Causal state binding is introduced as a framework that predicts action control in language agents, validated across large benchmarks and SWE-bench Lite where adding the measure raised issue-to-file hit@3 AUC from 0.873 to 0.935.
Connectionist models of recognition memory: Constraints imposed by learning and forgetting functions.Psychological Review, 97(2):285–308
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DG-Hard uses Donoho-Gavish hard thresholding on the fine-tuning weight delta to separate task-aligned signal from noise-like residual, recovering damaged capabilities while preserving target-task gains.
Eye contact norms create three recurring access barriers for visually impaired people in mixed-ability groups, reframing accessible design as support for explicit interaction contracts instead of gaze visibility.
PALMS is a computational tool implementing canonical and attentional Pavlovian learning models with support for large experiments and a new unified learning rate variant that combines Mackintosh and Pearce-Hall ideas.
Human-human interaction in RPS can produce higher Lempel-Ziv complexity sequences than RNG opponents via sensitivity to recent frequency biases, most evident in low-entropy opponent states.
Joint sparse coding and temporal dynamics in mPFC and computational networks reduce cross-context interference and enhance separability, enabling better retention in lifelong learning without extra heuristics.
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Causal state binding predicts action control in language agents
Causal state binding is introduced as a framework that predicts action control in language agents, validated across large benchmarks and SWE-bench Lite where adding the measure raised issue-to-file hit@3 AUC from 0.873 to 0.935.