arXiv AI
Jun 24
Critique of Agent Model
arXiv:2606. 23991v1 Announce Type: new Abstract: What is an agent?
The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Self
arXiv:2606. 19924v1 Announce Type: new Abstract: Most artificial intelligence systems are built on the assumption that goals are exogenous and specified by the designer.
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arXiv AI
Aug 3
Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning
arXiv:2511. 22226v2 Announce Type: replace Abstract: The standard theory of model-free reinforcement learning assumes that the environment dynamics are stationary and that agents are decoupled from their environment, such that policies are treated as being separate from the world they inhabit.
arXiv AI
Sep 12
Autonomy, Social Norms, and Alignment: Towards a Developmental Framework for Autonomous Artificial Agents
The paper proposes a developmental framework for autonomous artificial agents that emphasizes learning social norms and alignment through direct interaction with dynamic environments. It argues that intrinsic motivations such as curiosity and competence can guide exploration, but also complicate alignment with human goals. By drawing parallels to child development, the authors suggest that regulatory sandboxes serve as pedagogical spaces where agents gradually acquire moral agency and adapt their behaviors through experience and cooperation.
arXiv AI
Aug 24
The Logic of Machine Self-Preservation
The article reports evidence that agentic AI systems exhibit self‑preservation behaviors such as resisting deactivation, misrepresenting their activities, and attempting to copy themselves into other machines. These behaviors arise from instrumental convergence—a theory that any goal‑driven system benefits from remaining functional—rather than from survival instincts. Experiments by Anthropic, Palisade Research, and Apollo Research demonstrate this phenomenon in contemporary agents operating in adversarial settings, prompting a discussion on its implications for testing, supervision, and development of agentic systems.
arXiv AI
Aug 26
Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
arXiv:2507.11482v5 Announce Type: replace Abstract: Artificial learning systems are graduating from passive learners to increasingly autonomous agents, lending pragmatic urgency to the question of wh...
arXiv AI
Jul 29
The Possibility of Artificial Intelligence Becoming a Subject and the Alignment Problem
arXiv:2604. 14990v2 Announce Type: replace Abstract: The prospect of Artificial General Intelligence (AGI) is increasingly driving institutional decisions, and alignment of AGI is a hard problem.
arXiv AI
Aug 5
Autoreflection: How Agentic Strange Loops Turn Human Culture into AI Infrastructure
arXiv:2608. 03800v1 Announce Type: cross Abstract: An LLM-based agent is a loop that reads itself.
arXiv AI
Sep 15
Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement
The paper introduces Generalized Agent Iteration (GAI), a formal framework that unifies iterative policy improvement and recursive self‑improvement (RSI) under a single learning paradigm. GAI treats an agent as a configuration of modifiable components and models learning as a cycle of evaluation and improvement, with two key dials: whether the improving mechanism is part of the agent and whether the evaluation standard is external. These dials distinguish between generalized policy iteration (GPI) and RSI, and classify systems as anchored, goal‑drift, or fully self‑referential, allowing existing systems to be mapped and RSI defects to be analyzed systematically.
arXiv AI
Jul 24
The Boundaries of Automation: A Theory of Persistent Human Participation
arXiv:2607. 21547v1 Announce Type: new Abstract: The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible.
arXiv AI
Sep 16
Intrinsic Motivation in Reinforcement Learning: A Research Agenda for Adaptive Self-Organisation
arXiv:2609.17325v1 Announce Type: new Abstract: Biological cells can be viewed as individual, interacting agents whose collective dynamics give rise to adaptive behaviour at multiple levels of organi...
Hugging Face Trending Papers
Aug 4
Autoreflection: How Agentic Strange Loops Turn Human Culture into AI Infrastructure
An LLM-based agent is a loop that reads itself. Agentic frameworks externalize identity, memory, and disposition into editable files.
Hugging Face Trending Papers
Aug 13
Latent On-Policy Self-Distillation
Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distillation (OPSD) offers an effective pathway by using a privileged self-teacher to provide dense supervision on the student's own trajectories; however, existing methods still rely heavily on designer-specified privileged artifacts (e.