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新论文 · v1arXiv:2611.07420

Adaptive and High-Fidelity Matching Algorithm for Retrieval-Based Accent Conversion

Xinran Wang, Ning Chen, Yixin Luo, Hongqing Zhu, Li Tian

原文摘要

Accent conversion (AC) aims to transform accent-related acoustic characteristics of the source speech to a target accent while preserving linguistic content and speaker identity. Unlike conventional generative AC models, the retrieval-based ones, which employ k-nearest neighbors (kNN) to directly retrieve acoustic features from target-accent speech and so do not need source-accent speech during training, are more effective. However, existing retrieval-based AC frameworks face three issues: limited intelligibility due to incomplete phoneme coverage from sequential matching pool construction, limited adaptability to source speech variations, and low spectral continuity from coarse-grained style modulation. To address these limitations, we propose Anisotropy-Aware Adaptive k-Nearest Neighbor Accent Conversion (AA-kNN-AC), an enhanced retrieval-based AC model. First, we design a phoneme coverage-based matching pool construction strategy, employing greedy selection with diversity-aware scoring to achieve balanced and near-complete phonetic representation under limited target accent data. Second, we propose an anisotropy-aware adaptive kNN retrieval mechanism that dynamically determines the neighborhood size according to the singular value decomposition (SVD)-based anisotropy analysis, enhancing adaptability to source speech variations. Third, we present a progressive Adaptive Instance Normalization (AdaIN)-based fine-grained decoder by integrating temporal up-sampling within AdaIN blocks, enhancing spectral continuity of the converted speech. Experimental results on the L2-ARCTIC and VCTK datasets demonstrate that the proposed method outperforms existing retrieval-based and generative baselines in both objective and subjective evaluations, achieving high intelligibility, speaker similarity, and naturalness. Demo page is available at https://aa-knn-ac.github.io/demo/.

cs.SD

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.00908

Formalizing Heterogeneous Interaction Modeling for Multi-Agent Debate in Visual Geo-Localization

Zijie Zhou, Dandan Zhu, Heng Zhang, Yanming Yang, Xu Liu, Huishen Jiao

原文摘要

Visual geo-localization requires complex reasoning to infer image locations without GPS metadata. Traditional retrieval methods are limited by database quality while Large Vision Language Models often struggle with diverse scenes. Although existing multi-agent systems improve collaboration, they treat interactions uniformly and lack effective conflict resolution. We propose GraphGeo, a multi-agent debate framework based on heterogeneous graph neural networks for visual geo-localization. The framework models diverse debate relationships through typed edges and separates supportive collaboration, competitive argumentation and knowledge transfer. We introduce a dual-level debate mechanism combining node-level refinement and edge-level argumentation modeling. A cross-level topology refinement strategy enables co-evolution between graph structure and agent representations. Experiments on multiple benchmarks demonstrate GraphGeo outperforms single model and multi-agent baselines. The framework converts cognitive conflicts between agents into higher geo-localization accuracy through structured debate.

cs.CV · cs.GR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07417

MessyNav: Zero-Shot Navigation via Manipulation in Messy Environments

Kenneth Llontop, Cherie Ho, Dylan Zhou, Anuva Banwasi, Priya Sundaresan, Carlota Parés-Morlans, Jeannette Bohg

原文摘要

Real-world environments are messy, with paths often blocked by everyday objects like chairs and toys, requiring robots to perform interactive navigation by manipulating obstacles to clear a path. Existing methods rely on extensive simulation-based learning or per-scene instructions specifying which objects to avoid or manipulate. They struggle to generalize across novel objects, diverse environments, and varied contexts such as a cart of fragile glassware that should be avoided while an empty one can be pushed. We formalize this problem as open-world interactive navigation and present MessyNav, a zero-shot framework based on vision-language models (VLMs). At its core, MessyNav performs explicit VLM-based affordance reasoning before task planning to decide whether to swipe, pick up, or avoid an object and produce a high-level plan. Evaluated on a mobile manipulator across real-world indoor and outdoor environments, MessyNav improves success rate by 27 percentage points (pp) over the strongest baseline in real-robot trials and achieves statistically significant gains in a larger-scale offline evaluation, without task-specific training.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07415

Truncated backpropagation through time induces a two-sided gradient turnpike property

Julian D. Schiller, Matthias A. Müller

原文摘要

Truncated backpropagation through time reduces the computational burden of training recurrent models by restricting gradient computations to shorter temporal contexts. This induces two distinct boundary effects: missing past information perturbs the forward state evolution, while truncating the backward recursion omits future information. This motivates decoupling the interval over which gradient contributions are averaged for the parameter update from the surrounding temporal context over which they are computed. Under exponential decay of the state-transition Jacobians and mild regularity conditions, this leads to a two-sided gradient turnpike property: the error between truncated- and full-context gradient contributions decays exponentially with the distance from both context boundaries. We derive bounds for the averaged update gradient, yielding guarantees on gradient alignment and approximate full-context stationarity; under unbiased interval sampling and a Polyak-Lojasiewicz condition, these further imply a bound on the resulting optimality gap. Numerical experiments on the Wiener-Hammerstein benchmark illustrate the role of past and future context and the trade-off between predictive performance and computational cost.

eess.SY · cs.LG · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08544

GenStrain: Multi-Conditional Generative Diffusion Model To Synthesize High-quality Motion and Strain Maps From Cardiac Magnetic Resonance Images

Rishov Paul, Sona Ghadimi, Cristiane C. Singulane, Amit R. Patel, Frederick H. Epstein, Miaomiao Zhang

原文摘要

This paper presents GenStrain, a novel method for synthesizing high-quality cardiac motion-based strain maps from routinely collected cine cardiac magnetic resonance (CMR) images. Existing approaches typically rely on image registration to estimate motion (a sequence of displacements) from cine CMR images, but cine CMR presents very sparse trackable intramyocardial features, presenting challenges to estimate intramyocardial motion. An advanced imaging technique, Displacement Encoding with Stimulated Echoes (DENSE) CMR, is considered as the gold standard for strain assessment, but it requires additional acquisitions, which are not ideal for efficient clinical workflows. To address this gap, we introduce GenStrain, a novel framework designed to learn the data distribution of highly accurate cardiac motion captured by DENSE. In particular, we develop a diffusion process in motion space, where spatiotemporal continuity is carefully considered. To fully leverage both cine CMR images and their associated motion patterns, we introduce a multi-conditional conditioning strategy that integrates anatomical features and motion representations learned by a spatiotemporal registration model to guide the generation process. Experimental results demonstrate that our proposed model, GenStrain, outperforms existing strain analysis methods based on displacement estimates from both traditional registration approaches and state-of-the-art (SOTA) deep learning models. Our model represents a promising step toward more accurate cardiac function assessment and holds great potential for clinical translation. Our code is publicly available at https://github.com/Rishov-MIA/GenStrain.

eess.IV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.19540

Continuous Adversarial MeanFlow for Few-Step Generative Adaptation from Any Parameterization

Yara Bahram, Zahra Dehghani, Mélodie Desbos, Eric Granger, Pablo Piantanida, Mohammadhadi Shateri

原文摘要

Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model leaves its costly multi-step sampling intact, and existing acceleration methods are tied to the source parameterization (x, epsilon, v, or u), so heterogeneous pretrained models share no common acceleration target. Second, while adversarial post-training sharpens generation quality, existing criteria each cover only one end of the step range. Discrete criteria compare jump endpoints and lose their signal as the jump shrinks, while continuous criteria score the instantaneous velocity and misjudge the large jumps of few-step sampling. Neither fits MeanFlow (MF) models, which predict average velocities over intervals of any length. We propose MeanFlow-Transfer (MF-T), which adapts a source domain model of any parameterization and compresses it into a few-step MF generator through a common velocity map. We then introduce Continuous Adversarial MeanFlow (CAMF), which scores the change of a learned potential along each predicted interval jump of MF. CAMF needs no gradient penalty or Jacobian-vector product (JVP). We also prove it stays well-conditioned for every interval length and recovers the instantaneous criterion as the interval vanishes. Adapting four ImageNet-based source models (DiT, SiT, JiT, iMF) to five target domains, MF-T + CAMF approaches or exceeds the fine-tuned many-step teacher in FID and FDD at up to 125x fewer Neural Function Evaluations (NFEs). Further, CAMF improves MF-T's few-step FID by about 30% on average, outperforming prior adversarial post-training on iMF at every NFE.

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07411

Kinematic Hypothesis Context: Embodiment-Conditioned Learned Video Coding for Bandwidth-Limited Robot Policy Uplinks

Yuze Lu

原文摘要

Robots increasingly run large vision-language-action policies on remote servers, making the camera uplink the bottleneck. Existing video codecs spend bits on content the receiver could predict: the robot's own motion. We introduce the kinematic hypothesis context, a zero-rate conditioning signal that both ends build from joint states, the robot model and the camera calibration. Ego-motion homographies, per-link robot reprojections, an end-effector plane and a background memory warp the previous reconstruction into hypotheses that condition a learned video codec. We prove that history-only and global-pose conditioning with a limited receptive field cannot access this information under large motion, and that coding gains become task gains at the policy's success cliff. On LIBERO, the context saves 23.9% bit rate over a capacity-matched history-conditioned codec and 40-56% over x264, x265 and SVT-AV1, and needs 23.2% fewer bits than DCVC-RT at equal policy action deviation; trained to lower rates, it matches DCVC-RT in PSNR. In closed loop at equal rate, OpenVLA-OFT success nearly doubles from 22.8% to 43.1% at 6 kbps (72.6% with lower-rate training, 23.6 points above DCVC-RT), pi_0.5 success rises from 55.5% to 77.4%, and the gain grows under packet loss. The context transfers across three codec hosts and to real robot data.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07410

Spectral symmetry fingerprints in equivariant representations

Jiacheng Cen, Anyi Li, Ning Lin, Zihe Wang, Ji-Rong Wen, Wenbing Huang

原文摘要

Symmetry governs the organization of physical structures, while symmetry breaking reveals their transformations. In geometric deep learning, symmetry imposes rank constraints on equivariant representations, limiting the expressive capacity of equivariant graph neural networks (GNNs). Here we show that these constraints can instead be exploited: the singular-value spectra of equivariant representations provide a unified fingerprint of structural symmetry. We introduce spectral symmetry fingerprints (SymFP), which enable discrete point-group identification through rank-template matching and continuous symmetry analysis by quantifying spectral energy beyond target rank constraints. SymFP is orientation invariant and architecture agnostic, enabling a parameter-free GNN implementation. Its spectra can be reused across multiple target groups, and the resulting leakage scores satisfy subgroup monotonicity. Across synthetic structures, molecules, coordination complexes and large biological assemblies, SymFP accurately identifies point groups, tracks controlled symmetry breaking and reveals local and degree-resolved symmetry changes that can be obscured by global geometric measures. These results establish the spectral structure of equivariant representations as a general framework for characterizing symmetry across physical systems.

cs.LG · cond-mat.mtrl-sci · math-ph · physics.chem-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07408

PSGAgent: Enhancing MLLMs' Dynamic Visual Perception for Panoptic Scene Graph Generation via Agentic Reasoning

Qingyun Liu, Yuankun Liu, Jiaye Feng, Jie Huang, Zonghong Dai, Haochen Li, Jingkun Wang, Weiping Li 等 9 位作者

原文摘要

Panoptic Scene Graph generation (PSG) requires holistic yet fine-grained reasoning over complex relations among objects. However, current Multimodal Large Language Models (MLLMs) applied to PSG suffer from a perception bottleneck: they function as static observers limited by fixed-resolution views. As a result, models often overlook critical relations involving small or peripheral objects and struggle to differentiate ambiguous predicates in complex scenes. To address these limitations, we propose PSGAgent, an agentic framework that enhances visual perception for PSG through a think-act-observe loop. PSGAgent allows MLLMs to dynamically refine their visual perception using two tools: a Region Zoom-In Tool for local exploration of small or boundary relations and a Visual Highlight Tool for relation-focused segmentation that filters out irrelevant contextual noise. To optimize decision-making, we construct a two-stage training pipeline. First, we create a high-quality, error-aware Multimodal Chain-of-Thought (M-CoT) dataset that provides robust supervision for cold-start learning. Then, we design a novel hierarchical reward mechanism integrated with Group Sequence Policy Optimization (GSPO) to enable strategic and adaptive tool usage. Extensive experiments on the PSG benchmark show that PSGAgent achieves SOTA performance, demonstrating strong capability in both holistic and fine-grained scene understanding.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21569

Decoupled Safety Control: A Safety-Control Algorithm for Training-Free Safety Guidance

Huilin Zhou, Ruoxi Cheng, Yuhang Wang, Yuming Liu, Minghao Sun, Ruolong Ma, Lan Zhang

原文摘要

Diffusion models achieve strong performance in text-to-image generation, but they can still produce unsafe content. Training-free safety guidance offers a practical inference-time alternative that intervenes during sampling without modifying model weights. However, many existing safeguards still rely on a coupled safety-control chain. In this chain, the same unsafe condition simultaneously specifies what should be avoided, indicates the current unsafe signal, constructs the intervention, and determines when that intervention remains active. This coupling can work when unsafe factors are explicit, but becomes less reliable once harmful and benign semantics are entangled in the current generation state, often leaving residual unsafe content or unnecessarily degrading benign-generation quality or prompt fidelity. We address this problem with Decoupled Safety Control (DSC), a plug-in safety-control algorithm for training-free safety guidance. DSC reformulates training-free safety guidance as an explicit state-dependent control process, so the executed intervention is determined from the current generation state rather than inherited directly from a single unsafe condition. Experiments on standard safety benchmarks show that DSC improves representative latent- and text-space safeguards, achieving stronger unsafe-content suppression while preserving benign-generation quality and maintaining efficient inference.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07398

FreePath: Neural Mean Free Path for Image Dehazing

Rongrong Gao, Jingxuan Feng, Wenyu Xu

原文摘要

Single image dehazing requires recovering scene radiance from visual responses that are weakened and veiled by participating media. Existing methods either invert explicit scattering variables, which can be unstable under complex scenes, or learn direct restoration mappings, which often discard the physical organization of haze formation. We present \emph{FreePath}, a neural mean free path framework for image dehazing. Instead of estimating atmospheric parameters, FreePath represents dehazing as feature space path state restoration, inspired by the expected distance a visual response can propagate before interacting with haze. Within a residual encoder and decoder, the Mean Free Path Residual Block learns path and collision fields to guide haze constrained correction, while FreePath State Conservation Integration preserves restored path states during deep feature aggregation. Experiments on paired CDD-11 haze images and real RTTS scenes show that FreePath consistently improves full reference fidelity, no reference perceptual quality, and visual faithfulness over representative dehazing baselines.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07397

Vis2State: Visual Reasoning through Executable State Abstractions

Sheng Jin, Wentao Liu, Chen Qian

原文摘要

Solving visual puzzles requires both accurate interpretation of visual evidence and reliable computation under task-specific rules. Embedding visual assumptions and task constraints directly in solving code makes the represented problem difficult to inspect and revise independently. We introduce Vis2State, a training-free agentic framework that abstracts visual puzzles into explicit, revisable problem states linking visual inspection and execution. Visual inspection guides state construction and refinement, while generated programs solve the represented problem. The model constructs both the state and the program without predefined puzzle schemas or solving procedures. Inspection and execution feedback allow it to revise the problem representation and solving procedure independently. Vis2State achieves the state-of-the-art performance on four benchmarks. Cross-model evaluation further demonstrates consistent gains. Ablations, error analyses, and fixed-program state interventions further support the effectiveness of the state-centered design. The code will be made publicly available to facilitate future research.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.13780

Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery

Hengtong Shen, Li Yan, Hong Xie, Yaxuan Wei, Xinhao Li, Wenfei Shen, Peixian Lv, Fei Tan

原文摘要

Remote sensing (RS) change detection is essential for interpreting surface dynamics. Semantic change detection (SCD) further enables pixel-level understanding of multi-class transitions, yet remains sensitive to pseudo-changes induced by imaging conditions. Recent RS foundation models extract semantically consistent features across temporal and environmental variations, which is critical for mitigating pseudo-changes. However, existing SCD methods are often rigid and backbone-specific, lacking the flexibility to integrate diverse multi-scale features from emerging foundation models. To this end, we introduce a modular Cascaded Gated Decoder (CG-Decoder) that bridges various backbones and SCD tasks, processing multi-scale features in a coarse-to-fine manner while enabling adaptive change extraction. Building upon the RS foundation model PerA, we present PerASCD, a unified SCD framework. We further propose a Soft Semantic Consistency Loss (SSCLoss) to mitigate numerical instability in mixed-precision training. Extensive experiments on SECOND and LandsatSCD show that PerASCD achieves new state-of-the-art Sek scores (26.11% and 65.21%), surpassing the previous best by 0.61% and 4.95%, respectively. It also demonstrates exceptional data efficiency (outperforming the full-data baseline with 50% data), seamless cross-backbone generalization, and enhanced interpretability. Our approach maintains robust semantic consistency under radiometric variations, providing a reliable SCD solution. Code: https://github.com/SathShen/PerASCD.git.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07395

Rigorously Certified Neural-Network Computation of Nonlinear Water Waves at Near-Machine Precision

Michal Shavit, Yongji Wang, Tristan Buckmaster

原文摘要

We present a computational framework that combines machine-learning-based discovery with rigorous computer-assisted numerical certification and formal verification for strongly nonlinear partial differential equations. We demonstrate the approach for periodic gravity-capillary traveling water waves. Physics-informed neural networks are used as a search mechanism, with the wave profile, wave speed and, in continuation experiments, surface tension learned directly from the governing equations without training data. Through a staged training and refinement procedure, the neural networks achieve near-machine-precision residuals of the governing equations, even in strongly nonlinear regimes. A single formulation recovers high-amplitude Stokes waves, Crapper waves and Wilton ripples. Continuation from a high-amplitude Wilton ripple in surface tension reveals new high-amplitude candidate solutions. We freeze one candidate as an explicit finite representation and rigorously certify a uniform bound on its governing-equation residual using interval arithmetic, accounting for floating-point roundoff, Fourier truncation, aliasing and grid-to-continuum errors. This independently validates the neural-network accuracy. We formalize the certification argument in Lean, which machine-checks the deductions from certified numerical bounds to the residual theorem. Both the interval certification and formalization were developed with substantial assistance from a large language model, while the validity of the result remains independent of the language model: the numerical bounds are rigorously enclosed by interval arithmetic and the subsequent mathematical deductions are checked by the Lean kernel. Together, these steps provide a pipeline from neural discovery to rigorous numerical certification and machine-checked verification with auditable mathematical guarantees.

math.AP · cs.LG · physics.flu-dyn

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36980

AutoVend: Benchmarking LLM Agents on Long-Horizon Autonomous Vending Operations

Qiming Shi, Yikun Li, Beidi Luan, Rui Sun, Sinuo Wang, Delin Wang, Yan Ma, Jing Li 等 10 位作者

原文摘要

Large language model (LLM) agents are extending agent-mediated commerce on the Web from shopping for consumers to operating businesses for sellers. In online-to-offline (O2O) commerce, decisions are issued through Web-based platforms but realized through costly physical execution. Evaluating such agents requires going beyond individual transactions to ask whether decisions made online turn into sustained economic returns. We introduce AutoVend, a benchmark in which one LLM agent manages 20 vending machines across five site types through a Web-based operating interface for 180 simulated days. Its data-grounded simulator combines a shared warehouse, procurement lead times, paid replenishment, inventory expiration, and recurring operating costs. We evaluate five LLM agents against a rule-based inventory-control baseline and diagnose their site coordination, execution, and performance over time. Four of five evaluated LLM agents fall short of this baseline, and returns per dollar of inference cost rank models differently from returns alone. Operational analyses show that gains in individual metrics do not consistently yield stronger economic growth, as inventory and execution costs erode these gains. Trajectory analysis further reveals difficulties in grounding feasible actions, managing inventory commitments, and converting feedback into effective policy revisions.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07392

WeLM-Image: A Unified Image Generation Model towards Visual Intelligence

Canmiao Fu, Chen Li, Guohui Zhang, Hao Lu, Hongyang Wei, Huadong Wu, Huaisong Zhang, Hubery Yin 等 16 位作者

原文摘要

We present \textbf{WeLM-Image}, a unified image foundation model that integrates multimodal understanding into text-to-image generation, instruction-guided editing, and multi-reference synthesis. Designed for single-GPU deployment in WeChat's large-scale consumer applications, the model processes text, reference images, and target latents as one interleaved sequence. A Mixture-of-Transformers (MoT) backbone combines shared attention with independent understanding and generation parameters to limit optimization interference. Equipping each path with Mixture-of-Experts (MoE) modules provides sparse capacity, with 31.7B stored parameters and approximately 5.4B generation parameters activated per target latent token. A generation-oriented data engine combines a knowledge-grounded visual vocabulary with large-scale visual clustering to balance semantic coverage and editing tasks, while training-time evaluation guides iterative data refinement. Bilingual annotations and verified editing supervision support progressive training up to native 1.5K resolution. We optimize generation and editing separately with rubric- and checklist-based rewards, then consolidate the specialized policies through noise-aware on-policy distillation. In the reported overall comparisons, the 1536-resolution model ranks first among the evaluated open-source models on PRISM-Bench, Qwen-Image-Bench, and UnicBench. MeanFlow-based few-step distillation yields WeLM-Image-Turbo, which generates a $1024\times1024$ image in 4.98\,s on a single NVIDIA H20 GPU using eight guidance-free steps.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07390

Deep reinforcement learning-based optimization of multi-RAT handovers

Pavan Kumar Mangipudi, Satya Krishna Pothapragada, Alina Zare, Janise McNair

原文摘要

A major evolutionary goal and challenge for next-generation wireless systems is seamless multiple radio access technology (multi-RAT) mobility. In a multi-RAT environment, handover decisions must account for both user quality of service and network priorities, including signal quality, resource availability, energy consumption, latency, and security. Although WiFi/cellular mobility is well explored in research and standardization, many conventional handover mechanisms continue to rely primarily on received signal strength. Such decisions may overlook link delay, congestion, and the distinct operating characteristics of heterogeneous RATs. In this work, we present the Reinforced Flexible Multi-RAT Selection framework (ReFlexMRS), a configurable Deep Reinforcement Learning (DRL)-based approach for multi-criteria handover target selection. ReFlexMRS is evaluated using an SDN-enabled multi-RAT testbed. The decision problem is formulated as a Markov Decision Process with a finite action space and is addressed using a Deep Q-Network (DQN) with convolutional layers that extract relationships among network measurement features. The agent is trained offline from collected network observations using reward guided Q-value updates, then used to select handover targets during inference. A 5G-WiFi case study demonstrates that ReFlexMRS can trade a small amount of throughput for substantial improvements in link delay, handover stability, and WiFi offloading. Compared with RSSI-based selection, the proposed formulations retain approximately 90-95% of the baseline throughput while reducing total handovers by up to 18% and WiFi-to-5G handovers by up to 13%, while maintaining the required RSSI levels.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.02937

BEAST3D: Animal behavioral analysis and neural encoding from multi-view video via Gaussian splatting

Yanchen Wang, Lenny Aharon, Wangshu Zhu, Kyle Daruwalla, Linghua Zhang, Jiaru Zou, Selmaan Chettih, Helen Hou 等 10 位作者

原文摘要

Multi-view video recordings are increasingly used to capture the 3D movements of animals in experimental settings, yet extracting rich 3D representations from these recordings remains challenging. Supervised pose estimation requires extensive manual annotation, while general-purpose 3D reconstruction models trained on generic scene datasets fail on the specialized imagery and sparse-view setting of laboratory experiments. We address these limitations with BEAST3D, a self-supervised pretraining framework that learns 3D visual representations from unlabeled, calibrated multi-view video. BEAST3D uses a vision transformer to predict 3D Gaussian splats that reconstruct held-out views through differentiable rendering, while simultaneously segmenting the animal from the background. BEAST3D reconstructs 3D structure with as few as four views by conditioning directly on known camera parameters--unlike general-purpose models, which must estimate camera geometry from dense overlapping viewpoints that are seldom available in lab settings. Through comprehensive evaluation across four species, we demonstrate that BEAST3D produces rich, viewpoint-invariant features that transfer effectively to three downstream tasks: novel view synthesis, which validates the quality of the learned 3D representations; multi-view pose estimation, which provides the sparse keypoint trajectories widely used in behavioral analysis; and neural encoding, which relates 3D behavioral features to simultaneously recorded neural activity. BEAST3D thus establishes a versatile framework for behavioral analysis that leverages 3D structure in modern multi-view laboratory recordings.

q-bio.NC · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.22911

ThermoLLM: LLM-Based Multi-Zone HVAC Control with a Spatiotemporal Context

Kirtan Bhatt, Xiachong Lin, Matthew Amos, Flora D. Salim, Wen Hu

原文摘要

Multi-zone HVAC control is a spatial decision problem in which indoor thermal evolution and control decisions depend not only on outdoor conditions and internal heat gains but also on zone layout, physical adjacency, and delayed thermal interactions across the building. Recent LLM-based HVAC controllers have shown that prompt-based control is feasible. However, these methods produce inconsistent results, are difficult to generalize, and experience control-latency issues. Their performance also relies heavily on numeric reward values, textual feedback, or unstructured retrieval, which limits their ability to make control decisions that account for zone dynamics and thermodynamics. No existing LLM-based methods have used building semantics and dynamics, together with rolling history, as the centre of the LLM context while testing and comparing HVAC control performance with existing LLM-based HVAC approaches. This paper presents ThermoLLM, an LLM-based HVAC control framework tested in a multi-zone building environment. Spatiotemporal context grounding is a main component of ThermoLLM, which uses a spatial knowledge graph derived from Brick-style building semantics and linked with recent observations from a rolling history. We evaluate ThermoLLM against standard control methods and recent state-of-the-art baselines in a five-zone building environment across six climate-season scenarios. ThermoLLM consistently achieves the best energy-comfort trade-offs and outperforms all evaluated baselines in the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) rankings across all six scenarios.

cs.AI · cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07377

Designing with Cinematic Language: An Intent-Driven Approach to Human-AI Video Clip Co-Creation

Yi He, Zhengxi Yu, Feilin Han, Chuer Chen, Shengqi Dang, Nan Cao

原文摘要

Generative video models have made video creation more accessible, yet creators still struggle to translate high-level narrative intentions into coherent cinematic expressions. Existing systems primarily rely on prompt refinement or expose isolated camera controls, leaving a gap between what to express and how to film it. We present CineAssist, a human-AI co-creation system that treats cinematic language as an interactive design medium connecting creative intentions with executable video controls. We construct a Cinematic Language Design Space and an annotated dataset from 107 filmmaking sources and 1,436 film clips, linking communicative intentions to cinematic parameter values. CineAssist combines a multi-agent workflow for intent alignment and strategy recommendation with an interactive cinematic editor and a context-sensitive preference learner for user-controlled personalization. We evaluate the system through three studies examining end-to-end usability, cinematic quality, and preference evolution. Results show that CineAssist facilitates efficient, controllable, and personalized intent-driven cinematic video creation.

cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06046

DeepfakeGenome: Toward Next-Generation Deepfake Attribution

Shunli Wang, Xinyu Zhou, Yandan Zhao, Wenbin Liang, Taiping Yao, Ke-Yue Zhang, Zhiyuan Yan, Shouhong Ding 等 9 位作者

原文摘要

In recent years, AIGC technologies are capable of generating hyper-realistic forged facial images, posing severe threats to facial security safety. To tackle these challenges, early Deepfake research primarily centered on real-fake classification. Recently, growing efforts have been devoted to the Deepfake Attribution (DFA) of generated content. Nevertheless, existing research on Deepfake detection and attribution faces saturated performance in binary classification, limited diversity in datasets and algorithms, and imperfect evaluation protocols, which severely impede practical application. To address these limitations, we propose a comprehensive deepfake detection and attribution benchmark named DeepfakeGenome (DFG). It contains 100 facial forgery algorithms and 2M images in total, achieving 4x to 100x larger than prior DFA benchmarks. We further design 4 protocols for practical evaluation, including a novel retrieval-based attribution paradigm. Unlike previous open-set evaluation metrics, the proposed retrieval metrics are more aligned with the real-world active defense situation of blacklist registration mechanisms. Based on these elaborate designs, we investigate the performance ceiling of deepfake attribution task. Over 2k experimental evaluations are conducted, and 10 insightful findings are derived. We believe this work can provide new insights into the DFA research field. The Code and Benchmark of DFG are avaliable on https://huggingface.co/spaces/DeepfakeGenome/DFG_Page.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.00734

EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updates

Mocheng Li, Xiao Yan, Baotong Lu, James Cheng, Chenhao Ma

原文摘要

Filtering approximate nearest neighbor search (Filtering ANNS) retrieves approximate nearest neighbors subject to attribute constraints in applications such as recommendation, retrieval-augmented generation (RAG), and LLM agent memory. However, predicate-specialized methods target either range or label filtering and can incur substantial index construction time, whereas predicate-agnostic methods support arbitrary predicates but struggle to sustain throughput and recall across a wide range of selectivities. In this paper, we propose EMA, a Filtering ANNS algorithm that supports multi-predicate queries over mixed numerical and categorical attributes as well as efficient dynamic updates. EMA attaches compact summaries, called Markers, to graph edges to record the attributes of candidates removed by geometric pruning. During query processing, EMA performs Marker-guided joint search with bounded edge recovery, enabling efficient filtering while preserving graph navigability. On multi-predicate workloads, EMA achieves 1.20x-28.18x the query throughput of the best competing baseline at comparable recall. In our complete-record replacement experiment, EMA retains approximately 98% of its initial query throughput without rebuilding the graph.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07375

When Scores Conflict with Preferences: Pre-Alignment Diagnosis and Adaptive Correction of Constraint Drift in DPO

Ping Liu, Yan Yan

原文摘要

Direct Preference Optimization (DPO) is increasingly used in settings where pairwise preferences coexist with pointwise criteria such as response length, answer correctness, or safety scores. When the two disagree, standard DPO treats pairs whose preferred output scores worse on the pointwise criterion (conflict pairs) in the same way as the remaining (matching) pairs. We characterize a systematic failure mode, constraint drift: for sufficiently small updates, conflict pairs tend to increase constraint cost while matching pairs tend to repair it, and the net change depends on their balance. This balance yields a closed-form pre-alignment diagnostic, $ρ^{*}_{\mathrm{eq}}$, computable from dataset statistics, that indicates whether a dataset is self-correcting or requires intervention. Building on this diagnostic, we propose Conflict-aware DPO (CoDPO), which down-weights conflict pairs with a strength set from $ρ^{*}_{\mathrm{eq}}$ rather than tuned, and adds an adaptive margin shift that addresses residual drift during training. Empirically, drift grows monotonically with conflict exposure on the conflict-dominated domains, and the intervention decision given by $ρ^{*}_{\mathrm{eq}}$ matches the observed drift on every domain, including one that it identifies as self-correcting. On all four domains of the main comparison, CoDPO reduces drift while largely preserving the preference learning of vanilla DPO. Compared with hard filtering and recent constrained and multi-objective baselines, CoDPO also achieves a more favorable drift-preference trade-off.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07374

Design of Multi-UAV Cooperative Deep Semantic Autoencoders for Communication Networks

Xiaoling Han, Bin Lin, Nan Wu, Tony Q. S. Quek

原文摘要

Driven by the rapid development of the low-altitude economy, Uncrewed Aerial Vehicle (UAV) networks have become increasingly important for executing tasks such as search, rescue, and monitoring. However, under bandwidth-constrained scenarios, UAV networks still face significant challenges in task execution efficiency and intelligent processing capabilities. To address these issues, we propose Multi-UAV Cooperative Deep Semantic Autoencoders (MC-DSA) for communication networks. The architecture comprises one Task UAV (T-UAV) and multiple Support UAVs (S-UAVs), which jointly enhance communication efficiency and data-processing capability via semantic communication and cooperation. Specifically, we introduce a semantic matching and source selection mechanism, enabling the proposed MC-DSA to handle low-quality images while ensuring effective utilization of high-quality images. The T-UAV broadcasts a task query, and each S-UAV computes the semantic similarity between its local image and the query. Communication links are established only when the similarity exceeds a predefined threshold. The training algorithm optimizes network parameters through gradient descent, improving semantic feature extraction accuracy. The testing algorithm evaluates UAV collaboration efficiency and communication quality. Experimental results demonstrate that, compared with benchmarks, the proposed MC-DSA improves image semantic segmentation accuracy while maintaining task execution efficiency. As a result, it reduces redundant transmissions and offers an effective solution for multi-UAV communication networks.

cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05136

Information Loss Is All You Need: $TC^0$ Pseudorandomness from Worst-Case Lattices

Chen Qian

原文摘要

Can deterministic information loss yield post-quantum pseudorandomness with $TC^0$ evaluation? We show that deterministic rounding achieves this under quantum worst-case lattice hardness, with fixed polynomial parameters and no predetermined sample bound. We introduce learning with algebraic rounding (LAR), which applies an explicit integer rounding map to blocks of secret-dependent linear measurements and encodes the result in a lattice quotient. For one fixed family with polynomially bounded modulus and dimensions, deterministic labels on exactly uniform, secret-independent public matrices are computationally indistinguishable from independent uniform labels for every polynomial number of classical random examples. Our classical reduction from Gaussian LWE, combined with the quantum worst-case reduction to LWE, bases LAR on GapSVP and SIVP over general lattices at approximation factor $\widetilde{O}(q)$, where $q$ is the modulus. The proof uses source-independent affine transport: public affine maps sampled independently of the hidden errors transport the Gaussian error law close to uniform on the rounding map's zero-label fiber while preserving exactly uniform public matrices. For every fixed inverse-polynomial accuracy, we give a polynomial-time sampler for the same public parameter family. This yields the first post-quantum weak pseudorandom function with joint key-and-input evaluation in $TC^0$, secure against uniform quantum polynomial-time distinguishers given any polynomial number of classical uniform random examples. We also prove hardness of learning polynomial-size two-hidden-layer ReLU networks from noiseless uniform random examples, even for quantum polynomial-time improper learners.

cs.CC · cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00247

Taurus: LLM-in-the-Loop CT Annotation Matches Coding Agents at Half the Cost

Bing Hu, Rishabh Sharma

原文摘要

Dense labels of the aorta in CT are slow to draw, and the public aortic CT datasets that could stand in for them each label different structures under their own conventions. Large language model (LLM) agents can now write code and read images. We asked whether such a model labels these scans better as a free-form agent that writes its own segmentation code, or inside a harness, where an automated pass labels the scan and the model reviews the result. We built a harness, Taurus, and compared it with free-form agents by blind label recovery: we withheld the human labels of six public datasets (939 volumes) and scored every approach against each dataset's own classes. In the first version of Taurus, the model's review raised the primary metric on only two of six datasets, by less than the noise between two reviews of the same case. A free-form agent running Claude Opus 5.5 scored higher on most datasets. Moving what the free-form agents did well into the automated pass then carried most of the gain on 38 held-out cases. The final version smooths the label surfaces with a fixed rule and checks for the large errors the automated pass tends to make, such as an aneurysm drawn to half its size. Each check that fires puts a multiple-choice question to the model, with candidate corrections drawn on the scan. It matched the surface smoothness of the human references, and in the primary metric we found no difference from the free-form Opus agent, at under half its cost, $1.22 against $2.71 per case. Most of what made Taurus competitive came from the harness and its automation rather than from the language model itself. Taurus shows that this kind of annotation can be automated, so that expert time can move from drawing labels to checking them.

cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07370

Breaking $\mathsf{TraintoDefend}$ via Adaptive Neuron Clustering in Cryptanalytic Model Extraction

Haojie Wu, Haolin Liu, Tianshuo Cong, Guoyan Zhang

原文摘要

Cryptanalytic model extraction poses a fundamental threat to deep learning model confidentiality by enabling precise parameter recovery with theoretical guarantees of functional equivalence. To counter this growing threat, $\mathsf{TraintoDefend}$ (NeurIPS 2025) was proposed as the first dedicated defense against cryptanalytic extraction and remains the only defense believed to be effective to date. It encourages neurons to learn similar weights so that they switch activation states together, making their individual contributions harder to distinguish. However, we notice that neurons with similar weights need not switch activation states together under small perturbations. Instead, weight similarity can cause the original clustering rule to merge signatures from distinct neurons when only a subset of their normalized weight coordinates agree. Motivated by this observation, we introduce $\mathsf{SimBreak}$, an adaptive clustering rule that breaks the weight-similarity defense of $\mathsf{TraintoDefend}$. $\mathsf{SimBreak}$ estimates a relative scale from all jointly observed weight coordinates and uses the same scale to test both weight direction and bias consistency. Across six protected fully connected networks trained on MNIST, $\mathsf{SimBreak}$ raises target layer signature recovery from $0\%$ to $100\%$, with a maximum relative recovery error of $2^{-22.27}$. Our work challenges the use of neuron weight similarity as a sufficient basis for protection and establishes adaptive evaluation as essential to assessing cryptanalytic extraction defenses.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2502.21012

FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection

Silin Chen, Andy Liu, Kangjian Di, Yichu Xu, Han-Jia Ye, Wenhan Luo, Ningmu Zou

原文摘要

Unsupervised image anomaly detection (UAD) has become a critical process in industrial and medical applications, where models are trained on normal samples without requiring labeled anomalies. However, even normal images may contain sensitive product designs, manufacturing information, or institution-specific population characteristics, making centralized data collection difficult in practical deployments. Federated UAD provides a promising decentralized solution, but it remains challenging because clients use only normal training samples, their feature distributions remain highly heterogeneous within the same task, and communication should be efficient while limiting the exposure of sensitive client information. To address these challenges, we propose FedDyMem, an efficient federated learning method with dynamic memory and memory-reduce for unsupervised image anomaly detection. Instead of exchanging full model parameters, FedDyMem facilitates knowledge sharing between the clients and server through reduced dynamic memory banks. In the local clients, a memory generator and a metric loss are employed to improve the consistency of the feature distribution for normal samples, leveraging the local model to update the memory bank dynamically. For efficient communication and data privacy, a memory-reduce method based on weighted averages is proposed to significantly decrease the scale of memory banks. This representation reduces communication overhead and empirically lowers reconstruction fidelity under inversion attacks. On the server, global memory is constructed and distributed to individual clients through k-means aggregation. Experiments on six federated UAD tasks constructed from eleven public industrial and medical datasets show that FedDyMem consistently improves anomaly detection and localization while reducing communication cost.

cs.DC · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.25667

AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation

Junchen Ding, Jialiang Dong, Yichen Zhu, Yi Liu, Gelei Deng, Willy Susilo, Siqi Ma, Yuekang Li

原文摘要

The integration of Large Language Models (LLMs) into cybersecurity has transformed vulnerability assessment, but it has also produced a trustworthiness crisis driven by the unchecked proliferation of "AI slop." These artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports, impose a cognitive burden on human triage pipelines that mirrors a denial-of-service attack. This paper surveys the empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage. We formalize a taxonomy of AI slop grounded in a structured literature review and dissect its root cause: the gap between the causal deductive reasoning of security experts and the autoregressive probabilistic generation of current LLMs. We operationalize this gap through a measurable proxy, the Deductive Coverage Score, and show that chain-of-thought prompting and tool-using agents narrow but do not close it. We review mitigation strategies and argue that passive detection and watermarking target provenance rather than correctness, facing fundamental entropy constraints. We instead advocate for active neuro-symbolic verification, mapping each pipeline component to prior systems with documented limits on security inputs. Finally, we specify two evaluation instruments, CVE-Bench and Slop-Score, including dataset construction, metric formulas, and anti-gaming provisions. By shifting evaluation from linguistic fluency to mathematical verifiability, this survey provides a roadmap for securing emerging AI-driven triage systems.

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07367

Test-Time Self-Play: Scaling without a Verifier

Akira Yoshiyama, Akmal Ashirmatov, Jonas Hübotter, Ido Hakimi, Andreas Krause

原文摘要

Test-time scaling methods spend additional compute at deployment to improve a large language model's performance on a hard target problem. Most methods rely on a verifier that scores candidate solutions for correctness, using it to select among or refine the model's outputs. In many deployment regimes, however, a strong verifier on the target problem is unavailable. We introduce Test-Time Self-Play (TTSP), an approach to test-time scaling using asymmetric self-play without reliance on a verifier. On a difficulty-calibrated subset of LiveCodeBench v6, TTSP is the best-performing method at every verifier strength we evaluate, and the gap to the strongest baseline widens as the verifier weakens; in the no-verifier limit, TTSP yields a 35\% absolute improvement in mean@8. The gains hold across a range of verifier strengths, except when the base policy has near-zero coverage of the correct answer.

cs.LG · cs.AI

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arXiv ↗PDF ↗