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Neural systems can develop agency by distinguishing self-caused actions

By PulseAugur Editorial · [2 sources] ·

Researchers have detailed a developmental pathway for artificial agency in minimal neural systems, specifically a 192-dimensional GRU. The study outlines four sequential conditions necessary for a predictive system to distinguish self-caused actions from external events. These conditions include stable states, a causal action loop, proprioceptive feedback, and asynchronous learning, with agency gain proposed as a key metric. AI

IMPACT Establishes a theoretical framework and metric for developing agency in AI systems, potentially guiding future research in embodied AI and self-aware agents.

RANK_REASON The cluster contains a research paper detailing a novel approach to developing agency in neural networks.

Read on arXiv cs.LG →

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

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Evan Ye ·

    From Prediction to Self: Developmental Conditions for Agency in Minimal Neural Systems

    arXiv:2606.05605v1 Announce Type: new Abstract: How does a system that merely predicts the world come to distinguish its own causal influence from everything else? We trace this transition in a minimal 192-dimensional GRU through 40 controlled experiments arranged as a developmen…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Evan Ye ·

    From Prediction to Self: Developmental Conditions for Agency in Minimal Neural Systems

    How does a system that merely predicts the world come to distinguish its own causal influence from everything else? We trace this transition in a minimal 192-dimensional GRU through 40 controlled experiments arranged as a developmental sequence, adding components one at a time an…

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