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New cognitive extensions boost language agent performance in interactive tasks

Researchers have developed cognitive extensions for dual-process language agents to improve their performance in interactive environments. The Adaptive Memory Module (AMM) and Self-Reflection Module (SRM) were added to the SwiftSage agent, enhancing its ability to track long-horizon states, execute actions, and recover from failures. The full system, incorporating both AMM and SRM, achieved the highest scores in mean final score, success rate, and successful-step efficiency on the ScienceWorld benchmark. AI

IMPACT Enhances language agent capabilities in complex interactive scenarios, potentially improving their reliability and efficiency in real-world applications.

RANK_REASON The cluster contains an academic paper detailing a new method for improving language agents.

Read on arXiv cs.MA (Multiagent) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New cognitive extensions boost language agent performance in interactive tasks

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The cluster contains an academic paper detailing a new method for improving language agents.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jo\~ao Meneses dos Santos, Arlindo L. Oliveira ·

    Cognitive Extensions for Dual-Process Language Agents: Memory and Self-Reflection in Interactive Environments

    arXiv:2609.19128v1 Announce Type: new 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 a…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Arlindo L. Oliveira ·

    Cognitive Extensions for Dual-Process Language Agents: Memory and Self-Reflection in Interactive Environments

    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 …