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新论文 · v1arXiv:2610.36687

The Exact Weight Spectrum of $\RM(m-7,m$): A Counterexample to the Carlet-Solé Conjecture

Yueying Lou, Qichun Wang

原文摘要

Boolean functions are widely used in cryptography and coding theory. A key challenge lies in finding the weight spectrum for all $n$-variable Boolean functions of degree at most $r$, also known as the Reed--Muller code $\RM(r,n)$. In 2023, Carlet and Solé determined the weight spectra of $\RM(n-c,n)$ for $c=3,4$, and conjectured the weight spectrum of $\RM(n-c, n$) for $n\ge2c$. The weight spectra of $\RM(n-5, n)$ and $\RM(n-6,n)$ were determined by Carlet in 2024 and by Lou and Wang in 2025, respectively, confirming the Carlet-Solé conjecture for the cases $c=5,6$. Very recently, Leuenberger et al. and Wei et al. investigated the weight spectrum of $\RM(7,14)$ independently, and there are eight potential weights which have not been determined. Up until now, the weight spectra of the Reed--Muller codes $RM(r,n)$ are still unknown for $r=3,...,n-7$. In this paper, we determine the exact weight spectrum of $\RM(m-7, m)$ for every $m\ge 14$. By combining affine geometry and Fourier analysis, we exclude the conjectured eight weights for $\RM(7,14)$. We then extend the exclusions to all higher dimensions using a geometric transfer. Consequently, we provide a counterexample to the Carlet-Solé conjecture.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.32732

Plan-to-Synthesis: Cross-City Human Mobility Generation via Semantic Latent Flow Matching

Zhoufu Wang, Baoshen Guo, Zhiqing Hong, Junyi Li, Kailai Sun, Heye Huang, Alok Prakash, Shenhao Wang 等 9 位作者

原文摘要

Human mobility generation aims to synthesize realistic point-of-interest (POI) visitation trajectories and has become an important tool for travel behavior modeling, transportation management, and urban planning. Existing diffusion-based methods achieve high fidelity but require per-city generation, given the inherent heterogeneity of geospatial locations and POI categories, while large language model-based methods generalize across cities but remain too costly at scale, especially for long-horizon trajectory generation. To address this, we propose SeMoFlow, a Semantic human Mobility generation framework based on latent Flow matching. We first encode heterogeneous POIs from different cities into a shared cross-city representation space via hierarchical Semantic IDs, where shared prefixes capture transferable semantics, and successive codes progressively refine the representation toward individual POIs. Building on the semantic IDs, SeMoFlow follows a plan-to-synthesis hierarchical generation paradigm, in which an autoregressive planner generates coarse-grained semantic and recurrence patterns, and a flow matching realizer synthesizes fine-grained suffix latents. The generated latents are subsequently decoded and grounded to concrete POIs. Extensive experiments on large-scale multi-city datasets show that SeMoFlow achieves higher trajectory fidelity than existing baselines, preserves city-specific mobility motifs, and supports both joint multi-city generation and effective cross-city transfer. The code is available at \href{https://github.com/urban-mobility-generation/SeMoFlow}{https://github.com/urban-mobility-generation/SeMoFlow}.

cs.SI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36685

RoboSTD: Zero-Shot Single-to-Dual Transfer via Sagittal Mirroring for Bimanual Learning

Hongyu Wu, Chong Liu, Shaoxuan Xie, Shaqi Luo, Hanyu Feng, Shihan Wu, Guocai Yao

原文摘要

Bimanual robot manipulation plays an important role in real-world robotic applications, but the high cost of collecting bimanual demonstrations limits the scalable development of bimanual policy learning. Meanwhile, existing single-arm manipulation data are difficult to directly transfer to bimanual systems due to the Single-to-Dual gap in action spaces, coordination requirements, and arm-side spatial preference. To address this issue, we propose RoboSTD, a zero-shot Single-to-Dual data construction method for bimanual robot learning. RoboSTD first applies sagittal-plane mirroring to observations, proprioception, and actions to construct executable contralateral pseudo-demonstrations. It then uses an LLM to generate structured spatio-temporal coordination constraints that guide the reconstruction of pseudo-bimanual supervision across workspace regions and task stages. We evaluate RoboSTD on the AgileX ALOHA platform in both RoboTwin simulation and real-world settings. Experiments demonstrate the effectiveness of RoboSTD for bimanual policy learning. Without requiring additional bimanual demonstrations, RoboSTD increases the average number of collected cups to 2.11x that of the single-arm baseline in the cup collection task. Project page: https://huahungy.github.io/RoboSTD/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.19350

Pruning via Causal Attribution Preserves Reasoning Performance in Large Language Models

Amogh Sheth, Andrew Lin, Biruk Assefa, Yi Wen Huang, Yuhao Ge

原文摘要

Large language models (LLMs) excel at multi-step reasoning but incur substantial inference cost. We introduce Causal Attribution Pruning (CAP), a training-free method that identifies critical attention heads by measuring their causal impact on reasoning tasks and uses these head-level scores to guide fine-grained weight pruning. For each attention head, CAP estimates the expected performance degradation when the head is masked during forward passes on a small calibration set of reasoning problems. These causal scores are then converted into weight-level importance values for the corresponding projection matrices. Unlike magnitude-only or activation-based criteria, CAP's interventional measurement directly captures each head's functional contribution, yielding relative accuracy gains of up to 61% over Wanda on ARC-Challenge at 20% sparsity. We evaluate CAP on GSM8K, StrategyQA, and ARC-Challenge using Llama-3-8B-Instruct and Mistral-7B-Instruct at 10%, 20%, and 50% sparsity. At moderate sparsity (10-20%), CAP improves over Wanda in most model-benchmark configurations. with especially large gains on ARC-Challenge for Llama-3. Our results suggest that attention-head-level causal attribution can better preserve reasoning performance on downstream benchmarks than correlational pruning criteria at equivalent sparsity, while remaining limited by coarse MLP attribution at 50% sparsity.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.25435

Kom8ndor: An IEEE 802.11bn-Oriented Simulator for Wi-Fi 8 and Beyond

Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta

原文摘要

The upcoming IEEE 802.11bn amendment marks a paradigm shift in Wi-Fi, which will pose ambitious performance targets under the paradigm of Ultra-High Reliability (UHR). To understand the implications of such a new technology and to support early research and protocol design for Wi-Fi 8, we present Kom8ndor. This discrete-event network simulator extends the open-source Komondor platform (a simulator validated against ns-3 and other analytical tools) with 802.11bn features. Among the newly added functionalities, we highlight Multi-Access Point Coordination (MAPC)-including Coordinated Time-Division Multiple Access (Co-TDMA), Coordinated Spatial Reuse (Co-SR), and Coordinated Beamforming (Co-BF)-, Non-Primary Channel Access (NPCA), and Dynamic Subband Operation (DSO). Beyond Wi-Fi 8 implementations, Kom8ndor introduces novel functionalities (e.g., a machine learning wrapper for building AI-based protocols) and a modular design to boost the prototyping and research of future Wi-Fi technologies. Kom8ndor is open-source (GNU GPLv3) and available at https://github.com/wn-upf/Komondor.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36683

Feedback autocratic strategies in stochastic games

Masahiko Ueda

原文摘要

A stochastic game is an optimal control problem for multiple agents in a stochastic environment. Stochastic games contain important practical problems, related to environmental issues or traffic flows. If the number of environmental states is one, such stochastic games are called repeated games. In repeated games, several strategies which unilaterally control payoffs have been proposed, and these strategies work in a mechanism similar to feedback control. In this paper, we generalize these strategies as feedback strategies in stochastic games, and investigate their properties. In particular, we provide a necessary and sufficient condition for such feedback strategies to work as controllers of payoffs under some assumptions. We demonstrate the performance of such feedback strategies in several examples related to social dilemmas. Our findings connect the theory of payoff control in repeated games to the theory of feedback control in stochastic games.

cs.MA · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36682

An Empirical Study of Undisclosed Charges in Hook-Based Decentralized Exchanges

Kai Ma, Tianle Sun, Shun Zhou, Boyao Yang, Ningyu He, Haoyu Wang

原文摘要

Many decentralized exchanges use automated market makers (AMMs) to trade assets against liquidity pools. Modern AMMs allow third-party contracts, called Hooks, to customize this process. However, Hooks can impose additional deductions that fee breakdowns based on standard pool parameters do not itemize. We present, to the best of our knowledge, the first large-scale empirical study of the production Hook ecosystem, focusing on these undisclosed settlement charges. Our study covers 7,873 Hook deployments, 50,011 pools, and 296M pool events on Uniswap v4 and PancakeSwap Infinity across Ethereum and BNB Smart Chain (BSC) through August 1, 2026. We develop a bytecode analysis that reconstructs how Hooks change traders' payments and receipts, separating charges from exchanged assets and standard fees. We then reconstruct historical settlements to measure charging frequency and economic burden. We find that direct settlement adjustment is the dominant Hook capability, supported by 71.37% of registered Hooks. Charging capability is widespread and often exercised: 24.30% of Hooks can impose undisclosed deductions, and these deductions occur in 59.23% of accounted swaps involving such Hooks. These charges generate millions of dollars in revenue, with $3.97M recorded across Hooks with available token prices. These findings reveal a gap between standard fee displays and actual trading costs, motivating explicit disclosure of Hook deductions alongside standard fees.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.07853

Lost in the bf16 Cast: Exporting Ternary Language Models Can Revert Most Low-Learning-Rate Code Changes

Avichal Sahai, Nishant Raj, Animesh Srivastava

原文摘要

Ternary language models such as BitNet b1.58, Falcon-E and BitCPM are fine-tuned through higher-precision latents and deployed as ternary codes by an export that, in the labs' documented pipelines, first casts latents to bf16. We audit releases from three labs. In released checkpoints, fp32 quantization of the shipped latents disagrees with deployed codes on 0.83-1.77% of codes (Falcon-E, BitCPM) and 1.530% (2B-4T); most Falcon-E and BitCPM disagreements are bf16 products exactly on the threshold, which ties-to-even maps to zero, and the unmodified onebitllms exporter reproduces all four Falcon-E releases byte for byte. At fine-tuned endpoints with ULP-matched rates, the documented export lowers greedy GSM8K strict accuracy from 58.79% to 0.78% (Falcon-E-1B-Base) and 36.13% to 0.39% (BitCPM-CANN-0.5B), and a bf16 save and reload lowers 2B-4T's by 27.54 points while its last-number accuracy rises at that endpoint. The strict decrease recurs at each of three new seeds in all three models and has the same direction on 607 reserve test rows, but it is recipe-dependent: at Falcon-E's online-loss-selected rate the export was non-inferior, and after a longer recipe no difference was detected. Two remedies, writing the trained codes directly or adjusting bf16 inputs so unchanged tools emit them, met an inherited 4-point strict-accuracy non-inferiority criterion in all three models, also for the pooled mean over three fixed new trajectories per model and, separately, on the reserve rows. In a frozen study of bf16 latents in fine-tuning (one task, one trajectory per arm), native bf16 writes left 2B-4T's endpoint codes unchanged and changed one Falcon-E code; emulated stochastic-rounding and compensated writers changed codes and were non-inferior to fp32 training at that margin. In two model families, randomized threshold-distance interventions support distance-dependent selection of changed codes.

cs.LG · cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2502.09254

AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection

Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang

原文摘要

Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular and heterogeneous abnormality patterns in graphs from different domains. To address this challenge, we propose AnomalyGFM, a GAD-oriented graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD in diverse graph datasets. One key insight is that graph-agnostic representations for normal and abnormal classes are required to support effective zero/few-shot GAD across different graphs. Motivated by this, AnomalyGFM is pre-trained to align data-independent, learnable normal and abnormal class prototypes with node representation residuals (i.e., representation deviation of a node from its neighbors). The residual features essentially project the node information into a unified feature space where we can effectively measure the abnormality of nodes from different graphs in a consistent way. This provides a driving force for the learning of graph-agnostic, discriminative prototypes for the normal and abnormal classes, which can be used to enable zero-shot GAD on new graphs, including very large-scale graphs. If there are few-shot labeled normal nodes available in the new graphs, AnomalyGFM can further support prompt tuning to leverage these nodes for better adaptation. Comprehensive experiments on 11 widely-used GAD datasets with real anomalies, demonstrate that AnomalyGFM significantly outperforms state-of-the-art competing methods under both zero- and few-shot GAD settings.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37717

Learning Graph Beliefs for Active Radio Mapping With Decision-Certified Multi-Step Sensing

Wangqian Chen, Hao Wang, Yumeng Zhang, Chenghong Bian, Junting Chen, Jun Zhang

原文摘要

Radio maps provide essential wireless propagation knowledge for network optimization and autonomous platforms. Acquiring dense measurements, however, is prohibitively costly, requiring active sampling to prioritize informative measurements. Existing active sampling methods typically rely on variances from prescribed spatial models or learned pointwise uncertainty, which may not fully capture the spatial correlation of different measurements. This paper proposes GraphBARC, a graph-structured active radio map construction framework that integrates learned spatial modeling with analytical Bayesian inference. A graph network learns correlated node and edge factors using only acquired measurements, inducing a joint full-map belief that supports analytical observation updates without retraining. Rather than immediate one-step acquisition, it adopts a nonmyopic sensing strategy that evaluates joint map-wide Bayes risk reduction over feasible multi-step routes, accounting for information redundancy among planned measurements. We reuse candidate responses and derive residual-based bounds directly on route-value differences, retaining shared response-error structure to avoid uniformly high-accuracy evaluation. An error-aware continuation bound screens unpromising prefixes, after which only responses needed for unresolved comparisons are refined. We further establish a computable decision-gap bound and show that certified exclusions remain valid during refinement. Experiments show that GraphBARC reduces mean root-mean-square error (RMSE) by over 16% relative to the evaluated baselines and core planning time by 56% compared with the fixed-accuracy, unpruned implementation.

cs.NI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2609.11115

Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation

Koutian Wu, Wenjun Zhang, Junjie Zhou, Pengqian Han, Qiming Bao, Ergan Shang, Jiayu Wang, Junkai Wang 等 16 位作者

原文摘要

Benchmark researchers and developers of large language models (LLMs) and other AI systems need to find relevant evaluations, locate their benchmark datasets and code, and understand the settings behind reported scores. We present Benchmark Radar, a living database and search engine for retrieval and discovery of AI benchmarks, covering LLM evaluation, agentic and tool-use benchmarks, coding, reasoning, safety, and domain-specific evaluations. The system combines daily discovery of benchmark papers, repositories, datasets, and releases with a searchable benchmark catalog, mentions in model cards and technical reports, and score histories. It retains source identities and citations so readers can inspect candidate benchmarks and their evaluation evidence. Daily discovery draws on 37 sources: 13 direct connectors and 24 first-party research and engineering feeds. The catalog contains 1,283 source records drawn from 4 benchmark catalogs and 12,916 numeric observations on 790 records. We describe collection and retrieval, audit the full catalog, and examine benchmark saturation, adoption trends, and the limits of score comparisons. A worked example walks through a complete prior-art search, showing how to query the catalog and inspect benchmark evidence when designing a new evaluation. We release the web dashboard with a benchmark leaderboard, a Pareto frontier view of score against measured use, saturation and trend views, daily feeds, downloadable evidence, a command-line interface (CLI) for offline queries, and reproducible analysis.

cs.AI · cs.IR

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arXiv ↗PDF ↗
版本更新 · v8arXiv:2402.11887

Generative Semi-supervised Graph Anomaly Detection

Hezhe Qiao, Qingsong Wen, Xiaoli Li, Ee-Peng Lim, Guansong Pang

原文摘要

This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively explored unsupervised setting with a fully unlabeled graph. We reveal that having access to the normal nodes, even just a small percentage of normal nodes, helps enhance the detection performance of existing unsupervised GAD methods when they are adapted to the semi-supervised setting. However, their utilization of these normal nodes is limited. In this paper, we propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the normal nodes. The key idea is to generate pseudo anomaly nodes, referred to as 'outlier nodes', for providing effective negative node samples in training a discriminative one-class classifier. The main challenge here lies in the lack of ground truth information about real anomaly nodes. To address this challenge, GGAD is designed to leverage two important priors about the anomaly nodes -- asymmetric local affinity and egocentric closeness -- to generate reliable outlier nodes that assimilate anomaly nodes in both graph structure and feature representations. Comprehensive experiments on six real-world GAD datasets are performed to establish a benchmark for semi-supervised GAD and show that GGAD substantially outperforms state-of-the-art unsupervised and semi-supervised GAD methods with varying numbers of training normal nodes. Code will be made available at https://github.com/mala-lab/GGAD.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36679

Specialization, Diversity, and Scaling in Child-Scale Language Models

Steven Y. Feng, Michael C. Frank

原文摘要

Modern language models are trained on far more language than children experience, but we know much less about learning from small, naturalistic datasets. We train small GPT-2 models on transcripts of speech with children from dense home recordings (BabyView) and on synthetic child-directed dialogue (TinyDialogues), varying the amount, source, and diversity of training data. Models trained on one child's language environment predict that environment substantially better than other children's environments. Much of this specialization is lexical and distributional: a simple 5-gram model reproduces the cross-child pattern (r=.91). Training on data from multiple children clearly improves prediction of pooled held-out language relative to training on a single child's input without a similarly clear improvement on grammatical benchmarks. Across data sizes, naturalistic and synthetic training inputs also produce different grammatical and lexical-semantic patterns, with important differences between grammatical phenomena. Together, these results show that small-data language learning depends not only on how much language a model sees, but on which environments it comes from and how well that input matches what the model is asked to predict.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2308.01463

SemDiff: Binary Similarity Detection by Diffing Key-Semantics Graphs

Zian Liu, Xiaoxi Meng, Viet Vo, Siqi Ma, Chao Chen, Dongxi Liu, Jun Zhang, Shigang Liu 等 10 位作者

原文摘要

Binary similarity detection is a critical technique that has been applied in many real-world scenarios where source code is not available, e.g., bug search, malware analysis, and code plagiarism detection. Existing works are ineffective in detecting similar binaries in cases where different compiling optimizations, compilers, source code versions, or obfuscation are deployed. We observe that all the cases do not change a binary's key code behaviors although they significantly modify its syntax and structure. With this key observation, we extract a set of key instructions from a binary to capture its key code behaviors. By detecting the similarity between two binaries' key instructions, we can address well the ineffectiveness limitation of existing works. Specifically, we translate each extracted key instruction into a self-defined key expression, generating a key-semantics graph based on the binary's control flow. Each node in the key-semantics graph denotes a key instruction, and the node attribute is the key expression. To quantify the similarity between two given key-semantics graphs, we first serialize each graph into a sequence of key expressions by topological sort. Then, we tokenize and concatenate key expressions to generate token lists. We calculate the locality-sensitive hash value for all token lists and quantify their similarity. %We implement a prototype, called SemDiff, consisting of two modules: graph generation and graph diffing. The first module generates a pair of key-semantics graphs and the second module diffs the graphs. Our evaluation results show that overall, SemDiff outperforms state-of-the-art tools when detecting the similarity of binaries generated from different optimization levels, compilers, and obfuscations. SemDiff is also effective for library version search and finding similar vulnerabilities in firmware.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36674

Beyond the 1D Reality-Virtuality Continuum: A 2D Framework for Seven Extended Reality Paradigms

Rajashekhar Vachiravelu Saminathan

原文摘要

The Reality-Virtuality (RV) continuum helps in describing the relationship between physical and computer-generated environments. However, the increasing number of Extended Reality (XR) paradigms introduces new technologies. Their characteristics cannot be completely represented by a single real-virtual dimension. It can be noted that XR systems not only vary in their proportion of real and virtual content but also adds, suppresses, or transforms information available to the user. This work proposes a framework for characterizing seven XR realities, namely Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Augmented Virtuality (AV), Phenomenal Reality (PR), Annexing Reality (AnR), and Diminished Reality (DR). It uses a two-dimensional extension of the conventional 1D RV continuum. The first dimension helps in identification of the position of an XR technology between real and virtual environments. The second dimension captures information addition and subtraction. The proposed approach gives a qualitative basis for comparing existing and emerging XR paradigms within a unified XR framework.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36673

Atom as an Agent: Emergent Order by Collective Predictive Coding

Koji Hashimoto, Tadahiro Taniguchi

原文摘要

What is the intrinsic reason of emergence of macroscopic order parameter out of microscopic systems? In this article, we apply to physics the collective predictive coding (CPC), the theory of language/symbol emergence. As our language emerges out of our human communications, physical macroscopic order parameter emerges out of communications among atoms --- each atom is regarded as an agent who predicts each other's behavior through inference. We present three examples of classical spin systems: two-dimensional ferromagnetic and antiferromagnetic Ising models, a two-dimensional spin model with competing stripe orders, and the fully connected Sherrington--Kirkpatrick spin-glass model. In all of these examples, proper macroscopic order parameters including the replica overlap emerge automatically through communication informatics of CPC, only from observed spin configurations. We call the learned macroscopic coordinate the collective predictive order. Furthermore, for the ferromagnetic and antiferromagnetic Ising models, the equilibrium distribution of the collective predictive order yields an emergent potential with the symmetry and thermal evolution characteristic of a Landau potential. Notably, the emergence of the collective predictive order does not refer to any structure of microscopic Hamiltonians/Lagrangians. Thus our ``Atom-as-an-Agent" (AaaA) principle offers a completely data-driven method for the emergence of macroscopic order, that is, machine learning physics.

cs.AI · hep-th

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36672

Learning Where to Trust: Reliability-Guided Expert Routing and Velocity Gating for 3D Deformable Registration

Chenyu Guo, Wenjie Dai, Yiqian Chang, Zhiyuan Zhang, Yunfan Wei, Shuhan Qi

原文摘要

Deformable image registration relies on accurate anatomical correspondences. However, explicitly coupling local matching reliability with spatial context selection and deformation update strength remains insufficiently explored in learning-based registration. This challenge is particularly pronounced in homogeneous, weakly textured, and severely deformed regions, where ambiguous matches may induce erroneous deformation updates. We propose ReliMorph, a reliability-routed symmetric framework for 3D deformable registration that couples correspondence assessment with spatial context selection and update strength.Local correlation distributions characterize correspondence ambiguity using shared anatomical features with modality-specific adaptation. These reliability cues guide spatial expert fusion and gate residual velocities during coarse-to-fine refinement. Updated inverse-inconsistency feedback guides routing and gating at finer pyramid levels. To our knowledge, ReliMorph is the first 3D deformable registration framework to jointly condition spatial expert routing and residual velocity gating on correspondence reliability. Extensive experiments demonstrate that ReliMorph consistently outperforms competing methods in anatomical overlap, highlighting its effectiveness across unimodal and multimodal registration tasks.

cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36671HF 社区推荐

Look Only When Needed: Certificate-Guided Visual Routing for Efficient Diffusion VLMs

Donghyeon Cho, Joowon Kim, Seungho Shin, Eunho Yang

原文摘要

Diffusion vision-language models (DVLMs) process their entire input at every denoising step. Since visual tokens make up 89-97% of this input on the benchmarks we study, most of the decoding computation is spent repeatedly processing the same image. Visual token pruning methods partially relieve this burden by removing part of the visual tokens, but they lose the removed information and still process the remaining image at every step. To measure how often the image is actually needed, we compare LLaDA-V's predictions with and without the image at every decoding step and find that removing the image changes a token being unmasked at only 22.8% of steps on average and at as few as 7.9%. We therefore propose LOWN, a decoding method that decides at every step whether to process the image and how many tokens to unmask. LOWN predicts the masked tokens without the image and processes the image only when this prediction disagrees with the last image-conditioned prediction. Agreement certifies the earlier prediction for reuse, and the number of certified tokens sets how many are unmasked in that step. We further observe that the need for the image differs across benchmarks, so LOWN's adaptive decision is needed rather than a fixed schedule. Without training or a tuned threshold, LOWN retains 99.6% of LLaDA-V's score on ten benchmarks at 35.3% of the decoding FLOPs on average. LOWN also generalizes across LLaDA-based DVLMs, composes orthogonally with visual token pruning, and runs under key-value caching for further efficiency gains. Code and project page are available at https://hyeon9698.github.io/LOWN/

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36670

Design of DoF-Robust MIMO via Generalized Pinching Antennas

Zhiguo Ding, Robert Schober, H. Vincent Poor

原文摘要

Spatial degrees of freedom (DoF) are fundamental to the performance of multiple-input multiple-output (MIMO) systems. However, the use of conventional fixed-location antennas introduces disparities in the users' distances to a common base station, which makes the multi-user channel matrix more susceptible to rank deficiency and, consequently, a loss of spatial DoF. This paper shows that generalized pinching antennas can be used to design a new form of MIMO robust to the loss of spatial DoF. In particular, by deploying pinching antennas in close proximity to the users, the resulting multi-user channel Gram matrix can have a well-conditioned, almost diagonal structure, which reduces its sensitivity to the users' spatial deployment as well as the risk of rank deficiency. Two performance metrics, namely the rank-deficiency probability and the outage probability, are used to evaluate the performance of pinching-antenna-assisted MIMO transmission. The presented analytical and simulation results demonstrate the capability of pinching antennas to preserve spatial DoF and provide ubiquitous connectivity by effectively supporting users at spatially separated locations.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.20822

Interaction Effects Between Learner Characteristics and Dialogue Format in LLM- and TTS-Generated Dialogue-Based Lessons

Fumie Watanabe, Gendo Kumoi, Tota Suko, Takashi Ishida, Yuko Kuma, Manabu Kobayashi, Shigeichi Hirasawa

原文摘要

Large language models and text-to-speech technology enable low-cost production of dialogue-based lessons: videos in which two speakers converse. The speakers' roles can vary, but little is known about whether all learners respond alike to such differences. In a repeated-measures design, 222 first-year high school students viewed lessons in three formats: a teacher and a student (TS), two students (SS), and two teachers (TT). Within the aptitude-treatment interaction framework, linear mixed-effects models tested whether associations between learner characteristics and outcomes differed by format. The learner characteristics were experiential learning style - seeking challenging, new experiences (CE factor) and learning by reflecting on experience (RCE factor) - and critical thinking disposition; the outcomes were motivation based on the ARCS model, self-reported learning outcomes, and overall evaluation. For motivation, both factors interacted with format: the CE factor's association differed between TS and TT, and the RCE factor's slope was significantly positive in TS and SS and significantly larger in TS than TT. For learning outcomes, no interaction was detected, but critical thinking disposition was positively associated with them. For overall evaluation, again no interaction was detected, but TT was rated lowest. Given small-to-medium interaction effects, these findings are preliminary evidence to inform personalized learning design.

cs.CY

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新论文 · v1arXiv:2610.36669

Proof Repair and (Co)algebra Equivalences

E. G Plante, Eric Paul, Cosmo Viola, G. A. Kavvos, Talia Ringer

原文摘要

Maintaining formal proofs in response to breaking changes in their dependencies is a major challenge of proof engineering. Proof repair is a kind of proof automation that lessens the burden of this challenge by automatically adapting proofs in response to those changes. While proof repair is well understood through an algorithmic lens, it has, until now, been poorly understood through a theoretical lens. We address this gap by giving the first foundational treatment of proof repair, focusing in particular on type-theoretic proof repair in response to changes in datatypes. Our key insight is that the core component of a proof repair algorithm---a ``configuration''---is exactly an algebra isomorphism. We formalize and prove this insight, answering open questions from prior work. We then take this one step further and show that, by taking the dual of this representation, we obtain proof repair over coinductive types. All of our results are formalized in Agda.

cs.PL

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新论文 · v1arXiv:2610.36668

A Quantitative Approach to Asch Conformity in Frontier Large Language Models

Senthil Rajasekaran, Stylianos Loukas Vasileiou

原文摘要

The Asch conformity experiment has recently gained considerable attention as a way of measuring conformity in large language models (LLMs). While previous studies have focused on demonstrating conformity in LLMs, our work adds a further quantitative layer to this approach. Instead of sampling a frontier LLM on a suite of questions, our approach samples a single question repeatedly, leveraging the probabilistic nature of LLMs to produce a distribution of answers. In doing so, we can compare the distributions of answers before and after the model is presented with the manufactured consensus. Our experiments confirm that frontier LLMs do conform, reinforcing prior literature, and, through our distribution-to-distribution comparisons, provide additional quantitative data on the conditions that lead to conformity and the degree to which it occurs.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.32118

Beyond Correctness: How Novice Software Engineering Students Exercise Oversight of Generative AI

Ali Shakiba, Thomas Chaffey

原文摘要

Generative AI (GenAI) is shifting software engineering students' work from producing artefacts towards evaluating and supervising AI-generated outputs. However, little is known about what characteristics students identify in outputs when reporting interventions, or how they exercise oversight during authentic software engineering work. We analysed 962 student-AI interaction turns documented by 145 first-year software engineering students through structured AI Journals, including 172 student-declared interventions. Using a multi-dimensional coding framework, we characterised the output characteristics identified in these reported interventions, students' reasons for intervening, the actions they took, and how those actions were enacted. Fitness problems were identified in 74.4% of interventions, compared with 16.9% involving correctness problems, indicating that oversight more often concerned whether plausible AI outputs were suitable for the engineering task than whether they contained explicit errors. Among classifiable interventions, students most frequently modified generated outputs (51.0%) or refined their prompts (28.2%), with both response types commonly associated with engineering fit. Within-student analysis further showed that outputs perceived as containing minor flaws were associated with lower evaluation confidence than outputs perceived as perfect. Taken together, these findings characterise novice AI oversight as a process that extends beyond error detection to encompass both judgement of fitness for purpose and active intervention in AI-assisted engineering work.

cs.SE · cs.HC

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新论文 · v1arXiv:2610.36667

Japanese Discourse Structure Analysis Based on Segmented Discourse Representation Theory Using LLMs

Satomu Tomisaki, Yosuke Mikami, Amane Watahiki, Hitomi Yanaka

原文摘要

Discourse parsing based on Segmented Discourse Representation Theory (SDRT) is crucial for capturing the structural dynamics of dialogues. However, most prior research focuses on English, and its effectiveness for Japanese remains unclear owing to the lack of Japanese SDRT corpora. To address this issue, we construct Japanese SDRT corpora by translating existing English SDRT corpora using an LLM. Through a comparative evaluation of prompt-based and fine-tuned LLMs, we show that the models achieve comparable performance between English and Japanese, suggesting the cross-lingual applicability of SDRT-based discourse parsing. In order to investigate why the English and Japanese parsers achieve similar performance, we conduct ablation analyses on discourse markers and contextual inputs. Our results reveal that most of the Japanese discourse markers we examined have little impact, and the models' predictions depend heavily on contextual discourse relations rather than on the content of the context clauses. This structural reliance might explain the comparable performance despite the substantial typological differences between English and Japanese.

cs.CL

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新论文 · v1arXiv:2610.37716

Generation, Not Selection: LLM Proposals for Certified Access-Control Policy Mining

Gautam Kumar, Ravi Sundaram, Shamik Sural

原文摘要

Attribute-Based Access Control (ABAC) policy mining---recovering a compact, auditable policy from an access log---is posed as minimum set cover and solved greedily, which enjoys an $s^{*}(1+\ln m)$ guarantee. That guarantee is \emph{conditional} on a precondition the literature never states: the candidate pool handed to greedy must contain the optimal rules. Generation, not selection, is the binding constraint, and existing generators are purely \emph{syntactic}---they surface a rule only when enough log records witness it, so they fail on precisely the rare exception rules that fill a heavy-tailed policy. We replace the generator with a language model proposing rules \emph{semantically}, from attribute names, and keep the combinatorial selector to carry the guarantee: every proposal is filtered against the observed denials, so soundness is independent of the proposal mechanism and an unreliable proposer can only waste candidates, never grant access. Over five seeds on each of two enterprise domains, adding semantic proposal to a syntactic miner dominates it on \emph{ten of ten} runs---strictly better on seven---with near-optimal policies ($9.2$--$9.6$ rules against $s^{*}=9$, versus $11.2$--$14.6$) and false grants cut four- to sevenfold. Replacing the syntactic miner rather than augmenting it wins only where witnesses are scarce. An anonymization ablation isolates the mechanism: opaque attribute names cost two thirds of the model's rule recovery while leaving the syntactic baselines provably unmoved, and cost nothing where witnesses are plentiful. Semantic knowledge and log evidence are substitutes.

cs.CR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36666

Impossibility of No-Regret in Multi-Agent Bidding

Zongjun Yang, Rachitesh Kumar, Christian Kroer

原文摘要

We consider $T$ repeated second-price auctions across $n$ pacing bidders with i.i.d. item arrivals, and show that it is computationally impossible for all bidders to achieve no-regret simultaneously while satisfying their respective budget or return-on-investment constraints. Therefore, in the prominent internet auction systems where buyers simultaneously deploy pacing algorithms or automated bidding agents, we cannot hope to achieve joint no-regret guarantees in general. We establish a deterministic relationship that high variance in online bidding systems implies high regret. By showing that one can extract a pacing equilibrium from a low-variance system, we prove PPAD-hardness of attaining joint no-regret. For several widely used bidding algorithms, we also show evidence from dynamical systems and numerical simulations that they can indeed display high-variance behavior through cycling, even on simple market instances.

cs.GT · cs.CC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.12701

RPT*: Global Planning with Probabilistic Terminals for Target Search in Complex Environments

Yunpeng Lyu, Chao Cao, Ji Zhang, Howie Choset, Zhongqiang Ren

原文摘要

Routing problems such as Hamiltonian Path Problem (HPP), seek a path to visit all the vertices in a graph while minimizing the path cost. This paper studies a variant, HPP with Probabilistic Terminals (HPP-PT), where each vertex has a probability representing the likelihood that the robot's path terminates there, and the objective is to minimize the expected path cost. HPP-PT arises in target object search, where a mobile robot must visit all candidate locations to find an object, and prior knowledge of the object's location is expressed as vertex probabilities. While routing problems have been studied for decades, few of them consider uncertainty as required in this work. The challenge lies not only in optimality ordering the vertices, as in standard HPP, but also in handling history dependency: the expected path cost depends on the order in which vertices were previously visited. This makes many existing methods inefficient or inapplicable. To address the challenge, we propose a search-based approach RPT* with solution optimality guarantees, which leverages dynamic programming in a new state space to bypass the history dependency and novel heuristics to speed up the computation. Building on RPT*, we design a Hierarchical Autonomous Target Search (HATS) system that combines RPT* with either Bayesian filtering for lifelong target search with noisy sensors, or autonomous exploration to find targets in unknown environments. Experiments in both simulation and real robot show that our approach can naturally balance between exploitation and exploration, thereby finding targets more quickly on average than baseline methods.

cs.RO · cs.CG

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新论文 · v1arXiv:2610.36665

Haste Makes Waste? Towards Safe Few-Step Text-to-Image Models

Lior Ziv, Mahmood Sharif

原文摘要

While few-step distilled text-to-image models have gained traction due to their fast generation times, the speed-up implications on their unsafe generation rates remain unknown. We uncover an inherent tension between speed and safety: few-step models are often more unsafe than their teachers, and reducing the number of inference steps consistently raises unsafe generation rates. We further find that acceleration makes unsafe concepts harder to erase in few-step models, with existing concept-erasure methods markedly less effective than in teachers. We offer a mechanistic diagnosis for why erasure methods fail on few-step models, finding that the fragility of few-step models' generation process and the way generation guidance is usually distilled into those models may hinder established concept-erasure methods. To address the speed-safety trade-off, we propose DICE (Distillation-Integrated Concept Erasure), a post-distillation erasure method tailored for few-step models. DICE aligns both its latent construction and its generation-preservation loss with the distillation method that produced the model, keeping erasure faithful to few-step models. DICE demonstrates strong results in our evaluation. For example, on FLUX Schnell, DICE reduces unsafe-generation rate to 0.046 (vs. at least 0.161 for prior erasure methods we test), while mostly preserving benign generation quality.

cs.CV · cs.CR

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版本更新 · v2arXiv:2606.03217

An Asymptotic Theory of Chain-of-Thought in In-Context Learning

Kaito Takanami, Cengiz Pehlevan

原文摘要

Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermediate reasoning steps at inference time. Yet the scaling behavior of generalization with CoT depth remains poorly understood. To address this question, we study a theoretically solvable model of CoT for in-context weight prediction in linear regression, where test-time reasoning is represented as an iterative refinement of the weight-parameter estimate. Using tools from random matrix theory under high-dimensional asymptotics, we derive an exact formula for the generalization error as a function of reasoning depth, pretraining data amount, and context length. Our analysis reveals a sharp phase transition separating exponential and polynomial improvement, saturation, and overthinking, and characterizes how the optimal reasoning depth scales. We further show that deeper reasoning is most effective with sufficiently rich pretraining and in-context information, whereas limited pretraining or context makes longer reasoning prone to error amplification or saturation. We also validate these predictions through experiments on fully learned linear attention and softmax attention models. Our results provide a unified theoretical account of how test-time CoT depth affects generalization.

stat.ML · cs.LG

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版本更新 · v3arXiv:2512.16074

In-Context Multi-Operator Learning with DeepOSets

Shao-Ting Chiu, Aditya Nambiar, Ali Syed, Jonathan W. Siegel, Ulisses Braga-Neto

原文摘要

An important application of neural networks in scientific computing is non-linear operator learning. In this framework, a neural network is trained to fit a non-linear map between infinite dimensional spaces, for example, the solution operator of ordinary and partial differential equations. Recently, inspired by the discovery of in-context learning for large language models, an even more ambitious paradigm has been explored, called multi-operator learning. In this approach, a neural network is trained to learn many different operators at the same time. At inference time, the network is passed a small number of example input and output pairs to disambiguate the desired operator. In this work, we provide a precise mathematical formulation of the multi-operator learning problem, propose a parameter-efficient, attention-free architecture, called DeepOSets, and prove its universality in the in-context multi-operator learning task. We perform experiments demonstrating the efficacy of DeepOSets on several benchmarks, including Burgers' equation, 1D and 2D Darcy flow, and a variety of initial-value and boundary-value differential equations. In a comparison with the transformer-based ICON model on the 1D Darcy problem, under a small training budget (10k steps), DeepOSets attained lower error at every context size with 19% of the parameters, while with an extended budget (100k steps), ICON attains 26-41% lower error, but at the cost of roughly 218$\times$ more training time, 12$\times$ higher inference latency, and 5.3$\times$ more parameters.

cs.LG

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