论文检索

Social Aspects · Privacy

Zoë R Bell, Anvith Thudi, Olive Franzese-McLaughlin, Nicolas Papernot, Shafi Goldwasser

Training with differential privacy (DP) guarantees dataset members that they cannot be identified by users of the released model. However, those data providers, and, in general, the public, lack methods to efficiently verify that models trained on their data satisfy DP guarantees. The amount of compute needed to verify DP guarantees for current algorithms scales with the amount of computation required to train the model. In this paper we design the first DP algorithm with near optimal privacy-utility trade-offs but whose DP guarantees can be verified cheaper than training. We focus on DP stochastic convex optimization (DP-SCO), where optimal privacy-utility trade-offs are known. Here we show we can obtain tight privacy-utility trade-offs by privately minimizing a series of regularized objectives and only using the standard DP composition bound. Crucially, this method can be verified with much less compute than training. This leads to the first known DP-SCO algorithm with near optimal privacy-utility whose DP verification scales better than training cost, significantly reducing verification costs on large datasets.

Applications · Computer Vision

Lin Fu, Zheyuan Yang, Yang Wang, Tingyu Song, Arman Cohan, Yilun Zhao

We introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 430K video reasoning examples over 126K newly collected, CC-licensed, expert-domain videos. We develop an expert-in-the-loop, skill-oriented example generation pipeline that targets progressively deeper video reasoning capabilities while ensuring the difficulty, diversity, and reliability of both the examples and their CoT rationales. We also curate VideoKR-Eval, a new expert-annotated benchmark where questions require genuine video understanding and knowledge-intensive reasoning. Our experiments show that, under a standard SFT→GRPO pipeline, models post-trained on VideoKR already outperform prior post-training approaches on both general and knowledge-intensive video reasoning benchmarks, highlighting data design as a key driver of progress in video reasoning. We further conduct comprehensive ablations to isolate the contributions of VideoKR, providing actionable insights for future work.

Applications · Computer Vision

Yuying Chen, Liu, Linyan Jiang, Qifan Gao, Xianguo Zhang, Jianhou Gan, Wenqi Ren

Recent advances in video diffusion models have demonstrated remarkable generative capability, yet adapting these large pretrained text-to-video (T2V) models to video super‑resolution (VSR) typically encounters challenges, such as artifacts introduced by complex degradations in real-world scenarios and compromised fidelity due to the strong generative capacity of the powerful T2V models. We present WEVSR, a novel approach that adapts a pretrained flow-matching video diffusion transformer to RealVSR. First, we design a task-oriented adaptation strategy that leverages timestep sampling and noise augmentation to enhance detail restoration while preserving structural stability. Second, we propose a lightweight multi-level discrete wavelet transform (DWT) front-end for the VAE encoder, injecting explicit frequency priors into the latent space without modifying the pretrained decoder. Extensive experiments across multiple RealVSR benchmarks demonstrate that WEVSR achieves state-of-the-art performance against existing approaches.

Applications · Chemistry, Physics, and Earth Sciences

Matteo Rossi, Ryan Pederson, Miles Wang-Henderson, Benjamin Kaufman, Edward Williams, Carl Underkoffler, Owen Howell, Adrian Layer, Stephan Thaler, Narbe Mardirossian 等

We present CoarseBind, a foundation model for protein-ligand structure and binding affinity prediction that achieves 26$\times$ faster inference than state-of-the-art methods while improving affinity prediction accuracy by up to 20\%. Current deep learning approaches to structure-based drug design rely on expensive all-atom diffusion to generate 3D coordinates, creating inference bottlenecks that render large-scale compound screening computationally intractable. We challenge this paradigm with the hypothesis: full all-atom resolution is unnecessary for accurate small molecule pose and binding affinity prediction. CoarseBind tests this hypothesis through a coarse pocket-level representation (protein C$_\beta$ atoms and ligand heavy atoms only) within a multimodal architecture combining pretrained molecular encoders and ESM-2 protein embeddings that learns rich structural representations, which are used in a diffusion-free optimization module for pose generation and a binding affinity likelihood prediction module. On structure prediction benchmarks, CoarseBind matches diffusion-based baselines in ligand pose accuracy. For binding affinity, CoarseBind outperforms Boltz-2 by 16-20\% in Pearson correlation on both a public benchmark (CASP16) and a diverse private dataset (18 assays). The affinity module also provides well-calibrated uncertainty estimates, addressing a critical gap in compound prioritization for drug discovery. Furthermore, this module enables a continual learning framework and a hedged batch selection strategy that, in simulated drug discovery cycles, achieves 6$\times$ greater affinity improvement over greedy approaches.

Deep Learning · Graph Neural Networks

Maysam Behmanesh, Erkan Turan, Maks Ovsjanikov

Graph alignment, the problem of identifying corresponding nodes across multiple graphs, is fundamental to numerous applications. Most existing unsupervised methods embed node features into latent representations to enable cross-graph comparison without ground-truth correspondences. However, these methods suffer from two critical limitations: the degradation of node distinctiveness due to oversmoothing in GNN-based embeddings, and the misalignment of latent spaces across graphs caused by structural noise, feature heterogeneity, and training instability, ultimately leading to unreliable node correspondences. Our key insight is that rather than balancing these objectives purely in the feature space, a unified framework can simultaneously filter in both feature and map spaces. We propose a novel framework that employs a dual-pass encoder to inject high-frequency discriminability into node features, paired with a geometry-aware functional map module that operates on the correspondence itself. This functional map module learns bijective and isometric transformations that align latent spaces while acting as a low-pass filter on correspondences, enforcing smoothness and robustness as a structural prior in the map space. Extensive experiments on graph benchmarks demonstrate that our method consistently outperforms existing unsupervised alignment baselines, exhibiting superior robustness to structural inconsistencies and challenging alignment scenarios.

Applications · Computer Vision

Qunzhong Wang, Jie Liu, Jiajun Liang, Yuanxing Zhang, Yilei Jiang, Yaozhi ZHENG, Xintao Wang, Pengfei Wan, Xiangyu Yue, Jiaheng Liu

Recent advancements in multimodal reward models (RMs) have substantially improved post-training for visual generative models. However, current RMs face inherent limitations: **(1)** visual inputs consume large context budgets, forcing fewer frames and causing a loss of details; and **(2)** all visual information is packed into the initial prompt, exacerbating forgetting during chain-of-thought reasoning. To overcome these issues, we introduce **VR-Thinker**, a thinking-with-image RM equipped with visual reasoning operation and a configurable visual memory window. This allows the RM to actively acquire visual evidence, improving reasoning fidelity and reliability. We activate visual reasoning via a reinforcement fine-tuning pipeline: **(i)** **Cold Start** with curated visual chain-of-thought data to distill basic operation formatting; **(ii)** select samples with correct judgments, then conduct **Rejection sampling Fine-Tuning** on these high-quality traces to further enhance reasoning; and **(iii)** apply **Group Relative Policy Optimization** (GRPO) to strengthen reasoning. Our approach delivers state-of-the-art accuracy among open-source models on video preference benchmarks: a 7B **VR-Thinker** achieves 80.5\% on VideoGen Reward, 82.3\% on GenAI-Bench, and 75.6\% on MJ-Bench-Video.

Applications · Computer Vision

Ghadi Nehme, Yanxia Zhang, Dule Shu, Matthew Klenk, Faez Ahmed

Generating high-fidelity 3D geometries that satisfy specific parameter constraints has broad applications in design and engineering. However, current methods typically rely on large training datasets and struggle with controllability and generalization beyond the training distributions. To overcome these limitations, we introduce LAMP (Linear Affine Mixing of Parametric shapes), a data-efficient framework for controllable and interpretable 3D generation. LAMP first aligns signed distance function (SDF) decoders by overfitting each exemplar from a shared initialization, then synthesizes new geometries by solving a parameter-constrained mixing problem in the aligned weight space. To ensure robustness, we further propose a safety metric that detects geometry validity via linearity mismatch. We evaluate LAMP on two 3D parametric benchmarks: DrivAerNet++ and BlendedNet. We found that LAMP enables (i) controlled interpolation within bounds with as few as 100 samples, (ii) safe extrapolation by up to 100\% parameter difference beyond training ranges, (iii) physics performance-guided optimization under fixed parameters. LAMP significantly outperforms conditional autoencoder and Deep Network Interpolation (DNI) baselines in both extrapolation and data efficiency. Our results demonstrate that LAMP advances controllable, data-efficient, and safe 3D generation for design exploration, dataset generation, and performance-driven optimization.

Deep Learning · Self-Supervised Learning

Faris Chaudhry

We develop a geometric theory of projection heads in self-supervised learning by interpreting the head as a trainable metric on the backbone representation manifold. Our analysis reveals that head curvature and architectural asymmetry induce negative eigenvalues of the Hessian at collapsed equilibria in networks with smooth activation functions, yielding a destabilization mechanism which explains collapse avoidance in non-contrastive methods. We further show that linear heads perform implicit subspace whitening under induced metric geometry, while nonlinear heads adapt local metrics to satisfy the specific topological constraints of the loss. Finally, we characterize how metric degeneracy governs the information-invariance trade-off in learned representations. Our results apply to both contrastive and non-contrastive objectives including InfoNCE, BYOL, SimSiam, and decorrelation-based methods, demonstrating that the projection head acts as a universal geometric buffer that decouples the semantic backbone from the rigid constraints of the training objective.

Optimization · Discrete and Combinatorial Optimization

Shengyu Feng, Tarun Suresh, Yiming Yang

Neural solvers have recently emerged as powerful tools for combinatorial optimization (CO). Among them, diffusion models have shown strong promise due to their ability to capture highly multimodal solution distributions in CO through iterative generative processes. However, training diffusion models typically requires large collections of near-optimal solutions, which limits their scalability and generalization. We address this fundamental challenge by extending adjoint matching, a powerful unsupervised diffusion training framework based on chain-rule–style gradient propagation in continuous spaces, to discrete combinatorial domains. Our approach resolves the broken-gradient issue inherent to discrete data and unifies local and global training objectives within a single principled framework. Empirically, our method consistently outperforms existing unsupervised baselines and achieves performance comparable to supervised diffusion models.

Deep Learning · Generative Models and Autoencoders

Jianyuan Zhong, Wang Kaibo, Ding Ding, Zijin Feng, Haoli Bai, Yang Xiang, Jiacheng Sun, Qiang Xu

Diffusion Large Language Models (dLLMs) often exhibit severe instability during Group Relative Policy Optimization (GRPO) training, limiting the effectiveness of reinforcement learning for improving reasoning capabilities. In dLLMs, the importance ratios used by GRPO are derived from finite-sample estimates rather than exact likelihoods, making them inherently noisy. In this paper, we show that GRPO is highly sensitive to this noise, which drives training instability. Through theoretical analysis and empirical evidence, we identify a self-reinforcing instability loop in which noisy importance ratios induce gradient spikes and policy drift, further amplifying future importance ratio estimation variance. To address this issue, we propose StableDRL, a novel reinforcement learning framework for dLLMs. StableDRL stabilizes training via (i) unconditional clipping to suppress outlier-induced gradient spikes, and (ii) self-normalization to constrain gradients within the convex hull of per-sample updates. We further extend StableDRL to block-wise diffusion models via a staircase attention mechanism. StableDRL is the first method that enables stable, full-parameter reinforcement learning for dLLMs. It achieves the state-of-the-art performance, outperforming prior best full-attention baselines by 6% on MATH500 and block-diffusion baselines by 25.6% on AIME.

Applications · Computer Vision

Bin Lei, Nuo Xu, Ali Payani, Mingyi Hong, Chunhua Liao, Yu Cao, Caiwen Ding

Multimodal large language models (MLLMs) have markedly expanded the competence of graphical user-interface (GUI) systems, propelling them beyond controlled simulations into complex, real-world environments across diverse platforms. However, practical usefulness is still bounded by the reliability of visual grounding, i.e., mapping textual references to exact on-screen elements. This limitation prevents the system from accurately performing pointer-level actions such as clicking or dragging. To address it, we introduce GUI-Spotlight -- a model trained for image-grounded reasoning that dynamically invokes multiple specialized tools to iteratively narrow its focus to the relevant region of the screen, thereby substantially improving visual grounding accuracy. On the ScreenSpot-Pro benchmark, GUI-Spotlight trained with only 18.5K training samples achieves 52.8\% accuracy, surpassing V2P-7B (50.6\% with 9.6M training samples) and GTA-1-7B (50.1\% with 1.56M training samples).

Deep Learning · Everything Else

Songsong Ouyang, Le Wu, Yingying Zhu

Cross-view geo-localization (CVGL) aims to retrieve the corresponding satellite image given a street query and is critical for autonomous navigation. Although recent methods perform well on benchmarks, they often fail to generalize to unseen environments. A key limitation is the use of contrastive learning, which assigns equal labels to all negative samples and induces similarity-amplified repulsion. But should all negatives be treated equally? In CVGL, semi-positive samples that are geographically proximate to the positive often share important semantic cues. Treating them as ordinary negatives forces the model to overfit noise, leading to a collapse in generalization. To address this issue, we propose an uncertainty-aware framework grounded in Deep Evidential Regression (DER), modeling the Normal-Inverse-Gamma (NIG) distribution as a conjugate prior to quantify environmental complexity $u$ in a single forward pass. The estimated $u$ adaptively softens labels for hard negatives in Soft InfoNCE, mitigating excessive repulsion on semi-positive samples. An Uncertainty Head with cls-to-spatial cross-attention and attention statistics is designed to accurately fit the NIG distribution. Extensive experiments demonstrate state-of-the-art performance, including an average 18\% R@1 improvement in zero-shot cross-dataset transfer, filling the critical gap between laboratory benchmarks and robust real-world deployment.

Reinforcement Learning · Everything Else

Xiyue Peng, Lingkai Zu, Ziyu Shao, Xin Liu

We study episodic constrained Markov decision processes (CMDPs) with linear function approximation, where the goal is to achieve strong regret and constraint violation guarantees without allowing error cancellations. Unlike the existing work, which focuses on either tabular CMDP or model-based reinforcement learning methods. We propose a model-free policy APMPO that achieves near-optimal $\widetilde{O}(\sqrt{K})$ strong regret and strong constraint violation with Slater's condition (or strict feasibility assumption), where $K$ is the total number of episodes. It matches the best-known rates without requiring any prior knowledge of the feasibility gap reported in prior model-based work for tabular CMDPs. Besides, APMPO achieves $\widetilde{O}(K^{\frac{3}{4}})$ strong regret and $\widetilde{O}(K^\frac{3}{4})$ strong constraint violation without Slater's condition. To the best of our knowledge, this is the first sublinear result of CMDP w.r.t. the strong metrics without Slater's condition. APMPO achieves these results by a novel and adaptive design of a violation-aware penalty and learning rates to balance the strong regret and constraint violation, which is quite different from the (regularized) primal-dual methods imposing constraints via dual penalty in the literature. The experiments show APMPO significantly outperforms the strong baselines, which justify our design and theoretical performance.

Deep Learning · Large Language Models

Azim Ospanov, Zijin Feng, Jiacheng Sun, Haoli Bai, SHEN XIN, Farzan Farnia

Informal mathematics has been central to modern large language model (LLM) reasoning, offering flexibility and efficient construction of arguments. However, purely informal reasoning is prone to logical gaps and subtle errors that are difficult to detect and correct. In contrast, formal theorem proving provides rigorous, verifiable mathematical reasoning, where each inference step is checked by a trusted compiler, but lacks the exploratory freedom of informal problem-solving. This mismatch leaves current LLM-based math agents without a principled way to combine the strengths of both paradigms. In this work, we introduce Hermes, the first tool-assisted agent that explicitly interleaves informal reasoning with formally verified proofs in Lean. The framework performs intermediate formal checking to prevent reasoning drift and a memory module for proof continuity across multi-step reasoning chains, enabling both exploration and verification. We evaluate Hermes on four challenging mathematical reasoning benchmarks using LLMs of varying parameter scales, from small models to state-of-the-art systems. Across all settings, Hermes reliably improves the reasoning accuracy of base models while substantially reducing reasoning token usage and computational cost compared to reward-based approaches. On difficult datasets such as AIME and HARDMath2, Hermes@1 achieves up to a 40% accuracy improvement while using 80% fewer total inference FLOPs. When scaled at test time, Hermes@5 boosts accuracy further by 20%.

Deep Learning · Generative Models and Autoencoders

Xing Cong, Hanlin Tang, Kan Liu, Lan Tao, Lin Qu, chenhao xie

Diffusion Transformers (DiT) achieve strong performance in image generation but incur substantial inference costs. While prior work has reduced this cost via quantization and distillation, semi-structured sparsity—which can nearly halve FLOPs—remains underexplored. A key reason is that most existing approaches focus on weight sparsification, and pruning 50\% of the weights can remove critical model capacity and degrade generation quality. Our study, however, shows that DiT activations are intrinsically sparse and significantly more robust to N:M semi-structured sparsification than weights. Motivated by this observation, we advocate a paradigm shift from weight sparsification to activation sparsification. We propose RT-Lynx, which applies N:M sparsification to activations and incorporates error-compensation techniques to mitigate accuracy loss. We further implement highly optimized CUDA kernels tailored to this setting, achieving up to a 1.55× speedup on average in linear layers. Extensive experiments across multiple diffusion models demonstrate that our method preserves the generation quality of the original models while substantially accelerating inference.

Deep Learning · Graph Neural Networks

Junyi Yan, KE LIANG, Hao Yu, Meng Liu, Hao Tan, Tianrui Liu, Jun-Jie Huang, Xinwang Liu

Federated graph anomaly detection (GAD) aims to identify abnormal nodes in distributed subgraphs through collaborative learning. However, existing methods suffer from two limitations. 1) Their reliance on neighborhood aggregation assumes that anomalous information can be sufficiently captured, which often fails in federated learning with partitioned client subgraphs. 2) They overlook the detection bottleneck caused by weak attribute or structural anomalies. To tackle these challenges, we revisit federated GAD and reveal that weak anomalies exhibit harder-to-detect signals compared to strong anomalies. Specifically, we propose MV-FGAD, an efficient and effective federated GAD framework based on multi-view learning designed to mine anomalies of varying strengths. MV-FGAD introduces a federated knowledge learning module to aggregate and broadcast shared knowledge, which is further exploited to optimize local topological structures. Moreover, it designs a multi-view learning mechanism to capture diverse anomaly patterns, and adopts Mahalanobis distance–based scoring strategy to quantify node abnormality across views. Extensive experiments on real-world datasets of varying types and scales demonstrate MV-FGAD's efficiency and effectiveness.

Applications · Computer Vision

Zhaoyang Jia, Naifu Xue, Zihan Zheng, Jiahao Li, Bin Li, Xiaoyi Zhang, Zongyu Guo, Yuan Zhang, Houqiang Li, Yan Lu

Recent advanced diffusion methods typically derive strong generative priors by scaling diffusion transformers. However, scaling fails to generalize when adapted for real-time compression scenarios that demand lightweight models. In this paper, we explore the design of real-time and lightweight diffusion codecs by addressing two pivotal questions. First, does diffusion pre-training benefit lightweight diffusion codecs? Through systematic analysis, we find that generation-oriented pre-training is less effective at small model scales whereas compression-oriented pre-training yields consistently better performance. Second, are transformers essential? We find that while global attention is crucial for standard generation, lightweight convolutions suffice for compression-oriented diffusion when paired with distillation. Guided by these findings, we establish a one-step lightweight convolution diffusion codec that achieves real-time 60 FPS encoding and 42 FPS decoding at 1080p. Further enhanced by distillation and adversarial learning, the proposed codec reduces bitrate by 85% at a comparable FID to MS-ILLM, bridging the gap between generative compression and practical real-time deployment.

General Machine Learning · Unsupervised and Semi-supervised Learning

Long Tang, Keyang Pu, Yingjie Tian

Generalized Zero-Shot Learning (GZSL) suffers from severe seen-class bias, a challenge stemming from the label incompleteness inherent in mixed test distributions. To address this, we propose PUFE, a unified plug-and-play framework that recasts GZSL inference as a Positive-Unlabeled (PU) learning task by treating seen categories as positive and mixed test data as unlabeled. Serving as a seamless post-processing module, PUFE constructs a PU classifier in the semantic space, jointly estimating the seen-class posterior and labeling propensity via Maximum Likelihood Estimation (MLE) within a dual-head network. Furthermore, we introduce an adaptive prototype calibration strategy that leverages high-confidence pseudo-instances—identified by the PU classifier—to explicitly align semantic prototypes with the underlying test distribution. Extensive experiments demonstrate that PUFE consistently mitigates bias and significantly boosts the performance of various embedding-based baselines, yielding gains of up to 11.2 percentage points in the harmonic mean.

Reinforcement Learning · Batch/Offline

Siyu Wang, Xiaocong Chen, Mingming Gong, Yong Li, Quan Sheng, Lina Yao

Transformer sequence models such as Decision Transformer can learn strong offline policies from logged trajectories, but they can suffer from causal confusion: reliance on spurious correlations that predict reward in the data but do not reflect the true causal mechanisms of the environment. We propose CSET (Counterfactual State Editing Transformer), which improves robustness in strictly offline reinforcement learning without learning environment transition dynamics. CSET first fits a causal reward model as a conditional variational autoencoder to infer a posterior over reward disturbances for each transition. Conditioning on the factual action and a sampled disturbance, a counterfactual state generator proposes a minimally edited state whose predicted reward matches the factual reward; a normalized move-band constraint and an acceptance gate enforce state plausibility and reward consistency. We then augment trajectories by replacing only the observation token with the edited state while keeping the next observation factual, so the policy is not trained on synthetic successor transitions. On the model side, CSET uses a causally structured hybrid transformer: separate convolutional encoders process return-to-go, state, and action streams for local temporal structure, and a final attention block is softly supervised so action prediction focuses on its direct causal parents (state and return-to-go). Experiments on D4RL locomotion and AntMaze tasks and on offline recommendation benchmarks show consistent gains over transformer baselines and substantially improved robustness to injected spurious distractors.

Deep Learning · Generative Models and Autoencoders

Chaokang Jiang, Desen Zhou, Jiuming Liu, Li Sun

Closed-loop evaluation of autonomous-driving policies requires interactive simulation beyond log replay. However, existing generative world models often degrade in closed loop due to (i) history-free initialization that mismatches policy inputs, (ii) multi-step sampling latency that violates real-time budgets, and (iii) compounding kinematic infeasibility over long horizons. We propose VectorWorld, a streaming world model that incrementally generates ego-centric $64 \mathrm{m}\times 64\mathrm{m}$ lane--agent vector-graph tiles during rollout. VectorWorld aligns initialization with history-conditioned policies by producing a policy-compatible interaction state via a motion-aware gated VAE. It enables real-time outpainting via solver-free one-step masked completion with an edge-gated relational DiT trained with interval-conditioned MeanFlow and JVP-based large-step supervision. To stabilize long-horizon rollouts, we introduce $\Delta$Sim, a physics-aligned non-ego (NPC) policy with hybrid discrete--continuous actions and differentiable kinematic logit shaping. On Waymo open motion and nuPlan, VectorWorld improves map-structure fidelity and initialization validity, and supports stable, real-time $1\mathrm{km}+$ closed-loop rollouts.