Artificial Intelligence · Cross-listed from physics.plasm-ph
Advantage-level Aggregation Reinforcement Learning for X-point Target Magnetic Configuration Control in an EXL-50U Experiment-Calibrated Simulation Environment
Siqi Ding, Xuanhe Wang, Pei Guo et al. (+10 more)
Managing divertor heat loads is a central challenge for compact, high-power tokamaks. To increase local flux expansion and decouple the dissipation volume from the core, EHL-2 adopts the X-point target (XPT) divertor. This requires the secondary X-point to remain on the divertor leg; displacement degrades the
topology and exhaust geometry. Current experiments, including EXL-50U discharges, rely on precomputed feedforward waveforms with PID loops on global quantities. Lacking dedicated closed-loop feedback for the secondary null, XPT operation is repeatable but not routine. We formulate XPT feedback as a multi-objective reinforcement learning (RL) control problem in a free-boundary environment calibrated to EXL-50U discharge #13906. To address strong coupling among
plasma current, shape, and null constraints - where reward scalarisation collapses objective-specific temporal credit - we develop Advantage Aggregation (AdvA). AdvA preserves objective-wise temporal credit before worst-objective-aware nonlinear scalarisation and introduces a residual correction to policy updates. AdvA-PPO is evaluated against Reward-PPO and a feedforward-plus-PID baseline under nominal operation, measurement uncertainties, and unseen initial
equilibria. On a 500 ms rollout, AdvA-PPO raises the mean worst-channel score from 0.23 to 0.81 over Reward-PPO, reducing X-point flux RMSE by ~20x. Under combined measurement uncertainties, it is the only learned controller completing the horizon while retaining a usable XPT shape. Multi-initialization fine-tuning enables a single AdvA-PPO policy to complete full-horizon operation across divertor and limiter initial
equilibria. These results provide a simulation-based foundation for future real-time XPT validation on EXL-50U.
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All authors Siqi Ding, Xuanhe Wang, Pei Guo, Guoyang Shi, Changquan Yu, Yiting Wang, Xianming Song, Xiang Gu, Zhengyuan Chen, Lei Xing, Yapeng Zhang, Jianguo Chen, Tianyuan Liu
Computation and Language
STAR-OPD: Structured Aspect-Cascade-Aware On-Policy Reward Distillation for ABSA Quadruple Extraction
Tong Sun, Mingyang Ma and Jiayang Yu
Aspect-based sentiment analysis (ABSA) quadruple extraction requires jointly predicting target, aspect, opinion, and sentiment over reviews that often contain multiple fine-grained sentiment tuples. While large chain-of-thought (CoT) models perform well on this task, distilling them into smaller deployable models remains difficult. We identify a task-specific failure mode in distilled ABSA extraction: student errors at the target-aspect interface create structurally invalid states, such as broken target-aspect bindings and hallucinated targets, which then corrupt downstream predictions. Conventional off-policy
distillation is poorly suited to this setting because it trains only on teacher-generated trajectories and provides little supervision on the student-induced structural states that dominate inference. To address this mismatch, we propose
STAR-OPD (STructured Aspect-cascade-aware On-Policy Reward
Distillation), which builds on generic on-policy
distillation and instantiates it for ABSA quadruple extraction with cascade-aware, set-structured rewards.
STAR-OPD trains on student rollouts and applies set-structured rewards that directly target binding consistency, target grounding, and fine-grained aspect disambiguation. Experiments on E-ABSA20K and SemEval-2014 show that
STAR-OPD consistently outperforms off-policy and general on-policy baselines, reduces target
hallucination, and substantially improves performance on structurally hard cases. With Qwen3-4B,
STAR-OPD substantially narrows the student-teacher gap while improving inference efficiency, highlighting the importance of on-policy structural correction for distilled ABSA extraction.
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Computers and Society
Fine-tuning LLMs for Tourist Trajectory Prediction using Field Experiment Data
Tatsuya Amano and Hirozumi Yamaguchi
Evaluating mobility interventions at tourist destinations requires predicting visitor behavior under varying conditions. Traditional methods struggle because tourist decisions depend heavily on context like weather and fatigue, yet models cannot generalize to unobserved scenarios. Large
Language Models offer a solution by encoding commonsense knowledge about human behavior from pretraining, enabling
reasoning about context-dependent decisions, while natural
language representation flexibly integrates heterogeneous information. Fine-tuning on local trajectories adapts this general understanding to destination-specific patterns. We validate this approach using 566 trajectories from Wakayama Castle Park, Japan. Our fine-tuned Llama-3.1-8B achieves 49.1% next POI accuracy and maintains strong performance on undersampled scenarios like rainy days, demonstrating effective generalization. This establishes LLMs as high-fidelity behavior models for context-dependent tourist prediction, providing groundwork for counterfactual analysis of mobility interventions.
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Human-Computer Interaction
The Belief Update Gate: Separating Inertia from Learning in Human-AI Interaction
Shreyan Biswas, Alexander Erlei and Ujwal Gadiraju
Repeated human-AI interaction is often analyzed through pooled belief-updating slopes: users observe AI successes and failures, revise reported beliefs in the feedback-consistent direction, but appear conservative on average. We show that such averages can obscure an important distinction between whether an elicited belief report changes at all and how it changes conditional on movement. We refer to this measurement-aware decomposition as the belief update gate. Reanalyzing a multi-task human-AI decision-making
dataset with 240 participants, 7,200 trials, and three task domains, we find substantial non-movement in reported beliefs: 67.3% of trial-level belief changes are exactly zero, and 76.4% are smaller than five percentage points. Separating non-moving from moving reports changes the descriptive interpretation of pooled conservatism: the within-trajectory slope rises from 0.494 overall to 0.949 among rows with nonzero movement. Since this latter estimate conditions on observed movement, we interpret it as a descriptive decomposition rather than as evidence of a near-
Bayesian latent learning process. Complementary hurdle style analyses (i.e., modeling zero vs. non-zero changes before predicting update magnitude) show that the absolute discrepancy between feedback and entering belief predicts whether a report changes, while the signed feedback discrepancy predicts the direction and magnitude of change among reports that move. Importantly, observed non-movement does not distinguish genuine latent belief inertia from small unexpressed updates, rounding, or other reporting processes. These findings show that calibration analyses of repeated human–AI interaction should distinguish visible non-movement in elicited belief reports from updating conditional on movement rather than treating reported beliefs as a single continuous updating process.
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Digital Libraries
The Legibility Gap: How Gender Equity Interventions Redistribute Recognition Across Cultures
Binglu Wang, Jose Cervantez, Jiahui Xue et al. (+2 more)
Efforts to promote gender equity in science increasingly rely on name-based inference to quantify representation and guide policy and behavior. Yet linguistic cues that signal gender vary across cultures and are often obscured when names are transliterated into English. Here we identify a pattern we call the "legibility gap": when gender is inferred from names, equity interventions systematically benefit women whose names signal gender while bypassing those whose names lose such cues in translation. Using both observational and experimental evidence, we show how this gap reshapes recognition in science. Analyzing citation diversity statements-an emerging practice in which authors report the algorithmically estimated gender composition of their reference lists-we find that papers that include this practice cite women more frequently, but the gains accrue almost entirely to authors with gender-signaling Western names. By contrast, women whose names lose gender cues in English transliteration, predominantly those with East Asian names, receive fewer citations in these same papers. Two preregistered experiments (N = 2,250) corroborate this pattern and identify its mechanism: linguistic legibility, not cultural unfamiliarity, determines who is recognized as a woman and who benefits from policies designed to support women in science. Overall, these findings expose a previously unrecognized layer of inequity embedded in global equity infrastructures. As science becomes increasingly global and equity efforts increasingly algorithmic, the legibility gap reveals how uneven identity recognition reshapes
fairness. In global systems of recognition, equity depends not only on whether policies are effective on average, but also on whether they are equitable across cultures.
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All authors Binglu Wang, Jose Cervantez, Jiahui Xue, Katherine L. Milkman, Dashun Wang
Artificial Intelligence
Prediction certification cannot replace explanation certification: a competence envelope for trustworthy AI under compound stress
Nataliya Shakhovska, Ivan Izonin and Stergios-Aristoteles Mitoulis
Artificial intelligence systems increasingly make consequential judgments - which patient is deteriorating, which building is safe to enter, whether an image is authentic and are trusted on the strength of how accurately and confidently they predict. The safeguards that certify them are correspondingly prediction-based: accuracy, calibration and conformal coverage all measure how well a model performs. Whether such checks are sufficient to establish model trustworthiness has remained unclear. Here we prove that they cannot. We establish a separation
theorem showing that a reliable model and a compromised one can be identical under every prediction-side certificate, including accuracy, calibration and coverage, yet differ arbitrarily in explanation fidelity and deployment behaviour. Detecting this failure requires access to the model's decision mechanism in addition to its predictions. We introduce the competence envelope as an operational framework that combines prediction and explanation certification into a single deployable criterion. Across diverse
datasets and model classes, the proposed framework reveals failure modes that prediction-side certification alone does not capture. Certification against failures that are invisible in prediction behaviour therefore requires evidence about the model's decision mechanism as well as its outputs.
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Robotics
Natural Sit-to-Stand Motion Synthesis For Humanoids via Guided Assistance Curricula and Staged Rewards
Meet Pal Singh, Vyankatesh Ashtekar and Ashish Dutta
A
humanoid has infinitely many ways to stand up from sitting while maintaining balance, making sit-to-stand (STS) a challenging control problem. We synthesise natural
humanoid STS motion from scratch using reinforcement learning, without demonstrations or reference trajectories. A single Proximal Policy Optimisation policy learns smooth, human-like rising driven by three complementary components. (i) A coupled force/chair-height curriculum is used. A vertical pelvis-assist force aids early trajectory exploration and decays over training. Taller chairs are unlocked with decaying assisting force. This ensures that the policy masters a viable STS trajectory at each chair height before being exposed to harder ones, avoiding the premature distribution shift that otherwise collapses generalisation. (ii) Motion robustness is achieved by randomly sampling from a large number of inverse kinematics-generated initial and target poses spanning over eight chair heights. (iii) A set of rewards is defined inspired from biomechanics and optimal control studies. They shape the
robot's angular momentum for seat-off, and enable support-region transition via centre of pressure attraction function to ensure smooth low-effort actuation. On a deterministic force-free evaluator, the policy attains more than 97% balanced-standing success across eight chair heights. The policy generalises smooth motion across chair heights and enables the
robot to rise from substantially deep-seated postures as compared to the state of the art.
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Computers and Society
Interaction Effects Between Learner Characteristics and Dialogue Format in LLM- and TTS-Generated Dialogue-Based Lessons
Fumie Watanabe, Gendo Kumoi, Tota Suko et al. (+4 more)
Large
language models and text-to-
speech technology enable low-cost production of dialogue-based lessons:
videos in which two speakers converse. The speakers' roles can vary, but little is known about whether all learners respond alike to such differences. In a repeated-measures design, 222 first-year high school students viewed lessons in three formats: a teacher and a student (TS), two students (SS), and two teachers (TT). Within the aptitude-treatment interaction framework, linear mixed-effects models tested whether associations between learner characteristics and outcomes differed by format. The learner characteristics were experiential learning style - seeking challenging, new experiences (CE factor) and learning by reflecting on experience (RCE factor) - and critical thinking disposition; the outcomes were motivation based on the ARCS model, self-reported learning outcomes, and overall evaluation. For motivation, both factors interacted with format: the CE factor's association differed between TS and TT, and the RCE factor's slope was significantly positive in TS and SS and significantly larger in TS than TT. For learning outcomes, no interaction was detected, but critical thinking disposition was positively associated with them. For overall evaluation, again no interaction was detected, but TT was rated lowest. Given small-to-medium interaction effects, these findings are preliminary evidence to inform personalized learning design.
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All authors Fumie Watanabe, Gendo Kumoi, Tota Suko, Takashi Ishida, Yuko Kuma, Manabu Kobayashi, Shigeichi Hirasawa
Artificial Intelligence
Certified Multi-Turn Robustness for LLM Safety via Compositional Bounds and Safety Persistence
Yang Liu, Bin Chong, Wenkai Yang et al. (+9 more)
Large
language models (LLMs) are vulnerable to multi-turn jailbreak attacks that progressively manipulate conversation context. Existing certified robustness methods are limited to single-turn inputs; naive multi-turn composition yields bounds that degrade exponentially in the number of turns. We introduce Multi-Turn Certified Robustness (MTCR), a framework that models conversational safety via State-
Adversarial MDPs and defines $k$-turn certified robustness as the worst-case safety probability across $k$
adversarial turns. MTCR comprises: (i) compositional certification via
embedding-space mode decomposition, yielding tighter certified lower bounds than naive multiplication; (ii) $(\alpha,\beta)$-safety persistence, improving the degradation rate from $\underline{p}^{k}$ to $\beta^k$ (with $\beta > \underline{p}$) and yielding interpretable horizon estimates; (iii) matching information-theoretic upper bounds establishing tightness; and (iv) a unified
algorithm combining these results. Experiments on six LLMs under $\epsilon$-bounded and Crescendo-style attacks confirm that empirical safety consistently exceeds the certified bounds.
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All authors Yang Liu, Bin Chong, Wenkai Yang, Shuai Zhang, Yancheng Chen, Feiyu Han, GuoZhen, Cheng Zhang, Huaibing Xie, Changze Lv, Shihan Dou, Pluto Zhou
Distributed Computing
PRICE: Pricing-based Resource Incentives for Quality-of-Result-aware Computing at the Edge
Uwe Gropengießer, Sebastian Frenz and Max Mühlhäuser
Edge nodes are capacity-constrained by design, yet many edge workloads can trade result quality for resource efficiency at runtime. Existing edge pricing mechanisms largely treat requests as fixed-configuration submissions and rarely exploit per-request quality flexibility under overload. We present PRICE, an incentive mechanism that couples a utilization-dependent price signal to per-request quality selection. As utilization increases, rising acceptance prices make resource-intensive variants less likely to be selected, shifting accepted requests toward lighter execution and allowing the node to serve significantly more requests while operating near capacity. Evaluation on real hardware under sustained overload shows that PRICE outperforms both fixed-allocation and dynamic-pricing baselines in accepted throughput and CPU utilization. The results are robust across pricing function families, task-duration distributions, and client populations. Result-quality flexibility is a powerful but underused control dimension for overload management at the edge, and pricing is an effective mechanism to exploit it.
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Machine Learning
Chenghao Li, Xiao Han, Xinxin Huang et al. (+22 more)
The
matrix-aware optimizer
Muon improves large model training by balancing updates across singular directions, yet its scaling behavior and end-to-end efficiency on large
Diffusion Transformers (DiTs) remain unclear. We first establish
Muon's scaling behavior on DiTs from 1.3B to 15B parameters, showing that its optimization and generative quality advantages over AdamW persist across model scales. However, at scale, the 5-step Newton–Schulz iteration (NS5) performed at every optimization step, together with full-momentum materialization, introduces substantial computation and communication overhead that can offset
Muon's step-efficiency advantage. We introduce Periodic Row-wise
Muon, which performs a full NS5 spectral update once every \(K\) steps and applies a low compute and communication cost row-wise constrained update based on the current momentum at the remaining steps. We further co-design a distributed implementation that operates directly on sharded momentum during non-refresh steps and accelerates spectral refreshes through bucketed all-gather and communication–computation overlap. Across all scales,
Muon improves the best observed generative quality over AdamW by 12.9–19.1%. Compared with vanilla
Muon, Periodic Row-wise
Muon remains within 0.5% in best generative quality on the 1.3B–4B models and improves it by 4.5% at 9B. It reduces optimizer time by 46.9–54.3%, end-to-end step time by 15.7–24.3%, and logical communication volume by 66.7%, while reaching its respective best generative quality with 33.7–64.8% less active training time. These results show that Periodic Row-wise
Muon preserves
Muon's generative quality advantage while translating it into end-to-end training efficiency for large DiTs.
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All authors Chenghao Li, Xiao Han, Xinxin Huang, Wei Liu, Boyang Li, Bing Xiao, Heran Zhang, Juanma Perez Rua, Ke Xu, Kangning Liu, Linjun Kuang, Na Li, Tan Wang, Tian Xie, Wei Peng, Yang Pei, Yifan Xu, Yuanhao Zhai, Yuwei Lin, Zhe Wang, Zihao He, Daniel Li, Junbiao Tang, Ziyang Jiang, Dake Chen
Cryptography and Security
GhostTac: Manipulating Tactile Sensors without Physical Contact
Kun Wang, Xuancun Lu, Ruochen Zhou et al. (+6 more)
Tactile sensors are integral components of modern robotic systems, enabling
robots to perceive and interact with the physical environment through tactile feedback. Despite their importance, the physical-layer security of tactile sensors has received little
attention in prior work. In this paper, we present GhostTac, to the best of our knowledge, the first contactless attack that manipulates tactile sensing via electromagnetic interference (EMI). We identify that EMI exploits the nonlinear rectification and limited bandwidth amplification effects, allowing carefully crafted EMI signals to be converted into a persistent DC offset that bypasses on-board filtering and induces stable measurement deviations. Building on this mechanism, GhostTac enables fine-grained and controllable manipulation of sensor outputs by reshaping the spatial distribution and manipulating the magnitude at the targeted location. Such interference can induce unintended and harmful
robot behaviors, such as causing a domestic
robot to exert excessive force, resulting in physical damage or human injury. We evaluate GhostTac on 10 sensor modules and 2 dexterous hands, covering 15 tactile sensors of different types, and demonstrate consistent attack effectiveness across all tested devices. We further present three case studies on tactile grasping, slip detection, and material classification to illustrate practical impacts in real robotic tasks. We envision that our findings shed light on a new physical attack vector against tactile sensing in robotic systems.
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All authors Kun Wang, Xuancun Lu, Ruochen Zhou, Kai Wang, Tongjun Ye, Yihao Shao, Chen Yan, Xiaoyu Ji, Wenyuan Xu
Computer Vision
Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision
Beibei Zhang, Chao Xu, Jun Lan et al. (+4 more)
Though
Multimodal Large
Language Models (MLLMs) have shown impressive potential in
video understanding, long
video understanding (LVU) remains challenging since distracting noise in complex and lengthy contexts can obscure localized details, misleading MLLMs to produce incorrect answers. Recent works mitigate these issues by incentivizing deep
reasoning to include relevant evidence. However, these methods have two main problems: First, the reinforcement fine-tuning framework (RFT) they leveraged incurs substantial training overheads, including high annotation costs and complicated reward designs. Second, the self-reflective and iterative-perception mechanism in some methods causes lengthy outputs and high inference latency. To alleviate these problems, we propose a novel Segment-to-
Video Supervision} method (S2V) to efficiently enhance fine-grained
reasoning in LVU. Specifically, we generate question answer pairs (VQA) based on localized segments, and then transfer these segment-based VQA back to the whole
video for training. Due to focusing on short segments, segment-based VQA can naturally notice details which tend to be overlooked from a whole-
video perspective. Training on such data can enforce MLLMs to correctly associate fine-grained details with QA while avoiding distracting noise in the whole
video. The S2V training involves just reinforcement learning (RL) with a simple accuracy reward based on only 10K VQA samples and the resulting S2V model predicts answer using a single forward pass with limited output
tokens. Experimental results demonstrate that S2V can consistently improve LVU performance across multiple LVU
benchmarks, outperforming both general MLLMs and
reasoning-based methods not only in LVU accuracy but also in training and inference efficiency.
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All authors Beibei Zhang, Chao Xu, Jun Lan, Zongyi Li, Lai Wei, Huijia Zhu, Tongwei Ren
Machine Learning
Resolution-Consistent Greedy Neural Approximation on Infinite-Dimensional Spaces
Pablo M. Berná, Antonio Falcó and Diego Mondéjar
We develop constructive approximation and learning guarantees for shallow neural models with infinite-dimensional inputs observed through finitely many coordinates. The analysis is based on a parameter-normalized neural dictionary and its associated weighted variation class. Within this class, the approximation error separates into a distribution-dependent coordinate-truncation term and a greedy finite-width term. For empirical regression, a fully-corrective greedy procedure yields population guarantees whose statistical complexity is uniform in the retained input resolution. The same framework extends to
Hilbert-valued responses without an explicit dependence on the output dimension. The dimension-free statements are statistical, not computational: selecting a new
neuron still requires solving a nonconvex parameter-search problem. The quasi-Polish construction underlying recent infinite-dimensional universal approximation results provides a motivating example, and synthetic experiments illustrate the predicted resolution, width, and sample-size regimes.
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Multimedia
When Images Look Right and Retrieve Wrong: Coverage-Guided Cross-Scale Re-Indexing for Knowledge-Faithful Generative Perception
Guangyuan Dong, Chuang Liu, Haoyu Wang et al. (+10 more)
Multimodal information systems increasingly route generated visual content back through the same
vision-
language index that informed its production, so the output must remain retrievable by the queries it was meant to serve. When the scene contains entities at vastly different scales, existing
language-guided generators condition on a single, globally pooled text
embedding and quietly drop scale-specific concepts, breaking concept-query
retrieval even when pixel fidelity is high. We formalise this failure as semantic collapse and propose CERES, a closed-loop
multimodal indexing framework that builds a three-level semantic pyramid, mines implicit concepts via a co-occurrence-aware router, performs scale-routed cross-
attention into a lightweight U-Net generator, and verifies coverage by re-indexing the generated image with the same frozen VLM. A continuously differentiable soft-Jaccard coverage objective returns dense gradients to the 0.39 M-parameter generator under explicit non-degeneracy conditions, and coverage is verified by an independent DINOv2 linear probe trained only on external scene and object labels. On four pansharpening
benchmarks across seven settings, CERES delivers the new state of the art with the largest gains where scale variation is most extreme (+4.64% relative Q2n and +9.7 mAP for DOTA detection). It also improves concept-query
retrieval Recall@5 by +14.0 points and image-text mean reciprocal rank by 0.19 over the strongest baseline, showing that the closed loop preserves queryable content rather than self-referential feature consistency.
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All authors Guangyuan Dong, Chuang Liu, Haoyu Wang, Yangchen Zeng, Jiaqi Zhang, Li Jiuxing, Xiaoyang Yu, Pinlong Zhao, Yuchao Hou, Ziwei Li, Zheng Lin, Alexander Lim Han Yang, Yusen Wu
Computer Vision
TRACE: Training-time Report-guided and Clinically Ordered Concept Editing
Wentao Yue, Tianyou Lai, Jiayu Luo et al. (+4 more)
Breast ultrasound diagnosis relies on clinically meaningful semantic concepts, yet most deep learning methods adopt end-to-end image-to-label paradigms that lack interpretability and robustness. While concept-based approaches offer a promising alternative, they often assume complete annotations or require
multimodal inputs at inference, which significantly limits their real-world applicability. To tackle these issues, we propose Training-time Report-guided and Clinically Ordered Concept Editing (TRACE), a training-time report-guided framework that leverages structured radiology reports as privileged concept supervision while enabling image-only diagnosis at test time. TRACE refines image-derived concepts through a teacher-guided editing mechanism within a malignancy-aware ordered concept space. To address incomplete annotations, we introduce Strategic Concept Missing Training (SCMT) and train an image-only self-editor via edit
distillation for autonomous concept refinement. Besides, we introduce BUSC, a concept-enriched
benchmark linking images, labels, and structured attributes. Experiments across multiple
datasets demonstrate that TRACE achieves superior performance and improved cross-domain robustness compared to existing methods.
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All authors Wentao Yue, Tianyou Lai, Jiayu Luo, Qingyu Mao, Ziying Wang, Zhenyuan Ning, Qilei Li
Artificial Intelligence
Neuro-Geospatial Modelling of EEG Affective States Using Literature-Informed Environmental Context
Utsav Poudel, Jagannath Aryal and Subramaniyaswamy Vairavasundaram
Environmental exposures such as air pollution and greenness have been associated with affective and cognitive outcomes, but
EEG and environmental
datasets are rarely jointly georeferenced. We investigate whether literature-informed environmental priors can serve as an auxiliary geospatial modality for
EEG-based affective-state classification when individual-level exposure data are unavailable. We combine 30-channel
EEG from the EAV
benchmark (42 participants, aged 20-30 years) with environmental representations derived from OpenAQ, Sentinel-2, Sentinel-5P, and OpenStreetMap data for Astana. A dual-tower architecture combines
EEG-Conformer representations with a graph-based environmental encoder. Because the
datasets are not co-registered, environmental context is treated as a literature-informed prior rather than measured exposure. Subject-level repeated splits, permutation and label-shuffling controls, dose-response reversal, and domain-shift experiments distinguish architecture-level gains from prior-dependent gains. The
multimodal model achieves 76.2% accuracy versus 67.4% for
EEG alone. Controls disrupting environmental-label structure retain part of this gain, indicating that the improvement is not attributable solely to environmental information. Replacing the Astana environmental distribution with an independently modeled Singapore distribution reduces accuracy to 72.8%. These findings demonstrate technical feasibility but do not establish an observed or causal exposure-affect association. The study provides a framework for future jointly collected mobile
EEG-environment studies. Implementation: https://github.com/r11up/geo-cog
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Computer Vision
Routing Before Looking: Query-Adaptive Evidence Acquisition for Long-form Video Understanding
Tianyue Wang, Xuying Wu, Yuxiang Ma et al. (+7 more)
Long-form
video understanding remains challenging for
video agents due to the mismatch between query demands and evidence acquisition strategies. Although recent planning-before-perception methods outperform query-agnostic pipelines, they often rely on a single dominant strategy, either generation-based strategy or
retrieval-based strategy, limiting their ability to handle diverse query demands. We propose Route2Look, a lightweight and model-agnostic framework for query-adaptive evidence acquisition in long-form
video understanding. Route2Look operates in a Route-Look-Memorize loop with three tools: Global Browse for holistic context, Temporal Ground for explicit temporal cues, and Semantic Retrieve for semantic search. The core component is a routing policy that dynamically selects evidence acquisition tools based on the query. To build this policy, Route2Look adopts a two-stage design: first distilling the routing skill from differential contrastive analysis between generation-based and
retrieval-based trajectories, and then applying the distilled skill with hard routing rules and continue-or-stop criteria during inference. Experiments on challenging long-
video benchmarks show that Route2Look achieves state-of-the-art performance while maintaining strong frame efficiency across
datasets and query types. Oracle routing analysis further reveals the potential of query-adaptive evidence acquisition for future long-form
video understanding.
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All authors Tianyue Wang, Xuying Wu, Yuxiang Ma, Ruiming Liang, Jiaxuan Kang, Yanchao Hao, Zheng Wei, Leigang Qu, Haiyun Guo, Jinqiao Wang
Computation and Language
Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation
Yanglei Gan, Peng He, Run Lin et al. (+3 more)
Temporal Knowledge Graph (TKG) extrapolation seeks to infer future facts from time-varying relational histories. Recent
diffusion-based approaches improve uncertainty modeling through generative denoising, but their aggregated conditioning on subject histories may insufficiently distinguish query-specific evidence from non-salient historical facts, thereby diluting target-discriminative signals. To bridge this gap, we propose FreqDiff, a Frequency-aware
Diffusion framework for TKG extrapolation. Specifically, FreqDiff formulates future object prediction as query-slot denoising and develops a dual-stream denoiser that integrates temporal dependency modeling with context-aware spectral calibration. The spectral branch synthesizes history-conditioned filters from learnable bases to adaptively re-calibrate denoising representations, while a frequency-domain regularizer is proposed to align the denoised target with the gold object in spectral space. Experiments on four public TKG
benchmarks demonstrate that FreqDiff achieves state-of-the-art performance.
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All authors Yanglei Gan, Peng He, Run Lin, Peiyuan Jiang, Yifan Wang, Qiao Liu
Graphics
CubicSplat: Differentiable Vector Graphics via Error-Bounded Forward Relaxation
Chenglong Liu, Xin Zhang, Yimeng Zhu et al. (+5 more)
Vector graphics are prized for their resolution independence, compact storage, and direct editability, making differentiable optimization of their parametric primitives an attractive goal. Yet classical rasterization is discontinuous with respect to geometry, and existing remedies that smooth the forward pass demand increasingly elaborate heuristics as scene complexity grows. We trace this fragility to a gradient seesaw: design choices that improve forward
geometric exactness can systematically degrade the induced gradient signal, and vice versa. To navigate this tension we introduce CubicSplat, a differentiable vector rasterizer that replaces Bézier closest-point solvers with uniform polyline surrogates whose
geometric error is bounded at $O(S^{-2})$. The resulting static computation graph yields well-conditioned gradients by construction, while a compositing-derived visibility mechanism prunes degenerate primitives without auxiliary regularization. On DIV2K and Kodak
benchmarks CubicSplat achieves state-of-the-art reconstruction quality with over 2 dB PSNR gain in the closed-fill setting, while training up to 4x faster than prior methods. The code is available at https://github.com/CubicSplat/repo
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All authors Chenglong Liu, Xin Zhang, Yimeng Zhu, Liyang He, Yixiao Ma, Yu Su, Zhenya Huang, Qi Liu
Artificial Intelligence
SPARC: Single-Pass Scaling for Motion Forecasting with Conformal Bayesian Last Layers
Sakif Hossain, Julian Teusch and Jörg P. Müller
Human motion forecasters are increasingly accurate and fast, but reliable deployment requires uncertainty estimates that are structured, calibrated, and efficient.
Bayesian and ensemble-based uncertainty estimates often require repeated
stochastic inference [15, 26], while conformal calibration alone does not provide an epistemic signal or preserve trajectory covariance structure [14, 50]. We introduce SPARC (Single-Pass Adaptive Risk Calibration), a
Bayesian-conformal uncertainty layer for motion
forecasting. A deterministic MLP backbone predicts the future mean, and a conjugate
Bayesian last layer converts time-domain feature leverage into an analytic horizon-wise epistemic scale $\kappa_t(x)$. This scale inflates a graph-temporal Gaussian covariance without changing its correlation structure, and split conformal calibration produces 95% marginal prediction tubes with finite-sample validity under exchangeability. The key interface is the structured factorization $\kappa_t(x)\Sigma_{\mathrm{str},t}(x)$, which injects feature-space epistemic uncertainty into trajectory densities without Monte Carlo sampling. Across nine
dataset-protocol blocks and deterministic,
multimodal, and calibration baselines, SPARC ranks first on NLL and on the combined MPJPE+NLL criterion while retaining competitive point accuracy and efficient calibrated tubes. Ranking windows by $\kappa$ separates high-error cases, making the scale usable as a lightweight risk monitor.
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Information Retrieval
Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders
Dojun Hwang, Seunghan Lee, Cheonyoung Park et al. (+2 more)
While Large
Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains challenging. Real-world items are described by vast, heterogeneous, and unstructured metadata, where decision-relevant signals are often implicit, noisy, or buried in long descriptions. Moreover, feature salience is highly context-dependent, varying not only across items but also across users. Existing methods often rely on item titles, fixed attributes, or static item summaries, which limit personalized and fine-grained item understanding. To bridge this gap, we propose CAIRO, a user context-aware item profiling framework for LLM-based reranking. CAIRO first structures raw metadata and reviews into objective features and subjective traits, and employs a lightweight profiler to select the most relevant information for each user-item pair with limited serving-time overhead. The resulting profiles are concise and context-specific, providing relevant item-side evidence for the LLM's ranking decision. Experiments show that CAIRO consistently improves LLM-based reranking, highlighting the importance of item profiling that effectively exploits vast item-side information.
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All authors Dojun Hwang, Seunghan Lee, Cheonyoung Park, Sara Yu, SeongKu Kang
Artificial Intelligence
Dynamic Context Scheduling: Learning Beyond the Static Universe
Martin Mráz and André Biedenkapp
We study dynamic context scheduling as a training instrument for contextual re- inforcement learning. Rather than treating intra-episode context variation as a deployment reality, we treat it as a controlled shaping mechanism. Thereby, context evolves within each training episode according to a predetermined schedule, expos- ing the policy to a richer and more temporally structured region of the environment parameter space. We introduce DYNAMICCARLENV, a framework that wraps contextual environments with pluggable schedule families, such as sinusoidal off- sets or cosine annealing. Across CartPole, BipedalWalker and VehicleRacing with CARL contextualization, we show that dynamic schedules match or outperform static context baselines in the out-of-distribution (OOD) regimes. Interestingly, for the more complex BipedalWalker and VehicleRacing environments we also achieve higher in-distribution (ID) evaluation performance. Preliminary findings indicate that automatic search for multi-stage curricula can successfully discover schedules that improve generalization, performing comparably to extensive grid search over single-stage schedulers.
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Cryptography and Security
Beyond Explicit Generators: Distribution-Free Linear-Decomposition Attacks on Public-Key Encryption
Ziyan Chen and Ding-Xuan Zhou
Linear-decomposition attacks can break public-key schemes without recovering the secret algebraic action: when a target public state lies in a known linear span, its decomposition coefficients transfer through the unknown action to reveal the shared value. We study a setting in which the adversary uses only the public sampling-and-evaluation oracle available to honest participants, the induced distribution is arbitrary, and the goal is to attack future ciphertexts rather than recover the full algebraic span. We model public paired samples under a fixed secret linear transport and define the sampled-orbit dimension as the effective dimension of the encryption distribution. We prove distribution-free one-shot recovery, a high-probability certificate for the future-ciphertext coverage of a sampled span, and the optimal sampled-span complexity $m^\star_{\mathrm{span}}(r,\varepsilon,\delta) =\Theta((r+\log(1/\delta))/\varepsilon)$. These results yield a generic impossibility
theorem: publicly samplable linear key transport with
polynomial sampled-orbit dimension is incompatible with IND–CPA security when the transported value determines the decryption payload. We apply the framework to the 2024 probabilistic PKE from twisted–skew group rings. Its underlying Computational Twisted–Skew Problem admits a sampler-only linear attack using independently generated public protocol samples, yielding plaintext recovery and constant IND–CPA advantage. Experiments verify the linear transport and end-to-end recovery, and show that high future-ciphertext coverage may precede recovery of the full algebraic span.
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Artificial Intelligence
Automated Trajectory Evaluation for Mobile Agents via Step-Level Consequence Reasoning and Aggregation
Pengshuai Yang, Zijing Gao, Xue Yu et al. (+3 more)
Evaluating
language-guided mobile
agents has recently shifted from rule-based to model-based approaches to achieve scalable and automated assessments. However, existing holistic evaluation paradigms process entire trajectories at once, leading to substantial context overload. Moreover, they primarily focus on task completion while overlooking operational safety. To address these limitations, we introduce CRATE, a novel two-stage VLM-as-judge framework for automated mobile
agent evaluation that is compatible with both open- and closed-source models. Leveraging a step-level consequence
reasoning mechanism, CRATE independently extracts task-relevant visual clues and infers action-conditioned state changes at each step. The resulting step-level textual evidence is then synthesized through trajectory-level aggregation to deliver an evidence-grounded evaluation of task completion. Building upon this evaluation scheme, we further extend CRATE to CRATE-S for operational safety assessment. Extensive experiments validate the effectiveness and robustness of both CRATE and CRATE-S. Powered by Qwen2.5-VL-72B-Instruct, CRATE achieves an F1-score of 0.833 on AndroidWorld (outperforming SPA-Bench by 20%), while CRATE-S reaches an F1-score of 0.697 on MobileRisk, demonstrating strong
alignment with
benchmark ground truths. Code is available at https://anonymous.4open.science/r/CRATE-D580.
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All authors Pengshuai Yang, Zijing Gao, Xue Yu, Benhui Zhuang, Bo Yuan, Junlan Feng
Systems and Control
A Study on THz Plasmonics in a CMOS Continuum Transistor Array
Hossein Naghavi, Shruti Chakraborty, Zainulabideen Khalifa et al. (+6 more)
This work addresses the limitations of CMOS at terahertz (THz) frequencies, where charge transit time and parasitic capacitances restrict the maximum operating frequency, fmax. As transistor dimensions shrink, reduced current handling capabilities further challenge CMOS, necessitating novel
circuit design approaches for the THz domain. By leveraging the
plasma characteristics of electron channels in CMOS transistors, this study explores a potential solution for THz signal amplification. Key mechanisms in
plasma wave amplification within a continuum transistor array (CTA) formed by 28 nm fully depleted silicon-on-
insulator (FD-SOI) CMOS transistors are investigated. A hydrodynamic transport model combined with Pierce's theory is presented to describe
plasma wave propagation along the CTA. Simulations of gated amplifiers demonstrate the potential for THz signal amplification in advanced fabrication nodes. Finally, a proof-of-concept
plasma wave amplifier operating at 700 GHz has been designed and fabricated, exhibiting amplification along the
plasma wave propagation path.
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All authors Hossein Naghavi, Shruti Chakraborty, Zainulabideen Khalifa, James Gruber, Aditya Varma Muppala, Chen Shen, Denil Sabu, Andreia Cathelin, Ehsan Afshari
Artificial Intelligence
Knowing but Not Saying: Preventing Factual Access Failures in LLM SFT via Recall-Anchored Distillation
Haodong Chen, Yadong Wang, Shengtao Wen et al. (+2 more)
Supervised fine-tuning (SFT) can degrade factual behavior outside the target domain. This degradation is often described as catastrophic forgetting, yet open-ended factual failures do not necessarily imply that the underlying facts have been erased. In this work, we identify a more specific phenomenon, factual access failure: after domain SFT, models can still recognize or rank the correct answer under constrained evaluation, while failing to produce it in closed-book generation. Through
benchmark-level comparisons, same-fact multiple-choice and generation probes, and failure-mode analysis, we show that SFT-induced factual degradation reflects both genuine wrong-answer generations and expression-level failures such as verbosity, formatting mismatch, and exact-match artifacts. To address this problem, we introduce Recall-Anchored
Distillation (RAD), a base-anchored self-
distillation objective that preserves out-of-distribution generation behavior by aligning the adapted model with the original base model's soft continuation distribution on unlabeled OOD text. RAD requires no gold OOD answers, external judges, or labeled factual data. Across three backbones fine-tuned on MedMCQA, RAD recovers a consistent portion of the lost OOD recall while preserving target-domain adaptation. Compared with replay on the same OOD text, RAD shows that the key preservation signal is the base model's soft distribution rather than additional text exposure alone.
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All authors Haodong Chen, Yadong Wang, Shengtao Wen, Dong Liang, Xiang Chen
Computer Vision
CertVLA: Certified Defense against Physical Visual Attacks for Vision-Language-Action Models
Hui Lu, Zhijie Peng, Yuqi Lin et al. (+8 more)
Vision-
Language-Action (VLA) policies are vulnerable to localized physical perturbations, yet existing certified patch defenses target discrete labels and cannot directly certify continuous, temporally correlated actions. We introduce CertVLA, a certified defense for closed-loop VLA control under bounded patch and texture attacks. CertVLA proposes a calibrated region of behaviorally consistent actions, while deterministic covering masks ensure that at least one checked prediction is attack-free. Specifically, CertVLA normalizes action disagreement by the benign variation of each mask pair and accepts a single-mask anchor only when it remains consistent under every second mask. It then calibrates the resulting max-min-max episode score to provide finite-sample clean coverage. Conjoining query-level decisions extends the action certificate to the complete closed-loop rollout. Furthermore, we prove that against any adaptive attacker satisfying the bounded-support threat model, every rollout certified by CertVLA executes only action chunks consistent with attack-erased clean predictions. Under dual-mask rollout correctness, this consistency certificate further guarantees task success. The certificate is independent of patch content, generation method, and physical transformation. Experiments in simulation and the real world demonstrate the empirical and certified effectiveness of CertVLA against patch attacks, with additional simulation validation on texture attacks.
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All authors Hui Lu, Zhijie Peng, Yuqi Lin, Zaijia Yang, Jiaming He, Shuhan Ye, Yi Yu, Hanwei Zhu, Bingquan Shen, Alex Kot, Xudong Jiang
Human-Computer Interaction
Chat First, Worry Later: Understanding Individuals' Privacy Perceptions Using ChatGPT in a Work Context
Christoph Nirschl, Magdalena Glas, Gerhard Messmann et al. (+1 more)
Generative Artificial Intelligence (GenAI) tools like ChatGPT, which can generate human-like responses from vast amounts of textual data, are increasingly transforming work routines across various fields, including education, healthcare, and IT. This integration, however, raises
privacy concerns and questions the readiness of both environments and individuals. To investigate this issue, we conducted a user study with $N=224$ participants from a range of different employment sectors that have integrated ChatGPT into their work routines. We examined how proficiency in the utilization of ChatGPT, general
privacy concerns, and organizational policies for GenAI usage impact users' actual ChatGPT usage and how these factors interact. Our findings reveal organizational policies are significantly positively associated with
privacy-related ChatGPT proficiency, however, the overall proficiency is low. Higher
privacy concerns were found to negatively influence both the frequency of ChatGPT use and the diversity of its applications, especially among users in organizations without GenAI policies.
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All authors Christoph Nirschl, Magdalena Glas, Gerhard Messmann, Günther Pernul
Computer Vision
M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo
Byeonggwon Lee, Sanggi Lee, Siwoo Lee et al. (+2 more)
Deep learning-based Multi-View Stereo (MVS) has advanced significantly but often generalizes poorly to unseen scenes, particularly in occluded areas or regions with limited view overlap. To mitigate this, recent approaches integrate Depth Foundation Models (DFMs) into MVS pipelines to provide
monocular depth priors. However, existing methods typically rely on a static, one-way fusion scheme, which fails to fully exploit the complementary strengths of both modalities. We propose a novel framework that overcomes this limitation by tightly coupling a DFM with a cascade MVS pipeline through a bidirectional mutual refinement strategy. Our method leverages MVS depth to resolve the scale ambiguity in
monocular predictions, while the
monocular depth, in turn, enhances the structural completeness and fine-grained detail of the MVS estimate. Furthermore, we introduce a prior-guided cost volume refinement mechanism that effectively integrates multi-view and
monocular information via
attention-based fusion and discretized depth bins, thereby promoting local
geometric consistency. Extensive experiments demonstrate that our method outperforms state-of-the-art MVS approaches on standard
benchmarks, producing more complete and generalizable depth maps with sharp boundaries. Furthermore, although not explicitly designed for sparse-view settings, our framework generalizes remarkably well, competing favorably with even dedicated sparse-view methods while maintaining a superior accuracy-efficiency trade-off.
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All authors Byeonggwon Lee, Sanggi Lee, Siwoo Lee, Khang Truong Giang, Soohwan Song