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版本更新 · v2arXiv:2610.35852

DiffContact: Differentiable Contact Dynamics for Policy Learning and Contact-Implicit Trajectory Optimization

Gijeong Kim, Seungbin Oh, Dongyun Kang, Hae-Won Park

原文摘要

This paper proposes DiffContact, a smooth analytic gradient for frictional contact, and applies it to policy learning and contact-implicit trajectory optimization for legged locomotion. The exact contact sensitivity is available in closed form but describes changes within the current contact mode, providing no first-order information toward neighboring contact modes. The proposed gradient replaces this sensitivity only in the backward pass, leaving the hard-contact forward solve unchanged and the realized rollout feasible. A central-path relaxation of the de Saxcé-corrected second-order-cone complementarity problem for Coulomb friction smooths the conditions defining the separating, clamping, and sliding modes. The relaxed sensitivity is evaluated at a point on the central path obtained by a Newton iteration initialized from the hard-contact solution. The sensitivity remains continuous through contact-mode transitions and converges to the exact sensitivity as the relaxation vanishes. Using 100,000 samples and less than 2 min of training on a desktop PC, policy learning with the proposed gradient produces a policy that transfers to hardware without fine-tuning. On hardware, the robot under a trotting policy withstands pushes and traverses debris and slippery ground. In trajectory optimization, the proposed gradient reaches contact-rich solutions missed by exact and Signorini-relaxed gradients. Project page: https://diffcontact.github.io

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.02166

Beyond light cones: State preparation complexity in quantum spin glasses

Omar Al-Ghattas, David Gamarnik, Bobak T Kiani

原文摘要

We introduce a technique for studying state preparation complexity in the quantum $p$-spin model on $n$ qubits, a disordered quantum spin system with random interactions on every set of $p$ qubits. We reduce the task of proving state preparation lower bounds to bounding the local profile complexity of the class, a quantity derived from the metric entropy of the set of vectors recording each state's expectation values against all weight-$p$ Pauli strings. We show that, for sufficiently large fixed $p$, any class with local profile complexity $O(n^2)$ remains separated from the ground-state energy by $Ω(\sqrt n)$, a gap on the leading energy scale. When the profile complexity is $o(n^2)$, we prove the stronger conclusion that the class cannot outperform a benchmark class of simpler states at leading order. Product states always provide such a benchmark. Our framework strengthens known bounds and covers classes beyond the reach of previous methods. These include examples where circuit lightcones extend across the entire system, preventing an application of standard arguments. We obtain an $Ω(n^2/\log n)$ gate lower bound for preparing near-ground states and depth--width tradeoffs, allowing arbitrary one- and two-qubit gates and arbitrarily many discardable ancillas. We also derive lower bounds on entanglement depth and matrix product state bond dimension, and energy obstructions for forward and reverse magic at every fixed level of Parham's magic hierarchy (arXiv:2504.19966). For Clifford circuits doped with $T$-gates, we obtain sharper energy comparisons using product stabilizer states as a more restrictive benchmark. The proof combines an adaptation of a nonsymmetric quantum de Finetti theorem (arXiv:1810.12197) with metric-entropy bounds for the Gaussian process indexed by the local profiles of states in the class.

quant-ph · cond-mat.dis-nn · cs.CC · math.PR

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2312.06140

ICS-Sniper: A Targeted Blackhole Attack on Encrypted ICS Traffic

Gargi Mitra, Chanyuan Liu, Pritam Dash, Yingao Elaine Yao, Alain Zhiyanov, Aastha Mehta, Karthik Pattabiraman

原文摘要

Modern industrial control systems (ICS) increasingly host their Supervisory Control and Data Acquisition (SCADA) services in the cloud to reduce the costs of large-scale automation. To protect site-SCADA communications, ICS operators commonly use VPN tunneling and standard security practices. We show that, despite these security measures, an on-path Internet adversary can disrupt ICS operations without infiltrating the ICS perimeter, breaking encryption, or knowledge of the control logic. We present ICS-Sniper, a targeted blackhole attack that analyzes the VPN traffic metadata (sizes, direction, timing of packets) to identify narrow time windows, called critical superperiods, during which the site-SCADA traffic would likely contain highly critical commands or data. Post-analysis, in a subsequent operational cycle, ICS-Sniper drops a small set of payload-carrying packets in the critical superperiods to disrupt the ICS's operations. We demonstrate three attacks on two realistic modern Secure Water Treatment (SWaT) plant testbeds that can potentially violate the operational safety of the ICS while evading state-of-the-art ICS attack detectors.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.12358

Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube

Thomas Weinberger

原文摘要

For a known finite-dimensional model subspace, classical Gaussian regression theory guarantees that least squares eventually attains the parametric rate, with prediction error scaling as the subspace dimension divided by the sample size. Yet subspaces with the same number of unknown coefficients can require very different sample sizes to reach this regime. For subspaces of polynomials on the Boolean cube, a fundamental question is how restrictions on degree and ambient dimension jointly determine this threshold. Our main result sharply characterizes the worst-subspace minimax sample threshold over a substantial parameter range, revealing an exponential dependence on degree and precisely quantifying the joint effects of degree, ambient dimension, and subspace dimension. A small modification of our lower-bound construction reveals a strong separation: a single subspace can admit noiseless recovery with optimal sample complexity independent of degree, yet require essentially the worst-case sample size, exponential in degree, to attain the noisy parametric rate. To establish the sampling lower bound, we sharpen the Polyanskiy-Samorodnitsky uncertainty principle (J. Funct. Anal., '19) to uniformly optimal precision and extend its concentration guarantee to large subspaces in which every function concentrates most of its energy on one shared Hamming ball. The sampling upper bound follows from the sharp moment bound of Kirshner and Samorodnitsky (IEEE Trans. Inf. Theory, '21) combined with empirical-process bounds.

math.PR · cs.IT · cs.LG · math.CO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.10003

Probing the Limits of the Lie Detector Approach to LLM Deception

Tom-Felix Thormann

原文摘要

A strand of mechanistic approaches to deception in large language models (LLMs) relies on "lie detectors" - truth probes aimed to identify model outputs that are internally represented as false. This lie detector approach to LLM deception implicitly assumes that deception is coextensive with lying. This paper challenges that assumption by experimentally investigating whether LLMs can produce deceptive statements that are not false and whether truth probes (if naively applied as deception detectors) fail to detect such behavior. In doing so, we make four core contributions: First, we provide a dataset of manually written examples of deceptive non-falsities grounded in linguistic theory. Second, we show across six open-source LLMs of different sizes and families that some models can select these deceptive non-falsities in a forced-choice task. Third, we demonstrate that while truth probes trained on a standard true-false dataset perform well at detecting false outputs, they perform significantly worse at detecting deceptive non-falsities. We view this as substantiating the worry that naive applications of truth probes as deception detectors suffer from a critical blind spot. Fourth, we explore whether training deception probes in dialogical contexts or including misleading non-false statements in the training improves the deception detection rate.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.10789

An explicit shuffle construction of the homotopy span model of differential linear logic

Paul-André Melliès, Federico Olimpieri, Lionel Vaux Auclair

原文摘要

We give a direct and explicit construction of the homotopy span model of linear logic originally formulated by Melliès using abstract homotopy theory in the Quillen category Cat of small categories equipped with its natural model structure. Following the principles of homotopy theory, the formulas and proofs of linear logic are interpreted in the model as small categories up to categorical equivalence, the notion of weak homotopy equivalence underlying the natural Quillen model structure on Cat. Here, we explain how the original interpretation of proofs as fibrant spans can be replaced by a more liberal interpretation of proofs as separately fibrant spans. This relaxation from fibrant spans to separately fibrant spans enables us to give a simple and intuitive description of the homotopy span model, where the axiom and cut links are interpreted as identity spans, and where the symmetries of the exponential modality are captured by a shuffle structure on the interpretation of proofs.

cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03415

O2N2: Efficient Doubly Oblivious Approximate Nearest Neighbor Search

Zhechen Li, Zimai Guo, Mingxun Zhou, Chang Shi, Cheng Hong

原文摘要

Privacy-preserving approximate nearest neighbor (ANN) search allows clients to search from an untrusted server without revealing sensitive queries, and is useful in scenarios like medical diagnostics and credit scoring. Among the various privacy-preserving primitives, trusted execution environments (TEEs) have much lower overhead than heavy cryptographic ones, yet they leak memory access patterns, enabling adversaries to infer query contents. Oblivious RAM (ORAM) can be used to hide those access patterns at $\mathrm{O}(\log N)$ asymptotic cost, but existing high-performance ANN search algorithms (e.g., IVF or HNSW) exhibit complex, data-dependent access patterns that are unfriendly for ORAM. Furthermore, standard ORAM constructions typically assume there exists a small amount of trusted storage, which weakens the security guarantees and is not always feasible. Solutions that eliminate such trusted storage (known as {\em double obliviousness}) typically degrade performance further to $\mathrm{O}(\log^2 N)$. We introduce O2N2, a doubly oblivious ANN framework built around single-path search in randomly rotated $k$-d tree forests. A pointer-based Circuit ORAM stores the search structure, with separate storage for compact internal nodes and vector-bearing leaves. Each visited leaf is processed by an oblivious selection network, and fixed-schedule duplicate removal and selection combine the local results into the final top-$k$. We evaluate O2N2 on real-world datasets and further test its scalability on 100 million vectors. For 10-NN and 100-NN search at the specified high-recall targets, it achieves a $9.8$--$125.9\times$ speedup over the fastest doubly oblivious baselines.

cs.CR · cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03414

Finite-Memory Control of POMDPs: Fundamental Limits and Efficient Design

Emrecan Kutay, Atilla Eryilmaz, Ness Shroff

原文摘要

Optimal control of partially observable Markov decision processes (POMDPs), in general, requires a complete history. The belief state, a distribution over hidden states, summarizes this history and provides the classical sufficient statistic to control POMDPs. However, the continuous nature of the belief space generally prevents a finite-memory controller from maintaining it exactly. This raises the question we address in this work: what must the available finite memory preserve to achieve near-optimal control performance? We study this question through an information-theoretic converse that gives a fundamental lower bound on the loss of any finite-memory controller. The analysis introduces the concept of a decision witness that characterizes belief states whose confusion would lead to unavoidable loss. Motivated by this bound, we propose RECAP, a constructive quantization method for designing representative memory states. We prove an upper bound on the optimality gap of any RECAP codebook and use the components of this bound together with the converse analysis to define the codebook objective. Experiments on 11 POMDP benchmarks demonstrate that RECAP improves over finite-memory representation baselines under the same memory budget.

cs.LG · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03411

Low-Rank Clebsch--Gordan Coupling for Equivariant Spherical Forecasting

Yaojun Li, Zhixuan Zhao, Dongzhe Zheng, Christine Allen-Blanchette

原文摘要

Spherical-harmonic neural operators provide an efficient representation for global forecasting, but their discretized pointwise nonlinearities can break rotational equivariance through aliasing. Clebsch-Gordan (CG) tensor products provide exactly equivariant interactions between spherical-harmonic representations, but their computational cost grows rapidly with spectral bandwidth and has limited their use at forecasting scale. We introduce LCG-SFNO, which combines low-rank CG interactions within a controlled low-degree band with coefficient-space residual propagation and an exactly projected quadratic nonlinearity, preserving SO(3) equivariance throughout the learned operator. To make the CG interaction practical, we further develop SSHCG, a sparse GPU implementation with a factorized alternative for wider coupled bands. On spherical shallow-water dynamics and the ERA5-based WeatherBench benchmark, LCG-SFNO reduces rotational error to numerical precision (more than three orders of magnitude below SFNO on shallow water and five orders of magnitude below it on WeatherBench), while matching SFNO on shallow-water forecasting and substantially improving forecast skill relative to SFNO on WeatherBench. Matched ablations show that CG coupling accelerates convergence, while SSHCG keeps the coupling to about 10 percent of a training step at the operating bandwidth, making exact equivariant coupling practical at forecasting scale.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03410

OpenZilo: An Open Smart-Ring Platform for Human-Centric Sensing and Interaction with Robots

Qianggang Ding, Xingyao Wang, Rui Feng, Chi Qin, Weijian Lin, Lizhi Ding, Jiankai Tang, Yuntao Wang 等 10 位作者

原文摘要

Wearable robot interfaces must connect user input to timely physical action. We present the proposed platform, an open smart-ring platform for studying this connection across task stages. The ring integrates a microphone, an inertial measurement unit, photoplethysmography, and skin-temperature sensing. Its software architecture separates acquisition, event interpretation, interaction policy, and robot execution. We characterize seven operating modes and evaluate speech and gesture control with 10 participants. Across five commands and 250 trials per modality, speech and gestures achieved task success rates of 90.4% and 88.8%, respectively. Recognition accuracy exceeded task success for both modalities. Gaussian hidden Markov models achieved higher accuracy than a support vector machine across three vocabulary sizes, with longer scoring times. Following a shared spoken task start, gesture-based adjustments had shorter median response times than speech. Whole-task success was 70.0% with gestures and 64.0% with speech. We further examine physiological-event-triggered robot interaction using simulated event notifications. Mean notification-receipt-to-arrival times were 11.71 s for the robot and 9.20 s for an on-site person. These descriptive findings support evaluating wearable robot interfaces through both recognition and physical outcomes, with timing measures appropriate to each task stage.

cs.RO · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03409

On inverse aggregate network assignment games

Joseph Y. J. Chow

原文摘要

To design platforms for multi-operator mobility markets coordinating their operations or infrastructure to support them, models are needed to understand the behavior of these operators and travelers in a shared network. Assignment games can prove useful, but operationalizing them requires adopting a more generalized aggregate assignment game framework that allows for identification and estimation. We extend such a framework to aggregate network assignment games, proposing a path-based formulation and a PURC-based formulation along with solution algorithms. An inverse optimization model is proposed for the link-based formulation. It is solved by a quadratic program that separates into a buyer block, which retains the node potentials and imposes every observed zero-flow cell, and a seller block. This estimator, introduced in the literature for uncapacitated route choice, is extended here to a capacitated two-sided market, with rationing prices supported on observed binding links and operator supply scales estimated by group. Computational tests with a toy network demonstrate the economic value of these models. A large-scale Long Island Rail Road network example (897 nodes, 671 census tracts, 123 LIRR stations, 8,320 directed links) is calibrated to 2026 fares, timetabled seat supply and National Transit Database operating costs. With 10-50% of links sampled, the misspecified inverse model fits oracle link flows with R^2>=96%. Held-out link error falls from 29% to 19%, correlations with the true rationing prices rise from 49% to 75%, and social optimal gross surplus is recovered within 6%. Deadweight loss is overestimated by 10-17%. Ablation tests show that ignoring the inactive cells more than doubles the held-out error and, at 30-50% sampling, roughly halves the correlation of the pricing wedge, while ignoring the rationing prices degrades every metric.

cs.GT · econ.TH

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03406

From Reactive to Agentic Resilience: Multi-Timescale AI-RAN Orchestration Under Signaling Storms

Sotiris Chatzimiltis, Mahdi Boloursaz Mashhadi, Mohammad Shojafar, Rahim Tafazolli

原文摘要

Next-generation open radio access network (Open RAN) and AI-RAN deployments turn the RAN into a disaggregated, compute-aware platform whose service continuity depends on many control loops acting at different timescales. Resilience in this setting is best seen as a lifecycle spanning anticipation, absorption, adaptation, recovery, and evolution. Yet, existing mechanisms remain stage-specific and are rarely coordinated across the control hierarchy. This paper argues that agentic AI offers an orchestration layer that coordinates this lifecycle, turning operator intent into coordinated action while keeping slow language-model reasoning off the fast control path. Using mass-attachment signaling storms as a running study, we build a discrete-event Open RAN attach simulator that captures retry amplification, shared-compute contention, and non-instantaneous provisioning, and we propose a three-tier framework in which an orchestrator at the service management and orchestration layer sets a standing policy, a per-site non-real-time agent judges storms and anticipates scheduled crowds, and a deterministic near-real-time loop enforces every decision. Across experiments, the agentic controller approaches the resilience of a fully provisioned pool of servers at a quarter of its capacity by reading a scheduled event from a calendar and provisioning ahead of it. No reactive weight setting recovers this once the surge arrives. Free-text operator intents ground into control directives at 94% exact match on previously unseen intents, and a single instruction reshapes live behavior end to end. We also quantify the latency, cost, and contention limits that bound it. The supplementary material provides analytical proof of the derived Theorem on anticipation horizon, as well as the subsequent Corollary results on reactive provisioning limits.

cs.NI · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03404

VidDub: Replacement-Aware Distillation for Two-Step Video Dubbing

Yexin Liu, Yunlong Lin, Zehan Wang, Guangxin He, Xinli Xu, Jiajun Zha, Yaofu Liu, Yueze Wang 等 14 位作者

原文摘要

Video dubbing aims to synthesize lip movements that match target speech while preserving the speaker identity, head pose, and background of a source video. Existing editors are predominantly trained via self-reconstruction under original paired audio, even though the source mouth becomes a conflicting target when driving speech is replaced. We introduce replacement-aware cross-audio supervision: paired-audio samples use full-frame reconstruction, whereas unpaired replacement-audio samples mask out source-mouth reconstruction losses and supervise the mouth solely through audio-visual synchronization while preserving unmasked facial regions and background. Because offline dubbing provides the complete source video and target speech beforehand, we present VidDub, a full-context diffusion editor distilled into two denoising steps (2 NFE). Our replacement-aware teacher first learns accurate lip synchronization without regressing toward conflicting source mouth movements. Matched trajectory regression then transfers the teacher's CFG-guided trajectories to a two-step student, and distribution matching distillation recovers visual quality after step compression. We will release the curated training dataset, VidDub-Bench, fixed paired- and cross-audio evaluation manifests, preprocessing outputs, and unified evaluation code. Across three training seeds, masking out conflicting source-mouth reconstruction improves cross-audio Sync-C by 0.833; the final two-step model combines strong lip synchronization with superior visual quality, identity preservation, and visual speech recognition across four benchmarks.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.42097

ROOF: Learning Humanoid Fall Recovery under Overhead Constraints

Yidan Lu, Jiayuan Yu, Zongrui Cai, Jingwen Sun, Jinrui Han, Jing Wang, Ping Luo, Peng Lu

原文摘要

A fallen humanoid beneath an overhead obstacle faces a coupled recovery problem. It must create clearance while organizing support for a stable rise, since attempting to stand before making room can further increase obstruction. We introduce a reinforcement learning method with contact-supported geometric escape guidance, which rewards incremental reduction of robot-obstacle overlap without prescribing an escape path. To achieve this, a learned motion prior, together with posture-dependent support and joint-stall costs, is proposed to shape a single policy that learns when to clear the obstacle, how to establish support, and when to complete the rise, all using proprioception alone. In simulation, it recovers in 88.5% of trials beneath plates that can only be lifted, compared with 18.9% without geometric guidance, and in 98.6% of trials beneath free plates with randomized size, mass and placement. On a Unitree G1, the deployed policy recovered in 95 of 150 attempts across seven real-world obstacle scenes, including furniture and multi-object arrangements absent from training. Project page: https://lu-yidan.github.io/ROOF/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.21337

Multicategorical Semantics for Untyped Effects

Ariel Grunfeld, Liron Cohen

原文摘要

Completeness proofs in categorical semantics usually proceed by building a syntactic category whose composition is given by substitution. For untyped effectful Call-by-Value languages, this runs into a basic obstacle: there is no canonical notion of simultaneous substitution of computations, since evaluation order is semantically meaningful. We address this by taking single computation substitutions, that is, binding steps, as primitive, and representing computation substitution by finite sequential lists composed by concatenation. We formalize this idea in a one-object Freyd-multicategorical setting. We introduce Freyd operads, separating a cartesian operad of values from a symmetric Ren-cartesian preoperad of computations, connected by a Freyd functor, and from any Freyd operad we construct a corresponding Freyd PROP of substitutions. We prove that this construction is representable and, in the strict one-object setting, left adjoint to restriction to codomain 1. Using the induced term model, we interpret untyped computational lambda-calculus with procedures and higher-order functions in weakly closed Freyd operads, and prove soundness, initiality, and completeness. This yields a categorical semantics tailored to untyped effectful computation and broad enough to encompass realizability-oriented models such as monadic combinatory algebras.

cs.PL · math.CT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03400

In-depth data movement characterization of DNA read mapping algorithms

Berkan Şahin, Ecem İlgün, Klea Zambaku, Can Alkan

原文摘要

Read mapping algorithms represent a critical bottleneck in genomic analysis pipelines due to the resources required; a problem that is becoming more pronounced as sequencing technologies advance and datasets expand. We present an extensive workload characterization of leading read mapping algorithms across diverse sequencing technologies to identify hardware acceleration and optimization opportunities. We analyzed the following implementations: MashMap, wfmash, and Minimap2 for whole-genome alignment (WGA); NGMLR, Minimap2, and LRA for long reads; and Bowtie2, BWA-MEM, StrobeAlign, and minibwa for short reads. We used the Intel VTune Profiler for detailed memory analysis to identify memory- bound functions, cache usage patterns, and data transfer bottlenecks. We further analyzed a selected subset of alignment tools using the DAMOV-SIM simulator to obtain a more granular view of data movement characteristics for two different cache structures. Based on our results, Minimap2 emerges as the fastest alignment tool for long read scenarios, but falls behind in whole-genome alignment due to poor multithreading. Across all three categories, some programs (e.g., LRA, BWA-MEM) are highly memory-bound. More in-depth analysis via DAMOV-SIM revealed that some memory-bound tools, in particular, do not fully utilize the multi-level cache hierarchy in modern multi-core CPU architectures. Our results show that while some tools require algorithmic redesign for performance gains, some others can benefit from running on PIM architectures.

cs.AR · q-bio.GN

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03399

Image Guided Depth Upsampling using Anisotropic Total Generalized Variation

David Ferstl, Christian Reinbacher, Rene Ranftl, Matthias Rüther, Horst Bischof

原文摘要

In this work we present a novel method for the challenging problem of depth image upsampling. Modern depth cameras such as Kinect or Time of Flight cameras deliver dense, high quality depth measurements but are limited in their lateral resolution. To overcome this limitation we formulate a convex optimization problem using higher order regularization for depth image upsampling. In this optimization an anisotropic diffusion tensor, calculated from a high resolution intensity image, is used to guide the upsampling. We derive a numerical algorithm based on a primaldual formulation that is efficiently parallelized and runs at multiple frames per second. We show that this novel upsampling clearly outperforms state of the art approaches in terms of speed and accuracy on the widely used Middlebury 2007 datasets. Furthermore, we introduce novel datasets with highly accurate groundtruth, which, for the first time, enable to benchmark depth upsampling methods using real sensor data.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03397

Online Algorithms Cannot Find Isolated Perceptron Solutions

Shuangping Li, Gabe Schoenbach

原文摘要

The binary perceptron problem asks for a length-$n$ sign vector in the intersection of $\lfloor αn\rfloor$-many independent random Gaussian half-spaces. A striking feature of this model is strong freezing: at every constraint density $α> 0$, all but a vanishing fraction of solutions are isolated -- $Ω(n)$-far from all others, in Hamming distance. Such solutions are believed to be algorithmically inaccessible. Nevertheless, efficient algorithms find solutions at some constraint densities $α$. One possible resolution of this apparent contradiction is that the solution space also contains rare well-connected structures, and that algorithms succeed by locating them. Gong, Huang, Li and Sellke (FOCS 2026) proved this for stable algorithms, by showing that such algorithms cannot locate isolated solutions with probability more than $\approx 0.84$. In this work, we study online algorithms, which receive the columns of the constraint matrix one by one and sign each irrevocably upon arrival. This is a widely used class of algorithms in discrepancy minimization, and it includes the algorithm of Jo and Lee (2026), which has one of the best provable guarantees for the binary perceptron. We prove that at any constraint density, any online algorithm outputs an isolated solution with probability $o(1)$. Our proof is based on the observation that the signs an online algorithm has already fixed can be completed to at most one isolated solution, so a successful algorithm must choose the unique correct sign for each of the last few columns. Using the exchangeability of these columns, we show that such an algorithm must match a suffix-parallel baseline, which signs each of them as if it were the next column to arrive. We then use an anti-concentration argument to show that such a baseline rarely produces a unique completion.

cs.CC · cs.DS · math-ph · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03394

TaoMate-HD: Progressive-Resolution Autoregressive Audio-Video Generation

Qijun Gan, Chenwei Zhang, Xinyang Li, Meiguang Jin, Junfeng Ma, Shen Qiu

原文摘要

Streaming digital humans produce detailed video while continuing speech as new text arrives. High-resolution denoising is costly, and text updates can cause repeated or interrupted speech. We present TaoMate-HD, an autoregressive model for high-resolution audio-video streaming. For each short segment, or chunk, two denoising steps establish coarse video structure at low resolution. A final step refines the video to high resolution, with audio generated throughout the same trajectory. Each stage passes its audiovisual predictions to the same stage of the next chunk as history. Across text updates, the model retains this history to infer what has already been spoken. Continuation training utilizes asymmetric text conditioning, with separate text windows for generation and distillation. Experiments show a 1.6x speedup over direct high-resolution generation with the same model and hardware. Optimized stage-parallel deployment provides a further 1.9x overall speedup over serial progressive generation. Our continuation training reduces character error rate by 11.18 percentage points relative to the baseline. The model also supports hour-long audiovisual generation. Our project page is https://taoliveaigc.github.io/TaoMate-HD.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03392

TinyML Hits the Memory Wall: A Benchmark of Ten Sub-Watt Edge Devices

Pietro Bonazzi, Luigi Capogrosso, Ahmet Celik, Rosanna Manzo, Marco Giordano, Szymon Ruciński, Jonathan Hungerbuehler, Michele Magno

原文摘要

Neural network inference at the edge must fit tight energy budgets. Existing benchmarks compare few devices, do not report energy on a common basis, and omit the newest microcontrollers with an NPU or the 1 GHz Cortex-M85. Hardware-aware design also ranks networks by MAC count, yet no study has tested whether this proxy holds across accelerators. We benchmark sub-watt edge AI hardware by evaluating four CNNs (MobileNetV3, MCUNetV1, EfficientNet, SqueezeNet) and two tiny transformers (MobileViT-XXS, DeiT-tiny) on ten commercial devices: an in-sensor DSP (Sony IMX500), four MCUs with an NPU (STM32N6, Alif Ensemble E7/E8, GreenWaves GAP9), and five MCUs without an NPU (STM32U5, Apollo510B, ESP32-S3 Xtensa LX7, RA8M2, STM32V8). Four devices are novel (Alif E8, Apollo510B, RA8M2, STM32V8). Measured as energy above idle, the IMX500 (0.13-0.35 mJ) uses 1.3-3x less energy than the STM32N6 (0.31-1.05 mJ) on every model; the GAP9 uses the least on MobileNetV3 and the Alif E7 the least on MCUNetV1 and SqueezeNet. All four use 6-34x less energy than the STM32V8 (1.85-3.98 mJ), and the Alif E7 and IMX500 form the power-performance Pareto front. Latency on all four accelerators scales with memory traffic (bytes of weights and activations, r >= 0.98) and not with MACs, so they are memory-bound, while no MCU without an NPU is. The NPU advantage grows with intensity from 1.4x over a 1 GHz Cortex-M85 on MCUNetV1 to 8.2x on SqueezeNet. MAC count is therefore a misleading cost proxy for sub-watt accelerators.

cs.AR

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新论文 · v1arXiv:2611.03390

Progressive Multi-Scale Super-Resolution via Cascaded Bicubic Anchors and Inter-Scale Mutual Refinement

Chun-An Lin, Yu-Chia Chang, Chi-Mao Fan, Yen-Chieh Ouyang, Tsung-Jung Liu

原文摘要

Super-resolution networks are trained for one magnification factor, so several output resolutions require several models. We propose PMS-SR, a progressive single-input multiple-output network that produces x2, x4, and x8 results in one forward pass, with a Transformer backbone on the low-resolution grid. In Stage 1, each scale is predicted as a residual over the detached bicubic upsampling of the previous scale's prediction. With Dirac and zero initialization, training starts exactly from the bicubic cascade, and each head adds only the band the previous scale lacks. In Stage 2, inter-scale mutual refinement couples the scales in both directions through space-to-depth feedback, deformable alignment, and per-pixel fusion, so x8 features refine the x2 output. Trained on DF2K, the 7.95M-parameter model achieves a higher PSNR than every compared multi-scale method at x4 and x8 on all five standard benchmarks. At x8, it outperforms the x8-specific DRN-L on all of them, leads all compared methods on Urban100, and is within 0.13 dB of the best result elsewhere. Against three single-scale SwinIR-M models, PMS-SR uses 22% of the parameters, runs 2.9x faster on BSD100 inputs, and its outputs agree 6.7-12.4 dB more closely. Code and pretrained models will be released at https://github.com/julian135707/PMS-SR upon acceptance.

cs.CV · cs.LG · eess.IV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2510.13385

Probabilistic Prediction Markets with Intermittent Contributions

Michael Vitali, Pierre Pinson

原文摘要

Although both data availability and the demand for accurate forecasts are increasing, collaboration between stakeholders is often constrained by data ownership and competitive interests. In contrast to recent proposals within cooperative game-theoretical frameworks, we place ourselves in a more general framework, based on prediction markets. There, independent agents trade forecasts of uncertain future events in exchange for rewards. We introduce and analyse a prediction market operating with a fixed pool of agents that (i) accounts for the historical performance, (ii) adapts to time-varying conditions, while (iii) accommodates intermittent forecast submissions. The proposed design employs robust regression models to learn the optimal forecasts' combination whilst handling missing submissions. Moreover, we introduce a pay-off allocation mechanism that considers both in-sample and out-of-sample performance while satisfying several desirable economic properties. Case studies using simulated and real-world data demonstrate the effectiveness and adaptability of the proposed market design.

cs.LG

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新论文 · v1arXiv:2611.03389

CrowdForge: Effects of Audio and Visual Audience Participation Modalities on Agency, Collaboration, and Engagement in AI-Mediated MR Performances

Torin Hopkins, Shih-Yu Ma, Zander Trafton-Hancock, Ellen Yi-Luen Do, Luca Turchet

原文摘要

Technology-mediated audience participation (TMAP) enables audiences to influence live performances, yet balancing interaction with meaningful agency remains challenging. While AI-generated music and MR enable new forms of audio and visual audience participation, little is known about how these participation modalities influence audience experiences during live musical performance. In particular, it remains unclear how they shape perceived agency, collaboration, and engagement, and how they complement one another within TMAP systems. We present CrowdForge, an AI- and MR-mediated paradigm where audiences collectively control generative music and virtual environments during a live performance featuring a guitarist and DJ. Through a mixedmethods concert study evaluating three interaction conditions (audio, visual, and combined audio-visual), we demonstrate that observability is the primary driver of audience agency. While visual interaction provided easily observable feedback, audio parameters offered macro-structural control over the music. Audiences tended to use the control enabled by AI audio generation to test musical boundaries, introducing a productive adversarial tension between the crowd and the musicians. This dynamic pushed creative limits, deepened engagement, and generated moments of musical coherence when performers successfully resolved audience-induced challenges. We argue future TMAP systems should prioritize human-centered, highly observable interactions to build agency, while combining the expressiveness of music with the clarity of visual feedback.

cs.HC · cs.AI · cs.NI · cs.PF · cs.SI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.27428

Rethinking AI in Medical Imaging: Assumptions, Reality, and Reframing; Towards Physician-Centered Agentic Reasoning

Arman Rahmim, Nourhan Bayasi, Xiaoxiao Li, Babak Saboury, Fereshteh Yousefirizi

原文摘要

Medical imaging has served as primary proving ground for clinical AI, yet a decade of intense research has not translated into proportionate bedside impact. We argue that this gap is not primarily a product of insufficient algorithmic performance, inadequate regulation, or limited explainability. Rather, it reflects a structural misalignment, between how AI systems are designed and evaluated, and how clinical decisions are made. This work identifies 8 interconnected dimensions of this misalignment: 1) the dominance of pixel-only models in a multimodal clinical world; 2) the erosion of physician trust through opaque and inflexible systems; 3) the unfulfilled promise of foundation models in data-sparse medical domains; 4) the persistent bottleneck of non-shareable under-curated datasets; 5) the gap between validated algorithms and deployable clinical platforms; 6) the slow, unmonitored translation of evidence into routine practice; 7) the risk of AI displacing rather than extending physician judgment; and 8) the failure of prediction-centric AI to generate actionable, agentic clinical guidance. For each dimension, we reframe the problem and propose a path forward, culminating in a vision of physician-centered AI solutions with agentic reasoning that extend, rather than replace, clinical judgment.

physics.med-ph · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.28397

MAS-Guard: Unified Guardrails from Individual Agents to Multi-Agent LLM Systems

Mohammad Kachuee, Phu Mon Htut, Sheng Li, Nikolaos Pappas, Miguel Ballesteros, Yassine Benajiba, Sujith Ravi, Dan Roth

原文摘要

Agentic AI extends large language models (LLMs) from generating responses to planning and executing complex workflows, substantially expanding their utility while exposing applications to unsafe actions, information leakage, and adversarial manipulation. Multi-agent systems further expand these capabilities through specialization and collaboration, but distributed authority, heterogeneous roles, and opaque or potentially compromised participants make safeguarding even more challenging. Existing defenses address important individual threats but provide fragmented coverage across local execution, system-level interactions, and enforcement mechanisms. We introduce MAS-Guard, a unified framework that closes this gap for safeguarding single- and multi-agent AI applications containing both instrumentable and opaque agents. Within instrumentable agents, the proposed local Agent Guardrail (A-Guard) layer applies task-specific experts to assess agentic events. At the system level, developer-owned Agent Charters and a Control Plane combine deterministic structural checks of routes, agent capabilities, and message formats with semantic assessments of roles and message content. These complementary layers enable enforcement through a shared set of interventions: allow, reflection, confirmation, and block. Evaluated across six benchmarks covering diverse single- and multi-agent scenarios with three agent LLMs from different providers, MAS-Guard reduces the overall attack success rate by 71.5-86.3% relative to unguarded agents, while meeting the utility reference criterion on benign tasks.

cs.CR · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03388

LAND: Learning an Adaptive Number of Draws for Bayesian Best-of-N Selection

Koen Oostermeijer, Teryn Jones

原文摘要

Best-of-N decoding improves generation quality by sampling multiple candidates and selecting the best, but it allocates equal compute to sources regardless of how much they benefit from additional sampling: the expected benefit is small for sources with low-variance score distributions and potentially much larger for those with high-variance distributions. To address this shortcoming, we propose LAND (Learning an Adaptive Number of Draws), which dynamically allocates samples according to their posterior-predictive marginal value. LAND learns a discrete empirical mixture of source-conditioned score distributions from calibration data, updates its belief about a source as completion scores are observed, and stops sampling when the expected improvement of another draw falls below a shared compute price. Across machine-translation experiments with multiple models and language pairs, LAND achieves better MetricX scores than fixed best-of-N at matched average compute. Conversely, at matched translation quality, LAND requires approximately 20-40% fewer candidate generations. Adaptive allocation therefore provides a simple way to retain the benefits of best-of-N while substantially improving its compute efficiency.

cs.AI

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新论文 · v1arXiv:2611.03386

At What Cost? (In)efficiency in First-Price Procurement Auctions

Simone Palazzotto, Andrés Cristi

原文摘要

We study the efficiency of Bayes-Nash equilibria in first-price procurement auctions. We measure efficiency by the Price of Anarchy (PoA): the ratio between the expected social cost in equilibrium and the minimum expected social cost resulting from a socially optimal allocation. We first show that the standard first-price procurement auction has no bounded PoA guarantee. We construct equilibria in which every bidder is indifferent between all bids in a common interval, so that bids can be arbitrarily large, and the PoA is unbounded. If costs lie in an interval $[\underline c,\bar c]$ but bids are not restricted, the PoA equals the trivial ratio $\bar c/\underline c$. We hence study ways to restore a finite guarantee, first by characterizing the PoA when bids lie in $[\underline c,\bar c]$, which leads to the tight bound $1 + \frac1{e^2}(\frac{\bar c}{\underline c}-1)$. Alternatively, we introduce the notion of a phantom bidder: the auctioneer accepts the lowest bid only if it does not exceed a phantom bid, drawn independently of the costs, and otherwise buys from an outside option at cost $\bar c$. We show that a simple fixed phantom bid, i.e., a deterministic cap, gives PoA $=\frac1e\sqrt {\frac{\bar c}{\underline c}}+O(1)$. We further show that no phantom mechanism whose construction depends only on the cost interval, under a mild regularity condition, whatever distribution it uses and whatever it reveals about the phantom bid before bidding, can improve on the $\sqrt{\frac{\bar c}{\underline c}}$ dependence.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.13201

Compact Quantitative Equational Theories as Monad Restrictions

Matteo Mio, Colin Riba

原文摘要

Proofs in quantitative algebra are, generally, infinite objects due to the presence of infinitary rules in the deductive apparatus. A quantitative equational theory is called compact if all its consequences are derivable by means of finite proofs, that is, proofs not using any instances of the infinitary rules. This definition is purely logical. In this work we give an equivalent, but categorical, characterisation based on the notion of monad restriction.

cs.LO

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新论文 · v1arXiv:2611.03384

Can We Stop the Backtest Yet? Anytime-Valid Promotion Tests for Adaptive Financial Research Agents

Eric Benhamou, Jean-Jacques Ohana, Beatrice Guez, Ethan Setrouk, Chamyl Saadi, Thomas Jacquot

原文摘要

Financial research agents can generate and monitor trading rules faster than a human can document the resulting selection process. Candidates appear at data-dependent times, weak rules are abandoned, and promising ones are watched until they cross a reporting threshold. We introduce PROMOTE, a protocol that assigns every candidate predictable testing wealth at birth and accumulates a cost-aware e-process only from subsequent returns. If lifetime allocations sum to at most one, a weighted Ville argument bounds the probability of ever promoting a rule whose declared bounded net score has always had nonpositive conditional mean. The result permits dependent candidates, adaptive births, and indefinite monitoring. We audit the implementation in two settings. A retrospective pseudo-out-of-sample replay contains 24 panel--rule hypotheses, 3,000 shared-sign null paths, injected alternatives, and public ETF and industry data. Repeated unadjusted tests produce a family crossing in 94.5% of null paths, versus 0.0% for PROMOTE; at a 15-bp aligned ETF-component perturbation, PROMOTE has 70.1% target power and median crossing session 2,327. A separate exact mean-zero experiment lets an agent select candidate identities and birth times from past scores; the funded gate has 1.77% false-family crossings across 3,000 programs. The market replay produces zero promotions after costs. The certificate controls historical false promotion under the declared null; it is not a certificate that an earlier edge remains profitable at deployment. AI-use disclosure. Generative AI tools, including OpenAI Codex, assisted manuscript preparation. For this preprint version, Codex assisted with LaTeX editing, author and venue metadata, compilation, and layout checks.

cs.AI · q-fin.ST

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03382

ASCoR: Asymmetric Semantic-Spatial Learning for Remote Sensing Domain Generalization

Zitong Wang, Shuchang Lyu, Meng Li, Qiang Wang, Guangliang Cheng, Zhenwei Shi

原文摘要

Source-only domain generalization (DG) for remote sensing semantic segmentation seeks reliable predictions in unseen domains without target-domain data during training. Although vision foundation models offer transferable knowledge, category recognition remains sensitive to source-specific visual cues, while task-specific adaptation may weaken pretrained transferability. We propose ASCoR, an asymmetric semantic-spatial framework built on SAM3. Its text-conditioned semantic branch uses class-name prompts to provide explicit category guidance. Probe-Once Dynamic Adaptive LoRA distributes a fixed adaptation budget across encoder blocks to limit source-specific specialization. A complementary task-specific dense branch learns multi-scale spatial representations using a separate frozen SAM3 image encoder. A lightweight reliability router integrates the two branches through bounded spatial correction based on local prediction reliability. All components are trained using source-domain supervision only. Experiments on the ISPRS, LoveDA, and OpenEarthMap benchmarks cover nine cross-domain transfers. ASCoR achieves a state-of-the-art mean IoU of 68.61% across six ISPRS transfers, outperforming the previous best method by 2.69 percentage points. It also achieves the best results on all three LoveDA/OpenEarthMap transfers, with 59.07%, 47.95%, and 49.38% mIoU on Rural-to-Urban, Urban-to-Rural, and Urban-to-OpenEarthMap, respectively. Ablation studies, feature visualizations, and spatial reliability analyses support the effectiveness and complementarity of the proposed components. Code: https://github.com/WangZitong42/ASCoR.

cs.CV

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