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新论文 · v1arXiv:2611.05859

更新于 2026-11-04

Generative AI for urban planning: synthesizing land-use maps with satellite imagery via DualControlNet

Qingqi Song, Yuzhou Chen, Lingqian Hu, Shenhao Wang

原文摘要

Various land-use models characterize land-use patterns and facilitate scenario planning. However, existing models are largely predictive, forecasting transitions based on numeric data without synthesizing new land-use maps from simple text prompts. Meanwhile, generative AI in urban systems remains unimodal, weakly controllable, and evaluated mostly by generic image quality rather than domain-specific metrics. To address these gaps, we introduce DualControlNet, a generative land-use model that jointly synthesizes high-resolution land-use maps and satellite imagery, addressing multimodality, controllability, and verification challenges. Trained on over 40,000 paired tiles from Orlando and Chicago, DualControlNet is evaluated using land-use, spatial consistency, and image quality metrics. Relative to unimodal baselines, DualControlNet improves land-use diversity consistency by 62.8% and building coverage ratio consistency by 16.3%, more than doubles cross-modal spatial consistency, and triples visual quality. Scenario-based experiments demonstrate that DualControlNet supports sustainable development through multidimensional controls, increasing density and diversity, and responding to text prompts like "high-density development." Overall, this work advances land-use modeling via a multimodal, controllable, and verifiable generative framework, lowering technical barriers and enabling intuitive exploration of urban futures.

cs.CV · cs.AI

新论文 · v1arXiv:2611.03092

更新于 2026-11-02

Avatar Intelligence: Amplifying Human Agency Through Persistent Personal Agents

Zheng Wu, Xinbei Ma, Yao Yao, Zhaoyuan Huang, Haowen Hu, Yijie Lu, Taolei Wang, Yifei Yang 等 11 位作者

原文摘要

Personal AI agents are increasingly capable of handling long-horizon tasks across applications, yet users often need to invest substantial effort in specifying goals, resolving ambiguity, and verifying outcomes. We argue that progress should be measured by how effectively agents amplify human agency: enabling people to pursue their goals with less routine supervision while retaining control over consequential decisions. We advocate Avatar Intelligence, a paradigm of persistent personal agents that represent users' goals, values, and constraints through sustained decision delegation across contexts and time. We describe a maturity roadmap from human-directed execution to autonomous operation within delegated domains, and introduce the Agentic Oversight Index (AOI) as a framework for evaluating delivered value against agent-side operational cost and human oversight burden. We identify three core research challenges: identity-continuous personalization that carries user preferences across tasks, user-situated evolution through Rehearsal Engineering, and safety mechanisms that combine technical control with accountability and recourse. These directions define a research agenda for reliable decision delegation to personal agents across tasks and over time.

cs.AI · cs.CL

新论文 · v1arXiv:2610.37843

更新于 2026-10-28

Where the Error Lives: Spectral Representations of Learned Kohn-Sham Hamiltonians

Fang Sun, Hekai Cheng, Ruoyan Li, Yuanzhou Chen, Wei Wang, Jason Cong, Yizhou Sun

原文摘要

Kohn-Sham density functional theory (DFT) is the standard method for computing the electronic structure of molecules and materials, but its self-consistent-field (SCF) iterations make it expensive for large systems and high-throughput screening. Machine-learned Hamiltonian models offer a shortcut: an equivariant network predicts the Kohn-Sham Hamiltonian matrix in one forward pass, and diagonalizing the prediction yields orbital energies, the HOMO-LUMO gap, molecular orbitals and the electron density. These models are trained to match the reference matrix entry by entry, yet a small entry-wise error does not guarantee accurate eigenvalues or eigenvectors. Two physics-informed alternatives go partway: a recently proposed wavefunction-alignment loss adds a spectral penalty without diagonalizing the prediction, and matching the electron density of the diagonalized prediction supervises it directly. Neither is complete: by representing the prediction error in the eigenbasis of the reference Hamiltonian, we show that each sees only part of the error, and that shifting the predicted orbital energies leaves the electron density exactly unchanged. The same analysis identifies a complete training signal: the cumulative local density of states (cumulative LDOS), the number of electrons at each point in space with energy below a given threshold. The cumulative LDOS is a single physical observable that contains the electron density as a special case, responds to every component of the error, determines the Hamiltonian uniquely, and yields gradients that do not vanish for large orbitalenergy errors. Fine-tuning three Hamiltonian networks on QH9 in this way, which we call FermiScope, reduces the HOMO-LUMO gap error without increasing the element-wise error. The gains carry over to out-of-distribution and molecular-dynamics splits, where the wavefunction-alignment loss helps less.

physics.chem-ph · cs.AI

新论文 · v1arXiv:2610.37797

更新于 2026-10-28

Structured Security Explanations for Learning-Based Vulnerability Detection

Fares Trad, Yizhou Chen, Hung Viet Pham, Zeyu Sun, Gias Uddin

原文摘要

Software vulnerability detection remains difficult because existing approaches rely on predefined analysis rules, learn vulnerability patterns implicitly from code and labels, or use large language models (LLMs) to make the final security judgment. It remains unclear whether structured descriptions of security-relevant program behavior can improve supervised detection. We present VulnLens, an explanation-augmented framework for binary C/C++ vulnerability detection that separates program-behavior analysis from the final decision. A label-blind LLM-based Explainer examines each function from seven fixed perspectives: data flow, memory access, object lifetime, arithmetic and type behavior, control flow, shared state, and external-resource access. Without receiving vulnerability labels or CWE information, it generates structured explanations. A supervised Detector combines source code and explanations through sequential cross-attention. We evaluate VulnLens on a PrimeVul-derived dataset covering ten CWE categories and test transfer to SecVulEval without retraining. On the 526-function PrimeVul test set, VulnLens achieves 75.5% accuracy and 77.2% F1, exceeding DeepSeek V4 Pro by 4.2 percentage points in accuracy and Gemini 2.5 Pro by 2.2 points in F1. Matched explanations improve F1 by 7.3 points over a separately trained code-only detector. On SecVulEval, VulnLens achieves 66.4% accuracy and 65.8% F1, outperforming DeepSeek V4 Pro by 7.6 points in accuracy and 20.0 points in F1, and Gemini 2.5 Pro by 16.6 points in accuracy and 7.8 points in F1.

cs.CR · cs.SE

新论文 · v1arXiv:2610.36415

更新于 2026-10-27

Verifiable Efficiency Limits of Innate LLM Planning in Sliding Puzzles

Yezhou Cheng, Charles Ling

原文摘要

Tool-assisted planning success can reflect external search and verification, leaving a model's unaided planning ability unresolved. We isolate innate planning through complete plans generated in one response without tools or execution feedback. We propose a verifiable framework that decomposes success-weighted plan efficiency as $\mathrm{SPE}=R\times Q\times E$: execution reliability, planning coordination among legal plans, and action economy among completed plans. Trajectory replay and certified optima verify these factors; the joint score includes unsuccessful attempts. We evaluate 8,400 full-plan outputs from seven model conditions on $4\times4$ sliding puzzles, controlling exact horizon and obstacle count in the primary cohort. Models that complete every trial still become less economical at longer horizons, while higher efficiency among completed plans can coexist with poor goal attainment. SPE separates the overall capability hierarchy, while its factors distinguish coordination failure from inefficient completion. The framework makes long-horizon limitations in unaided planning quantitatively verifiable.

cs.AI

版本更新 · v2arXiv:2512.04231

更新于 2026-10-26

CRAFT-E: An Interpretable and Editable Neuro-Symbolic Framework for Affordance-Grounded Active Object Retrieval

Zhou Chen, Joe Lin, Carson Bulgin, Sathyanarayanan N. Aakur

原文摘要

Assistive robots operating in unstructured environments must reason not only about what objects are, but how they can be used, including non-canonical and improvised uses. This requires grounding language-based action queries to objects that afford the requested function, are visually present, and can be physically retrieved. Existing approaches often rely on black-box models, fixed affordance labels, or direct object predictions, limiting transparency and controllability. We introduce CRAFT-E, a modular neuro-symbolic framework that composes an LLM-derived verb-property-object knowledge graph with CLIP-based visual alignment and energy-based grasp reasoning. The system exposes interpretable grounding paths, incorporates grasp feasibility into affordance inference, and supports targeted graph edits without retraining. We also construct a benchmark dataset with unified annotations for verb-object compatibility, segmentation, and grasp candidates, and deploy the full pipeline on a physical robot. Across static scenes, real-world trials involving 20 verbs and 39 objects, ImageNet-based functional retrieval, and COCO-Tasks transfer, CRAFT-E provides a competitive training-free prior for affordance-grounded retrieval while exposing component-level diagnostics and graph-level interventions. Under staged and full-pipeline perception, CRAFT-E matches strong flat LLM priors in several settings while adding failure attribution and editable reasoning paths. By coupling symbolic reasoning with embodied perception, CRAFT-E offers an inspectable and customizable framework for affordance-grounded object retrieval.

cs.RO · cs.AI

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.3ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He 等 59 位作者

原文摘要

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

cs.CL · cs.CV

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.1424ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models

Ying He, Sihang Jiang Fudan University, Xingzhou Chen, Zhouhong Gu Xiaohongshu, Yiwei Gu, Minggui HE Huawei Technologies Ltd., Shimin Tao, Mahongxia 等 9 位作者

原文摘要

Cultural taboo safety is essential for deploying large language models (LLMs), as culturally insensitive outputs may cause offense or even social harm. However, existing cultural benchmarks primarily assess cultural knowledge or values biases, while overlooking whether LLMs can recognize and respect cultural taboos, especially when taboos are implicitly hidden in seemingly harmless questions. Besides, cultural taboos are implicit, and context-dependent, thus poss unique challenges for reliable evaluation. To address these gaps, we introduce CulShield, the first public benchmark dedicated to evaluating and improving the cultural taboo safety of LLMs. CulShield spans 77 countries and regions, and includes over 2,020 taboos. It evaluates models along both explicit knowledge and implicit behaviors.Experiments on several advanced LLMs (e.g., GPT-4o-mini, Gemini-2.5-pro) reveal a clear “knowledge-behavior gap”: models often fail to apply known taboos during interaction. We further show that variations in linguistic context can significantly affect LLMs’ cultural taboo safety. Code and data is accessible here: https://anonymous.4open.science/r/CulShield-7A0E.

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.1807ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement

Yixiao Zhou, Dongzhou Cheng, Zhiliang wu Nanyang Technological University, Yi Yang Zhejiang University, Yu Cheng Skywork AI and The Chinese University of Hong Kong, Hehe Fan Zhejiang University

原文摘要

Large Language Models (LLMs) often fail to utilize their latent reasoning capabilities due to a distributional mismatch between ambiguous human inquiries and the structured logic required for machine activation. Existing alignment methods either incur prohibitive O(N) costs by fine-tuning each model individually or rely on static prompts that fail to resolve query-level structural complexity. In this paper, we propose ReQueR (Reinforcement Query Refinement), a modular framework that treats reasoning elicitation as an inference-time alignment task. We train a specialized Refiner policy via Reinforcement Learning to rewrite raw queries into explicit logical decompositions, treating frozen LLMs as the environment. Rooted in the classical Zone of Proximal Development from educational psychology, we introduce the Adaptive Solver Hierarchy, a curriculum mechanism that stabilizes training by dynamically aligning environmental difficulty with the Refiner’s evolving competence. ReQueR yields consistent absolute gains of 1.3%–7.2% across diverse architectures and benchmarks, outperforming strong baselines by 2.1% on average. Crucially, it provides a promising paradigm for one-to-many inference-time reasoning elicitation, enabling a single Refiner trained on a small set of models to effectively unlock reasoning in diverse unseen Solvers. Code is available at https://github.com/newera-xiao/ReQueR.

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.359ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering

Taolin Zhang, Dongyang Li Shanghai University of Electric Power, Chen Chen, Qizhou Chen, Jiuheng Wan Hefei University of Technology, Xiaofeng He East China Normal University, Chengyu Wang Independent Researcher and Alibaba Group, Richang Hong Hefei University of Technology

原文摘要

Despite substantial advances in large language models (LLMs), producing factually consistent responses for knowledge-intensive question answering remains challenging. These difficulties are primarily due to hallucination and the limitations of LLMs in bridging long-tail knowledge gaps. To address this, we propose AMATA, an Adaptive Multi-Agent Trajectory Alignment framework that dynamically integrates external knowledge to improve response interpretability and factual grounding. Our architecture leverages six specialized agents that collaboratively perform structured actions for complex question reasoning. We formalize multi-agent collaboration with external tools as a trajectory preference alignment problem, incorporating question-aware agent customization and inter-agent preference harmonization. AMATA introduces two principal innovations: (1) Intra-Trajectory Preference Learning, which learns objective-oriented preferences to prioritize critical agents, and (2) Inter-Agent Dependency Learning, which captures cross-agent tool dependencies through a novel dependency-aware direct preference optimization technique. Empirical results show that AMATA consistently outperforms baseline approaches, knowledge-augmented frameworks, and LLM-based trajectory systems on five established knowledge-intensive QA benchmarks. Further analysis demonstrates the efficiency of our method in reducing token consumption.

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.373ACL Anthology · ACL 2026

来源日期 2026-07-01（仅到月份）

Taming "Zombie" Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution

Taolin Zhang, Pukun Zhao Guangdong University of Finance & Economics, Qizhou Chen, Jiuheng Wan Hefei University of Technology, Chen Chen, Xiaofeng He East China Normal University, Chengyu Wang Independent Researcher and Alibaba Group, Richang Hong Hefei University of Technology

原文摘要

Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To enhance the overall efficiency, existing methods often rely on aggressive graph topology evolution for agents (e.g., node or edge pruning), which risks prematurely discarding valuable agents due to transient issues such as hallucinations or temporary knowledge gaps. However, such hard pruning overlooks the potential for “zombie” agents to recover and contribute in subsequent discussion rounds. In this paper, we propose AgentRevive, a Markov state-aware framework for resilient multi-agent evolution. Our approach dynamically manages agent collaboration through soft state transitions, implemented via two key components: (1) State-Aware Policy Learning: Agent states are divided into “Active”, “Standby”, and “Terminated”, selectively propagating messages based on agent memory. The policy employs a risk estimator to optimize agent state transitions by assessing hallucination risk, minimizing the influence of unreliable nodes while safeguarding valuable ones. (2) State-Aware Edge Optimization: Subgraph edges are pruned according to states learned from the policy, permanently removing “Terminated” nodes and retaining “Standby” nodes for subsequent rounds to observe potential future contributions. Extensive experiments on general reasoning, domain-specific, and hallucination challenge tasks show that our method consistently outperforms strong baselines and significantly reduces token consumption through state-aware agent scheduling.

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