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

Wencheng Zhang, Long Li, Huayi Qin, Zongjuan Wu, Jing Li, Wanghu Chen

Effective time series representation is critical for revealing temporal dynamics in many fields. However, existing approaches encounter fundamental limitations. Discrete-time representations struggle with irregular sampling and the tradeoff of fidelity and efficiency, while traditional implicit neural representations suffer from spectral bias and frequency entanglement. To address these challenges, we conceptualize time series as the superposition of continuous trends and discrete events from a continuous-time perspective and propose DualTimesField, a framework that utilizes dual implicit neural fields. Its Continuous Time Field captures smooth trends through bandwidth-limited parameterization, while a Discrete Geometric Field models transient events using learnable Gabor atoms, gated sparsity, and coarse-to-fine scale annealing. This explicit field separation effectively overcomes both limitations. Experiments on nine real-world benchmarks demonstrate substantial improvements in representation fidelity, achieving 51.2% average MSE reduction over discrete-time baselines and competitive interpolation on irregular data. Code is available at https://anonymous.4open.science/r/DualTimesField-AF32.

Theory · Deep Learning

Shengtai Yao, Eitan Levin, Mateo Diaz

Several machine learning models are defined for inputs of any size, such as graphs with different numbers of nodes and point clouds with varying numbers of points. The universality properties of such any-dimensional models remain poorly understood, as universality is traditionally studied for models accepting inputs of a fixed size, defined on a compact subset of their domain. In sharp contrast, any-dimensional models can be viewed as sequences of functions defined on growing-sized inputs, and it is not clear in which sense they can be universal. We develop a systematic approach to establish any-dimensional universality by identifying any-dimensional functions with a unique function that takes inputs in a suitable infinite-dimensional limit space containing inputs of all finite sizes, as well as their limits. Using the symmetries of these inputs and relations between inputs of different sizes, we show that this limit space admits a natural topology with rich families of compact sets on which any-dimensional universality can be established. We illustrate our approach by showing that several existing architectures fail to be universal, and we propose simple modifications that restore universality.

Applications · Robotics

Abdul Monaf Chowdhury, Akm Moshiur Rahman Mazumder, Safaeid Arib, Rabeya Akter

Robotic manipulation benefits from foundation models that describe goals, but today's agents still lack a principled way to learn from their own mistakes. We ask whether natural language can serve as feedback, an error-reasoning signal that helps embodied agents diagnose what went wrong and correct course. We introduce LAGEA (Language Guided Embodied Agents), a framework that turns episodic, schema-constrained reflections from a vision language model (VLM) into temporally grounded guidance for reinforcement learning. LAGEA summarizes each attempt in concise language, localizes the decisive moments in the trajectory, aligns feedback with visual state in a shared representation, and converts goal progress and feedback agreement into bounded, step-wise shaping rewardswhose influence is modulated by an adaptive, failure-aware coefficient. This design yields dense signals early when exploration needs direction and gracefully recedes as competence grows. On the Meta-World MT10 and Robotic Fetch embodied manipulation benchmarks, LAGEA improves average success over the state-of-the-art (SOTA) methods by 9.0% on random goals, 5.3% on fixed goals, and 17% on fetch tasks, while converging faster. These results support our hypothesis: language, when structured and grounded in time, is an effective mechanism for teaching robots to self-reflect on mistakes and make better choices. Code will be released soon.

Deep Learning · Large Language Models

Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Ali Subhan, Hassan Rizwan, Ayesha Mohsin, Dean Hougen

Test-time compute scaling allocates inference computation uniformly, uses fixed sampling strategies, and applies verification only for reranking. In contrast, we propose a verifier-guided adaptive framework treating reasoning as iterative trajectory generation and selection. For each problem, the agent runs multiple inference iterations. In each iteration, it optionally produces a high-level plan, selects a set of reasoning tools and a compute strategy together with an exploration parameter, and then generates a candidate reasoning trajectory. A process reward model (PRM) serves as a unified control signal: within each iteration, step-level PRM scores are aggregated to guide pruning and expansion during generation, and across iterations, aggregated trajectory rewards are used to select the final response. Across datasets, our dynamic, PRM-guided approach consistently outperforms direct test-time scaling, yielding large gains on MATH-500 and several-fold improvements on harder benchmarks such as AIME24 and AMO-Bench. We characterize efficiency using theoretical FLOPs and a compute intensity metric penalizing wasted generation and tool overhead, demonstrating that verification-guided allocation concentrates computation on high-utility reasoning paths.

Reinforcement Learning · Planning

Yunxiang LI, Mark Schmidt, Reza Babanezhad, Sharan Vaswani

Temporal difference (TD) learning is a fundamental algorithm for estimating value functions in reinforcement learning. Recent finite-time analyses of TD with linear function approximation quantify its theoretical convergence rate. However, they often require setting the algorithm parameters using problem-dependent quantities that are difficult to estimate in practice --- such as the minimum eigenvalue of the feature covariance ($\omega$) or the mixing time of the underlying Markov chain ($\tau_\text{mix}$). In addition, some analyses rely on nonstandard and impractical modifications, exacerbating the gap between theory and practice. To address these limitations, we use an exponential step-size schedule with the standard TD(0) algorithm. We analyze the resulting method under two sampling regimes: independent and identically distributed (i.i.d.) sampling from the stationary distribution, and the more practical Markovian sampling along a single trajectory. In the i.i.d. setting, the proposed algorithm does not require the knowledge of problem-dependent quantities such as $\omega$, and attains the optimal bias-variance trade-off for the last iterate. In the Markovian setting, we propose a regularized TD(0) algorithm with an exponential step-size schedule. The resulting algorithm achieves a comparable convergence rate to prior works, without requiring projections, iterate averaging, or knowledge of $\tau_\text{mix}$ or $\omega$.

Deep Learning · Graph Neural Networks

David R Johnson, Alexander Sietsema, Rishabh Anand, Deanna Needell, Smita Krishnaswamy, Michael Perlmutter

We introduce vector diffusion wavelets (VDWs), a novel family of wavelets inspired by the vector diffusion maps algorithm that was introduced to analyze data lying in the tangent bundle of a Riemannian manifold. We show that these wavelets may be effectively incorporated into a family of geometric graph neural networks, which we refer to as VDW-GNNs. We demonstrate that such networks are effective on synthetic point cloud data, as well as on real-world data derived from wind-field measurements and neural activity data. Theoretically, we prove that these new wavelets have desirable frame theoretic properties, similar to traditional diffusion wavelets. Additionally, we prove that these wavelets have desirable symmetries with respect to rotations and translations.

Social Aspects · Accountability, Transparency, and Interpretability

Raghu Arghal, Fade Chen, Niall Dalton, Evgenii Kortukov, Calum McNamara, Angelos Nalmpantis, Moksh Nirvaan, Gabriele Sarti, Mario Giulianelli

Understanding an agent's goals helps explain and predict its behaviour, yet there is no established methodology for reliably attributing goals to agentic systems. We propose a framework for evaluating goal-directedness that integrates behavioural evaluation with interpretability-based analyses of models' internal representations. As a case study, we examine an LLM agent navigating a 2D grid world toward a goal state. Behaviourally, we evaluate the agent against an optimal policy across varying grid sizes, obstacle densities, and goal structures, finding that performance scales with task difficulty while remaining robust to difficulty-preserving transformations and complex goal structures. We then use probing methods to decode the agent's internal representations of the environment state and its multi-step action plans. We find that the LLM agent non-linearly encodes a coarse spatial map of the environment, preserving approximate task-relevant cues about its position and the goal location; that its actions are broadly consistent with these internal representations; and that reasoning reorganises them, shifting from broader environment structural cues toward information supporting immediate action selection. Our findings support the view that introspective examination is required beyond behavioural evaluations to characterise how agents represent and pursue their objectives.

Theory · Probabilistic Methods

Andrej Bogdanov, Alon Rosen, Neekon Vafa

We show how an adversarial model trainer can plant backdoors in a large class of deep, feedforward neural networks. These backdoors are statistically undetectable in the white-box setting, meaning that the backdoored and honestly trained models are close in total variation distance, even given the full descriptions of the models (e.g., all of the weights). The backdoor provides access to invariance-based adversarial examples for every input, mapping distant inputs to unusually close outputs. However, without the backdoor, it is provably impossible (under standard cryptographic assumptions) to generate any such adversarial examples in polynomial time. Our theoretical and preliminary empirical findings demonstrate a fundamental power asymmetry between model trainers and model users.

Applications · Energy

Chao Shen, Zihan Guo, Xu Wan, Zhenghao Yang, Yifan Zhang, Wenqi Huang, Jie Song, Zongyan Zhang, Mingyang Sun

Growing renewable penetration introduces substantial uncertainty into power system operations, necessitating frequent adaptation of dispatch objectives and constraints and challenging expertise-intensive, near-real-time modeling workflows. Large Language Models (LLMs) provide a promising avenue for automating this process by translating natural-language (NL) operational requirements into executable optimization models via semantic reasoning and code synthesis. Yet existing LLM datasets and benchmarks for optimization modeling primarily target coarse-grained cross-domain generalization, offering limited, rigorous evaluation in powersystem settings, particularly for Optimal Power Flow (OPF). We therefore introduce ProOPFD and ProOPF-B, a dataset and benchmark for professional-grade OPF modeling: ProOPF-D contains 12K instances pairing NL requests with parameter adjustments and structural extensions to a canonical OPF, together with executable implementations; ProOPF-B provides 121 expertannotated test cases with ground-truth code, enabling end-to-end evaluation under both concrete and abstract OPF modeling regimes.

Deep Learning · Foundation Models

Xueying Ding, Haomin Wen, Simon Klüttermann, Leman Akoglu

Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes algorithm and hyperparameter selection notoriously hard. Foundation models (FMs) have transformed ML, and OD is no exception: Shen et al. (2025) introduced FOMO-0D, the first FM for OD, achieving remarkable performance against numerous baselines. This work introduces OUTFORMER, which advances FOMO-0D with (1) a mixture of synthetic priors and (2) self-evolving curriculum training. OUTFORMER is pretrained solely on synthetic labeled datasets and infers test labels of a new task by using its training data as in-context input. Inference is fast and zero-shot, requiring merely forward pass and no labeled outliers. Thanks to in-context learning, it requires zero additional work—no OD model training or bespoke model selection—enabling truly plug-and-play deployment. OUTFORMER achieves state-of-the-art performance on the prominent ADBench, as well as two new large-scale OD benchmarks that we introduce, comprising over 1,500 datasets, while maintaining speedy inference.

Deep Learning · Large Language Models

Jie He, Chao Chen, Weidong Bao, Zhengyi Zhong, Shuai Zhang, Ji Wang

Task-vector–based model merging enables low-cost, training-free multi-task learning for large language models, but suffers from severe performance degradation due to task conflict. Prior mitigation strategies largely rely on validation data for costly hyperparameter tuning, limiting both interpretability and practicality. We therefore propose OPIC, an evolutionary optimization–based model merging framework. Our preliminary experiments reveal that the degradation of In-Context Learning (ICL) capabilities is a primary driver of task conflict. Motivated by this insight, we formulate model merging as an optimization problem with ICL preservation as the objective. OPIC introduces a hierarchical refinement operators and optimizes it using self-generated data, effectively eliminating the reliance on external validation sets. Experimental results demonstrate that OPIC achieves an average performance retention of 80.73%, outperforming SOTA methods and improving by up to 11.1% over recent validation-free approaches. In addition, OPIC is compatible with existing merging pipelines, offering a new alternative solution for deploying without validation dependencies. Code is available at: https://anonymous.4open.science/r/OPIC-CFFE.

General Machine Learning · Evaluation

Priyaranjan Pattnayak, Ishan Banerjee

Large language models (LLMs) are increasingly used as assistive interfaces for users who cannot reliably produce clean text due to accessibility constraints, yet existing evaluations assume iterative input repair and focus on task accuracy or generic noise robustness. We introduce Assistive Prompt Mediation (APM), a theory-grounded evaluation paradigm that reframes assistance as a constrained mediation problem: recovering latent user intent from accessibility-impaired input without clarification, while minimizing cognitive burden and hallucination risk. APM decomposes assistive quality along these axes and is instantiated across 8 languages, 4 accessibility-driven noise classes, and 10 frontier LLMs, with impairment severity yielding accessibility sensitivity curves. Results show that apparent robustness often masks trade-offs—high intent preservation frequently coincides with increased burden or hallucinated mediation, hallucination rates vary by more than $2\times$ across noise types, and assistive decisions exhibit bounded entropy ($<0.81$ normalized), indicating systematic rather than unstable behavior. These findings demonstrate that standard robustness metrics substantially overestimate assistive reliability and motivate evaluating LLMs as constrained mediators under accessibility-driven input degradation.

Applications · Computer Vision

Yuan Zeng, Yujia Shi, Tiao Tan, Xingting Li, Yaqi Qin, Zongqing Lu, Wenming Yang, Jing-Hao Xue, Qingmin Liao

Estimating full-hand grasp pressure from egocentric video is critical for immersive VR and robotic manipulation, yet dense tactile sensing often relies on intrusive hardware. Existing vision-based methods predominantly rely on planar surfaces or fingertip contacts, failing to generalize to complex 3D object interactions. Therefore, we introduce EgoTactile, a benchmark pairing egocentric video with full-hand pressure supervision for diverse everyday objects, incorporating a bare-hand transfer subset to enable generalization to natural scenarios. Leveraging this benchmark, we first establish EgoPressureFormer as a discriminative baseline. Beyond this, to explicitly address the uncertainty in partial observations, we propose EgoPressureDiff, a conditional diffusion framework that adapts a large-scale pre-trained video diffusion backbone. By combining rich world knowledge priors with a Physically-Informed Feature Rectification layer to inject semantic constraints, our approach effectively hallucinates plausible contact patterns and resolves visual-physical ambiguities. Extensive experiments demonstrate that our method achieves superior performance on the benchmark and robust transferability to in-the-wild scenarios. Our project page is at https://egotactile.github.io/.

Deep Learning · Large Language Models

Carla Troper

Recent work uses human cognitive benchmarks to evaluate how LLMs represent concepts, claiming to assess "human-like" understanding. This position paper argues that this approach is misguided: these benchmarks come from narrow, typically Western populations yet are treated as universal standards, despite cross-cultural research showing culture shapes how people think, not just what they think about. LLMs trained on global multilingual data should not be expected to mirror thinking patterns from limited groups. Moreover, LLM outputs can shift with minor changes in prompting, unlike the stable human mental structures these benchmarks were designed to measure. These problems show up as contradictory findings across studies, making benchmark results poor evidence for claims about how LLMs represent concepts. We call for evaluation approaches designed for what LLMs actually are—systems trained on diverse global data—rather than tests measuring how closely they match a single population’s way of thinking.

Optimization · Zero-order and Black-box Optimization

Sichen Wang, Zhipeng Lu

Noisy evolution strategies commonly mitigate ranking uncertainty by improving per-generation fidelity—for example, by allocating budget to resampling candidates or using robust aggregation to stabilize the within-generation ordering. Under strict fixed evaluation budgets, however, any additional intra-generation querying directly reduces the number of generations the algorithm can execute, shortening the optimization trajectory. This dynamic can be characterized as prioritizing fidelity over depth. We propose a paradigm shift in fixed-budget regimes toward depth over fidelity, arguing that the cumulative progress from a long sequence of noise-smoothed updates often outweighs that of a short sequence of rigorously denoised ones. We operationalize this principle via probabilistic elite membership, replacing hard truncation with conditional expected rank weights that integrate over ranking uncertainty. This shifts noise handling from the evaluation stage to the selection stage: rather than repeatedly reevaluating candidates to denoise their objective values, we directly smooth the selection signal driving the update. We instantiate this approach using residual bootstrapping: we perform sparse reevaluations near the selection boundary, store standardized noise residuals in a reusable pool, and generate bootstrap rankings to estimate expected weights. Recognizing that residual pool mismatch constitutes a potential statistical risk, we derive a falsifiable error decomposition and provide runtime diagnostics to ensure estimator validity. To prevent oversmoothing in low-noise regimes, we introduce an adaptive probe-and-switch mechanism that leverages a low-cost rank disagreement metric to dynamically select between standard CMA-ES and our bootstrap-based updates. Extensive evaluations across the COCO bbob-noisy suite and diverse external tasks—including RL policy search and noisy HPO—demonstrate consistent gains. Specifically, in high-misranking regimes constrained by strict budgets, our residual-bootstrap approach achieves substantially steeper progress curves than both uncertainty-handling CMA-ES and fixed-k resampling baselines. These results substantiate a testable thesis: when budgets are limited and ranking uncertainty is high, integrating uncertainty at the selection stage is more sample-efficient than reducing it at the evaluation stage.

Applications · Computer Vision

Yuejiao Su, Xinshen ZHANG, Zhen Ye, Lei Yao, Lap-Pui Chau, Yi Wang

A precise and comprehensive understanding of human-environment interactions in egocentric vision is essential for next-generation intelligent agents, such as assistive robotics. While existing multimodal large language models (MLLMs) support unified reasoning from scene-level analysis to instance-specific grounding, their accuracy and generalization remain limited. To this end, this paper introduces a novel Egocentric Analysis-guided RL-based method (EARL) that employs Group Relative Policy Optimization (GRPO) to enhance the interaction understanding of MLLMs in first-person vision. Specifically, EARL adopts a two-stage parsing framework including coarse-grained interpretation and fine-grained response. The first stage holistically interprets egocentric interactions and generates a structured textual description. The second stage produces the language answer and corresponding pixel-level grounding mask in response to the user query. To bridge the two stages, we extract a global interaction descriptor from the first stage and treat it as a semantic prior, which is then integrated via a novel Analysis-guided Feature Synthesizer (AFS) to support query-oriented reasoning. Furthermore, to effectively guide policy optimization, we design a sophisticated, multi-faceted reward mechanism that incorporates format correctness, answer relevance, and grounding accuracy. Experimental results demonstrate that EARL achieves an impressive 65.48% cIoU on the Ego-IRGBench benchmark for pixel grounding, surpassing previous state-of-the-art RL-based methods by 8.37%. Superior performance in out-of-distribution evaluations further validates EARL's generalization capability.

Probabilistic Methods · Everything Else

Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest, Thomas Bonald, Marine Le Morvan, Gael Varoquaux, Matthieu Labeau

Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation. This disconnect is especially critical in Inverse Probability Weighting (IPW) for causal inference, where propensity score errors near $0$ and $1$ often lead to high bias and variance. We propose a principled framework for deriving task-specific strictly proper scoring rules by matching the local curvature of the downstream error metric. We apply this to the Average Treatment Effect (ATE) estimation, deriving a closed-form loss and its corresponding canonical probability mapping that can be readily integrated with any model like a neural network or a gradient boosting algorithm. Extensive evaluations on causal inference benchmarks demonstrate that our tailored objective consistently outperforms standard likelihood-based and covariate-balancing approaches.

Marianne Arriola, Volodymyr Kuleshov

Masked discrete diffusion models have improved steadily, but still lag behind autoregressive (AR) models in quality, require fixed-length generation, and cannot exploit key-value (KV) caching. Block Diffusion partially bridges diffusion and AR by unmasking left-to-right token blocks, but sacrifices infilling flexibility and KV caching within blocks. Our key insight is that interpolating generation orderings between autoregression and fully-random decoding, rather than committing to a fixed block length, offers a better interpolation between diffusion and AR. We present a new class of language models, Set Diffusion, comprised of 1) a tighter likelihood bound induced by an order-informed noise process and 2) a causal diffusion architecture that enables KV caching under stochastic token orderings. We bias the noise process toward left-to-right generation, rather than enforcing a strict block factorization, such that tokens can be decoded in sliding-window sets for faster inference and greater flexibility for any-order decoding. Set Diffusion achieves better speed-quality tradeoffs on mathematical reasoning, summarization, and unconditional generation compared to prior diffusion language models while offering stronger infilling performance than Block Diffusion.

Ruiqi Lyu, Alistair Turcan, Bryan Wilder

Concept shift occurs when the distribution of labels conditioned on the features changes between domains, making even a well-tuned ML model to have learned a fundamentally incorrect representation. Identifying these shifted features provides unique insight into how one dataset differs from another, considering the difference may be across a scientifically relevant dimension, such as time, disease status, population, etc. In this paper, we propose SGShift, a model for detecting concept shift in tabular data and attributing reduced model performance to a sparse set of shifted features. We frame concept shift as a feature selection task to learn the features that can explain performance differences between models in the source and target domain. This framework enables SGShift to adapt powerful statistical tools such as generalized additive models, knockoffs, and absorption towards identifying these shifted features. We conduct extensive experiments in synthetic and real data across various ML models and find SGShift can identify shifted features much more accurately than baseline methods, requires few samples in the shifted domain, and is robust to complex cases of concept shift.

Anka Reuel, Avijit Ghosh, Jenny Chim, Andrew Tran, Yanan Long, Jennifer Mickel, Usman Gohar, Srishti Yadav, Pawan Sasanka Ammanamanchi, Mowafak Allaham 等

Foundation models are increasingly central to high-stakes AI systems, and governance frameworks now depend on evaluations to assess their risks and capabilities. Although general capability evaluations are widespread, social impact assessments covering bias, fairness, privacy, environmental costs, and labor remain uneven. To characterize this landscape, we conduct the first comprehensive analysis of social impact evaluation reporting, examining 186 first-party release reports and 248 third-party evaluation sources, supplemented by developer interviews. We find a stark division of labor: first-party reporting is sparse, often superficial, and declining in areas like environmental impact and bias, while third-party evaluators provide broader, more rigorous coverage of bias, harmful content, and performance disparities. However, only developers can authoritatively report on data provenance, content moderation labor, costs, and infrastructure, yet interviews reveal these disclosures are deprioritized unless tied to product adoption or compliance. Current practices leave major gaps in assessing societal impacts, underscoring the need for policies that mandate developer transparency, strengthen independent evaluation ecosystems, and create shared infrastructure for aggregating third-party evaluations.