Cognitive Extensions for Dual-Process Language Agents: Memory and Self-Reflection in Interactive Environments
cs.AI, cs.LG, cs.MA
Submitted: 2026-09-16
Updated: 2026-09-16
Comments: 13 pages, 1 figure
License: http://creativecommons.org/licenses/by/4.0/
The gist: Language agents remain brittle in interactive environments, where success requires long-horizon state tracking, valid action execution, and recovery from failed steps.
Terminology
Abstract
Language agents remain brittle in interactive environments, where success requires long-horizon state tracking, valid action execution, and recovery from failed steps. We extend SwiftSage, a dual-process agent that combines a fast action proposer with a slower planner, using two modular cognitive extensions: 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. Both modules are implemented as feature-flagged extensions over the same execution substrate, enabling controlled ablations on ScienceWorld. Across four configurations---baseline, baseline+AMM, baseline+SRM, and the full system---the full system achieves the best mean final score (64.62), success rate (43.17%), and successful-step efficiency (19.33 steps), while SRM is the strongest standalone contributor. The results suggest that execution-time control is the dominant bottleneck in this setting, while episodic memory becomes most useful once the runtime loop is stabilized.
Sources
- MemGPT: Towards LLMs as Operating Systems
- ART: Automatic multi-step reasoning and tool-use for large language models
- Large Language Models Cannot Self-Correct Reasoning Yet
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