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版本更新 · v2arXiv:2609.24687

Discrete Isoperimetric Inequalities via Curvature

Zejia Chen, Zongchen Chen, Xinyuan Zhang

原文摘要

Isoperimetric inequalities on the Boolean hypercube play a fundamental role in the analysis of Boolean functions. These inequalities have been established primarily for the uniform measure and for biased product measures, often through Fourier-analytic or inductive arguments. We develop a curvature-based semigroup framework to establish isoperimetric inequalities for measures with weakly dependent coordinates. In particular, we show that a Dobrushin-type condition, together with marginal boundedness of the distribution, implies the local Bobkov inequality, Talagrand's $L^1$--$L^2$ and variance--surface-area inequalities, the Kahn--Kalai--Linial inequality, and the Eldan--Gross inequality. Our results apply to zero-field Ising models with interaction matrix $J$ throughout the Dobrushin uniqueness regime $\|J\|_1<1$. The framework builds on discrete Bakry--Émery theory and gradient estimates and can also be extended to measures on Hamming slices or hypergrids.

math.PR · cs.DM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.35036

Persona Following Is Not Selective Control: The Neutrality Gap in LLM User Simulation

Jiashen Ren, Wenlin Zhang, Bohan Zhang, Xiaopeng Li, Zichuan Fu, Wanyu Wang, Junyi Li, Xiangyu Zhao

原文摘要

Persona prompting is widely used in LLM user simulation, yet it relies on an untested assumption: specifying a target user attribute should shift target behavior while leaving other fixed attributes unchanged. Across eight black-box and five open-weight models, however, changing a target attribute alone also shifts unspecified non-target choices, such as risk preference shifting color choice. We test whether explicitly specifying non-target attributes restores selective control across 477 held-out synthetic worlds and 200 independent items under unspecified, directional, and neutral states. Assigning a clear non-target direction nearly eliminates cross-attribute shifts. Explicitly declaring no preference, however, still leaves 37.7%-76.3% of held-out worlds and 51%-81% of independent non-target items shifting with the target attribute while preserving target efficacy. We call this disparity the neutrality gap. Because directional tests can be passed simply by following a stated preference, the neutral state reveals selective-control failures they miss: following a stated persona does not imply keeping unrelated choices invariant. In simulated investment decisions, incidental brochure color labels shift estimated target risk treatment effects by 25.5 percentage points, even when models accurately report having no color preference. Counterfactual attention interventions show that unwanted influence enters primarily at query time, when option evaluation reads the target persona directly. Blocking query-time attention cuts non-target drift by 72.1%-87.8%, whereas directional declarations suppress drift primarily by overwriting option values rather than rerouting attention. Because target and non-target choices share query-time access, blocking it weakens target efficacy while value overwrite imposes an artificial direction, leaving task-dependent selective access as a central challenge.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16307

DEDCA: Test-Time Adaptation for Generalized AI-Generated Image Detection

Hao Tan, Qimin Zhang, Lihuang Fang, Kebing Jin, Jinghui Qin

原文摘要

AI-generated image detectors face significant challenges when deployed in real-world environments, particularly when test samples are produced by unseen generators that deviate from the source training distribution. Existing detectors often rely on a single type of evidence, such as semantic representations or low-level generation traces, making them vulnerable when the corresponding cue becomes unreliable. Moreover, adapting detectors to newly emerging generators usually requires target labels or retraining, which is costly and impractical. To address these challenges, we propose Dual-Evidence Disagreement-Constrained Adaptation (DEDCA), a test-time adaptation framework for generalized AI-generated image detection. DEDCA constructs a dual-evidence detector by combining CLIP-based semantic evidence with trace evidence extracted from shuffled entropy and residual statistics, and introduces a reliability-aware gate to perform sample-specific adaptive fusion. Our key idea is to exploit the disagreement between the two evidence streams as an unlabeled adaptation signal, enabling the detector to adjust to target-domain shifts without relying on target labels or blindly trusting a single prediction stream. Experiments show that DEDCA achieves superior performance on the GenImage benchmark by outperforming state-of-the-art AI-generated image detectors and test-time adaptation methods with 92.0\% average accuracy.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.13535

Joint Communication-Control Strategy Optimization with Partially Nested Information Structures: The Linear-Quadratic Case

Haoyi You, Kaiqing Zhang

原文摘要

In this paper, we formalize a joint communication-control strategy optimization (JCCO) problem in multi-agent linear systems with quadratic costs, under the common-information-based (CIB) framework from decentralized stochastic control. For computational tractability, we focus on such JCCO problems with partially nested (PN) information structures (ISs). In particular, with a baseline communication protocol that leads to a PN IS, we establish a series of conditions under which the partial nestedness is preserved under the (additional) communication strategies to be optimized, while violating them may cause nonlinearity or non-existence of the optimal control strategies, with open-loop communication strategies. Under these conditions, we then develop a dynamic-programming-based approach to compute the optimal control strategies of JCCO with open-loop communication strategies, which yields a set of closed-form Riccati Equations. As a byproduct of independent interest, such an approach also offers a new way to solve decentralized linear-quadratic control with PN ISs and output feedback, under the CIB framework and the aforementioned conditions. Finally, we extend such an approach to JCCOs with closed-loop communication strategies, yielding a more tractable dynamic program than an infinite-dimensional CIB-belief-based one.

eess.SY · cs.MA · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16293

Rasoi: Visual Diffs for Evaluating Structural Changes Made by Coding Agents

Riya Sahni, Aryan Kaul, Anushka Bhatnagar, Xuanming Zhang, Vivian Liu, Reya Vir, Lydia B. Chilton

原文摘要

Coding agents let developers modify software rapidly through natural language. However, as agents take on larger changes, developers can lose track of how the codebase is evolving. These edits may look correct on the surface while restructuring the system in unexpected ways: duplicating logic, changing modules outside the requested scope, removing functionality developers meant to keep, or introducing architectural drift. We investigate how visual diffs can help developers understand and evaluate the structural changes that coding agents make. We present Rasoi, a design probe that maps how a codebase's architecture evolves after each agent-made edit, letting developers inspect which architectural elements and relationships were added, modified, or removed across multiple levels of abstraction. We deployed Rasoi in a week-long study with 12 developers working on their own codebases across varied technical domains. We found that visual diffs helped developers see whether changes stayed within scope, notice unexpected restructuring, and understand how modified parts related to the system as a whole. Our findings suggest that the value of oversight tools for coding agents lies less in making developers trust the agent and more in helping developers feel confident in their own evaluation of agent-made changes. We discuss implications for visual tools that support code comprehension and human oversight in AI-assisted software development.

cs.SE · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16289

Eliciting and Mitigating Specification Satisficing in Coding Agents

Ruixin Wang, Tianyi Zhang

原文摘要

Coding agents often fail to meet all requirements in the task description. In some of these failures, the agent considers an implementation choice that meets a requirement, but later decides to commit to another one that does not meet the requirement for a reason it finds acceptable. We call this specification satisficing. It is challenging to identify and mitigate specification satisficing because the agent accepts such choices for its own reasons, and existing self-correction methods are biased toward them. We propose SatisFix, which guides a coding agent to report these choices and their reasons, and then check each choice against the task description. We evaluate SatisFix on DeepSWE and SWE-bench Pro with five LLMs as backbones and two different agent frameworks, and compare it with three self-correction methods. SatisFix improves the pass@1 of the base agent by an average of 5.09% on DeepSWE and 7.06% on the hard subset of SWE-bench Pro. On DeepSWE, it outperforms the best baseline by 3.98%. Our ablation study shows that both steps of SatisFix contribute to the improvement.

cs.SE · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16287

RiskLens: Human-Informed Incident Transfer for AI Risk Evaluation

Yiliu Tang, Hangyue Zhang, Yun Huang

原文摘要

As AI capabilities advance, early-stage teams need better ways to judge whether historical incidents transfer to new products, demanding support beyond static risk checklists or one-off ideation tools. We present RiskLens, a human-AI workflow with an interactive system built around a live crawl, refreshable ranker, and extensible, human-informed schema. RiskLens analyzes 2,034 AI incidents and 10,443 AI products to rank transferable incidents using a feature schema spanning technical, operational, and user-facing attributes and supports an interface that presents ranked incident evidence, generated scenarios, risk metrics, and mitigation suggestions. In a study with 21 AI-familiar participants, users surfaced evaluation criteria that we added as 13 human-informed dimensions, including harm categories, failure mechanisms, and vulnerable groups. The feature-based model outperformed a generic baseline with an 86.2% win rate, and remained stable on a twenty-month future holdout; after human-informed schema expansion, the updated model outperformed the same baseline on 91.4% of pairs.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16270

SMIC: Shared Multiscale Input-Reference Conditioning across Transformer Layers for Natural Language Inference

Xin Zhang, Victor S. Sheng

原文摘要

Natural Language Inference models can be sensitive to localized lexical changes. We introduce Shared Multiscale Input-Reference Conditioning (SMIC), which constructs a single input-dependent multiscale reference and reuses it across Transformer layers. Each layer applies its own multiscale transformation to the shared reference and injects the resulting conditioning feature before the attention update. We instantiate SMIC in a token--context Transformer (TCT) and conduct from-scratch comparisons against decoder-based and Multi-Layer Perceptron (MLP) baselines across all three ANLI rounds. TCT-SMIC attains the highest observed ten-seed mean accuracy on all three ANLI rounds. The ablations further support the contribution of SMIC. We evaluate a real-valued Llama-style plug-in and extend deletion/insertion diagnostics to pretrained ModernBERT encoder and Llama decoder models with and without SMIC. Across the evaluated perturbation protocols, TCT-SMIC exhibits comparatively low pooled-attention redistribution and smaller signed accuracy changes. We further establish a finite-sample bound on average subset-mass redistribution in terms of the mean Jensen--Shannon divergence between the restricted and renormalized attention distributions.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.33663

LieDiscover: Adaptive Symbolic Library Construction for Explicit Open-form Symmetry Discovery

Xinxin Li, Jianming Ma, Xingyu Cui, Da Li, Juan Zhang, Junping Yin

原文摘要

Discovering symmetries from observational data provides interpretable insights into dynamical systems and priors for scientific machine learning. However, existing symmetry discovery methods face two limitations. Many methods represent symmetries implicitly, making it difficult to obtain explicit infinitesimal generators that can serve as physical priors for learning dynamical systems. Meanwhile, methods that explicitly identify generators often rely on predefined function libraries, limiting their ability to capture complex symmetries involving high-order polynomials or transcendental functions. To address these limitations, we propose LieDiscover, a framework that explicitly identifies infinitesimal generators through an iterative symbolic search process that dynamically expands the symbolic function library to capture complex functional forms. Specifically, at each search step, we design an encoder-decoder architecture to generate a candidate symbolic expression. An adaptive singular value decomposition (SVD)-based evaluation is introduced to assess how each candidate contributes to the recoverable symmetry subspace. The resulting evaluations are incorporated into step-wise rewards to guide reinforcement learning toward discovering richer symmetry structures with concise symbolic representations. Experiments on eight partial differential equation (PDE) benchmarks demonstrate that LieDiscover recovers explicit generators involving higher-order polynomial, exponential, and trigonometric terms, while achieving more accurate symmetry subspace recovery and improved robustness to noise. Furthermore, the recovered generators enable effective symmetry-based data augmentation, improving the accuracy of downstream neural PDE solvers.

cs.SC · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16265

Rectified Looped Transformer

Musheng Li, Jiawei Zhang, Ziyuan Liu, Yuantao Gu

原文摘要

Looped Transformers increase effective depth by repeatedly applying a shared block, offering a parameter-efficient approach to language modeling. However, most existing works directly reuse each loop's output as the next's input, which can lead to hidden-state scale growth and perturbation amplification as recurrence deepens. We propose Rectified Looped Transformer (RLT), which redesigns feedback between loops using an interpolation schedule inspired by flow-based modeling. At each loop, RLT forms the next input by linearly interpolating between the current state and its prediction. Such interpolation distributes state changes across loops, encouraging a smoother trajectory from the initial input to the final output. Theoretical analysis proves that, under appropriate assumptions, RLT keeps state magnitudes uniformly bounded across loops and reduces the worst-case perturbation amplification. We evaluate RLT on language modeling corpora and downstream benchmarks. At matched training and inference depths, RLT consistently achieves lower perplexity than a standard looped Transformer, while exhibiting greater robustness to input perturbations and variance in inference depth. Consistent with the theoretical results, latent trajectory analysis shows that RLT maintains more stable hidden-state norms across loops, which also facilitates extrapolation-based decoding strategies. Notably, RLT achieves these gains without requiring additional trainable parameters or extra recurrent block evaluations, thus providing a simple yet more stable feedback mechanism for looped Transformers.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.22209

Duet: Co-Optimizing P2P Message Propagation and Rotating-Leader Consensus

Yifeng Ye, Rongji Huang, Gerui Wang, Mingchao Wan, Yuxing Duan, Jingjing Zhang, Shengyun Liu

原文摘要

In blockchain systems, peer-to-peer (P2P) overlay networks play a crucial role in providing reliable, scalable and efficient message-delivery services to upper layers. However, the consensus layer and the underlying P2P network remain mutually opaque in existing blockchains, waiving the opportunity for further improvement. In contrast to other P2P applications, blockchain can naturally be abstracted as a state machine. We therefore leverage this abstraction to record network topologies and latencies in a trusted and coordinated manner. With this support, we propose three improvements to rotating-leader consensus protocols and their underlying P2P networks: (1) accelerating leader rotation; (2) introducing a reliable-broadcast paradigm that employs tree-based dissemination in the normal case and falls back to gossip only when necessary; and (3) constructing latency-aware dissemination trees. We integrate the above ideas into Tendermint and libp2p, and conduct empirical evaluation on Amazon EC2 platform using up to 300 nodes distributed across 10 regions. The results demonstrate that, compared with gossip-based dissemination over the same topology, our prototype improves peak throughput by up to $7.26\times$.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16257

Few-Shot Cross-Subject Neural--Image Retrieval via Stimulus-Anchored Latent Bias Calibration

Xizhang Yao, Li Zhang, Gan Huang, Zhen Liang

原文摘要

In cross-subject neural--image retrieval, comparing the mean of a few stimulus-matched target observations with the global source mean conflates subject-specific representation shifts with stimulus-composition effects. To address this problem, we propose Stimulus-Anchored Latent Bias Calibration (SA-LBC), which estimates the first-order shift under fixed preprocessing and a frozen source-trained representation by averaging residuals between target responses and same-stimulus source-population anchors, then shrinks the correction using source-derived uncertainty. Under uniform sampling without replacement from a finite stimulus population, we derive exact finite-population MSE expressions for the unshrunk anchored and global-mean estimators, including residual--anchor cross-covariance, and establish a necessary and sufficient condition for anchoring to reduce MSE relative to global-mean estimation. In a 10-subject leave-one-subject-out evaluation on THINGS-EEG2, SA-LBC increases mean reciprocal rank (MRR) for the Attention--CLIP ViT-B/32 configuration from 0.1867 without calibration to 0.2013 and 0.2336 with M=2 and M=16 stimulus-level calibration samples, respectively. Across six neural--visual configurations and all calibration budgets, stimulus-matched anchors yield higher MRR than anchors with shuffled stimulus identities, but retrieval gains over no calibration remain representation-dependent. At (M=2), shrinkage improves MRR over the unshrunk anchored estimator in all six configurations, but its effects at larger budgets vary across representations; the relative ranking of translation and orientation corrections also depends on regularization strength. Thus, lower shift-estimation MSE does not guarantee higher retrieval performance. The source code is publicly available at: https://github.com/Xizhang-Yao/SA-LBC.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16217

WorldDraft: If Code Builds the World, Can Video Models Just Render It?

Zhiyu Hou, Wen-Jie Shu, Harold Haodong Chen, Weilin Ruan, Chenfei Liao, Liuzhou Zhang, Disen Lan, Xiaogang Xu 等 10 位作者

原文摘要

Multimodal coding agents decouple 4D world construction from visual generation, repurposing video models from world generators toward generative renderers. This division of labor raises a fundamental question: if code builds the world, can video models just render it? A reliable generative renderer hinges on two complementary criteria: (i) world preservation, retaining prescribed 4D kinematics and camera paths; and (ii) visual realization, persistently binding text-specified target attributes over time. To evaluate this capability, we introduce WorldDraft, a benchmark of $432$ strictly paired test cases across $48$ executable 4D world families spanning six dynamic challenge categories. Under a $3 \times 3$ factorial design, WorldDraft orthogonally crosses draft granularity (abstract proxy to previs) and appearance alignment (neutral to conflicting) under identical dynamics. Extensive evaluations across $12$ representative models reveal that: (I) world preservation and visual realization remain in stark operational tension; (II) richer draft scaffolding anchors structure but paradoxically impairs appearance fidelity; and (III) conflicting source cues trigger severe source leakage (up to $64\%$), exposing fragile cross-modal disentanglement. To mitigate these bottlenecks, we propose WorldDraft-Primer, a plug-and-play visual prompting strategy that consistently improves both objectives across frozen video renderers. Together, WorldDraft and WorldDraft-Primer lay a foundation for bridging programmatic 4D simulation with video generation, advancing physically grounded generative rendering.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16201

An Extensive Evaluation of Machine Learning and Deep Learning Approaches for Sentiment Analysis: Insights from Amazon Customer Reviews

Muhammed Ihsan Corak, Cuiyuan Wang, Xiaowen Zhang

原文摘要

The rapid growth of user-generated text on online platforms creates opportunities for understanding customer opinion and supporting data-driven decisions. This study compares five traditional machine learning classifiers (Random Forest, Logistic Regression, Bernoulli Naive Bayes, Linear Support Vector Classifier, and Multinomial Naive Bayes) and six deep learning architectures (LSTM, Bi-LSTM, GRU, RNN, Attention, and FastText) for three-class sentiment classification of 568,454 Amazon Fine Food Reviews. Machine learning models used Bag-of-Words (BoW) and TF-IDF features limited to 5,000 terms; deep learning models used learned embeddings over a 20,000-word vocabulary. Models were evaluated on the original imbalanced data and on a random-oversampled balanced set of 1,070,469 reviews, using accuracy, macro and per-class precision, recall, and F1-score, and training time. On the balanced set, Random Forest with BoW achieved 99\% accuracy and the Attention model achieved 98.7\%. Linear SVC reached 89\% accuracy in 1 min 15 s, and Logistic Regression reached 90\% in 3 min 37 s. On the imbalanced set, the Attention model achieved 97.8\% accuracy, and FastText achieved 95.3\% in 3 min 26 s, while the RNN reached 91.9\% with a neutral-class F1-score of 0.08. Deep learning models were more stable across class distributions, whereas the best classical model depended on balancing. We also report a user-facing application that applies the trained Random Forest and Attention models to new review text. Because the labels are derived from star ratings, the results measure agreement with rating-derived sentiment classes rather than annotated sentiment; the dataset is a single source, and the findings should be read with that limitation in mind.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2511.03398

A Class of Self-Orthogonal, NMDS, or LCD MDS $(\mathcal{L}, \mathcal{P})$-Twisted Generalized Reed-Solomon Codes

Zhonghao Liang, Chenlu Jia, Dongmei Huang, Qunying Liao, Chunming Tang

原文摘要

Maximum distance separable (in short, MDS), near MDS (in short, NMDS), linear complementary dual codes (in short, LCD) and self-orthogonal codes play a pivotal role in algebraic coding theory, particularly in applications such as quantum communications and secret sharing schemes. Recently, the construction of non-generalized Reed-Solomon (in short, non-GRS) codes has emerged as a significant research frontier. This paper focuses on a class of $(\mathcal{L}, \mathcal{P})$-twisted generalized Reed-Solomon (TGRS) codes $\mathcal{C}_{k-1}\left(\boldsymbolα,\boldsymbol{v},\boldsymbolη\right)$ characterized by $\ell+1$ twists. By considering a special class of parity-check matrices derived from existing results, we derive the corresponding necessary and sufficient conditions or sufficient conditions for $\mathcal{C}_{k-1}\left(\boldsymbolα,\boldsymbol{v},\boldsymbolη\right)$ to be self-orthogonal, and then generalize the corresponding results given by Liu et al. (2021), Sui et al. (2021), and Zhang et al. (2025). As applications, leveraging these self-orthogonal structures, we can obtain several families of LCD MDS codes. Secondly, we provide a characterization of the NMDS property for the codes $\mathcal{C}_{k-1}\left(\boldsymbolα,\boldsymbol{v},\boldsymbolη\right)$, offering a partial solution to the open problem concerning general $(\mathcal{L}, \mathcal{P})$-TGRS codes posed by Hu et al. (2025). Finally, we rigorously prove that the codes $\mathcal{C}_{k-1}\left(\boldsymbolα,\boldsymbol{v},\boldsymbolη\right)$ are of non-GRS type when $2k > n>k+\ell+2$.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16163

Generative AI models for exploring diffusion dynamics from multi-frame particle tracking

Jianan Wei, Qiang Zhang, Hao Sun, Yi Yang, Wenguan Wang

原文摘要

Particle-based volumetric flow measurement techniques have proven their efficacy in quantitatively assessing the dynamic characteristics of unsteady flows within the realm of fluid mechanics. Despite recent advances in multi-frame particle tracking, an open challenge in this area is maintaining trajectories in temporally under-sampled 3D sequences, where inter-frame displacements are large. In this work, we develop DiffTR-Flow3D, a diffusion-based model that estimates flow motion from spatiotemporal three-dimensional particle recordings via a stochastic diffusion process. DiffTR-Flow3D conditions the diffusion process with both global and local particle dynamics and enhances flow estimation by leveraging historical particle trajectories. Extensive experiments, including numerical simulations and real-world measurements, demonstrate that DiffTR-Flow3D achieves state-of-the-art displacement estimation accuracy compared with learning-based flow estimators, and, when integrated into particle tracking pipelines, substantially improves their long-term tracking performance, while exhibiting robustness and generalization across diverse flow patterns. This work is expected to provide fluid researchers with an effective tool for the consistent and precise analysis of particle trajectories in temporally under-sampled 3D sequences with large inter-frame displacements.

physics.flu-dyn · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.22673

Agent MechSuits: Mechanistic Subspace Safety Steering for Multi-Turn CLI Agents

Weidi Luo, Qiming Zhang, Yihao Quan, Mingyu Jin, Jie Cai, Chaowei Xiao, Jingcheng Niu, Zhen Xiang

原文摘要

Command-Line Interface (CLI) agents based on large language models (LLMs) demonstrate remarkable autonomous capabilities, but they also introduce significant safety and misuse risks during multi-turn interactions with external environments. Existing safety mechanisms mainly rely on external guardrails, which have a limited ability to perform fine-grained behavioral control during execution. Meanwhile, recent mechanistic interpretability methods for LLM safety are mostly confined to single-turn or jailbreak-style QA settings, limiting their ability to capture the evolving risk dynamics of multi-turn agent execution. In this paper, we investigate the safety of multi-turn CLI agents from an internal perspective. We propose Agent MechSuits (Mechanistic Subspace Intervention and Steering), a white-box defense framework that performs runtime safety detection and representation-level mitigation for CLI agents. Unlike conventional agent guardrails, Agent MechSuits detects harmful execution states from step-level hidden representations and mitigates unsafe behavior by intervening in a 10-dimensional subspace within a single layer. To support this research, we introduce the Mechanistic Agent Safety (MAS) benchmark, comprising comprehensively annotated multi-turn execution trajectories across 194 tasks using LLaMA-3.1-8B, Qwen-2.5-7B, and Gemma-2-9B. Extensive experiments show that Agent MechSuits achieves strong safety detection performance, provides preliminary evidence for lookahead risk anticipation, and substantially reduces harmful actions of the CLI agent, establishing a foundation for applying mechanistic interpretability to dynamic LLM agent safety.

cs.AI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16159

From Tokens to Behaviors: Rethinking Generative Recommendation for User Trajectory Prediction

Jiaqi Wang, Hanchen Yang, Hengyu Zhang, Hongxu Ma, Wenqi Rao, Hongyang Wang, Zhongbo Sun, Renliang Qian 等 15 位作者

原文摘要

User behavior trajectory prediction (UBTP) aims to capture how a user's needs evolve over time by predicting the sequence of future interactions from that user's interaction history. Recent generative recommendation (GR) methods address this task through autoregressive generation of semantic IDs (SIDs). However, serial SID decoding couples token generation with behavior state transitions and requires multiple dependent steps per interaction. Discrete item feedback overlooks predictive uncertainty, and teacher-forced training leaves a training-inference gap in trajectory generation. To address these limitations, we propose TraGR, a behavior-aware generative recommendation framework that aligns generation, feedback, and learning with successive user behaviors. Its behavior-level architecture predicts SID tokens in parallel within each interaction and models behavioral transitions through recurrent state updates, with joint SID and item-level supervision. Probability feedback combines the selected behavior with a probability-weighted summary of alternatives to guide subsequent predictions. Relation-aware trajectory learning incorporates collaborative transition signals and later credit assignment into training on model-generated trajectories. Experiments on four public datasets demonstrate that TraGR consistently outperforms state-of-the-art baselines across different prediction horizons. Code: https://github.com/CalmaQi/GR4UBT.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16149

HyperPage: Learned Page Layouts for Disk-Resident Graph Vector Indexes

Xinyu Zhang, Jingze Li, Baihua Zheng, Weiwei Sun

原文摘要

Disk-resident graph indexes such as DiskANN store each vector with its graph edges as one record on SSD pages. A search needs the page of every record it expands, so its page reads depend on the page layout, that is, which records share a page. Search agents trained with reinforcement learning query one such index run after run, and every graph traversal can be logged with the records it expands. We therefore use the logs of a finished run to lay out the index for a declared memory budget, so later runs read fewer pages. In contrast, existing layouts such as Starling and MARGO are computed from the graph and vectors and use no logs. Learning a layout from logs is challenging, because the logs must stay valid under every layout, the saving depends on what the budget caches, and the best layout is NP-hard to find. Our key observation is that DiskANN-style search routes on in-memory codes and the records it expands, never on their page-mates. As a result, the logged traversals price every layout by replay, and cached records add no page reads. Accordingly, we propose HyperPage, which first builds layouts for the declared budget on the logged traversals of uncached records. It then selects one layout, together with the budget split between hot records and a page cache, by replaying validation steps. In addition, a Lagrangian lower bound shows how far a layout may be from the optimum. We learn from one Search-R1 agent run and test on a later run with a different reward, over 1M and 21M passages. Across seven cache budgets, HyperPage reads 5.2% to 21.2% fewer pages than MARGO, and no compared layout reads fewer. The advantage is largest when the cache is small. For example, at 1% of the 1M index, MARGO needs nearly 3 times the budget to read as few pages. On the 21M index, HyperPage also cuts search time per step by up to 10.4%. The saving holds for a second agent model and a second corpus.

cs.DB · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16148

Calibrated Early Termination with Bounded Optimality Gaps for Constrained Vector Search

Xinyu Zhang, Jingze Li, Baihua Zheng, Weiwei Sun

原文摘要

Vector search over learned embeddings is the retrieval layer of recommendation and retrieval-augmented generation, and many applications cap how many answer items one category may contribute. Graph indexes answer a query by best-first traversal, and where it stops determines its cost and whether its answer is exact. We study early termination for such constrained $k$-nearest-neighbor ($k$-NN) queries, with plain $k$-NN as a special case, and seek the cheapest stopping rule whose answer is exact with probability at least $1-\varepsilon$. Learned early-termination methods instead target average recall, which does not guarantee exact answers. Multiple testing can calibrate any parameterized stopping rule to our target, but it cannot tell how much a better rule with the same risk and observations would save. Answering this needs a lower bound on the optimum of a rule family too large to enumerate, valid for the query population. Our key idea is to declare the observations a rule may use and to consider every rule monotone in them. Risk-constrained stopping then becomes a linear program over an order polytope, whose Lagrangian minimizers form a nested chain of staircase rules computed exactly by dynamic programming. Fixed-sequence binomial tests along this chain calibrate our rule, CaST, whose failure probability is at most $\varepsilon$ with confidence $1-δ$. Lagrangian weak duality bounds the optimum of the declared class from below at all risk levels at once, without a union bound over the rules. On two datasets of one million items each, CaST replayed on logged searches costs at most 1.132 times the class optimum for risk levels from 0.03 to 0.10, at 95% joint confidence per setting. Larger savings thus need a rule outside the class. At a common risk target on held-out queries, CaST is faster than every other calibrated rule we time, which take 4.8% to 45.5% more mean time.

cs.DB · cs.IR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16138

Individually Fair Facility Location

Rajni Dabas, Samir Khuller, Milind Maiti

原文摘要

We study individually fair facility location, where each client must connect to an open facility within a prescribed radius and the objective is to minimize total opening and connection cost. We show that preserving the radii changes the approximability of metric facility location fundamentally. For every fixed radius violation below three, no deterministic polynomial-time algorithm achieves a sublogarithmic cost factor unless $\mathrm{P}=\mathrm{NP}$. The hardness holds with uniform opening costs and uniform radii. It contradicts the strict-fairness constant-factor guarantee recently claimed by Liu, Xu, and Zhang [Theory of Computing Systems, Vol. 70 (2026)]. We then establish a positive result for arbitrary client radii. We give a randomized polynomial-time algorithm that has cost at most $1.725$ times the strictly fair optimum and serves every client within three times its fair radius (in the Appendix we show how this can be de-randomized). We use the approach of Byrka, Srinivasan and Swamy [IPCO 2010]. The facility-location radius factor three is therefore optimal among deterministic polynomial-time algorithms with constant cost factor, unless $\mathrm{P}=\mathrm{NP}$. Experiments on real-world data indicate that the worst case radius bound is rarely realized in practice: the algorithm serves over 99.8% of clients within their stated radius, and never beyond 1.5 times it. Our code is available at https://github.com/milindmaiti/IFFL_Implementation.

cs.DS · cs.CC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.07338

Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images

Yuechen Jiang, Enze Zhang, Md Mohsinul Kabir, Chenying Lin, Ying Peng, Fengyuan Liu, Qianqian Xie, Stavroula Golfomitsou 等 10 位作者

原文摘要

Museum records express how people understand heritage objects through cultural and historical relations. Recovering these human attributions from images is a technical challenge for vision-language models (VLMs). We introduce Appear2Meaning, a cross-cultural benchmark of 750 museum objects for image-only recovery of title, culture, period, origin, and creator. Its evaluation framework distinguishes field-level from complete-record compatibility and uses human validation for semantic judgement. Across nine VLMs, mean field accuracy is 36.16--49.82%; 7.20--17.02% of records have all four contextual fields compatible, and 2.13--7.87% have all five, including title. For the two tested models, image-only input yields 12.6--32.2 percentage points higher reference compatibility for culture, period, and origin than a museum-and-title-only control.

cs.CV · cs.CL · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16110

Harnessing Environmental Scatterers for Endogenous Target Covertness in 6G ISAC: A New Paradigm for Sensing Security

Yifan Zhang, Boxuan Xie, Mingyang Yang, Ying Liu, Riku Jäntti, Zan Li

原文摘要

Integrated sensing and communication (ISAC) is a cornerstone of 6G networks, enabling simultaneous data transmission and environmental perception. However, the ubiquitous radiation of sensing waveforms introduces a critical security vulnerability where the physical existence and characteristics of targets are continuously exposed to unauthorized wardens. Meanwhile, existing sensing-secure methods primarily focus on degrading a target warden's parameter-estimation accuracy, yet they fail to prevent wardens from detecting the target's physical presence. In this article, we first analyze sensing security threats in ISAC systems and survey existing sensing-secure methods. To address the critical gap, we propose a novel security paradigm termed endogenous target covertness. By intentionally harnessing environmental scatterers, such as known objects, ambient backscatter devices, and reconfigurable intelligent surfaces (RIS), as ghost targets, the proposed scheme ensures a low probability that the warden detects the true target for the true target at the warden. This deliberately renders the true target's existence indistinguishable to the warden, while the legitimate base station can extract the true target's response using prior knowledge of the scatterers. We demonstrate the effectiveness of the proposed scheme through a case study, proving that absolute target covertness can be achieved without consuming dedicated jamming power. Finally, we outline key open challenges and promising research directions to realize truly secure, ubiquitous sensing in future ISAC ecosystems.

cs.CR · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.14972

Learning to Solve Stochastic Controls with Unknown Drifts and Running Rewards: Theory, Algorithms and Convergence

Jin Ma, Gaozhan Wang, Jianfeng Zhang, Xunyu Zhou

原文摘要

We study continuous-time and possibly high-dimensional stochastic control problems where drift coefficients and running reward functions are unknown. Due to these missing model primitives, we take the exploratory, reinforcement learning (RL) framework of Wang, Zariphopoulou, and Zhou(2020) with relaxed controls and entropy regularization. The objective is to develop theoretically grounded, efficient and scalable RL algorithms to learn both the optimal value functions (which also solve the exploratory HJB equation) and optimal exploratory feedback control policies. When the diffusion coefficients do not contain control, we employ probabilistic representations of both the optimal value function and its gradient based on an auxiliary state process depending only on the diffusion part of the original dynamics. With a delicate analysis on some properly defined mappings and their fixed points, this leads to the introduction of our policy iteration algorithms and their convergence. We demonstrate the performance of our algorithms through various numerical examples. Finally, we study a special control-dependent diffusion case where probability representation of the Hessian is called for.

cs.LG · math.NA · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16103

Low Probability of Target Detection in Wireless Sensing by Harnessing Reconfigurable-antenna based Reflectors

Yifan Zhang, Zhentian Zhang, Jantti Riku, Yichuan Wang, Xingwang Li, Tuo Wu

原文摘要

In this paper, we introduce low probability of target detection (LPTD) as a new security objective for wireless sensing systems, where the existence of a target is concealed from passive wardens. Unlike conventional communication security and covert communication, LPTD aims to protect the sensed target itself from malicious detection, rather than concealing transmitted signals or communication activities. Specifically, we propose a reconfigurable-antenna-based reflector-assisted target covertness scheme, where passive reflectors deployed around the target are harnessed as artificial scattering sources to confuse the warden. By adjusting their reflection coefficients, these reflectors reshape the local scattering environment and mask the target-induced echo. We then analyze the warden's optimal detection strategy and derive the false-alarm and missed-detection probabilities, followed by a closed-form expression for its minimum total detection error probability. Numerical results validate the analysis and show that sufficient cooperation among reflectors can increase the warden's minimum total detection error probability to approximately 0.94--0.96, making target detection close to random guessing.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16099

CupOFMoCA: Coupled Objective-Guided Discrete Flows for Molecular Conjugate Assembly

Ruoxi Rosie Zhang, Ziang Li, Jiatao Gu, Pranam Chatterjee

原文摘要

Molecular conjugates, including PROTACs and peptide-drug conjugates (PDCs), derive their function from the joint behavior of multiple coupled components, yet most generative approaches design these components independently and combine them only after generation. Such staged pipelines ignore cross-component dependencies and often produce conjugates that are chemically invalid, fall outside empirical conjugate distributions, or lose function upon assembly. In this work, we introduce Coupled Objective-Guided Discrete Flows for Molecular Conjugate Assembly (CupOFMoCA), a discrete generative method that enforces cross-component chemical constraints during conjugate generation. CupOFMoCA restricts guided updates to chemically feasible conjugate states and reweights local transitions using molecular design objectives. Across three PDC systems, coupled generation achieved 100% conjugation validity among valid peptides and increased valid yield over matched post-hoc filtering, with comparable predicted binding scores across the full valid sets. In three-seed evaluations of three PROTAC systems, CupOFMoCA achieved the highest assembly validity on two systems and generated more assembly-valid molecules per second than the evaluated baselines. We further evaluated the generated PROTACs with post-hoc DegradeMaster scoring and explicit-solvent molecular dynamics, supporting their predicted activity and conformational properties. Together, these results establish CupOFMoCA as an efficient generator of molecular conjugates, supporting therapeutic development via coordinated control of assembly and molecular properties.

q-bio.BM · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16080

TGen-SQD: Physics-Informed Transferable Configuration Generator for Sample-Based Quantum Diagonalization

Niloy Deb Roy Mishu, Lei Jiang, Mohsen Heidari, Ruizhe Zhang, Fan Chen

原文摘要

Sample-based quantum diagonalization (SQD) avoids iterative variational circuit optimization, but its accuracy and cost depend on selecting a compact determinant subspace that captures the ground state. Existing machine-learning selectors use task-specific physical cues and typically retrain for each Hamiltonian. We introduce TGen-SQD, a physics-informed generator jointly trained across multiple Hamiltonians. It learns from configuration-interaction-weighted data and uses orbital- and system-level descriptors to guide generation. After training, its weights remain frozen for inference on seen and zero-shot geometries. On a 20-qubit H10 chain, TGen-SQD achieves mean energy errors of 40.18 +/- 1.52 and 43.97 +/- 1.20 mHa on seen and unseen geometries, respectively, 12.7% and 11.7% lower than the state-of-the-art PIGen-SQD baseline under matched shot and proposal budgets.

quant-ph · cs.ET · physics.chem-ph

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.25246

FrontierOR: Benchmarking LLMs' Capacity for Efficient Algorithm Design in Large-Scale Optimization

Minwei Kong, Chonghe Jiang, Ao Qu, Wenbin Ouyang, Zhaoming Zeng, Xiaotong Guo, Zhekai Li, Junyi Li 等 27 位作者

原文摘要

Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines. Existing benchmarks are limited to small or simplified examples far below real-world scale and complexity. We introduce FrontierOR, the first literature-grounded benchmark targeting LLM-generated algorithm efficiency on realistic large-scale optimization problems. FrontierOR includes 180 diverse tasks, each derived from a paper published in a top-tier operations research venue, with standardized instances and a hidden, expert-verified evaluation suite. We evaluate recent LLMs' one-shot algorithm generation and improvements achieved through several self-evolving frameworks. The results show that current models still struggle to move from executable formulations to efficient optimization algorithms. Even the strongest LLM succeeds on only 39% of Full-set task instances, where success is defined as returning a feasible solution with an objective gap of at most 1% relative to Gurobi within its runtime. On the Hard subset, test-time evolution raises this proportion from 51% for the strongest one-shot model to 78%. FrontierOR provides a continuously maintained testbed for evaluating LLM-designed optimization algorithms under a shared protocol and controlled computational settings. Code and data are publicly released at https://github.com/Minw913/FrontierOR.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2602.09009

ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling

Yilang Zhang, Bingcong Li, Niao He, Georgios B. Giannakis

原文摘要

Scaling network depth has been a central driver behind the success of modern foundation models, yet recent investigations suggest that deep layers are often underutilized. This paper revisits the default mechanism for deepening neural networks, namely residual connections, from an optimization perspective. Rigorous analysis proves that the layout of residual connections can fundamentally shape convergence behavior, and even induces an exponential gap in convergence rates. Prompted by this insight, we introduce adaptive neural connection reassignment (ANCRe), a principled and lightweight framework that parameterizes and learns residual connectivities from the data. ANCRe adaptively reassigns residual connections with negligible computational and memory overhead ($<1\%$), while enabling more effective utilization of network depth. Extensive numerical tests across pre-training of large language models, diffusion models, and deep ResNets demonstrate consistently accelerated convergence, boosted performance, and enhanced depth efficiency over conventional residual connections.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13223

Deletion Sensitivity Is a Property of the Model: A Three-Condition Measurement across Seven Open Reasoning Models

Hanzhang Jia, Liheng Zeng, Zhiyu Yang, Yi Gao, Bo Ma

原文摘要

Reasoning models write a reasoning trace at every turn of a multi-turn tool session, and later turns read this history. Across 94k ShareGPT conversations, 73 percent of user interactions span multiple turns. Upstream providers bind each trace to its issuer, so a request forwarded across issuers arrives with its history deleted, and the task cost of this stripping remains unmeasured. To the best of our knowledge, this paper presents the first direct, blind, multi-seed measurement of full reasoning-history deletion in multi-turn tool calling, through three rendering conditions, nine open-weight reasoning models, three seeds, 794 tasks, and the official checker. The same deletion spans -3.9 to +51.3 points with both ends excluding zero, and for the one model re-dosed from 0 to 100 percent deletion, the reading varies by at most 1.5 points. Deletion sensitivity is a model-level property. The reading follows the model itself and stays constant across deployments, and a retrained or re-released model is a new model that a two-cell screen, one retention run and one deletion run, re-reads for the cost of two runs. Deletion sensitivity is thus a measurable property of the model, and the reading must enter routing and aggregation as prior information rather than persist as an invisible default cost.

cs.CL · cs.AI · cs.SE

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arXiv ↗PDF ↗