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新论文 · v1arXiv:2611.15315

MIGRO: A Decoupled and Portable Long-Term Memory System for Language Models

Mingxi Zou, Yinghui Xu, Jiaxiang Chen, Langzhang Liang, Wei Zhu, Peng Ye, Runkai Zhao, Ruoxi Jiang 等 13 位作者

原文摘要

Language models evolve, but the observations, revisions, and context accumulated through user interactions should remain usable. Each new model must be able to retrieve past evidence, track revisions, and add new observations. We present MIGRO, a decoupled long-term memory system that treats experience as a persistent state shared across successive language models. MIGRO follows a simple principle: preserve historical evidence exactly, organize it approximately, and decide what to retrieve only when the question is known. We call this asymmetric fidelity with deferred selectivity. A shared semantic index supports retrieval, while exact records retain observations and their historical relationships. Because models differ in vocabulary and tokenization, host-specific interfaces convert selected evidence into inputs the receiving model can use. Reversible encoding reduces archival storage while enabling exact recovery. The shared state and its access interfaces let successive hosts read and extend the accumulated experience. On matched LoCoMo states, MIGRO scores 7.3% higher and uses 99.3% less private memory than the strongest quality baseline. After handoff, continued writing retains 99.9% of full-history rebuilding quality by extending the inherited state. On million-token BEAM histories, MIGRO uses 57.5% less private storage on average than the most compact baseline. MIGRO combines high answer quality, continued memory growth across hosts, and low storage cost. Code is available at https://github.com/zOUMINGXI/MIGRO.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15314

AcrossPlace: Do Vision--Language Models Recognize Places Beyond Their Source Views? A Comprehensive Benchmark

Zhenyu Li, Tianyi Shang, Shuhan Dong, Ruotong Xu, Qingyu Zhu

原文摘要

Language-guided place recognition requires recovering geographic identity across visual observations beyond the image from which a description originates. However, aggregate retrieval scores often entangle observation transfer, gallery competition, and linguistic evidence, obscuring the underlying recognition capability. We introduce AcrossPlace, a provenance-aware benchmark establishing a systematic evaluation standard for bidirectional language--vision place recognition. Built upon GSV-Cities, AcrossPlace comprises 56,726 places across 23 cities, 156,800 automatically generated, rule-filtered descriptions, and 113,404 target images. Each description retains its source-image provenance, while retrieval operates on disjoint target observations. Text-to-image (T2I) and image-to-text (I2T) retrieval share a unified place-identity relevance criterion supporting multiple valid matches. Two geographic protocols evaluate unseen places within represented cities (P0) and generalization to held-out cities (P1). Paired observation comparisons, gallery-controlled evaluations, fixed-query scaling, and blinded annotation auditing systematically characterize retrieval performance. Across three training seeds, place-supervised SigLIP2 T2I Recall@1 decreases from 19.69% on source images to 12.31% on independent targets under P0. For fixed P1 queries, expanding the gallery from 500 to 8,425 places reduces Recall@1 from 14.67% to 2.87%. A 240-description audit further reveals discrepancies between automated acceptance and factual evidence. These findings establish AcrossPlace as a reproducible evaluation standard for assessing transferable place identity across visual observations and geographic environments.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.09203

Copies Are Not Confirmations: How News Duplication Fools LLM Judges and Continual Learners

Abdelkader Metales, Miriam Grabenheinrich, Aicha Metales

原文摘要

A language model that keeps learning from the news sees the same claim many times, but many reports are copies of one source: wire stories, content shared within a media group, syndication and press releases. We ask whether LLM judges and continual learners treat such copies as independent confirmations. In 200,000 English articles from CC-News, a near-duplicate detector identifies copy links to another outlet for 18.9% of the articles, a detector-based lower bound. In a controlled experiment with claims about invented people, three identical reprints instead of one report raised the lies accepted by Llama 3.2 from 0.06 to 0.71 and by phi4 from 0.21 to 0.90; Gemini 2.5 Flash already believed a single report, and Gemini 2.5 Pro followed provenance information most consistently. In a simulated continual-learning stream, showing the judge one report per detected copy chain cut the copied lies accepted by 0.10-0.24 across four judges, at a mean cost of at most 4.2 points of accepted truths. Text similarity links none of the copies reworded by three LLMs. An entity-novelty test, the share of a report's named entities absent from an earlier look-alike, separates these rewrites from 100 author-labelled independent reports of the same event (AUC 0.98-0.99), statistically indistinguishable from TF-IDF and the best tested embedding model, but recognises only 17% of natural same-source derivatives. With both detectors run on 15,084 real articles carrying inserted claims, one report per chain cut the lies accepted by phi4 from 0.57-0.67 to 0.09 (copies), 0.14 (rewrites) and 0.34 (rewrites with invented details), and by Llama 3.2 from 0.56-0.62 to 0.07, 0.09 and 0.25, at a cost of 2.4 and 10 points of accepted truths. Counting reports or chains without a judge is unreliable, and colluding sources that do not copy each other remain an open problem.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15313

Influence of Assortativity on the SICP Spreading Process in Hypergraphs

Jelmer Stolwijk, Renze Dijkhuizen, H. A., Peters, Huijuan Wang

原文摘要

Many real-world systems involve group interactions that cannot be captured by pairwise graphs. They can be better represented by hypergraphs where each hyperedge could connect an arbitrary number of nodes. The Susceptible-Infected Contact Process (SICP) has been proposed to model the spreading of information and epidemics on hypergraphs. However, how higher-order topological properties of a hypergraph influence the SICP spreading process remains understudied. In this paper, we examine the impact of hypergraph assortativity based on the Neighborhood Coefficient (NC)-the average size of hyperedges incident to a node-on the SICP spreading process. By introducing a low-complexity measure of hypergraph assortativity grounded in analysis of variance, we apply hyperdegree-preserving rewirings to real-world networks and synthetic hypergraph models to tune their assortativity. We find and explain why lower NC assortativity yields higher average epidemic prevalence and lower prevalence variance across different seed selections. This phenomenon is evident when hyperdegree-preserving rewirings are applied to real-world networks with heterogeneous topological structures, but it is largely absent when comparing synthetic random hypergraphs to their rewired counterparts.

cs.SI · physics.soc-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15311

Arbor: When Labels Are Not Coarsened at Random

Faisal Mohammad, Mohamed Younis

原文摘要

Merging datasets labelled at different levels of a taxonomy leaves some images with only a coarse label, and some classes with no fine-grained label at all. Standard training scores a coarse label by the total probability of the classes under it. Coarse labels name superclasses, and the standard objective is exact only when every class within a superclass is equally likely to receive a coarse label. We call a class novel if it is never labelled finely, and known otherwise. A novel class violates the assumption, and the classifier assigns its images to a known sibling. We propose Arbor, which models how labels become coarse. In each superclass with a novel class, Arbor adds a virtual novel class, and a logistic gate divides the superclass's probability between it and the known classes. The gate is trained with the likelihood of the observed labels under this model, the coarsening likelihood. We prove that at its population optimum the gate equals the Elkan-Noto correction of positive-unlabelled learning, and show that training the correction into the classifier improves on applying it after training. Across Cars, CUB and Aircraft with 90% coarse labels, Arbor correctly assigns 76-85% of novel-class images to their superclass, more than hierarchical classifiers, hierarchical novelty detection and the Elkan-Noto correction. Its top-1 accuracy over the leaf classes on known-class images is within 1.6 points of the best baseline, although its final predictions return the superclass more often. On the more challenging iNaturalist-2019 benchmark, Arbor improves novel-class accuracy from 46% (uniform target, the strongest baseline) to 68%.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15310

Technical Debt Risks in AI-Assisted Software Development: Insights from Practitioners

Nazia Jahan, Nafisa Alam, Sourov Debnath, Arafat Rahman, S. M. Waesh Ahmed, Md Farhad Alam Bhuiyan, Mia Mohammad Imran

原文摘要

AI coding assistants increasingly support software development, raising concerns about technical debt associated with AI-generated code and development practices. We investigate these concerns through a sequential mixed-methods study involving 137 survey respondents and 20 follow-up interviews with software professionals in Bangladesh. Drawing on ten established technical debt dimensions, we examine perceived risks, contributing circumstances, maintenance consequences, validation practices, and developer understanding. Respondents expressed the strongest concerns about People Debt (73.0\%), Management Debt (70.1\%), and Architecture Debt (68.6\%), suggesting that risks extend beyond code quality. Participants associated ambiguous requirements, insufficient project context, and unrequested AI-generated changes with architectural inconsistencies, rework, and maintenance difficulties. Their experiences revealed contrasting outcomes: AI assistance supported refactoring, maintainability improvements, and testing, but also introduced rework and context misalignment. Although 89.8\% reported regularly verifying AI-generated code, participants expressed concerns about AI dependence, reduced code understanding, and review effort. They also emphasized the need for stronger architectural awareness, requirements clarification, and validation support in coding assistants. Our findings suggest that managing technical debt risks requires attention to project context, implementation decisions, and developer understanding alongside generated code quality.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15309

Where Autonomy Belongs: A Separation of Powers for Agentic Program Evolution

Zhengyang Ai, Tong Liu, Jingxian Tang, Xiaobing Du, Tao Xiao, Fengshi Zhang, Zishuo Li, Xiaodong Ai 等 9 位作者

原文摘要

LLM-driven program evolution rests on three decisions: govern directs the search from accumulated evidence, propose constructs the next candidate, and judge settles validity and fitness. All three appear in every system, and existing work splits into two lines over who makes each. Controller-centric systems strengthen govern and neglect the autonomy of propose, which caps what evolution can reach. Agent-centric systems let a coding agent take over both govern and propose, at a cognitive and computational cost. Neither line protects the independent authority of judge, so benchmark reliability suffers. These failures share one root: autonomy is misallocated across the three decisions. We address this problem with EvoTrias, a separation-of-powers architecture in which each decision has one owner: Structured Governance for what spans evolution rounds, Open Proposal for construction inside a task, and Protected Judgment behind a private evaluator. These owners exchange typed contracts only, and runtime hooks reinforce the boundaries. This separation also lets EvoTrias run as a plugin that turns an existing coding agent into an evolution system. EvoTrias sets a new state of the art on mathematical, systems, and algorithm-design benchmarks. Three further results show what each owner buys: separating Governance from Proposal cuts token cost by 64%, Open Proposal extends evolution to a 680K-line industrial repository, and Protected Judgment keeps benchmark scores faithful to transfer.

cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.13961

MarsRetrieval: Benchmarking Vision-Language Models for Planetary-Scale Geospatial Retrieval on Mars

Shuoyuan Wang, Yiran Wang, Hongxin Wei

原文摘要

Data-driven approaches like deep learning are rapidly advancing planetary science, particularly in Mars exploration. Despite recent progress, most existing benchmarks remain confined to closed-set supervised visual tasks and do not support text-guided retrieval for geospatial discovery. We introduce MarsRetrieval, a retrieval benchmark for evaluating vision-language models for Martian geospatial discovery. MarsRetrieval includes three tasks: (1) paired image-text retrieval, (2) landform retrieval, and (3) global geo-localization, covering multiple spatial scales and diverse geomorphic origins. We propose a unified retrieval-centric protocol to benchmark multimodal embedding architectures, including contrastive dual-tower encoders and generative vision-language models. Our evaluation shows MarsRetrieval is challenging: even strong foundation models often fail to capture domain-specific geomorphic distinctions. We further show that domain-specific fine-tuning is critical for generalizable geospatial discovery in planetary settings. Our code is available at https://github.com/ml-stat-Sustech/MarsRetrieval

cs.CV · astro-ph.IM · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15307

Floating-point, exact and certified computation for control: paradigms, pitfalls, and how to combine them

Christina Katsamaki, Fabrice Rouillier

原文摘要

Many questions in linear systems theory have a discrete answer: is a polynomial Hurwitz, is a 2-D system structurally stable, is the peak gain finite, which solution of a Riccati equation is stabilizing? Algorithms implemented in floating point usually answer such questions, but often with a limited, or even no, guarantee on the result, which depends on a tolerance fixed a priori. Computer algebra and interval arithmetic provide other ways of computing, which give guarantees on the result, but usually at a higher cost. In this paper, we propose a short guide to these approaches: floating-point, exact, multiprecision and interval arithmetic, certified root isolation and certified Newton methods. Using small examples from control theory (each of them runs in a few seconds), we show what each approach guarantees and in which situations it fails, and how several approaches can be combined, for example by certifying a numerical solution, or by reducing a problem exactly before solving it with certified real solving. We then revisit the structural stability of 2-D systems (with and without parameters) and the computation of the $H_\infty$ norm, and we list the available implementations. The main examples are given as reproducible worksheets based on the PACE ecosystem.

cs.SC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15306

Beyond the Explicit: Benchmarking and Detecting Implicit Harm in Short Videos

Chenyang Li, Qifan Yu, Saihui Hou, Zhaofeng He

原文摘要

Short videos have become a dominant online medium, where harmful content is increasingly conveyed in implicit and fragmented forms. Prior work largely focuses on explicit categories, such as violence and hate speech, and often fails to detect videos that subtly promote misleading values through narrative context, emotional framing, or cross-modal cues, partly due to the lack of dedicated benchmarks for implicit harm. To bridge this gap, we construct DeepHarm-7K, a large-scale harmful short-video dataset comprising 7,110 samples annotated according to a fine-grained harmful-content taxonomy. The dataset systematically covers implicitly harmful videos across diverse real-world scenarios and is constructed through a rigorous multi-stage quality-control process. Building on this dataset, we propose DeepHarm-VL, a multimodal detection framework that integrates visual, audio, and cross-modal reasoning. The framework employs a two-round reasoning strategy to progressively identify surface-level evidence and infer latent harmful implications. Experimental results show consistent improvements over strong baselines and state-of-the-art methods across multiple open-source video-language models, demonstrating its effectiveness in detecting both explicit and implicit harmful short videos.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2412.19551

Boolean combinations of graphs

Sarosh Adenwalla, Samuel Braunfeld, John Sylvester, Viktor Zamaraev

原文摘要

Boolean combinations allow combining given combinatorial objects to obtain new, potentially more complicated, objects. In this paper, we initiate a systematic study of this idea applied to graphs. In order to understand expressive power and limitations of boolean combinations in this context, we investigate how they affect different combinatorial and structural properties of graphs, in particular $χ$-boundedness, as well as characterize the structure of boolean combinations of graphs from various classes.

math.CO · cs.DM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15302

Browser Immune System (BIS): Innate and Adaptive Threat Detection in the Browser with a Cybersecurity Transformer

Víctor Mayoral-Vilches, Eduardo Worrel

原文摘要

Security decisions about a web page are best made where the page is: in the browser, before anything leaves the machine. We present the Browser Immune System (BIS), a browser extension that screens navigation with a cybersecurity transformer running entirely on the user's GPU, and alias-web, the model that powers it: a 4B-parameter model that starts from the dual-regime cybersecurity transformer (CT0), extends its public release alias3-micro with a context-category sensor, and is quantised to 4 bits and compiled to a 2.44 GB graph for WebGPU. alias-web is the first in-browser realisation of that model's typed decision mode, System 1, with its official pieces: the marker-token mapping, the complete pointer head and the final normalisation that the exported graph omits. It answers a typed security question in about 150 ms on a laptop GPU, and the same weights generate explanations at 10.5 tokens/s on demand. BIS treats the model as an immune system: an innate response, one System 1 question per threat class, runs on every page; an adaptive response, System 2 text, explains a verdict only when the user asks. On BISBENCH-8, eight synthetic sites of which six execute real but harmless malicious behaviour and two are adversarial controls, BIS returns the expected page verdict on 7/8 sites with both controls green and a class-level micro-F1 of 0.71, scans a page in 0.9 to 6.4 s, and gives identical verdicts across samples and across Chrome and Firefox. Single-variable diagnostics trace both benchmark errors to what the model was shown and, with a first pass over eleven real public pages, show that two of the seven questions answer yes on ordinary scripts and login forms: specificity on the real web is the open problem. We report every error, the engineering obstacles between a research checkpoint and a working extension, and what the evidence does not yet support.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2504.16771

Projective Variety Recovery from Unknown Linear Projections

Yirmeyahu Kaminski

原文摘要

We study how a smooth irreducible variety $X$ of dimension $n$ and degree $d$ in $\mathbb{P}^m$, $m\geq n+2$, can be recovered from the equations of its images under two generic projections from unknown centers. This extends earlier results on curves in $\mathbb{P}^3$ and surfaces in $\mathbb{P}^4$ to any dimension. Since $X$ is the normalization of each image, one image determines $X$ modulo the projective group when $X$ is linearly normal; what two images add is the configuration, that is the pair of projections. For complete intersections we prove unconditionally that the set of configurations compatible with the two images and their Hilbert polynomial is finite, of size at most $|\mathrm{Lin}(X)|$, the projective automorphism group of $X$. The argument is effective and the bound is attained. We then introduce an equation generalizing Kruppa's equation, relating the duals of the images to a fundamental matrix, encoding the pair of projections modulo the projective group. We give a necessary condition for its solution variety to be finite, a bound on its isolated points, and a sufficient condition under which the true fundamental matrix is rigid: no quadric may contain a generic line $L$ together with the locus of points of $X$ whose tangent space meets $L$. Rigidity is unconditional for curves; in higher dimension it holds under a first-order liftability hypothesis, which we certify for a del Pezzo quartic surface in $\mathbb{P}^4$. Global finiteness of that solution variety remains open, necessarily so, as it contains solutions realized by no variety; for $\dim X<\mathrm{codim}\,X-1$ the obstruction amounts to knowing the fundamental matrix itself. Once the projections are known, $X$ is recovered from the two cones over the images: in codimension two their intersection is $X$ plus a component of degree $d(d-1)$; in higher codimension $X$ is its unique component of maximal dimension.

math.AG · cs.SC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15300

Sharp Covering-Number Bounds for Feedforward ReLU Networks via Parameter Symmetries

Ruizhi Deng, Guohao Shen, Tangrui Xiong

原文摘要

In neural networks, distinct parameter configurations often realize the same input--output mapping, indicating that nominal parameter counts can overstate functional complexity. In this paper, we characterize the parameter-space symmetries of ReLU and Leaky ReLU networks across fully connected, convolutional, and residual architectures. We prove that every quasianalytic parameter map preserving the network function is a composition of positive channel scaling and channel permutation. By quotienting out both symmetries, we derive uniform covering-number upper bounds in which positive scaling reduces the complexity exponent from the total parameter count $S$ to $S-H$ (with $H$ denoting the number of scalable channels) and permutation symmetry divides the bound by the factorial count $G$ of channel permutations. We establish matching lower bounds showing that the exponent $S-H$ is sharp in general and that the divisor $G$ is attained for one-hidden-layer and generically identifiable networks. We further extend these results to general continuous piecewise-linear activated networks. Finally, we apply our covering-number bounds to derive statistical risk guarantees for empirical risk minimization and capacity control beyond i.i.d.\ settings, and show how the underlying symmetries help explain weight rebalancing in function approximation and flat directions in empirical risk optimization.

cs.LG · math.ST · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15299

Small classes have linear neighbourhood complexity

John Sylvester, Viktor Zamaraev, Maksim Zhukovskii

原文摘要

A hereditary graph class is small if it contains at most $n!c^n$ labelled graphs on $n$ vertices for some constant $c$. Small classes include proper minor-closed classes and classes of bounded twin-width. We prove that every small class has linear neighbourhood complexity, that is, in every graph in the class, vertex neighbourhoods intersect any $k$-vertex set in only $O(k)$ distinct ways. This resolves a conjecture of Bonnet, Duron, and the first two authors (ITCS 2025) and makes the algorithmic, structural, and representation consequences of linear neighbourhood complexity available for every small class. In particular, every small class admits an adjacency labelling scheme with $O(\log^2 n)$-bit labels, improving the previous $O(\log^3 n)$ bound.

math.CO · cs.DM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15298

Quantum Recurrent Architectures for Financial Time Series Prediction

Danyal Maheshwari, Gerhard Hellstern, Martin Zaefferer, Martin Braun, Tanja Döhler

原文摘要

This study evaluates quantum classical hybrid recurrent architectures for financial time-series forecasting: a Quantum Long Short-Term Memory (QLSTM) network and Quantum Reservoir Computing (QRC) with lag-based inputs. Classical lag vectors are encoded into quantum states via amplitude encoding, which compactly represents normalized observations but requires nontrivial state preparation; the QLSTM gates and the QRC reservoir are implemented with parameterized quantum circuits, while classical optimizers train the interface and readout layers. On a monthly revenue series extended to 1056 months by a Gaussian-process--hidden-Markov generator, with a fixed chronological 70/15/15 split and one-step-ahead forecasts, we compare both models against a full-size LSTM, a classical control matched in parameter budget and interface architecture, a small plain LSTM, and classical reservoir computing, over ten seeds and against persistence, seasonal-naive and SARIMA benchmark forecasts. The QLSTM, with 2,105 parameters, is statistically indistinguishable from an LSTM 126 times larger, but equally indistinguishable from the matched classical control and from a plain LSTM of the same budget, so its parameter efficiency cannot be attributed to the quantum circuits. The quantum reservoir is worse than a classical reservoir of equal feature dimension in all ten seeds and worse than SARIMA, whereas all recurrent models and both classical reservoirs improve on SARIMA by about 2\% in RMSE. In a 24-month rolling-origin backtest the classical LSTMs degrade more slowly than the QLSTM. We find no evidence of a quantum accuracy or parameter-efficiency advantage in this low-dimensional setting.

quant-ph · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15296

APG-Planner: Learning Vision-Based High-Speed Trajectory Planning via Analytic Policy Gradient

Jinwoo Lee, Geunsik Bae, Changseung Kim, Jiwoo Kim, Woojae Shin, Hyondong Oh

原文摘要

Vision-based high-speed navigation requires a trajectory planner that can reactively generate smooth, collision-free trajectories from onboard visual observations. Deep reinforcement learning offers a promising approach to vision-based trajectory planning, as it enables the optimization of sequential decision-making policies in complex environments. However, model-free policy gradient methods rely on sample-based policy gradient estimation, which yields noisy gradient estimates and inefficient training. To this end, we propose a framework for learning trajectory planning policies using the analytic policy gradient (APG) method and use it to train APG-Planner, a vision-based high-speed trajectory planner. The proposed framework leverages differential flatness to analytically reconstruct vehicle states from planned trajectories, thereby enabling differentiable trajectory-level state transitions without requiring an explicit low-level dynamics model. By iteratively performing trajectory planning and differentiable state transitions, we construct a differentiable episode rollout and analytically compute policy gradients from episode-level losses, improving both training efficiency and policy performance. After approximately 30 minutes of training on a single GPU, APG-Planner demonstrates comparable or superior performance to existing baseline methods in simulation and achieves speeds of up to 11\,m/s in real-world flight experiments. We further validate the proposed framework on a skid-steer ground robot, demonstrating its applicability to differentially flat robotic systems beyond quadrotors.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15291

Learning Rate Transfer and Feature Learning Across Depth for Constrained Spectral Optimizers: Complete Scion

Eline Pot, Laurent Lefebvre, Mathieu Segond, Antonio Silveti-Falls

原文摘要

Hyperparameter transfer across model depth is instrumental in training deep models, as it enables learning rates calibrated on shallow networks to be effectively applied to deeper architectures. The recent CompleteP framework establishes a $1/L$ residual scaling rule that demonstrates this for Adam-family optimizers. We study whether similar transfer extends to constrained optimization methods, specifically Frank-Wolfe and projected non-Euclidean normalized steepest descent, where updates are determined by linear minimization oracles. We derive scaling rules for these methods which confirm that the $1/L$ residual scaling applies, although the arguments differ from the Adam-family optimizers. Our analysis supports depth-wise transfer and width-wise transfer of the learning rate for the Scion algorithm while ensuring feature learning and avoiding the kernel-regime. In experiments on NLP transformers (nanoGPT on FineWeb), vision transformers (DeiT on ImageNet), and vision MLPs (MLP-Mixer on CIFAR-10), we observe learning rate transfer across depth and feature learning for appropriately chosen residual scaling.

cs.LG · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.15729

Constructing Generalizable Parallel Algorithm Portfolios for Multi-Objective Binary Optimization via General-Purpose Instance Generation

Zhiyuan Wang, Shengcai Liu, Shaofeng Zhang, Ke Tang

原文摘要

Despite progress in constructing generalizable parallel algorithm portfolios~(PAPs), no general-purpose method is yet available for multi-objective binary optimization problems~(MOBOPs) without problem-specific customization. To fill this gap, this work proposes domain-agnostic co-evolution of parameterized search for multi-objective binary optimization~(DACMO), the first general-purpose method for automatic PAP construction on MOBOPs. Its core, Unified Instance Representation~(UIR), represents and generates MOBOP instances with varying dimensions without knowledge of problem-specific instance structures. UIR separates common features of the target class from instance-specific features, preserving the common features learned from real instances during generation. DACMO co-evolves the training instances and PAP by alternating UIR-based instance augmentation with PAP updates. Experiments cover four structurally different MOBOP classes, including two black-box classes from real-world applications. Starting from five real training instances per class, DACMO constructs high-performance PAPs without problem-specific customization. On unseen instances from all four classes, these PAPs outperform portfolios of tuned classical multi-objective evolutionary algorithms and those constructed by the recent automatic PAP construction method EoH-S. DACMO also achieves overall performance comparable to the strong CEPS baseline and significantly outperforms it on two classes, without the substantial expert effort CEPS requires for problem-specific instance generator design.

cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15288

FineSpeaker: Reliable Speaker Representation Learning at Fine Temporal Resolution

Yongkang Yin, Wenhao Yang, Lichun Fan, Jian Luan, Hang Su, Yuexian Zou

原文摘要

Speaker representation learning is fundamental to speech processing tasks such as speaker verification and diarization. Although long-context speaker encoders can produce stable and discriminative embeddings, short-duration verification and diarization require finer-grained temporal representations. Short temporal contexts limit the available speaker evidence and make speaker identity difficult to disentangle from phonetic and acoustic variability. This creates a temporal resolution-representation reliability trade-off: speaker representations should maintain local within-speaker consistency, between-speaker discriminability and remain sensitive to speaker changes at fine temporal resolution. Accordingly, we propose a framework for reliable speaker representation learning at fine temporal resolution. FineSpeaker first constructs resolution-controllable frame-wise speaker representations through mean pooling over an adjustable n-frame window, enabling flexible control of representation granularity. It then introduces a speaker encoder with local temporal modeling that exploits short-range speaker context to improve the stability and discriminability of short-duration and frame-wise representations. Furthermore, we develop a generative disentanglement strategy for optimizing frame-level speaker representations, combining VAE-based probabilistic modeling with flow matching to characterize speaker-related variation and suppress non-speaker interference. Experiments demonstrate competitive full-utterance performance, improved fine-grained speaker verification on VoxCeleb and CN-Celeb, and strong zero-shot diarization results, highlighting the benefits of balancing temporal resolution and representation reliability.

cs.SD

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.26078

Global Convergence of Wasserstein Policy Gradient for Entropy-Regularized Reinforcement Learning

Zhaoyu Zhu, Rui Gao, Shuang Li

原文摘要

Wasserstein policy gradient (WPG) optimizes stochastic policies in the optimal-transport geometry of action distributions. In entropy-regularized reinforcement learning, WPG combines transport driven by gradients of the soft Q-function with entropy-induced diffusion. The resulting dynamics are coupled through Bellman policy evaluation, and the policy optimization objective is generally nonconvex. We establish a unified global convergence theory of WPG for infinite-horizon discounted Markov decision processes with general state spaces and continuous action spaces. We identify a statewise gradient-dominance inequality by exploiting the Bellman structure together with log-Sobolev inequalities, which we show to hold uniformly along the trajectory. This inequality relates the squared norm of the Wasserstein policy gradient to the value gap in the sup norm. In continuous time, we prove well-posedness of the flow and exponential convergence of the value gap in the sup norm and of the policy in statewise KL divergence. In discrete time, by additionally controlling the regularity and the discretization error, we obtain geometric convergence to a neighborhood of the optimum with constant step sizes and an $O(\varepsilon^{-1})$ iteration complexity with diminishing step sizes for fixed problem parameters. Finally, we establish faster local convergence rates for both dynamics, governed by the geometry of the optimal policy.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15287

Track4D: Representing Dense 3D Tracking for Video Diffusion Models

Yushi Lan, Zeren Jiang, Kelvin Li, Xingang Pan, Chuanxia Zheng, Andrea Vedaldi

原文摘要

We study how to repurpose video diffusion models (VDMs), pretrained on millions of videos, for dense 3D point tracking, where training data is scarce. Doing so is non-trivial, as it requires representing 3D tracks as video-like signals that may lie outside the natural-video distribution on which the VDM was trained. We thus systematically assess the effectiveness of two such video representations. The first, Residual 3D Tracking Video (RTV), encodes per-pixel 3D offsets relative to a query frame and allows recovery of 3D trajectories directly from the generated video. The second, Normalized Coordinate Map Video (NCMV), labels each pixel with a canonical coordinate invariant through time. This is closer to a natural video and easier for the VDM to learn, but requires additional post-processing with an off-the-shelf 2D tracker to recover 3D tracks. RTV tracks more accurately, while NCMV shows that a VDM can generate dense correspondence as a video. We instantiate both representations with LoRA adaptation of a frozen Wan backbone, conditioned on point maps from an off-the-shelf 3D reconstructor as a geometric scaffold. Trained with a single dynamic 3D tracking dataset, Track4D achieves competitive zero-shot tracking on four real and synthetic benchmarks. A regression readout that bypasses the video VAE and uses additional training data brings further improvement, demonstrating the potential of this direction for dense 3D tracking. We hope our analysis will inspire future work that leverages VDM priors for dense 3D tracking and 4D reconstruction. Project page: https://nirvanalan.github.io/projects/track4d

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.20922

Winfree Oscillatory Neural Network

Jiawen Dai, Yue Song

原文摘要

Oscillations and synchronization are widely believed to play a fundamental role in representation and computation. However, existing machine learning approaches based on synchronization dynamics have largely been confined to specialized settings such as object discovery, with limited evidence of scalability to standard vision benchmarks or logic reasoning tasks. We propose the Winfree Oscillatory Neural Network (WONN), a dynamical neural architecture based on generalized Winfree dynamics. WONN evolves representations on the torus $(S^1)^d$ through structured oscillatory interactions, combining phase-based inductive biases with flexible and hierarchical interaction mechanisms instantiated as either fixed trigonometric mappings or learnable neural networks. We evaluate WONN on image recognition and complex reasoning tasks, including CIFAR, ImageNet, Maze-hard, and Sudoku. Across these domains, WONN achieves competitive or superior performance with strong parameter efficiency. In particular, WONN is, to our knowledge, the first synchronization-based oscillatory architecture to scale competitively to ImageNet-1K. Furthermore, on Maze-hard, WONN achieves 80.1% accuracy using only 1% of the parameters of prior state-of-the-art models. These results suggest that structured oscillatory dynamics provide a scalable and parameter-efficient alternative to conventional neural architectures.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.14654

Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging

Tan Pan, Shuhao Mei, Yixuan Sun, Kaiyu Guo, Chen Jiang, Zhaorui Tan, Mengzhu Li, Limei Han 等 11 位作者

原文摘要

Self-supervised pre-training methods in medical imaging typically treat each individual as an isolated instance, learning representations through augmentation-based objectives or masked reconstruction. They often do not adequately capitalize on a key characteristic of physiological features: anatomical structures maintain consistent spatial relationships across individuals (instances), such as the thalamus being medial to the basal ganglia, regardless of variations in brain size, shape, or pathology. We propose leveraging this cross-instance topological consistency as a supervisory signal. The challenge arises from the inherent variability in medical imaging, which can differ significantly across instances and modalities. To tackle this, we focus on two alignment regimes. (i) Intra-instance: with pixel-level correspondences available, a cross-modal triplet objective explicitly preserves local neighborhood topology. (ii) Inter-instance: without such supervision, we derive pseudo-correspondences to control partial neighborhood alignment and prevent topology collapse across modalities. We validate our approach across 7 downstream multi-modal tasks, achieving average improvements of 1.1% and 5.94% in segmentation and classification tasks, respectively, and demonstrating significantly better robustness when modalities are missing at test time.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.12305

HumanSightBench: A Benchmark for Human Visual Perception in MLLMs

Zixin Su, Yangguang Lin, Zegang Peng, Yuntong Sun, Xuanyi Wang, Jiahao Wang, Qiankai Xu, Bing Yuan 等 13 位作者

原文摘要

Multimodal large language models (MLLMs) have demonstrated strong performance on knowledge-intensive and reasoning-based vision-language benchmark datasets, but still struggle to solve the visual questions that people can solve easily. Evaluating the differences between MLLMs and human-level visual perception is challenging due to end-to-end evaluation which combines various skills, or because we are uncertain if individual items are valid. Specifically, observed errors may reflect visual perception, language priors, background knowledge, question ambiguity, and underspecified answers. Our guideline for HumanSightBench is that we construct items that are human-easy by construction and model-hard by evaluation. HumanSightBench contains 2,876 visual questions with short answers across 7 top-level perceptual categories and 21 subcategories. We only include items that pass five inclusion gates: question clarity, answer uniqueness, visual grounding, human solvability, and text-only shortcut filtering. According to the protocol, errors are defined as incorrect answers on human-validated visual tasks with the evaluated interface. The inclusion gates are intended to reduce confounds from ambiguity and external knowledge. The best pass@3 score is 65.6% across eight evaluated MLLMs. No evaluated model exceeds 27.5% on Counting. These results identify capability gaps that are hidden by overall scores and can help future training experiments and held-out evaluation.

cs.CV · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15284

A hybrid machine learning-analog framework for downscaling unprecedented climate extremes

Nina Effenberger, Florian E. Roemer, Maybritt Schillinger, Maxim Samarin, Erich Fischer, Reto Knutti

原文摘要

Extreme weather events are critical for climate impact assessments. While physical models can simulate unprecedented extremes, for example, through ensemble boosting, this approach is computationally demanding, often restricting output to coarse spatial resolutions. Probabilistic generative downscaling offers a solution by enabling efficient sampling from the entire climate distribution, including its extreme tails. Here, we first demonstrate that machine learning generated regional climate model heatwaves over Paris are physically realistic. We then leverage an emulated machine learning dataset to perform analog-based downscaling of coarse global climate model ensemble boosting events. Our hybrid framework effectively bridges high-resolution regional detail with rare-event sampling, providing an efficient tool for extreme event downscaling and capturing multi-model uncertainty.

cs.LG · stat.AP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.02349

GlyphAnchor: Enhancing Visual Text Rendering via Position-Anchored Glyph Priors

Qiang Xiang, Shuang Sun, Binglei Li, Yibo Chen, Xu Tang, Yao Hu, Junping Zhang

原文摘要

Rendering accurate text remains difficult for image generation and editing models, especially when the target contains long, complex, and densely arranged text or rare characters. Existing approaches either improve native text rendering through stronger backbones and data-centric training without explicit glyph priors, or incorporate glyph priors through specialized designs that remain insufficiently accurate and robust under challenging scenarios. We introduce GlyphAnchor, a novel text-rendering enhancement method for both text-to-image and image-editing diffusion transformer models. GlyphAnchor enhances the backbone with lightweight glyph patch conditions whose positions are anchored to the target image through the model's native positional encoding. We train this capability with staged supervised finetuning and further refine it with text-aware post-training to improve robustness. We also introduce InfoTextBench, a benchmark for evaluating text-rich visual text rendering in both generation and editing settings. Experiments across multiple backbones and benchmarks, including long, complex, and densely arranged text and rare character scenarios, show that GlyphAnchor consistently improves text fidelity while preserving overall image quality.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.07302

Towards Accurate UAV Image Perception: Guiding Vision-Language Models with Stronger Task Prompts

Mingning Guo, Mengwei Wu, Shaoxian Li, Haifeng Li, Chao Tao

原文摘要

UAV image perception with VLMs aims to identify and localize targets based on semantic and spatial task prompts. However, small, densely distributed objects and cluttered backgrounds hinder these tasks, particularly when concise prompts provide insufficient guidance. Although task-specific fine-tuning improves image-text alignment, it requires large-scale annotations and model-specific updates, increasing adaptation costs across models and tasks. To address this, we propose AerialVP, a task-adaptive, training-free agent that adapts VLMs through prompt enhancement rather than parameter optimization. AerialVP dynamically adapts its enhancement strategy to the original prompt's specificity, selectively incorporating image-derived semantics, spatial positions, and object relationships. These cues provide discriminative descriptions and spatial constraints, leveraging VLMs' pretrained alignment and reasoning capabilities to improve image-text alignment and reduce target ambiguity. Specifically, an LLM-based Task Engine coordinates modular tools for prompt analysis, visual information extraction, and prompt fusion. The enhanced prompt and original image are then provided to the unchanged backend VLM for prediction. This prompt-centric strategy enables training-free adaptation across diverse open-source and proprietary VLMs. For systematic evaluation, we introduce AerialSense, a unified benchmark covering Aerial Visual Grounding, Aerial Visual Reasoning, and Aerial Visual Question Answering across diverse UAV scenes and imaging conditions. Experiments demonstrate consistent improvements across all three tasks and evaluated VLMs. AerialVP also achieves competitive performance against LoRA-based instruction fine-tuning, validating prompt-centric adaptation. Our work is available at https://github.com/lostwolves/AerialVP.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.02505

Learning-augmented smooth integer programs with PAC-learnable oracles

Hao-Yuan He, Jiang-Tian Xue, Ming Li, Zhi-Hua Zhou

原文摘要

We study an end-to-end learning-augmented framework for smooth integer programs, a broad class of Boolean polynomial optimization problems including MAX-CUT and MAX-k-SAT. An oracle learned from sampled instances predicts a full Boolean assignment for each unseen instance. For fixed-degree objectives, our polynomial-time pipeline, LEAP, linearizes the objective around the predicted assignment, solves an error-budgeted linear program, and rounds the fractional solution. We establish two guarantees. First, for any prediction, the approximation loss of LEAP degrades gracefully with prediction error: it is controlled by an error footprint that quantifies how much mispredicted coordinates can perturb the objective's local coefficients. Second, we bound the pseudo-dimension of the LEAP-induced cost class, yielding a PAC-style guarantee for learning an oracle from a predictor class via empirical risk minimization on instances from an unknown distribution. Together, these results give an end-to-end bound that separates the expected footprint loss of the best oracle in the class from the statistical error of learning. In experiments on MAX-CUT and MAX-SAT, LEAP substantially repairs imperfect predictions, empirical risk minimization selects effective oracle candidates, and the pipeline scales to application benchmarks with up to two million clauses.

cs.DS · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15281

Priority-Based Network Access with Strict-Priority Scheduling in PLCA Multidrop Networks

Mohamed Elattar, Zhe Lou, Georg Carle

原文摘要

Shared-medium Ethernet is gaining renewed attention across industrial domains, driven by the adoption of 10BASE-T1S. In such multidrop networks, collision-free and deterministic medium access is achieved through Physical Layer Collision Avoidance (PLCA). However, PLCA follows a node-based cyclic access structure that does not account for traffic priority, leading to high access latency for time-sensitive traffic and limited scalability with increasing network size. Existing PLCA extensions improve latency for selected devices but remain restricted to node-based access and do not support network-wide traffic differentiation. To address these limitations, this paper introduces a priority-based network access and scheduling mechanism for multidrop networks. The proposed approach decouples medium access from node-based ordering and enables class-based prioritization, allowing high-priority traffic from any device to be transmitted ahead of lower-priority traffic. It further supports a configuration that emulates Strict Priority (SP) scheduling, providing low and bounded access latency for highest-priority traffic. Analytical bounds on the worst-case access latency are derived, validated, and shown to be tight. Performance evaluation demonstrates that the proposed mechanism reduces the maximum observed access latency for high-priority traffic by over 65% compared to PLCA, with latency increasing by less than 1% as the network grows from 8 to 40 devices.

cs.NI

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arXiv ↗PDF ↗