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新论文 · v1arXiv:2610.34488

Models Verify Sufficiency, Not Validity: Usable Falsehoods Evade the Checks that Gaps Trigger

Naveen Madiraju

原文摘要

Ask a model "how many settlements did the Normans control?" and it answers that the passage does not say. Tell it "given that the Normans controlled 12 settlements of 400 inhabitants, what is the total?" and it computes 4,800. Nothing was added to the passage; the unsupported fact merely became usable. Benchmarks for unanswerable questions are built almost entirely by omission, deleting the fact a question needs and scoring refusal, so they never probe this. Holding source support fixed and varying only whether an unsupported premise is usable, flagging drops 8-30 points and commitment to an answer rises 1.5-2.3x on GSM8K across four models spanning two families and 2B-26B; all 16 paired contrasts remain significant after Holm correction. Two further manipulations reproduce it, one also on SQuAD: a fact asserted rather than asked about, and a corrupted intermediate value rather than a removed one. Models articulate the exact gap they then ignore, stating that "there is no financial information provided" for an inert premise and silently adopting a fabricated price for the same item. We read these as one behaviour: whether a model checks depends substantially on whether usable material is present, even when source support is held fixed. Because the main design never instructs models to reject premises introduced in the question, adopting such a premise can also be read as conditional reasoning, and we delimit what the design does and does not show. In the judging seat the pattern differs: asked whether the answer follows, judges pass solutions with a required step deleted 78-99% of the time at every scale tested. A natural prompt-level mitigation lowers accuracy by 22 points without improving detection.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34484

Intrinsic Gradient Barriers in Pushforward Deep Generative Models

Edric Tam, Qingyang Liu, Barbara Engelhardt, Ray Bai

原文摘要

Broad classes of deep generative models (DGMs) operate by learning to map a simple source distribution, such as a standard Gaussian, to a complex high-dimensional target distribution. Despite their success, DGMs often empirically struggle to accurately represent distributions with heavy tails or well-separated modes. In this work, we show that these difficulties reflect intrinsic properties of the underlying transport problem rather than particular neural network architectures or training procedures. We establish quantitative lower bounds on the gradients and Jacobians of pushforward mappings that transport log-concave source distributions to such targets. For heavy-tailed target distributions, the required gradient magnitude grows as the target develops increasingly large moments. For multimodal target distributions, the low-probability regions between well-separated modes similarly force large gradients in the transport map. We discuss how such large derivatives relate to trade-offs between expressivity and numerical stability. Our results hold under substantially weaker regularity conditions than previous analyses and remain stable under perturbations of the target distribution. Finally, we show that these obstructions can be mitigated when the goal is approximation of a given density rather than generative modeling. Specifically, we propose simple classes of bijective reparametrizations that can alleviate the difficult geometric structure and reduce the gradient burden placed on the learned generative map. Numerical experiments support our theoretical analysis.

stat.ML · cs.LG · math.ST

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35967

Data-driven Discovery of Lie Symmetries and First Integrals with Latent ODEs

Matt L. Wiemman, Peter Melchior

原文摘要

We present LatentLieODE, a novel neural ODE method to discover linear Lie symmetries and first integrals of the learned dynamics from timeseries observations such as video inputs. It starts with an autoencoder, which learns a low dimensional representation of the observed system and then maps trajectories to decorrelated latent coordinates $z \in \mathbb{R}^d$. A bank of linear infinitesimal generators $\mathbf{A}_k \in \mathbb{R}^{d \times d}$ is fitted jointly against a differentiable Lie-bracket residual $\mathbf{J}_f(z)\mathbf{A}z - \mathbf{A} f(z)$, while an MLP optimises the latent ODE $f(z) = \mathrm{d}z/\mathrm{d}t$ to reconstruct the observation-space timeseries. After suitable generators and $f$ are found, we fit a third network $H_φ$ to generate the first integral of the system. Across six benchmark systems (harmonic oscillator, simple and twin pendulums, Lotka-Volterra, pixel pendulum, sphere geodesics) we find each system's relevant symmetries, and the discovered invariants match ground-truth conserved quantities at $|ρ| \geq 0.978$. On the sphere, the pipeline recovers a non-abelian $\mathfrak{so}(3)$, certified by the negative-definite Killing form of its recovered structure constants. This work presents significant advances in data-driven discovery of symmetries and provides a new pathway for neural surrogates of dynamical systems.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21072

0.4633-Approximation for Online and Offline on-Monotone DR-Submodular Maximization

Vaneet Aggarwal, Donglei Du

原文摘要

We obtain the approximation coefficient $α_0=54440750000/117486474169>0.463378$ for maximizing nonnegative, non-monotone DR-submodular functions over compact convex down-closed subsets of the unit cube, with queries allowed throughout the cube. This improves the previous best coefficient, $4/9$. A coordinatewise map, exponential on small coordinates and linear-fractional on large ones, turns the learner's state, a feasible point, into a feasible action whose value is at least $α_0$ times that of any feasible point, up to a linear term. The proof is analytical and reduces to one-dimensional inequalities. The linear term has an unbiased estimator from one noisy-gradient query, so online maximization reduces to linear learning. Under a uniform bound on the gradients, the expected approximate regret is $O(\sqrt T)$ with projected ascent and $O(T^{3/4})$ with one linear-maximization call per round; with one noisy value per round, it is $O(T^{3/4})$ and $O(T^{7/8})$. Online-to-batch conversion gives offline guarantees. With exact values and gradients, contracting the objective toward a feasible interior point yields an expected $(α_0-\varepsilon)$-approximation for every nonnegative $C^1$ DR-submodular objective, with oracle counts polynomial in $n$ and $\varepsilon^{-1}$ and no gradient or smoothness parameter. Sampling and standard rounding give $(α_0-\varepsilon)$-approximation for submodular set functions under one matroid or a fixed number of knapsack constraints.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34481

From Outranking Flow Scores to Linear Programs: Group Decision Making in PROMETHEE II

Alexandre Flachs, Yves De Smet

原文摘要

We study inverse and consensus analysis for PROMETHEE II ranking problems when the performance table is fixed. Existing approaches in PROMETHEE-based sensitivity and consensus analysis usually focus on deviations of criteria weights while treating preference functions as fixed. Nevertheless both sets of parameters jointly determine the outranking flow scores. We introduce a linear reparametrization of the PROMETHEE II net flow scores based on weighted preference increments over the observed differences of performances. This representation yields a linear expression of the net flows and clarifies how weights and preference functions interact. Building on this result, we derive linear optimization models for three settings: minimizing weight deviations while allowing preference functions to vary; minimizing deviations over the full set of preference parameters; and multiple decision-maker consensus analysis (with objectives based either on total deviation or on the maximal individual deviation to promote fairness). An illustrative example shows that allowing preference functions to vary can dramatically change the inverse-analysis conclusions obtained from weight-only models, and that the proposed group formulations distinguish efficient consensuses from fairer ones.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.30563

Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective

Jiaqi Zhang, Zheng Pang, Mengting Li, Yiqi Wang, Guangyuan Dong, Chao Xue, Yusen Wu, Zihao Li 等 12 位作者

原文摘要

Multimodal Emotion Recognition (MER) systems often suffer from missing modalities in real-world scenarios. Existing methods usually generate, align, or distill missing modalities as a whole, overlooking the heterogeneous nature of the information carried by each modality. Such holistic treatment mixes inferable shared semantics with uncertain modality-specific details, yielding unstable representations and degrading robustness. To address this issue, we propose the Primitive Memory Distillation (PriMD) framework. Unlike existing methods, PriMD takes an intra-modal perspective and focuses on how different types of information within a modality differ in recoverability. PriMD first disentangles cross-modal shared semantics from modality-specific representations, and then discretizes the latter into learnable semantic primitives to construct modality-specific memory banks. When modalities are missing, PriMD is a teacher-student framework that the student model uses the shared semantics of available modalities as queries to dynamically retrieve primitives. It compensates for missing modality-specific information within a constrained memory space and aligns with the teacher model. Extensive experiments on IEMOCAP, CMU-MOSI, and CMU-MOSEI demonstrate that PriMD achieves state-of-the-art performance and consistently stronger robustness across a wide range of missing-modality settings, while mitigating the instability caused by holistic feature inference. Our code and project website are available at https://www.jiaqizhang-sengoku.com/Code/PriMD and https://www.jiaqizhang-sengoku.com/PriMD/ , respectively.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35966

EMMA: Structured Episodic Memory for Multi-Agent Workflow Synthesis

Ruijia Niu, Kaiwen Liu, Rose Yu, Kristina Klinkner

原文摘要

Deploying LLM agents in specialized domains often require fine-grained task-specific procedures and knowledge a general model lacks, yet parameter training is costly and non-interpretable, and hand-engineered agent systems shift the cost to expert labor. Episodic memory updates at context level, but single agent adaptation struggles to alleviate cross-task interference, especially with complex tasks such as multi-application navigation or data pool analysis. Although multi-agent evolution alleviates this problem, existing methods lack clear definitions on update rules and rely on the backbone model to decide where and how much the system changes. This ill-scoped evolution makes learning follow the backbone's structural preferences rather than the observed failures: one backbone accumulates skills while another spawns agents. We present EMMA (structured Episodic Memory for Multi-Agent workflow synthesis), which treats the configuration of a multi-agent system (controller routing memory, specialists inheriting a generalist prompt, and per-step procedural skills) as episodic memory. EMMA makes the ownership and admissibility of updates explicit: a reflector assigns each diagnosed failure to the scope owning its correction and proposes one edit within it, and backbone-independent guards keep every accepted edit scoped, incremental and bounded. Across KramaBench, AppWorld and BBEH with five backbones and four baselines (ACE, GEPA, EvoMAS and ADAS), EMMA attains the best overall performance on every benchmark, up to 32.5% over the strongest baseline, maintains consistent improvements across backbones, and uses 17-38% fewer training executions than the most efficient baseline.

cs.MA · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34477

Finite-Sample Sensitivity of Kernel Metrics for Generative Evaluation

Tomasz Radzikowski, Mateusz Modrzejewski

原文摘要

Kernel distances such as the Kernel Inception Distance (KID) and the Kernel Audio Distance (KAD) compare generated and reference samples in an embedding space, and model comparisons rest on their values. Each ships with one fixed kernel, and a reported value does not say which changes that kernel detects at the sample size used. We run each default as a permutation two-sample test on real embeddings of symbolic music, audio and images, comparing a sample with an independent sample changed by a mean shift, an isotropic contraction, or a rotation that keeps the mean and the covariance trace and is matched to the shift in transport distance. At 780 samples per side, both defaults rarely detect the rotations in five evaluation spaces, while they detect the shifts in every run. The median-bandwidth Gaussian kernel of KAD detects the contraction and regains power against rotations on image embeddings with an order of magnitude more samples. The cubic kernel of KID contains second- and third-moment information but misses the contraction in most runs, and our experiments do not establish a high-power rotation regime for it over the tested sample sizes. Narrower Gaussian bandwidths restore sensitivity in music and audio, an $\ell_1$ Laplace kernel does so for images, and aggregated tests combine both at nominal level. We recommend that a reported kernel distance state its sample budget and the changes its configuration detects at that budget.

cs.LG · cs.SD

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.18714

PLANA3R: Zero-shot Metric Planar 3D Reconstruction via Feed-Forward Planar Splatting

Changkun Liu, Bin Tan, Zeran Ke, Shangzhan Zhang, Jiachen Liu, Ming Qian, Nan Xue, Yujun Shen 等 9 位作者

原文摘要

This paper addresses metric 3D reconstruction of indoor scenes by exploiting their inherent geometric regularities with compact representations. Using planar 3D primitives - a well-suited representation for man-made environments - we introduce PLANA3R, a pose-free framework for metric Planar 3D Reconstruction from unposed two-view images. Our approach employs Vision Transformers to extract a set of sparse planar primitives, estimate relative camera poses, and supervise geometry learning via planar splatting, where gradients are propagated through high-resolution rendered depth and normal maps of primitives. Unlike prior feedforward methods that require 3D plane annotations during training, PLANA3R learns planar 3D structures without explicit plane supervision, enabling scalable training on large-scale stereo datasets using only depth and normal annotations. We validate PLANA3R on multiple indoor-scene datasets with metric supervision and demonstrate strong generalization to out-of-domain indoor environments across diverse tasks under metric evaluation protocols, including 3D surface reconstruction, depth estimation, and relative pose estimation. Furthermore, by formulating with planar 3D representation, our method emerges with the ability for accurate plane segmentation. The project page is available at https://lck666666.github.io/plana3r

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34473

PhysCreator: Creating Physically-Attributed 3D Assets at Will

Shiyan Zheng, Ming Li, Lei Han, Xiangyu Xu

原文摘要

Significant advancements in 3D asset creation have enabled high-fidelity 3D generation from images. However, most existing representations remain inherently static, lacking the ability to model physically plausible behaviors under dynamic interactions. We present \textbf{PhysCreator}, a unified framework that directly generates simulation-ready 3D representations with spatially varying physical properties from images. Unlike prior approaches that treat physical attributes as simple post-hoc predictions or rely on expensive per-instance optimization, we reformulate physical property estimation within a unified 3D representation learning framework. We introduce a voxel-aligned latent space by lifting image observations into a spatially structured 3D representation, in which geometry, appearance, and physical properties are jointly modeled and inferred in a feed-forward manner. To enable scalable learning without densely annotated physical ground truth, we leverage vision-language distillation to transfer material-aware semantic priors from powerful vision-language models into spatially grounded voxel-level supervision. Experiments demonstrate that our method achieves superior visual quality, physical plausibility, and generalization compared to prior approaches.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2506.13261

Building Automotive Security on Internet Standards: In-Car Service Authentication with DNSSEC, DANE, and DANCE

Timo Salomon, Mehmet Mueller, Philipp Meyer, Thomas C. Schmidt

原文摘要

The automotive industry trends to softwarizing cars according to flexible, service-oriented architectures. At the same time, connected cars become networked computer systems but lack proper security measures. In this paper, we propose to authenticate and authorize in-vehicle services by integrating DNSSEC, DANE, and DANCE with automotive middleware. Our approach decouples the cryptographic authentication of the service from that of the service deployment with the help of DNSSEC and thereby largely simplifies key management. We propose to authenticate in-vehicle services by certificates that are solely generated by the service suppliers but published on deployment via DNSSEC TLSA records solely signed by the OEM. We further show how these established Internet standards secure the service discovery and invocation, ensure interoperability with various protocols, and scale management of credentials for millions of connected vehicles at robust security levels. We back our design proposal by a security analysis using the STRIDE threat model and by evaluations in an emulation of a realistic in-vehicle setup that demonstrate its effectiveness.

cs.CR · cs.NI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.22191

Partial Rewriting and Value Interpretation of Logically Constrained Terms (Full Version)

Takahito Aoto, Naoki Nishida, Jonas Schöpf

原文摘要

Logically constrained term rewrite systems (LCTRSs) are a rewriting formalism that naturally supports built-in data structures, including integers and bit-vectors. The recent framework of existentially constrained terms and most general constrained rewriting on them (Takahata et al., 2025) has many advantages over the original approach of rewriting constrained terms. In this paper, we introduce partial constrained rewriting, a variant of rewriting existentially constrained terms whose underlying idea has already appeared implicitly in previous analyses and applications of LCTRSs. We examine the differences between these two notions of constrained rewriting. First, we establish a direct correspondence between them, leveraging subsumption and equivalence of constrained terms where appropriate. Then we give characterizations of each of them, using the interpretation of existentially constrained terms by instantiation. We further introduce the novel notion of value interpretation, that highlights subtle differences between partial and most general rewriting.

cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34472

Reliability Assessment of FRP-RC Columns Using AK-MCS with a Piecewise Two-Branch Approach

Arndt Goldack, Richard Koehler, Ahmed Usama Syed

原文摘要

Slender concrete columns reinforced with fiber-reinforced polymer (FRP) bars fail at medium to high axial load levels either by concrete crushing or by loss of stability, while the tensile strains in the reinforcement remain far below the rupture strain. Each non-linear finite element (FE) analysis yields only the capacity of the governing failure mode, and the governing mode changes over a broad zone in which both capacities are nearly equal. Both characteristics conflict with active-learning Kriging-assisted Monte Carlo simulation (AK-MCS), which assumes a single, smooth limit-state function. This paper proposes a piecewise, two-branch AK-MCS framework in which each failure mode is represented by its own Kriging model, trained only on the FE analyses in which that mode governs, and the governing prediction is the minimum of both. Safeguards against data starvation keep rarely governing branches usable. After convergence, additional FE analyses are placed based on a leave-one-out calibration check and on the width of the confidence interval on the reliability index. A design-point search on the surrogate and importance sampling at the design points provide the reliability index even where direct Monte Carlo simulation finds no failure. For an FRP-reinforced concrete column example with mixed failure, 79% and 62% of the training points lie near the mode boundary. The reliability index from the design-point search agrees with the Monte Carlo estimate to within 0.05, and an FE analysis at the design point confirms the surrogate to within 0.6%.

cs.CE · stat.AP · stat.CO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.17736

Machine learning kinetics from molecular dynamics data

Jonathan Weare, Aaron R. Dinner

原文摘要

Most molecular transitions occur on time scales far beyond direct molecular dynamics simulations. The committor, the probability that a configuration reaches a product state before a reactant state, is a central kinetic statistic, providing a mechanism-independent reaction coordinate and a foundation for transition path theory and the calculation of rates. This review surveys modern approaches for estimating the committor and related kinetic statistics from molecular simulations, with an emphasis on self-supervised methods that learn solutions of their defining dynamical equations rather than relying on labeled shooting data. We develop a common operator viewpoint connecting generator-based partial differential equations, variational principles, Markov state models, dynamical Galerkin approximation, and neural networks. Empirical and theoretical evidence points to the efficiency of these methods. We provide theoretical and practical guidance for realizing their full potential in applications, including strategies for treating non-Markovian effects and for sampling. We conclude by identifying opportunities for further research, including connections to reinforcement learning and generative modeling.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2506.18814

Online Learning in Stabilized Linear Dynamical Games with Adversarial Disturbances

Anas Barakat, John Lazarsfeld, Georgios Piliouras, Antonios Varvitsiotis

原文摘要

We study online learning in linear dynamical games where multiple strategic agents act on a shared state evolving according to a linear dynamical system subject to adversarial disturbances. This setting lies beyond both single-agent nonstochastic online control and classical linear-quadratic games, which typically focus on quadratic objectives and noiseless or stochastic dynamics. Each agent seeks to minimize its own sequence of convex losses under full-state observability. Following the stabilizing-baseline paradigm in nonstochastic online control, we assume access to linear controllers that stabilize the noiseless system and focus on online adaptation by learning disturbance-action corrections. This extends adversarial online control to strategic multi-agent shared-state systems, where each agent's actions shape the state trajectory and hence the realized losses of all learners. Under state-only and aggregate-input feedback models, we analyze agents running online gradient descent with memory to update their own disturbance-action policies. We prove per-agent regret bounds that are sublinear and near-optimal in the time horizon, with population dependence that varies under the specified feedback, opponent policy, and stability assumptions. In the common-interest case, where all agents have identical cost functions, we show that the induced online problem forms a time-varying potential game and derive equilibrium tracking guarantees. Together, our results provide a theoretical framework for adversarial online learning in stabilized linear dynamical games, connecting online control with learning in games.

cs.LG · cs.GT · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34471

Extrapolating learned dynamics across bifurcations based on generalized synchronization

Lingfeng Chen, Masanobu Inubushi

原文摘要

Predicting dynamics across bifurcations by extrapolating trained models is crucial in diverse fields of physics. Generalized synchronization provides a framework for understanding dynamical reconstruction by neural networks, especially in the context of reservoir computing. Building on this framework, we derive a formula combining pretrained readouts to reconstruct unseen chaotic dynamics. Using classical Lagrange interpolation theory, we derive rigorous bounds on readout errors and conjugacy residuals. Numerical examples demonstrate accurate prediction of chaotic dynamics across bifurcations.

nlin.CD · cs.LG · math.DS

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.07544

From Pixels to Prompts: Vision-Language Models

Khang Nhat Hoang Vo

原文摘要

When you read a paper about a new Vision-Language Model today, it can be easy to forget how strange this idea would have sounded not so long ago. Teaching machines to see was already hard. Teaching them to read and generate language was already hard. Asking them to do both at once - and then to reason, answer questions, follow instructions, and sometimes even surprise us - still carries a quiet trace of science fiction, even as it becomes routine. This book was born from a simple feeling: it is too easy to get lost. The field moves quickly, new model names appear constantly, and the gap between "I know the buzzwords" and "I actually understand how this works" can feel uncomfortably wide. I have felt that gap many times. If you are holding this book, you probably have too. My goal is not to provide an exhaustive catalog of every dataset, benchmark, and new model variant. Instead, I want to offer something more modest - and, I hope, more durable: a clear mental map of Vision-Language Models. Enough structure that you can read new papers with confidence; enough intuition that you can design your own systems without feeling as if you are assembling LEGO bricks blindly.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2608.06981

Local Epistemic Uncertainty Guided Active Sampling for Plug-and-play Diffusive Image Restoration

Jiaqi Zhang, Zheng Pang, Rongrong Gao, Qiyuan Zhang, Yang Yang

原文摘要

Diffusion models have demonstrated remarkable effectiveness in image restoration tasks. However, when guiding image reconstruction, existing Diffusion Model-based Image Restoration (DMIR) methods typically rely on fixed data constraints and uniform step sizes, thereby overlooking the dynamic nature of the generative process. Such rigid designs render the models vulnerable to spatially non-uniform degradations, thus resulting in structural distortions and loss of fine details. Meanwhile, uniform step sizes introduce computational redundancy, whereas naïve step reduction strategies tend to accumulate approximation errors. To address these limitations, we propose a Local Epistemic Uncertainty Guided Active Sampling framework (LEADer). In the spatial domain, LEADer leverages pixel-wise uncertainty to dynamically modulate the prior strength within the null space, which effectively balances detail preservation and artifact suppression. In the temporal domain, it quantifies sampling stability via the uncertainty trace to enable adaptive trajectory pruning, thereby accelerating convergence. Theoretical proofs demonstrate that our framework achieves strict data consistency, while the trajectory pruning strategy admits a deterministic error bound, thereby guaranteeing stable convergence under skip sampling. Notably, our plug-and-play method can be seamlessly integrated into various DMIR baselines. Extensive experiments show that LEADer improves the performance of multiple state-of-the-art DMIR methods, while significantly reducing sampling time with negligible memory overhead. Code is available at https://www.jiaqizhang-sengoku.com/Code/LEADer.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34466

IntNet: A Communication-Driven Multi-Agent Reinforcement Learning Framework for Cooperative Autonomous Driving

Leandro Parada, Kevin Yu, Panagiotis Angeloudis

原文摘要

Achieving safety in autonomous driving through Multi-Agent Reinforcement Learning (MARL) is a critical yet challenging task due to non-stationarity, partial observability, and the need for effective coordination among agents. Although earlier cooperative MARL methods have aimed to improve coordination by sharing encoded state observations, we find these strategies are insufficient in safety-critical scenarios. To address this gap, we present IntNet, a novel MARL framework that enhances safety by incorporating the transmission of vehicle intent and adaptive communication scheduling into a unified end-to-end learning paradigm, jointly optimising all components for safe coordination. Central to our approach is an observation prediction module that enables agents to forecast subsequent policy outputs, which they can then share across a vehicle-to-vehicle network. Our model-free architecture employs a Graph Attention Network to process incoming messages and a scheduler component to dynamically optimise the communication graph for bandwidth efficiency. We compare IntNet against state-of-the-art communicative MARL methods in complex urban environments with both autonomous and human-operated vehicles, achieving improved coordination, lower collision rates, and reducing communication efforts by up to 60\%. Through extensive experiments, we assess the framework's learning efficiency, scalability, and the balance between information sharing and bandwidth usage for collision-free trajectories.

cs.RO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34462

End-to-End Neural Modeling of EM Response and Design Performance for Free-Form RFIC Passives

Yuhao Mao, Chenhao Chu, Martin Vechev, Hua Wang

原文摘要

Passive matching networks are critical components in radio-frequency integrated circuits (RFICs), but their design is bottlenecked by expensive finite-difference-based electromagnetic (EM) simulation. We propose an end-to-end neural framework for free-form RFIC passives that predicts both EM response and downstream design performance under varying frequency and circuit contexts, combining a frequency-conditioned neural simulator with a circuit-conditioned neural solver. To improve design performance prediction, the simulator outputs a neural EM response consisting of both the explicit EM response and a complementary response, together with a dependence regularizer that encourages the two responses to capture orthogonal information. To evaluate the proposed framework, we construct a large-scale dataset of free-form passive layouts, synthesized from both expert-designed contours and random paths. Our 21M-parameter model achieves high accuracy across a broad frequency range, circuit contexts and passive matching settings, with $R^2>0.98$ for almost all targets, while providing orders-of-magnitude faster evaluation than traditional EM simulation. Code available at https://github.com/ETH-IDEAS/RFIC-NeuralSim.

eess.SP · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34461

Task Relevance-Driven Prompt Expansion for Federated Continual Learning

Jiahua Dong, Wenqi Liang, Yang Cong, Yichen Li, Hongyuan Yu, Henghui Ding, Yulun Zhang, Salman Khan 等 9 位作者

原文摘要

Federated continual learning (FCL) has attracted considerable interest by enabling local clients to continually acquire knowledge from new tasks. However, most FCL methods assume that the global server has prior knowledge of the arrival times and number of categories for new tasks, which is unrealistic in the real world. Furthermore, they neglect the importance of positive inter-task semantic relationships when learning new tasks and are significantly affected by irrelevant relationships from old tasks, thereby exacerbating catastrophic forgetting. To resolve the above challenges, we develop a novel Task Relevance-driven Prompt Expansion (TRPE) model. Specifically, to eliminate the reliance on prior knowledge of new tasks, we propose an out-of-distribution detector to identify the arrival of new tasks within local clients, along with a radius-adaptive DBSCAN to infer the quantity of categories in new tasks on the proxy server. Moreover, to explore positive inter-task relationships, we calculate the affinity between the original gradient for learning new task and its gradient orthogonal to the feature space of old tasks to evaluate semantic relevance between old and new tasks from a global perspective. Then, we use this global task relevance to determine the key-prompt pair for new tasks and identify highly related old tasks. After concatenating their prompts for training, we can leverage such positive inter-task relations to improve the continual learning of newly arriving tasks. To balance the learning of new tasks and the forgetting of old ones, a soft orthogonality constraint is applied to the local key and prompt gradients. Experiments spanning diverse datasets and backbones confirm the efficacy of our TRPE model.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34459

From Explaining to Enabling: Actionable Transparency in Algorithmic Insurance Pricing

Agathe Fernandes Machado, Fei Huang

原文摘要

Machine learning (ML) systems increasingly make consequential decisions about individuals, including credit, hiring, and insurance. Research on ML-driven insurance pricing has asked how the pricing mechanism itself can be made accountable. Yet it remains unclear how an individual policyholder's algorithmically determined premium should be explained to them. We argue that a useful explanation must be valid, actionable, and reliable. A valid recommendation lowers the premium according to the model. An actionable recommendation changes only characteristics the policyholder can control. A reliable recommendation has an actual outcome that is likely to meet the target, even in sparsely observed regions of the feature space where the model is uncertain. We formalize these three requirements as axioms and develop a method that turns an algorithmic premium into recommendations that meet them. The method meets the third requirement with a Conformal Prediction constraint, which screens out recommendations that the model is too uncertain about and thereby bounds the rate of unreliable recommendations. We apply four Counterfactual Explanation techniques to 57,832 real Florida flood-insurance claims and 98,058 synthetic driver-telematics policies. We adapt these techniques for insurance applications, so that their recommendations change only controllable characteristics and resemble policyholders observed in the data. In our illustrations, the constrained recommendations for high-risk policyholders are both more reliable and associated with larger predicted risk reductions than the unconstrained ones. These results suggest that insurers can give policyholders explanations that are both actionable and trustworthy. The work contributes to research on algorithmic accountability and offers implications for insurers and regulators.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.07600

Mathematical Reasoning via Intervention-Based Time-Series Causal Discovery Using LLMs as Concept Mastery Simulators

Tsuyoshi Okita

原文摘要

Recent methods for improving LLM mathematical reasoning, whether through MCTS-based test-time search or causal graph-guided knowledge injection, cannot identify which concepts causally contribute to a correct answer, as the observed association may be spurious, driven by confounders such as problem difficulty. We propose CIKA (Causal Intervention for Knowledge Activation), a framework that uses the LLM itself as an interventional simulator: a prompt sets the concept state to ``mastered'' and the correctness change estimates the causal effect. We formalize this quantity as an Interventional Capability Probe (ICP), which diagnoses whether the LLM can use a given concept -- distinct from merely possessing knowledge. Because the intervention exogenously sets the concept state independently of problem difficulty, ICP separates confounding that observational methods cannot. On 67 screened problems, the ICP of the top-ranked concept (+0.219) is significantly larger than that of the negative control (+0.039; paired $t$-test, $p < 10^{-6}$, Cohen's $d = 0.86$), confirming that the probe discriminates causally relevant concepts from irrelevant ones. Analysis of 601 Omni-MATH problems further shows that solved problems have 6.1$\times$ higher ATE than unsolved ones (0.338 vs. 0.055), confirming that ICP is predictive of problem-solving success. With a 7B-parameter LLM whose weights are entirely frozen, CIKA achieves 69.7\% on the contamination-free Omni-MATH-Rule benchmark and 64.0\% overall, compared to 60.5\% for o1-mini, and 97.2\% on GSM8K, 46--50\% on AIME 2024--2026, and 46.2\% on MathArena. The Causal Knowledge Activation component contributes 33.8\% of correct answers on problems where the base model alone fails, demonstrating that the LLM already possessed but had not activated the requisite knowledge.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34458

py-ARGOS: A Python package for automatic, statistically principled discovery of ordinary differential equations from data

Yuzheng Zhang, Rui Carvalho

原文摘要

Recovering the governing equations of a dynamical system from data is a central problem in data-driven science, yet the available Python tooling forces a compromise among automation, statistical rigor, and reliability. We present py-ARGOS, a Python package implementing two automatic algorithms, bootstrap ARGOS and Bayesian-ARGOS, behind a uniform interface. Both are statistically principled throughout: automated model selection reduces the candidate library, each coefficient is reported with a bootstrap confidence or Bayesian credible interval that quantifies its uncertainty, and a term is retained when this interval excludes zero. Built-in diagnostics additionally flag unreliable fits. On chaotic benchmark systems, the two algorithms improve on the SINDy family's identification performance.

cs.MS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34457

Toward Neuroadaptive Communication Interfaces: A Framework for Personalized Speech and Language Support

Carlos Bruno, Luis Ricardo Santos

原文摘要

Speech and language technologies have expanded access to communication, but the support they provide is often fixed. The same linguistic or visual aid may help one user and hinder another, or become less useful as task demands change. This project proposes a framework for adapting language presentation to individual abilities and ongoing interaction. It separates access to a message from the estimation of user needs and from the selection of support, including sentence segmentation, pictograms, and optional spoken output. Behavioral feedback provides the primary evidence for adaptation; electroencephalography is considered an additional, uncertainty-aware source of information rather than a direct measure of comprehension. We specify a staged evaluation comparing fixed, behavior-adaptive, and neuroadaptive interfaces. The central question is whether neural information improves comprehension and communication efficiency beyond a strong behavioral baseline.

cs.HC · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34456

Trajectory Generation and Control for Quadrotors

Sandesh Thapa

原文摘要

In this project, I explored controller design and trajectory generation for a quadrotor. Differential flatness was used for trajectory generation based on one of the references. A minimum snap trajectory is generated from the given algorithm based on waypoint navigation, and a simple proportional plus derivative (PD) controller is used to track the trajectories. Simulation results are presented which generate smooth trajectories for position and velocity of the quadrotor.

eess.SY · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.21856

The Illusion of Reasoning: Exposing Evasive Data Contamination in LLMs via Zero-CoT Truncation

Yifan Lan, Yuanpu Cao, Hanyu Wang, Lu Lin, Jinghui Chen

原文摘要

Large language models (LLMs) have demonstrated impressive reasoning abilities across a wide range of tasks, but data contamination undermines the objective evaluation of these capabilities. This problem is further exacerbated by malicious model publishers who use evasive, or indirect, contamination strategies, such as paraphrasing benchmark data to evade existing detection methods and artificially boost leaderboard performance. Current approaches struggle to reliably detect such stealthy contamination. In this work, we uncover a critical phenomenon: a model's generated reasoning steps actively mask its underlying memorization. Inspired by this, we propose the Zero-CoT Probe (ZCP), a novel black-box detection method that deliberately truncates the entire Chain-of-Thought (CoT) process to expose latent shortcut mappings. To further isolate memorization from the model's intrinsic problem-solving capabilities, ZCP compares the model's zero-CoT performance on the original benchmark against an isomorphically perturbed reference dataset. Furthermore, we introduce Contamination Confidence, a metric that quantifies both the likelihood and severity of contamination, moving beyond simple binary classifications. Extensive experiments on both previously identified contaminated models and specially fine-tuned contaminated models demonstrate that ZCP robustly detects both direct and evasive data contamination. The code for ZCP is accessible at https://github.com/Yifan-Lan/zero-cot-probe.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.08185

k-SUM Hardness Implies Treewidth-SETH

Michael Lampis

原文摘要

We show that if k-SUM is hard, in the sense that the standard algorithm is essentially optimal, then a variant of the SETH called the Primal Treewidth SETH is true. Formally: if there is an $\varepsilon>0$ and an algorithm which solves SAT in time $(2-\varepsilon)^{tw}|φ|^{O(1)}$, where $tw$ is the width of a given tree decomposition of the primal graph of the input, then there exists a randomized algorithm which solves k-SUM in time $n^{(1-δ)\frac{k}{2}}$ for some $δ>0$ and all sufficiently large $k$. We also establish an analogous result for the k-XOR problem, where integer addition is replaced by component-wise addition modulo $2$. As an application of our reduction we are able to revisit tight lower bounds on the complexity of several fundamental problems parameterized by treewidth (Independent Set, Max Cut, $k$-Coloring). Our results imply that these bounds, which were initially shown under the SETH, also hold if one assumes the k-SUM or k-XOR Hypotheses, arguably increasing our confidence in their validity.

cs.CC · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34454

Nonlinear Control of Aerial Manipulator

Sandesh Thapa

原文摘要

This paper presents two controllers for a quadrotor equipped with a two-DOF robot manipulator that physically interacts with the environment. Cartesian impedance and adaptive sliding mode controllers are implemented and compared for a simple hovering task in the presence of some bounded disturbance. A full dynamic model is developed, and the two above controllers are implemented with the same initial conditions and desired position.

eess.SY · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34453

Scaling Dexterous Manipulation via Action Representation Pretraining

Yanjinghao Xu, Jiexin Zhang, Huan Zhao, Jianjia Mo, Zhenghao Yin, Dingkang Liang, Xiang Bai, Han Ding

原文摘要

Large-scale vision, language, and video pretraining has substantially improved the perception and generalization capabilities of robotic manipulation models, but action generation for complex dexterous manipulation remains constrained by the scarcity of robot demonstrations. Limited robot demonstrations must be used to learn both action representations and task-conditioned action distributions, making it difficult to adequately learn action representations and, in turn, limiting the ability of downstream generative models to generate complex dexterous actions. To address this limitation, we propose ARPG (Action Representation Pretraining and Generation), which explicitly separates action representation learning from action generation: we first pretrain an action representation encoder on large-scale human manipulation data. We then freeze the encoder and train a compact generative backbone (130M parameters) to learn task-conditioned action distributions in the encoder's representation space. Experimental results show that ARPG achieves state-of-the-art performance on the DexJoCo benchmark and improves the average success rate on bimanual dexterous manipulation from 10.1% for $π_{0.5}$ to 44.7%. Scaling experiments further show that broader coverage of human manipulation behaviors effectively improves downstream performance. These results indicate that action representation pretraining provides an independent scaling dimension for improving complex dexterous manipulation capabilities.

cs.RO

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arXiv ↗PDF ↗