FCGraft synthesizes code policies for embodied agents by grafting KV caches from a library of validated functions, claiming 18.31% higher success rate and 2.3x faster synthesis than prompt-level caching.
Nesyc: A neuro- symbolic continual learner for complex embodied tasks in open domains
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Constraint-aware decoding refines TAS predictions by embedding data-derived structural priors into modified Viterbi inference for error correction without model changes.
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.
citing papers explorer
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Functional Cache Grafting: Robust and Rapid Code-Policy Synthesis for Embodied Agents
FCGraft synthesizes code policies for embodied agents by grafting KV caches from a library of validated functions, claiming 18.31% higher success rate and 2.3x faster synthesis than prompt-level caching.
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Improving Temporal Action Segmentation via Constraint-Aware Decoding
Constraint-aware decoding refines TAS predictions by embedding data-derived structural priors into modified Viterbi inference for error correction without model changes.
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Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.