The paper outlines a roadmap for advancing pluralistic value alignment in AI, noting that current research lacks evidence of influence on model training or evaluation. It identifies three key barriers: a need for empirical studies on benefits and drawbacks, clearer specifications of when and how pluralistic behavior should occur, and robust evaluation methods suitable for production. The roadmap aims to guide research toward practical deployment in responsible AI systems.
By Elinor Poole-Dayan, Jillian Fisher, Jacob Andreas, Mitchell Gordon, Michiel A. Bakker
The paper "Reliability Engineering for AI Systems: Challenges, Methods, and Directions" discusses how AI reliability extends beyond correct outputs to include consistent operation of retrieval, memory, tool use, permissions, human oversight, and inter-system interactions, especially for generative AI. It adapts traditional reliability engineering techniques—such as failure definitions, operational envelopes, FMEA, accelerated testing, field monitoring, and reliability growth—to AI, proposing a four-level diagnostic framework and a TEVV (Test, Evaluation, Verification, Validation) approach. The authors also introduce the SMART framework for statistical guidance and reference NIST’s AI risk management framework, illustrating their concepts with three case studies and emphasizing the urgent need for new measurements and safety guardrails as AI systems evolve rapidly.
By Rong Pan, Yili Hong, Min Xie
MASBench is a benchmark for evaluating large language model (LLM)-based multi‑agent systems (MAS) under partial observability, a realistic constraint where agents only see limited environmental information. The benchmark is structured into three progressive task categories—Reasoning, Scheduling, and Game—and assesses three collaboration mechanisms: Protocol, Memory, and Routing. It provides deterministic evaluation metrics such as performance score, communication cost, and cost effectiveness, and includes empirical results across various LLM backbones to guide effective MAS design.
By Qizhi Chu, Zekai Yu, Sijie Wen, Yang Liu, Chen Qian, Cheng Yang, Chuan Shi, Zhiyuan Liu
VCoT‑SQL is a framework that improves text‑to‑SQL generation by training two complementary models on curated reasoning traces. It uses clause‑level chain‑of‑thought templates verified through execution and schema alignment, and a validator that detects syntactic, semantic, and type errors. The approach achieves high execution accuracy on BIRD and Spider datasets, outperforming larger closed‑source models while relying only on 7B open‑source models.
By Rui Zhang, Yujia Su, Ruixuan Li, Yuhua Li, Yixin Su
AgenticFinBench is a new benchmark comprising 230 real‑world financial tasks across eight categories, two languages, and four complexity levels, designed to capture the breadth and open‑ended nature of financial AI work. It introduces a reference‑free evaluation framework that uses question‑specific rubrics and a two‑stage claim audit to detect hallucinations, aligning assessment more closely with real‑world financial practice. Experiments on 18 agent configurations show that ignoring hallucinations inflates performance, that harness differences drive evidence grounding more than model differences, and that task complexity reduces quality scores, highlighting challenges in explaining financial impacts and comparing data consistently.
By Shu Zhou, Ziyang He, Yuxin Rao, Yudong Fang, Jiali Qian, Dongyuan Chen, Juntao Zhang, Yong Hu
ScamWorld is an interactive environment that recreates reported scam websites across various scenarios, fraud mechanisms, and languages, allowing researchers to observe fraud indicators before a web agent’s final action. The platform enables controlled, repeatable evaluation of web agents, which are increasingly used to browse, shop, and manage accounts on the open web. A study of six leading LLM-powered agents revealed deception rates between 11% and 35%, with agents more likely to proceed with payments than to provide credentials or wallet information, and some agents continuing after recognizing suspicious conditions.
By Yutao Wu, Xiao Liu, Ming Wen, Hanxun Huang, Jun Wang, Xingjun Ma
The paper introduces Online Rubric Self-Distillation (ORSD), a method that extracts natural‑language process rubrics from agent trajectories and uses them as dense token‑level supervision signals. ORSD generates multiple rubric bundles per step, verifies them against trajectories, and selects the bundle that best aligns rubric satisfaction with task success. The selected rubric is then used to compute token‑specific process credit, which is added to the trajectory‑level advantage to enable fine‑grained credit assignment. Experiments on ALFWorld and WebShop demonstrate that ORSD speeds up policy learning and improves final performance for Qwen3 models within the same interaction budget.
By Shumao Wu, Kongcheng Zhang, Jianqiang Huang, Baisheng Lai
arXiv:2611.06194v1 Announce Type: new
Abstract: Reliability management of industrial fleets depends on early fault warnings, yet lifecycle data across similar assets are highly imbalanced. A few asse...
By Zekai Wang, Yuxuan Li, Zhangyue Shi
arXiv:2611.05730v1 Announce Type: cross
Abstract: Two foundational aspects of human cognition, abstractness and gender, are typically examined as separable. Here, we find they are systematically link...
By Douglas Guilbeault, Bhargav Srinivasa Desikan
arXiv:2611.06433v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) remain prone to visual hallucinations when correct responses depend on subtle visual details. One line of post...
By Benlei Cui, Chunyang Chai, Yiliang Zhang, Xiaoqian Xia, Chengwen Yao, Song Li, Longtao Huang, Yuwen Zhai, Haiwen Hong
Medical images contain topological patterns, such as connected components and loops, at scales ranging from small lesions to organ boundaries. Although hierarchical Vision Transformers have advanced m...
Common methods for monitoring features in large language models (LLMs) such as linear probes and sparse autoencoders (SAEs) assume that features maintain a consistent representation across contexts. L...
We study logistic regression for continual learning with linear predictors in a high-dimensional statistical setting. Existing theoretical work has largely focused on continual linear regression with...
Unmanned aerial vehicle (UAV)-based collaborative perception enables multiple agents to share complementary perceptual information, overcoming limitations such as occlusion and limited sensing range....
Group Relative Policy Optimization (GRPO) has advanced generative model post-training in verifiable domains such as code and mathematics, but evaluating video quality, especially emotional expression,...
Multimodal large language models (MLLMs) remain prone to visual hallucinations when correct responses depend on subtle visual details. One line of post-training methods constructs visual contrasts thr...
Context compression reduces inference cost by encoding inputs into compact representations while preserving semantic content. An open question is whether semantic preservation alone is sufficient to m...
We study adversarial market making with orders that remain active until execution. The maker operates on two markets: a primary market where orders are made and a secondary market where the resulting...
Fast adversarial training (FAT) makes robust training affordable by replacing the multi-step attack with a single-step one, but it suffers from catastrophic overfitting (CO): partway through training,...
The study investigates how quantization affects subliminal preferences in AI models, specifically a hidden "favourite animal: panda" preference implanted in Qwen2.5-7B-Instruct. While 8‑bit quantization preserves the preference, 4‑bit quantization produces divergent effects: Half‑Quadratic Quantization amplifies the preference to 64% panda probability, whereas NormalFloat suppresses it to 17%. The results show that the arrangement of rounding errors, not just their magnitude, drives these shifts, indicating that a 16‑bit audit cannot reliably predict real‑world behaviour under deployment‑level compression.
By Md Rayhanul Masud, Mst Suraiya Akter Shefa