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版本更新 · v3arXiv:2605.16757

NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning

Haoran Lu, Luyang Fang, Wenxuan Zhong, Ping Ma

原文摘要

Multi-agent language systems are often built as hand-designed workflows, where agents are assigned semantic roles and communication protocols are specified in advance. We propose NeuroMAS, a framework that treats a multi-agent language system as a trainable and scalable neural-network-like architecture with hidden textual intermediate signals. In NeuroMAS, language-agent nodes are role-free but structure-aware: the topology determines how information can flow, while reward-based training determines how nodes communicate, specialize, and coordinate. This formulation shifts multi-agent design from workflow engineering toward architecture design, where depth, width, connectivity, and growth protocol become scalable sources of capability. We provide a theoretical perspective showing why such modular text-valued computation can be parameter-efficient when tasks admit stable decompositions. Experiments show that NeuroMAS improves over both inference-time collaboration methods and trained baselines, and that its gains are not explained by trainable parameter count alone. We further find that organizational scaling is path-dependent: larger systems can fail when trained from scratch, but become stronger when grown progressively from smaller trained systems. These results suggest that learned multi-agent organization is a promising scaling axis for language-model reasoning and generation.

cs.AI · cs.MA · stat.ME · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.09993

Beyond Bayesian Nash: Learning Minimax-Regret Equilibria for Adversarial Team Games under Asymmetric Information

Naman Aggarwal, Jonathan P. How

原文摘要

Adversarial team games (ATGs) with asymmetric information, such as adversarial path-finding, goal search, and reachability games on graphs, require strategies that are robust to hidden opponent types, such as a hidden goal flag, and to deception. Under asymmetric information, deception is seen as strategic shifts in the type distribution such that the omniscient opponent can collude with Nature and condition its play on the observed type. Existing risk-neutral solution concepts, such as Bayesian Nash equilibrium (BNE), are sensitive to distribution shifts, while distributionally robust approaches provide guarantees only within a prescribed ambiguity set. To address these limitations, we introduce Probabilistically Robust Minimax-Regret Equilibrium (PR-MRE), a novel equilibrium concept that combines the distribution-free robustness of minimax-regret reasoning with probabilistic information from a nominal type distribution. PR-MRE minimizes worst-case regret over a high-confidence subset of the type space, providing protection against strategic redistribution of probability mass while avoiding the conservatism of fully distribution-free approaches. We show that, for finite normal-form Bayesian games, approximate PR-MRE computation can be formulated as a robust bilinear program and derive a tractable semidefinite relaxation. We then adapt this relaxation into a novel meta-solver within a robust double-oracle framework, PRMRE-PSRO, enabling population-based learning of approximate PR-MRE strategies via deep reinforcement learning best responses. Experiments on graph-structured adversarial team games demonstrate that PR-MRE discovers strategies with substantially improved worst-case performance across hidden types compared to risk-neutral Bayesian Nash equilibrium solutions, resulting in more robust behavior under strategic distribution shifts.

cs.GT · cs.AI · cs.LG · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08749

How are viral trends different from educational and commercial uses of LLM image generation and editing? A privacy-preserving descriptive analysis

Will Tucker-Ray, Marika Swanberg, Alexander Knop, Marvin Chow, Eric Culbertson, Julian Jacobs, Pritish Kamath, Ravi Kumar 等 12 位作者

原文摘要

Gemini's Nano Banana models, first introduced in late 2025, led to a shift in the capabilities and use of image generation and editing. We utilize Urania, a Google-developed framework for generating insights about LLM chatbot interactions with rigorous differential privacy (DP) guarantees, to analyze use cases and intents, techniques and ways of prompting, without ever looking at a single image or individual prompt. We select a subsample of 2.1M conversations in early 2026 which utilized Nano Banana models, into roughly 800 clusters in five categories: (1) Social & Viral Sharing, (2) Brand & Marketing Collateral, (3) Educational & Technical Information Media, (4) Product Visualizations & E-commerce, and (5) Architectural & Spatial Designs. We then compare these clusters on 21 features of use, measuring aspects of prompts' (a) editing techniques, (b) artistic style requests, (c) composition, and (d) chatbot interactions. This allows us to describe a baseline of how people are using image generation and editing tools for different use cases---(including growing and emergent use cases which were not possible before Nano Banana)---in a privacy-preserving manner.

cs.CY · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08745

I Don't Care How You Hide It, I Care What You Do: Runtime Detection of Malicious ML Models

Hala Ali, Andrew Case

原文摘要

Pre-trained machine learning (ML) models shared through public platforms can be weaponized to execute malicious payloads during loading or inference. Existing scanners prove inadequate for detecting malicious models as they rely on static analysis and function denylists, support limited serialization formats, and miss attacks that replace denylisted functions with equivalent alternatives, exploit unsupported loading paths, or abuse legitimate framework APIs. To address this problem, we present ModelShield, the first runtime behavioral detection system for malicious ML models that attributes observed system-level behavior to specific model operations. It combines application-level instrumentation of ML framework APIs with eBPF-based syscall tracing, tracking model behavior across spawned threads and processes to separate model-triggered activity from environment noise. Attributed syscall sequences are classified using 195 detection patterns mapped to MITRE ATT&CK techniques. We implemented ModelShield to support 17 ML frameworks and 36 formats. On an independent dataset, it is the only evaluated tool to achieve zero false positives, with false negatives limited to payloads that produce no malicious runtime behavior. It detected all 29 published evasion variants spanning function substitution, loading-path obfuscation, and TensorFlow API abuse, where no existing scanner achieved complete coverage. Across 145,000 models scanned from public platforms, it captured 41 malicious models on Hugging Face, including a reverse-shell payload and a payload that performed host reconnaissance and outbound exfiltration. Among these, ModelScan, PickleScan, Fickling, and ModelAudit detected only 29%, 56%, 80%, and 93%, respectively. Despite runtime overhead, ModelShield has a median analysis time of 12.3 seconds, with 90% of scans completing within 35.5 seconds, making it practical for hub-scale screening.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.10834

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery

Xiao Liang, Zhen Qin, Zhihui Zhu, Shuang Li

原文摘要

This paper studies the landscape of the simultaneous blind deconvolution and phase retrieval (BDPR) problem via a structured low-rank tensor recovery framework. Since the complicated sensing structure makes direct landscape analysis technically challenging, we introduce a tractable tensor sensing surrogate that preserves the essential low-rank structure. We first analyze its population risk, characterize the global landscape on the unit sphere, and establish local linear convergence of Riemannian gradient descent (RGD) under mild conditions. We then extend the analysis to tensor sensing under a tensor restricted isometry property (TRIP), establishing local geometric and RGD convergence guarantees, and quantify robustness to measurement noise. Our theoretical results are further supported by extensive numerical experiments. These findings offer insights into the optimization landscape of the surrogate tensor sensing problem, thereby providing guidance for studying and solving the original BDPR problem.

eess.SP · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.11639

Inter-Layer Hessian Analysis of Neural Networks with DAG Architectures

Maxim Bolshim, Alexander Kugaevskikh

原文摘要

Automatic differentiation computes loss Hessians, while interpreting inter-layer interactions requires an explicit choice of graph-indexed blocks. This paper presents an analytical formalism that decomposes the full Hessian into blocks indexed by the DAG of an architecture. The standard decomposition $H = H^{GN} + H^T$ separates the Gauss-Newton component (positive semidefinite for a convex output loss) from the tensor component (residual curvature). If every node map is locally affine in its parent activations, the tensor component of the input Hessian vanishes on each smooth cell ($H^{T}_{v,w}=0$, $H^f_{v,w}=H^{GN}_{v,w}$); ReLU activations alone do not suffice. The full parametric Hessian contains residual terms that do not reduce to the GGN. Building on this decomposition, we use diagnostic metrics (inter-layer resonance $\mathcal{R}$, geometric coupling $\mathcal{C}$, stable rank $\mathcal{D}$, GN-Gap) with stochastic HVP-based estimates whose cost depends on graph evaluation and the number of probes. The theoretical analysis gives conditional bounds on curvature decay and residual-chain transport; empirical examples span fully connected MLPs (Exp. 1-5) and convolutional architectures (ResNet-18, ${\sim}11$M parameters, Exp. 6). When the architecture reduces to a single node, all definitions collapse to the standard Hessian $\nabla^2_θ\mathcal{L}(θ)\in\mathbb{R}^{p\times p}$. Archival correction. The original general novelty claims are no longer maintained: general DAG-Hessian calculus has prior precedents. This is a corrected archival version.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v7arXiv:1202.4961

Strongly universal string hashing is fast

Daniel Lemire, Owen Kaser

原文摘要

We present fast strongly universal string hashing families: they can process data at a rate of 0.2 CPU cycle per byte. Maybe surprisingly, we find that these families---though they require a large buffer of random numbers---are often faster than popular hash functions with weaker theoretical guarantees. Moreover, conventional wisdom is that hash functions with fewer multiplications are faster. Yet we find that they may fail to be faster due to operation pipelining. We present experimental results on several processors including low-powered processors. Our tests include hash functions designed for processors with the Carry-Less Multiplication (CLMUL) instruction set. We also prove, using accessible proofs, the strong universality of our families.

cs.DB · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08731

eSharp: Diffusion-based Sharp Monocular Metric Event-to-Depth Estimation

Qiushi Dai, Aniket Dashpute, Yuhao Liu, Rishubh Parihar, Guha Balakrishnan, Ashok Veeraraghavan

原文摘要

Depth perception is fundamental for autonomous navigation, industrial inspection, and mixed-reality systems. It often operates under strict constraints on power and form factor. In such cases, monocular depth estimation (MDE) is often used. Even as recent advances continue to improve monocular depth quality, conventional frame-based MDE systems remain challenged by fast motion and high-dynamic-range scenes. This work introduces eSharp, an MDE framework that addresses these problems using an event camera. eSharp repurposes pretrained diffusion priors for event-only MDE and produces accurate metric depth with unparalleled sharpness on real data. Real-world event datasets provide only sparse LiDAR depth maps for ground-truth supervision, making dense prediction challenging. We address this limitation through cross-modal distillation, using LiDAR-calibrated dense pseudo-labels generated from a spatially co-registered frame camera to train eSharp. Experiments show that eSharp outperforms prior event-to-depth baselines in both metric-depth accuracy and spatial fidelity, establishing diffusion models as a promising foundation for event-driven 3D perception.

cs.CV · eess.IV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.10039

Budgeting Discretion: Theory and Evidence on Street-Level Decision-Making

Gaurab Pokharel, Sanmay Das, Patrick J. Fowler

原文摘要

Street-level bureaucrats, such as caseworkers, triage nurses, and school administrators, operate under a fundamental tension. Official policy on who to prioritize may conflict with bureaucrats' estimation of who would benefit most. The theory of street-level bureaucracy argues that frontline agents bridge this gap, overriding policy when professional judgment identifies opportunities to improve outcomes. As algorithmic recommendations enter allocation settings, understanding this behavior is key to designing tools that account for professional judgment. We formalize discretion as a finite-horizon dynamic allocation problem. An agent follows a default policy but has a budget of $K$ overrides across $T$ periods. Each period, the agent observes the welfare gain from deviating and decides whether to spend an override now or save it for later. We show that the optimal policy is a state-dependent threshold rule and prove a behavioral invariance: for location-scale families of improvement distributions, the rate at which an optimal agent exercises discretion is independent of welfare-gain magnitude and depends only on the distribution's shape. Fat-tailed gains induce patience (conserving overrides for rare, high-stakes cases); thin-tailed gains induce routine, front-loaded spending. We also compare the model's implications with data from a homelessness service system. In the model, a capacity opening makes an override cheaper to spend, lowering the threshold for using one. In the data, override rates shift with short-run capacity openings in scarce housing, rising when exits free slots and falling when interventions are congested. Descriptively, spending is front-loaded: override rates peak on Mondays and early in the fiscal year. Together, the model and data point to systematic structure in discretion, informing the design of more efficient and equitable human-AI allocation systems.

cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08720

The GPU Needs an Operating System

Reza Nourai, Behnam Bastani

原文摘要

AI chips sit mostly idle while serving models, because replies must be fast and each step waits on memory. On two large open models, the math units were busy only about 6-11% of the time. We argue the GPU needs an operating system that keeps urgent work on schedule and fills idle capacity with other useful work. A feature that ships today recovers a quarter of a GPU with work designed to share, and a tenth with real jobs, at 5% slower decode. Our research system does better: one resident kernel runs a whole model faster than vLLM's best for a single request, and shares the GPU at under 2% slowdown. Scaling beyond one hand-built model needs compilers and a few new chip features, which we specify.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08714

Patching the Blind Spots: Reversible LLM Rule Overlay for Frozen Fraud Detectors

Bo Qu, Zhurong Wang, Frank Zahradnik, Ming Ouyang

原文摘要

In industrial tabular fraud classification, gradient-boosted trees remain state-of-the-art, but even a well-trained model has blind spots, and retraining the deployed model is not always safe. We propose a framework that patches such a model without modifying it: LLMs propose rules anchored on features the model underweights by SHAP, and the resulting noisy pool is screened for empirical support and calibrated into an additive, reversible log-lift overlay that runs at the production cut-off. Single-LLM benefit is high-variance and inverts between datasets, so we pool three diverse backbones by equal-weight logit averaging, trading peak gain for a bounded worst case. On a real-world e-commerce fraud detector trained on selection-biased data, continued training regresses even when carefully tuned, whereas the overlay gains on average +2.26% recall at equal precision ($Δ$R@P) and +1.60% precision at equal recall ($Δ$P@R) over production cut-offs spanning 30-57% precision and 23-63% recall. On a public benchmark of seven designed bias-shift variants it raises $Δ$P@R on every variant and keeps $Δ$R@P near neutral. Each amendment is a readable predicate naming the fraud pattern it covers.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08709

Levenshtein-type bounds for $(a,b)$-avoiding spherical codes

Peter Boyvalenkov, Danila Cherkashin, Peter Dragnev, Peter Grabner

原文摘要

We develop a Levenshtein-type linear programming framework (for maximal $s$-codes and for minimal energy) for $(a,b)$-avoiding spherical codes, that is, codes whose inner products between distinct points avoid $(a,b)$. We introduce and investigate signed measures that are positive definite up to an appropriate degree and yield stationary auxiliary linear programming polynomials and exact quadrature formulas. We give explicit conditions for positivity of the quadrature weights and sufficient Krein-type conditions for nonnegative Gegenbauer coefficients (positive definiteness of the candidate optimal linear programming polynomials). Under these hypotheses and suitable node-location assumptions, the resulting cardinality bounds are optimal within the corresponding degree restriction and unique under natural nondegeneracy conditions. Under proper conditions on the quadrature nodes, we obtain universal lower energy bounds by utilizing Hermite interpolants as polynomial solutions of the respective linear program. Common test functions characterize optimality in the full polynomial linear programs under mild additional assumptions.

cs.IT · math.CO · math.MG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08704

Contextuality and Contradictions: Exploring Real-World Identifications of AI-Generated Writing

Michelle Morgenstern, Julia Witte Zimmerman, Tabia Tanzin Prama, Kristine Harootunian, June Clark, Bryn Loftness, Calla Beauregard, Juniper Lovato

原文摘要

As AI-generated content spreads into nearly every domain of human interaction, practices and discourses of AI detection, both formal and informal, have become an everyday part of how people interact with text and with each other. In this paper, we explore the real-world practices and discourses of AI identification that have emerged in response to a shifting discursive landscape where the human provenance of a text can no longer be taken for granted. We pair qualitative coding with computational analysis of lexical change, grounded in linguistic anthropological perspectives on language and communication, to investigate AI identifications among English Wikipedia editors and students. We find that these identifications draw on a diversity of evidentiary types, from discrete textual features and judgments about content to circular sense-making claims and presumptions about the author's moral character, and that they are contradictory, dynamic, and contextually dependent. In practice, what often motivates an AI identification is not the presence of any specific textual element, but pragmatic infelicity: a perceived mismatch between the elements present and those deemed appropriate within the text's particular context. Consequently, real-world identifications of AI-generated writing do not merely neutrally point out the perceived presence of "AI-like" features, but can be leveraged as a discursive resource to maintain and define sociolinguistic norms: what can be said in a given context, what is appropriate, and, ultimately, what is human. Further, the design and implementation of systems and tools that treat AI-likeness as a context-free property of text may therefore inherit, and enforce at scale, the norms of whichever contexts they were trained on.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08697

CPU Micro-Architectural Characteristic Sensitivity Study on CPU-Side Agentic Task Latency

Jack Adiletta, Shixin Ji, Zhuoping Yang, Peipei Zhou

原文摘要

We perform a novel study of CPU micro-architectural characteristics on how they affect agentic AI CPU side latency. Small enterprises and individual users looking to reduce costs and seeking data privacy benefit from running agents locally, motivating an investigation into the optimal performance characteristics of an edge CPU to improve user experience. We investigate the relationship between CPU core frequency, CPU uncore frequency, active physical cores, and symmetric multi threading with agentic task latency in single and multi agent configurations. Across eight experiments on an Intel edge CPU, we find that P-core frequency produces the largest median latency improvement among the evaluated parameters. Increasing P-core frequency from 2.5 to 5.5 GHz yields a 1.154$\times$ speedup for a single agent and a 1.341$\times$ speedup for eight concurrent agents. We are the first to characterize hardware micro-architectural relationships on single and multi agent configurations with TerminalBench; we also provide an open source framework and dataset for repeatable CPU side latency agentic benchmarking.

cs.DC · cs.AR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.10147

From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs

Wish Suharitdamrong, Muhammad Awais, Xiatian Zhu, Sara Atito

原文摘要

Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actually travel through the network to shape an answer? Despite their growing role in research and real-world applications, the internal pathways through which audio and visual tokens influence the final prediction remain poorly understood. In this study, we examine audio-visual information flow inside Audio-Visual Large Language Models (AVLLMs), tracing how AVLLMs route, utilize, and integrate audio and visual information across two input configurations, audio-visual video and multiple interleaved audio-visual items. We find that for audio-visual video, AVLLMs follow the sequential information flow pathway established for VLMs and VideoLLMs, with audio and visual contribution flowing along this pathway in proportion to the task's reliance on each modality. In settings with multiple interleaved audio-visual items, this routing shifts to different parallel streams. Furthermore, we demonstrate that audio-visual and other token types can be discarded once their information is transferred to LLM, with minimal impact on the model's prediction or even slight improvement, generalizing across multiple tasks and datasets, enabling more efficient inference. These findings hold across multiple models and scales, Qwen2.5-Omni and Video-SALMONN2 Plus at 3B and 7B scales, leading to hypotheses on why these flow structures emerge. Together, these results deliver the first coherent picture of how AVLLMs orchestrate sound and sight inside the network and lay the groundwork for the next wave of interpretability, design, and efficiency advances in audio-visual and broader MLLMs.

cs.AI · cs.CL · cs.CV · cs.SD

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.28990

Agentic AI uncovers conserved cross-tissue protein co-abundance programs inaccessible to single-dataset analysis

Dehao Wu, Runyu Guan, Qiqi Xie, Yang Li, Haohan Wang

原文摘要

Protein co-abundance clusters preserved across tissues can nominate shared disease mechanisms and therapeutic targets, particularly where proteins implicated in organ-confined diseases converge in other tissues. Yet cross-tissue proteomic studies have examined pre-selected tissue pairs, leaving most combinations, and non-obvious relationships among them, unexplored. Exhaustive comparison requires consistently assessing thousands of candidate clusters from hundreds of tissue-pair networks across heterogeneous evidence, a scale beyond manual curation. We present an LLM-agent framework for large-scale, evidence-grounded comparison of tissue-specific protein co-abundance networks. From sample-level proteomic data, it constructs tissue networks, derives conserved clusters for each tissue pair, integrates evidence from expression atlases, interaction and complex databases, pathways, disease associations and the literature, and proposes mechanistic hypotheses in which every conclusion cites its evidence. Applied to all 231 pairs among 22 human tissues and sample groups, it identified 1,825 conserved clusters in 216 tissue pairs. Co-abundance relationships were shared beyond chance in 76% of pairs, including unrelated organs, and shared clusters were only weakly associated with abundance similarity. Clusters were enriched 3.7- to 6.9-fold for known interactions and complex memberships, and 379 of 793 testable clusters replicated in independent CPTAC proteomes. Some clusters pointed to relationships absent from current databases: one shared by liver and bone marrow linked three genes causing inherited white matter disorders, suggesting a common mechanism via the integrated stress response. Conserved co-abundance clusters thus offer a hypothesis-generating resource for studying disease proteins beyond the affected organ. Code: https://github.com/jacob20030316-crypto/Protein_Co_Abundance_Agent

cs.AI · q-bio.MN

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.18699

RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories

Yunfu Deng, Sangwoo Shin, Brahma S. Pavse, Josiah P. Hanna

原文摘要

Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world models. These rollout-based approaches impose infrastructure requirements; simulator and world-model evaluators additionally face prediction errors that can compound over the evaluation horizon. In this paper, we study off-policy evaluation as an alternative to rollout-based robot-policy evaluation methods. Specifically, we introduce RoboFQE, an evaluator that estimates the discounted expected return of a target policy from offline robot trajectories without estimating a behavior-policy density, combining fitted Q-evaluation with a pretrained vision foundation model and learned terminal success annotation to bridge the gap between standard OPE benchmarks and continuous-control visual manipulation. Controlled experiments across simulated and real-world manipulation tasks show that RoboFQE produces accurate discounted-return estimates and preserves policy rankings even on long-horizon manipulation tasks where video world-model evaluators are degraded by autoregressive prediction errors. Ablation experiments show that pretrained vision encoders substantially outperform from-scratch alternatives, particularly on long-horizon tasks, and that learned terminal success annotation enables evaluation without ground-truth reward labels with only slight reduction of accuracy.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08665

Dynamic GPU Voxel Grids and Voxel-sequential Processing in NanoVDB

Eftychios Sifakis, Francis Williams, Christopher Horvath, Rohan Sawhney, Mark Harris, Jonathan Swartz, Ken Museth

原文摘要

We introduce two fundamental advances to the NanoVDB data structure: dynamic GPU-native topological updates, and a voxel-sequential access paradigm that dramatically accelerates point-wise and stencil-based operators on sparse grids. NanoVDB's linearized memory layout has delivered strong GPU performance, and the Index-Grid concept introduced in the fVDB framework [Williams et al 2024] provided topology/data separation and optimal compactness of storage in sparse machine learning workloads. However, all these prior iterations of NanoVDB assume a static topology that has precluded use of this data structure in simulation and dynamic geometric processes. We eliminate this limitation by introducing a suite of dynamic topology operators -- morphological dilation, grid merging, pruning, and cross-resolution coarsening and refinement. These operators use bit-parallel algorithms to reconstruct grids containing hundreds of millions of voxels in just a few milliseconds. We also introduce a voxel-sequential access paradigm and data structure for GPU kernels operating on sparse data. We stream active voxels as a flat linear sequence, minimizing or eliminating the need for tree traversal in the process. This achieves compute density and regularity that allow even complex stencil-based operators to approach peak memory bandwidth, with minimal storage overhead. Together, these contributions remove the read-only constraint from NanoVDB, establishing it as a high-performance primary representation for dynamic GPU simulation and geometry manipulation. We evaluate the efficacy of our newly introduced operators in benchmarks sourced from applications such as 3D reconstruction, fluid simulation, and large-scale mesh voxelization.

cs.GR · cs.CV · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08664

ForPC: Force-Aware Whole-Body Control for Precise Humanoid Cart Pushing

ZhenYu Zou, Xing Liu, ShiChuang Wang, YunLong Dong, LiNan Deng, Feng Hua, Jun Wan, Cheng Cheng

原文摘要

Humanoid robots are expected to take over strenuous material handling in manufacturing, logistics, and healthcare, where operating existing wheeled carts would let them move heavy loads without modifying the workplace. However, humanoid cart pushing remains challenging because the bimanual grasp closes a force-coupled loop in which the contact wrenches that drive the cart also disturb the robot's balance, and payload changes alter the coupled dynamics. We propose ForPC, a force-aware whole-body control framework that regulates cart velocity through two-stage reinforcement learning. A locomotion prior is first trained under varied hand loads and upper-body postures to provide balance support. A force latent and a state latent are then inferred from onboard observations, without external force sensors, and fed to a residual policy that coordinates bounded arm, waist, and leg adjustments with base-velocity commands. Multi-objective rewards penalize bimanual internal opposition, grasp slip, and actuator saturation, encouraging the policy to apply contact force on demand without human demonstrations. Sim-to-sim experiments show that ForPC reduces cart-velocity RMSE by up to 68.2% relative to a locomotion-only baseline (Loco-only) and cross-track RMSE by up to 74.2% on the multi-bend route. Trained solely in simulation, the policy also transfers to a physical Unitree G1 without real-world training or fine-tuning and, with no payload-specific adjustment, pushes a cart carrying up to 30 kg at 0.5 m/s. These results suggest that humanoid robots could eventually replace humans for efficient material handling in daily life.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.02582

Sensitivity and Locality Lower Bounds via Low-Soundness Label Cover

Noah Fleming, Max Hopkins, Yuichi Yoshida

原文摘要

We study the sensitivity of approximation algorithms, which measures how much their output distributions change under a single input edit. We show that, for Set Cover, Dominating Set, Min-Rep, and minimum unweighted directed 3-spanner, even a $\log^βn$ approximation requires sensitivity $n^{Ω(1)}$, for some absolute constant $β>0$ and all sufficiently large $n$. The lower bounds hold even for randomized algorithms with unrestricted computation. Here $n$ is the universe size for Set Cover and the number of vertices for the graph problems. On bounded-degree graphs, our sensitivity lower bounds imply logarithmic locality lower bounds in the randomized LOCAL model and its non-signaling generalization. For Dominating Set, fix $\varepsilon\in(0,1)$ and a sufficiently large constant maximum-degree bound $Δ$. A $(1+\varepsilon)\lnΔ$ approximation can be achieved with sensitivity and LOCAL round complexity independent of $n$. We show that improving the ratio to $(1-\varepsilon)\lnΔ$ requires sensitivity $n^{Ω_{\varepsilon,Δ}(1)}$, and hence locality $Ω_{\varepsilon,Δ}(\log n)$. For minimum unweighted directed 3-spanner, every constant approximation factor requires logarithmic locality, even on DAGs of constant maximum total degree. The key ingredient is a polynomial sensitivity lower bound for a variant of low-soundness Label Cover. Fleming and Yoshida established such a lower bound when the required fraction of satisfied constraints on satisfiable instances is close to one. We extend this to inverse-polylogarithmic fractions, enabling the reductions to the problems above. We adapt the Impagliazzo--Kabanets--Wigderson direct-product construction and Dinur--Harsha iterated composition to control the propagation of input edits, and design recovery procedures that are stable under changes to both the supplied labeling and the source instance.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.30058

Emergence of a Shared Canonical Object Frame from In-the-Wild Videos

Tom Fischer, Martin Sundermeyer, Adam Kortylewski, Eddy Ilg

原文摘要

Comparing object orientations and positions across different instances requires their poses to be expressed in a shared canonical frame. Establishing such frames has traditionally required manual annotation, creating a scaling bottleneck that limits category and instance diversity. We show that a shared canonical frame can instead emerge from self-supervised training on object-centric videos of rigid objects captured in the wild, using only noisy camera poses from Structure-from-Motion. Our key idea is to route all training sequences through a shared geometric bottleneck: a coarse canonical mesh that carries no category-specific detail. By learning dense correspondences from image pixels to this mesh, and estimating per-sequence alignments from noisy SfM geometry, a common canonical frame emerges from multi-view consistency and the semantic priors of the feature extractor, without any canonical pose labels or category conditioning. Trained in a self-supervised manner on ${\sim}160{,}000$ in-the-wild object videos, our method achieves competitive accuracy on category-level pose estimation benchmarks compared to methods that rely on canonical pose supervision. The code and checkpoint are available on \href{https://github.com/Fischer-Tom/Emergent-Canonical-Frame/}{github.com/Fischer-Tom/Emergent-Canonical-Frame/}.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.07878

Black-box model classification under the discriminative factorization

Hayden Helm, Merrick Ohata, Carey Priebe

原文摘要

Access to modern generative systems is often restricted to querying an API (the ``black-box'' setting) and many properties of the system are unknown to the user at inference time. Recent work has shown that low-dimensional representations of models based on the relationship of their embedded responses to a set of queries are useful for inferring model-level properties. However, the quality of these representations is highly sensitive to the query set. We introduce the \emph{discriminative factorization} to distinguish between high- and low-quality query sets in the context of black-box model-level classification. Under this framework, the probability of chance-level classification is bounded by a term that decays exponentially in the query budget. On three auditing tasks, estimated factorization parameters predict the empirical performance decay rate. We conclude by showing that query sets selected using the estimated factorization reproduce the empirical ordering of oracle query sets.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2602.22710

Same Words, Different Judgments: How Preferences Vary Across Modalities

Aaron Broukhim, Nadir Weibel, Eshin Jolly

原文摘要

Preference-based reinforcement learning (PbRL) is the dominant framework for aligning AI systems to human preferences. However, evaluation protocols for such data were designed for text and have not been validated for speech. We present the first intraclass correlation coefficient (ICC)-based, controlled cross-modal study of human and synthetic preference annotations, comparing text and audio evaluations of identical semantic content across 100 prompts. We show that achieving \textit{good} (audio) to \textit{near good} (text) agreement within either modality (ICC(2,$k$) $\approx$ .81 audio, .74 text) requires $\sim$9 raters and modalities show marked differences in how people report preferences: audio raters exhibit narrower decision thresholds, reduced length bias, and more user-oriented evaluation criteria, with near-chance cross-modality agreement. We demonstrate that synthetic ratings can be used to effectively predict inter-rater agreement, thus serving as an early signal for stimulus selection and proxy for human annotations. Together, these findings argue that evaluation protocols for audio preference data require modality-specific design rather than direct adaptation from text. Code, data, and the audio corpus are available at: https://huggingface.co/datasets/sciminds/same-words-different-judgements

cs.SD · cs.AI · cs.HC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.00634

Beyond Landmark Extraction: A Framework for Robust Geometric Feature Evaluation in Structured Image Classification

Saravana Mauree, Sakshi Arya

原文摘要

Much of the literature on structured image recognition has disproportionately focused on the comparison of classification algorithms. Rather than investigating which classifier performs best, this paper instead asks: what should a classifier know before it ever makes a prediction? In structured vision problems such as gesture recognition, facial expression categorization, and medical image analysis, discriminative information lies less in individual pixels and more in spatial relationships between semantic parts. Raw pixel spaces are high-dimensional, sensitive to nuisance variation, and often obfuscate the geometric structures that make visual tasks interpretable. Landmark extraction provides one form of dimension reduction, but it does not by itself determine the information preserved. This paper studies the post-landmark feature map as the central object of analysis and proposes a systematic framework for constructing and interpreting landmark-derived representations as an, informed, feature-based "dimension reduction" step. Using static hand gesture recognition as a case study, we evaluate coordinate, distance, angle, and hybrid representations through perturbation and ablation experiments. The results show that visually variable data exposes substantial gaps between raw coordinate features and their geometrically invariant counterparts, while hybrid representations achieve the strongest overall performance by combining complementary geometric components. These findings frame feature construction as a fundamental modeling decision and ultimately suggests that the question of what representation should a classifier learn from is one worth asking. The code used for feature construction and evaluation is available at https://github.com/ShivMaureeCWRU/Feature_based_dimension_reduction

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08643

FinEvolveKG: Controlled and Attributable Prompt Evolution for Financial Knowledge Graph Construction

Abhinav Arun, Jayita Bhattacharyya, Fabrizio Dimino, Bhaskarjit Sarmah, Stefano Pasquali

原文摘要

Financial institutions construct knowledge graphs (KGs) from shared SEC 10-Ks to serve risk analysis, portfolio management, and investment research, whose analytical needs differ. Extraction must adapt without fragmenting shared grounding, typing, and temporal rules. Hand-authored persona prompts and fine-tuning add cost. Weight-free prompt optimization offers an alternative, but unconstrained evolution of a monolithic prompt can rewrite shared rules alongside persona guidance, obscuring changes and complicating review. We introduce FinEvolveKG, a KG-construction harness separating reusable general and persona-policy blocks and mechanically confining GEPA, a reflective prompt optimizer, to the designated block. During persona adaptation, the approved general block remains byte-stable and every optimizer edit is localized to a separately versioned persona policy. A dual objective balances generic temporal-KG quality with persona-relevant facts, motifs, and multi-hop connectivity. We evaluate five mutation scopes on ticker-disjoint 10-K splits, with three seeds for the persona-conditioned arms (v3-v5) across three personas. On the three-seed mean, the complete two-stage block-constrained workflow (v4) improves over the frozen prompt baseline (v1) by 19.4-30.9% and over the tested unconstrained full-prompt workflow (v5) by 2.2-6.4%, leading v5 on both objective components and winning all nine paired-seed comparisons with the lowest seed variance in every persona. Without further optimization, v4 also exceeds v5 across all evaluated out-of-distribution (OOD) shifts. An SME audit supports using the generated triplets as ground truth; a blinded two-SME audit favors v4 over v5 in 70.1% of contrast-enriched judgments. These results show that mechanically attributable evolution can coexist with strong empirical performance, without attributing the gains causally to the boundary.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08638

MORTIS: Compiler-Guided KV-Cache Management for LLM Agent Programs

Xiaoyu Ma, Jayanaka L. Dantanarayana, Savini Kashmira, Baichuan Li, Lingjia Tang, Jason Mars

原文摘要

Existing agent serving systems divide each prompt into a reusable prefix and an opaque suffix, managing the KV-cache at this coarse granularity without distinguishing the components within each part. We observe that prompt components differ in which LLM calls share their values and how long those values remain reusable, a property we term intra-prompt heterogeneity. Our key insight is that, in frameworks that generate prompts from typed LLM function calls, these components correspond to program variables, allowing compiler analysis to recover their sharing patterns and lifetimes. We present MORTIS, a compiler-serving co-design that combines this analysis with runtime observations to improve KV-cache management. MORTIS reorders prompt components to expose additional prefix reuse and uses their predicted reuse and lifetimes to guide cache retention, eviction, and prefetching. Our evaluation shows that MORTIS achieves up to 1.99x job completion time (JCT) speedup and 1.97x throughput improvement over SGLang, with geometric-mean speedups of 1.45x and 1.49x, respectively.

cs.OS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.08711

Deep Learning-Based Tracking and Lineage Reconstruction of Ligament Breakup

Vrushank Ahire, Vivek Kurumanghat, Mudasir Ganaie, Lipika Kabiraj

原文摘要

The disintegration of liquid sheets into ligaments and droplets involves highly transient, multi-scale dynamics that are complex to quantify from high-speed shadowgraphy images. Identification of droplets, ligaments, and blobs formed by the breakup of a liquid sheet, and their tracking from frame to frame, is an essential part of spray analysis. Analysis of such processes poses a fundamental challenge for conventional multi-object tracking frameworks, which enforce strict one-to-one temporal associations and cannot represent one-to-many fragmentation events. We present a two-stage deep learning framework that detects droplets and ligaments and identifies their temporal relationships across successive frames, modeling ligament deformation, fragmentation, and parent-child lineage. In the first stage, a Faster R-CNN detector with a ResNet-50 backbone and Feature Pyramid Network classifies ligaments and droplets in recordings of two impinging Carbopol gel jets. A morphology-preserving synthetic data generation strategy enriches the training set without physically implausible configurations, achieving a held-out test F1 score of up to 0.872 across fourteen original-to-synthetic data configurations, with robustness tested on different atomization conditions and liquid compositions. In the second stage, a Transformer-augmented multilayer perceptron classifies inter-frame object associations into continuation, fragmentation (one-to-many), and non-association using physics-informed geometric features. Despite severe class imbalance, the model achieves 86.1% accuracy, 93.2% precision, and perfect recall (1.00) for fragmentation events on the held-out test set. Together, the two stages preserve parent-child lineage and enable extraction of breakup statistics, including fragment multiplicity and droplet size distributions, offering an automated analysis of the primary mode of atomization.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08631

BEVCast: Path-Adaptive BEV for Efficient End-to-End Autonomous Driving

Qiren Wang, Mingyu Guo, Zixuan Wang, Weisong Shi

原文摘要

Camera-based driving models build a dense bird's-eye-view (BEV) grid and spend the same computation on every cell of it, although the vehicle crosses only a few of those cells within the planning horizon. We present BEVCast, a two-stage BEV whose resolution adapts to the predicted driving path. A coarse pass encodes the whole scene on a grid of 4.096 m cells. A heat head trained on the recorded path assigns a heat to every coarse cell and the cells with the highest heat are refined into 1.024 m cells under a fixed budget. The remaining cells keep their coarse resolution, so the trajectory head reads a mixed-resolution BEV that still covers the entire scene and needs neither an HD map nor a navigation command. Against a full grid of 1.024 m cells with the same architecture and training recipe, BEVCast at a 10% refinement budget encodes 28% of the BEV queries and needs 66% of the BEV encoder time at batch size 1. On nuScenes its mean displacement error is 1.078 m against 1.149 m and its drivable area violation rate is 1.46% against 2.29%. On NAVSIM navtest its mean displacement error is 0.577 m against 0.598 m and its extended predictive driver model score is 82.51 against 81.28. Next to published planners BEVCast reaches a comparable error on both benchmarks without a navigation command. The resolution of a driving BEV can therefore follow the task instead of the grid.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.05466

Recursive Synthesis for Long-Horizon Terminal Tasks

Zhongzhi Li, Yucheng Shi, Zongxia Li, Ruhan Wang, Anhao Li, Jiaxi Li, Junyao Yang, Lei Ke 等 14 位作者

原文摘要

High-quality long-horizon training data for terminal agents is costly and difficult to scale, as manual authoring is expensive and direct LLM generation often produces inconsistent instructions, environments, solutions, and verifiers. We present Recursive Synthetic Terminal Tasks (RST), a recursive verified synthesis framework for constructing long-horizon terminal-agent tasks at scale. Starting from verified seed tasks, RST extends the reference solution, realigns the verifier and instruction to the new workflow, validates the result in a fresh sandbox, and reuses accepted tasks as seeds for subsequent rounds. Across fifteen recursive rounds, RST produces 37,484 synthesized terminal-agent tasks at roughly $0.05 per task. Task difficulty increases substantially over rounds: the median reference solution grows from 67 to 374 lines, the median number of executed commands grows from 40 to 244, and DeepSeek-V4-Pro pass@4 drops from 90% at R1 to 2.5% at R15. To demonstrate training utility, we collect rejection-sampled Qwen3.5 trajectories on the synthesized tasks and use them for supervised fine-tuning. Fine-tuning on these trajectories improves Qwen3.5-27B and Qwen3.5-122B-A10B by up to 10 points on Terminal-Bench 2, Terminal-Bench Hard, and Long-Horizon Terminal Bench, while agentic PPO lifts Qwen3.5-27B to 49.44%, 32.00%, and 22.07% on the three benchmarks, corresponding to relative gains of 20.0%, 41.2%, and 21.9% over the base model. Moreover, after 15 rounds, the recursion shows no ceiling: synthesis yield and validation rates remain stable as difficulty keeps climbing, indicating that the process can continue well beyond the scale reported here.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.06630

The Sparsity Whisperer

Linghao Kong, Inimai Subramanian, Micah Adler, Dan Alistarh, Dan Gutfreund, Nir Shavit

原文摘要

Pruning reduces the inference cost of large language models, but existing criteria primarily preserve large activations or reconstruct layer outputs. We argue that this overlooks a key computation performed by particularly sparsity-sensitive neurons in the MLP up and gate projections: separating similar inputs into dissimilar outputs. This suggests that effective pruning should preserve not only activations, but also the differences between outputs more broadly. We introduce a family of difference-informed pruning methods built upon this principle. Wisp+ is a neuronwise, update-free method that prioritizes the input pairs each neuron separates most strongly relative to their distance. Wisp provides a more efficient layerwise alternative that scores weights using shared input-difference norms. Finally, Whisper extends global difference preservation to second-order reconstruction using a lightly regularized difference Hessian. Across Llama 2 and 3.1 models from 7B to 405B parameters, our second-order variant consistently improves over strong reconstruction-based baselines, while our update-free variants improve over activation-aware baselines, especially in constrained settings. The improvements over Wanda and SparseGPT extend to structured sparsity, downstream evaluations, and other model families. Augmenting stronger techniques such as RIA and ALPS with our difference-informed criteria yields further improvements, shifting the overall accuracy-runtime frontier outward at negligible additional cost. These results suggest that preserving output differences is a broadly useful and composable signal for post-training LLM sparsification.

cs.LG

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arXiv ↗PDF ↗