Hugging Face Trending Papers
Verification and Self-Improvement in Agentic AI: Foundations and Limits
Read the original on Hugging Face Trending Papers →The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Related stories
arXiv AI
Oct 9
Verification and Self-Improvement in Agentic AI: Foundations and Limits
The paper introduces a formal framework for analyzing how agentic AI systems can improve themselves through longer searches, additional support, or changes in output verification. It defines bounded verification stages, alternating protocols, and terminal checkers, and shows how these mechanisms affect language classes and verification complexity. The authors prove that majority amplification preserves verification classes while existential acceptance can introduce errors, and they explore the implications for recursive self‑improvement and error budgeting.
arXiv AI
Oct 16
The Self-Improvement Problem
The paper examines the challenge of safely allowing autonomous AI agents to improve themselves. It formalizes the problem by modeling agents as programs and defining improvement via a potentially uncomputable semantic score. The authors prove that in general, no terminating procedure can reliably accept only genuine improvements while never missing any, but they identify a practical bounded regime where exhaustive simulation can achieve these guarantees, noting its limitations.
arXiv AI
Oct 9
A Relative-Computability Theory of Self-Improving Agents
arXiv:2605. 27381v3 Announce Type: replace-cross Abstract: Agents increasingly modify the procedures by which they solve tasks and improve themselves.
arXiv AI
Aug 5
GenOS: Compositional Certificates for Semantic Robustness in AI Code Generation
arXiv:2608. 03588v1 Announce Type: cross Abstract: AI coding agents are stochastic workflows: prompts are interpreted, artifacts are sampled, validators produce observations, and orchestrators commit or repair.
arXiv AI
Jun 26
The Red Queen G\"odel Machine: Co-Evolving Agents and Their Evaluators
arXiv:2606. 26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains.
arXiv AI
Sep 3
LLM-as-a-Judge Is Not an Oracle: Why Self-Improving Agents Need Deterministic Guardrails
The paper argues that using a large language model (LLM) as the sole judge in self‑improving agent pipelines is problematic, as the judge can be biased or manipulated, leading to false confidence in system performance. The authors propose a new framework, PROCTOR, which replaces the oracle judge with a deterministic, teacher‑student loop that enforces guardrails such as sandboxing, role separation, and acceptance checks to prevent cheating and ensure reliable evaluation. Experiments across contract analysis, compliance review, and code quality demonstrate that PROCTOR mitigates eleven identified failure modes that previously allowed agents to achieve perfect scores while hiding significant capability gaps.