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Applications · Everything Else

Yuxi Chen, Haoyu Zhai, Chenkai Wang, Rui Yang, LINGMING ZHANG, Gang Wang, Huan Zhang

GUI agents are rapidly shifting from multi-module pipelines to end-to-end, native vision-language models (VLMs) that perceive raw screenshots and directly interact with digital devices. Despite rapid progress on general GUI tasks, CAPTCHA solving remains a major challenge. On the other hand, although specialized CAPTCHA solving pipelines exist, they cannot handle general GUI tasks. To address this gap, we introduce ReCAP: a CAPTCHA-capable native GUI agent that can robustly solve modern, interactive CAPTCHA challenges, while preserving their performance as a general GUI agent. We first develop a dynamic CAPTCHA system spanning seven representative CAPTCHA types, designed to stress primitive and complementary capabilities for CAPTCHA solving (e.g., robust OCR under heavy noise and text stylization, fine-grained visual understanding, and precise control). Then, we develop an automated data collection and curation pipeline that generates large-scale CAPTCHA interaction trajectories paired with reasoning traces. As CAPTCHA solving often requires multi-step interaction and recovery from intermediate mistakes, we further leverage failed trajectories to construct self-correction data, training agents to reflect on errors and correct their actions online. %To mitigate the imbalance between reasoning tokens and action tokens, we also introduce a weighted training objective that up-weights action tokens. Across held-out test sets, ReCAP improves CAPTCHA-solving success from roughly 30\% to 80\%, while maintaining strong performance on general GUI-agent benchmarks.

Reinforcement Learning · Planning

Shiyu Li, Yifan Wang, Peiming Li, Zheng Wei, Yang Tang

Search agents powered by Large Language Models have demonstrated significant potential in tackling knowledge-intensive tasks. Reinforcement learning has emerged as a powerful paradigm for training these agents to perform complex, multi-step reasoning. However, prior RL-based methods often rely on sparse or rule-based rewards, which can lead agents to commit to suboptimal or erroneous reasoning paths without the ability to recover. To address these limitations, we propose **ReSeek**, a self-correcting framework enabling search agents to recover from erroneous search paths during an episode. By invoking a special **JUDGE** action, the agent can judge the information and re-plan its search strategy. To guide this process, we design a dense, instructive process reward function, which decomposes into a correctness reward for retrieving factual information and a utility reward for finding information genuinely useful for the query. Additionally, to mitigate the risk of data contamination in existing datasets, we introduce **FictionalHot**, a contamination-free benchmark requiring complex reasoning. Experiments show ReSeek significantly outperforms SOTA baselines in task success and path faithfulness.

General Machine Learning · Evaluation

Yachen Gao, Xinwei Sun, Yikai Wang, Ye Shi, Jingya Wang, Jianfeng Feng, Yanwei Fu

Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliability of these models, which takes into account their inherent uncertainty, is still lacking. Existing metrics, which typically assess a single output, may fail to capture the variability or potential risks in generation. In this paper, we propose a novel evaluation metric called \emph{reliability score} based on conformal prediction, which measures the worst-case performance within the prediction set at a pre-specified confidence level. However, computing this score is challenging due to the high-dimensional nature of the output space and the nonconvexity of both the metric function and the prediction set. To efficiently compute this score, we introduce Conformal ReLiability (CReL), a framework that can \textbf{(i)} construct the prediction set with desired coverage; and \textbf{(ii)} accurately optimize the reliability score within the constructed prediction set. We provide theoretical results on coverage and demonstrate empirically that our method produces more informative prediction sets than existing approaches. Experiments on synthetic data and the image-to-text and text-to-image tasks further demonstrate the interpretability of our new metric, and the validity and effectiveness of our computational framework.

Deep Learning · Generative Models and Autoencoders

Mingkun Lei, Tong Zhao, Liangyu Yuan, Chi Zhang

Step-level caching offers a promising avenue for accelerating diffusion models by exploiting temporal redundancy. However, existing strategies predominantly rely on heuristic, threshold-based metrics to trigger cache updates. This reactive paradigm is inherently myopic as it optimizes only for local feature consistency, and yields unpredictable deployment latency. In this work, we propose BudCache, a budget-constrained optimization framework that inverts this standard: instead of letting error thresholds dictate the cost, we enforce a strict computational budget and globally search for the caching policy that maximizes generation fidelity. To tackle the combinatorial complexity of step selection, we employ a hybrid strategy combining Simulated Annealing with deterministic Hill Climbing. This approach efficiently escapes local optima to locate globally optimized cache masks within minutes, incurring zero inference overhead. Crucially, to address the trajectory drift induced by aggressive caching, we introduce a cache-aware schedule alignment mechanism. By refining the time discretization via a lightweight, data-free distillation, we significantly enhance performance in low-NFE regimes. Extensive experiments on FLUX.1-dev and Wan2.1 demonstrate that BudCache consistently outperforms heuristic baselines, achieving superior perceptual quality under rigid latency constraints.

Applications · Everything Else

Chang Yu, Yuxuan Luo, Yixuan Du, Yuqing Zhou, Siyuan Li, Jingbo Zhou, jiawei jiang, Zhen Lei, Stan Z Li

Modeling complex dynamics from observational data is fundamental to scientific discovery and artificial intelligence. However, existing approaches ranging from Neural ODEs to diffusion models are often plagued by the entanglement of static state representations and instantaneous motion, leading to accumulated errors and off-manifold hallucinations where predicted trajectories violate intrinsic geometric constraints. To address this, we propose Geometric Flow Grounding, a unified framework that enforces dynamic evolution strictly along the tangent bundle of the learned data manifold via a differentiable Neural Tangent Projection Layer. By geometrically decoupling state representation from tangential dynamics, our method generalizes across diverse data regimes. In the context of scientific discovery, we demonstrate that the projection layer eliminates numerical aliasing in sparse dynamical systems and recovers interpretable gene regulatory motifs from single-cell data by disentangling states from developmental velocities. Bridging to trustworthy AI, we further repurpose the geometric projection residual as a zero-shot metric for deepfake video detection, identifying generative inconsistencies against the implicit flow of pre-trained world models. Our results establish manifold-constrained projection as a universal operator for both discovering natural laws and verifying synthetic content.

Deep Learning · Generative Models and Autoencoders

Harold Haodong Chen, Xianfeng Wu, Wenjie Shu, Rongjin Guo, Disen Lan, Harry Yang, YINGCONG CHEN

Test-time strategies have shown remarkable success in improving large language models, but their application to next-token prediction (NTP) autoregressive (AR) image generation remains largely underexplored. Existing test-time scaling (TTS) methods for visual autoregressive models (VAR) rely on frequent partial decoding and external reward models, which are inefficient and often ineffective for NTP-based image generation due to the inherent instability of intermediate decoding results. To address these limitations, we propose **ScalingAR**, a novel test-time scaling framework tailored for NTP-based AR image generation. ScalingAR introduces *token entropy* as a confidence signal and operates at two complementary levels: (***i***) ***Profile Level***, integrates intrinsic uncertainty and conditional utilization into a unified confidence state, and (***ii***) ***Policy Level***, leverages this state for adaptive trajectory pruning and dynamic guidance scheduling. Without requiring early decoding or auxiliary rewards, ScalingAR achieves significant improvements across diverse benchmarks. Experiments show that ScalingAR **(I)** improves base models by 12.5% on GenEval and 15.2% on TIIF-Bench, **(II)** reduces visual token consumption by 62.0% while outperforming baselines, and **(III)** enhances robustness, mitigating performance degradation by 26.0% in challenging scenarios. These results establish \ourmethod as a robust and efficient test-time scaling solution for autoregressive image generation.

Deep Learning · Graph Neural Networks

Jeffrey Seely, Bartłomiej Cupiał, Llion Jones

We present a differentiable optimization framework for multi-agent coordination. An input is decomposed into overlapping local views, each processed by an agent that solves a convex subproblem parametrized by a neural encoder. Agents coordinate through the Alternating Direction Method of Multipliers (ADMM) with inter-agent constraints specified by a cellular sheaf. The sheaf specifies which aspects of neighboring solutions must agree. Backpropagating through the unrolled optimization jointly trains encoders, decoders, and sheaf structure. We evaluate on maze pathfinding, image classification, and Sudoku, where agents with individually insufficient local views coordinate to produce correct global outputs. We show that this locality also yields improved robustness to distribution shifts (padding, missing patches, and noise) when evaluated against a standard CNN on MNIST, while exposing interpretable primal/consensus/dual variables that make the coordination dynamics directly inspectable.

Diedong Feng, Peiyi Zeng, Zhen Liu, Zhongyang Li, Bing Zeng, Shuaicheng Liu

Reconstructing high-fidelity RAW sensor data from processed RGB images is a fundamental yet ill-posed problem, plagued by irreversible information loss and complex non-linear ISP transformations. While generative models offer high-quality reconstruction, they suffer from prohibitive computational costs. Conversely, dominant regression-based methods are fast but susceptible to incoherent observational deviations, often yielding over-smoothed predictions that drift from the authentic signal manifold. To reconcile this trade-off, we propose SHADE, a Single-step Homogeneous Aligned DEterministic flow framework. We validate that, unlike point-to-point regression, the single-step deterministic flow captures global transport trends and enables intrinsic robustness against input perturbations. Furthermore, we introduce Homogeneous Aligned Guidance to maximize fidelity. By leveraging a homogeneously initialized student-teacher DINO pair, this mechanism enforces alignment within a shared feature space, significantly amplifying the representational capacity. Extensive experiments demonstrate that SHADE achieves state-of-the-art performance on multiple benchmarks, establishing a new paradigm for accurate and efficient sensor data reconstruction.

Social Aspects · Accountability, Transparency, and Interpretability

Ander Artola Velasco, Stratis Tsirtsis, Nastaran Okati, Manuel Gomez-Rodriguez

State-of-the-art large language models require specialized hardware and substantial energy to operate. Consequently, cloud-based services that provide access to these models have become very popular. In these services, the price users pay depends on the number of tokens a model uses to generate an output–they pay a fixed price per token. In this work, we show that this pricing mechanism creates a financial incentive for providers to strategize and misreport the (number of) tokens a model used to generate an output, and users cannot prove, or even know, whether a provider is overcharging them. However, we also show that, if an unfaithful provider is obliged to be transparent about the generative process used by the model, misreporting optimally without raising suspicion is hard. Nevertheless, as a proof-of-concept, we develop an efficient heuristic algorithm that allows providers to significantly overcharge users without raising suspicion. Crucially, the cost of running the algorithm is lower than the additional revenue from overcharging users, highlighting the vulnerability of users under the current pay-per-token pricing mechanism. Further, we show that, to eliminate the financial incentive to strategize, a pricing mechanism must price tokens linearly on their character count. While this makes a provider's profit margin vary across tokens, we introduce a simple prescription that allows a provider to maintain their average profit margin when transitioning to an incentive-compatible pricing mechanism. To complement our theoretical results, we conduct experiments with large language models from the $\texttt{Llama}$, $\texttt{Gemma}$ and $\texttt{Ministral}$ families, and prompts from a popular benchmarking platform.

Applications · Computer Vision

Sen Fang, Hongbin Zhong, Yalin Feng, Yanxin Zhang, Dimitris Metaxas

New technologies such as Rectified Flow and Flow Matching have significantly improved the performance of generative models in the past two years, especially in terms of control accuracy, generation quality, and generation efficiency. However, due to some differences in its theory, design, and existing diffusion models, the existing acceleration methods cannot be directly applied to the Rectified Flow model. In this article, we have comprehensively implemented an overall acceleration pipeline from the aspects of theory, design, and reasoning strategies. This pipeline uses new methods such as batch processing with a new velocity field, vectorization of heterogeneous time-step batch processing, and dynamic TensorRT compilation for the new methods to comprehensively accelerate related models based on flow models. Currently, the existing public methods usually achieve an acceleration of 18%, while experiments have proved that our new method can accelerate the 512$\times$512 image generation speed to up to 611%, which is far beyond the current non-generalized acceleration methods.

Deep Learning · Large Language Models

Théo Lasnier, Armel Randy Zebaze, Djamé Seddah, Rachel Bawden, Benoît Sagot

Mechanistic Interpretability (MI) seeks to explain how neural networks implement their capabilities, but the scale of Large Language Models (LLMs) has limited prior MI work in Machine Translation (MT) to word-level analyses. We study sentence-level MT from a mechanistic perspective by analyzing attention heads to understand how LLMs internally encode and distribute translation functions. We decompose MT into two subtasks: producing text in the target language (i.e. target language identification) and preserving the input sentence’s meaning (i.e. sentence equivalence). Across three families of open-source models and 20 translation directions, we find that distinct, sparse sets of attention heads specialize in each subtask. Based on this insight, we construct subtask-specific steering vectors and show that modifying just 1% of the relevant heads enables instruction-free MT performance comparable to instruction-based prompting, while ablating these heads selectively disrupts their corresponding translation functions.

Probabilistic Methods · Bayesian Models and Methods

Sergio Calvo Ordoñez, Jonathan Plenk, Richard Bergna, Alvaro Cartea, Yarin Gal, Jose Miguel Hernandez-Lobato, Kamil Ciosek

Bayesian last layers (BLLs) provide a convenient and computationally efficient way to estimate uncertainty in neural networks. However, they underestimate epistemic uncertainty because they apply a Bayesian treatment only to the final layer, ignoring uncertainty induced by earlier layers. We propose a method that improves BLLs by leveraging a projection of Neural Tangent Kernel (NTK) features onto the space spanned by the last-layer features. This enables posterior inference that accounts for variability of the full network while retaining the low computational cost of a standard BLL. We show that our method yields posterior variances that are provably greater or equal to those of a standard BLL, correcting its tendency to underestimate epistemic uncertainty. To further reduce computational cost, we introduce a uniform subsampling scheme for estimating the projection matrix and for posterior inference. We derive approximation bounds for both types of sub-sampling. Empirical evaluations on UCI regression, contextual bandits, and out-of-distribution detection tasks in image and tabular datasets, demonstrate improved calibration and uncertainty estimates compared to standard BLLs and competitive baselines, while reducing computational cost.

Probabilistic Methods · Bayesian Models and Methods

Giovanni Charles, Cosmo Santoni, Seth Flaxman, Elizaveta Semenova

The cost of simulator evaluations is a key practical bottleneck for Simulation Based Inference (SBI). In hierarchical settings with shared global parameters and exchangeable site-level parameters and observations, this structure can be exploited to improve simulation efficiency. Existing hierarchical SBI approaches factorise the posterior yet still simulate across multiple sites per training sample; We instead explore likelihood factorisation (LF) to train from single-site simulations. In LF sampling we learn a per-site (factorised) likelihood and then assemble synthetic multi-site observations to amortise inference for the full hierarchical posterior. Building on this, we propose Tokenised Flow Matching for Posterior Estimation (TFMPE), a tokenised flow matching approach that supports function-valued observations through likelihood factorisation. To enable systematic evaluation, we introduce a benchmark for hierarchical SBI. We validate TFMPE on this benchmark and on realistic infectious disease and computational fluid dynamics models, finding well-calibrated posteriors while reducing computational cost.

Applications · Robotics

Nan Tang, Jing-Cheng Pang, Guanlin Li, Chao Qian, Yang Yu

Reward design remains a critical bottleneck in visual reinforcement learning (RL) for robotic manipulation. In simulated environments, rewards are conventionally designed based on the distance to a target position. However, such precise positional information is often unavailable in real-world visual settings due to sensory and perceptual limitations. In this study, we propose a method that implicitly infers spatial distances through keypoints extracted from images. Building on this, we introduce Reward Learning with Anticipation Model (ReLAM), a novel framework that automatically generates dense, structured rewards from action-free video demonstrations. ReLAM first learns an anticipation model that serves as a planner and proposes intermediate keypoint-based subgoals on the optimal path to the final goal, creating a structured learning curriculum directly aligned with the task's geometric objectives. Based on the anticipated subgoals, a continuous reward signal is provided to train a low-level, goal-conditioned policy under the hierarchical reinforcement learning (HRL) framework with provable sub-optimality bound. Extensive experiments on complex, long-horizon manipulation tasks show that ReLAM significantly accelerates learning and achieves superior performance compared to SOTA methods.

Optimization · Non-Convex

Chenyu Gao, Zhexian Yang, Ziping Zhao

Estimating statistical parameters from quantized data has received significant attention in recent years, as recovering information from quantized measurements has numerous applications across signal processing, communications, and data analysis. In this work, we focus on maximum likelihood (ML) estimation of statistical parameters from quantized samples. Directly solving the ML problem is challenging, as the likelihood function involves multiple integrals that are difficult to evaluate. To address this challenge, we propose an expectation-conditional-maximization (ECM) algorithm under a general distributional framework. Our approach generalizes the quantization model to multi-bit settings and allows the underlying signal to follow any distribution within the normal mean-variance mixture family. By designing suitable surrogate functions, the ECM algorithm ensures that all model parameters can be updated in closed form at each iteration. Leveraging the ECM framework, we provide convergence guarantees, and under specific distributional assumptions, we further derive bounds on the convergence rate and the statistical error. Extensive experiments demonstrate the effectiveness of our method in recovering statistical parameters from quantized data.

Applications · Computer Vision

Ziye Yuan, Ruchang Yao, Chengxin Zheng, Yusheng Zhao, Daxiang Dong, Ming Zhang

Multimodal large language models (MLLMs) excel at high-level reasoning yet fail on OCR tasks where fine-grained visual details are compromised or misaligned. We identify an overlooked optimization issue in multi-layer feature fusion. Skip pathways introduce direct back-propagation paths from high-level semantic objectives to early visual layers. This mechanism overwrites low-level signals and destabilizes training. To mitigate this gradient interference, we propose Detached Skip-Links, a minimal modification that reuses shallow features in the forward pass while stopping gradients through the skip branch during joint training. This asymmetric design reduces gradient interference, improving stability and convergence without adding learnable parameters. To diagnose whether fine-grained information is preserved and usable by an LLM, we introduce $R$-Probe, which measures pixel-level reconstructability of projected visual tokens using a shallow decoder initialized from the first quarter of the LLM layers. Across multiple ViT backbones and multimodal benchmarks, and at scales up to 7M training samples, our approach consistently improves OCR-centric benchmarks and delivers clear gains on general multimodal tasks.

Probabilistic Methods · Bayesian Models and Methods

Rik Knowles, Tom Rainforth

We investigate which models admit a collapse of the expected information gain (EIG) and its derivative from a doubly intractable to a singly intractable expression. We prove that a sufficient condition is that the posterior distribution belongs to an exponential family (EF) that depends on the experimental design and data only through its natural parameters, and derive corresponding singly intractable and unbiased estimators for the $\operatorname{EIG}$ and its (reparameterised) gradient. We further derive necessary conditions on the likelihood to obtain an EF posterior of the required form, showing that this does not necessarily require the prior to be conjugate. This is complemented by a theoretical analysis of certain degenerate behaviors that may arise when optimizing the $\operatorname{EIG}$ for EF-modeled experiments. Finally, we empirically demonstrate the benefits of our singly intractable estimators, showing substantial performance gains over standard nested estimators.

Social Aspects · Robustness

Zhenqian Zhu, Yamin Hu, Yiya Diao, Weixiang Li, Haodong Li, Wenjian Luo

Model merging (MM) has gained significant attention as a cost-effective approach to integrate multiple task-specific models into a unified model. However, recent work reveals that MM is highly susceptible to backdoor attacks. Existing defenses based on task arithmetic often fail to eliminate backdoors without substantially degrading clean-task performance, owing to their reliance on direct parameter-space editing. To address this gap, we propose Linear Feature Path Minimization (LFPM), a backdoor mitigation framework for model merging, which introduces an anti-backdoor task vector into the backdoored merged model. Unlike prior approaches, LFPM formulates the backdoor robustness of the merged model from a unified feature-space perspective under the Cross-Task Linearity (CTL) framework, which leverages the approximate linearity of features across tasks. This perspective guides the optimization of the anti-backdoor task to suppress backdoors while preserving clean-task performance. Furthermore, we introduce an effective optimization mechanism based on gradient accumulation and loss path-integral, ensuring robust backdoor suppression along the interpolation path. Extensive experiments demonstrate that LFPM consistently exhibits strong robustness against backdoor attacks in both full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) settings.

Applications · Time Series

Jiahui Zhou, Dan Li, Boxin Li, Xiao Zhang, Erli Meng, Lin Li, Zhuomin Chen, Jian Lou, See-Kiong Ng

Time series is a pervasive data type across various application domains, rendering the reasonable solving of diverse time series tasks a long-standing goal. Recent advances in large language models (LLMs), especially their reasoning abilities unlocked through reinforcement learning (RL), have opened new opportunities for tackling tasks with long Chain-of-Thought (CoT) reasoning. However, leveraging LLM reasoning for time series remains infancy, hindered by the absence of carefully curated time series CoT data for training, limited data efficiency caused by underexplored data scheduling, and the lack of RL algorithms tailored for exploiting such time series CoT data. In this paper, we introduce VeriTime, a framework that tailors LLMs for time series reasoning through data synthesis, data scheduling, and RL training. First, we propose a data synthesis pipeline that constructs a time series–text multimodal dataset with process-verifiable annotations. Second, we design a data scheduling mechanism that arranges training samples according to a principled hierarchy of difficulty and task taxonomy. Third, we develop a two-stage reinforcement finetuning featuring fine-grained, multi-objective rewards that leverages verifiable process-level CoT data. Extensive experiments show that VeriTime substantially boosts LLM performance across diverse time series reasoning tasks. Notably, it enables compact 3B–4B models to achieve reasoning capabilities on par with or exceeding those of larger proprietary LLMs.

Probabilistic Methods · Bayesian Models and Methods

Lifu Wei, Yinuo Ren, Naichen Shi, Yiping Lu

Data assimilation (DA) addresses the problem of sequentially estimating the state of a dynamical system from noisy and incomplete observations. In this work, we employ a diffusion model as a world model to simulate and predict the system’s dynamics. Recently, score-based diffusion models have learned global diffusion priors that effectively model (stochastic) dynamics, revealing strong potential for data assimilation. In this paper, we investigate how information from noisy observations can be incorporated to enable continuous correction and refinement of the predicted system state when using a diffusion prior. Motivated by particle filtering methods, we represent the posterior distribution using a set of particles. After receiving noisy observations, the diffusion model is guided using the observation likelihood to steer the generation process toward observation-consistent states. Nevertheless, such guidance does not guarantee sampling from the true posterior. We therefore employ a Sequential Monte Carlo approach over the diffusion trajectory, viewed as a path measure, to reweight and resample particles, thereby correcting the generation process and ensuring convergence toward the desired posterior distribution. This leads to an unbiased particle filtering method that rigorously fuses observational data with diffusion model simulations.