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Social Aspects · Accountability, Transparency, and Interpretability

Zehao Wang, shilong jin, Zhao Cao, Lanjun Wang

LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge when evaluating plan feasibility, a phenomenon we term epistemic miscalibration in planning. Unlike execution errors, epistemic miscalibration is latent during planning, as generated plans can remain self-consistent and executable without observable errors; the miscalibration is also dynamic, as new information can alter feasibility assessments, potentially obscuring past miscalibration signals and causing them to recur over time. To address this, we propose the Epistemic Planning Calibration Agentic Workflow (EPC-AW), which assesses whether plans remain supported under varying information conditions rather than directly verifying feasibility. EPC-AW employs Information-consistency-based Plan Selection, selecting plans whose evaluations are stable across agents, together with Consistency-guided Epistemic State Refinement to adapt calibration over time by leveraging past discrepancies to guide future planning. Experiments show that EPC-AW improves system-level success by an average of 9.75\%.

Deep Learning · Other Representation Learning

Manuel Hirth, Lukas Koberg, Nasser Jazdi, Enkelejda Kasneci

While diffusion models have advanced anomaly detection, their reliance on multi-step noise schedules introduces significant computational complexity. In this paper, we demonstrate that the generative capability of diffusion is not required for tabular one-class anomaly detection. We revisit core principles of denoising without targeting data generation and present a deep-learning approach that streamlines these objectives into a fixed-noise formulation. Unlike standard denoising autoencoders that rely on reconstruction error, our method utilizes a preconditioning with an explicit linear reference channel. We train a denoising predictor to recover clean samples from perturbed observations and derive anomaly scores from the expected deviation under repeated perturbations. We theoretically motivate this with a stability proxy by a first-order approximation rather than just distance to the manifold. On the well-established ADBench benchmark, our approach achieves improvements over existing methods of 1.22\% in AUCROC and 1.13\% in AUCPR, the most informative and threshold-independent metrics. Our approach emphasizes structural simplicity and efficiency, indicating that a single-step, stability-based objective outperforms complex generative schedules for tabular data.

Jun-Peng Jiang, Shiyin Lu, An-Yang Ji, Yinglun Li, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, De-Chuan Zhan, Han-Jia Ye

This paper presents OvisOCR, a lightweight and strictly end-to-end Multimodal Language Model (MLLM) tailored for document parsing. Unlike current methods that rely on complex "Crop-OCR-Merge" cascades to handle high-resolution inputs, OvisOCR directly maps full-page visual signals to structured Markdown without localized slicing or layout detection dependencies. Through extensive evaluations on the OmniDoc benchmark, OvisOCR achieves SOTA performance, demonstrating that a compact E2E model can effectively ``digest'' the capabilities of intricate pipelines and surpass specialized and general methods. Technically, OvisOCR establishes a holistic paradigm that synergizes specialized perception with general reasoning, distilling fine-grained recognition from OCR engines and semantic correction from LLMs into a unified model. To balance the performance across diverse document constituents, we design category-specific reward mechanisms for distinct element types, such as dense text, complex tables, and formulas, and ensure the model enhances its formatting strengths for each modality concurrently. This approach effectively resolves the optimization conflict, guaranteeing that improvements in structural layout parsing do not come at the expense of omitting fine-grained textual details. Empirical results confirm that OvisOCR eliminates the error propagation inherent in split-and-merge architectures, offering a streamlined path for next-generation document intelligence.

Theory · Deep Learning

Bodie Rubacher

Softmax attention is increasingly treated as a reusable inference primitive in transformers. Much prior theory covers Gaussian/linear models or assumes one shared quadratic query--key metric, which can fail for non-Gaussian exponential-family likelihoods with varying curvature. This risks overreading learned similarities as a global metric or as proof of Bayes-optimality. For discrete latent symbols with exponential-family observations, we show a single head can implement Bayes posteriors (and posterior means) by setting logits to log prior plus log likelihood, and we characterize single-head posteriors as exactly the log-linear (exponential-family) class. Using convex duality, we rewrite log-likelihoods as negative Bregman divergences on mean/sufficient-statistic space, making Bayes rule a soft nearest-neighbor computation; this yields a sharp boundary for globally shared quadratic metrics and a multi-head curvature-atlas approximation with head-count scaling, and we extend these guarantees to in-context estimation with consistency and finite-sample stability bounds. In synthetic Gaussian and Bernoulli in-context estimation, trained minimal attention models validate these predictions: performance approaches a Bayes-oracle baseline as prompt length grows; learned metrics align with noise precision in the Gaussian case, while Bernoulli retains a gap consistent with curvature variation. Together, these results explain when Fisher geometry should emerge, when a single metric is justified, and when multiple heads are necessary for Bayes-like in-context estimators beyond Gaussians.

Applications · Everything Else

shilong jin, Lanjun Wang, Zhuosheng Zhang

Autonomous Graphical User Interface (GUI) agents often struggle with multi-step tasks due to constrained context windows and static policies that fail to adapt to dynamic environments. To address these limitations, this work proposes the Self-Evolving GUI Agent (SE-GA), a novel framework that integrates hierarchical memory structures with an iterative self-improvement mechanism. At the core of our approach is Test-Time Memory Extension (TTME), which facilitates long-term planning by dynamically retrieving episodic, semantic, and experiential memories to provide salient contexts during inference. To ensure continuous learning, we introduce Memory-Augmented Self-Evolution (MASE), which is a training pipeline that adopts the data collected by TTME to stabilize and enhance the agent's foundational policy. Extensive evaluations across both offline and online benchmarks demonstrate SE-GA achieves state-of-the-art performance, reaching success rates of 89.0\% on ScreenSpot and 75.8\% on the challenging AndroidControl-High dataset. Furthermore, significant improvements on the AndroidWorld benchmark highlight the superior generalization to dynamic environments.

General Machine Learning · Representation Learning

Rong Hu, Ling Chen

Disentangled representation learning is a powerful paradigm for robust attribute prediction. While recent methods address attribute correlations, hidden correlations remain underexplored, where data under the value of a certain attribute exhibit underlying modes correlated with other attributes. To preserve mode information and achieve disentanglement, we jointly discover modes and enforce mode-based conditional independence. Yet, the interdependency between these two modules may lead to error amplification under naive iterations. We propose Coordinated Disentanglement with Iterative mode Discovery (CoDID), an end-to-end framework featuring a dynamic architecture that adapts to evolving number of modes, and a coordination mechanism that mitigates error amplification via meta-optimization. Empirical results demonstrate the state-of-the-art performance on diverse tasks. Codes are available at anonymous Github https://anonymous.4open.science/r/CoDID-B038.

Social Aspects · Accountability, Transparency, and Interpretability

Soyeon Kim, Seongwoo Lim, Kyowoon Lee, Jaesik Choi

Feature attribution is central to diagnosing and trusting deep neural networks, and Integrated Gradients (IG) is widely used due to its axiomatic properties. However, IG can yield unreliable explanations when the integration path between a baseline and the input passes through regions with noisy gradients. While Guided Integrated Gradients reduces this sensitivity by adaptively updating low-gradient-magnitude features, input-space guidance still produces intermediate inputs that deviate from the data manifold. To address this limitation, we propose **Manifold-Aligned Guided Integrated Gradients** (MA-GIG), which constructs attribution paths in the latent space of a pre-trained variational autoencoder. By ensuring that decoded intermediate images remain aligned with the data manifold, MA-GIG constrains gradient evaluation to statistically valid regions. Through qualitative and quantitative evaluations, we demonstrate that MA-GIG produces faithful explanations by aggregating gradients on path features proximal to the input. Consequently, our method suppresses off-manifold noise and outperforms prior path-based attribution methods across multiple datasets and classifiers.

Deep Learning · Large Language Models

Haoren Zhao, Tianyi Chen, Zhen Wang

Multimodal Large Language Models (MLLMs) have revolutionized GUI automation, yet their efficacy is largely established on idealized, single-layer interfaces. This paper identifies a critical reliability gap: state-of-the-art agents face distinct robustness challenges in real-world desktop environments characterized by multi-window stacking, occlusion, and visual clutter. To address this, we introduce WinDeskGround, a novel benchmark and synthesis framework tailored for evaluating GUI grounding robustness. Unlike static datasets, our framework parametrically generates complex desktop scenarios by controlling window occlusion, layout density, and semantic similarity, thereby simulating the distribution shifts of authentic workflows. We construct a diverse meta-dataset of 1,356 high-fidelity instruction-target pairs and conduct comprehensive evaluations of five leading MLLMs. Our results demonstrate that while top-tier agents excel in simplified settings, their accuracy declines under partial occlusion. WinDeskGround provides a valuable benchmark to facilitate the assessment and advancement of GUI agent robustness in realistic environments.

Social Aspects · Robustness

Soroush H. Zargarbashi, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski

Robust conformal prediction (RCP) addresses confidence miscalibration in machine learning models by producing prediction sets with guaranteed coverage — these sets are guaranteed to include the true label with a user-specified high probability, even under worst-case noise. Recent works use randomized smoothing, as it provides robustness for black-box models at larger radii. Currently, there exist two setups for smoothing-based RCP: one requires extensive Monte Carlo sampling at calibration and test time but results in smaller prediction sets; the other setup produces larger prediction sets but uses a single sample at both stages. Since calibration is a one-time preprocessing step, it can accommodate substantially higher computational overhead than inference. Inspired by that, we offer procedures in between: we increase the sample rate at calibration time while using only one or few samples at test time. Increased calibration-time sampling can reduce the size of the prediction sets. With a large enough test set (which is often the case in production), our Front-Loaded RCPs have the same computational complexity as the state of the art, while producing considerably smaller sets at larger radii.

Applications · Computer Vision

Renjie Ding, Yaonan Wang, Min Liu, Jialin Zhu, Jiazheng Wang, Jiahao Zhao, tan xiao, Feixiang He, xiang chen

3D Gaussian Splatting (3DGS) enables real-time, high-fidelity view synthesis via explicit scene representations and has recently been extended to dynamic scene modeling. In spite of excellent quality and interpretability, we find explicit Dynamic GS often exhibits generalization degradation in large-motion scenes. Motivated by generalization insights from deep learning and the characteristics of Gaussian primitive optimization, we propose an update mixture strategy. This work focuses on two representative open-source explicit Dynamic GS pipelines and our approach includes: (i) a space–time dependent Strictly Sparse Update with additional regularization to stabilize adaptive updates; (ii) a constant-corrected adaptive algorithm to attenuate the over-scaling of primitive gradients, yielding a stable mixture of adaptive and non-adaptive steps; and (iii) attributes mixture via Stochastic Attribute Averaging to mitigate frame-preference under motion disturbances. Experiments show consistent improvements and reduced generalization issues, highlighting the role of non-adaptive updates and the influence from frame-preference in explicit Dynamic GS optimization.

General Machine Learning · Hardware and Software

Chonghao Zhong, Shi Linfeng, ChenHua, Tiecheng Sun, Hao Zhao, Binhang Yuan, Chaojian Li

Training 3D Gaussian Splatting (3DGS) at billion-primitive scale is fundamentally memory-bound: each Gaussian carries a large attribute vector, and the aggregate parameter table quickly exceeds GPU capacity, limiting prior systems to tens of millions of Gaussians on consumer hardware. We observe that 3DGS training is inherently sparse and trajectory-conditioned: each iteration activates only the Gaussians visible from the current camera batch, so GPU memory can serve as a working-set cache rather than a persistent parameter store. Building on this insight, we introduce \textbf{TideGS}, an out-of-core training framework that manages parameters across an SSD--CPU--GPU hierarchy via three synergistic techniques: block-virtualized geometry for SSD-aligned spatial locality, a hierarchical asynchronous pipeline to overlap I/O with computation, and trajectory-adaptive differential streaming that transfers only incremental working-set deltas between iterations. Experiments show that TideGS enables training with \textbf{over one billion Gaussians} on a single consumer GPU while achieving state-of-the-art reconstruction quality on large-scale scenes, exceeding prior out-of-core baselines (e.g., $\sim$100M Gaussians) and standard in-memory training (e.g., $\sim$11M Gaussians).

Theory · Online Learning and Bandits

Yanlin Qu, Hongseok Namkoong, Assaf Zeevi

Thompson Sampling is one of the most widely used and studied bandit algorithms, known for its simple structure, low regret performance, and solid theoretical guarantees. Yet, in stark contrast to most other families of bandit algorithms, the exact mechanism through which posterior sampling (as introduced by Thompson) is able to "properly" balance exploration and exploitation, remains a mystery. In this paper, we show that the core insight to address this question stems from recasting Thompson Sampling as an online optimization algorithm. To distill this, we introduce a time invariant notion of regret that summarizes cumulative regret across horizons (through a regret bound), leading to a time invariant Bellman-optimal policy. It turns out that Thompson Sampling admits an online optimization form that mimics the structure of the Bellman-optimal policy, where greediness is regularized by a measure of residual uncertainty. When viewed through this new lens of online optimization, Thompson Sampling can be understood and improved in a principled manner, by comparing it against the Bellman-optimal benchmark.

General Machine Learning · Sequential, Network, and Time Series Modeling

Xiaokai Luo, Haotian Xu, Carlos Misael Madrid Padilla, OSCAR HERNAN MADRID PADILLA

We study online change point detection for multivariate inhomogeneous Poisson point process time series. This setting arises commonly in applications such as earthquake seismology, climate monitoring, and epidemic surveillance, yet remains underexplored in the machine learning and statistics literature. We propose a method that uses low-rank matrices to represent the multivariate Poisson intensity functions, resulting in an adaptive nonparametric detection procedure. Our algorithm is single-pass and requires only constant computational cost per new observation, independent of the elapsed length of the time series. We provide theoretical guarantees to control the overall false alarm probability and characterize the detection delay under temporal dependence. We also develop a new Matrix Bernstein inequality for temporally dependent Poisson point process time series, which may be of independent interest. Numerical experiments demonstrate that our method is both statistically robust and computationally efficient.

Applications · Robotics

Xukun Li, Yu Sun, Lei Zhang, Bo-Sheng Huang, Yibo Peng, Yuan Meng, Haojun Jiang, Shaoxuan Xie, Guocai Yao, Alois Knoll 等

Bimanual dexterous manipulation relies on integrating multimodal inputs to perform complex real-world tasks. To address the challenges of effectively combining these modalities, we propose DECO, a decoupled multimodal diffusion transformer that disentangles vision, proprioception, and tactile signals through specialized conditioning pathways, enabling structured and controllable integration of multimodal inputs, with a lightweight adapter for parameter-efficient injection of additional signals. Alongside DECO, we release DECO-50 dataset for bimanual dexterous manipulation with tactile sensing, consisting of 50 hours of data and over 5M frames, collected via teleoperation on real dual-arm robots. We train DECO on DECO-50 and conduct extensive real-world evaluation with over 2,000 robot rollouts. Experimental results show that DECO achieves the best performance across all tasks, with a 72.25\% average success rate and a 21\% improvement over the baseline. Moreover, the tactile adapter brings an additional 10.25\% average success rate across all tasks and a 20\% gain on complex contact-rich tasks while tuning less than 10\% of the model parameters.

Applications · Robotics

Letian Fu, Justin Yu, Karim El-Refai, Ethan Kou, Haoru Xue, Huang Huang, Wenli Xiao, Li Fei-Fei, Guanya Shi, Jiajun Wu 等

“Code-as-Policy” considers how executable code can complement data-intensive Vision-LanguageAction (VLA) methods, yet their effectiveness as autonomous controllers for embodied manipulation remains underexplored. We present CaPX, an open-access framework for systematically studying Code-as-Policy agents in robot manipulation. At its core is CaP-Gym, an interactive environment in which agents control robots by synthesizing and executing programs that compose perception and control primitives. Building on this foundation, CaP-Bench evaluates frontier language and vision-language models across varying levels of abstraction, interaction, and perceptual grounding. Across 7 simulation tasks and 12 models, CaP-Bench reveals a consistent trend: performance improves with human-crafted abstractions but degrades as these priors are removed, exposing a dependence on designer scaffolding. At the same time, we observe that this gap can be mitigated through scaling agentic test-time computation–through multi-turn interaction, structured execution feedback, visual differencing, automatic skill synthesis, and ensembled reasoning–substantially improves robustness even when agents operate over low-level primitives. These findings allow us to derive CaP-Agent0, a training-free framework that recovers human-level reliability on several manipulation tasks in simulation and on real embodiments. We further introduce CaP-RL, showing reinforcement learning with verifiable rewards improves success rates and transfers from sim2real with minimal gap. Together, CaP-X provides a principled, open-access platform for advancing embodied coding agents. Project page: https://cap-x-anonymous.github.io

Deep Learning · Large Language Models

Yujie Wang, Siwei Chen, Longzan Luo, Xinyi Liu, Xupeng Miao, Fangcheng Fu, Bin Cui

Reinforcement Learning (RL) has become pivotal for improving model capabilities yet suffers from rollout efficiency bottlenecks due to the long-tail response length distribution. While existing works mitigate the impact of long tails via prompt-level tail scheduling, we focus on the root source of inefficiency: the distribution itself. Specifically, we characterize the long-tail distribution at a finer granularity, identifying intra-prompt long tails, and revealing that they frequently consist of ineffective verbosity. To address this, we propose a novel paradigm of active distribution shaping to shape the rollout distribution towards conciseness and certainty, thereby fundamentally resolving tail-induced overheads. We achieve this through a distribution-aware trajectory sampling mechanism, which selects trajectories from a redundant exploration space for each prompt, and an adaptive redundancy allocation scheme to maximize both shaping effectiveness and system efficiency. Experiments demonstrate significant acceleration over state-of-the-art systems by up to 1.77$\times$ without compromising model performance.

Deep Learning · Large Language Models

Xinwu Ye, Yicheng Mao, Jia Zhang, Yimeng (Yoyo) Liu, Hao Li, Fang Wu, Zhiwei Li, Yuxuan Liao, Zehong Wang, Zhiyuan Liu 等

Current chemical large language models (LLMs) predominantly rely on explicit Chain-of-Thought (CoT) to solve complex reasoning problems. However, forcing nonverbal tacit chemical logic into discrete natural language imposes a fundamental ``modality mismatch,'' creating an artificial bottleneck for reasoning. To investigate this, we introduce LatentChem, a reasoning interface that decouples chemical logic from linguistic generation, enabling the model to process information via continuous thought vectors and dynamic perception. Our investigation reveals a pivotal emergent behavior: spontaneous internalization. When optimized for task success, the model voluntarily abandons verbose textual derivations in favor of implicit latent computation, suggesting that it autonomously identifies the continuous manifold as a more native substrate for chemical logic. This paradigm shift also proves to be a superior computational strategy: LatentChem achieves a 59.88\% non-tie win rate against the strong CoT baseline on the rigorous ChemCoTBench, while delivering a broad 10.84$\times$ average speedup across all evaluated benchmarks. This empirically validates that chemical logic is inherently better modeled by continuous latent dynamics than by linear linguistic approximations.

Probabilistic Methods · Gaussian Processes

Jihao Andreas Lin, Sebastian Ament, Louis Tiao, David Eriksson, Maximilian Balandat, Eytan Bakshy

Gaussian processes (GPs) are powerful and widely used probabilistic regression models, but their effectiveness in practice is often limited by the choice of kernel function. This kernel function is typically handcrafted from a small set of standard functions, a process that requires expert knowledge, results in limited adaptivity to data, and imposes strong assumptions on the hypothesis space. We study Empirical GPs, a principled framework for constructing flexible, data-driven GP priors that overcome these limitations. Rather than relying on standard parametric kernels, we estimate the mean and covariance functions empirically from a corpus of historical observations, enabling the prior to reflect rich, non-trivial covariance structures present in the data. Theoretically, we show that the resulting model converges to the GP that is closest (in KL-divergence sense) to the real data generating process. Practically, we formulate the problem of learning the GP prior from independent datasets as likelihood estimation and derive an Expectation-Maximization algorithm with closed-form updates, allowing the model handle heterogeneous observation locations across datasets. We demonstrate that Empirical GPs achieve competitive performance on learning curve extrapolation and time series forecasting benchmarks.

General Machine Learning · Transfer, Multitask and Meta-learning

Hongwei Wen, Can Zhang, Haoyu He, Hanyuan Hang, Minglong Lei

We propose *Progressive Structure Adjustment for Homophily Shift* (*PSAHS*), a lightweight method for *Graph Domain Adaptation* (*GDA*) that explicitly addresses cross-domain mismatch in node-level homophily. PSAHS enhances node homophily in the source graph to a prescribed level by reweighting edges and introducing additional intra-class connections for low-homophily nodes, and conservatively refines the target graph using agreement-consistent predictions from a structure-aware *Graph Neural Network* (*GNN*) and an attribute-only *Multi-Layer Perceptron* (*MLP*) to ensure reliability under label scarcity. After each structural refinement, domain-adversarial training is employed to align node representations across domains. PSAHS employs a progressive training scheme that alternates between structure adjustment and representation alignment, where increasingly informative representations enable safer homophily correction, and the refined structure in turn improves representation learning. Extensive experiments on multiple GDA benchmarks demonstrate that PSAHS consistently outperforms strong baselines, with particularly large gains under severe homophily mismatch, highlighting the importance of explicit homophily alignment for effective cross-graph transfer.

Applications · Chemistry, Physics, and Earth Sciences

Yanming Xia, Angelica Aviles-Rivero

Neural operators perform well on structured domains, yet their behaviour on irregular geometries remains poorly understood. We show that this limitation is not merely an encoding issue, but a depth-wise failure mode inherent to deep operator architectures. We formalise the *Geometric Forgetting Hypothesis*: due to the Markovian structure of operator layers and their reliance on global mixing mechanisms, neural operators progressively lose access to domain geometry as depth increases. Using layer-wise geometric probing, we demonstrate that both spectral and attention-based operators systematically lose geometric fidelity. We show that this geometric forgetting degrades accuracy, stability, and generalisation. To counteract it, we introduce a lightweight *geometry memory injection* mechanism that restores geometric constraints at intermediate depths with minimal architectural overhead. This simple intervention consistently mitigates forgetting and exposes a *geometric shortcut* instability in transformer-based operators, revealing that geometric retention is a structural requirement rather than a design choice.