Introduces evaluation of LLMs' implicit software world models via prediction of execution resources on real software tasks, finding modest and brittle performance across models including frontier ones.
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4 Pith papers cite this work, alongside 560 external citations. Polarity classification is still indexing.
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DeCo-DETR builds hierarchical semantic prototypes offline and uses decoupled training streams to deliver competitive zero-shot open-vocabulary detection with improved inference speed.
Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.
LLMs form an inner monologue from closed-loop language feedback to improve high-level instruction completion in simulated and real robotic rearrangement and kitchen manipulation tasks.
citing papers explorer
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Towards Evaluation of Implicit Software World Models in Coding LLMs
Introduces evaluation of LLMs' implicit software world models via prediction of execution resources on real software tasks, finding modest and brittle performance across models including frontier ones.
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DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection
DeCo-DETR builds hierarchical semantic prototypes offline and uses decoupled training streams to deliver competitive zero-shot open-vocabulary detection with improved inference speed.
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Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy
Unsupervised domain adaptation via feature alignment raises radioisotope identification accuracy on real LaBr3 gamma spectra from 0.754 to 0.904 for models trained only on synthetic data.
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Inner Monologue: Embodied Reasoning through Planning with Language Models
LLMs form an inner monologue from closed-loop language feedback to improve high-level instruction completion in simulated and real robotic rearrangement and kitchen manipulation tasks.