Agent-BRACE improves LLM agent performance on long-horizon partially observable tasks by 5.3-14.5% through a decoupled belief state of verbalized atomic claims with certainty labels that keeps context length constant.
ALFRED : A benchmark for interpreting grounded instructions for everyday tasks
3 Pith papers cite this work. Polarity classification is still indexing.
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SkillOps maintains LLM skill libraries via Skill Contracts and ecosystem graphs, raising ALFWorld task success to 79.5% as a standalone agent and improving retrieval baselines by up to 2.9 points with near-zero library-time LLM cost.
Introduces Image Reconstruction Game benchmark showing describer model dominates reconstruction quality in multi-turn VLM-generator dialogue, with math images hardest and token budget affecting convergence.
citing papers explorer
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Agent-BRACE: Decoupling Beliefs from Actions in Long-Horizon Tasks via Verbalized State Uncertainty
Agent-BRACE improves LLM agent performance on long-horizon partially observable tasks by 5.3-14.5% through a decoupled belief state of verbalized atomic claims with certainty labels that keeps context length constant.
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SkillOps: Managing LLM Agent Skill Libraries as Self-Maintaining Software Ecosystems
SkillOps maintains LLM skill libraries via Skill Contracts and ecosystem graphs, raising ALFWorld task success to 79.5% as a standalone agent and improving retrieval baselines by up to 2.9 points with near-zero library-time LLM cost.
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The Image Reconstruction Game: Drawing Common Ground Through Iterative Multimodal Dialogue
Introduces Image Reconstruction Game benchmark showing describer model dominates reconstruction quality in multi-turn VLM-generator dialogue, with math images hardest and token budget affecting convergence.