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新论文 · v1arXiv:2611.08696

Hybrid Quantum-Classical Annealing for Protein Side-Chain Optimization: A Feasibility Study

Valeria B. C. Visani, Simone Reale, Alfonso Gautieri, Elisabetta Di Nitto

原文摘要

Hybrid quantum-classical annealing solvers are moving from research laboratories toward industrial use, but their advantage over classical optimization appears to be confined to a narrow class of problems. A recent systematic benchmark of the D-Wave hybrid solver against industry-leading classical solvers, across several classes of optimization problems, found it to be most competitive on binary quadratic programs, while noting that few industrial problems take this form. Therefore, identifying real-world industrial problems that can be modeled with this structure is a key challenge. We propose protein side-chain optimization, a core step of computational protein design, as one of these problems: it is natively a binary quadratic program, and its application context requires pools of low-energy solutions, which annealers naturally return. In this feasibility study, we test whether the competitiveness of the hybrid solver against industry-leading classical solvers extends to this problem. On 40 instances derived from 20 experimentally determined protein structures with up to about 200 amino acids and 2000 binary variables, we compare the results achieved with a hybrid solver, run for 5 seconds, against those of the commercial solver Gurobi, run for 10 seconds, 1000 seconds, and 4 hours. The hybrid solver reaches the certified optimum whenever one is available, matches Gurobi's performance on smaller instances, and outperforms it on larger instances, finding lower-energy solutions. These results show that the competitiveness of the hybrid solver on binary quadratic programs, so far observed only on a synthetic benchmark, extends to a real-world problem. This motivates comparisons with classical heuristics specific to protein side-chain optimization.

cs.ET · q-bio.BM · quant-ph

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新论文 · v1arXiv:2611.08695

FoleyForge: Free-form, Controllable, Rectified-flow Editing of Sound Effects

Giovana Morais, Hugo Flores García, Prem Seetharaman, Johannes Imort, Justin Salamon, Magdalena Fuentes, Oriol Nieto

原文摘要

We present FoleyForge, a generative model for instruction-based editing of sound effects. Given an input clip and a free-form instruction describing the desired change, it applies the edit while preserving the input's identity. A single model handles semantic, stylistic, and DSP-like changes. We introduce a data pipeline that guides an audio language model to author both DSP-based acoustic edits and similarity-preserving semantic edits, thus addressing the lack of publicly available audio editing data. Such data is then used to fine-tune a rectified-flow audio generation model, which we extend to expose attribute controls for loudness change, edit strength, and an input-derived contour envelope, each tunable via classifier-free guidance, yielding a more expressive editing model. FoleyForge achieves state-of-the-art instruction adherence at competitive audio quality and source faithfulness.

cs.SD · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08693

Enabling Retraining-Free Accurate Inference in Analog Compute in Memory Accelerators

George Karfakis, Puneet Gupta

原文摘要

Analog compute-in-memory (CIM) accelerators compute matrix-vector products directly inside memory arrays, eliminating weight movement and promising order-of-magnitude gains in inference efficiency. However, converter and device errors make pretrained models unusable on these arrays, and hardware-aware retraining is costly. We enable retraining-free, accurate inference of pretrained models on simple analog arrays with low-cost, shared calibration. Our key observation is that analog error depends on how each layer is represented on the array, and that this representation can be changed offline without changing the computation. We center the inputs and apply Hadamard rotations designed for analog arrays, which keep shared reference errors spread and confine input mixing to each array. Across CNNs, vision transformers and LLMs up to 14B parameters on PCM and ReRAM, the method brings vision models and larger LLMs within a few points of floating-point accuracy and matches or approaches published hardware-aware training without training any weights. On ReRAM with 6-bit ADCs, it restores ViT-B/16 from 37.4% to 82.9% and Phi-4 from 27.0% to 72.2%, both within 2.2 points of floating-point accuracy.

cs.AR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08689

A Gamma-Mixture Solution for Definite Noncentral Complex Gaussian Quadratic Forms

Fernando D. Almeida García, Carlos R. Nogueira da Silva, Felipe A. P. de Figueiredo, Rausley A. A. de Souza, Michel Daoud Yacoub, José Cândido Silveira Santos Filho

原文摘要

Finding exact solutions for the statistics of definite noncentral complex Gaussian quadratic forms (CGQFs) has long been a challenging problem. Among existing exact approaches, Raphaeli's formulation [1] remains the most computationally efficient. However, its analytical tractability is hindered by the dependence of its series coefficients on the evaluation variable, which confines their statistics to the probability density function (PDF) and cumulative distribution function (CDF). This paper overcomes this limitation by deriving novel Gamma-mixture representations for the PDF and CDF of positive- and negative-definite noncentral CGQFs. The resulting structure broadens Raphaeli's statistical framework beyond the PDF and CDF to a wider class of CGQF functionals and a substantial range of practical applications. In particular, expectations of integrable functions of definite noncentral CGQFs are expressed as weighted sums of the corresponding expectations over Gamma random variables, enabling the direct derivation of moments, transforms, and performance measures. Moreover, the proposed formulation reduces the single-point complexity from \(O\!\left(n^2K+nK^2\right)\) for Raphaeli's method to \(O\!\left(nK+K^2\right)\), where \(n\) denotes the quadratic-form dimension and \(K\) the truncation order. The proposed Gamma-mixture structure also enables closed-form approximations that, by construction, remain valid probability distributions for any truncation order. In addition, uniform PDF and CDF truncation-error bounds are derived, allowing prescribed-accuracy truncation. As a sample application, we derive the first reported exact average bit error rate expression for maximal-ratio combining receivers with an arbitrary number of branches under an arbitrary positive-definite Rician correlation structure.

eess.SP · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08686

Compressed Per-Player Local Methods for Multiplayer Federated Learning: Exact Convergence via Increment Sharing

Bhavika Balani, TaeHo Yoon, Nicolas Loizou

原文摘要

Multiplayer Federated Learning (MpFL), introduced by Yoon et al. (2025), extends classical federated learning to settings where clients are rational players in a game with individual objectives and strategies, seeking an equilibrium. A natural baseline is Per-Player Local SGD (PEARL-SGD), where each player performs several local SGD steps before exchanging strategies through a central server. However, the original PEARL-SGD formulation does not extend cleanly to compressed communication. Directly compressing the players' strategies can prevent convergence to the equilibrium, even with deterministic gradients. To address this issue, we reformulate PEARL-SGD so that players communicate local strategy increments rather than the strategies themselves. Without compression, the two formulations are equivalent, but with compression, increment sharing preserves exact convergence, allowing us to design the communication-efficient compressed PEARL-SGD (C-PEARL-SGD). Beyond compression, we also consider variance reduction as another axis of improvement, introducing PEARL-SVRG and its compressed variant C-PEARL-SVRG. Under the original MpFL assumptions, we establish convergence guarantees for all variants through a unified parametric inequality. Numerical experiments, including distributed spectrum sensing, demonstrate the practical benefits of our methods over vanilla PEARL-SGD.

math.OC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.10910

Equivariant Reinforcement Learning for Clifford Quantum Circuit Synthesis

Richie Yeung, Aleks Kissinger, Rob Cornish

原文摘要

We consider the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. We approach this task as a reinforcement learning problem in which an agent learns to discover a sequence of elementary Clifford gates that reduces a given symplectic matrix representation of a Clifford circuit to the identity. This formulation permits a simple learning curriculum based on random walks from the identity. We introduce a novel neural network architecture that is equivariant to qubit relabelings of the symplectic matrix representation, and which is size-agnostic, allowing a single learned policy to be applied across different qubit counts without circuit splicing or network reparameterization. On six-qubit Clifford circuits, the largest regime for which optimal references are available, our agent finds circuits within one two-qubit gate of optimality in milliseconds per instance, and finds optimal circuits in 99.2% of instances within seconds per instance. After continued training on ten-qubit instances, the agent scales to unseen Clifford tableaus with up to thirty qubits, including targets generated from circuits with over a thousand Clifford gates, where it achieves lower average two-qubit gate counts than Qiskit's Aaronson-Gottesman and greedy Clifford synthesizers.

quant-ph · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08683

Compression of Emotion in Language Model Representations

Laya Iyer, Sanmi Koyejo, Chen Shani

原文摘要

Large language models (LLMs) have been evaluated on their ability to identify emotions expressed in text and produce fluent emotional language. However, output predictions alone do not reveal how fine-grained emotion distinctions are organized internally. We study seven LLMs on GoEmotions, an English Reddit dataset annotated with 27 fine-grained emotion categories plus neutral. We compare model predictions and emotion-prompted final-token representations with the 28 GoEmotions categories and with coarser emotion-family and sentiment groupings. We find a consistent pattern of $\textit{overcompression}$ relative to the fine-grained GoEmotions taxonomy. Under the tested KMeans protocol, when we cluster their hidden representations without using emotion labels, the clearest split is into two groups that largely separate positive from negative emotion. Coarser emotion groupings are also recovered with fewer clusters than the full 28-category taxonomy. Yet linear probes retain substantial fine-grained predictive information. These results suggest that fine-grained emotion information remains accessible even though broader affective structure more strongly organizes the unsupervised geometry.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08681

National and State-Level Approaches to Automated Vehicle Certification: A Comparative Framework for State Operational Governance

Sk Nahia Ahsan, Md Abdullah Al Hasan, Danjue Chen, George List

原文摘要

Automated vehicle (AV) deployment in the United States is expanding without a unified federal certification framework, leading to substantial variation in how states authorize and oversee AV testing and deployment. This paper examines how U.S. states regulate Society of Automotive Engineers (SAE) Level 4-5 automated driving systems (ADS), identifies common certification approaches, compares them with international practices, and evaluates the implications for North Carolina's future regulatory strategy. Publicly available state statutes, administrative regulations, agency guidance, and federal policy documents were reviewed. Ten states, selected to capture the range of regulatory approaches, were analyzed using a structured framework covering operational scope, governance and oversight, federal interaction, and implementation outcomes. Certification practices in the European Union, China, Japan, and Australia were reviewed for comparative context. Three state certification models were identified, reflecting increasing regulatory involvement: baseline legal compliance, administrative self-certification with ongoing oversight, and permit-based authorization requiring formal agency review before deployment. North Carolina currently aligns with the baseline compliance model. International frameworks are converging toward scenario-based testing, operational-design-domain-specific approval, safety-case documentation, cybersecurity assurance, and lifecycle oversight, although direct transferability to the U.S. is limited. The paper develops a three-model typology for comparing state AV certification approaches and integrates state practices with international frameworks, providing a consistent basis for evaluating differences in regulatory involvement, governance complexity, and institutional responsibilities across jurisdictions.

cs.RO · cs.SE

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新论文 · v1arXiv:2611.08675

A Complete Calculus for Hamiltonian Quantum Programming

Jacques Carette, Chris Heunen, Robin Kaarsgaard, Neil J. Ross, Amr Sabry

原文摘要

Given the forces acting on a quantum particle, a Hamiltonian models its continuous evolution. Quantum computers evolve Hamiltonians to simulate physical systems, to solve optimisation problems, and to estimate eigenvalues, and even the popular circuit abstraction is compiled to Hamiltonians on the hardware. We introduce Hamilton, a process calculus whose parallel operator is the sum of interactions acting simultaneously. Its operational semantics is the Lie-Trotter product formula, which recovers a sum as the limit of ever finer sequential interleavings; compiling a program means halving it to a chosen precision and unrolling the interleaving into a circuit. The semantics is fully abstract, the expressible Hamiltonians are exactly those whose Pauli coefficients have the form $m/2^k$, the precision that successive halving reaches, and the equational theory is complete and decidable. A full implementation compiles Hamilton programs to circuits and decides their equivalence, and Agda proofs sanity-check the core metatheory.

cs.PL · quant-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08673

Optimal Contention Resolution from Samples

Shaddin Dughmi, Vasilis Livanos, Pranav Nuti

原文摘要

Contention resolution schemes convert random sets which are ex-ante feasible for some constraint into sets which are feasible ex post, and their online variants have broad applications in Bayesian and stochastic optimization, including prophet inequalities and stochastic probing. In many such applications, however, full distributional knowledge is an unattainable requirement. This motivates the study of contention resolution schemes which employ a limited number of samples from the underlying distributions. We study sample-based contention resolution for $k$-uniform matroids, for both uniformly random and adversarial arrival orders, and compare their balance guarantees to the best possible with full distributional knowledge. For a single-item constraint, we show that one sample per element suffices for an optimal $(1-1/e)$-balanced ROCRS. For adversarial arrivals, however, we prove that no single-sample OCRS can be $1/2$-balanced, even for known arrival orders, separating contention resolution from prophet inequalities in the single-sample setting. Perhaps surprisingly, we show that two samples are necessary and sufficient for the optimal $1/2$ guarantee. We also present a single-sample $0.48$-balanced scheme. For general $k$, we give a simple single-sample ROCRS that achieves an optimal balance ratio of $1-e^{-k}k^k/k!$. In adversarial order, we obtain a single-sample $(1 - O(1/\sqrt{k}))$-balanced scheme, matching the optimal asymptotic dependence on $k$. Finally, we complete the picture by characterizing the minimum number of samples needed to match the optimal balance ratio achievable with full distributional knowledge. For random arrivals, one sample suffices for $k = 1,2,3,4$, and zero samples suffice for $k \geq 5$. For an adversarial arrival order, even known in advance, and any $k \geq 2$, we show that no constant number of samples suffices to achieve optimal balance.

cs.DS · cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08672

Real-World Adoption, Use, and Governance of an LLM-Based Electronic Health Record Search and Synthesis Platform

Jiunn-Tyng Yeh, Michael Gao, Marshall Nichols, Anika Mohan, Tareq Aljurf, Michael Revoir, Matthew Gardner, Bradley Hintze 等 11 位作者

原文摘要

We evaluated the longitudinal adoption and use of Scout, an electronic health record-integrated large language model platform that provides cited search and synthesis of patient records, during staged deployment across a multi-hospital academic health system. Over 11 months, 2,635 users completed 80,465 tasks. 1,768 clinical users initiated use within the first 29 days of system-wide launch, which included a required training. Daily initiation increased 21.2-fold at launch, from 2.9 to 61.0 users per day. Of the 495 users who initiated use during the launch week, 310 did so in the first two days. Use spanned more than 20 recorded specialty categories, five care-practitioner roles, and 46,485 distinct prompts involving retrieval, synthesis, extraction, evaluation, and drafting, with 752 tasks saved for reuse. Among care practitioners who completed the required course, 77.9% went on to use Scout, with a median of 1.7 days after course completion. The highest-intensity user quintile accounted for 59.0% of tasks and 61.9% of tokens. Exploratory prompt analysis showed use across retrieval, synthesis, extraction, evaluation, and note-drafting. These findings suggest that rapid, broad adoption requires clinician-led task design and evaluation, traceable governance proportional to reach, centralized capacity planning and user support, and training integrated with access.

cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.18261

DeepRitzSplit: A Variational Neural Operator Framework for Gradient Flows with Application to Dendritic Growth

Benoît Appolaire, Ludovick Gagnon, Chih-Kang Huang, Miha Založnik

原文摘要

Phase-field models are widely used to simulate microstructure evolution but their numerical solution remains computationally demanding. Neural operator surrogates provide a promising alternative, yet existing approaches are data-driven, requiring large datasets and exhibiting limited robustness when extrapolating to unseen configurations. We introduce DeepRitzSplit, an unsupervised neural operator framework for gradient-flow systems based on a variational formulation of energy-stable splitting schemes. It learns the time evolution semigroup associated with a convex-concave free-energy decomposition by minimizing the underlying variational problem at each time step. It is designed to inherit the dissipative structure of the underlying gradient flow without requiring trajectory datasets. We further introduce a Reaction-Diffusion Neural Operator (RDNO) architecture motivated by splitting schemes, with separate reaction and diffusion components to improve training efficiency and interpretability. We assess the approach on the Allen-Cahn equation and apply it to a phase-field model of anisotropic dendritic growth during solidification. In the Allen-Cahn benchmark, DeepRitzSplit shows better generalization than the supervised baseline on the out-of-distribution configurations tested, using 180 training configurations. In dendritic growth, the method captures the overall phase morphology and dendrite tip velocity well, while the tip radius is more sensitive to the training strategy. Refinement based on the residual of the splitting scheme markedly improves this second-order morphological quantity. The trained operators retain good predictive capabilities on configurations substantially different from the training set. These results demonstrate the potential of variationally trained neural operators as physically informed surrogates for accelerated microstructure simulations.

math.AP · cs.LG · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08666

Pdb or Not to Be: Comparing Debugging Interfaces With Pass$_{X}$@$k$

Ryan Magnuson, Ellie Kitanidis, Ariel Lee

原文摘要

Interactive debuggers are indispensable to human software development, yet coding agents overwhelmingly rely on edit-execute-observe loops, using common bash tools, test scripts, and print statements to debug via repeated shell commands. Stepping through a debugger provides live access to runtime state, which may enable different problem-solving capabilities. However, prior work typically introduces the debugger as an additional tool or through a new subagent, making its individual contribution difficult to isolate from the associated scaffold changes. To address this, we compare a debugger-only agent against a bash-only agent on all 500 SWE-bench Verified instances, holding the rest of the scaffold fixed. We evaluate both architectures on two models, GPT-OSS-120B and Nemotron-3-Ultra-550B, with three independent runs per architecture $\times$ model configuration. Crucially, to distinguish interface specialization from expanded coverage under repeated sampling, we introduce pass$_X$@$k$, a cross-agent pass@$k$ that provides an unbiased estimator of the probability that at least one of $k$ agents is successful. Although the bash and debugger agents solve partially disjoint subsets of the benchmark in any single pair of runs, our pass$_X$@$2$ estimator reveals that this is largely due to run-to-run variance rather than tool complementarity. Across both models, we find that two bash rollouts outperform the combination of one bash and one debugger rollout. Our findings highlight the importance of accounting for stochastic variance in evaluating agent and tool diversity, and we hope that pass$_X$@$k$ will serve as a useful framework for the community.

cs.AI · cs.SE

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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:2506.11945

Subjective Experience in AI Systems: What Do AI Researchers and the Public Believe?

Noemi Dreksler, Lucius Caviola, David Chalmers, Carter Allen, Alex Rand, Joshua Lewis, Philip Waggoner, Kate Mays 等 9 位作者

原文摘要

Whether AI systems are perceived to have subjective experience has scientific, ethical, and governance implications. Yet little is known about how AI researchers and the public view this possibility. We surveyed 582 AI researchers who published in leading AI venues and 825 nationally representative US adults about the likelihood, identification, and governance of AI systems with subjective experience. We find that both AI researchers and the public think it is unlikely that AI systems have subjective experience now, but believe it is more likely than not they will by century's end. Both groups believe multidisciplinary expertise will be important in identifying AI subjective experience, support developers implementing safeguards now, and agree that, if created, subjectively experiencing AI systems should have moral responsibilities and be held accountable. Both groups also show substantial disagreement on many moral and policy questions. Our results underline the importance of building more detailed, evidence-based understanding of public and expert opinion on this contentious issue.

cs.CY · cs.AI

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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 · v3arXiv:2606.30444

SGD Provably Prioritizes a Shortcut Spurious Feature in the XOR Model

Tyler LaBonte, Vidya Muthukumar

原文摘要

Neural networks are known to be susceptible to over-reliance on spurious correlations. However, the precise mechanism by which models exploit shortcut features is not fully understood, and algorithms to mitigate this behavior rely on as yet unjustified assumptions about the learned representations. In this work, we provide the first end-to-end theoretical characterization of spurious feature learning for two-layer ReLU neural networks trained by online minibatch SGD on the logistic loss. We consider data drawn from the high-dimensional Boolean hypercube with a quadratic signal function (namely XOR) and a linear spurious correlation. We show that SGD learns the spurious feature first, and exponentially fast. Moreover, the optimization dynamics couple the spurious and signal features, with a stronger spurious component inhibiting signal feature learning. Our analysis reveals precise phase transitions in the learning dynamics. In the first phase, alignment between the signs of the spurious feature and second-layer weight drives rapid growth of the spurious feature. In the second phase, large majority group margin slows learning and the signal feature remains suppressed. When the spurious correlation is maximally strong, we show theoretically that the spurious feature dominates even at the sample complexity threshold where XOR would be learned in isolation (i.e., if the spurious feature was absent). In contrast, when the correlation strength is constant, we provide preliminary evidence that the model can eventually learn the XOR signal, although the spurious feature is not forgotten.

stat.ML · cs.LG

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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:2605.25120

Evidence-Linked Radiology Reporting: A Human-Supervised Reference Architecture for Structured Imaging Intelligence

Houman Kazemzadeh, Kamyar Naderi

原文摘要

Radiology reports remain the primary mechanism by which imaging findings are communicated to clinical teams. However, much of the structured information behind these reports, including measurements, image evidence, prior comparisons, lesion identity, uncertainty, and terminology, often remains trapped in free text or fragmented across picture archiving and communication systems, radiology information systems, reporting workstations, worksheets, advanced visualization tools, and electronic health records. This paper proposes a human-supervised, evidence-linked reference architecture for structured radiology reporting. The framework combines exam-specific templates, speech-to-structure processing, measurement and segmentation capture, controlled AI-assisted drafting, and standards-based interoperability using DICOM, DICOM Structured Reporting, DICOM Segmentation, HL7 FHIR, RadLex, SNOMED CT, LOINC, and UCUM. The system is positioned not as an autonomous report generator, but as a structured intelligence layer for enterprise imaging that supports reviewed reporting, longitudinal comparison, clinical data reuse, governance, and integration with PACS, RIS, EHR, analytics, and registry workflows. The paper also discusses modality-specific deployment considerations, clinical safety risks, validation requirements, cybersecurity, privacy, quality management, and regulatory boundaries for AI-assisted radiology reporting systems.

cs.CL · cs.AI · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08657

JEPAhead: From Inpainting to Causal Forecasting for Urban Driving

Daniel G. P. Petrini, Davi Pincinato, Fabio Grassiotto, Pedro L. F. Prado, Alexandre F. S. Osorio, Raphael V. Ramos, Felipe Pamboukian

原文摘要

We fine-tune a pre-trained V-JEPA1 predictor from bidirectional latent inpainting to action-free causal forecasting while keeping the encoder frozen. Using contiguous past observations, horizon-aligned targets, and group-level causal attention, the model forecasts eight future token groups, corresponding to sixteen frames, in one forward pass. On CARLA Town12, it retrieves the correct future among 591 candidates in 69.0% of cases, compared with 55.3% for persistence, with greater gains at longer horizons and on tokens exhibiting greater change. It also outperforms a compact predictor trained from scratch with six times fewer parameters. Semantic analysis indicates driving-relevant information in predicted features: a fixed linear probe trained on real encoder tokens achieves 0.16 mIoU on patches undergoing semantic-class changes, versus 0.13 for persistence. Vehicle-conditioned retrieval improves over persistence by 15 percentage points at the longest horizon. Pixel-space evaluation shows modest L1 gains but little improvement under LPIPS, motivating direct evaluation through latent and semantic measures alongside an oracle reconstruction reference. These findings support further investigation of latent forecasting for driving perception, while downstream utility remains unverified.

cs.CV · cs.AI

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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 ↗
版本更新 · v2arXiv:2605.11135

Control Charts for Multi-agent Systems

Hayden Helm, Carey Priebe, Brandon Duderstadt

原文摘要

Generative agents have proven to be powerful assistants in a wide variety of contexts. Given this success, users are now deploying agents with minimal restrictions in open ended, multi-agent environments. Current methods for monitoring the dynamics of open-ended multi-agent systems are limited to aggregate statistics, qualitative inspection, or detecting change between two fixed timepoints. In this paper, we extend the process-theoretic notion of adaptive control charts to multi-agent systems to enable automated monitoring. Using simulation, we demonstrate that adaptive control charts are necessary for monitoring multi-agent systems that can learn from their environment. We further demonstrate, both empirically and theoretically, that adaptive control charts are susceptible to adversarial agents that defect sufficiently slowly. These results suggest a fundamental tradeoff in multi-agent system control: monitors with fixed baselines flag legitimate learning, while monitors with adaptive baselines admit slow adversaries. We conjecture this holds for any monitor.

cs.MA · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08655

Scoring, Not Learning: Decomposing Catalog-Consolidation Gains in Web-Scraped Retail Product Recognition

Kaden Leis, Xiang Ma

原文摘要

Retail product catalogs assembled from web image search are cheap to build and pervasively noisy: queries return packaging shots instead of products, and entries proliferate under names that cannot be distinguished visually. Consolidating such a catalog can look like an alternative to scaling the model. We show this is not a comparison between two ways of improving a recognizer, but between improving a recognizer and changing what counts as correct. We study a public archive of 165,769 cheese images across 1,824 query-named entries. A packaging filter and near-duplicate removal reduce it to a 998-class recognition set on one fixed, group-aware split. Canonicalizing entries to their base type (844 classes) raises top-1 accuracy by 4.0 points. Scoring the unchanged model against the merged label space, by summing its predicted probability within each canonical group, recovers +4.27 points with no retraining (+4.25 over six seeds). Retraining on the consolidated catalog then adds -0.10$\pm$0.53, +0.08$\pm$0.65 and -0.10$\pm$0.65 points at 28, 50 and 88 M parameters, indistinguishable from zero at every capacity ($\pm$ is the across-seed standard deviation). That component is specific, not mechanical: random merges with the identical size profile yield only +0.68 points, and the three backbones agree to within 0.1 points. But it is overwhelmingly category coarsening, not alias correction: across the 37 merged groups, type coarsenings supply 94% of the gain and alias collapses only 7%. We also detect no interaction between consolidation and backbone size: tripling backbone parameters is worth +1.50$\pm$0.57 points on the raw catalog and +1.42$\pm$00.44 on the canonical one, a within-seed interaction of -0.08 [-0.79,+0.63]. On this archive, the reported consolidation gain primarily reflects the evaluation label space, and separating that out costs one evaluation

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08654

CS-Pi: Constraint-Satisfying Flow Policy Sampling

Soumojit Bhattacharya, Oliver Kroemer, Shubham Tulsiani

原文摘要

A flow-matching policy deployed in the field must often satisfy a constraint that was absent from its demonstrations, such as avoiding an obstacle it has never seen. Retraining for every new constraint is impractical, so the constraint must be enforced at inference time. Enforcing it must not discard what the policy learned: an action the policy would rarely sample may avoid the obstacle yet fail the task. We therefore want actions that are both safe, satisfying the constraint, and plausible, remaining likely under the policy. Gradient guidance, the most common approach, steers noisy states, so it must evaluate its cost on the policy's running estimate of the clean action; when the policy is uncertain between passing left or right of an obstacle, this estimate is their average and can pass straight through it. We instead build on rejection and projection. Rejection redraws actions until one is safe: every output is plausible, but the wait is intractable when safe actions are rare. Projection moves an action to the nearest safe one: it is efficient, but the result need not be plausible. Our key insight is that flow-matching sampling lets us combine the two, keeping projection's efficiency and rejection's plausibility. CS-Pi, our draw-project-renoise sampler, projects each draw to the nearest safe action, adds noise back scaled to how far the projection moved it, and re-runs the unmodified policy until a draw is safe. Replacing only the projection step extends CS-Pi to continuous costs. Across a 2D diagnostic, a 7-DoF shelf pick-and-place, an orientation preference, and a zero-shot Franka deployment, CS-Pi reaches 81% safe success on the shelf and 55% on hardware, against 55% and 40% for projection, while staying plausible.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.38377

PhyProbe: Rethinking Physical Consistency Evaluation in Generated Videos

Max Ku, Jiaojiao Fan, Zekun Hao, Francesco Ferroni, Heng Wang, Wenhu Chen, Ming-Yu Liu, Prithvijit Chattopadhyay

原文摘要

Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language models, which can often be myopic to physical dynamics, or fine-tuned evaluators trained on human annotations, which overfit to dataset-specific cues and fail to generalize. A key challenge is that existing supervision sources provide either relative ordering or absolute scores, but not both reliably and consistently across varied settings. To this end, we introduce PhyProbe, an evaluator that extracts features from a frozen pretrained spatio-temporal encoder and maps them to a scalar physical consistency violation score via a lightweight scoring head. PhyProbe is trained through a unified objective combining pairwise ranking, regression on noisy scalar annotations, and anchor-based calibration over a curated set of heterogeneous supervision sources. Experiments show that PhyProbe outperforms prior methods on most pairwise benchmarks spanning real-generated and generated-generated pairs under varying correspondence, with the largest gains in no-correspondence and generated-generated settings where existing fine-tuned evaluators degrade sharply. PhyProbe achieves strong correlation with human judgments, with close agreement between rank-based and linear metrics, indicating that scores are both well ordered and anchored to a stable [0, 1] scale. Further, despite being trained on supervision indicative of physical consistency, without explicit general-preference labels, PhyProbe also performs competitively on human preference benchmarks: consistent with the observation that physics violations are entangled with broader quality degradations.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38076

Prompt Lottery in LLM-as-a-Judge: Measuring and Mitigating Sensitivity to Syntactic Rephrasing

Ilija Subasic, Brett Levert, Andrew Rabinovich, Sajjad Pakdamansavoji, Zhao Chen

原文摘要

LLM-as-a-Judge has become a standard for evaluating large language models, yet most approaches implicitly assume that a single judge prompt functions as a stable evaluation instrument. Prior work has primarily explored deliberate prompt modifications such as scoring rubrics, role descriptions, or chain-of-thought reasoning in order to optimize accuracy. In contrast, we investigate whether validated prompt variants intended to preserve evaluation semantics while differing in surface realization (\textit{paraphrastic rephrasings}) yield consistent judgments under a fixed evaluation task. We introduce \textit{Prompt Lottery}, a phenomenon in which paraphrastic rephrasings produce different LLMaJ judgments. Across a large-scale study spanning diverse evaluation tasks, judge families, and model backbones, we find that these variations can alter evaluation outcomes and, in some settings, reverse relative model rankings. A user study with 10 human annotators shows that LLMaJ judges exhibit 6-9$\times$ larger agreement degradation under prompt rephrasings than humans, falling below the generally accepted threshold in usability studies. This indicates that single-prompt evaluation is insufficiently robust for model ranking. Finally, we show that multi-prompt aggregation substantially improves robustness by emphasizing judgments that remain consistent across prompt variants.

cs.CL · cs.AI

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