Machine Learning
A Comprehensive View of Fairness through Distributional Stability
Gayane Taturyan, Charlotte Laclau and Stephan Clémencon
We view
fairness as a property of distributional stability. Rather than assessing a predictor under a fixed data distribution, we study how its predictions change under perturbations that modify the composition of protected groups. A predictor is fair if it remains stable under such shifts. Under this perspective, several classical notions of
fairness arise as stability with respect to specific perturbations, with the associated unfairness gap given by a Lipschitz constant of a prediction-rate functional. This formulation also yields guarantees that hold uniformly over a range of demographic compositions at test time, without requiring knowledge of the deployment distribution. It leads to a learning procedure based on convex combinations of reweighted predictors, formulated as a second-order cone program, for which we establish generalization bounds. Experiments on standard
benchmarks illustrate the approach.
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Machine Learning
Message Passing Does More with Less for In-Context Learning on Graphs
Dooho Lee, Jinmo Lee, Minho Jeong et al. (+2 more)
Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each
dataset, incurring repeated costs and effort. Graph in-context learning (ICL) avoids this by using a single pretrained model to predict unknown node labels directly from labeled context nodes. Existing approaches, however, rely on dense
attention across nodes, making inference increasingly expensive as graphs grow. In this work, we present Ephris, a new graph in-context learner built on sparse message passing, scaling linearly with the number of node-feature entries and graph edges. Ephris is pretrained entirely on synthetic graphs generated from structural causal models with diverse graph structures and relational dynamics, exposing the model to varied dependencies among
topology, features, and labels. We evaluate Ephris on 51 node-classification
datasets against 15 extensively tuned GNNs and existing graph ICL methods under both high- and low-label train/validation/test splits. Across both settings, Ephris ranks first on all four aggregate measures: Elo, improvability, average rank, and accuracy. Its inference cost remains comparable to training a single GNN once, while being over 10 times faster than previous graph ICL models. Together, these results advance the performance-runtime Pareto frontier, demonstrating that strong graph ICL does not require dense
attention. Code and model weights are available at https://github.com/nums-ai/ephris.
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All authors Dooho Lee, Jinmo Lee, Minho Jeong, Kijung Shin, Jaemin Yoo
Information Theory
Evolving Towards Better Codes: LLM-Guided Search for High-Distance Binary Linear Codes
Amal Seddas, Vladyslav Shashkov, Maryna Viazovska et al. (+1 more)
Evolutionary program search driven by large
language models (LLMs) has produced record-breaking constructions for open problems in combinatorics and beyond. We apply this approach to the longstanding problem of improving the best-known bounds for binary linear codes. Building on the EvoTune evolutionary framework and the ShinkaEvolve codebase, we introduce LinCodeEvolve, which evolves code-construction programs against an exact minimum-distance evaluator. A strategy loop combines diversity-driven search and expert supervision: when progress plateaus, new strategies are used to redirect the search. LinCodeEvolve discovers seven record-breaking codes, $[172,21,66]$, $[173,20,68]$, $[176,21,68]$, $[181,21,70]$, $[184,21,72]$, $[189,22,72]$ and $[200,21,77]$, six of which have concise quasi-cyclic descriptions. With standard code modification techniques, they improve $22$ entries of the tables. Every code is verified by exhaustive enumeration. These results suggest that LLM-guided search can help find improved codes and complement existing methods in coding theory.
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All authors Amal Seddas, Vladyslav Shashkov, Maryna Viazovska, Emmanuel Abbe
Computer Vision
Spatial-OPSD: Self-Improving Spatial Reasoning via Label-Free Self-Distillation
Zhenyu Liu, Zhangquan Chen, Keyi Chen et al. (+4 more)
Vision-
language models (VLMs) increasingly operate in
embodied and spatially grounded settings, where accurate understanding of depth, viewpoint, and three-dimensional relations is essential. However, improving spatial
reasoning typically relies on ground-truth answers, answer-derived rewards, or other forms of task-specific supervision. We introduce Spatial-OPSD, a label-free self-improvement framework that instead exploits spatial structure naturally available from perception and reconstruction tools. During training, a privileged teacher receives automatically obtainable spatial priors, such as depth, reconstructed 3D relations, and camera geometry, while the student observes only the original visual-
language input. On trajectories sampled by the student itself, the teacher provides dense
token-level supervision, allowing the student to internalize spatial knowledge without ground-truth answer labels or privileged information at inference time. To extend this supervision beyond a single round, we adopt a round-wise recursive training scheme: the teacher remains frozen within each round to provide a stable learning target, and the improved student initializes both teacher and student in the next round, where privileged spatial priors re-establish an informative teacher–student asymmetry. This enables repeated self-improvement while avoiding a rapidly moving teacher during optimization. Across four VLM families, a single round of Spatial-OPSD consistently improves the five-
benchmark average, while three rounds further push a strong spatially specialized model to the open-source frontier, achieving the highest average among the open models and the best results on three of five spatial
reasoning benchmarks. Our code is available at https://github.com/vermouth599/Spatial-OPSD.
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All authors Zhenyu Liu, Zhangquan Chen, Keyi Chen, Mingze Sun, Xiang An, Haodong Jing, Ruqi Huang
Artificial Intelligence
ACTR: Aligning Thoughts and Responses for Multilingual Safety in Reasoning LLMs
Xianhui Zhang, Jian Yu, Chengyu Xie et al. (+6 more)
Ensuring the safety of
reasoning large
language models (LLMs) across
languages is essential for their reliable deployment. However, when exposed to jailbreak attacks in non-high-resource
languages, these models may generate unsafe responses even when their
reasoning traces identify safety risks. To address this issue, we propose aligning cross-lingual thoughts and responses (ACTR), a framework that improves
multilingual safety
alignment by strengthening the use of existing safety
reasoning. Specifically, we first present the think gap score (TGS) to compare the normalized contributions of
reasoning traces to
attention outputs during response generation across
languages, and use
reasoning- trace substitution to measure the cross-lingual safety gap. Next, using a corpus of jailbreak queries, we assess
neuron importance through changes in response representations caused by
neuron masking and compare the high-importance
neuron sets obtained with
reasoning enabled and disabled to identify safety think
neurons that support the use of safety
reasoning. Finally, we devise
neuron-selective consistency optimization (NSCO), which uses a frozen judge model to reward agreement between the safety categories of
reasoning traces and responses while updating only the parameters associated with the selected
neurons, requiring no human-annotated responses or preference data. Across two
reasoning models, ACTR achieves lower average attack success rates than the evaluated state-of-the-art methods on AdvBench-X and MultiJail, with safety gains extending to unseen
languages, while preserving or improving average performance on
multilingual knowledge and mathematical
reasoning tasks and limiting false refusals of benign requests. Warning: this paper contains examples with unsafe content.
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All authors Xianhui Zhang, Jian Yu, Chengyu Xie, Chenhang Cui, Shuyi Miao, Pengyang Shao, Yu Zheng, Fei Shen, Tat-Seng Chua
Artificial Intelligence
MatToolBench: Benchmarking Multimodal Agents in Real-World Materials Science Workflows
Mei Wu, Rui Xie, Runyu Zhang et al. (+6 more)
Multimodal GUI
agents have achieved impressive results on general software
benchmarks, yet their ability to operate professional scientific software remains largely unexplored. In materials science, sparse domain-specific web data, specialized interfaces, and tacit workflow conventions create blind spots that general-purpose pretraining cannot readily bridge. We present MatToolBench, the first real-environment
benchmark for evaluating
multimodal GUI
agents on professional materials science software, comprising 204 tasks across 10 tools in three modalities: GUI operation, OriginPro scripting, and code-based database queries, all executed inside a Windows 11 VM. Each task is decomposed into fine-grained sub-criteria by domain experts, enabling interpretable partial-credit scoring; the GUI component of our multi-level evaluation pipeline achieves an average F1 of 0.98. For OriginPro figure-generation tasks, we further conduct a human-LLM agreement study to validate the use of a
multimodal judge for secondary aesthetic assessment. Our experiments show that strong performance on general
benchmarks does not transfer to professional scientific workflows, and that this gap is not a visual-grounding problem alone: failures arise from domain-specific operational knowledge, sparse pretraining coverage of scientific software, weak cross-tool artifact handoff, and critical states exposed only visually. Even the best model reaches only 25% success rate on GUI tasks and 45% on code tasks. MatToolBench therefore serves as a challenging diagnostic
benchmark and real-environment testbed for data-scarce, knowledge-intensive scientific workflows.
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All authors Mei Wu, Rui Xie, Runyu Zhang, Yuqiang Li, Tianfan Fu, Bo Chen, Kai Yu, Xin Chen, Lu Chen
Computer Vision
OmniRoute: Mapping Temporal Semantic Evidence to Audio-Visual Token Budgets for Efficient Omnimodal Large Language Models
Yuchen Deng, Zidang Cai, Feidiao Yang et al. (+4 more)
Omnimodal large
language models (Omni-LLMs) encode audio and visual streams into temporally interleaved
token sequences for
multimodal reasoning. However, processing long audio-visual
token sequences incurs substantial prefill costs. Existing compression methods have made progress, but often overlook temporal changes in audio-visual semantic relevance. Motivated by temporal variation and local continuity, we propose OmniRoute, a training-free, two-stage compression framework. First, Temporal Evidence-Guided Budgeting (TEGB) derives chunk-wise modality preferences and initial leading-modality budgets from semantic relevance and local content variation. Second, Budget-Constrained Semantic Compression (BCSC) compresses the leading modality and then calibrates the follower's retention target using the actual retained fraction. For
video, it combines spatiotemporal grouping with query-guided selection; for audio, it selects
tokens based on encoder
attention and query relevance, then merges residual
tokens into context anchors under visual guidance. Experiments on four representative
benchmarks demonstrate a better trade-off between inference efficiency and performance than competitive baselines. The code and interface will be released to facilitate further research.
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All authors Yuchen Deng, Zidang Cai, Feidiao Yang, Yufei Wang, Jie Wang, Hai-Tao Zheng, Yuxing Han
Computer Vision
NHO: A Neural Hamiltonian Operator for Anchor-based Region Localization and Dense Correspondence
Jing Li, Yawei Luo, Xiangze Meng et al. (+3 more)
Non-rigid partial-to-full shape correspondence from sparse anchors requires identifying the corresponding region on the full surface and recovering dense correspondences between the partial shape and that region. We present NHO, which combines sparse anchors with the intrinsic geometry of the partial shape to learn a neural
Hamiltonian operator whose localized eigenspace encodes both the region support and intrinsic coordinates for dense correspondence. NHO parameterizes the
Hamiltonian potential as an intrinsic neural field and optimizes it using anchor evidence together with spectral and
geometric constraints. To resolve the spatial ambiguity left by sparse anchors, we introduce reciprocal refinement between operator estimation and correspondence recovery. At each round, the current eigenspace provides spectral coordinates and restricts matching to its induced support, while geometrically reliable correspondences provide additional evidence for updating the potential. After refinement, aggregated eigenfunction energy yields the final localization, and the recovered map initializes dense correspondence refinement. Experiments demonstrate competitive accuracy on both tasks and robustness to uniform scaling and rotation.
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All authors Jing Li, Yawei Luo, Xiangze Meng, Ying Li, Tieru Wu, Rui Ma
Neural and Evolutionary Computing
Multi-Depth Temporal Fusion for Feedforward, Locally Trained Spiking Neural Networks
Aidin Attar, Eleonora Cicciarella and Michele Rossi
We propose a new spiking neural network (SNN) design to process static images and event streams using time-to-first-spike (TTFS) latencies. Our key research question is which architectural choices best accommodate local and online learning in multi-layer convolutional SNNs. This question is addressed via an original framework combining residual-like connections with multi-depth feature aggregation and consensus. The full SNN pipeline features an early-
vision front end, to convert raw visual data into sparse spike latencies, a four-layer convolutional backbone trained layerwise with unsupervised spike-timing-dependent plasticity (STDP), a deterministic Multi-Depth Temporal Fusion (MDTF) and a final classifier trained with reward-modulated spike-timing-dependent plasticity (R-STDP). Rather than replacing early features in deeper layers, the proposed MDTF preserves early temporal evidence, adding sparse residual events from intermediate layers, and incorporating deeper features only when they agree in time with earlier representations. The resulting architecture is experimentally validated across MNIST, Fashion-MNIST, CIFAR-10, and N-MNIST, delivering strong classification performance under a fully local learning regime. Selective multi-depth fusion significantly outperforms traditional STDP/R-STDP baselines on higher-variability visual tasks (achieving +18.2 pp on Fashion-MNIST and +29.2 pp on CIFAR-10). Furthermore, activity-budget analyses show that the network retains high accuracy even when removing a large fraction of late or weak spike events, confirming its high data efficiency and reduced event-processing requirements. The codebase is publicly available at github.com/aidinattar/multi-depth-temporal-fusion-snn.
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Computer Vision
Speed in the Blind Spot: An Interpretability Analysis of Dynamic Perception in VLMs for Autonomous Driving
Katharina Winter, Stefan Englmeier and Fabian B. Flohr
Vision-
Language Models are increasingly used in autonomous-driving systems, yet their ability to recover dynamic physical state from visual input remains insufficiently characterized. We study velocity understanding as a controlled diagnostic across three tasks: surrounding-
agent speed, current ego speed, and short-horizon future ego-speed proposal. On nuScenes, we evaluate open-weight general-purpose and PhysicalAI VLMs, together with the driving-oriented Alpamayo-1.5
Vision-
Language-Action model, using multiple input and output formulations. We combine verbal evaluation with temporal perturbations, counterfactual ego-speed hints and linear probes of hidden representations. The tasks exhibit distinct failure modes. Surrounding-
agent speed is weakly encoded in an
agent-specific form, whereas current ego speed is often internally accessible but poorly verbalized: continuous probes achieve 4.7-5.8 km/h MAE compared with 10.2-16.8 km/h MAE for verbal outputs. Multiple frames provide inconsistent verbal gains to single frame inputs, and frame order is rarely exploited. Under non-optimized QLoRA, task-specific adaptation improves both task-relevant latent speed representations and verbal readout, but continuous surrounding-
agent speed estimation remains weak, while most future-speed gains survive frame shuffling, indicating limited temporal grounding. Driving specialized Alpamayo-1.5 shows stronger latent representations for surrounding-
agent and future ego speed, while current ego-speed decodability is comparable and substantial probe-verbal gaps remain. Thus, driving specialization can strengthen motion representations but does not guarantee stronger encoding across both scene and ego states or reliable readout. The results show that plausible planning outputs do not necessarily imply reliable recovery or temporal grounding of the underlying dynamic state.
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Software Engineering
GitCF: Reducing Incomplete Changes by Exploiting Multiple Similarities Among Commits
Yuto Kitabayashi and Takashi Kobayashi
Software developers often struggle to identify all locations that their changes affect, and incomplete changes frequently result from these omissions. To mitigate this problem, several approaches have been proposed to recommend additional locations that should be modified together with a developer's current change. A large class of these techniques mines co-change rules from repository revision histories, but because they rely solely on which elements have been modified together, they cannot recommend elements that have rarely co-changed, and they disregard the textual information that accompanies commits. A second class of techniques does exploit textual information, but it derives it from the source code or from a change request rather than from commit history, and it does not use the developer's current change as its input. To address these limitations, we propose GitCF, a change recommendation method that compares a developer's current change with past changes in the revision history and recommends additional change locations by combining two sources of similarity: the set of modified elements and the textual content of commits, including commit messages, code diffs, and linked issue descriptions. We evaluate GitCF on 543 incomplete changes from 17 open-source projects, where an incomplete change denotes a commit in which an omitted modification is supplied in a later commit, comparing it against three co-change-rule-based techniques. GitCF outperforms the strongest baseline on all five metrics (MAP, Recall@10, Recall@20, Hit@10, and Hit@20); in particular, it raises Hit@10 from 0.322 to 0.403, and the improvement in average precision is statistically significant.
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Computation and Language
Learning from Think-Mode Advantage via On-Policy Distillation
Wanqi Ren, Jianxiang Wang, Danxuan Liu et al. (+2 more)
Explicit intermediate
reasoning gives large
language models (LLMs) a stronger problem-solving mode. We study learning from this think-mode advantage via on-policy
distillation (OPD). OPD preserves student-generated trajectories and provides dense
token-level teacher targets at student-visited prefixes. Privileged
reasoning is used during
distillation rather than student inference. Uniform ThinkOPD, a natural think-enabled OPD baseline, conditions a fixed teacher on one shared think trace and uniformly distills every sibling student response. Although its prefixes are on-policy, the trace need not follow a route compatible with every complete response: the same privileged trace can induce different teacher-student discrepancies even when responses reach the same outcome. We summarize this interaction with trace-response divergence (TRD) and introduce ThinkOPD, which routes supervision at the response level by combining group-relative reward gain with a TRD-based compatibility proxy. Final response weights are normalized within each rollout group. Across mathematical
reasoning and code generation, ThinkOPD outperforms Uniform ThinkOPD in both same-model settings and both cross-model teacher-student pairs, and it exceeds representative rationale and self-
distillation baselines in a controlled comparison. Controlled interventions show that outcome benefit and the TRD-based proxy provide complementary routing signals in this setting. Think-enabled OPD provides a controlled setting for studying how teacher advantage becomes transferable along student responses.
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All authors Wanqi Ren, Jianxiang Wang, Danxuan Liu, Linyi Ding, Huaixiao Tou
Machine Learning
Iterative Exact Discrete Guidance for Energy-Based Sampling
Yuwen Qian, Yidong Ouyang, Zhengyan Wan et al. (+1 more)
Sampling from unnormalized distributions over large discrete state spaces becomes difficult when a
multimodal target is far from a tractable reference. We introduce Iterative Exact Discrete Guidance (IEDG), a population-exact, trajectory-wise guidance framework for unnormalized discrete targets. Rather than learn the full reference-to-target correction in one step, IEDG introduces a global Boltzmann tilt along an annealing trajectory. Each stage learns a stage-local posterior correction for an incremental Boltzmann tilt of the current source, while the resulting corrections are accumulated relative to a fixed analytic posterior. At the population optimum, exact stage posteriors recover the correct reverse dynamics, whose exact simulation reproduces the target distribution. IEDG chooses stage increments by relative effective sample size (rESS), which controls Rényi-2 displacement and locally adapts the step size to the thermodynamic geometry of the annealing path. Our stagewise total-variation analysis shows that limited overlap amplifies Bregman fitting error by $1/\sqrt{\mathrm{rESS}}$, while posterior, simulation, and truncation errors enter additively. IEDG improves all distribution-level errors over the neural baselines on ordered, exactly enumerated Ising $4\times4$, while substantially reducing one-shot errors on Ising/Potts $16\times16$ across thermodynamic regimes and attaining the best neural-sampler result on several reported local-statistic and phase-coverage metrics. On Max-Cut, its best-of-512 and average-sample ratios exceed all the baselines. Code and artifacts are available at https://github.com/StillFantasy123/iterative-exact-discrete-guidance.
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All authors Yuwen Qian, Yidong Ouyang, Zhengyan Wan, Hongyuan Zha
Computer Vision
GleanVID: Complementary Token Selection for Efficient Video Large Language Models
Shuo Yang, Changbai Li, Rui Tang et al. (+3 more)
Video Large
Language Models (VideoLLMs) have achieved strong
video understanding capabilities but incur substantial inference overhead due to the large number of visual
tokens. Existing VideoLLM
token compression methods largely rely on selection-independent scoring, overlooking cross-frame complementarity and consequently retaining redundant evidence across frames. Instead, we view
video token selection as a progressive evidence accumulation process. It aims to retain visual evidence that is individually informative and collectively complementary under a limited
token budget. Building on this insight, we introduce GleanVID, a training-free inference acceleration framework for VideoLLMs. Specifically, GleanVID first allocates the global
token budget across frames according to temporal novelty and then selects
tokens by jointly considering local representativeness and subspace complementarity, thereby preserving richer and less redundant visual evidence. Extensive experiments across diverse VideoLLMs and
benchmarks demonstrate that GleanVID consistently achieves state-of-the-art performance. Notably, with only 25% of visual
tokens, GleanVID preserves 98.6% of Qwen3-VL's original performance while reducing its prefill latency by 44.7%. On LLaVA-OV-7B, GleanVID at a 25% retention ratio even slightly surpasses the original model.
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All authors Shuo Yang, Changbai Li, Rui Tang, Xinyu Zhao, Linlin Yang, Baochang Zhang
Machine Learning
Beam Search as Test-Time Self-Distillation via Counterfactual Contexts
Su Ee Tan, Xiaotong Ji, Rasul Tutunov et al. (+2 more)
Self-
Distillation Fine-Tuning (SDFT) enables a
language model to act as its own teacher: by conditioning on a demonstration, the model produces an implicit reward via pointwise mutual information, which guides on-policy learning without external supervision. However, SDFT operates at training time: it requires gradient updates and access to expert demonstrations, making it inapplicable at inference. We propose test-time self-
distillation, a decoding-time method that extracts a steering signal from the self-
distillation framework without any parameter updates, reward models, or training data. Our key insight is that counterfactual contexts, i.e. fixed textual templates that hypothetically
prime the model for excellent versus poor
reasoning, can substitute for the demonstration. The log-odds ratio of a candidate answer under these two counterfactual conditions defines a new reward signal. We derive the optimal KL-regularized policy under this reward, which takes the form of a Gibbs reweighting of the base distribution. Crucially, this reweighting is global: it cannot be decomposed into independent per-
token operations without ignoring future trajectory quality. We therefore approximate the target distribution via beam search. Experiments on mathematical
reasoning (MATH500), code generation (HumanEval), and graduate-level science QA (GPQA) across multiple model scales show that test-time self-
distillation improves over standard sampling, low temperature, beam search and power sampling baselines on average, demonstrating that the self-
distillation principle can be operationalized at inference time.
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All authors Su Ee Tan, Xiaotong Ji, Rasul Tutunov, Haitham Bou-Ammar, Matthieu Zimmer
Computation and Language
Selecting The Most Informative Tokens in Natural Language Autoencoders
Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez et al. (+4 more)
Natural
language autoencoders translate a
language model's internal activations into readable explanations. Explaining every
token position is costly. Which positions should an auditor inspect to understand a potential threat? We study this question across $4.7$ million explanations on
prompt injection and concealment. We compare signals from model computation with a ranker trained only on chat structure. Chat structure usually selects more relevant explanations than the computational signals, without requiring a model forward pass for position selection. On three of four
datasets, explaining just $5\%$ of positions retains nearly all of the success rate from explaining every position, where success means obtaining an explanation about the threat. The benefit varies with the audit task. We also show that pretrained verbalizers recover words that models have learned to conceal through fine-tuning, without additional verbalizer training. These results identify where auditors can concentrate explanation generation and show that useful explanations can extend beyond the model a verbalizer was trained to describe.
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All authors Federico Torrielli, Gianluca Barmina, Andrea Blasi Núñez, Amon Rapp, Luigi Di Caro, Peter Schneider-Kamp, Lukas Galke Poech
Machine Learning
NowcastDiT: Diffusion Transformers are Effective Precipitation Nowcasters
Haoran Xu, Xingzhuo Guo, Yuchen Zhang et al. (+3 more)
Precipitation nowcasting demands accurate short-term forecasts under strong spatiotemporal variability.
Diffusion models are well suited to modeling complex precipitation distributions, yet existing approaches often introduce increasingly specialized designs, leaving the capability of a standard
diffusion architecture underexplored. We show that a standard
Diffusion Transformer already provides a simple and scalable foundation for precipitation nowcasting, with domain-specific requirements accommodated naturally within its design space. Based on this principle, we develop NowcastDiT and instantiate this flexibility through two complementary adaptations: a dynamics-aware noise prior for temporally coherent forecasts, and end-to-end reinforcement learning with timestep-aware rewards for meteorological skill. Experiments on SEVIR and MRMS
benchmarks show that NowcastDiT achieves state-of-the-art performance in both perceptual quality and meteorological skill. These results suggest that standard DiT can serve as an effective foundation for precipitation nowcasting.
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All authors Haoran Xu, Xingzhuo Guo, Yuchen Zhang, Jincheng Zhong, Jianmin Wang, Mingsheng Long
Machine Learning
High-Resolution Dynamic Functional Connectivity Generation with Graph-Variate Flow Matching
Om Roy, Yashar Moshfeghi and Keith Malcolm Smith
High-resolution dynamic functional connectivity (DFC) can reveal rapidly evolving
brain-network interactions, but short temporal windows yield noisy, often low-rank covariance estimates. Graph-Variate Dynamic (GVD) connectivity addresses this by modulating fast instantaneous interactions with stable trial-level support. This suppresses spurious fluctuations and emphasizes persistent, informative connections. We show that the Hadamard construction lifts low-rank instantaneous connectivity from the positive-semidefinite to the positive-definite cone, keeping high-resolution trajectories on the SPD
manifold without ridge regularisation or post-hoc projection. We introduce GVD-CFM, a class-conditional generative model for high-resolution dynamic connectivity. Each trial is represented as SPD GVD
matrices on a product Riemannian
manifold, then mapped through a global log-Euclidean diffeomorphism and an invertible temporal DCT basis. A
Transformer-based conditional flow models all spectral modes jointly and generates the full trajectory non-autoregressively in Euclidean coordinates while preserving exact correspondence with valid SPD sequences. Retaining the full DCT basis also enables decoding on denser temporal grids without retraining. Across multiple
EEG motor-imagery
datasets, GVD-CFM delivers the strongest overall results for held-out distributional fidelity, temporal-dynamics preservation, and synthetic-to-real classification. It also remains computationally efficient relative to strong raw-signal and direct GVD-space generative baselines. GVD-CFM therefore provides a practical framework for realistic, temporally coherent, high-resolution
brain-network generation with preserved
manifold structure and resolution-flexible decoding from a single trained model.
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Artificial Intelligence
Watch-Think-Interact: Bootstrapping Long-Horizon Multi-Turn Streaming Video Reasoning with Reinforcement Learning
Ziheng Huang, Yicheng Bao, Xueheng Li et al. (+8 more)
Streaming
video assistance requires models to answer asynchronous questions from an observed prefix under a fixed context budget. Existing approaches model response timing or compress history, but an online state formed before future questions are known can omit visual details before later questions reveal their relevance; the retained state alone cannot recover them. We introduce Watch-Think-Interact (WTI), a closed-loop framework for multi-question streaming
video reasoning. WTI maintains compact natural-
language memory entries tagged with source-
video time ranges; these entries support direct
reasoning when sufficient and otherwise anchor selective recall of finer visual evidence. For each question, WTI answers when current context and
memory suffice, continues watching when required evidence has not appeared, or recalls a relevant past interval and decides again after incorporating the returned chunks, without replaying the full observed history. To train this behavior, we construct WTI-82K, comprising 82,335 timed questions across 4,812 causally aligned trajectories, and develop Stream-GDPO to optimize complete multi-question streaming rollouts using trajectory-level feedback for response timing, source-
video recall, and
memory updates. WTI achieves state-of-the-art aggregate performance among the compared open-source streaming baselines, reaching 83.3% on StreamingBench and 73.6% weighted overall accuracy on OVO-Bench.
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All authors Ziheng Huang, Yicheng Bao, Xueheng Li, Zhenkun Gao, Bangwei Liu, Kunquan Li, Yuxiang Shen, Bangyan Li, Xuejiao Wang, Changbo Wang, Gaoqi He
Artificial Intelligence
FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning
Mengjun Yi, Huaian Gu, Yinghao Ai et al. (+2 more)
Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but statistical heterogeneity makes a single global adapter insufficient for personalized prediction. Existing personalized methods typically use the same low-rank structure for both shared and private adaptation, overlooking their distinct requirements for aggregation and personalization. We propose FedLAFP, a role-aware framework that couples a compact, globally aggregated LoRA branch with a client-private, full-rank-capable RandLoRA branch. The shared branch provides an efficient interface for transferring common knowledge, whereas the private branch combines fixed random low-rank bases with learned scaling coefficients to provide expressive client-specific adaptation without additional communication. Client- and layer-specific mixing coefficients jointly fuse the two branches, and only the shared LoRA parameters are exchanged. A controlled linear study supports this role assignment: LoRA yields more aligned client updates and lower aggregation error, while RandLoRA more accurately recovers client-specific residuals. Experiments across four visual recognition
benchmarks show that FedLAFP consistently outperforms local-only and
federated LoRA baselines, achieving an average personalized accuracy of $86.93\%$ and exceeding the best baseline average by $1.30$ percentage points.
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All authors Mengjun Yi, Huaian Gu, Yinghao Ai, Furao Shen, Jian Zhao
Computer Vision
UniBuild: Unified Building Mapping From Multi-Source Optical Remote Sensing Imagery With Detail Decoding and Geometry Regularization
Wei Huang, Chenying Liu, Yilei Shi et al. (+1 more)
Building extraction from optical remote sensing (RS) imagery is fundamental to urban mapping, yet existing methods are often
dataset-specific and generalize poorly to unseen domains. Their practical use is also limited by insufficient detail recovery and weak
geometric regularization, leading to blurred boundaries, irregular shapes, and merged adjacent buildings. To address these issues, we propose UniBuild, a unified building extraction framework for multi-source RGB optical RS imagery. First, a unified multi-
dataset training scheme is constructed over heterogeneous RGB optical
datasets to learn transferable building representations across sensors and resolutions. Second, a novel detail-preserving HR-DPT decoder is designed to integrate high-level semantic features with high-resolution spatial features, enhancing building detail recovery. Third, geometry-aware regularization is introduced through a structure-tensor-based direction-aware loss for boundary direction consistency and a saddle-aware loss for suppressing false activations in narrow inter-building gaps under low-resolution conditions. We train and evaluate UniBuild on multi-source RGB optical
datasets, including 10 public high-resolution
datasets and two self-collected low-resolution
datasets. Experiments show that UniBuild consistently improves building-region accuracy, boundary sharpness, and adjacent-building separation across diverse
datasets. It also generalizes well to unseen domains and supports practical building extraction from RGB optical RS imagery up to 10\,m resolution. The predicted masks can be further converted into GIS-compatible building footprints through simple polygonization. The trained model and inference code are released at https://github.com/zhu-xlab/UniBuild.
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All authors Wei Huang, Chenying Liu, Yilei Shi, Xiao Xiang Zhu
Computer Vision
MotionInsight: Diagnosing Object Motion Deficiencies in Generated Videos
Jiahao Zhan, Yongrui Ma, Qunliang Xing et al. (+6 more)
Despite rapid progress in
video generation models, they still exhibit obvious motion deficiencies, often manifested as incorrect object motion. However, most existing
video quality evaluations focus on aesthetic quality or text-
video alignment. To address this gap, we study object-centric motion fidelity assessment, evaluating target objects along object consistency, motion continuity, and physical plausibility. To achieve this, we first introduce VidMotion, a diagnostic
dataset of 6,879
videos with designated moving objects and fine-grained annotations including dimension-wise scores and failure causes. We further propose MotionInsight, a diagnostic evaluator that shifts assessment from implicit RGB-frame observation to explicit motion-space diagnosis. By constructing motion-aware representations, MotionInsight makes subtle motion deficiencies more observable. We also introduce motion-specific rewards during GRPO to transform observed motion into a diagnostic assessment. Experiments demonstrate that MotionInsight provides an effective basis for diagnosing object motion deficiencies, producing human-aligned scores along three dimensions and grounded explanations.
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All authors Jiahao Zhan, Yongrui Ma, Qunliang Xing, Xuanyu Zhang, Jingqi Tong, Junlin Li, Li zhang, Shijie Zhao, Tianfan Xue
Machine Learning
LongSpark: Efficient speculative decoding with a fixed-cost parallel drafter
Hao-Yuan He, Peng-Fei Liu, Chen Chen et al. (+6 more)
Speculative decoding accelerates autoregressive inference by verifying multiple draft
tokens in a single target forward pass. However, as the context grows, existing state-of-the-art drafters become increasingly expensive, eroding the very efficiency advantage they are designed to provide. We argue that this scaling is unnecessary. A standalone
language model must grow with its prefix because it is solely responsible for every
token it produces. A drafter, by contrast, only proposes candidates; the target catches and corrects every error before any
token is committed. The drafter's decoding cost can therefore be made entirely independent of the prefix length. We introduce LongSpark, a block-
diffusion drafter that achieves this by extracting fixed-size, multiscale views from the target's verification pass, thereby eliminating the need for a growing persistent state. Extensive evaluations demonstrate that LongSpark achieves state-of-the-art end-to-end efficiency across multiple model scales and realistic serving conditions. Notably, it delivers the lowest time-per-output-
token on long-context tasks while reducing the drafter's context state by several orders of magnitude.
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All authors Hao-Yuan He, Peng-Fei Liu, Chen Chen, Si Shen, Quan He, Runzhou Liu, Xinqi Li, Zhenhua Dong, Ming Li
Sound
RAWD-TTS: Ratio-Free Reward Alignment for Discrete-Diffusion Voice Cloning
Maxim Maslov, Kirill Borodin, Vasilii Kudryavtsev et al. (+2 more)
Zero-shot text-to-
speech synthesizes new utterances in a speaker's voice from a short reference recording. Voice cloning requires accurate content and preserved speaker identity, but supervised acoustic-
token prediction does not directly optimize these waveform-level properties. Reward-based post-training addresses this mismatch, but in discrete
diffusion,
token choices and reveal positions jointly define the sampling trajectory, complicating
alignment. We introduce RAWD-TTS (Ratio-free Advantage-Weighted Denoising), which scores decoded samples with recognition and speaker rewards and uses group-relative advantages to weight masked-
token reconstruction of those samples, without reverse-trajectory likelihoods or target audio. On 500 Russian CV3-Eval voice-cloning
prompts, joint
alignment reduces word error rate from 3.18% to 2.42% at the reward-selected checkpoint (24.0% relative) and to 2.58% at the final checkpoint (19.0%), while WavLM speaker cosine rises from 0.733 to 0.748 and 0.755. Controlled experiments characterize recognition-identity trade-offs and the effects of corruption count, group composition, and weighting.
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All authors Maxim Maslov, Kirill Borodin, Vasilii Kudryavtsev, Nikita Vasiliev, Grach Mkrtchian
Artificial Intelligence
Beyond Low-Rank Parameterization: Narrowing the Gap Between LoRA and Full Fine-Tuning via Gradient Decomposition
Yihao Ouyang, Shiwei Li, Haozhao Wang et al. (+5 more)
Low-Rank Adaptation (LoRA) is a widely used approach to parameter-efficient fine-tuning (PEFT), yet a performance gap can remain relative to full fine-tuning (FFT). Many LoRA variants improve the initialization or optimization of low-rank factors. At each training step, however, their first-order weight-space directions are constrained by the current parameterization. We characterize the corresponding LoRA-accessible gradient space and show that it coincides with the tangent space induced by the current LoRA parameterization. This characterization yields an orthogonal decomposition of the full weight gradient at the current model parameters. We term the component orthogonal to this space the normal gradient. Based on this decomposition, we propose GDLoRA (Gradient-Decomposed Low-Rank Adaptation). GDLoRA reconstructs the full weight gradient from forward activations and backward signals, extracts its normal component, and directly updates the base weights with this component, while retaining standard AdamW optimization for the LoRA factors. GDLoRA incorporates complementary normal gradients without increasing standard LoRA's optimizer-state
memory budget under matched adapter and optimizer configurations. Experiments on natural
language understanding, mathematical
reasoning, commonsense
reasoning, and image classification show that GDLoRA consistently improves over LoRA and narrows the performance gap to FFT. The code is available at https://anonymous.4open.science/r/GDLoRA.
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All authors Yihao Ouyang, Shiwei Li, Haozhao Wang, Xiandi Luo, Zhuoqi Hu, Jinglun Yu, Yichen Li, Ruixuan Li
Artificial Intelligence
AnyAct: Universal Action for Self-Evolving Agents
Lingrui Xu, Yangqin Jiang, Jiachang Zhang et al. (+2 more)
As large
language models (LLMs) advance, AI
agents are increasingly deployed in open-world environments to tackle complex sequential tasks (e.g., document processing, cross-application collaboration), relying heavily on actions ranging from GUI operations to semantic APIs. However, three core challenges persist: the "scale dilemma" of massive tool ecosystems exceeding LLM context windows, the "non-stationarity" of tool quality due to updates or outages, and the "heterogeneity" of feedback formats (pixels, text, structured data) creating information silos. To address these, we propose AnyAct, a universal action layer that unifies available capabilities into a self-evolving action space, enabling
agents to operate efficiently and reliably in large-scale, dynamic tool ecosystems. AnyAct's core design focuses on two objectives: constructing this action space via hierarchical progressive
retrieval (filtering task-relevant actions) and test-time reliability
evolution (
pruning unreliable actions), and enabling reliability-aware action orchestration through a heterogeneous observation grounding module that unifies multi-modal feedback. Additionally, it defines a hybrid action space (primitive + semantic actions) and optimizes for a balance between task success rate and execution cost. Evaluations on LiveMCPBench and OSMCP (a new
benchmark we developed for multi-granularity action collaboration) demonstrate state-of-the-art performance. AnyAct delivers substantial performance gains over baseline methods across various LLM base models on LiveMCPBench and improvements are particularly notable for models with constrained native capabilities. On OSMCP, it achieves 77.27% overall success with only 50 steps, which is half the steps required by most competitors.
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All authors Lingrui Xu, Yangqin Jiang, Jiachang Zhang, Xubin Ren, Chao Huang
Artificial Intelligence
Language as the Interface: Foundation-Model Contrastive Learning Links Transcriptomes and Electrophysiology
Junbo Shen, Jinying Gao and Bo Lei
Integrating transcriptomic and electrophysiological data is essential for building
multimodal foundation models for neuroscience. Patch-seq provides paired measurements of gene expression and intrinsic electrophysiology from the same
neuron, establishing a basis for training cross-modal models. Here we introduce LangPatch, a foundation-model-based contrastive learning framework that uses paired Patch-seq data to align pretrained GenePT representations with electrophysiological phenotypes through a
language-based interface. Gene descriptions and verbalized electrophysiological profiles are embedded by the same frozen text encoder. A context adapter and projection modules connect the modalities through paired contrastive learning. Across mouse visual, mouse motor, and human cortical cohorts, LangPatch achieves the highest mean transcriptome-to-electrophysiology prediction correlation among the evaluated foundation-model and representation-learning methods. It also improves held-out cross-modal
alignment in the two mouse cohorts (FOSCTTM 0.107/0.135 vs. 0.208/0.222 for JAMIE, an existing cross-modal Patch-seq imputation method). It predicts transcriptomic family, type, cortical layer, and marker-gene expression from electrophysiology, exceeding other baselines on most endpoints. More importantly, the method transfers across
brain areas and species: a model trained on mouse visual cortex predicts electrophysiology in motor cortex with approximately 70% correlation retention and in human cortex with 47% (58% on acute-slice recordings). Together, these results demonstrate
alignment between molecular and functional representations of
neurons, providing a building block for
multimodal foundation models in neuroscience.
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Artificial Intelligence
Physics-Informed Multi-Agent Coordination for Hospital Patient Flow Optimization
Guoqing Zhang, Rafik Hadfi and Takayuki Ito
Efficient patient flow coordination across autonomous hospital departments is critical for mitigating overcrowding and balancing resource utilization. While classical queueing theory, specifically open Baskett–Chandy–Muntz–Palacios (BCMP) networks, provides an interpretable mathematical
topology for healthcare operations, analytical models rely on stationary assumptions and fixed routing
matrices that degrade under state-dependent real-world dynamics. Conversely, centralized reinforcement learning approaches struggle to accommodate the decentralized structure of hospital governance, where individual clinical departments function with localized observations, heterogeneous resources, and divergent operational objectives. In this paper, we present a Multi-
Agent Systems (MAS) framework titled Physics-Informed Multi-
Agent Coordination, which embeds empirically calibrated BCMP queueing
topologies as physical priors within a decentralized multi-
agent reinforcement learning architecture. Formulated as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) under coupled resource constraints, our method enables autonomous departmental
agents to cooperatively negotiate patient routing and dynamic service scaling. To mitigate environmental non-stationarity without inducing excessive communication overhead,
agents exchange localized action fingerprints along network edges and optimize a spatially decomposed reward structure. Empirical evaluations driven by real-world MIMIC-IV patient trajectories indicate that this cooperative multi-
agent approach substantially reduces cumulative system delay compared to static Markovian approximations, heuristic dispatching, and independent multi-
agent baselines, while maintaining clinical safety constraints.
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Hardware Architecture
Low-level optimizations in high-level HDLs: Is there a benefit?
Oliver Keszocze, Tjark Petersen, Arved Friedemann et al. (+1 more)
This paper explores the applicability of functional programming to the design of Application-specific Integrated
Circuits (ASICs). We investigate the impact of designing ASICs using high-level, abstract Hardware Description
Language (HDL) features versus employing low-level optimizations on the area of the synthesized
circuits. The aim is to determine whether using low-level optimizations is beneficial and, if so, whether it is worth the added implementation effort. To carry out the investigation, we implement an unsigned bit-serial multiply-accumulate (MAC) unit in 16 different configurations using the functional HDL Clash. We make use of both low-level bit-manipulation techniques as well as Clash's high-level constructs for
circuit design. The experimental evaluation shows that some high-level constructs of Clash have negligible influence on the resulting
circuit size, suggesting that using the full power of functional programming is a viable approach to hardware design. To evaluate the impact of the used HDL itself, we also implemented versions of the MAC in Verilog. The experiments clearly show that there seems to be an inherent overhead in using Clash compared to Verilog code written by a seasoned engineer.
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All authors Oliver Keszocze, Tjark Petersen, Arved Friedemann, Matthias Bo Stuart
Numerical Analysis · Cross-listed from math.PR
Consistency and Convergence of the Backward-Euler Scheme for Stochastic Functional Differential Equations Driven by Fractional Brownian Motion
Alexander Abreu, Lisandro Fermin, Ernesto Mordecki et al. (+1 more)
We study the backward-Euler scheme for a class of
stochastic functional differential equations (SFDEs) with
memory driven by a fractional Brownian motion with Hurst parameter H > 1/2. We first establish the local consistency of the method, with a local truncation error of order H -$\rho$- $\beta$ + 1, and then, as the main result, prove the uniform global pathwise convergence of the numerical approximation, on a set of probability one, with order H -$\rho$ -$\beta$, for $\beta$ $\in$ (1-H, 1/2) and 0 < $\rho$ < H -$\beta$. A uniform high-probability formulation with deterministic constants follows as a corollary. In particular, the convergence order can be taken arbitrarily close to 2H -1, matching the order obtained for the explicit Euler scheme. The convergence proof relies on an exact integral representation of the scheme at mesh points, an a priori bound for the numerical solution, fractional calculus estimates, and a Gronwall inequality for weakly singular kernels.
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All authors Alexander Abreu, Lisandro Fermin, Ernesto Mordecki, Soledad Torres