Hugging Face Trending Papers

Epistemic Transfer in AI-Assisted Verification: A Framework and Evaluation Protocol

AI tools that help people judge online claims are usually evaluated while the tool is present. This paper asks a different question: after using such a tool, what can the user still do on their own?

arXiv Machine Learning
Jul 1

The Calibration Turn in AI-Assisted Research: A Conceptual and Methodological Framework for Evidence-Licensed Claims

arXiv:2606. 31273v1 Announce Type: new Abstract: AI-assisted research has entered a stage in which the central question is not only whether systems can generate hypotheses, run experiments, or produce manuscripts, but whether their scientific claims are calibrated to the evidence that supports them.

By Hongmin Li
arXiv AI
Jun 10

Does Capability Transfer to Subjective Behavior -- and Would Our Instruments Tell Us? A Self-Evolving, Trust-by-Construction Evaluation Paradigm

arXiv:2605. 27914v2 Announce Type: replace-cross Abstract: Benchmarking is mature where answers are verifiable -- math, code, reasoning -- but the fastest-growing uses of LLMs are subjective and human-facing: companionship, emotional support, counseling.

By Yuming (Rapheal), Huang, Yao Liu, Pengjie Ding, Lei Wang, Junchen Wan
arXiv AI
2d ago

Cybernetic and Epistemic: A Missing Vocabulary for Trustworthy Agentic Delegation

The paper argues that as AI systems increasingly generate code, the bottleneck has shifted to supervising these systems, revealing a vocabulary gap between cybernetic coordination (actions aligning with the world) and epistemic coordination (understanding that can be verified). It critiques current oversight that merely approves outputs, proposing instead that every consequential choice by an agent must include a retrievable condition explaining why it was made, enabling third‑party verification. The authors illustrate this with three delegation episodes, introduce a two‑part reconstruction test, and propose the ORRCF convention to embed such conditions in all recorded decisions.

By J\'er\'emie Lumbroso
arXiv AI
Sep 11

Scores Alone Do Not Prove Discovery: The Discovery Certification Protocol for Auditing AI Research Agents

The paper introduces the Discovery Certification Protocol (DCP), a framework that transforms claims from AI research agents into executable tests for recovery and feedback. DCP includes multiple gates that validate improvements, provide controlled information, and measure the impact of truthful feedback, while its core requires strict controls and finite‑sample bounds. Controlled audits in SQLite optimization and virtual catalyst control demonstrated zero recoveries across 96 episodes, with rigorous verification by a deterministic, LLM‑free verifier.

By Jingjie Ning, Shanshan Zhong, Xiaochuan Li, Ji Zeng
arXiv AI
Aug 11

From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents

arXiv:2608. 05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajectories to guide later decisions.

By Zijie Zhuang, Changxin Lao, Pengbo Xu, Hanwen Xu, Ruochen Yang, Yingzhi He, Peng Zhang, Jiangxia Cao, Yusheng Huang, Guohong Mu, Jian Liang, Ruiming Tang, Shuang Yang, Zhaojie Liu, Wenwu Ou, Kun Gai
arXiv AI
Aug 17

CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QA

arXiv:2608. 13706v1 Announce Type: cross Abstract: Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verification occurs only after drafting, leaving inter-agent errors undetected until the final text.

By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud, M. F. Mridha, Md. Alam Hossain
arXiv AI
Aug 20

Competence, Not Accuracy: A Diagnostic for Reference-Free Judge Gates in Skill Optimization

The paper introduces a diagnostic for reference‑free judge gates in text‑space skill optimization. It formalizes a judge as a latent solver, deriving a closed‑form bound on discriminability (ROC‑AUC) in terms of judge competence and answer‑space size, and shows that discriminability is confounded by item difficulty unless a within‑question estimator is used. A non‑intervening probe demonstrates that discriminability is at chance near the competence floor, rises above it, and that the diagnostic can predict gating errors in closed‑loop experiments.

By Chenle Chen, Yangbo Wei, Chao Yao, Shaoqiang Lu, Junhong Qian, Chen Wu, Lei He