Self-Emergence Agent Architecture:Behavior-Inertia HMM, Reflexive Metacognition,and Social-Contrastive Self-Modeling
Paper recorded by Signals 4 on 2026-09-15 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16
Category: cs.AI · 人工智能 · first seen 2026-09-16
Abstract
Large language model (LLM) agents exhibit strong language-generation and problem-solving capabilities, yet suffer from three structural limitations: personality drift, non-evolutionary reflection, and the absence of a self-other boundary. Existing generative-agent simulations rely on static memory and fixed prompts, maintaining neither behavioral inertia nor endogenous self-evolution. We propose t
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Cite this page: Self-Emergence Agent Architecture:Behavior-Inertia HMM, Reflexive Metacognition,and Social-Contrastive Self-Modeling: the #60 most recent of 300 cs.AI papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/self-emergence-agent-architecture-behavior-inertia-hmm-reflexive-metacognition-a.html
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