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新论文 · v1arXiv:2611.09336

RegNC-Mamba3: Shared Structural Feature Learning Using Non-Causal State Space Principles for Multi-Modal Image Registration

Zhikang Li, Yan Wu, Xin Hu, Yi Dai, Ming Li

原文摘要

The main challenge of multi-modal image registration is shared structural feature extraction with higher accuracy and lower computational complexity. Compared with a State Space Model (SSM), the State Space Principles (SSP) formulation of Mamba-3 uses a more accurate discretization with lower truncation error while maintaining linear complexity. However, vanilla SSP is designed for natural language processing (NLP) and is not directly suitable for images. To address this issue, we propose a novel non-causal SSP (NC-SSP) with shared structure awareness and develop a coarse-to-fine registration method named RegNC-Mamba3. First, NC-SSP is designed to eliminate the dependence of the integral computation on the scanning path. By combining relative-weight normalization with aggregation over all circular scan paths, NC-SSP constructs a globally hidden state without long-range decay, which provides a wider receptive field and higher efficiency than the original Mamba-3. Second, NC-SSP is modified to strengthen the extraction of cross-modal structural features. When computing hidden states and feature interactions, we construct complex-valued sequences in NC-SSP by applying learnable Log-Gabor kernels, with different orientations to each head for frequency-domain modulation. This improves the perception of shared structural information across modalities and increases both registration accuracy and quality. Experiments on VIS-SAR and VIS-IR demonstrate that RegNC-Mamba3 outperforms the compared detector-based and semi-dense multi-modal registration methods.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.16981

Rethinking the State Update Gate for Long-Sequence Recurrent 3D Reconstruction

Kejun Ren, Lei Jin, Tianxin Huang, Lianming Xu, Li Wang

原文摘要

Streaming 3D reconstruction under a strict constant-memory budget hinges on how the recurrent state is updated as the stream evolves. We profile TTT3R-style per-token gates across five benchmarks and discover a structural bottleneck: the gate is intrinsically bounded in magnitude (median $0.31$; never exceeding $0.6$) and nearly frame-invariant, yielding an effective memory horizon of only $\sim$3 frames per state token, which serves as the structural origin of long-sequence drift. We trace this to a missing axis: existing inference-time methods modulate updates only at the per-token, intra-frame level, while the orthogonal frame-level question of \emph{how strongly each frame should contribute to the state} has been treated as content-independent. We close this gap with a scalar frame-level gate $α_t \in (0, 1]$ derived in closed form from frame-to-frame changes of internal features---a graded write weight, inspired by classical Simultaneous Localization and Mapping (SLAM) keyframe selection, that never discards a frame and requires no parameters, no training, and no extra forward pass. Across six benchmarks spanning camera pose, video depth, and 3D reconstruction at sequence lengths up to $4,661$ frames, our gate cuts ATE by $51\%$ on long TUM-RGBD pose sequences, reduces AbsRel by $13.0\%$ on Bonn video depth, and on KITTI long-sequence pose estimation surpasses both LongStream and Keyframe-VO in average ATE, while retaining strictly constant memory at zero training cost. Code is available at https://github.com/rkj12345/AFG.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38897

RARE: Response-Aware Rational Equilibrium in Budget-Constrained Pricing Games

Bhaskar Ray Chaudhury, Jugal Garg, Xin Huang, Eklavya Sharma

原文摘要

We study a pricing game in markets with budget-constrained buyers, where each seller offers a single good and sets its price. We consider both non-rivalrous goods, such as datasets, and rivalrous goods, such as physical goods. For non-rivalrous goods, each buyer chooses a utility-maximizing bundle within her budget. For rivalrous goods, an allocation rule selects a Pareto-optimal allocation subject to the buyers' budgets and the goods' supply constraints. Pure Nash equilibria often fail to exist in this game. A key reason is that Nash equilibrium assumes that the other sellers keep their prices fixed when one seller deviates. In practice, however, sellers harmed by a price change may respond, for example, by matching the new price. We propose a new equilibrium notion called Response-Aware Rational Equilibrium (RARE). At a RARE, every deviation by a seller can be deterred by a response from the other sellers, in which no responder earns less than she did right after the deviation. Every Nash equilibrium is a RARE. Assuming that the allocation rule is continuous in prices, we show that a RARE always exists for rivalrous goods, and for non-rivalrous goods when buyers have equal budgets. Moreover, the RARE that we construct extracts the buyers' entire budget. For non-rivalrous goods with unequal budgets, we show that an approximate RARE exists, where a deviating seller can increase her revenue by a factor equal to the ratio of the average budget to the minimum budget. We also study a natural dynamic in which sellers repeatedly raise their prices to earn the revenue that they could guarantee themselves by deviating. We show that, in the settings where we prove exact existence, this dynamic reaches a RARE with additive error $δ$ within $\lfloor B/δ\rfloor$ rounds, where $B$ is the buyers' total budget. This paper's main results have been formally verified in Lean 4.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.13056

A Strongly Polynomial-Time 7/9-Approximation Algorithm for Maximin Share Allocations

Xin Huang, Shengwei Zhou

原文摘要

We study maximin share (MMS) allocations of indivisible goods under nonnegative additive valuations. We give a strongly polynomial-time algorithm that allocates to every agent a bundle worth at least $7/9$ of her MMS, improving the previous $10/13$ guarantee of Heidari et al. (SODA 2026). Our analysis uses a dynamic witness-allocation framework that tracks the remaining goods from each agent's perspective throughout the allocation process. We obtain the computational result by iteratively refining upper bounds on MMS values, using only polynomially many runs of the allocation algorithm. Finally, we formalize and verify our main results in Lean 4.

cs.GT · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06105

XuanJian-DINOSAR: A SAR Foundation Model Based on Global and Local Semantic Consistency

Chunxiao Wu, Huixin Huang, Jialei Pan, Qiang Liang, Tao Wu, Cheng Wu, Long Huang, Yuanji Li 等 12 位作者

原文摘要

Self-supervised pre-training on synthetic aperture radar (SAR) imagery enables transferable representations, but the optical-SAR domain gap and speckle perturbations limit representation stability. Most SAR foundation models use masked image modeling (MIM) through pixel reconstruction or structural feature prediction, which is sensitive to speckle or relies on predefined operators. Joint modeling of cross-view semantics and perturbation stability at corresponding spatial locations remains underexplored. We propose XuanJian-DINOSAR, a SAR foundation model based on global and local semantic consistency. We curate over 360K publicly available SAR target chips, detection images, and scene images spanning diverse targets, scenes, and sensors, and adapt an optically pre-trained DINOv2 model to SAR through self-supervised learning. The framework combines DINO global semantic self-distillation, iBOT local masked modeling, and KoLeo feature-distribution regularization to learn globally discriminative and locally structure-aware representations. We introduce a local semantic consistency branch tailored to SAR intensity statistics and coherent imaging characteristics. Shared geometric transformations produce spatially aligned teacher weak views and student strong views. Intensity transformations and Gamma multiplicative speckle perturbations are applied to student views, while a cosine consistency loss aligns patch tokens at corresponding locations, promoting stability under radiometric perturbations. This objective complements the cross-region and cross-scale semantic consistency of DINOv2 multi-crop training, jointly enforcing global and local semantic consistency. Experiments on multiple downstream classification and detection datasets demonstrate state-of-the-art performance on most evaluation metrics, validating the generality and transferability of the learned representations.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06052

Alleviating Limited Effectiveness and Multi-Class Collapse in Deep Graph-level Clustering

Xiaoyang Lin, Renchi Yang, Yuqi Chen, Xin Huang

原文摘要

Given a collection of $N$ graphs, graph-level clustering (GLC) seeks to partition the $N$ graphs into semantically meaningful groups, which serves as a fundamental task in analyzing graph-structured data. While Graph Isomorphism Networks (GINs) have become the de facto encoders for GLC, they still fail to distinguish graphs due to their neglect of global topology. On top of that, existing deep clustering frameworks suffer from severe "clustering collapse" issues, particularly in imbalanced multi-class scenarios. To address these challenges, we propose S$^3$GLC, a novel framework that integrates global topological characteristics with local neighborhood features for balanced GLC. Theoretically, we leverage the Hoffman-Wielandt inequality to construct Spectral Signature as a rigorous lower bound for graph edit distance (GED), providing a computationally efficient proxy for global topological features. We additionally propose a joint manifold-preserving sparse regression module to fuse Spectral Signature with Spatial Representation, eliminating noise while preserving the complementary information. Furthermore, to mitigate clustering collapse, we introduce a balanced clustering objective based on optimal transport, harnessing the Sinkhorn-Knopp normalization to enforce equipartition constraints on cluster assignments. Extensive experiments on 10 real-world datasets demonstrate that S$^3$GLC significantly outperforms 15 state-of-the-art baselines, achieving remarkable improvements on benchmark datasets such as REDDIT-12K and OGBG-PPA.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04257

ProGeo-RC: Prototype-guided Semantic Reliability and Geometric Error Calibration for Semi-supervised Cardiac Image Segmentation

Yunfei Zhu, Wenfeng Zhang, Pan Zeng, Xin Huang, Tao Jia, Weiting Shao, Wei Hu, Jianming Hu

原文摘要

Semi-supervised medical image segmentation aims to train reliable segmentation models using limited labeled data and abundant unlabeled data. However, existing methods typically rely on prediction confidence or model consistency to estimate pseudo-label reliability, but these criteria are insufficient to characterize both semantic reliability and geometric plausibility. To address this issue, we propose ProGeo-RC, a prototype-guided semantic reliability and geometric error calibration framework for semi-supervised medical image segmentation. ProGeo-RC consists of two key modules: Prototype-guided Semantic Reliability Estimation (PSRE) and Structured Semantic-Geometric Error Calibration (S-SGEC). PSRE uses probabilistic class prototypes to estimate the semantic reliability of unlabeled predictions and calibrate pixel-wise teacher pseudo labels, while S-SGEC introduces signed-distance-field-based geometric constraints to refine ambiguous boundaries and suppress spurious foreground responses outside target structures. Experiments on the ACDC and MVAA-CT datasets show that ProGeo-RC achieves better performance compared with representative semi-supervised segmentation methods. Under the 10\% labeled setting on ACDC, ProGeo-RC improves DSC and Jaccard by 0.37\% and 0.49\% over the corresponding second-best results, while reducing 95HD and ASD by 0.61 and 0.21, respectively. These results indicate that joint semantic reliability estimation and geometric error calibration can provide more reliable pseudo supervision for semi-supervised medical image segmentation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02806

CARE: Contrastive Representation Learning with Environment-Invariance Regularization for Behavioural Intention Prediction

Shang Gao, Zhixin Huang, Franz Götz-Hahn, Konrad Doll, Bernhard Sick

原文摘要

Trajectory-based behavioural intention prediction faces environmental variation, and representations learned with a pooled contrastive objective may retain environment-specific regularities. We present CARE, a self-supervised trajectory-learning framework that couples temporally asymmetric contrastive views with an invariant-risk-minimisation (IRM)-style gradient penalty on environment-wise contrastive risks. Each input window yields an anchor, a partially masked positive view, and a temporally perturbed negative view. Masked reconstruction and observability-aware evidence consistency complement this objective by promoting temporal information retention under partial input. On an IMPTC-derived four-class intention task, CARE achieves 0.840 accuracy and 0.023 expected calibration error (ECE) under IID fine-tuning, and 0.806 accuracy with a Brier score of 0.308 on the reported held-out environment. Under frozen-encoder linear probing, reconstruction provides the largest individual held-out accuracy gain, whereas the complete objective yields the lowest held-out ECE (0.049) among tested variants. These results support the combined objective as a competitive representation-learning approach under the evaluated shift, while the IRM-style regulariser is not taken as evidence of formal invariance.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.26015

SemantiScene-Edit: Expert-Synthesized Supervision and Dual-Path Source Conditioning for Instruction-Based Video Editing

Xinyu Chen, Song Wu, Dingbao Shao, Jiahang Li, Jiang Lin, Yuhang Liu, Duo Li, Jiaxin Hu 等 15 位作者

原文摘要

Instruction-guided video editing requires executing a requested change while preserving the source video's appearance and temporal structure. We present SemantiScene-Edit, a video editor that couples a multimodal encoder with a pretrained video diffusion transformer. The encoder jointly processes source frames and the editing instruction, and learnable queries convey their semantic context to the generator through a trainable connector and cross-attention. A complementary visual pathway concatenates source-video latents with noisy target latents before patch embedding, supplying dense spatial and temporal information for content preservation. We train the model with flow matching and a three-stage image--video curriculum comprising semantic alignment, joint adaptation, and high-resolution refinement. To support training, we construct over 1.2 million instruction-conditioned video-editing records covering object addition, removal, replacement, and attribute modification. We also construct a 100-case benchmark for evaluating instruction execution, source preservation, temporal consistency, and visual quality. Comparative experiments show that SemantiScene-Edit effectively follows editing instructions while retaining source content. At inference, the model requires only a source video and a natural-language instruction.

cs.AI · cs.GR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39273

PFA: Attention Can Be Progressively Focused in Long-Sequence Generative Recommendation

Pengjie Zhou, Weiqin Yang, Haibo Xing, Hao Deng, Jinxin Hu

原文摘要

Long user behavioral sequences improve the performance of generative recommendation (GR), but computing full attention over long histories during autoregressive beam search decoding incurs substantial online inference latency. To accelerate long-sequence decoding, existing approaches typically rely on static history compression or dynamic top-k sparse attention; however, the former risks losing critical semantics, whereas the latter incurs significant overhead from repeated token selection and non-contiguous memory access. In this work, we thoroughly analyze attention patterns during GR decoding and reveal two key properties: (i) progressive attention focusing, where attention progressively focuses on a small subset of critical historical tokens; and (ii) cross-step ranking consistency, where their importance rankings remain relatively stable across subsequent decoding steps. Motivated by these observations, we propose Progressively Focused Attention (PFA), an efficient sparse decoding framework for long-sequence GR. PFA retains full attention at the initial decoding step to explore global historical preferences. It then establishes a shared importance ranking through cross-beam weighted aggregation and reorders the key-value cache of the selected tokens into a contiguous memory layout. Subsequent decoding steps reuse this ranking and geometrically reduce the token budget, preserving critical semantics while lowering computational and memory access overhead. Extensive experiments on two public benchmarks and a large-scale industrial dataset demonstrate that PFA outperforms state-of-the-art baselines. A 7-day online A/B test on a large-scale e-commerce advertising platform further confirms that PFA delivers substantial business gains while maintaining low inference latency. Our code is publicly available at https://anonymous.4open.science/r/PFA-review-088D.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.20709

DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents

Rupam Patir, Keyan Guo, Suvadra Barua, Abhijeet Pathak, Dinesh Gudimetla, Jiawei Guo, Hongxin Hu, Haipeng Cai

原文摘要

Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and secure remains a challenge. We present DualGauge, the first fully automated framework for jointly evaluating correctness and security of specification-only code generation, supported by DualGauge-Bench, a language-agnostic benchmark of 307 coding tasks each paired with functional and security tests derived from the same specification. Evaluating 10 representative LLMs across Python, C++, and JavaScript, we find that functional correctness substantially overestimates reliable code generation: even the strongest model remains below 15% joint security-functionality success in every language. Common model-side factors--scale, extended thinking, quantization, instruction tuning, and code specialization--do not reliably improve joint performance, suggesting secure-and-correct code generation does not simply emerge from stronger coding capability. Evaluation of 3 leading coding agents (Codex, OpenHands, and Claude Code) shows that iterative scaffolding is not a reliable remedy on specification-only tasks: results are strongly scaffold-dependent, with two of the three agents failing to outperform a comparable direct model. A qualitative audit reveals that these failures concentrate at the output contract boundary and in guards that exist but are insufficient--patterns that only joint benchmarking reliably exposes.

cs.SE · cs.AI · cs.CR

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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 ↗
版本更新 · v4arXiv:2606.30097

CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking in Mobile Robots

Buyin Deng, Kai Luo, Lingxin Huang, Xinqi Liu, Fei Cheng, Hang Zheng, Liming Yin, Kailun Yang

原文摘要

Maintaining consistent target identities and observation histories is essential for mobile robot perception. Panoramic Multi-Object Tracking (MOT) benefits from broad visual coverage but must address coordinate discontinuities at the equirectangular image seam and fluctuations in relative-depth estimates. Such fluctuations can undermine depth-aware association, highlighting the need for temporally consistent depth cues. We propose CylindTrack, a depth-aware cylindrical tracking-by-detection framework that jointly models panoramic topology and temporal depth consistency. Depth-Temporal Trajectory Modeling (DTM) maintains instance depth and its rate of change as filtered trajectory states. Spherical Spatio-Temporal Consistency Learning (SSTC) combines a Temporal Mixer with Spherical Geometry-aware Attention to refine depth-aware representations. A Topology-Aware Cylindrical Motion Model (TCMM) represents horizontal motion in an unwrapped angular state and incorporates periodic overlap, angular consistency, and filtered depth cues into association. Experiments on QuadTrack and JRDB demonstrate improved identity consistency and trajectory continuity, with HOTA scores of 33.67 and 31.12 and IDF1 scores of 40.45 and 34.33, respectively. The framework processes 28.56 and 21.34 frames per second, respectively, on an NVIDIA GeForce RTX 3090 GPU under the reported evaluation settings. These results support persistent panoramic multi-object tracking for mobile robot perception. The source code will be released at https://github.com/warriordby/CylindTrack.

cs.CV · cs.RO · eess.IV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.22724

AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification

Tian Zhang, Yiwei Xu, Juan Wang, Keyan Guo, Xiaoyang Xu, Bowen Xiao, Quanlong Guan, Jinlin Fan 等 11 位作者

原文摘要

Tool-augmented large language model (LLM) agents aggregate dialogue history, tool outputs, and retrieved content into an accumulated runtime state to complete multi-step tasks. This execution model exposes agents to indirect prompt injection (IPI) attacks, in which attacker-controlled instructions embedded in external artifacts enter the agent state and later influence tool use. Multi-turn IPI is especially challenging because injected content can persist across steps and trigger unauthorized state-changing actions only after merging with user intent. This creates a source-attribution problem: existing inference-time defenses can often detect suspicious behavior but do not directly distinguish whether a pending action is supported by the user task or influenced by injected content. They therefore resort to conservative blocking, termination, or broad filtering. We propose AgentSentry, an inference-time defense that operationalizes temporal causal diagnostics through boundary-local counterfactual re-executions at tool-return boundaries. It substitutes the mediator under a restored snapshot, records whether the pending proposal survives, and aggregates user-view and probe scores across adjacent boundaries. When a proposal is mediator-sensitive, AgentSentry purifies the implicated mediator content while preserving task-relevant evidence and resumes execution from the sanitized state. We evaluate AgentSentry on AgentDojo across four task suites, three IPI attack families, and three black-box LLM backbones. It records no observed successful attack in the evaluated cases (ASR = 0%) and achieves an average Utility Under Attack (UA) of 76.10%. Over the strongest evaluated baseline, it improves average UA by 1.84--7.60 percentage points, with benign FPRs of 2.06%--5.15%. We further evaluate long-horizon and adaptive attacks, imperfect purification, runtime overhead, and an OpenClaw case study.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33894

WMEval: Evaluating Interactive World Models Grounded on Physical Simulations

Lanxiang Hu, Yixin Huang, Abhilash Shankarampeta, Zilin Dai, Yuyuan Wu, Kun Qian, Junze Liu, Arvind Srinivasan 等 16 位作者

原文摘要

Interactive world models hold immense potential for high-stakes applications in robotics, game engines, and immersive digital twins, yet existing benchmarks primarily focus on perceptual similarity rather than the physical-causal coherence essential for real-world utility. We introduce WMEval, a comprehensive benchmark that evaluates interactive world models against Unity-based physical simulations. By leveraging Unity as a high-fidelity ground-truth oracle, we shift evaluation beyond perceptual visual assessment toward the objective state-based verification required for downstream applications. Spanning 9 diverse scenes, WMEval assesses action controllability, scene and character consistency, prompt adherence, kinematics, fluid mechanics, physical causality, and lighting. By executing matched action sequences in both the simulator and the world model, WMEval enables evaluation at both the perceptual level and the physical-causal level, using simulator-derived states as reference. Experiments on state-of-the-art models reveal significant room for improvement in dimensions that remain hidden by standard perceptual metrics but are vital for real-world deployments. Our code is available at: https://github.com/hao-ai-lab/WMEval.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36973

KL-MOPD: Discrepancy-Aware Multi-Objective On-Policy Distillation for Generative Recommendation

Weiqin Yang, Yandong Wang, Yuan Gao, Hao Deng, Jinxin Hu, Jing Cao, Jiawei Chen

原文摘要

Coordinating multiple recommendation objectives (e.g., click and conversion) is critical for real-world generative recommendation (GR). However, since a positive item in GR can be retrieved only when its tokens are successively selected at every decoding step, traditional item-wise methods lack fine-grained token-wise supervision to guide intermediate steps. Motivated by its success in foundation models, we consider introducing multi-objective on-policy distillation (MOPD) into GR to distill two single-objective teachers into a student at each token. Yet, we find that in vanilla MOPD, (i) teachers exhibit unequal distillation difficulty at each token (i.e., token-level imbalance), and (ii) the harder-to-distill teacher varies across tokens (i.e., cross-token inconsistency), leaving each objective poorly learned at certain steps and hindering positive item retrieval for both objectives, leading to suboptimal performance. To address this challenge, we propose KL-MOPD (KL-Weighted Multi-Objective On-Policy Distillation), a simple yet effective distillation method that allocates token-wise teacher weights proportional to the initial student-teacher token-wise KL divergences, thereby strengthening supervision for the harder-to-distill teacher at every decoding step. Despite its simplicity, we theoretically prove that KL-MOPD strictly outperforms vanilla MOPD in the additive joint recommendation objective under mild conditions. Moreover, KL-MOPD is plug-and-play and computationally efficient, requiring only a one-time pre-training weight calibration with negligible training overhead. Extensive experiments on three public benchmarks, a billion-scale industrial dataset, and online A/B tests on our e-commerce advertising platform demonstrate that KL-MOPD significantly improves both individual and joint recommendation performance. The anonymous code is provided for peer review.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36553

NextEval: An Agent-Based Solver and Benchmark for Derivative-Free Optimization

Shuo Chen, Li Cui, Cunxin Huang, Ya-Feng Liu, Minghang Xie, Meng Xu, Zaikun Zhang

原文摘要

Derivative-free optimization (DFO) addresses problems where derivatives are unavailable or costly to obtain. This setting often arises in simulation-based and experimental applications, where evaluating the black-box objective can itself be costly. Thus, it remains a central challenge in DFO to effectively exploit accumulated observations to select informative evaluation points under a limited budget. In this light, we introduce the NextEval solver, an agent-based DFO solver, which integrates the capabilities of a large language model (LLM) to guide the sequential selection of evaluation points. Specifically, we establish a system that combines an LLM agent with a numerical environment. The environment provides tools for analyzing past evaluations and executing numerical methods, while maintaining a shared evaluation history and method-specific state to support continuation across method switches. Using observations and tool feedback, the LLM agent selects tools, configures their parameters, and allocates the evaluation budget, while numerical methods compute and evaluate candidate points. Building on this system, we also introduce NextEval Bench, a benchmark protocol for comparing agent configurations on shared DFO tasks using the same numerical tools under matched evaluation budgets and information constraints. Numerical results on low-dimensional DFO problems demonstrate that the NextEval solver outperforms the state-of-the-art DFO baselines in function-evaluation efficiency.

cs.LG · cs.AI · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35523

Ranking Function Generation with LLMs for Termination

Rongkang Li, Jiaxin Hu, Taolue Chen, Yong Li, Fu Song, Zhilin Wu

原文摘要

Ranking functions are a fundamental technique for proving loop termination, but constructing them manually is challenging and requires substantial expertise. We propose RAINFALL, a neuro-symbolic framework which follows the generate-verify-refine paradigm and tightly integrates LLMs with symbolic verification. RAINFALL formulates ranking function synthesis as a code-completion task by inserting placeholders into the input program and constraining the LLM to fill four predefined slots for each loop. The completed program is then checked by a symbolic verifier. If verification succeeds, a valid ranking function is obtained; otherwise, the verifier's diagnostics are translated into natural-language feedback that guides iterative refinement of the LLM's output. We evaluate RAINFALL on two public benchmark suites comprising 358 terminating C programs. Using MiniMax V2.7, RAINFALL synthesizes verifiable ranking functions for 323 programs (90.22%), outperforming all promising baselines. In particular, it solves 12 more programs than PROTON 2.1, the best-performing baseline and winner of the SV-COMP 2026 Termination category. Furthermore, combining RAINFALL with PROTON 2.1 increases the success rate to 96.65% (346/358 programs). We also conduct ablation study confirming individual contributions of placeholder contracts, few-shot examples, symbolic verification, and diagnostic information feedback. Experiments across LLMs further confirm the effectiveness of RAINFALL. Our results suggest a generic, sound framework for ranking-function synthesis and, beyond termination, LLM-assisted synthesis of formally checkable liveness certificates, both of which represent new directions of software engineering research.

cs.SE · cs.AI · cs.LO

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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.1160ACL Anthology · ACL 2026

GMFL: Efficient Global Masking for Federated LLM Fine-tuning

Xin Huang, Yan Hu, Yue-Jiao Gong, Xinglin Zhang South China University of Technology

原文摘要

Low-Rank Adaptation (LoRA) has emerged as a prominent solution to mitigate the communication and computation costs in federated fine-tuning of Large Language Models (LLMs). However, we observe that even within low-rank adapters, a substantial portion of parameters manifest negligible updates during federated training, leading to redundant communication and wasted local computation. To address this, we propose GMFL, a plug-and-play layer freezing mechanism designed to seamlessly integrate with existing federated fine-tuning frameworks. Specifically, the server monitors the global update magnitude of each LoRA layer to dynamically generate freezing masks. These masks are updated periodically with a fixed freezing rate, ensuring stable convergence by robustly identifying “saturated” layers. Theoretical analysis confirms the convergence of GMFL, where the freezing mechanism yields a bounded error that scales with client heterogeneity. Extensive experiments across multiple tasks (GLUE, Commonsense Reasoning, Math Reasoning and General Generation) demonstrate that GMFL reduces communication overhead and lowers computational costs while preserving the performance of the underlying federated fine-tuning methods. Our work provides a practical, versatile solution for deploying large-scale federated LLM fine-tuning in resource-constrained environments. Our code is available at: https://github.com/tunx-cyber/GMFL.

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.1909ACL Anthology · ACL 2026

Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation

Fangda Ye National University of Singapore, Kuicai Dong Huawei Technologies Ltd., Xie Zhifei, Yuxin Hu, Yihang Yin University of Edinburgh, Shurui Huang, Shikai Dong, Chen Zhang Huawei International Pte Ltd 等 10 位作者

原文摘要

Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. However, they remain text-centric, overlooking the multimodal evidence that characterizes real-world expert reports. We introduce a pressing task: multimodal long-form generation. Accordingly, we propose Deep-Reporter, a unified agentic framework for grounded multimodal long-form generation. It orchestrates: (i) Agentic Multimodal Search and Filtering to retrieve and filter textual passages and information-dense visuals; (ii) Checklist-Guided Incremental Synthesis to ensure coherent image-text integration and optimal citation placement; and (iii) Recurrent Context Management to balance long-range coherence with local fluency. We develop a rigorous curation pipeline producing 8K high-quality agentic traces for model optimization. We further introduce M^2LongBench, a comprehensive testbed comprising 247 research tasks across 9 domains and a stable multimodal sandbox. It enables unified multimodal assessment, fair comparison, and accessible evaluation without commercial APIs. Extensive experiments demonstrate that long-form multimodal generation is a challenging task, especially in multimodal selection and integration, and effective post-training can bridge the gap. Our code is available at https://github.com/fangda-ye/Deep-Report.

cs.CL · cs.AI · cs.CV

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.384ACL Anthology · ACL 2026

ReportLogic: Evaluating Logical Quality in Deep Research Reports

Jujia Zhao, Zhaoxin Huan, Zihan Wang, Xiaolu Zhang Ant Group, Jun Zhou Ant Group, Suzan Verberne Universiteit Leiden, Zhaochun Ren Leiden University

原文摘要

Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports that support understanding and action. In this context, the practical reliability of such reports hinges on logical quality: whether the report’s claims and arguments are explicitly supported and can be trusted as a basis for downstream use, rather than merely appearing fluent or informative. However, current evaluation frameworks largely overlook this requirement. To bridge this gap, we introduce ReportLogic, a benchmark that quantifies report-level logical quality through a reader-centric lens of auditability. Specifically, ReportLogic adopts a hierarchical taxonomy that evaluates whether readers can (1) trace an on-topic report structure with a unified analytical arc (Macro-Logic), (2) understand the progression with necessary context (Expositional-Logic), and (3) verify conclusions via explicit claim–support (Structural-Logic). Based on this taxonomy, we construct a human-annotated rubric-guided dataset and train an open-source LogicJudge for scalable evaluation. We further evaluate judge robustness via adversarial attacks, showing that off-the-shelf LLM judges are frequently influenced by superficial cues (e.g., verbosity), and reasoning modes can mask broken support relations. Overall, our results provide actionable guidance for building more robust logic evaluators and improving the logical reliability of LLM-generated reports.

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.967ACL Anthology · ACL 2026

Probing the Safety Robustness of LLMs in Latent Space

Tianle Gu, Kexin Huang, Zongqi Wang, Yixu Wang, Jie Li Shanghai Artificial Intelligence Laboratory, Xin Wang Fudan University, Yang Yao, Yujiu Yang Tsinghua University 等 10 位作者

原文摘要

Safety alignment is a fundamental prerequisite for building trustworthy artificial general intelligence. Despite substantial progress in safety alignment techniques, empirical evidence shows that aligned large language models can still produce unsafe responses under minor internal perturbations, revealing a robustness gap in existing safety mechanisms at the latent representation level. In this paper, we study the robustness evaluation of safety alignment under latent-space perturbations. We introduce Activation Steering Attack (ASA), and leverage the Negative Log-Likelihood (NLL) as a diagnostic signal to probe the local sensitivity of safety behaviors in latent space. By measuring a model’s likelihood under controlled perturbations to its hidden representations, we assess the stability of its original responses. The probing signal is model-agnostic and supervision-free, enabling a general and reproducible diagnostic metric for analyzing safety robustness. Leveraging these probes, we systematically uncover a set of previously underexplored empirical findings, including (1) non-stationarity of layer vulnerabilities, revealing that the most vulnerable layer is an unstable property and even relocates after robustness training; (2) instance-level alignment with cross-layer consistency, where specific inputs remain universally vulnerable across the entire model hierarchy; (3) compositional effects of ASA, characterized by its incremental accumulation across sequential decoding steps and its potential for prompt-level jailbreak effectiveness.

cs.CL

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