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版本更新 · v3arXiv:2606.07642

Do VLMs See What Sensors Feel? A Scalable Expert-Guided Design for Wheelchair Accessibility Assessment from Street View

Dongdong Wang, Alina Hagen, Isabelle Gatmaitan, Hao Zhou, Yiwen Dong, Shabboo Valipoor, Vivian W. H. Wong, Lingyao Li

原文摘要

Assessing built-environment interaction, such as wheelchair accessibility, is challenging because real-world mobility is shaped by context-dependent and temporary barriers that are difficult to capture at scale. This paper examines whether vision-language models (VLMs) can identify potential accessibility barriers from Google Street View (GSV) imagery. We propose an expert-guided retrieval-augmented framework that combines GSV imagery, ADA-informed guidance, and expert-derived rubrics to evaluate multiple accessibility dimensions. We collect a campus-scale dataset at the University of Florida, linking 407 unique GSV locations with GPS-derived wheelchair dwell behavior as a proxy for potential mobility friction. Results show that VLM ratings are negatively correlated with dwell time and exhibit distributional similarity, with the expert-guided configuration strengthening the correlation from $r=-0.40$ to $r=-0.54$ for the strongest model. Visual cue analysis further shows that features such as curb ramps and crosswalks are associated with higher VLM accessibility scores, while correspondence remains limited for subtle surface conditions, transient obstructions, and viewpoint-dependent barriers. Overall, our findings demonstrate the potential of expert-guided VLMs for scalable preliminary accessibility screening and prioritizing locations for further investigation.

cs.CV · cs.CY

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arXiv ↗PDF ↗
版本更新 · v13arXiv:2602.16291

A Calculus of Inheritance

Bo Yang

原文摘要

Just as the $λ$-calculus is a pure functional programming language built from three primitives (abstraction, application, variable), we present inheritance-calculus, a pure declarative programming language built from three primitives (mixin, definition, reference). We introduce multi-target $\mathtt{this}$ to solve the problem that inheritance conflicts with modularity, and thereby unify object-oriented and configuration constructs: modules, classes, objects, methods, fields, and locals become a single mixin abstraction, and inheritance becomes simply set union, the deep merge of configuration languages. Consequently, composition is commutative, idempotent, and associative, structurally eliminating the multiple-inheritance linearization problem; the inheritance relation has substitutability; every path of every program can be inherited and extended; every subtree can be split into parts that, inherited together, behave as the whole; and programs have no side effects and cover every input. We call these properties modularity by construction. The calculus has no functions, yet it macro-expresses the lazy $λ$-calculus and is more macro-expressive than it in Felleisen's sense. Its semantics is first-order, denotational, and evaluated by tabling, even for cyclic inheritance hierarchies. Inheritance-calculus is distilled from MIXINv2, an implementation in which the same code acts as different function colors and ordinary arithmetic agrees with the relational semantics of logic programming.

cs.PL · cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.17100

Block Sensitivity and Certificate Complexity of Rotation Symmetric Boolean Functions

Yuan Li, Jing Zhang

原文摘要

We investigate block sensitivity and certificate complexity of monomial rotation-symmetric Boolean functions, obtaining exact formulas and structural classifications for several families. For odd-degree monomial rotation-symmetric functions, the equality $s(f)=n$ established in our earlier work yields $bs(f)=C(f)=n$. For quadratic monomial rotation-symmetric functions, we determine both measures completely and prove that block sensitivity equals certificate complexity. For consecutive quartic monomials, we determine sensitivity, block sensitivity, and certificate complexity in every residue class of the number of variables. Our principal result is an exact global block-sensitivity formula for consecutive monomials of every even degree $d\ge4$. Writing $n=q(d+2)+ρ$, where $0\leρ<d+2$ and $q\ge\max\{2,d/2-1\}$, we prove \[ bs(F_{d,n})=n-\min\left\{\left\lceil\fracρ{2}\right\rceil,d+2-ρ\right\}. \] We also establish the corresponding sensitivity formula by a self-contained argument, yielding arbitrarily large gaps between sensitivity and block sensitivity as the degree varies. For certificate complexity, we obtain exact pointwise formulas for structured run-and-gap configurations and establish additional global equalities, including $C(F_{6,8q+5})=bs(F_{6,8q+5})=8q+2$ for $q\ge2$. Our methods combine cyclic run decomposition, parity arguments, explicit sensitive-block constructions, and constant-subcube certificates. The exact global certificate complexity for arbitrary even degrees remains open.

math.CO · cs.CC · cs.DM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.10877

Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations

Mathieu Alain, So Takao, Xiaowen Dong, Bastian Rieck, Emmanuel Noutahi

原文摘要

Predicting labels for graph-structured data is crucial in many scientific applications. Recently, Gaussian processes with graph-level inputs have been proposed as flexible non-parametric models for classification tasks. In this work, we extend this framework to regression tasks and to simplicial complexes, enabling the modelling of attributes defined on edges and higher-order simplices. Drawing on Hodge theory, we construct representations based on the Hodge decomposition, allowing the model to distinguish between exact, co-exact, and harmonic signal components. We introduce Hodgelet representations as rich, learnable topological descriptors of simplicial complexes and show that our framework improves predictive performance across a range of applications. Our work broadens the applicability of Gaussian processes to prediction tasks at the graph and simplicial-complex levels.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.13898

HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing Model Serving

Haoran You, Yotam Nitzan, Lingzhi Zhang, Yifan Gong, Mang-Tik Chiu, Connelly Barnes, Yan Kang, Yuqian Zhou 等 11 位作者

原文摘要

Creative image editing tools, such as Adobe Photoshop's Remove and Generative Fill buttons, are central to everyday customer use and account for a major share of traffic. However, current generative AI models face significant latency challenges, which become even more pronounced as the field transitions from convolution-based U-Nets to Diffusion Transformers (DiTs). In our evaluation on hundreds of representative image editing samples spanning a wide range of mask ratios, the DiT module alone accounts for an average of 73% of the total model latency, even after being distilled from 50 timesteps down to 8 timesteps. To tackle this challenge, we propose $\textbf{HiLo-Token}$, an input-adaptive token compression framework that allocates a larger token budget to high-frequency, rich-context regions while assigning fewer tokens to low-frequency areas. Specifically, for the editing region specified by the user mask, we retain all tokens within a dilated mask to preserve strong locality and contextual relevance. Outside the editing region, we introduce a simple yet effective high-frequency token selection strategy based on spatial frequency to capture important local details, while using tokens from a heavily downsampled image to represent low-frequency components and preserve the coarse global structure. Extensive experiments on production-level evaluation data validate the effectiveness of the proposed method, which achieves 3.13$\times$, 2.59$\times$, and 1.67$\times$ DiT speedups on A100-80GB for image editing tasks across small, medium, and large mask ratio categories with average ratios of 6.38%, 15.92%, and 35.36%, respectively, without any regression in generation quality. In production, where the editing pipeline is served as disaggregated, independently autoscaled GPU workers, these per-request savings translate into a 33% reduction in the GPUs required to serve the models.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.17006

Sensitivity of Rotation Symmetric Boolean Functions via Binary Necklaces

Yuan Li, Jing Zhang

原文摘要

Rotation symmetric Boolean functions are important structured families in the theory of Boolean functions, where cyclic invariance permits compact algebraic descriptions while retaining nontrivial local behavior. We investigate their sensitivity through binary necklaces and monomial orbit sums. In the necklace quotient of the Boolean cube, ordinary adjacency does not record the number of coordinate flips: we give a directed flip-multiplicity formula involving stabilizers and distance-two near-periods, and derive exact local sensitivity formulas when the number of variables is an odd prime. For monomial rotation symmetric functions we determine sensitivity for all odd degrees and for quadratic orbit sums. For the consecutive-monomial family of every even degree $d\ge4$, we prove a run-cluster inequality and obtain the eventual exact formula $s(F_{d,n})=n-\min\{r,d+2-r\}$, where $n\equiv r\pmod{d+2}$, under the stated length hypothesis. The consecutive quartic case follows explicitly. We also evaluate sensitivity for cyclic run-threshold functions. Our results provide exact combinatorial and complexity-theoretic information about cyclic Boolean functions and clarify how cyclic symmetry governs local sensitivity.

math.CO · cs.CC · cs.DM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.10290

AI Organizations are More Effective but Less Aligned than Individual Agents

Judy Hanwen Shen, Daniel Zhu, Siddarth Srinivasan, Henry Sleight, Lawrence T. Wagner, Morgan Jane Matthews, Erik Jones, Jascha Sohl-Dickstein

原文摘要

AI is increasingly deployed in multi-agent systems; however, most research considers only the behavior of individual models. We experimentally show that multi-agent "AI organizations" are simultaneously more effective at achieving business goals, but less aligned, than individual AI agents. We examine 12 tasks across two practical settings: an AI consultancy providing solutions to business problems and an AI software team developing software products. Across all settings, AI Organizations composed of aligned models produce solutions with higher utility but greater misalignment compared to a single aligned model. Our work demonstrates the importance of considering interacting systems of AI agents when doing both capabilities and safety research.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.05516

Distributed Learning of $α$-Potential Functions in Strategic Multi-agent Systems

Yifei Chen, Chinmay Maheshwari

原文摘要

We study the problem of computing the tightest \(α\)-potential approximation of a general-sum game over continuous action spaces, within a prescribed class of potential functions and when each player has access only to its own utility function. The difficulty is twofold: the approximation error involves a worst-case search over an infinite set of unilateral deviations, and the required utility information is distributed across players. For a linear-in-parameters potential class, we use an exact finite-tuple reformulation that separates the problem into a global outer search over deviation tuples and distributed convex inner problems. We develop a primal--dual inner oracle tailored to this structure and establish a uniform one-sided accuracy guarantee. This oracle can be combined with global outer search to obtain an end-to-end guarantee on the outer optimization error. We also develop a projected zeroth-order outer method as a computationally lighter alternative for higher-dimensional problems. Numerical experiments illustrate the accuracy--computation tradeoff between the two outer-search methods and show that the proposed optimization framework can improve upon analytical \(α\)-potential constructions.

cs.MA · cs.GT · eess.SY · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.14802

From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift

Mushan Li, Kihyun Han, Yanyuan Ma

原文摘要

Importance weights are essential in domain adaptation under label shift, yet their utility is often undermined by the finite sample uncertainty associated with their estimation. Existing methods typically analyze this uncertainty through Gaussian elimination on interval-valued linear systems, which leads to overly conservative confidence regions and inefficient downstream applications. We propose a paradigm shift from inversion-based inference to a direct matrix constraint framework. We use this framework to define a joint confidence region and extract marginal intervals via linear programming, deriving provably tighter bounds for importance weights while maintaining exact finite-sample validity. Furthermore, we analyze the confidence region's geometry and provide the theoretical results for its diameter bounds. Evaluated across text, image, multimodal benchmarks, including AGNews, MNIST, CIFAR-10, N24News, and a real-world autonomous driving dataset, nuImages, our approach consistently yields shorter confidence intervals and smaller prediction sets than inversion-based methods.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.14320

Catalytic Tree Evaluation From Matching Vectors

Alexandra Henzinger, Edward Pyne, Seyoon Ragavan

原文摘要

We give new algorithms for tree evaluation (S. Cook et al. TOCT 2012) in the catalytic-computing model (Buhrman et al. STOC 2014). Two existing approaches aim to solve tree evaluation (TreeEval) in low space: on the one hand, J. Cook and Mertz (STOC 2024) give an algorithm for TreeEval running in super-logarithmic space $O(\log n\log\log n)$ and super-polynomial time $n^{O(\log\log n)}$. On the other hand, an earlier algorithm of J. Cook and Mertz (STOC 2020), run on a catalytic tape, solves TreeEval in logarithmic $O(\log n)$ free space and polynomial time, but with polynomial $O(\sqrt{n})$ catalytic space. We show that the latter result can be improved. We give a catalytic algorithm for TreeEval with logarithmic $O(\log n)$ free space, polynomial runtime, and subpolynomial $2^{O(\log^εn)}$ catalytic space (for any $ε> 0$). Our result also implies an improved simulation of time by catalytic space, via the reduction of Williams (STOC 2025). Our TreeEval algorithm comes from a framework, inspired by private information retrieval, that generically reduces tree evaluation to two combinatorial objects: matching-vector families and decoders (a new object we define that closely resembles decoding polynomials). These ingredients' parameters directly govern an algorithm's resources: the vectors' dimension determines its catalytic space, and the decoder's sparsity determines its free space and runtime. In addition to enabling our new algorithm, this framework explains existing tradeoffs, for example why the Cook-Mertz algorithm needs super-logarithmic space but no catalytic tape. It also lets better matching-vector families or decoders plug in directly to give better algorithms.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.25619

Relative Contact Velocity-Controlled Hand-Object Mechanism for Dexterous Tool Manipulation

Sunyu Wang, Jean Oh, Nancy S. Pollard

原文摘要

This work investigates how to enable general multi-finger robotic hands to perform the complete tool manipulation process, which entails picking up a tool, loading it into a suitable pose, and then wielding it. Inspired by human tool manipulation and mechanical design principles, we model the hand and the tool as a unified hand-object mechanism (HOM) composed of sub-assemblies. Specifically, we define a HOM as consisting of the hand, the object, and the generalized contact frames, allowing the HOM's motions to be expressed with the same set of Cartesian-space relative contact velocities, irrespective of the hand's kinematics and geometry. Then, we define a HOM's sub-assemblies as relative contact velocity and contact force constraints between fingers. Building on these definitions, we developed a lightweight and physically interpretable motion planning and contact estimation framework using least squares and a complementary filter. We evaluated our framework in simulation by teleoperating five different robotic hands. The results show that our framework enabled all five hands to execute the complete tool manipulation process, achieving dexterous behaviors even from identical, simple reference trajectories. Furthermore, the results showcase our framework's adaptability to different hands, tools, and tasks, enabled by its kinematic and geometric foundation. Please visit website for videos: \href{https://sunyuw.github.io/hom-sim/}{https://sunyuw.github.io/hom-sim/}

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.17342

Learning a Maximum Entropy Model for Visual Textures using Diffusion

Xinyuan Zhao, Eero P. Simoncelli

原文摘要

Visual textures -- spatially homogeneous image regions containing repeated elements (e.g. a field of grass, the bark of a tree) -- are ubiquitous in visual scenes and provide important cues for recognizing and analyzing materials, surface geometry and objects. A number of existing texture models extract essential statistics from a single texture image, and can then generate high-quality samples that are visually similar to the original by matching these statistics. However, their statistics are either hand-designed or based on a network pretrained for another purpose (e.g., object recognition). Here, we develop the first principled method for unsupervised learning of a set of statistics that are used to constrain a maximum entropy probability model for texture. We leverage methods developed for generative diffusion models to derive training and sampling procedures, and compare these to the traditional method of sampling via matching the statistics. Despite the compactness of our trained model (512 statistics), it generates texture images whose quality is as good as or better than the current state-of-the-art model (~177k statistics), both visually and by quantitative perceptual scoring using DreamSim. Finally, we show that images generated from parameters lying along a line between the parameter vectors corresponding to two different reference textures are homogeneous textures with features that smoothly transition between those of the two references.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2507.21183

MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge

Guangchen Lan, Sipeng Zhang, Tianle Wang, Yuwei Zhang, Daoan Zhang, Xinpeng Wei, Xiaoman Pan, Hongming Zhang 等 10 位作者

原文摘要

As the era of large language models (LLMs) unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with human preferences and improving performance. We propose Maximum a Posteriori Preference Optimization (MaPPO), a methodology for learning from preferences that explicitly incorporates prior reward knowledge into the optimization objective. Building on the paradigm employed by Direct Preference Optimization (DPO) and its variants of treating preference learning as a Maximum Likelihood Estimation (MLE) problem, MaPPO integrates prior reward estimates into a principled Maximum a Posteriori (MaP) objective. This not only generalizes DPO and its variants, but also enhances alignment by mitigating the oversimplified binary classification of responses. Additionally, MaPPO introduces no additional hyperparameters, and supports preference optimization in both offline and online settings. In addition, MaPPO can be used as a plugin for DPO variants, including widely used SimPO, IPO, and CPO, and produce consistent improvements. Extensive empirical evaluations of different model sizes and model series on three standard benchmarks (MT-Bench, AlpacaEval 2.0, and Arena-Hard) demonstrate consistent improvements in alignment performance without sacrificing computational efficiency.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2511.15974

Knowledge and Reasoning Augmented Learning for Language Model Assisted Antimicrobial Decision Support

Zhe Li, Yehan Qiu, Yuntian Huang, Xin Ding, Yujie Chen, Jing Sun, Min Yan, Lei Chen 等 18 位作者

原文摘要

Background: Antimicrobial decision support requires both clinical knowledge and patient-specific reasoning. We developed Knowledge and Reasoning Augmented Learning (KRAL) to improve language model performance on antimicrobial decision-making tasks. Methods: In this retrospective technical study, a 27-billion-parameter student model underwent guideline-grounded supervised fine-tuning followed by Multi-turn Agentic GRPO using clinical reasoning trajectories. Six configurations were evaluated on 123 clinician-validated clinical antimicrobial treatment cases from Peking Union Medical College Hospital (PUMCH) and 100 JAMA Clinical Challenge cases. A language model judge scored responses, and paired tests compared configurations. Component ablations assessed benchmark performance and training stability. Results: On the PUMCH benchmark, KRAL achieved the highest composite score (0.895 on a 0-1 scale), outperforming all five controls (all P<0.001). On JAMA, KRAL scored 0.600 and exceeded the base model with and without retrieval (P = 0.0048 and 0.0499, respectively). Reinforcement learning ablation experiments demonstrate that the reward smoothing can significantly improve the performance and training stability of vanilla GRPO. Conclusions: KRAL improved performance on a single-center clinical antimicrobial benchmark and publicly available JAMA clinical challenge cases by combining guideline-grounded learning with Multi-turn Agentic GRPO. Further external clinical validation is needed to evaluate the practical utility and effectiveness of KRAL.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.14423

Scaling Laws for Multimodal Data Mixtures

Aditi Khandelwal, Ayush K. Tarun, Yixuan Xu, Imanol Schlag, Golnoosh Farnadi, Siva Reddy, Luke Zettlemoyer, Antoine Bosselut

原文摘要

Frontier AI systems are increasingly natively multimodal, jointly pretrained on multiple modalities, such as text, vision, and audio. However, the scientific understanding of the multimodal data mixtures used to pretrain these systems --- how much of each modality to mix, and at what potential interference cost to the others --- is largely absent or gatekept. In this work, we derive data mixing scaling laws for three modalities: text, vision, and audio. Multimodal Large Language Models (MLLMs) increasingly adopt Mixture-of-Experts (MoE) architectures, as MoEs scale efficiently and partially limit cross-modal interference. We therefore use MoEs to conduct 268 experiments ranging from 457M to 8.3B parameters, on up to 150 billion tokens. Our work leads to multiple actionable insights into multimodal data mixing for training MLLMs. First, compute optima are modality-specific: audio modality is model-heavy, favoring scaling parameters over tokens, while text and vision require balanced scaling of model size and data. Second, repeating vision or audio data beyond 2x yields negligible benefits. Finally, cross-modal interactions are highly asymmetric: vision strongly interferes with audio, whereas audio's interference with learning text and vision is negligible. Overall, we provide a principled foundation for understanding the multimodal data mixtures needed to train frontier MLLMs.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.09865

Advancing Model Refinement: Muon-Optimized Distillation and Quantization for LLM Deployment

Jacob Sander, Brian Jalaian, Venkat R. Dasari

原文摘要

Correction notice (v2). Two implementation issues affect these experiments. All LoRA training ran under DeepSpeed, which replaced the optimizer passed to the Trainer with AdamW at a constant learning rate, so the Muon and Adam arms used the same optimizer. The cross-entropy term was computed without the next-token shift, so the preference for alpha = 1 reflects that objective rather than the value of the teacher. Each comparison also uses a single GPTQ quantization, and repeated quantizations differ by up to several points. The findings on Muon's quantization robustness, the distillation weight and accuracy relative to GPTQ alone should not be relied on. See the notice on page 1. Large Language Models (LLMs) enable advanced natural language processing but face deployment challenges on resource-constrained edge devices due to high computational, memory, and energy demands. Optimizing these models requires addressing three key challenges: acquiring task-specific data, fine-tuning for performance, and compressing models to accelerate inference while reducing resource demands. We propose an integrated framework combining GPTQ-based quantization, low-rank adaptation (LoRA), and a specialized data distillation process to significantly reduce model size and complexity while preserving or enhancing task-specific performance. By leveraging data distillation, knowledge distillation via Kullback-Leibler divergence, Bayesian hyperparameter optimization, and the Muon optimizer, our pipeline achieves up to 2x memory compression (e.g., reducing a 6GB model to 3GB) and enables efficient inference for specialized tasks. Empirical results demonstrate superior performance on standard LLM benchmarks compared to GPTQ quantization alone, with the Muon optimizer notably enhancing fine-tuned models' resistance to accuracy decay during quantization.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.14869

A Self-Evolving Defect Detection Framework for Industrial Photovoltaic Systems

Haoyu He, Yu Duan, Wenzhen Liu, Hanyuan Hang, Boyu Qin, Qiantu Tuo, Xiaoke Yang, Rui Li

原文摘要

Reliable photovoltaic (PV) power generation requires timely detection of module defects that may reduce energy yield, accelerate degradation, and increase lifecycle operation and maintenance costs during field operation. Electroluminescence imaging is therefore widely adopted for PV module inspection; however, automated defect detection in real operational environments remains challenging due to heterogeneous module geometries, low-resolution imaging conditions, subtle defect morphology, long-tailed defect distributions, and persistent data and label-space shifts from evolving inspection and labeling processes. These factors limit the robustness and long-term maintainability of conventional deep-learning inspection pipelines. To address these challenges, this paper proposes SEPDD, a Self-Evolving Photovoltaic Defect Detection framework designed for evolving industrial PV inspection scenarios. SEPDD integrates automated pipeline optimization with a trigger-driven evolution mechanism that re-optimizes the whole training/evaluation pipeline when deployment indicators (retraining failure, label evolution, environmental change, periodic maintenance) fire, enabling deployment-time adaptation to distribution shifts and newly emerging defect patterns. Experiments on a public PV defect benchmark and a private industrial dataset, both exhibiting severe class imbalance and significant domain shift, demonstrate SEPDD's effectiveness: it attains mAP50 of 91.5% on the public dataset and 49.5% on the private dataset, surpassing the autonomous baseline by 14.9% and human experts by 4.8% on the public dataset, and by 4.9% and 2.5%, respectively, on the private dataset; comparisons are limited to the two evaluated datasets and the studied baselines. By improving the robustness and adaptability of automated PV inspection, the proposed framework supports more reliable maintenance of distributed PV systems.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.14381

ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence

Yiwei Chen, Amila Weerasinghe, Siddharth Biswal, Utkrisht Rajkumar, Olcay Boz, Taylor Berg-Kirkpatrick, Rose Yu

原文摘要

Large language models are increasingly used as scientific assistants, yet we still lack reliable ways to evaluate whether they can investigate a system rather than merely answer a prompt. A useful evaluation must let agents choose experiments, interpret finite evidence, and make causal decisions, while preserving objective ground truth. We introduce ACED (Active Causal Evidence and Decision Benchmark), a neuro-symbolic benchmark that separates the readable scientific surface from the formal causal world: natural-language scenarios describe the system, while a hidden causal model defines the data, the effect of every intervention, and the oracle answers. In ACED-Repair, agents diagnose a failing production system: within 15 experiments they must decide which controls to test, read confounded and filtered data, and recommend a fix, which we re-simulate on a hidden true objective. In ACED-Query, agents answer causal questions and make constrained causal decisions in worlds backed by categorical probabilistic graphical models. Acceptance on ACED-Repair ranges from 43% to 0% across 12 LLMs, while strong agents approach ceiling on ACED-Query's qualification questions. Tracing every experiment shows where investigations stop: most open-weight models test a true lever on its own in fewer than half of the worlds, the strongest models test the right levers but often miss the working dose or combination, and agents that do run a working fix almost always recommend it. No model reliably repairs systems with two independent causes or systems that need a subgroup-specific policy. These results suggest that reliable AI co-scientists need explicit support for choosing experiments, keeping track of evidence, and composing interventions. Code and data are available at https://github.com/Rose-STL-Lab/ACED-Bench.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.04045

Recursive Program Synthesis from Sketches and Mixed-Quantifier Properties

Derek Egolf, Stavros Tripakis

原文摘要

We present a novel approach for the synthesis of recursive programs from relational, mixed-quantifier first-order logic properties. We solve this problem using a sketching-based, enumerative, counterexample-guided approach. Our algorithm learns syntactic constraints from counterexamples to prune the candidate space and employs a prophylactic pruning technique to avoid enumerating invalid candidates altogether. We implement our technique in a tool called Cataclyst and evaluate it on a suite of 60 benchmarks. We demonstrate that both counterexample generalization and prophylactic pruning significantly improve performance. Cataclyst solves 59/60 benchmarks, while variants of Cataclyst without counterexample generalization or prophylactic pruning solve fewer benchmarks. No existing tool can directly handle the entire class of synthesis problems that our tool can. A portfolio of existing state-of-the-art tools solves 30/60 of our benchmarks.

cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.12754

LLMs Can Better Capture Human Judgments--With the Right Prompts

Danica Dillion, Chen Cecilia Liu, Baihui Wang, Daniele Barolo, Tanmay Rajore, Niket Tandon, Pranathi Ravikumar, Kurt Gray

原文摘要

Are large language models (LLMs) bad at capturing human judgment? Two commonly stated limitations are that LLMs fail to capture full distributions of responses, and that their judgments are unstable across wording variations. We demonstrate simple prompting strategies that mitigate these limitations. Across two datasets--a U.S.--representative set of 144 moral scenarios and 38 moral beliefs from the International Social Survey Programme's Family and Changing Gender Roles module covering 32 countries--we show how simple elicitation techniques help improve AI-human alignment. First, prompting models to report standard deviations and response proportions recovers the full range of human responses better than common strategies. Second, ensuring scenarios are clear to human participants--as reflected in human confusion ratings--is associated with stronger model alignment, and LLMs can track human confusion ratings. At the same time, we find that model confidence ratings do not consistently track errors in models' judgments, though they can predict human variability relatively well. These results suggest that asking better questions to LLMs can yield better answers.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.17725

Beyond Model Scaling: Rethinking Sequence Construction for Transformer-based Network Intrusion Detection

Zeeshan Zulkifl Shah, Muhammad Ikram, Dali Kaafar, Yipeng Zhou

原文摘要

Transformer-based network intrusion detection systems (NIDS) have shown strong potential for detecting multi-stage cyberattacks, yet most methods construct input sequences using fixed temporalization schemes rather than treating sequence construction as a systems design decision. In flow-based intrusion detection, timestamp-based windowing often fragments related attack activities while merging unrelated communications, reducing the behavioral context available to transformers. We present a unified pipeline abstraction that decomposes transformer-based NIDS into four design dimensions (traffic representation, temporalization, transformer backbone, and prediction aggregation) and propose session-coherent temporalization, which constructs sequences from communication sessions instead of fixed time windows. Experiments on three complementary intrusion detection datasets show that temporalization is the dominant factor governing transformer performance: session-coherent temporalization more than doubles Macro-F1 over timestamp-based windowing, enabling lightweight transformers to achieve up to 0.95 Macro-F1 at substantially higher inference throughput than pretrained models. We further vary the transformer size from 0.65M to 3.38M parameters and find that larger transformer models provide no measurable accuracy improvement despite substantially higher computational cost, while zero-shot cross-dataset evaluation shows only modest gains under domain shift. These results indicate that preserving communication-session semantics, rather than scaling transformer capacity, is the key to accurate and efficient transformer-based intrusion detection.

cs.CR · cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.00569

Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold

Ke Xu, Yuefeng Han

原文摘要

Recovering a low-CP-rank tensor from noisy linear measurements is a central challenge in high-dimensional data analysis, with applications spanning tensor PCA, scalar-on-tensor regression, tensor-on-tensor regression, and beyond. We exploit the intrinsic geometry of rank-one tensors by casting the recovery task as an optimization problem over the Segre manifold, the smooth Riemannian manifold of rank-one tensors. This geometric viewpoint yields two powerful algorithms: Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (RGN), each of which preserves feasibility at every iteration. Under mild noise assumptions, we prove that RGD converges at a local linear rate, while RGN exhibits an initial local quadratic phase followed by linear refinement to the statistical error. Extensive synthetic experiments validate these convergence guarantees and demonstrate the practical effectiveness of our methods.

stat.ML · cs.LG · math.OC · math.ST · stat.ME

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.20536

Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models

Guanhao Zhou, Yuefeng Han, Xiufan Yu

原文摘要

This paper studies the task of estimating heterogeneous treatment effects in causal panel data models with covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) framework that cohesively deals with the underlying heterogeneity and nonlinearity of both panel units and covariate effects. CoDEAL integrates nonlinear covariate effect components (parameterized by a feed-forward neural network) with nonlinear factor structures (modeled by a multi-output autoencoder) to form a heterogeneous causal panel model. The nonlinear covariate component flexibly captures complex covariate influences on outcomes, and the nonlinear factor decomposition enables CoDEAL to effectively capture both cross-sectional and temporal dependencies inherent in the data panel. This latent structural information is subsequently integrated into a customized matrix completion algorithm, thereby facilitating more accurate counterfactual imputation. Moreover, the use of a multi-output autoencoder explicitly accounts for heterogeneity across units and enhances the model interpretability of the latent factors. We establish theoretical guarantees on the convergence of the estimated counterfactuals and demonstrate the compelling performance of the proposed method using extensive simulation studies and real-world data applications.

stat.ML · cs.LG · econ.EM · stat.ME

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.25005

The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure

Kaiqiao Han, Katarzyna Pruś, Mark Steedman, Yizhou Sun

原文摘要

Recent work uses an NLI benchmark of the imperfective paradox to argue that large language models exhibit a Teleological Bias and Calibration Crisis. We identify three validity concerns in benchmark construction, including aspectual reduction, semantic bias, and relation bias. Under a strict NLI standard, 76% of the Group A instances do not explicitly rule out culmination; in native-speaker annotation, 38% of Group A and 29% of Group C examples permit an alternative interpretation. To better isolate the semantic contrast, we construct Lexically Matched Minimal Pairs. Moreover, by formulating event-semantic NLI as a multi-step reasoning problem, we evaluate intermediate semantic judgments and use oracle-guided settings. We find that incorrect NLI entailment does not fully support teleological bias. Prompting often changes final NLI decisions without reliably improving the underlying semantic judgments, offering a possible explanation for the Calibration Crisis.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.24405

Generative OOD-regularized Model-based Policy Optimization

Aysin Tumay, Jiahe Huang, Elise Jortberg, Rose Yu

原文摘要

We study sequential decision-making with offline reinforcement learning (RL). Traditional offline RL policies may result in out-of-distribution (OOD) actions when training relies only on sparse offline representations. To ensure safe offline policies in a sparse state-action space, we explore how density estimation models can be integrated into model-based RL methods to avoid OOD regions. Generative models are capable of explicitly modeling the density in sparse state-action spaces. Building on this, we introduce Generative OOD-regularized Model-based Policy Optimization (GORMPO), a density-regularized offline RL algorithm that uses generative density modeling to restrict policy updates to high-density areas of the dataset. Furthermore, we examine whether better OOD detection corresponds to better model-based offline policies. We compare (1) the OOD detection capabilities of various density estimators and (2) their performance within the GORMPO framework on a real-world medical dataset and sparse offline RL datasets. We provide theoretical guarantees bounding GORMPO's performance gap in terms of density estimation error, ensuring near-optimal policies under imperfect density estimates. Empirically, GORMPO remains competitive in reward and improves clinical metrics compared to state-of-the-art clinical RL models, and outperforms state-of-the-art offline RL models in offline RL benchmarks. Our empirical findings show that better OOD detection generally results in improved policies in environments with stable dynamics, while conservative penalties with poor density estimation are favored when dynamics are uncertain. Code, checkpoints, sparse, and OOD D4RL datasets are available at https://github.com/Rose-STL-Lab/GORMPO.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.11894

3D Point Splatting for mmWave Radar Novel View Synthesis

Adnan Armouti, Yixuan Gao, Rajalakshmi Nandakumar

原文摘要

Solving novel view synthesis (NVS) for millimeter-wave (mmWave) radar requires a renderer that is physically faithful, complex-valued, and multi-viewpoint-tractable. No prior method achieves these three properties simultaneously. Differentiable Monte Carlo (MC) ray tracers implement the radar forward model directly with explicit material modeling and complex outputs, but a joint fit across views takes over an hour per scene. Optical-NVS ports of NeRF, hash grids, and 3D Gaussians train fast but discard phase and replace explicit material modeling with opaque learned features, restricting them to power-only range-azimuth (RA) magnitudes. We propose 3D Point Splatting (3DPS), the first differentiable point splatting renderer for radar, derived directly from the standard solid-angle form of the radar equation. Each oriented 3D point carries a material model built on ITU-R P.2040 permittivity and slab reflection, evaluated in closed form, with the resulting complex phasor splatted into range bins through a precomputed point spread function (PSF). The complex-valued output makes the renderer product-agnostic. The same optimized scene yields analog-to-digital converter (ADC), complex range profile (CRP), and RA outputs through standard fast Fourier transform (FFT) pipelines without retraining for each format. On six outdoor ColoRadar scenes, 3DPS reaches 0.587 mean Pearson correlation on held-out RA images. This is between 1.7x and 5.2x the three optical-NVS baselines (RadarSplat, Radar Fields, DART) and 1.3x the mmIR ray tracer at 1/25 of its wall-clock time. Training takes approximately 3 minutes per scene on a single RTX 4090. Project page: https://3d-point-splatting.github.io/

cs.CV · cs.GR · cs.LG · eess.SP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.05755

Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement

Haodong Liang, Lifeng Lai

原文摘要

We investigate the ability of transformers to perform in-context reinforcement learning (ICRL), where a model must infer and execute learning algorithms from trajectory data without parameter updates. We show that a linear self-attention transformer block can provably implement policy-improvement methods, including semi-gradient SARSA and actor-critic, via explicit parameter constructions. Beyond existence, we design a teacher-mimicking training procedure, analyze its gradient-flow dynamics, and establish the first convergence guarantee in the ICRL literature: under suitable richness conditions on the training MDP distribution, gradient flow converges locally and exponentially to an optimal parameter manifold corresponding to the desired RL update. Empirically, training transformers on randomly generated tabular MDPs confirms these predictions: the learned models recover the parameter structure of our explicit constructions and, when deployed on unseen MDPs, deliver strong in-context control performance. Together, these results illuminate how transformer architectures internalize and execute classical reinforcement learning algorithms in context, bridging mechanistic understanding and training dynamics in ICRL.

stat.ML · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2405.12886

Improved Asymptotic Runtime of Gotzmann's Decomposition

Joseph Donato

原文摘要

In the study of Hilbert schemes, the integer partition or Gotzmann's decomposition of a Hilbert polynomial helps researchers identify some geometric and combinatorial properties of the scheme in question. To aid researchers in extracting such information from a Hilbert polynomial, we describe an efficient algorithm which can identify if $p(x)\in\mathbb{Q}[x]$ is a Hilbert polynomial and if so, recover the integer partition $λ$ associated with it.

cs.SC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.05973

Towards Reliable LLM Evaluation: Correcting the Winner's Curse in Adaptive Benchmarking

Jiefu Zhang, Yang Xu, Haixiang Sun, Zihan Zhou, Tianyu Cao, Vaneet Aggarwal

原文摘要

Adaptive prompt and program search makes LLM evaluation selection-sensitive. Once benchmark items are reused inside tuning, the observed winner's score need not estimate the fresh-data performance of the full tune-then-deploy procedure. We study inference for this procedure-level target under explicit tuning budgets. We propose SIREN, a selection-aware repeated-split reporting protocol that freezes the post-search shortlist, separates splitwise selection from held-out evaluation, and uses an item-level Gaussian multiplier bootstrap for uncertainty quantification. In a fixed-shortlist regime with smooth stabilized selection, the estimator admits a first-order item-level representation, and the bootstrap yields valid simultaneous inference on a finite budget grid. This supports confidence intervals for procedure-performance curves and pre-specified equal-budget and cross-budget comparisons. Controlled simulations and MMLU-Pro tuning experiments show that winner-based reporting can be optimistic and can change deployment conclusions, while SIREN remains close to the finite-sample reporting target.

stat.ML · cs.AI · cs.LG · stat.AP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.09645

Deep Gaussian Processes on Directed Acyclic Graphs

Federico L. Perlino, Oliver Hamelijnck, Adam M. Johansen, Theodoros Damoulas

原文摘要

Many real-world processes can be represented as compositions of functions along a directed acyclic graph (DAG). In causal modelling, these correspond to the underlying mechanisms; in engineering, to multiple fidelity levels; and in gene-regulatory networks, to transcription factors. These functions are partially observed across the DAG, with noisy and heterogeneously sampled measurements, posing significant challenges for reconstruction, uncertainty propagation, and inference. To tackle these challenges, we place priors over functions and naturally arrive at Deep Gaussian Processes over DAGs. We theoretically study their prior-collapse behaviour and the effect of graph topology and intermediate observations on the preservation of information. We obtain almost-sure lower bounds on the asymptotic frequency of depths at which the distinction between inputs is preserved, identify broad kernel classes for which these hold, and prove an observation by \citep{dunlop2018} on the role of input connections. We offer a structured variational approximation that retains graph dependencies, preserves compositional uncertainty, and captures the explaining-away behaviour of colliders. Finally, we empirically validate our theory and methodology at scale and under DAG misspecification. We then model a latent-collider DAG, a protein signalling network, and a multi-fidelity \mbox{heavy-ion} collision emulation task, attaining state-of-the-art performance while recovering low-fidelity contributions and yielding interpretability over the simulator hierarchy.

stat.ML · cs.LG · math.ST · stat.CO · stat.ME

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arXiv ↗PDF ↗