arXiv AI
What Does a System Modify When It Modifies Itself?
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Sep 10
When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems
arXiv:2609.07741v1 Announce Type: new Abstract: Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be trul...
arXiv AI
Sep 11
Cognitive Amplification vs Cognitive Delegation in Human-AI Systems: A Metric Framework
The paper proposes a metric framework to differentiate cognitive amplification—where AI enhances human performance without eroding human capability—from cognitive delegation, which relies heavily on AI reasoning. It introduces four metrics (CAI*, D, HRI, HCDR) and tests them in NetLogo simulations across various reliance and dependency scenarios. The results show that positive collaborative gain is only achievable when an explicit interaction term is added, indicating that mere prevention of capability erosion is insufficient for genuine amplification.
arXiv AI
Jul 9
Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops
arXiv:2607. 07663v1 Announce Type: new Abstract: AI systems increasingly participate in their own improvement: revising their outputs, adapting their own harnesses during deployment, training on data they generate, and, increasingly, conducting AI research itself.
Hugging Face Trending Papers
Jul 8
Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops
AI systems increasingly participate in their own improvement: revising their outputs, adapting their own harnesses during deployment, training on data they generate, and, increasingly, conducting AI research itself. This literature is described under a vocabulary ("self-refine," "self-reward," "self-play," "self-evolve") that conflates fundamentally different ambitions.
arXiv AI
Jun 6
The Self-Correction Illusion: LLMs Correct Others but Not Themselves
arXiv:2606. 05976v1 Announce Type: new Abstract: Recent work shows that LLM agents struggle to correct errors in their own reasoning traces yet show markedly higher correction rates when identical claims appear under external sources.
arXiv AI
Aug 12
Inferential Capability Does Not Determine Legal Scope
arXiv:2608. 10601v1 Announce Type: cross Abstract: Two instruments of EU digital law place inference at their centre and mean different things by it.