Machine Learning
Follow the Perturbed Leader for Linear Bandits: $\sqrt{T}$ Regret over General Convex Sets
Chen-Yu Wei
We give the first
adversarial linear bandit
algorithm based on Follow-the-Perturbed-Leader (FTPL) that achieves $\sqrt{T}$ regret over general convex action sets. Our
algorithm attains $\widetilde{\mathcal{O}}(d^{5/4}\sqrt{T})$ regret using only linear optimization oracles, and the near-optimal $\widetilde{\mathcal{O}}(d\sqrt{T})$ regret when an optimal-design exploration distribution is further available. This improves on the $T^{2/3}$ regret of the FTPL
algorithm of Dani and Hayes (2006) for general convex sets, and generalizes the $\sqrt{T}$ regret of Lévy et al. (2026) over two special action sets (hypercube and Euclidean ball). Compared to previous oracle-efficient linear bandit
algorithms based on continuous exponential weights, the FTPL approach generally has better computational efficiency, and is much simpler to implement. The key algorithmic element is a perturbation distribution that adapts to the past reward
estimators. Its simplest form, an adaptive Gaussian perturbation, is straightforward to implement and already attains the regret bound above. We further extend it to two general constructions, random-weighting and elliptical perturbations, each covering distributions from light tails (Gaussian) to heavy tails (Student-$t_2$). On the technical side, we develop a systematic way to analyze the stability term in the FTPL regret through the Fisher information of the perturbation distribution. This builds a general framework for FTPL beyond existing ones such as the hazard rate and the FTRL-FTPL duality.
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Artificial Intelligence
Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution
Pengcheng Huang, Zhenghao Liu, Shixin Ren et al. (+5 more)
Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-hosted models. However, as LLM
agents increasingly operate in long-horizon scenarios, cloud participation becomes interleaved throughout execution rather than a one-shot decision for each query. Such interleaved participation introduces trajectory-level effects that remain insufficiently understood. To address this gap, we introduce EC2-Bench, a
benchmark for evaluating edge-cloud collaboration in long-horizon
agent execution. It provides executable digital-workspace
agent tasks and a unified evaluation framework covering task performance, resource usage, and
privacy exposure. Using this
benchmark, we empirically study diverse forms of edge-cloud collaboration. Our study reveals three findings. First, collaboration achieves a favorable balance among task performance, resource usage, and
privacy exposure compared with edge-only and cloud-only execution. Second, as edge models become stronger, collaborative execution approaches cloud-only performance, and performance differences across strategies shrink, while
privacy exposure remains strongly strategy-dependent. Third, stronger edge capability shifts preferred collaboration from externally controlled cloud participation and direct cloud execution toward more edge-controlled collaboration. Further analyses identify cloud-invocation decision-making and instruction following as important edge-side abilities for effective collaboration. These findings provide a systematic understanding of edge-cloud collaboration in long-horizon
agent execution and insights for designing future edge
agents that effectively collaborate with cloud models. Code and resources are available at https://github.com/OpenBMB/EC2-Bench.
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All authors Pengcheng Huang, Zhenghao Liu, Shixin Ren, Shi Yu, Sijia Yao, Yukun Yan, Chaojun Xiao, Maosong Sun
Information Retrieval
When Inputs Receive No Score: Evaluating Open-Web Event Admission on the Complete Stream
Shengqi Zhou, Zhigang Liu, Wenxiang Zhu et al. (+4 more)
A Web item can link to the correct event without contributing information worth organizing. Evaluating item-level admission therefore begins with complete inputs, including those a processing pipeline fails to score. We study how this upstream selection changes ranking comparisons and constrains operational decisions. Across six instruction-model backbones, three strategies, and 602 human-annotated evaluation items, systems score different populations with different class compositions. Comparing rankings on shared inputs separates these population differences from discrimination. Score availability also limits attainable retention: four configurations score only ten of twelve positive development items, making a 90% full-stream target unreachable by threshold adjustment. Paired precision experiments show that this constraint depends on execution configuration as well as model choice. Qwen scores the same number of evaluation items under INT4 and INT8, yet exchanges four scored items for four others. Extraction-recovery diagnostics further show that obtaining more scores can increase false acceptance under an unchanged threshold. We connect these observations through a complete-stream evaluation protocol that checks calibration feasibility and separates upstream score loss from decision loss. The resulting account explains which inputs a system can score, what its operating point can achieve, and what it actually admits.
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All authors Shengqi Zhou, Zhigang Liu, Wenxiang Zhu, Boyong Du, Kairui Yang, Haicheng Huang, Weijian Bu
Sound
SSEmb: Training a Generalizable Speech Retrieval Model Using Only Automatic Speech Recognition Data
Sangkwon Park, Dongwook Lee, Youngho Cho et al. (+3 more)
End-to-end
speech-to-text
retrieval is becoming an important capability for voice
agents, allowing spoken queries to retrieve relevant documents without intermediate transcription. Training these retrievers typically depends on large collections of spoken query-document pairs that are costly to acquire and difficult to extend across domains and
languages. We introduce \ours, which replaces such task-specific supervision with
speech-transcript pairs from standard ASR corpora. A lightweight adapter projects the output of a frozen
speech encoder into the
retrieval space of a frozen pretrained text
embedding model. \ours is competitive with cascaded ASR-then-retrieve systems while reducing inference latency, and outperforms existing publicly available
speech embedding models. It also transfers to text-to-
speech and
speech-to-
speech retrieval without additional training. Controlled analyses validate the
speech-transcript
alignment paradigm, demonstrating robustness to reduced training data and consistent gains from larger corpora. Further ablations of loss functions and trainable modules establish practical guidelines for the framework. Model weights and codes are to be released.
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All authors Sangkwon Park, Dongwook Lee, Youngho Cho, Donghun Kang, Heeseung Kim, Sungroh Yoon
Computation and Language
Perturbation, Not Provenance: Why Activation Steering Does Not Make a Robust Text Watermark
Sergey Pletenev
Linear directions in the activation space of large
language models causally and universally influence model behavior. This suggests that they might serve as a basis for a robust text watermark. The idea is to steer the generation along a specific semantic direction and then recover the mark by probing the activations of the re-encoded text. We test this hypothesis by conducting a series of experiments. We vary the strength of the steering and use four different directions (formality, sentiment, complexity, and a random control) across two different model families. We also test two paraphrasing attacks: a sentence-level one and a stronger paragraph-level one. The results show that activation-steering watermarks are outperformed by a standard logit-based watermark (KGW) in terms of detection accuracy. At a matched level of fluency (within 1.15 times the original text's perplexity), KGW achieved an AUC of 0.81 against a sentence-level paraphraser, while the best steering direction only reached 0.65. A stronger paragraph-level paraphraser lowered both (0.65 for KGW and 0.59 for steering), narrowing the gap but not reversing it. On Qwen, it left the two indistinguishable but at near-chance levels for both. We further showed that the detectability provided by steering was not semantic: a random direction was just as detectable as a meaningful one. The signal was independent of which layer was probed and whether a surface feature control was used. We conclude that, in this case, semantic activation does not encode a localized, recoverable semantic mark, but rather a global, content-independent shift that can only be strongly detected when the text is degraded.
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Computer Vision
SPARCL: Spatially Adaptive Regional Coverage for Active Learning in Chest X-ray Analysis
Juho Jung, Seongjae Kang, Mijoo Kim et al. (+4 more)
Pixel-level annotation for chest X-ray (CXR) lesion segmentation is expensive, making active learning (AL) essential. However, existing AL strategies fail to account for the spatial distribution of selected samples, resulting in inconsistent performance across focal (e.g., aortic calcification) and diffuse (e.g., nodules) pathologies. In this paper, we propose SPARCL (Spatially Adaptive Regional Coverage for Active Learning) to dynamically balance predictive uncertainty with cumulative spatial coverage. By employing a concave coverage function, SPARCL imposes diminishing returns on previously annotated regions, naturally directing the annotation budget toward under-labeled areas. Furthermore, an
entropy gate reverts the
algorithm to standard uncertainty sampling when spatial variation is absent. This coverage objective is monotone submodular, guaranteeing near-optimal greedy batch selection. To address how aggregate metrics like Dice obscure regional failures, we further introduce two metrics—Spatial Inconsistency (SI) and Performance Coverage (PC)—to quantify anatomical
fairness across AL rounds. Evaluated on an in-house calcification cohort and public CXR
benchmarks, SPARCL reaches 95% of fully supervised performance using only 24% of the data, compared to 29% for the best baseline. Ultimately, SPARCL ensures robust spatial reliability, surfacing regional gains that aggregate metrics miss.
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All authors Juho Jung, Seongjae Kang, Mijoo Kim, Sungduk Cho, Yunkyoung Jun, Sunghoon Joo, Yoona Hwang
Robotics
Which Trajectory Is Safer? Combining Rule and Learning for Safety Evaluation
Xuewen Luo, Linlian Jiang, Bo Yu et al. (+3 more)
Collision rate remains a dominant metric for evaluating autonomous driving trajectory safety, but its binary nature obscures substantial safety differences among collision-free trajectories. Existing surrogate-safety metrics provide finer-grained assessments, yet often trade off physical interpretability against adaptivity to interaction context. We introduce FutureRisk, a continuous metric that quantifies the latent future risk of a trajectory by how closely and persistently its realized interactions approach unsafe states. At its core, our Context-Adaptive Risk Model (CARM) combines a context-dependent safety-time distribution with an independently supervised critical-event-time distribution. Its safety criterion is bounded around a closed-form physical prior, while a bounded residual provides additional adaptation without abandoning this physical anchor. Experiments on DeepAccident and Bench2Drive show that FutureRisk achieves stronger agreement with native collision labels than existing surrogate-safety measures, discriminates risk among collision-free trajectories, and transfers zero-shot across
datasets. Comparisons across four planners further show that FutureRisk reveals safety differences hidden by binary collision outcomes. Together, these results establish FutureRisk as a fine-grained, interpretable, and context-adaptive metric for trajectory safety evaluation. Code is available at https://github.com/Xuewen2025/safety-evaluation.
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All authors Xuewen Luo, Linlian Jiang, Bo Yu, Fan Ding, Xinxin Zuo, Yang Wang
Computation and Language
BRIDGE: Selective Boundary Repair with Dependency Graph Evidence in Multi-Agent Systems
Encheng Su, Pengze Li, Ziheng Hua et al. (+12 more)
Multi-
agent collaboration has emerged as a critical paradigm for tackling complex, long-horizon tasks; however, while coordination protocols continue to advance, systemic failures at the inter-
agent collaboration interface remain a pervasive bottleneck. In collaborative workflows, systems frequently suffer from broken handoffs—where critical task context is omitted, misrouted, or ignored despite each
agent emitting locally plausible responses. To address this challenge, we present \method, a verifiable runtime control framework that treats handoff repair as selective intervention over explicit dependency graphs. Central to \method is an integrated fault-attribution and calibration pipeline, \dataset, which decouples execution dependencies and injects counterfactual handoff perturbations to provide empirical supervision for runtime intervention. Leveraging this diagnostic foundation, \method dynamically compiles observable interaction traces into a provenance graph to pinpoint unresolved dependencies, without accessing private
agent states. It then evaluates candidate repairs through an empirical gate that weighs expected gains against disruption risks, and executes repairs only when formal local postconditions hold. Extensive evaluations across demanding
benchmarks demonstrate that \method boosts task success by up to 12.3 absolute percentage points on SILO-BENCH and 11.1 on TeamBench. Across both backbones, \method maintains strict cost-effectiveness, adding as little as 2.0% marginal
token overhead. Furthermore, on 562 handoff-relevant traces from the MAST
benchmark, \method achieves 69.8% F1 in a single pass, establishing a reliable operational boundary for verifiable coordination repair.
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All authors Encheng Su, Pengze Li, Ziheng Hua, Qiucheng Yu, Jianyu Wu, Chen Tang, Yizhou Wang, Lintao Wang, Junjia Xiang, Xinzhu Ma, Xi Chen, Shixiang Tang, Houqiang Li, Xi Chen, Aoran Wang
Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
Learning from fracture: Teaching language models to design through physical experience
Bo Ni
How can
language models learn to optimize physical designs through experience? We introduce FracLearn, an experience-abstraction-generalization framework evaluated through a deliberately unfamiliar, Tetris-inspired fracture game. The model first proposes architectures and examines simulated fracture responses to accumulate experience. Abstraction organizes these observations into conditional principles linking reinforcement placement, crack
evolution and load transfer, with supporting evidence and failure conditions. Generalization challenges the model to combine these principles in new architectures and surpass the earlier design record. Campaigns demonstrate progressive refinement and recovery through hypothesis revision, yielding a best composite score 11.0% above the experience-stage record. Fracture analyses connect improved performance to crack deflection and temporary trapping as toughening mechanisms, alongside greater residual load capacity. Learning occurs through external knowledge and decisions, without model-weight updates. This approach offers a route to scientific
agents that turn computational experiments into reusable physical knowledge for materials discovery and engineering design.
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Graphics
Construction of Simple Quadrangulations of the Sphere at Skeletal Joints
Suil Hwang and Q Youn Hong
We present a method for constructing a simple quadrangulation of the sphere from an unordered set of $n$ direction vectors, with application to parametric volumetric models of skeletal joints. We first select a quadrangulation encoding the joint
topology by exhaustively enumerating candidate dual graphs, prioritizing convexity and regularity of the resulting quads. We then realize this
topology on the sphere without crossings by solving a conflict-constrained assignment problem with lazy cyclic-order separation, and recover the corresponding primal graph. Experiments on direction sets from spherical Fibonacci sampling of size $3$ to $20$ show that the method robustly produces valid, high-quality quadrangulations across a range of input configurations. The resulting primal graph is directly converted into exact rational Bézier shell and column volumes, yielding a compact and precise volumetric representation of the joint. The approach extends naturally to multi-joint assemblies, enabling the construction of truss-based
lattice structures within 3D domains.
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Robotics
Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations
Bo-Ruei Huang, Daniel Seita and Erdem B{\i}y{\i}k
Robot manipulation has traditionally relied on end-effector-centric interactions. In contrast, humans routinely exploit whole-arm contacts to improve efficiency, robustness, and adaptability, especially when the hands are occupied or when precise grasping is unnecessary. Enabling similar capabilities in
robots remains challenging: the
robot must select where on its arm to make contact, generate effective non-prehensile interactions, and satisfy task-specific pose or safety constraints on the end-effector. We propose Whole-Arm Contact Learning (WACL), a hierarchical framework that describes each whole-arm contact task with three
robot-agnostic representations and hands them to a quasi-static surface-contact optimizer: semantic keypoints that summarize the scene, a reference contact trajectory that specifies where and how the arm presses on the object, and safety-aware path constraints that a
vision-
language model generates from the task description to protect the end-effector's task. The
robot's kinematic chain enters only in this optimizer, which grounds these representations onto the arm by jointly searching over which link makes contact, where on that link, and the joint configuration that realizes it. This decomposition lets WACL learn the contact trajectory from 10 bare-hand human demonstrations per task collected without teleoperation, while new end-effector constraints change only the optimizer's input rather than any trained component. Real-world experiments on a 7-DoF arm across four whole-arm tasks show that WACL sustains contact under end-effector constraints where a strong link-pose baseline fails (78% vs. 33% success) and generalizes to new constraints without retraining. Website: https://wacl-
robot.github.io
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Computer Vision
Multimodal Fusion of RGB and Complementary Modalities for Semantic Segmentation
Hu Cao, Yifeng Cheng, Mu He et al. (+3 more)
Multi-modal semantic segmentation adds an auxiliary sensing stream to RGB images (RGB-X), such as thermal,
LiDAR, depth, event, polarization, or light field. This can improve robustness in poor lighting and under motion blur. But different sensors often produce features that do not match well, and fusion can become unstable. To alleviate these issues, we propose a bidirectional polarity-aware cross-modality fusion (BPCF) module that effectively captures complementary cues while enhancing feature
alignment. We evaluate the framework on six modality pairings: RGB-Thermal, RGB-
LiDAR, RGB-Depth, RGB-Event, RGB-Polarization, and RGB-Light Field. Our model achieve state-of-the-art (SOTA) results on eight public
datasets, including MFNet, KITTI-360, DELIVER, DDD17, DSEC, MCubeS, ZJU, and UrbanLF. The codes and trained models will be released upon acceptance.
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All authors Hu Cao, Yifeng Cheng, Mu He, Mengyu Li, Yinlong Liu, Alois Knoll
Robotics
Beyond Safe Execution: Safe Online Reinforcement Learning for Autonomous Driving with Value Calibration and Adaptive Guidance
Zeqiao Li, Yijing Wang, Haoyu Wang et al. (+2 more)
Online reinforcement learning (RL) enables driving policies to adapt to changing deployment conditions, but requires safe exploration throughout training. Safety filtering alone does not ensure stable policy improvement or reduced dependence on corrections. We propose Safe Online Adaptive Refinement (SOAR-Flow), a framework for safe and stable online fine-tuning of offline-initialized Flow-based driving policies. A control barrier function (CBF)–model predictive control (MPC) filter corrects potentially unsafe actions before execution. To support stable policy updates, we introduce Gaussian-return-aware value calibration based on calibrated Q-learning (Cal-QL), using recorded behavior returns and predicted return variability to smooth conservative critic updates and mitigate policy degradation. Paired corrective learning teaches feasible action adjustments, while an intervention-to-go predictor encourages decisions requiring fewer future corrections. Its predictions also regulate guidance strength, allowing recovery supervision to weaken as predicted dependence decreases. Together with gradually reduced calibration regularization, this supports continued task improvement while preserving hard execution constraints. We analyze conditions for safe execution at control steps and diminishing auxiliary learning influences. We evaluate SOAR-Flow in simulation and on an uncrewed ground vehicle (UGV). The results demonstrate improved driving performance and reduced reliance on safety corrections during protected online fine-tuning. The demonstration
video and project overview are available at https://lzqw.github.io/SOAR-Flow/.
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All authors Zeqiao Li, Yijing Wang, Haoyu Wang, Zheng Li, Zhiqiang Zuo
Distributed Computing
Quota-Based Aging for Prediction-Guided Scheduling in LLM Serving
Hanzhi Zhang, Anxuan Che, Hang Qi et al. (+2 more)
Large
language model (LLM) serving systems use
token-level continuous batching to improve resource utilization, but heterogeneous response lengths complicate request scheduling. Prediction-guided short-first scheduling reduces average latency but can severely delay long requests, while unrestricted aging may over-promote delayed long requests under congestion. We propose Quota-Based Aging (QBA), a lightweight policy that combines predicted short-first admission with quota-bounded, debt-enforced promotion of aged predicted-long requests. We also use a reduced M/G/n queueing surrogate, calibrated from simulator traces, to explain first-order mean-latency trends. QBA is evaluated using a serving-aware event-driven simulator that models decode-priority batching, chunked prefill,
token-budget constraints, and KV-cache-safe admission. We compare QBA with FCFS, predicted short-first, and aggressive aging under asymmetric prediction errors on synthetic and production-derived workloads. The evaluation considers queueing delay, throughput, utilization, and class-conditional tail performance. Results show that QBA provides a controllable middle ground between aggressive short-first prioritization and unrestricted aging, substantially reducing long-request tail latency while retaining much of the mean-latency benefit of prediction-guided scheduling.
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All authors Hanzhi Zhang, Anxuan Che, Hang Qi, Qiyue Li, Jingjin Wu
Machine Learning
GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation
Xu Liu, Hanshu Yan, Taha Aksu et al. (+6 more)
Contextual forecasting seeks to improve time series forecasting by incorporating forward-looking information, such as planned events, external conditions, and expert expectations. However, reliable evaluation remains difficult: existing
benchmarks often have limited domain coverage, lack standardized history-future splits, or provide text that is not guaranteed to inform the future. We introduce GIFT-Eval-CTX, a
benchmark that isolates whether forecasting methods can translate verified future information into accurate numerical trajectories. Built upon GIFT-Eval, GIFT-Eval-CTX contains 10,000 instances across 20
datasets. Each instance provides structured effects derived from the realized future: Macro effects describe horizon-wide behavior, while Micro effects specify localized changes. We construct these annotations with an agentic pipeline combining LLM-based discovery and deterministic code validation. We
benchmark TSFMs, frontier LLMs, and tool-using agentic systems. The results show that structured effects reduce aggregate relative MASE by 40.7% for GPT-5.6 Sol and 29.6% for Claude Opus 4.8 relative to history-only prompting. The evaluated MoiraiAgent system achieves the best overall result. We further analyze the contributions of structured effects and agentic systems to performance, and identify opportunities for future research.
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All authors Xu Liu, Hanshu Yan, Taha Aksu, Quang Pham, Zhongzheng Qiao, Jun Hao Liew, Silvio Savarese, Doyen Sahoo, Junnan Li
Systems and Control
A Programming, Robotics and Automatic Control Intervention for Secondary Education Students in Sweden and Italy
A. S. Yamashita-Fridesjö, C. Hertzberg, M. Morano et al. (+3 more)
This study evaluates the AutoSTEM workshop, an intervention for secondary education that combines a serious game with PID-controlled hardware. Seven workshops were conducted across Sweden and Italy. The six Swedish workshops adopted an explicitly SDT-informed facilitation framework, while the Italian workshop followed the existing educational robotics format without a formally defined SDT facilitation protocol. Self-reports, an objective knowledge quiz, and qualitative responses were analysed using multiple site-adjusted statistical approaches. Technical-specific self-efficacy increased substantially in both conditions, with large effect sizes, while objective knowledge showed only modest, non-significant gains. No significant site-adjusted differences were found between SDT and control conditions for the primary outcomes. Perceived engagement was higher in the control condition, although this finding should be interpreted cautiously. The objective knowledge composite remained positive but non-significant across sensitivity analyses. Female participants reported smaller PID self-efficacy gains than males. Qualitative findings supported evidence of PID learning while identifying technical and pacing barriers. Overall, the results suggest that the technology-rich intervention supports competence development, regardless of SDT-informed facilitation.
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All authors A. S. Yamashita-Fridesjö, C. Hertzberg, M. Morano, G. Fiara, B. Castagna, D. Scaradozzi
Computer Vision
MHVF: Training-Free GUI Grounding via Verification-Guided Multi-Hypothesis Search
Minghua Chen, Xingming Liao, Weiqiang Zhong et al. (+1 more)
GUI grounding enables web
agents to translate natural-
language instructions into actionable interface locations. However, dense layouts and small, visually similar controls can lead models toward plausible but incorrect elements, while refinement around a single prediction may leave alternative targets unexplored. We propose the Multi-Hypothesis Verification Framework (MHVF) for training-free GUI grounding. It combines multi-hypothesis exploration with local visual verification using a single pretrained
vision-
language model. To explore alternative targets, MHVF constructs spatial hypotheses from multiple full-screen predictions and examines the corresponding regions through zoomed re-localization. To resolve ambiguity among the resulting candidates, it verifies their agreement with the instruction in marked local views. Verification scores then guide a second round of local search and rank candidates retained from both search levels for final selection. Experiments with five general-purpose and GUI-specific models across eight
benchmark settings demonstrate improvements in grounding accuracy in most settings. MHVF improves UI-Venus-Ground-7B by 4.3 and 2.1 percentage points on Mind2Web and VisualWebBench, respectively, and raises Qwen2.5-VL-7B's accuracy on ScreenSpot-Pro from 23.6% to 53.6%.
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All authors Minghua Chen, Xingming Liao, Weiqiang Zhong, Zhuowei Wang
Computer Vision
When, Where, and Why: Hierarchical Causal Explainability for Video Face Understanding with MLLMs
Anubhav Kataria, Shreya Ghosh, Jingwen Ye et al. (+1 more)
Understanding facial behaviour in
video requires answering three fundamental questions: when a key facial expression occurs, where the model attends on the face at that moment, and why that evidence is necessary rather than merely correlated. Today's
multimodal large
language models (MLLMs) can label and describe
video, but they rarely provide faithful explanations, especially for small facial movements that change over time. We present VideoFace-X-LLM, a framework for explainable facial
video understanding comprising two key components. First, we introduce ExplainFace, a weakly supervised
video-text corpus for facial behaviour understanding, automatically constructed from CelebV-HQ
videos and validated through human evaluation and a two-model audit (at most 2.3% contradictions), demonstrating consistent gains across two backbone architectures. Second, H-TCC (Hierarchical Temporal Spatial Causal Concepts), which finds the key temporal moments (When), the facial regions (Where), and verifies its causal necessity (Why) through targeted occlusion. Fine-tuning on ExplainFace raises appearance Macro F1 from 19.48% to 32.87% (+13.39), with gains of +4.38 on actions, +6.77 on temporal emotion, and +11.84 F1 on deepfake detection. We also measure the explanations pointing lift, face/edge lift, stability, an Adebayo sanity check, and a necessity test rather than only showing examples. Our models, code, and corpus are available on request for research use.
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All authors Anubhav Kataria, Shreya Ghosh, Jingwen Ye, Abhinav Dhall
Robotics
DexStitch: Learning and Composing Dexterous Skills from Human Videos via Contact-Aware Robotic Inpainting
Byeonggyeol Choi, Seongmin Jung, Unghui Nam et al. (+5 more)
A long-horizon manipulation task consists of primitive skills and the transitions connecting them. Learning skills that can be executed in sequence requires supervision for both, which is costly to collect on a
robot. Human
videos offer a scalable source of demonstrations, but transferring them to
robots requires extracting
robot-compatible observations and actions together with their skill structure. We present DexStitch, a framework that cuts long-horizon human
videos into skills and transitions, converts them into aligned
robot observation-action-skill data, and stitches the learned skills into a requested sequence. A skill classifier first segments the
videos into manipulation skills and transitions. DexStitch then converts the human demonstrations into
robot observations and actions while retaining their corresponding skill labels. During conversion, per-finger human-object contacts guide dexterous hand retargeting and robotic inpainting, preserving contact geometry and finger-object occlusion ordering. To stitch the learned skills into a sequence, a shared skill-conditioned policy is pretrained on the resulting synthetic data and finetuned on a mixture of synthetic data and a small set of single-skill teleoperation episodes. Conditioning on the current and next skills guides each transition toward the following skill, while predicted skill progression enables closed-loop switching. On a held-out session, the classifier agrees with manual annotations on 92.4% of frames. Across four household skills, DexStitch achieves 80.0% single-skill success and 77.5% sequence success.
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All authors Byeonggyeol Choi, Seongmin Jung, Unghui Nam, Youngbo Song, Sunhong Min, MinHyung Lee, Byonghyo Shim, Yongseok Lee
Machine Learning
Posterior-Averaged Bellman Flow Matching for Distributional Critics
Zeqiao Li, Yijing Wang, Haoyu Wang et al. (+2 more)
Distributional critics model the full return distribution, providing richer signals for exploration and risk-sensitive control. Recent work has shown that flow-based critics can model these distributions flexibly and provide effective supervision across flow time for temporal-difference learning. However, unlike standard flow matching with fixed data endpoints, a flow critic is trained on Bellman targets recursively generated by a learned target critic under the current policy. Existing objectives therefore either use noisy single-endpoint supervision or rely on the learned flow itself for additional guidance. Our key observation is that an intermediate bridge state may be compatible with several Bellman endpoints, so its velocity should average their directions under the corresponding posterior. We propose Posterior-Averaged Bellman Flow Matching (PA-BFM), which samples an endpoint pool, infers the closed-form posterior over the latent anchor, and regresses to the posterior-mean velocity. Uniform anchor selection makes this target an exact finite-pool Rao–Blackwellization of the single-endpoint target: for every \(K\), it preserves the population target, cannot increase conditional variance, and its conditional variance scales as \(O(K^{-1})\). The endpoint pool supports conservative updates, while the twin return flows provide critic-disagreement and unsupported-mass diagnostics. Controlled studies show that PA-BFM reduces fixed-query target mean-squared error by \(79.7\%\) and target-induced gradient variance by up to \(98.0\%\), while improving return-distribution fidelity. Across 50 OGBench tasks and four offline-to-online scenarios, PA-BFM achieves higher aggregate performance than Value Flows.
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All authors Zeqiao Li, Yijing Wang, Haoyu Wang, Zheng Li, Zhiqiang Zuo
Artificial Intelligence
Agentic4PDM: Host-Governed Evidence Admissibility for Industrial Fault Diagnosis
Nam V. Nguyen, Hoang Van Truong, Hieu D. D. et al. (+3 more)
We study industrial fault diagnosis as a classification task that assigns sensor time-series windows to predefined health states or fault types. Scarce labels and changing operating conditions make repeated training of specialized classifiers costly. Yet structures such as periodicity, trends, and transients can remain diagnostically informative and be examined through reusable analytical tools. LLM
agents can select and interpret these tools, but fixed rules or training-derived guidance may not resolve each signal's ambiguity, while unconstrained interpretation may overstate weak or conflicting results. We introduce Agentic4PDM, a framework that separates adaptive investigation from control over evidence use. LLM
agents compare each signal with labeled references from every candidate class, identify unresolved distinctions, and propose follow-up measurements. A deterministic software controller, called the host, enforces contract-checked gated adjudication (CCGA): it checks requests, requires commitment to a complete tool batch before results are observed, and assigns evidence roles based on tool checks against labeled references. These roles distinguish corroborative support from advisory information. The Judge
Agent proposes a diagnosis, while the controller checks protocol compliance and records the investigation. Agentic4PDM supports training-free diagnosis using pretrained representations and labeled references without task-specific diagnostic models, and a standard setting that additionally investigates trained-model predictions. Experiments on selected bearing and motor diagnosis challenges and an unfiltered industrial test set supplied by our customer show gains in aggregate macro F1 over the evaluated diagnostic and agentic baselines. Ablations further support the value of the governed workflow.
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All authors Nam V. Nguyen, Hoang Van Truong, Hieu D. D., Duy Khuong Nguyen, Quang Pham, Tung Kieu
Information Theory
Learned Score-Based Turbo Message Passing for Channel Recovery From Spatially Compressed Measurements
Yiming Wei and Xiaojun Yuan
Hybrid beamforming (HBF) reduces the hardware cost of massive
MIMO base stations, but its channel acquisition requires recovering the full spatial-domain channel from highly compressed beam-domain observations. This paper proposes a learned score-based turbo message passing (L-STMP) framework for uplink HBF channel recovery under extreme spatial compression. By deeply unfolding the turbo message-passing loop and introducing trainable calibration parameters and learnable score priors in each layer, L-STMP liberates STMP from the idealized compression-
matrix requirements of right-rotational invariance, and reduces inference complexity to only several unfolded layers. In addition, to preserve dominant channel components that random discrete
Fourier transform (DFT) selection may miss, we introduce precoding
matrix indicator (PMI)-assisted beam selection as a low-overhead directional prior. Simulation results demonstrate that L-STMP consistently outperforms existing baselines and obtains substantial reconstruction gains under PMI-assisted compression in channel reconstruction.
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Computation and Language
Where Words Begin and End
Husein Zolkepli
Forced aligners locate each word of a known transcript in the audio, and many
speech pipelines depend on them. In our own data, a widely used CTC aligner inserts a pause at nearly every word boundary, and we found almost no aligner evaluated on telephone audio. We compare nine aligner configurations against human word boundaries on read, conversational and telephone English. The Montreal Forced Aligner (MFA) is the most precise, while CTC aligners consistently start words late, because most frames inside a word go to the blank
token. We then fine-tune Whisper with LoRA to predict each word's start and end as timestamp
tokens, training only on boundaries placed by people: 47 hours in 87
languages. The resulting aligner is more precise than MFA on every English test condition, and than MFA's own Korean and Dutch models. With FA-Bench's protocol and code on TIMIT, its word-boundary error is 11.6 ms, against 18.2 ms for the best published system. Two choices matter most on telephone conversations: placing the timestamp slots after the word they time, and training on noisy transcripts, which also makes the aligner nearly insensitive to errors in the transcript. The released model reaches 16.0 ms on real landline calls, against 36.5 ms for MFA. The models are available at https://huggingface.co/collections/Scicom-intl/force-
alignment
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Computation and Language
Full-duplex Spoken Language Modeling with Discrete Diffusion
Chen Chen, Pin-Jui Ku, Ankita Pasad et al. (+3 more)
We introduce Diffuplex, a full-duplex spoken
language model based on discrete
diffusion that achieves an unprecedented balance between linguistic intelligence and response efficiency. With thinking mode, Diffuplex delivers 13.0
tokens per forward pass, reaching an average of 1872
tokens per second and substantially surpassing the frontier autoregressive (AR) models in decoding speed. Without thinking, Diffuplex matches the first-audio latency of existing full-duplex models while achieving markedly stronger linguistic intelligence. This capability is enabled by separating when to say from what to say at the training level, rather than compressing both into a single autoregressive timeline. During inference, Diffuplex continuously tracks the user
speech through an AR interaction policy, while independently generating and refining response content through parallel
diffusion. Consequently, semantic computation is no longer bounded by the perception audio frame rate, allowing intelligence and responsiveness to improve simultaneously. Taken together, these results establish a new paradigm for spoken
agents that can sustain real-time dialogue while devoting parallel computation to intelligent response generation. Training code and checkpoints will be avaliable soon.
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All authors Chen Chen, Pin-Jui Ku, Ankita Pasad, Ante Jukić, Zhehuai Chen, Boris Ginsburg
Machine Learning
Hybrid Parallel Bessel KAN Model: a Kolmogorov Arnold Network: integrated framework for developing a novel architecture to reconstruct the fused-basis approach via a parameter free quadratic interaction
Mintu Pathak, Meshv Patel, Mohendra Roy et al. (+1 more)
The Kolmogorov-Arnold Network (KAN) challenges traditional MLPs by placing learnable activation functions on network edges. However, scaling KANs to spatial data exposes limitations in default B-Splines. While hybrid architectures mix multiple basis families on a single edge to resolve this, monolithic superposition causes
geometric overlap, parameter explosion, and severe numerical ill-conditioning ($\kappa(G) \approx 10^{16}$).To overcome this, we propose Parallel BesselKAN, which isolates B-Spline and low-order Bessel branches into parallel processing pipelines integrated via a parameter-free quadratic interaction. This decoupled architecture acts as an efficient internal ensemble, bypassing monolithic stability bottlenecks. It compresses the parameter footprint by $35\%$ while achieving $97.99\%$ accuracy on MNIST. Although standard MLPs retain a hardware speed advantage for pure
vision tasks, Parallel BesselKAN provides a robust, parameter-efficient framework stabilizing mixed-basis networks and paving the way for scalable spatial adaptations like Convolutional KANs.
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All authors Mintu Pathak, Meshv Patel, Mohendra Roy, Jwngsar Brahma
Computation and Language
GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria
Luise Koch, Manuel Tonneau, Niyati Malhotra et al. (+5 more)
Research on gender-related online content is often operationalized through harm-centered tasks that collapse non-harmful gender-related and unrelated content, while existing
datasets remain concentrated in high-resource
languages and Global North settings. To bridge these gaps, we introduce GENDIS, a
dataset of 9,600 multilingual, human-annotated YouTube comments from India, Kenya, and Nigeria (3,200 per country), annotated using a two-level framework that first identifies whether gender is referenced and then classifies gender-related content as negative, positive, or neutral. This separation provides a denominator for gender-related discourse independent of harm, enabling both prevalence estimation and characterization of its evaluative range. In the uniform random stratum, 23.8-35.0% of comments are gender-related, and among those with a resolved stance, 82% are neutral or positive. Across models, gender-reference detection is substantially easier than stance classification, especially for positive and neutral
speech. Lightly supervised classifiers match frontier zero-shot large
language models at substantially lower cost and outperform generic toxicity detection, while cross-country transfer remains limited. GENDIS provides a locally grounded resource for measuring when gender is invoked online and how that reference is framed.
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All authors Luise Koch, Manuel Tonneau, Niyati Malhotra, Sunny Rai, Ana Maria Munoz Boudet, Victor Orozco-Olvera, Samuel P. Fraiberger, Sharath Chandra Guntuku
Computation and Language
Beyond Belief Nesting: Rethinking Higher-Order Theory of Mind in Large Language Models
Shanshan You, Zhengqi Wang, Hao Liu et al. (+4 more)
Theory of mind (ToM), the ability to reason about others' mental states, underlies effective communication and coordination. Its higher-order form enables
reasoning over nested perspectives in strategic interactions. However, existing evaluations often equate belief-nesting depth with
reasoning order, allowing nominally higher-order questions to admit lower-order shortcuts. We formalize effective order through the distinct events supporting successive belief prefixes, revealing widespread order inflation across existing
benchmarks. We introduce HopToM, with certified full-order questions and controls for lower-order alternatives. Under direct prompting, mean accuracy across four frontier LLMs falls from 73.5% at first order to 20.0% at fourth order, and human participants also struggle. Causal interventions link a recurring class of errors to evidence for shallower belief prefixes. We introduce HopTrace, a plug-and-play inference-time method that encodes each interaction once into perspective records and resolves nested queries deterministically. On certified narrative questions, it improves fourth- and fifth-order accuracy by 22.0 and 38.5 percentage points over strong prompting baselines, while using 20-39 times fewer
tokens than multi-call methods. These results sharpen how higher-order ToM is evaluated and offer a practical way to improve perspective
reasoning in interactive
agents.
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All authors Shanshan You, Zhengqi Wang, Hao Liu, Yuqin Liu, Ya He, Xin Hao, Liyuan Wang
Graphics
TempFit: Panoptic Symbol Spotting via Template Fitting
Honghao Xu, Hao Wang, Xiang Liu et al. (+4 more)
In this paper, we introduce TempFit, a novel approach for the problem of Panoptic Symbol Spotting on CAD drawings. Existing methods treat symbol spotting as a per-primitive classification or mask-prediction task, with no explicit constraint enforcing that the primitives assigned to one instance form a spatially connected group. As a result, predictions may erroneously merge primitives from distant symbols into a single instance or fragment a coherent symbol across multiple predictions. Motivated by the professional "retrieve-and-transform" CAD design workflow, we reformulate this task as a Template Fitting problem, where each foreground instance is detected by retrieving and aligning a canonical template from a library, enforcing structural completeness by construction. Building on this formulation, we present an autoregressive
transformer that unifies template
retrieval and pose prediction within a single end-to-end pipeline. The framework treats the template library as an explicit
geometric memory, parsing background regions in parallel via learnable background
tokens and synthesizing foreground instances autoregressively while attending to template-specific geometry at each step. Experiments on FloorplanCAD and our newly collected IndustryCAD
dataset, a more challenging
benchmark with complex industrial symbols, heavy occlusion, and wide scale variation, demonstrate state-of-the-art performance with particularly large gains on IndustryCAD. Project page: https://tempfit-project.github.io/.
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All authors Honghao Xu, Hao Wang, Xiang Liu, Juzhan Xu, Manfred Lau, Kai Xu, Ruizhen Hu
Computation and Language
Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders
Xin Li, Hao Jiang, Jinghao Guo et al. (+2 more)
A recommendation request on Reddit draws several replies, each naming different titles. When an LLM recommender is trained on such threads with reinforcement learning (RL), which replies should a generated list be rewarded against? The state-of-the-art recipe, Rank-GRPO, picks one reply per training example, so the advantage mostly records which reply was drawn (85% of its variance) rather than how good the list is. We argue that RL should train on the request, not on each answer, and propose ReMaRk: it scores every list once against the union of all replies' titles and samples each request equally. This makes RL cheaper. On seven backbones and three
datasets, ReMaRk reaches the validation recall of 2,000 per-reply RL steps in 25 to 53% of the steps and leads at that budget; trained until neither improves, it is never significantly behind Rank-GRPO and in four of seven pairs reaches Rank-GRPO's best with fewer GPU-hours. The same reward helps on ProtoQA, where about 100 people answer each question, which points to a simple rule for RL with several valid human answers: reward them jointly.
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All authors Xin Li, Hao Jiang, Jinghao Guo, Weisi Lin, Chau Yuen
Information Retrieval
More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026
Kazuki Nakayashiki and Keisuke Watanabe
A model trained on readers' highlights can show a newcomer where earlier readers converged on a page, even on pages with few readers. A year of stronger
language models suggests two upgrades: use them directly, or use them as teachers. We compare both with a third, more reader data, on one social highlighting platform, with the comparisons, the selection rules and a sealed set of fresh pages fixed in advance. On 215 development pages, seven
language models released by October 2026 rank sentences zero-shot at an average precision of at most 0.3744 against the sentences half the readers marked most, and 0.3931 when fused with reading position, while a 3-billion-parameter model trained on reader highlights alone reaches 0.4346. Used as the teacher that breaks ties among readers' counts, the stronger fusion changes a trained model by +0.0013 [-0.0012, +0.0040], whereas going from half to all of its training pages adds +0.0242 [+0.0179, +0.0301]. A 27-billion-parameter backbone on the reader-only recipe adds +0.0225 on the development pages; on 131 fresh pages, read once and too few for a confirmatory comparison, its difference from the 3-billion model is unresolved, both score above the fusion, and in an exploratory split their lead over the previous model sits on pages from domains that their fresh training pages also cover. In this recipe, more reader data improves a trained model where a stronger tie-breaking teacher yields no material gain, and frontier models used as rankers still trail a small model trained on readers.
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