arXiv AI June 30, 2026 By Aojie Yuan, Yi Nian, Haiyue Zhang, Zijian Su, Yue Zhao

SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution

arXiv:2606. 29713v1 Announce Type: cross Abstract: Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers emit only opaque binary labels, leaving agents unable to self-correct and operators unable to audit.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 17

Reward-Free Evolving Agents via Pairwise Validator

arXiv:2607. 14408v1 Announce Type: new Abstract: A self-evolving agentic loop repeatedly proposes a tweaked version of an agent (its prompt template or program) and accepts or rejects the change based on a per-iteration quality signal.

By Minghao Liu, Yu Wang, Jiayun Wang, Wei Wei

llms agents

arXiv Machine Learning

Jun 30

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives

arXiv:2510. 06096v3 Announce Type: replace Abstract: The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a grand challenge.

By Matthieu Bou, Nyal Patel, Arjun Jagota, Satyapriya Krishna, Sonali Parbhoo

llms reinforcement-learning safety

arXiv Machine Learning

Jul 8

More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges

arXiv:2607. 05904v1 Announce Type: new Abstract: Training a language model against its own reference-free judgments (the premise of self-rewarding, self-play, and LLM-as-a-judge pipelines) assumes a model's verdict on a shown answer tracks correctness.

By Chenyu Zhou

llms reinforcement-learning

arXiv Machine Learning

Jun 5

A Pre-Registered Causal Partition of Self-Consistency Elicitation and Reward Design in RLVR

arXiv:2606. 05932v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards (RLVR) improves reasoning even when the reward signal is spurious -- assigning credit to the group-plurality answer rather than a ground-truth verifier.

By Yuze Gao

reinforcement-learning safety

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.

By Alex Iacob, Andrej Jovanovi\'c, William F. Shen, Daniel Burkhardt, Meghdad Kurmanji, Nurbek Tastan, Lorenzo Sani, Niccol\`o Alberto Elia Venanzi, Ambroise Odonnat, Zeyu Cao, Bill Marino, Xinchi Qiu, Nicholas D. Lane

agents benchmarks safety

arXiv AI

Jul 14

LLMs as a Jury: Cross-Model Consensus Can Outperform Process Reward Models for LLM Reasoning

arXiv:2607. 10139v1 Announce Type: cross Abstract: Selecting the correct answer from a pool of candidate reasoning chains is the engine of test-time scaling, yet the standard selectors each carry a cost: self-consistency inherits the errors of the single model it resamples, and trained reward models need labeled data and transfer poorly off-distribution.

By Ning Liu

llms benchmarks

Related stories

Reward-Free Evolving Agents via Pairwise Validator

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives

More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges

A Pre-Registered Causal Partition of Self-Consistency Elicitation and Reward Design in RLVR

The Red Queen G\"odel Machine: Co-Evolving Agents and Their Evaluators

LLMs as a Jury: Cross-Model Consensus Can Outperform Process Reward Models for LLM Reasoning