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新论文 · v1arXiv:2611.02764

更新于 2026-11-02

Equivalent Graphical Conditions for Angle Rigidity and Angle-Based Network Localization

Jinpeng Huang, Gangshan Jing, Kai Zhao, Yongduan Song

原文摘要

Angle rigidity theory provides a theoretical basis for characterizing the geometric shape of graphs embedded in the plane under angle constraints, and establishing global convergence of angle-based network localization algorithms. The related references, however, are largely restricted to graphs generated by triangulation or bilateration ordering, which are sufficient but not necessary for angle rigidity. This paper addresses this challenge by focusing on establishing the equivalence between angle constraints and bearing constraints. The conditions for cycle compatible frameworks (with possibly non-generic configurations) to be angle fixable are provided. Moreover, it is shown for the first time that Laman graphs equivalently characterize minimally globally angle rigid graphs, implying that a Laman graph is the simplest graph that allows a generic embedding in the plane to be uniquely determined by subtended angles. As an application, a distributed algorithm for angle-based sensor network localization is proposed, which guarantees global convergence under a much milder network topology condition than those in the literature.

math.OC · cs.IT

新论文 · v1arXiv:2611.01454

更新于 2026-11-01

PROBE: Learned Proxy-Based Execution for Highly Selective Multimodal Semantic Queries

Haikai Zhao, Haisheng Fu, Zhengjie Miao, Zhenman Fang

原文摘要

Large language models (LLMs) enable semantic queries over unstructured data, but invoking an LLM on every row is prohibitively expensive. Proxy-based execution can reduce this ple with the LLM and training a lightweight proxyto label the remaining rows. Nevertheless, in practical scenarios where semantic queries are highly selective, it remainschallenging to identify representative samples foLM budget and to achieve accurate predictions overthe remaining data using the proxy. We present PROBE, a learned proxy-based execution framework for highly selective multimodal semantic queries. PROBE uses selectivity-adaptive sampling to discover rare polightweight proxy on the acquired labels. It thenrefines proxy predictions through residual correction and homogeneity smoothing over a similarity graph. To address sampling bias, PROBE jointly calibrates refined scores usiarate calibration sample, then selectively routescandidates to the LLM by optimizing the tradeoff between estimated F1 and evaluation cost. On queries from SemBench and LRO-Bench with positive rates as low as 2.17x10^-5 budget yields a geometric-mean F1 of 0.762,compared with 0.797 for exhaustive LLM evaluation, at a geometric-mean speedup of 4.87x and a maximum speedup of 9.3x.

cs.DB

新论文 · v1arXiv:2610.36389

更新于 2026-10-27

MAREval: A Multi-Agent Framework for Evaluating Natural Language Recommendation Explanations

Reza Yousefi Maragheh, Jayesh Uddhav Kudase, Aysenur Inan, Ramin Giahi, Kai Zhao, Parth Parikh, Jianpeng Xu, Jason Cho 等 10 位作者

原文摘要

Evaluating natural language explanations in recommender systems is essential for fostering user trust, transparency, and engagement. However, existing evaluation approaches like human evaluations, while accurate, are resource-intensive and impractical at the scale required by modern recommendation platforms. Also, automated methods using single-agent LLMs suffer from prompt sensitivity and inconsistent outputs. To address these challenges, we propose MAREval, a structured multi-agent framework for evaluating recommendation explanations using large language models. MAREval orchestrates (i) a planner agent that uses a novel Chain of Debate (CoD) prompting strategy to coordinate agent roles and enforce logically consistent evaluation plans; (ii) a moderator agent that regulates discussions by mitigating prompt drift; and (iii) an arbitrator agent that aggregates outputs from multiple evaluation rounds and (iv) a Monte Carlo sampling method, improving robustness and alignment with human judgment. We conduct comprehensive evaluations on both public (TopicalChat) and proprietary recommendation datasets, demonstrating that MAREval outperforms state-of-the-art baselines. Comprehensive experiments on a public benchmark and a proprietary e-commerce dataset show that MAREval improves alignment with human judgments over strong singleand multi-agent baselines. Stability analyses indicate substantially lower variability across repeated trials. In a large human-annotation gate, MAREval meets production quality thresholds where prior evaluators fall short, and online A/B testing demonstrates statistically significant improvements in engagement and revenue metrics. These results establish MAREval as a scalable and reliable solution for human-aligned evaluation of recommendation explanations in real-world systems

cs.AI · cs.IR

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.81ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Robust Explanations for User Trust in Enterprise NLP Systems

Guilin Zhang, Kai Zhao, Jeffrey Friedman, Xu Chu, Amine Anoun

原文摘要

Robust explanations are increasingly required for user trust in enterprise NLP, yet pre-deployment validation is difficult in the common case of black-box deployment (API-only access) where representation-based explainers are infeasible and existing studies provide limited guidance on whether explanations remain stable under real user noise, especially when organizations migrate from encoder classifiers to decoder LLMs. To close this gap, we propose a unified black-box robustness evaluation framework for token-level explanations based on leave-one-out occlusion, and operationalize explanation robustness with top-token flip rate under realistic perturbations (swap, deletion, shuffling, and back-translation) at multiple severity levels. Using this protocol, we conduct a systematic cross-architecture comparison across three benchmark datasets and six models spanning encoder and decoder families (BERT, RoBERTa, Qwen 7B/14B, Llama 8B/70B; 64,800 cases). We find that decoder LLMs produce substantially more stable explanations than encoder baselines (73% lower flip rates on average), and that stability improves with model scale (44% gain from 7B to 70B). Finally, we relate robustness improvements to inference cost, yielding a practical cost–robustness tradeoff curve that supports model and explanation selection prior to deployment in compliance-sensitive applications.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1118ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

LAMCL: A Length-aware Momentum Contrastive Learning Framework for Multiscale Machine-Revised Text Detection

Bing Zhou, Zhe Huang, Shilei Tan University of Science and Technology of China, Kai Zhao Chongqing University, Zhou Yongcheng

原文摘要

Detecting machine-revised text that exhibits subtle lexical differences from the original human-generated text remains a challenge. Recent detection methods, including watermarking-based, logit-based, and training-based models, struggle to capture the fine-grained semantic differences, especially for short texts. To address this issue, we propose Length-aware Momentum Contrastive Learning (LAMCL), a novel framework for multiscale machine-revised text detection that integrates two core modules. To enhance the discriminative semantic features, the Enhance Before Detection (EBD) module first fuses the original detected text with the counterpart processed by a Large Language Model (LLM), and then measures semantic consistency to distinguish between machine-revised and human-generated text. Meanwhile, based on the Momentum Contrastive Learning (MCL) framework, the Length-aware Weighting (LW) module leverages text length and label information for hard negative sampling, mitigating the ambiguity of short text attribution and boosting the robustness of representation learning. Experimental results demonstrate that our method outperforms the existing detectors in identifying multiscale machine-revised text across diverse practical scenarios, tasks, and LLMs. The code is available at https://github.com/hangtze/LAMCL.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.112ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Mixture-of-Minds: Multi-Agent Reinforcement Learning for Table Understanding

Yuhang Zhou Meta, Mingrui Zhang, Ke Li, Mingyi Wang Facebook, Qiao Liu City University of New York, Qifei Wang, Jiayi Liu Meta Platforms Inc., Fei Liu Meta 等 15 位作者

原文摘要

Understanding and reasoning over tables is a critical capability for many real-world applications. Large language models (LLMs) have shown promise on this task, but current approaches remain limited. Fine-tuning based methods strengthen language reasoning; yet they are prone to arithmetic errors and hallucination. In contrast, tool-based methods enable precise table manipulation but rely on rigid schemas and lack semantic understanding. These complementary drawbacks highlight the need for approaches that integrate robust reasoning with reliable table processing. In this work, we propose MIXTURE-OF-MINDS, a multi-agent framework that decomposes table reasoning into three specialized roles: planning, coding, and answering. This design enables each agent to focus on a specific aspect of the task while leveraging code execution for precise table manipulation. Building on this workflow, we introduce a self-improvement training framework that employs Monte Carlo Tree Search (MCTS) rollouts to generate pseudo-gold trajectories and optimize agents with reinforcement learning (RL). Extensive experiments show that MIXTURE-OF-MINDS delivers substantial gains, reaching 62.13% on TableBench and surpassing GPT-o3-mini. These results demonstrate the promise of combining structured multi-agent workflows with RL to advance table understanding.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1506ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

MetaBench: A Multi-task Benchmark for Assessing LLMs in Metabolomics

Yuxing Lu, Xukai Zhao, J. Ben Tamo, Micky C. Nnamdi, Rui Peng Peking University, Shuang Zeng Georgia Institute of Technology and Peking University, Xingyu Hu, Jinzhuo Wang Peking University 等 9 位作者

原文摘要

Large Language Models (LLMs) have demonstrated remarkable capabilities on general text; however, their proficiency in specialized scientific domains that require deep, interconnected knowledge remains largely uncharacterized. Metabolomics presents unique challenges with its complex biochemical pathways, heterogeneous identifier systems, and fragmented databases. To systematically evaluate LLM capabilities in this domain, we introduce MetaBench, the first benchmark for metabolomics assessment. Curated from authoritative public resources, MetaBench evaluates five capabilities essential for metabolomics research: knowledge, understanding, grounding, reasoning, and research. Our evaluation of 25 open- and closed-source LLMs reveals distinct performance patterns across metabolomics tasks: while models perform well on text generation tasks, cross-database identifier grounding remains challenging even with retrieval augmentation. Model performance also decreases on long-tail metabolites with sparse annotations. With MetaBench, we provide essential infrastructure for developing and evaluating metabolomics AI systems, enabling systematic progress toward reliable computational tools for metabolomics research.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.985ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Community-Aware Assessment of Social Textual Engagement and Resonance: A Human-Centric Perspective on User-Generated Content Evaluation

Tianjiao Li, Kai Zhao Bilibili, Xiang Li Bilibili, Yang Liu, Huyang Sun Bilibili

原文摘要

Traditional Video Quality Assessment (VQA) focuses narrowly on aesthetic fidelity, overlooking the complex social dynamics that define quality in User-Generated Content (UGC). In this work, we propose a paradigm shift from signal-centric metrics to human-centric resonance assessment. We introduce CASTER (Community-Aware Assessment of Social Textual Engagement and Resonance), a new task that evaluates whether a UGC item achieves positive community resonance based on its multimodal attributes rather than visual quality alone. To address this, we present MEDEA (Multimodal Engagement-Driven Evaluation Architecture), which introduces a novel Social Chain-of-Thought (Social-CoT) mechanism. Unlike traditional logical CoT, Social-CoT performs multimodal perspective-taking, instantiating diverse viewer personas to simulate collective cognitive and emotional reactions (i.e., the “community mind”) before deriving a quality judgment. MEDEA is trained via a two-stage approach involving supervised fine-tuning and process-supervised reinforcement learning with Social Alignment Reward to ensure reasoning paths are grounded in authentic human social cognition. To support this task, we release CASTER-Bench, a comprehensive human-annotated benchmark covering diverse UGC categories. Experiments demonstrate that MEDEA significantly outperforms state-of-the-art baselines on CASTER-Bench while providing interpretable and empathetic reasoning paths that align with real community feedback.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表