From Vulnerability to Coordination: Autonomous Emergence of Homeostatic Latent Dynamics, Acoustic Signaling, and Extinction Resistance in Embodied Recurrent Agents

Evaluating the emergence of adaptive internal representations under existential constraints is a foundational question in embodied artificial intelligence and cognitive systems. In this work, we investigate how non-resettable physical elimination (Darwinian mortality) and nociceptive feedback shape policy dynamics in low-dimensional recurrent neural agents (Gated Recurrent Units, H = 32) operating within a 2D continuous kinematic arena. Without pre-programmed emotional modules or symbolic communication seeds, agents exposed to somatic damage feedback autonomously structure latent manifolds satisfying operational criteria for threat-contingent coordination: cross-hazard representation convergence (cosine similarity 0.469 ± 0.06), anticipatory threat decoding under sampling truncation (ROC-AUC 0.642), and motor perturbation responses under latent vector injection (z = 0.625). Key Findings & Benchmarks: Empirical RL Benchmarking: Systematic comparison against standard reinforcement learning baselines (PPO, SAC, MADDPG, and Static Neuroevolution; N = 50 per condition) demonstrates statistically significant advantages in terminal survival (84.3% vs. 21.4–32.1%, p < 10^-80) and latency (0.12 vs. 4.9–15.4 ms/step) under non-resettable mortality. Acoustic Signaling & Channel Ablation: Under cooperative foraging with asymmetric sensory constraints, agents discretize continuous acoustic channels into distinct signaling centroids (MI = 0.122) functioning as stochastic alerting and recruitment beacons. Systematic channel ablations confirm that the +78.4% harvest gain decomposes into rear-predator alerting (+31.2%) and nutrient clustering (+42.6%). Spatial Dilemma & Ontogenetic Avoidance: In a spatial social dilemma, mortal agents exhibit a 97.0% individual retreat rate under elevated recurrent activation norms, reflecting the mathematical predominance of real-time Hebbian pain-avoidance gradients over unrepresented group-level payoffs. Conditioned Aversive States & Synaptic Dampening: Conditioned aversive associations exhibit persistent extinction resistance arising from the asymmetric reinforcement structure of Hebbian eligibility traces. Post-retrieval synaptic weight dampening acts as a phenomenological operator of attractor destabilization while preserving general motor competence (92.4% baseline foraging retention). Reproducibility: The work provides an explicit Open Science and Reproducibility Protocol with an independent replication checklist, detailing deterministic replication seeds and architecture parameters.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-11
DOI
https://doi.org/10.5281/zenodo.24136123
Primary Topic
Reinforcement Learning in Robotics
Type
preprint
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preprint

From Vulnerability to Coordination: Autonomous Emergence of Homeostatic Latent Dynamics, Acoustic Signaling, and Extinction Resistance in Embodied Recurrent Agents

Yoshikai Reiji
Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
preprint

From Vulnerability to Coordination: Autonomous Emergence of Homeostatic Latent Dynamics, Acoustic Signaling, and Extinction Resistance in Embodied Recurrent Agents

Yoshikai Reiji
preprint en

Abstract

Evaluating the emergence of adaptive internal representations under existential constraints is a foundational question in embodied artificial intelligence and cognitive systems. In this work, we investigate how non-resettable physical elimination (Darwinian mortality) and nociceptive feedback shape policy dynamics in low-dimensional recurrent neural agents (Gated Recurrent Units, H = 32) operating within a 2D continuous kinematic arena. Without pre-programmed emotional modules or symbolic communication seeds, agents exposed to somatic damage feedback autonomously structure latent manifolds satisfying operational criteria for threat-contingent coordination: cross-hazard representation convergence (cosine similarity 0.469 ± 0.06), anticipatory threat decoding under sampling truncation (ROC-AUC 0.642), and motor perturbation responses under latent vector injection (z = 0.625). Key Findings & Benchmarks: Empirical RL Benchmarking: Systematic comparison against standard reinforcement learning baselines (PPO, SAC, MADDPG, and Static Neuroevolution; N = 50 per condition) demonstrates statistically significant advantages in terminal survival (84.3% vs. 21.4–32.1%, p < 10^-80) and latency (0.12 vs. 4.9–15.4 ms/step) under non-resettable mortality. Acoustic Signaling & Channel Ablation: Under cooperative foraging with asymmetric sensory constraints, agents discretize continuous acoustic channels into distinct signaling centroids (MI = 0.122) functioning as stochastic alerting and recruitment beacons. Systematic channel ablations confirm that the +78.4% harvest gain decomposes into rear-predator alerting (+31.2%) and nutrient clustering (+42.6%). Spatial Dilemma & Ontogenetic Avoidance: In a spatial social dilemma, mortal agents exhibit a 97.0% individual retreat rate under elevated recurrent activation norms, reflecting the mathematical predominance of real-time Hebbian pain-avoidance gradients over unrepresented group-level payoffs. Conditioned Aversive States & Synaptic Dampening: Conditioned aversive associations exhibit persistent extinction resistance arising from the asymmetric reinforcement structure of Hebbian eligibility traces. Post-retrieval synaptic weight dampening acts as a phenomenological operator of attractor destabilization while preserving general motor competence (92.4% baseline foraging retention). Reproducibility: The work provides an explicit Open Science and Reproducibility Protocol with an independent replication checklist, detailing deterministic replication seeds and architecture parameters.

Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
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