and self-reflection modules, improving scores in interactive environments.
The work adds an Adaptive Memory Module (AMM) for salience-gated episodic storage and trigger-driven retrieval, and a Self-Reflection Module (SRM) for bounded execution-time validation and corrective intervention, to the SwiftSage agent. Controlled ablations on ScienceWorld across four configurations show the full system achieves the best mean final score (64.62), success rate (43.17%), and successful-step efficiency (19.33 steps). SRM is the strongest standalone contributor, suggesting execution-time control is the dominant bottleneck while episodic memory helps once the runtime loop is stable.
- AMM provides salience-gated episodic memory with trigger-driven retrieval
- SRM performs bounded execution-time validation and corrective intervention
- Full system reaches 64.62 mean final score and 43.17% success on ScienceWorld
- Self-reflection beats episodic memory as a standalone improvement
- Products
- SwiftSageScienceWorld
From the original article
1 min read at arxiv.orgLanguage agents remain brittle in interactive environments, where success requires long-horizon state tracking, valid action execution, and recovery from failed steps.
By João Meneses dos Santos · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.