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新论文 · v1arXiv:2611.14910

MuEdit: An efficient multi-task editing method towards inter-domain knowledge conflicts

Songshi Liang, Ting-En Lin, Zixuan Huang, Yuchuan Wu, Yongbin Li, Zihe Wang, Rui Yan

原文摘要

Large language models (LLMs) require frequent knowledge updates across heterogeneous domains, but existing editing methods fail when applied sequentially to multi-task settings. We trace this collapse to inter-domain null-space misalignment: heterogeneous tasks induce distinct Jacobian geometries, causing their common null space to shrink dramatically and leaving insufficient admissible directions for conflict-free updates. We propose the Conflict Index to quantify this geometric interference, and introduce Mu-Edit, which mitigates multi-task conflicts via (1) conflict-aware ordering to minimize cumulative interference, and (2) dynamic low-rank approximation to expand the null space when ordering alone is insufficient. Experiments across five functional domains and three backbones show that Mu-Edit significantly outperforms existing baselines in multi-task editing performance while preserving general capabilities, and remains effective under incremental task arrival.

cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.07062

Learning to Simulate Individuals from Macro Social Signals

Yining Zhao, Bushi Liu, Haofei Yu, Zhengyang Qi, Shanyong Wang, Chuyue Li, Yuxiang Liu, Jiaxuan You

原文摘要

Large language models are increasingly used to simulate how individuals respond to new situations, yet the behavioral reasoning behind these responses is either inherited from pretraining or learned from individual-level annotations, which offer limited behavioral diversity and little supervision of the reasoning itself. We propose to learn behavioral reasoning from prediction markets, whose price trajectories record how populations respond to real-world events at scale. We introduce macro2mind, which trains a language model with GRPO using market signals. A social behavioral decomposition makes behavioral reasoning an explicit step of forecasting: the model infers representative groups of market participants, predicts how each interprets the news and updates its beliefs, reasons about their interactions, and aggregates these responses into a price. A hindsight-regret curriculum with difficulty-aware sampling focuses training on transitions where hindsight-identified groups substantially improve the forecast while prioritizing examples that remain learnable for the current policy. The learned reasoning applies to user simulation without further training. On SWM-Bench, macro2mind achieves state-of-the-art directional accuracy and correlation on Polymarket. Trained on market data, it transfers zero-shot to four user-simulation benchmarks (Humanual, OvertonBench, PRISM, and CAD) and has competitive performance among zero-shot methods. Used as a data generator, macro2mind also raises a downstream simulator's accuracy on unseen users by 15.5 points, outperforming data generated by its backbone by 13.2 points.

cs.LG · cs.AI · cs.CL · cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10058

From Delegation to Action Approval: An Exploratory Study of Developers' Practices in Collaborating with AI Coding Agents

Yinan Wu, Dominik Wermke, Ze Shi Li, Kathryn Thomasset Stolee, Bowen Xu

原文摘要

Developers now collaborate with AI coding agents that write code and take actions on their behalf. Beyond reviewing this code, developers decide what to delegate and which agent actions to approve. Some of these actions, such as running shell commands or reading files, can have effects that never appear in the resulting code. How developers make these decisions and how much effort this requires are not well understood. To better understand this collaboration, we surveyed 203 developers who use AI coding agents. We found that careful code review does not necessarily extend to action approval: 61% of developers who carefully reviewed most or all generated code still often approved at least one type of agent action without close review. Developers aware of security or privacy incidents or concerning practices did so more often than those who were not. We also found that autonomy varied more between developers than between development activities. Although developers reported a lighter workload, supervising agents remained mentally demanding. When asked what would make them grant more autonomy, developers wanted more than reliability: more than half of their responses called for agents that are bounded, observable, or reversible. Our findings suggest that as agents take on more of the coding, the challenge shifts toward supporting the developers who supervise them.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31229

Iterative Randomized Rounding for $k$-Median in General and Euclidean Metrics

Jarosław Byrka, Yuhao Guo, Yang S. Hu, Shi Li, Yongho Shin, Phanu Vajanopath, Chengzhang Wan, Zaixuan Wang

原文摘要

We refine the iterative rounding framework of Byrka, Guo, Hu, Li, Wan, and Wang [FOCS'26], which gives a $(2+\varepsilon)$-approximation for $k$-median. Our algorithm opens $k+O(1)$ facilities with expected connection cost at most $1.9999$ times the standard LP value. Despite the small numerical gain, this shows that the LP integrality gap is below $2$ with a constant additive facility allowance, partially answering Li and Svensson's open question [SICOMP'16]. Their reduction yields a $1.9999$-approximation opening at most $k$ facilities. Our preprocessing makes the LP solution $\mathrm{poly}(\varepsilon)$-integral with a $(1+\varepsilon)$ loss in each client's expected connection cost and $O(1)$ extra facilities, without special treatment of clients with concentrated fractional facilities. We replace batch processing with single-facility openings and combine a tighter analysis for clients with unequal distances to their facilities with a complementary algorithm for nearly uniform distances. In Euclidean metrics, we obtain a $1.71955$-approximation for $k$-median and a $1.4561$-approximation for uncapacitated facility location (UFL). For every $p\ge2$ and sufficiently small $\varepsilon>0$, we obtain a $((3^p+2)/3+\varepsilon)$-approximation for $k$-clustering with $p$-th power distance costs. For $p=2$, our $(11/3+\varepsilon)$-approximation for $k$-means improves the $(3+\ln2+\varepsilon)$ bound of Anand, Charikar, Cohen-Addad, Gao, Grandoni, Lee, Sharma, and van Wijland [SODA'27]. The Euclidean $k$-median, $k$-means, and UFL ratios are below the respective general-metric hardness bounds $1+2/e$, $1+8/e$ [STOC'02], and $1.463$ [J. Algorithms'99]. The Euclidean $k$-median bound relies on a geometric inequality proved via a nonnegativity certificate constructed from positive-semidefinite kernels.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05229

Santa Claus and Weighted Nash Social Welfare with Subadditive Valuations

Yuhang Chen, Yuda Feng, Weijiang Hu, Shi Li, Ruilong Zhang

原文摘要

We study max-min allocation (the Santa Claus problem) and weighted Nash social welfare for $n$ agents with subadditive valuations over $m$ indivisible items. Using demand queries, we give randomized polynomial-time approximation algorithms with factors $O(\sqrt n)$ and $O(Γ(w))$, respectively, where $Γ(w)=\exp((\ln n-H(w))/2)$ and $H(w)=-\sum_i w_i\ln w_i$ for positive weights summing to one. Both ratios are independent of $m$. The max-min result improves the $2n-1$ bound of Khot and Ponnuswami (2007). We also give simpler $O(\sqrt n)$ algorithms for additive valuations and for submodular valuations with value queries, improving the $O(\sqrt{n\log n})$ bound of Saha and Srinivasan (2018) in the additive case. For Nash welfare, $Γ(w)=1$ for equal weights and $Γ(w)\le\sqrt n$ in general. We prove a configuration-LP integrality gap of $Θ(Γ(w))$ for every prescribed weight vector, even for XOS valuations. For every fixed $D>0$, we also prove information-theoretic approximation lower bounds of $Ω_D(\sqrt{n/\ln n})$ and $Ω_D(Γ(w)/\sqrt{\ln n})$, respectively, for randomized algorithms making at most $n^D$ adaptive queries. These hold on XOS instances with $m=Θ(n)$ items, even with simultaneous access to value, demand, and XOS supporting-clause oracles. They apply both to guarantees with success probability at least $2/3$ and to guarantees in expected welfare. The weighted bound holds for every prescribed weight vector and is vacuous below one. Thus the oracle lower bounds are within an $O(\sqrt{\ln n})$ factor of our upper bounds, while the configuration-LP gaps match them up to constants. Our algorithms build on random matchings for large items and charge the aggregate loss across configurations to the at most $n$ matched items.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07542

FlashOffloading: Low-Latency KV Cache Offloading for Long-Context LLM Serving

Xuan Wang, Zhuanhao Wu, Tingzhu Fang, Xushi Li, Sicheng Zhang, Zhenyu Yang, Feng Liu, Zhaohui Fan 等 20 位作者

原文摘要

In large language model (LLM) serving systems, the key-value (KV) cache grows linearly with sequence length and concurrency, so long-context inference easily hits the high-bandwidth memory (HBM) capacity wall and results in low concurrency. Offloading the KV cache to host dynamic random-access memory (DRAM) frees HBM space and raises concurrency, but the additional cache management and host-to-device (H2D) load overhead inflates inference latency, higher concurrency fails to translate into effective throughput. We present FlashOffloading, a host-DRAM-centric inference architecture that fully offloads the KV cache in both the prefill and decode phases. FlashOffloading builds rolling HBM cache for prefill, a hot cache for decode, a reverse sparse index with zero extra miss-lookup overhead, and a compute--load overlapped kernel, optimizing KV offloading latency across the entire pipeline and delivering throughput gains under a time-per-output-token (TPOT) service-level objective (SLO). In experiments deploying GLM-5.2 with multi-token prediction (MTP3) speculative decoding on an Ascend910C prefill--decode (PD)-disaggregated platform, FlashOffloading improves prefill throughput by up to 1.38\(\times\) and decode throughput by up to 1.91\(\times\) under a 30 ms TPOT constraint. FlashOffloading is integrated into an open-source serving framework: https://github.com/vllm-project/vllm-ascend/tree/releases/v0.30.0.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39524

Last-Iterate Convergence for Constrained MDPs: From Dual Descent to Primal-Dual

Michael Lu, Max Qiushi Lin, Mo Chen, Sharan Vaswani

原文摘要

We study policy optimization for infinite-horizon, discounted constrained Markov decision processes. While primal-dual ($\texttt{PD}$) methods are widely used in practice, their theoretical guarantees typically hold for an impractical mixture policy. In contrast, dual descent ($\texttt{DD}$) methods output a single policy ("last iterate") with guarantees on its performance. However, such methods require iteratively solving entropy-regularized MDPs in an inner-loop, limiting their practical effectiveness. Moreover, existing analyses rely on strong coverage assumptions that are difficult to satisfy in practice. Consequently, we first provide a new analysis of $\texttt{DD}$ methods that does not require coverage assumptions. We then employ entropy-regularized natural policy gradient and dual regularization to eliminate the inner loop, resulting in an efficient $\texttt{PD}$ algorithm. We develop a novel tracking analysis which shows that, compared to existing methods, the proposed algorithm achieves improved last-iterate convergence guarantees. Furthermore, our theoretical framework allows us to analyze a truncated bilevel method that interpolates between $\texttt{DD}$ and $\texttt{PD}$ methods. Finally, we generalize our analysis to handle linear function approximation. Compared to prior last-iterate methods, we prove stronger guarantees under less restrictive assumptions.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.02673

dots.tts.edit: Precisely Controlled Speech Editing with a Continuous Autoregressive Model

Hankun Wang, Bohan Li, Shi Lian, Xiaoyu Gu, Jing Peng, Da Zheng, Yiwei Guo, Colin Zhang 等 10 位作者

原文摘要

Speech editing for content creation requires precise control over both what an edit should do and where it should apply. Free-form natural language provides a flexible interface for expressing edit requests, but its ambiguity may leave the intended operation, parameters, or target region underspecified. We study a semi-formal transcript-grounded edit instruction that specifies operation categories and parameters and binds them to transcript spans or boundaries. This semantic timeline avoids explicit timestamp alignment and provides an externally inspectable contract for compositional edits. We instantiate the interface in dots$.$tts$.$edit, a speech editing model built on a continuous autoregressive speech generation architecture. Four representative speech-creation controls cover lexical content, affective expression, pitch and speaking-rate delivery, and temporal phrasing through text, emotion, prosody, and pause editing. Task-specific data pipelines construct operation- and scope-controlled pairs while retaining source-derived context outside each target region. We further introduce doteBench, a bilingual benchmark that measures precise instruction following, local preservation, and audio quality across the four controls and their composition. Experiments show leading overall instruction following and local preservation across its five editing categories, while audio quality remains comparable to existing open-source systems. Across three Seed-TTS-Eval shards, the model shows negligible differences from the base model in zero-shot TTS recognition error rate and speaker similarity.

cs.SD · cs.AI · cs.CL · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38355

FlatCake: A Flat KVCache Storage Architecture for LLM Serving

Xin Huang, Teng Ma, Zhuoran Li, Yibo Zhao, Zhaoshi Li, Qianchao Zhu, Wei Yang, Chenxi Wang 等 10 位作者

原文摘要

KV cache reuse accelerates LLM serving, but existing systems manage host DRAM and NVMe SSD as a tiered hierarchy where DRAM acts as an intermediate cache. Production LLM workloads break both premises of this hierarchy. First, bimodal reuse defeats DRAM temporal filtering, since workloads are dominated either by one-shot blocks with long reuse distances or by multi-gigabyte agentic sessions that exceed DRAM capacity, putting SSD on the critical read path. Second, DRAM staging and eviction-ordered layout bottleneck SSD restoration. Holding host staging buffers for in-flight prefixes causes contention under load that aborts prefetches, while incremental eviction scatters a session's blocks across the SSD and prevents large sequential reads. We present FlatCake, a flat KV storage architecture that exposes host DRAM and NVMe SSD as peer media in a unified 64-bit address space. Exploiting the fact that KV blocks are write-once, immutable, and reused at session scope, FlatCake places each block directly onto its target medium in session-contiguous, layer-major order at creation time and never migrates it. FlatCake streams coalesced SSD reads directly into a bounded GPU window via GPUDirect Storage and decouples DRAM and SSD request queues to prevent head-of-line blocking. On a production Claude Code trace with DeepSeek-V4-Flash, FlatCake sustains a 94% cache hit rate as load increases (versus 64% for Mooncake), eliminates Mooncake's $6.5\times$--$6.7\times$ SSD read amplification, and reduces peak-load mean TTFT by $1.6\times$ while improving total token throughput by $1.6\times$. Across 8K--128K contexts on Llama-3.1-70B, FlatCake further reduces mean TTFT by up to $2.4\times$ over Mooncake and $1.8\times$ over Mooncake-GDS.

cs.DC · cs.OS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36539

CARD: Contact-Anchored Retargeting for Dexterous Manipulation

Cunxi Dai, Botao He, Francois R Hogan, Wei Pan, Rao Fu, Wen-Han Hsieh, Ruoshi Liu, Jitendra Malik 等 10 位作者

原文摘要

Humans are dexterous, but traditional data collection paradigms such as teleoperation, kinesthetic teaching, and wearable exoskeletons force humans to act through the robot embodiment, bottlenecking their dexterity. Retargeting can avoid this. However, existing methods purely translate kinematics and ignore the real-world contact dynamics essential for task success. We present CARD: Contact-Anchored Retargeting for Dexterous Manipulation, a simulator and object-free framework that retargets human demonstrations to robot actions by explicitly preserving contact force targets. CARD unifies human data sources including video, motion capture, and tactile gloves via a shared hand--contact representation and generates explicit contact force targets in addition to kinematics. To maintain these contact targets during hardware open-loop replay, CARD also introduces a tactile tracking controller. For video which does not contain an explicit tactile source, we present Vid-HACO, our video-to-contact model that sets a new state of the art. Across our video, glove, motion-capture data and the GRAB dataset, CARD maintains contact on $43$--$78\%$ of contact frames, while kinematic and object-aware retargeting methods maintain contact on $<10\%$ and $22.5\%$ of the frames respectively. Further, integrating our formulation into physics-based retargeting improves strict task success from 12\% to 32\% on GRAB dataset. Finally, we show that our tactile tracker is critical to reproduce demonstrated forces during hardware replay. These results establish that contact preservation via CARD is a scalable interface for transferring human dexterity to robots.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22340

PlayPolicy: Dexterity from Supervised Play

Botao He, Yiming Dou, Yiannis Aloimonos, Jitendra Malik, Akash Sharma, Ruoshi Liu

原文摘要

How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present PlayPolicy: Dexterity from supervised play (DSP), a framework in which a robot learns a play policy from minutes of human play through behavior cloning, then deploys it autonomously to collect hours of interaction data to train a world model. During inference, the same play policy then guides model-predictive control (MPC) by proposing candidate action sequences for evaluation under the learned world model. To accelerate inference, we also introduce a method for distilling a closed-loop, goal-conditioned policy through self-play within the learned world model. We evaluate this framework on underactuated pushing and any-to-any in-hand pose reorientation with a multifingered hand, in simulation and real world. In real world experiment, our method outperforms hindsight behavior cloning by 69.6% in success rate and achieves comparable success to reinforcement learning with 490 times less interaction data. These results demonstrate that supervised play can autonomously collect interaction data, guide model-based planning, and enable policy learning without any task-specific demonstrations or sim-to-real transfer. Project page: https://play-policy.github.io/

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34968

Guarded Agent-in-the-Loop Residual Adaptation for Evolving Wind Power Regimes

Yuhang Dong, Min Xia, Jinghong Li, Yue Dai, Xinyue Wang, Shi Liang

原文摘要

Wind power forecasting models are often deployed as fixed temporal backbones, but the errors they make can vary across meteorological regimes such as cold waves, heat-and-calm periods, productive typhoon, and destructive storms. We present a guarded Agent-in-the-loop residual adaptation framework that leaves the temporal backbone unchanged and adapts only a small set of scene-conditioned residual experts. An Agent layer interprets external scene information, identifies the current regime, selects residual experts, and applies a correction only when recent observed errors support it. New experts enter the pool after they outperform their initializations on a validation set, keeping model growth under explicit control. Backbone-only and Agent-enhanced forecasts are compared at lead times of 4, 8, 12, 24, and 48 across nine time-series backbones. Across these runs, the guarded framework lowers both MAE and RMSE for all 45 backbone-horizon combinations, with average MAE reductions ranging from 11.21% to 20.26% across backbones. Online-selection baselines, component ablations, and routing analyses identify the roles of residual experts, scene-aware routing, and the online acceptance rule. Candidate-scene runs evaluate the promotion path under controlled conditions; long-term unattended deployment was not evaluated.

cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34735

iPrefBench: A User-Centric Benchmark for Personalized Mobile Use Agents

Yuting Ning, Ruijia Cheng, Sushobhan Ghosh, Peitong Duan, HungShi Lin, Abhishek Sundararajan, Shreyash Pandey, Huan Sun 等 10 位作者

原文摘要

An agent that truly knows and acts on behalf of its user has been a long-standing promise of AI assistants. Computer-use agents bring this promise within reach by capably executing fully specified tasks across apps. However, real-world user requests are often underspecified. To act correctly for the particular user, an agent must infer some missing information from the user's activity history and ask for other information that only the user can provide, without making unsupported assumptions or burdening the user with unnecessary questions. We present iPrefBench, a benchmark for evaluating the infer and ask capabilities of personal mobile-use agents in an executable iOS environment with an interactive simulated user. Grounded in a coherent persona with 3,051 past activity events, iPrefBench comprises 130 underspecified user tasks across 26 apps. Each omitted user-dependent requirement is, by construction, either history-inferable or ask-only. iPrefBench evaluates personalized mobile agents on both whether agents complete tasks correctly for the user (i.e., personalized task completion) and whether they seek user input when necessary (i.e., user-agent interaction). Evaluation of 7 frontier agents reveals a substantial gap between execution and personalization. Agents achieve high action completion scores (up to 91.6%) but low full task success (up to 47.7%) because personalization lags behind. They especially struggle with ask-only information, failing to request enough necessary information. Our findings highlight a challenge for personalized agents in knowing what to infer and when to ask. We hope that iPrefBench can serve as a valuable testbed for building agents that know their user and work with them.

cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1048ACL Anthology · ACL 2026

The Proxy Presumption: From Semantic Embeddings to Valid Social Measures

Baishi Li, Ta Yu, Kelvin J.L. Koa National University of Singapore, Ke-Wei Huang

原文摘要

Natural Language Processing is rapidly evolving into a primary instrument for Computational Social Science, with researchers increasingly using embeddings to measure latent constructs such as novelty, creativity, and bias. However, this transition faces a fundamental validity challenge: the “Proxy Presumption,” or the reliance on geometric properties (e.g., cosine distance) as direct measures of social concepts. We argue that without explicit validation, unsupervised representations remain entangled mixtures of the target construct (C) and confounding attributes (Z) like topic, style, and authorship. To bridge the gap between semantic embeddings and valid social measures, we introduce the Construct Validity Protocol (CVP). Drawing on causal representation learning and psychometrics, the CVP offers a rigorous pipeline from conceptualization to quantitative verification. We further propose Counterfactual Neutralization, a novel method using LLMs to reduce confounding in embedding space. By providing a standardized Validity Suite—including tests for discriminant, incremental, and predictive validity—this work offers the community a toolkit to transform heuristic proxies into robust, scientifically defensible instruments.

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.1088ACL Anthology · ACL 2026

LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models

Jian Gao, Richeng Xuan Tencent, Zhaolu Kang, Dingshi Liao, Wenxin Huang, Zongmou Huang, Yangdi Xu, Bowen Qin Beijing Academy of Artificial Intelligence 等 12 位作者

原文摘要

The rapid advancement of large language models (LLMs) has not been matched by their evaluation in low-resource languages, especially Southeast Asian languages like Lao. To fill this gap, we introduce LaoBench, the first large-scale, high-quality, and multidimensional benchmark for assessing LLM language understanding and reasoning in Lao. LaoBench contains 17,000+ expert-curated samples across three dimensions: culturally grounded knowledge application, curriculum-aligned K12 education, and bilingual translation among Lao, Chinese, and English. It includes open-source and held-out subsets, where the held-out portion enables secure black-box evaluation via a controlled service to improve fairness and data security. We construct LaoBench with a hybrid pipeline that combines expert authoring with agent-assisted verification, ensuring linguistic accuracy, cultural relevance, and educational validity. We evaluate diverse state-of-the-art open-source and closed-source LLMs, and find that even strong multilingual models lag behind human experts, particularly in culturally grounded reasoning and translation fidelity. We hope LaoBench will catalyze research on Lao and other underrepresented Southeast Asian languages for more inclusive multilingual evaluation.

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.1492ACL Anthology · ACL 2026

RLSeek: Evidence-Grounded Reasoning for RAG Hallucination Detection

Zhaoheng Huang Alibaba Group, Dacheng Wen, Yutao Zhu, Xiaoying Lian Microsoft, Yushi Liang Microsoft, Kai Hao Microsoft, Nan Li Microsoft, Liangjie Zhang 等 12 位作者

原文摘要

Large language models (LLMs) in retrieval-augmented generation systems can still produce hallucinations, generating content that is unsupported or contradicted by the source texts and undermines reliability. Recent work addressed this problem by training span-level hallucination detectors using reinforcement learning (RL) and chain-of-thought (CoT) reasoning. In this work, we show through error analysis that incorrect predictions by existing reasoning-based detectors are strongly associated with CoT processes that lack explicit grounding in source evidence, particularly when verification steps do not quote or verify claims against the retrieved documents. This behaviour contrasts with human verification practices in benchmarks such as RAGTruth, where evidence quotation is a prerequisite for determining hallucinated spans. Motivated by this observation, we propose an evidence-grounded RL framework, namely RLSeek, to explicitly enforce active evidence seeking during CoT reasoning by requiring quotation of relevant source segments at each verification step. Experiments on the RAGTruth and NewsSum dataset demonstrate consistent improvements in hallucination span detection performance, with limited additional reasoning overhead and improved robustness in out-of-domain settings.

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.416ACL Anthology · ACL 2026

CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments

Haotian Xu, Yue Hu National University of Defense Technology, Zhengqiu Zhu National University of Defense Technology, Chen Gao Tsinghua University, Ziyou Wang, Junreng Rao, Wenhao Lu, Weishi Li 等 10 位作者

原文摘要

Cross-view spatial reasoning is essential for embodied AI, underpinning spatial understanding, mental simulation and planning in complex environments. Existing benchmarks primarily emphasize indoor or street settings, overlooking the unique challenges of open-ended urban spaces characterized by rich semantics, complex geometries, and view variations. To address this, we introduce CityCube, a systematic benchmark designed to probe cross-view reasoning capabilities of current VLMs in urban settings. CityCube integrates four viewpoint dynamics to mimic camera movements and spans a wide spectrum of perspectives from multiple platforms, e.g., vehicles, drones and satellites. For a comprehensive assessment, it features 5,022 meticulously annotated multi-view QA pairs categorized into five cognitive dimensions and three spatial relation expressions. A comprehensive evaluation of 33 VLMs reveals a significant performance disparity with humans: even large-scale models struggle to exceed 54.1% accuracy, remaining 34.2% below human performance. By contrast, small-scale fine-tuned VLMs achieve over 60.0% accuracy, highlighting the necessity of our benchmark. Further analyses indicate the task correlations and fundamental cognitive disparity between VLMs and human-like reasoning.

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