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Deep Learning · Large Language Models

Mustafa Omer Gul, Claire Cardie, Tanya Goyal

LLMs cannot reliably recognize their parametric knowledge boundaries and often hallucinate answers to outside-of-boundary questions. In this paper, we introduce MASH (Modeling Abstention via Selective Help-seeking), a training framework that readily extracts abstentions from LLMs. Our key idea is that any external help-seeking by an LLM, i.e. search tool use, can serve as a proxy for abstention if the external help (search) is appropriately penalized while also rewarding answer accuracy. MASH operationalizes this idea using reinforcement learning with a pay-per-search reward. We run experiments on three knowledge-intensive QA datasets. Our results show that MASH substantially improves upon the selective help-seeking performance of prior efficient search approaches; on multi-hop datasets, it improves answer accuracy by 7.6%. Furthermore, MASH demonstrates strong off-the-shelf abstention performance, showcasing behavior competitive with prior abstention methods that additionally require predetermining model knowledge boundaries to construct training data. Overall, we show that MASH training effectively aligns search tool use with parametric knowledge, which can be successfully leveraged for making abstention decisions and efficient search tool use.

Reinforcement Learning · Planning

Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni, Marc Bellemare, Alessandro Lazaric, Ahmed Touati

The ability to plan with temporal abstractions is central to intelligent decision-making. Rather than reasoning over primitive actions, we study agents that compose pre-trained policies as temporally extended actions, enabling solutions to complex tasks that no constituent alone could solve. Such compositional planning remains elusive as compounding errors in long-horizon predictions make it challenging to estimate the visitation distribution induced by sequencing policies. Motivated by the *geometric policy composition* framework introduced in Thakoor et al. (2022), we address these challenges by learning predictive models of multi-step dynamics, so-called *jumpy world models*, that capture state occupancies induced by pre-trained policies across multiple timescales in an off-policy manner. Building on Temporal Difference Flows (Farebrother et al., 2025), we enhance these models with a novel consistency objective that aligns predictions across timescales, improving long-horizon predictive accuracy. We further demonstrate how to combine these generative predictions to estimate the value of executing arbitrary sequences of policies over varying timescales. Empirically, we find that compositional planning with jumpy world models significantly improves zero-shot performance across a wide range of base policies on challenging manipulation and navigation tasks, yielding an average 200% relative improvement over planning with primitive actions on long-horizon tasks.

Theory · Deep Learning

Swagatam Das, Vaclav Snasel

Many geometric-statistics and manifold-learning pipelines produce bundle-valued observations (for example, tangent vectors) that lie in different fibers. Forming empirical averages, therefore, requires transporting data to a common reference fiber, which can introduce curvature- and holonomy-driven effects. We study transported empirical means for a measurable section s: M → E of a rank-k vector bundle (E, π, M) equipped with a bundle metric and a compatible metric connection, and we derive finite-sample, dimension-free concentration bounds in the reference fiber E_{x₀}. Using sharp Hilbert-space inequalities due to Pinelis, we obtain Hoeffding- and Bernstein-type tail bounds controlled by a uniform per-sample bound B and a variance proxy σ². When minimizing geodesics are not unique, we isolate a deterministic holonomy ambiguity term Δ_{hol} and bound it in terms of bundle curvature and loop geometry; for the specific case E = T S²_{r} we give sharp area-based formulas. The resulting bias–variance decomposition provides practical uncertainty quantification for bundle-valued averaging and clarifies when curvature-driven transport ambiguity is negligible relative to sampling error.

Applications · Neuroscience, Cognitive Science

Dulhan Jayalath, ʻŌiwi Parker Jones

Clinical brain-to-text interfaces are designed for paralysed patients who cannot provide extensive training recordings. Pre-training improves data-efficient generalisation by learning statistical priors across subjects, but these priors critically depend on context. While natural speech might unfold gradually over minutes, most methods pre-train with only a few seconds of context. Thus, we propose *MEG-XL*, a model pre-trained with 2.5 minutes of MEG context per sample, 5-300× longer than prior work, and equivalent to 191k tokens, capturing extended neural context. Fine-tuning on the task of word decoding from brain data, MEG-XL matches supervised performance with a fraction of the data (e.g. 1hr vs 50hrs) and outperforms brain foundation models. We find that models pre-trained with longer contexts learn representations that transfer better to word decoding. Our results indicate that long-context pre-training helps exploit extended neural context that other methods unnecessarily discard.

Deep Learning · Theory

Changlong Wu, Jin Sima, Wojciech Szpankowski

Modern foundation models are evaluated through broad capabilities such as arithmetic, reasoning, safety, and robustness, yet it remains unclear in a principled sense when *finite tests* can meaningfully certify such claims. We develop a rigorous theory of capability evaluation by formalizing evaluation as inference over a task family and asking when guarantees over the full family can be inferred from a strict subset of tests. We analyze two canonical regimes. In stochastic multi-environment evaluation, we characterize when uniform certification is possible across multiple environments and show that the sample complexity is governed by a $\chi^2$-radius of the environment family, yielding near-optimal evaluation protocols with matching lower bounds under a natural overlap condition. In contrast, for worst-case, rule-like capabilities, we establish fundamental impossibility results. Even for structured model classes such as Boolean circuits of bounded size, black-box evaluation cannot, in general, certify global properties. Together, these results provide a principled framework for understanding when finite evaluation can and cannot certify capabilities.

Social Aspects · Robustness

Yuhan Xie, Chen Lyu, Jingrong Huang

Split Federated Learning (SFL) enables privacy-preserving collaborative training by partitioning models between clients and a server. However, under non-IID data distributions, SFL often suffers from biased optimization and unstable convergence, while existing solutions largely adapt techniques from conventional federated learning. In this work, we observe that the split architecture of SFL inherently alters how client information is represented and coordinated, opening opportunities for bias compensation beyond parameter-level aggregation. Based on this insight, we propose BESplit, an architecture-aware framework that exploits the intrinsic structure of SFL to mitigate non-IID effects. First, to prevent biased local data from dominating global updates, we introduce Evidential Aggregation (EA) to perform fine-grained reweighting of client contributions based on evidential uncertainty. Second, to further reduce distributional skew, we develop Bias-Compensated Collaboration (BCC) to align split-layer representations by pairing complementary clients. Finally, Dual-Teacher Distillation (DTD) is incorporated to synchronize knowledge between decoupled client and server models, enabling independent local inference. Extensive experiments on five benchmark datasets demonstrate that BESplit consistently outperforms state-of-the-art methods in accuracy, convergence stability, and computational efficiency under diverse non-IID settings.

Optimization · Discrete and Combinatorial Optimization

Shraman Pal, Can Li

Many learning tasks in science and engineering are characterized by sparse datasets, which limits the effectiveness of purely data-driven approaches. At the same time, these problems are often accompanied by rich domain knowledge derived from physical laws, operational requirements, and expert heuristics. Such knowledge is frequently expressed as rules involving logical propositions and linear inequalities. Existing neuro-symbolic methods typically enforce these rules approximately through soft penalties, assume input-independent rules when designing specialized architectures, or rely on non-differentiable post-processing at inference time to achieve hard constraint satisfaction. While recent advances in differentiable optimization layers enable end-to-end feasibility enforcement within neural networks, extending these approaches to logical or mixed-integer rules remains challenging due to inherent nonconvexity. In this work, we propose a unified end-to-end framework for enforcing hard, input-dependent mixed integer linear constraints within neural networks. Our approach represents rules as disjunctive constraints and applies hierarchical convex relaxations to obtain convex hull formulations. These relaxations yield tractable linear constraints that can be embedded as differentiable optimization layers while enabling exact rule satisfaction. We demonstrate the effectiveness of the proposed framework on real-world datasets, achieving perfect rule satisfaction and strong predictive performance.

Applications · Computer Vision

Ben Wan, Yan Feng, Zihan Tang, Weizhe Huang, Yuting Zeng, Jia Wang, Tongxuan Liu

DeepSeek-OCR leverages visual–text compression to reduce long-text processing costs and accelerate inference, yet visual tokens remain prone to redundant textual and structural information. Moreover, current token pruning methods for conventional vision–language models (VLMs) fail to preserve textual fidelity due to improper compression mechanisms. By analyzing the decoding process of DeepSeek-OCR, we find that a distinct two-stage reading trajectory: the model initially prioritizes the majority of high-norm tokens, then subsequently redistributes its attention to the remaining ones. Motivated by this insight, we propose *RTPrune*, a two-stage token pruning method tailored for DeepSeek-OCR. In the first stage, we prioritize high-norm visual tokens that capture salient textual and structural information. In the second stage, the remaining tokens are paired and merged based on optimal transport theory to achieve efficient feature aggregation. We further introduce a dynamic pruning ratio that adapts to token similarity and textual density for OCR tasks, enabling a better efficiency–accuracy trade-off. Extensive experiments demonstrate state-of-the-art performance, as evidenced by 99.47\% accuracy and 1.23× faster prefill on OmniDocBench, achieved with 84.25\% token retention when applied to DeepSeek-OCR-Large. Code is released.

Applications · Health / Medicine

Yaowei Jin, Junjie Wang, Cheng Cao, Penglei Wang, Duo An, Qian Shi

Structure-Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean and probabilistic spaces for continuous atomic coordinates and discrete chemical categories, leading to a mismatch with the underlying statistical manifolds. We address this issue from an information-geometric perspective by modeling molecules as composite exponential-family distributions and defining generative flows along exponential geodesics under the Fisher–Rao metric. To avoid the instantaneous trajectory collapse induced by geodesics directly targeting Dirac distributions, we propose Evolving Exponential Geodesic Flow for SBDD (EvoEGF-Mol), which replaces static Dirac targets with dynamically concentrating distributions, ensuring stable training via a progressive-parameter-refinement architecture. Our model approaches a reference-level PoseBusters passing rate (93.4%) on CrossDock, demonstrating remarkable geometric precision and interaction fidelity, while outperforming baselines on real-world MolGenBench tasks by recovering bioactive scaffolds and generating candidates that meet established MedChem filters.

General Machine Learning · Causality

Wenxin Chen, Weishen Pan, Kyra Gan, Fei Wang

Comparative evaluation of *multiple* dynamic treatment policies is essential for healthcare and policy decisions, yet conventional longitudinal causal inference methods estimate each in *isolation*, preventing information sharing across counterfactuals. We demonstrate that this separate estimation paradigm induces a structurally uncontrolled second-order bias, inflating finite-sample variance even after standard debiasing with *longitudinal targeted maximum likelihood estimation* (LTMLE). To address this, we propose a policy-aware reparameterization of *Iterative Conditional Expectation* (ICE) Q-functions that enables joint estimation through shared representations. We implement this approach in the **Policy-Encoded Q Network (PEQ-Net)**, an architecture centered on a shared policy encoder. The encoder is trained using kernel mean embeddings, ensuring that the learned representation space reflects population-level policy dissimilarities. After applying an LTMLE correction step, we prove this design imposes a structural constraint on the second-order remainder, thereby stabilizing finite-sample variance. Experiments on semi-synthetic datasets demonstrate that PEQ-Net consistently outperforms existing ICE-based methods, reducing root-mean-square error by 13–62\%.

Xinrui Ruan, Yingfei Wang, Waverly Wei, Jingshen Wang

Semi‑supervised statistical inference plays a key role in biomedical research, where labeled data often have higher quality but are limited due to costly clinical annotation. Yet, existing semi‑supervised statistical inference methods rely heavily on structured variables and strictly matched covariates between labeled and unlabeled datasets -- limitations ill‑suited for the heterogeneity and unstructured nature of real-world biomedical data. Modern biomedical studies increasingly collect unstructured data (clinical notes, patient audio and video recordings), with inconsistent protocols across datasets causing covariate misalignment (for instance, detailed medication histories may be recorded in one study but not another). Recent advances in pre‑trained multimodal large language models (LLMs), which excel at handling unstructured data, present an attractive potential solution. To transform this potential into rigorous semi-supervised statistical inference methods for biomedical research, two key challenges must be addressed: (1) How can we reliably integrate LLMs to enhance semi-supervised inference efficiency without compromising statistical validity? (2) How can those efficiency gains persist despite mismatched covariates between labeled and unlabeled datasets? In this paper, we tackle these challenges by systematically *calibrating pseudo-labels* provided LLMs with a novel *prediction-invariance identification* strategy. Our resulting semi‑supervised inference framework improves parameter estimation efficiency while maintaining full statistical validity, as demonstrated through our theoretical results and illustrated in a case study for identifying key biomarkers in Alzheimer’s disease detection with speech data.

Applications · Time Series

Haoxin Liu, Yichen Zhou, Rajat Sen, B. Aditya Prakash, Abhimanyu Das

We introduce a new context-enriched, multimodal time series forecasting benchmark TimesX. TimesX contains a wide selection of high-quality real-world time series with diverse domains and textual contexts obtained from an automated data generation pipeline, which helps address three main issues of existing multimodal forecasting benchmarks: (1) poor generalization due to the small scale and synthetic nature of benchmark data, (2) very limited types of textual contexts in the benchmarks, and (3) an inability to mitigate data leakage in evaluation. We conduct a thorough empirical study of zero-shot multimodal forecasting approaches on TimesX. Our results suggest that many approaches that perform well on existing benchmarks may fail on TimesX. In contrast, simple ensemble methods that leverage rich textual context accompanying time-series can outperform strong baselines on the TimesX benchmark.

Applications · Robotics

Kunyun Wang, Yuhang Zheng, Jieru Zhao, Yupeng Zheng, Wenchao Ding

Modern robotic policies increasingly rely on action chunking to execute complex tasks in the physical world. While action chunking improves temporal consistency at moderate action frequencies, it becomes insufficient when the action frequency is further increased (e.g., to 60~Hz). At such high frequencies, policies often fail to generate actions that are both temporally smooth and spatially consistent. We address this challenge by shifting high-frequency action learning from the action space to a latent space with variational autoencoder (VAE). This formulation significantly improves both temporal and spatial consistency of high-frequency control. To enable smooth real-time execution, we further introduce Reuse-then-Refine, a chunk-level refine strategy that improves continuity between adjacent action chunks under asynchronous inference. As a result, robots controlled by our policy can execute complex contact-rich tasks continuously, with less pauses and jerky motions. Experiments on three real-world contact-rich robotic tasks show that our approach consistently completes tasks with smooth motions.

Applications · Language, Speech and Dialog

Ziyin Zhang, Zihan Liao, Hang Yu, Peng Di, Rui Wang

The development of high-quality text embeddings is increasingly drifting toward an exclusionary future, defined by three critical barriers: prohibitive computational costs, a narrow linguistic focus that neglects most of the world's languages, and a lack of transparency from closed-source or open-weight models that stifles research. To dismantle these barriers, we introduce ML-Embed, a suite of inclusive and efficient models built upon a new framework: 3-Dimensional Matryoshka Learning (3D-ML). Our framework addresses the computational challenge with comprehensive efficiency across the entire model lifecycle. Beyond the storage benefits of Matryoshka Representation Learning (MRL), we integrate Matryoshka Layer Learning (MLL) for flexible inference-time depth and Matryoshka Embedding Learning (MEL) for enhanced parameter efficiency. To address the linguistic challenge, we curate a massively multilingual dataset and train a suite of models ranging from 140M to 8B parameters. In a direct commitment to transparency, we release all models, data, and code. Extensive evaluation on 430 tasks demonstrates that our models set new records on 9 of 17 evaluated MTEB benchmarks, with particularly strong results in low-resource languages, providing a reproducible blueprint for building globally equitable and computationally efficient AI systems.

Deep Learning · Algorithms

Vladimír Macko, Vladimír Boža

Sparse Matrix-Vector Multiplication (SpMV) is a fundamental operation in the inference of sparse Large Language Models (LLMs). Because existing SpMV methods perform poorly under the low, unstructured sparsity ($30-90\\%$) commonly observed in pruned LLMs, unstructured pruning provides only limited memory reduction and speedup. We propose **DELTA4-SpMV**, a GPU-optimized format and kernel co-designed to reduce storage overhead while remaining compatible with the GPU’s execution model. This enables efficient SpMV for unstructured sparsity without specialized hardware units or precomputation. We identify memory bandwidth as the primary limiting factor of SpMV and analyze the storage overhead of DELTA4. At $50\\%$ sparsity, DELTA4 is the first approach to achieve $1.5\times$ memory reduction and $1.2-1.5\times$ speedup over the dense baseline as well as substantial improvements over other SpMV methods: cuSPARSE ($2.8-13.0\times$), Sputnik ($1.9-2.6\times$), and DASP ($2.2-2.5\times$). An LLM pruned with Wanda to sparsity $50\\%$ requires $1.5\times$ less memory and achieves $1.5\times$ faster inference at fp16 precision. As a result, **unstructured pruning at $50\\%$ sparsity becomes practical** for real-world LLM workloads and **bridges the efficiency gap with structured 2:4 sparsity**.

General Machine Learning · Supervised Learning

Shuai Li, Xinyan Liang, Yuhua Qian, Li Lv

This paper studies a fundamental yet often overlooked premise in evolutionary multi-view classification (EMVC): the impact of label noise on EMVC, such as distorting fitness landscapes shaped by individual fitness values (e.g., test accuracy). Traditional EMVC assumes training labels are noise-free, yet this often fails in practice. As a result, label noise introduces harmful supervision during the training phase, resulting in distorted fitness landscapes and the emergence of fitness evaluation bias (FEB). This bias misguides the evolutionary trajectory, causing the search process to stagnate in local optima. Given that label noise largely stems from the mislabeling of samples near their decision boundaries by human annotators, we thus compared the decision boundaries of human annotators and models, and found discrepancies between the two. Based on this observation, we propose a simple yet effective ``detect-then-calibrate" data purification framework that leverages outlier analysis in the gradient space (i.e., treating outliers as noisy samples) and prototype calibration in the feature space (i.e., utilizing feature prototypes of noise-free samples to correct the labels of noisy samples). Experimental results demonstrate that this strategy can effectively purify the data and alleviate FEB; furthermore, it can improve the performance of various multi-view learning paradigms in label noise scenarios. (The code will be published.)

Applications · Neuroscience, Cognitive Science

Xiubo Liang, Jinxing Han, Yuke Li, Haoqi Zhu, Yu Zhao, Hongzhi Wang

Spiking Neural Networks (SNNs) enable event-driven computation with sparse activations, but building multimodal Transformers on SNNs is hindered by unstable training in deep spiking stacks and a mismatch between dense softmax attention and spike-based communication. We propose SMM Transformer, an SNN-based multimodal Transformer framework that combines (i) a Parallel LIF with Multistage Learnable Parameters (PLMP) neuron and a tailored P-STBP algorithm to stabilize training, (ii) a spike-driven attention approximation (SMSA) with a lightweight self-compensation branch, and (iii) a spiking mixture-of-experts (SMoE) module for modality-aware fusion. Across visual and multimodal benchmarks, SMM Transformer achieves competitive accuracy compared to ANN baselines while reducing the estimated compute energy of the attention module by up to 97\% under a standard MAC/AC cost model.

Deep Learning · Theory

Yoshihiro Maruyama

We develop order-equivariant neural networks (OENN), which generalize standard graph message passing and sheaf neural networks via the face-poset viewpoint. We (i) characterize all linear order-equivariant maps, (ii) build OENN layers, and (iii) prove a universal approximation theorem (UAT) for continuous order-equivariant maps, which is a new result even when restricted to sheaf neural networks (for which no UAT was known before). We illustrate the framework on graph and sheaf models. Our results can also be seen as extending the UAT for graph neural networks to a more general setting that subsumes sheaf neural networks as well.

General Machine Learning · Everything Else

Dang Nguyen, Tu Anh Hoang Nguyen, Thuc Le, Svetha Venkatesh, Trung Le, Sunil Gupta

Anomaly detection (AD) methods often ignore causal dependencies and treat anomalies as outliers, which is brittle when anomalies are primarily mechanism violations rather than extreme values. We propose CausalAno, a causal-aware detector that trains a causal GAN on normal data and leverages its discriminator to learn mechanism-consistent representations. We score test samples by fitting a Gaussian model in this feature space and computing the Mahalanobis distance, measuring deviation from the normal causal manifold. We demonstrate the effectiveness of CausalAno with extensive experiments on 28 tabular datasets (18 continuous-only and 10 mixed-type), comparing against 16 SOTA baselines. Our results show consistent improvements across both mixed-type and numerical-only settings. Our ablation studies further confirm that the gains come from the causal factorization in the causal GAN rather than a generic GAN backbone. Our CausalAno offers a practical and effective solution for real-world applications where anomalies often arise from dependency-breaking behaviors.

Theory · Learning Theory

Xiangchao Li, Xiao Han, Qing Yang, Xin Tong

This paper presents a theoretical investigation into the generalization capabilities of cross-domain knowledge distillation. Utilizing a high-dimensional asymptotic analysis of a linear teacher–student model, we characterize the excess risk while accounting for both model and covariate shifts. Our results provide a formal guarantee for the efficacy of distillation: even when the source and target domains differ substantially, there still may exist a regime where the student model achieves superior generalization ability over the student-only baseline. Moreover, we identify a \textit{crossed double descent} phenomenon: the excess risk can vary non-monotonically with the teacher’s and student’s dimension-to-sample-size ratios. These results provide rigorous insight into when and why distillation helps across domains.