Computer Vision
Relevance Does Not Imply Applicability: Experience Activation for Personal GUI Agents
Fuyao Zhang, Xuan Wang, Zherui Li et al. (+3 more)
Personal Graphical User Interface (GUI)
agents rely on interaction history to infer what a user wants from ambiguous instructions and to anticipate recurring routines. Existing approaches retrieve task-relevant history and append it to the policy's context, implicitly assuming that experience relevant to a task remains useful for each decision within it. We find that this help is largely spent at the first decision: retrieved history strongly improves the opening step of an episode, yet provides little sustained benefit over the remaining 90% of steps, and offers weak guidance on whether a proactive suggestion is warranted. A relevant record may tell the
agent where to begin, but not which past action applies to the current screen or whether a routine is due now. The underlying issue is that relevance does not imply applicability}: relevance is determined at the task level, whereas applicability depends on the situation at decision time. We therefore recast personalization as experience activation and introduce ExpActivator, a training-free framework that activates only the experience applicable to the current situation. During execution, ExpActivator matches each new screen to historical states in the frozen GUI backbone's latent space and supplies the corresponding action as a reference. Before execution, it activates a recurring intent only when the current time and scenario provide sufficient support, and otherwise abstains. Across four GUI backbones, ExpActivator improves within-trajectory step success by 28% on average, achieves the best personalized execution on every backbone while using about one-fifth as many history
tokens, and reaches approximately 2.3$\times$ the Matthews correlation coefficient of the strongest proactive baseline. Experience pays where it is activated, not where it is appended.
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All authors Fuyao Zhang, Xuan Wang, Zherui Li, Jiaming Zhang, Longtao Huang, Wei Yang Bryan Lim
Machine Learning
BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting
Kangjia Yan, Linfeng Wang, Tianen Shen et al. (+7 more)
Despite recent progress in irregular time series
forecasting, the field still lacks a unified
benchmark for fair and comprehensive evaluation. Existing evaluations are often conducted on a limited set of
datasets with inconsistent experimental protocols and predominantly error-based metrics, rendering it difficult to compare and assess methods fairly and comprehensively across diverse settings. To eliminate these limitations and accelerate progress, we propose BITS, a standardized, reproducible, and extensible
benchmark for advancing research on irregular time series
forecasting. BITS covers eleven
datasets from nine domains with diverse irregularity characteristics, and it characterizes the
datasets according to their missing rate, missing pattern complexity, sampling irregularity, and skewness. Further, it offers a unified pipeline for data preprocessing, model integration and evaluation, and reporting. It accommodates regular and irregular time series
forecasting methods, including time series foundation models, under consistent settings, incorporating both error-based and non-error-based evaluation metrics. Findings include that method performance varies substantially across irregularity characteristics, with no single modeling strategy consistently dominating. We also find that using error-based or non-error-based metrics can yield different model rankings, highlighting the need for multi-dimensional evaluation. The code can be found at https://anonymous.4open.science/r/BITS-8F2E/.
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All authors Kangjia Yan, Linfeng Wang, Tianen Shen, Xiangfei Qiu, Ruitong Zhang, Hao Miao, Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen
Computation and Language
Jianguo Huang, Lipeng Wan, Yanchen Deng et al. (+1 more)
Diffusion large
language models (dLLMs) generate text by iterative unmasking. At each denoising step, a dLLM proposes a
token at every masked position, but the decoder commits only a confident subset of these proposals. Trace-based on-policy
distillation (TOPD) builds on this process by matching the student to a stronger teacher, yet only at the committed positions. We argue that this discards much of the useful signal, which resides in the uncommitted proposals, where the student has made a prediction but is not yet confident enough to commit it. We call these proposals hesitations. In our pilot study on an SDAR-4B student, hesitations make up only 24% of supervisable state-position pairs but carry 66% of the teacher-student divergence. To exploit this signal, we propose Hesitation-Aware On-Policy
Distillation (HOPD), which extends teacher distribution matching to every masked position of each denoising step. Because hesitations are not equally informative, we further allocate supervision using hindsight from the completed trajectory, placing more weight on positions whose proposal was later disagreed with the final
token and on blocks where first-step proposals rarely survive. Since both models already produce distributions at all masked positions, HOPD requires no additional forward passes over TOPD. The only extra cost is evaluating the loss at more positions. With SDAR-1.7B and SDAR-4B students distilled from TraDo-8B-Instruct, HOPD achieves the best average score among the evaluated methods on five math and coding
benchmarks, under both static and dynamic decoding and at both scales. It also speeds up decoding. On SDAR-4B, the HOPD student hesitates less and commits 11% more
tokens per denoising step than TOPD, while reaching higher accuracy.
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All authors Jianguo Huang, Lipeng Wan, Yanchen Deng, Bo An
Robotics
AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents
Cunhao Zhu, Yifeng Wang, Dongliang Xu et al. (+3 more)
World Action Models (WAMs) are becoming increasingly important and useful for
embodied intelligence, as they enable
robots to anticipate the consequences of candidate actions before interacting with the physical environment. However,
underwater robots are usually subject to passive dynamics, such as inertia, buoyancy, hydrodynamic drag, and persistent drift, which can continue to affect the vehicle even after an action is completed. Existing WAMs, which primarily predict action-conditioned visual observations, are not explicitly designed to capture such passive motion dynamics. In this paper, we present AquaWAM, the first World Action Model designed for
underwater embodied agents. Instead of predicting future images, AquaWAM models both action-conditioned and passive physical dynamics, including the thruster dead band, the inertial glide that outlasts each command, and ambient currents. Specifically, it senses through the DVL, IMU, pressure sensor and joint encoders, while cameras supply only semantics for understanding goals and target pose. By modeling compact
navigation states rather than high-dimensional visual observations, AquaWAM substantially reduces the model size and computational cost compared with conventional WAMs. Experimentally, AquaWAM achieves a 72.6% task success rate across 20
underwater tasks on the USIM
benchmark, outperforming existing methods while making action decisions 2.7x faster than U0 on an NVIDIA Jetson AGX Orin. Our model also remains effective when some onboard sensor measurements are unavailable. For example, without DVL velocity measurements, our method still achieves a 61.6% success rate, compared with 39.4% for U0.
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All authors Cunhao Zhu, Yifeng Wang, Dongliang Xu, Yunzhong Hou, Yue Yao, Chi Harold Liu
Machine Learning
Direct Hidden-State Alignment: Mapping and Controlling Preference Expression in LLMs
Fansheng Zhang, Shengran Guo, Zexiao Wang et al. (+3 more)
In many settings, post-training need not create the target behavior from scratch: the base model can already produce it, but not reliably. This shifts part of preference
alignment from capability acquisition to behavioral expression. We ask how a specified preference is represented in native model computation, what prevents target-supporting computation from reliably dominating generation, and whether this structure can directly guide control. We introduce Residual Competition Maps (RCMs), which map a behavioral preference onto signed causal effects of native residual computation. Across preference domains, RCMs reveal coexisting target-supporting and target-competing effects, input-dependent component roles, and cases where a single native-component intervention reverses the preference outcome. DPO substantially reorganizes these effects and can weaken opposition without guaranteeing its removal. We then propose Direct Hidden-State
Alignment (DHSA), which treats inference-time hidden states rather than base-model weights as the direct adaptation space. RCM-guided Causal Activation State Transition (CAST) implements DHSA through local state interventions at a small number of preference-relevant interfaces while freezing the base model. With only 256-16,384 controller parameters, CAST reaches DPO-competitive operating points across three preference domains, can complement DPO-trained models, and can be enabled or removed at inference time.
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All authors Fansheng Zhang, Shengran Guo, Zexiao Wang, Liang Yuan, Jiyuan Chen, Ruikun Luo
Artificial Intelligence
The Error You See Is Not the Error You Made: Progression-aware Reasoning Origin for Reasoning Error Localization
Yiguo Wang, Ziyuan Yang, Yi Zou et al. (+3 more)
Verifying multi-step LLM
reasoning requires more than determining whether a trace is correct: a useful verifier should identify where the
reasoning first goes wrong. However, existing holistic methods provide little positional evidence, while forward sequential verification often treats the first rejected step as the error source. Under error propagation, this assumption can fail, since an earlier mistake may remain locally plausible and become observable only through its downstream consequences. We therefore rethink
reasoning verification as a progression-aware error-source localization problem: rather than asking only where a
reasoning trace first appears inconsistent, we ask which earlier step best explains how that inconsistency emerges along the trajectory. Based on this view, we propose Progression-aware
Reasoning Origin (PRO), a training-free framework for first-error localization. PRO jointly models incoming support from the preceding context and outgoing compatibility with subsequent
reasoning, selectively refines regions where these signals disagree, and finally performs detector-conditioned source attribution with intervention-based evidence to distinguish the true error origin from its propagated manifestations. We further formalize the gap between forward rejection and structural exposure, showing why incoming-side evidence alone is insufficient for reliable localization under error propagation. Experiments across open-form, medical, and structured
reasoning tasks demonstrate consistent improvements over strong verification baselines, supporting progression-aware source attribution as a more faithful formulation of
reasoning verification.
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All authors Yiguo Wang, Ziyuan Yang, Yi Zou, Dan Lin, Rongsheng Li, Yi Zhang
Computation and Language
BaatCheet: A Multilingual Corpus for Dialogue Translation in Indian Languages
Priyanka Dasari, Yuvrajsinh D. Bodana, Vandan Mujadia et al. (+3 more)
Existing translation models are typically trained on sentence-level and formal text, limiting their ability to capture everyday conversational dialogue phenomena such as informality, speaker interaction, and discourse coherence. Most existing Indic translation resources and evaluation
benchmarks focus on sentence-level or formal text, making it difficult to assess translation quality of the dialogue phenomena. In this work, we introduce BaatCheet, a
multilingual dialogue corpus named after the Hindi term for conversation or chitchat, containing approximately 49,000 dialogues for dialogue translation across five translation directions. We fine-tune five open-source LLMs across seven training data configurations and find that fine-tuning yields substantial gains over zero- and few-shot baselines. To comprehensively evaluate dialogue translation quality, we employ multiple evaluation strategies, including automatic metrics, LLM-as-judge, and human assessments using an SQM-guided Direct Assessment (DA) Protocol.
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All authors Priyanka Dasari, Yuvrajsinh D. Bodana, Vandan Mujadia, Arafat Ahsan, Dipti Misra Sharma, Parameswari Krishnamurthy
Artificial Intelligence
TraceDance: An Automated System for Building Agent Behavior Benchmarks from Real-World Agent Deployment Traces
Dehai Min, Daoan Zhang, Yiming Zeng et al. (+13 more)
An
agent can complete a task while exhibiting undesirable behavior during execution. Developers need tests for the specific behaviors encountered in deployment, beyond fixed
benchmark suites. We present TraceDance, an
agent system that constructs targeted
benchmarks from deployment traces for user-specified undesirable behaviors. For efficient construction, Anchor-and-Confirm combines programmable
retrieval with candidate-level confirmation by a Flash large
language model (LLM), while the Anchor Synthesis Loop generates and revises specifications for custom behaviors. The
benchmarks use decision-point continuation to evaluate an LLM's next turn at a recorded decision point with a behavior-specific rubric, without a reference answer or environment replay. Experiments in coding and general tool use draw on 252,557 sessions and produce 107
benchmarks with 4,125 instances, fulfilling 95.3% of build-target requests. Both human annotators confirm the requested behavior in 84% of sampled instances, and the automated grader's agreement with human pass/fail judgments is comparable to that between the annotators. Nine frontier LLMs achieve a mean pass rate of only 26.7%, showing that they still struggle to respond appropriately at the evaluated decision points. Analysis across behavior-specific
benchmarks further reveals weaknesses in how current LLMs behave as
agents. By turning deployment problems into targeted
benchmarks, TraceDance could serve as a key component of the recursive self-improvement (RSI) loop.
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All authors Dehai Min, Daoan Zhang, Yiming Zeng, Huayi Zhang, Ziyi Chen, Yan Zhang, Qinbo Bai, Mengyuan Chao, Jing Ning, Qiyue Hua, Huiyi Chen, Hanrong Zhang, Henry Peng Zou, Jie Yang, Wei Xu, Philip S. Yu
Data Structures and Algorithms
On the Complexity of Forcing and Anti-Forcing Minimum Cuts
Tatsuya Gima, Yasuaki Kobayashi, Hiraku Morimoto et al. (+1 more)
For an instance of a combinatorial optimization problem, a forcing set is a set of elements such that there is a unique optimal solution including it. Symmetrically, an anti-forcing set is a set of elements such that there is a unique optimal solution excluding it. In this paper, we study the problems of computing smallest forcing and anti-forcing sets for two classical cut problems, Global Min Cut and \textsc{Min $s$–$t$ Cut}. We also consider variants in which the optimal cut to be uniquely determined is given as input. For each of these problems, we either give a
polynomial-time
algorithm or prove \NP-completeness.
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All authors Tatsuya Gima, Yasuaki Kobayashi, Hiraku Morimoto, Yota Otachi
Information Theory · Cross-listed from stat.ML
The Statistical Benefits of Multiple Responses for Learning from Demonstrations
Chandramauli Chakraborty and Cong Ma
Many generative systems return multiple candidate responses and are evaluated according to the best one. Recent work shows that, when demonstrations are optimal, pass@$k$ can reduce the sample complexity of learning from demonstrations by a logarithmic factor in $k$. We ask what happens when the demonstrator is not assumed to be optimal. We find that multiple responses provide a qualitatively stronger benefit in this setting. In a finite reward-class model with no reward feedback, moving from pass@$1$ to any pass@$k$ with $k\ge2$ changes the worst-case dependence on target accuracy from $1/\varepsilon^2$ to $1/\varepsilon$, uniformly over demonstrator quality. Under standard evaluation, where an unknown reward is fixed before training, increasing $k$ provides an additional and distinct benefit: the optimal dependence on a reward class of size $N$ improves from $\log N$ to $\log N/\log k$. We further show that these two effects can be separated. Under robust evaluation, where one learned policy must compete with the demonstrator simultaneously for every reward in the class, the fast $1/\varepsilon$ dependence persists, while the $1/\log k$ improvement can disappear. We establish matching upper and lower bounds in the corresponding regimes and give a greedy multiplicative-weights learner achieving the upper bounds without any assumption on demonstrator quality.
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Computation and Language
Calibration, Not Answer Selection: Distilling Internal Confidence in Reasoning Models
Yadong Xi, Rongsheng Zhang, Tangjie Lv et al. (+2 more)
Reinforcement learning with binary correctness rewards trains correctness, not calibrated confidence. The confidence that
reasoning models verbalize is systematically overconfident, and the problem is not merely one of scale: verbalized confidence tracks how willing a model is to commit to an answer, not how likely the answer is to be right. Post-hoc rescaling therefore fits one distribution but rarely transfers. We look inside the model instead. On factual question answering, a linear probe on the hidden state between the chain of thought and the answer is substantially better calibrated: its expected calibration error is 5 to 38 times lower than that of the verbalized score across four
benchmarks and two model families. However, when used to pick among N sampled answers, that same probe nearly ties majority voting yet falls far short of the oracle. Internal states answer "how certain am I" well and "which answer is right" poorly, so the signal should be reported as a confidence rather than used to select answers. As a result, we introduce probe-guided self-
distillation (Probe-SD): score a model's own sampled traces with the probe, overwrite the confidence each trace states, and finetune the base checkpoint of the same family, so nothing but the model itself remains at test time. On Qwen3-14B, Probe-SD cuts ECE from 0.178 to 0.024 in-domain and from 0.542 to 0.113 out-of-domain, where it also beats post-hoc recalibration and self-consistency
distillation. The resulting confidence is well-calibrated and useful for weighted voting, behaviors previously attributed to online RL, here obtained with supervised finetuning alone.
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All authors Yadong Xi, Rongsheng Zhang, Tangjie Lv, Ziyang Luo, Ruochen Zhao
Artificial Intelligence
Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets
Shuze Daniel Liu, Claire Chen, Jiuqi Wang et al. (+2 more)
Autonomous large
language model (LLM)
agents operating in multi-product
markets must make sequential decisions under information asymmetry and resource constraints. We develop a machine learning approach for training such
agents to act effectively as sellers in a multi-item bargaining environment, where a seller concurrently negotiates a catalog of substitutable assets across a pool of independent buyers. Buyers hold private, heterogeneous valuations across products, and each can purchase at most one item. Facing limits on total communication turns, the seller must dynamically match buyers with the most profitable products considering their private valuations, while strategically allocating its limited interaction budget toward combinations of greater potential value. We formalize this problem as a Partially Observable Markov Decision Process using a structured, four-part message protocol that maps natural
language into a parsable and regulated decision space. Using this formalization, we design a post-training method using Reinforcement Learning from Verifiable Rewards (RLVR). To evaluate this framework, we construct a multidimensional metric suite that quantifies constraint adherence, seller surplus extraction, and allocation quality. Our trained seller
agent learns to match limited inventory to buyers more effectively, matching or outperforming trillion-parameter frontier models in both seller surplus extraction and buyer-product allocation quality. Finally, these learned strategies generalize robustly to unseen
market structures, correlated valuation distributions, and price ranges not encountered during training.
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All authors Shuze Daniel Liu, Claire Chen, Jiuqi Wang, David Simchi-Levi, Thorsten Joachims
Computer Vision
Informative Viewpoint Selection for Episodic-Memory Embodied Question Answering using Omnidirectional Images
Kaname Kitamura and Asako Kanezaki
Embodied Question Answering (EQA) requires
agents to answer natural
language questions about surrounding environments from visual observations. In this work, we focus on open-vocabulary episodic-
memory EQA (EM-EQA), where an
agent answers free-form questions using recorded observation histories. Omnidirectional images are promising for this task, as they provide wide field-of-view observations that can capture surrounding context without requiring explicit camera rotations. However, omnidirectional images introduce two challenges for EQA: (i) equirectangular projection causes severe
geometric distortion that degrades
vision-
language model (VLM) recognition accuracy, and (ii) feeding equirectangular images directly into VLMs introduces excessive irrelevant background information, reducing answer accuracy and increasing the visual-
token burden. To address these challenges, we propose a viewpoint selection method for EM-EQA using omnidirectional images. Our method converts equirectangular observations into perspective views via cubemap projection, estimates question-conditioned relevance with fine-tuned BLIP-2, and selects informative and diverse viewpoints through diversity-aware greedy selection. Experiments on the Habitat-Matterport 3D (HM3D) subset of OpenEQA show that our method achieves state-of-the-art model performance among the reported model results with equirectangular observations. Moreover, after removing rotation views, which reduces observation frames by 65.5%, our method largely maintains its answer accuracy.
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Artificial Intelligence
Feedback Makes Perfect: A Closed-Loop Framework for NL-to-STL Translation
Bowen Ye and Xiang Yin
Signal Temporal Logic (STL) enables rigorous verification and control of cyber-physical systems, but writing correct specifications requires expertise that most requirement holders lack. Large
language models can translate natural-
language (NL) requirements into STL, yet stronger translators alone approach an accuracy ceiling. We argue that this ceiling stems from how the task is posed: one-shot, open-loop translation is somewhat ill-defined. Natural
language is ambiguous, and, more fundamentally, what a person writes may not always be what they intend, so the target specification is not fully contained in the input text. We therefore reformulate NL-to-STL translation as a closed-loop feedback process. Each generated formula is translated back into natural
language for the user to check, and natural-
language corrections drive revision until the user accepts the specification. Users never read or write formal syntax. This framework rests on an asymmetry familiar from feedback control theory. The forward path, from ambiguous
language to formal logic, is hard and error-prone. The feedback path, from structured STL back to
language, can be made highly precise, and a precise feedback path lets an imprecise forward path achieve precise closed-loop behavior. Experiments on 500 expert-authored requirements and seven LLMs support this view. Back-translated explanations agree with expert judgments in 99.5% of cases. Closed-loop refinement raises strong models from about 89% open-loop accuracy to 98.0–99.2%, and yields gains of over 30 percentage points for weaker models (e.g., 17.6%$\rightarrow$48.0%). Ablations show these gains come from the semantic content of the feedback rather than from repeated attempts. An expert audit and a 280-session user study further confirm the reliability of the loop. We also identify a capability threshold above which feedback no longer helps.
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Computer Vision
InfoEdit: Probing Global Layout Reasoning in Infographic Editing
Cheng Yang, Chufan Shi, Huijuan Wang et al. (+6 more)
Multimodal foundation models edit natural photographs at production quality, yet the same models struggle with structured visual content such as infographics. Unlike photographs, infographics encode information through logical relations; editing one element often requires surrounding elements to be adapted. We refer to this global layout
reasoning capability as reflow. Existing image-editing
benchmarks neither provide a dedicated setting for structured visual content nor evaluate the reflow capability. We introduce InfoEdit, a novel
benchmark of 1,000 infographics across eight logical-relation families, paired with 4,000 editing instructions across four editing tasks, and a reflow-aware evaluation protocol. Across eight frontier editors, only GPT-Image-2 clears 60% average success rate; most models fall below 7%, and no editor exceeds 36% on the Swap-Block task even with perfect target localization. We further show that code-level editing can match the strongest pixel-level editor, revealing complementary strengths across tasks. InfoEdit identifies reflow as a central challenge in structured visual content editing and provides a diagnostic
benchmark to facilitate future progress.
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All authors Cheng Yang, Chufan Shi, Huijuan Wang, Bo Shui, Yaokang Wu, Muzi Tao, Yibo Yan, Xuezhe Ma, Taylor Berg-Kirkpatrick
Artificial Intelligence
RINI: Seeing the Prior Is Not Enough
Hongyi Du, Tianyi Zhang, Heng Wang et al. (+7 more)
A research proposal can describe an established mechanism correctly while claiming to introduce it. We study whether providing the earlier paper corrects such contribution claims. Three controlled experiments compare proposals generated with a contribution-bearing prior and a same-topic control. Providing the prior yields no clear aggregate reduction in unsupported novelty. Human analysis of 175 interpretable exposed proposals finds that 137 recognize the prior's relevance, but 61 correctly attribute the established contribution. Of 71 proposed remaining distinctions, 37 are covered by the same prior. We introduce Research Idea Novelty Inspection (RINI), which audits contribution claims against evidence, checks the remaining distinction, and applies local revisions. Five human annotators evaluate 1,080 original-revision pairs across three methods. On the same 240 originals judged to require correction, successful repair is 11.7% for Self-Revision, 39.1% for Retrieve-and-Revise, and 72.2% for RINI, with research tasks weighted equally. The improvement over same-evidence direct revision is 33.0 percentage points. The revised proposals retain their research questions and technical methods. These results motivate explicit contribution attribution when using literature to generate and revise research proposals.
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All authors Hongyi Du, Tianyi Zhang, Heng Wang, Zhelun Gao, Yimei Liu, Ambrose Luo, Annie Hao, Jiayan Ni, Jiawei Han, Jiaxuan You
Artificial Intelligence
Multi-Dimensional Comparative Scale Construction for Efficient Personalized Subjective Judgment in High-Traffic Applications
Xianglong Shi, Shifeng Liu, Sirui Zhao et al. (+2 more)
Subjective judgments are central to many high-traffic applications, but subjective intensity is difficult to quantify and perceptions vary substantially across individuals. To address these challenges, we propose a pairwise comparative framework for multi-dimensional scale construction. By comparing case-person pairs along case and profile dimensions, the framework constructs relative scales that capture both fine-grained intensity and individual variation. To support practical high-traffic deployment, we optimize both offline scale construction and online inference. For scale construction, we combine sparse Elo comparisons with multi-judge voting, cutting the comparison cost from $O(N^2)$ to $O(NK)$ for $N$ objects and a budget of $K$ opponents per object, while limiting reliance on any single judge. For inference, we propose SubJudge, a System One model for personalized scoring with Batchwise Preference Optimization (BPO). Using Bradley-Terry comparisons, BPO trains the model to learn relative orderings, and SubJudge reads a continuous score from digit-
token probabilities at the first response position, requiring only one forward pass per criterion and reducing the inference complexity to $O(1)$. Experiments on PluriHarms and iNews show that our 9B models match or surpass the evaluated frontier LLMs on multiple metrics. On the H100 GPU, SubJudge achieves an approximately $1.29\times$ to $261\times$ speedup in mean inference latency over Qwen3.5-9B with different thinking budgets. The code is available at https://github.com/Longchentong/SubJudge.
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All authors Xianglong Shi, Shifeng Liu, Sirui Zhao, Shengming Yuan, Enhong Chen
Cryptography and Security
Application Agnostic EM Side-Channel Emanations of the FPGA Clock Distribution Network
Ashish Sharma, Alia Long, Keith D. Mecham et al. (+1 more)
Electromagnetic side channels have been extensively utilized in non-invasive attacks or analyses to extract critical information from deployed systems. Most of the attacks or analyses are typically executed on cryptographic
algorithms under the assumption that the underlying implementation remains fixed. The assumption is valid in the context of fixed hardware, where modifications to the
circuit are not made after manufacturing. However, in the case of adaptive hardware, including field-programmable gate arrays (FPGAs), the assumption no longer holds as modifications to the configuration of the array are permitted post-manufacturing. Consequently, this study introduces a methodology for the analysis of electromagnetic side channels of an FPGA through the study of deterministic finite state machines (DFSMs), where the effects of placement, routing, and operating frequency on the EM emanations of the FPGA due to the utilized clocking resources are characterized. To analyze the impact of frequency, device placement, and resource allocation on the EM emanations from the FPGA, statistical analysis including mean difference and variance difference calculations of measured EM fields were utilized to identify points of high EM activity on a set grid of sampled points. A 1.32x increase in flagged grid points was observed in the analysis of the mean difference when the percentage of utilized clock resources was increased from 25% to 90%. Similarly, a 1.39x increase in flagged grid points was observed when the variance difference was calculated for the same increase in clock resources.
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All authors Ashish Sharma, Alia Long, Keith D. Mecham, Ioannis Savidis
Computation and Language
The Text Beside the Image: Detection, Utility and Leakage for Trustworthy Multimodal Medical Data and Beyond
Andreas Maier, Monica Hinrichs-Mayer, Franziska Weber et al. (+4 more)
Medical images are released with the reports that describe them, and protecting the image does not protect the report. This paper measures the text component of such releases. We measure identifier detection, downstream utility and residual identity leakage on the same documents, with the pseudonymisation policy as the variable under test: 15 detectors, three release conditions and four corpora of medical reports, legal judgments, news and other genres, and e-mail, in German, English, Chinese and Arabic. A fixed 13-detector union reaches a person sensitivity of 0.9998 at specificity 0.8686 on the medical reports, 0.9958 at 0.8504 on the legal judgments, 0.9352 at 0.9318 on news and other genres, and 0.9906 at 0.6235 on e-mail. With this ensemble, frequency matching with a public name list recovers zero identities by
alignment across the four corpora; the names it got right were ones the detector missed, left in clear text. Cross-document linkage ranks the correct person first for 0.93% of e-mail queries without training and 3.94% with it, against 1/3697 chance and 71.98% on unmodified text. On the medical reports it recovers nothing without training and 0.71% of 138 queries with it, against 1/207 chance and a 2.73% ceiling on unmodified text.
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All authors Andreas Maier, Monica Hinrichs-Mayer, Franziska Weber, Niklas Lackner, Matthias May, Bernhard Kainz, Siming Bayer
Machine Learning
Domain Generalization under Sampling Pattern Shifts in Irregular Time Series
Changhun Kim, Joohyung Lee, Kwanhyung Lee et al. (+3 more)
Irregularly sampled multivariate time series (ISMTS) are prevalent in real-world applications, where both observation times and available measurements can vary substantially across domains. While recent models increasingly exploit such sampling information for prediction, its robustness under sampling pattern shifts remains underexplored. We introduce HAR-C, to the best of our knowledge the first controlled
benchmark for sampling pattern shifts in ISMTS, and show that sampling shifts alone can substantially degrade performance, induce sampling-specific shortcuts, and remain challenging for existing domain generalization (DG) methods. Motivated by these findings, we propose PRISM, a DG framework that first learns complementary feature-centric and sampling-centric representations without task labels, and subsequently performs robust supervised training across diverse sampling variations to discourage brittle shortcut reliance. Extensive experiments on controlled and real-world ISMTS
benchmarks demonstrate that PRISM consistently improves robustness to unseen sampling shifts over existing methods. Our code is available at https://anonymous.4open.science/r/PRISM.
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All authors Changhun Kim, Joohyung Lee, Kwanhyung Lee, Donghwee Yoon, Grigorios Chrysos, Eunho Yang
Artificial Intelligence
ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation
Changhun Kim, Sunguk Jang, Jeongjun Lee et al. (+5 more)
Recent advances in generative modeling have substantially improved time series generation, yet most existing methods either assume a regular temporal grid or focus on feature dynamics under a given sampling structure. This makes them illsuited for generating irregular time series in their native form, where a model must capture not only feature values, but also how many observations occur, when they occur, and which features are observed together. To address this heterogeneous generation problem, we propose ChronoFlow, a unified hierarchical flow matching framework organized by statistical granularity. Following a coarse-to-fine hierarchy, ChronoFlow first generates observation counts and feature-wise frequencies, then jointly generates observation times and feature co-observation patterns, and finally generates values conditioned on the realized pattern. This turns a complex joint generation problem into structurally aligned subproblems while preserving their dependencies. To evaluate complete irregular time series generation, we introduce complementary metrics spanning sample realism, sampling structure, value fidelity, and temporal and cross-feature dependencies, and validate them through controlled corruptions. Across five
benchmarks, ChronoFlow achieves strong improvements in generation fidelity over existing baselines, while factorization studies support the proposed hierarchy. Our code is available at https://anonymous.4open.science/r/ChronoFlow.
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All authors Changhun Kim, Sunguk Jang, Jeongjun Lee, Juhwan Choi, Sangchul Hahn, Grigorios Chrysos, Eunho Yang, Juho Lee
Computational Engineering
Modelling non-linear aeroelastic loads in long-span bridges with extreme learning machines
Gledson Rodrigo Tondo, Samir Chawdhury, Sergio Andres Castro Giraldo et al. (+1 more)
Accurate modelling of aerodynamic loads is essential for predicting instabilities and ensuring the safety of long-span bridges. A methodology is introduced for modelling aerodynamic self-excited forces in bridge-deck cross-sections using extreme learning machines (ELMs). ELMs, as single-layer feedforward neural networks, offer efficient training and accurate predictions. Forced-oscillation
datasets from computational fluid dynamics (CFD) or wind-tunnel experiments are used for training, enabling systematic data selection to capture non-linear aerodynamic behaviour often missed by semi-analytical approaches. Once trained, the model predicts self-excited loads for any arbitrary motion composed by frequencies and amplitudes within the training domain. Comparisons with analytical, semi-analytical, and CFD results show superior accuracy in capturing non-linear force components and close agreement for aerodynamic loads and flutter wind speeds. Training required about 1.1% of the time of a conventional neural network, and coupled flutter analysis runs in seconds, providing orders-of-magnitude speed-ups over CFD. These results indicate that ELM-based frameworks are accurate, practical, and efficient alternatives for modelling self-excited loads, particularly when preliminary CFD or wind-tunnel data are available. The presented approach offers a reliable data-driven technique for aeroelastic load modelling in long-span bridges.
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All authors Gledson Rodrigo Tondo, Samir Chawdhury, Sergio Andres Castro Giraldo, Guido Morgenthal
Machine Learning
Towards Identifiable Representations under Misspecified Structure
Yuke Li, Yujia Zheng, Ziyi Chen et al. (+2 more)
The presence of noise that depends on the latent variables poses a fundamental challenge to identifiability. Existing results rely on conditional independence among the observations given the latent variables. We study a more general misspecified structure, where this conditional factorization does not hold, and establish both precise and approximate identifiability guarantees. We characterize structural misspecification as a perturbed factor analysis problem. For precise identifiability, we establish subspace identifiability under spectral separation and controlled perturbation, followed by component-wise identifiability under structural sparsity. When the precise condition is not guaranteed, we derive an approximate subspace-identifiability
theorem. Based on these results, we develop an unsupervised variational
estimator for recovering latent variables. Experiments demonstrate the effectiveness of the proposed framework.
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All authors Yuke Li, Yujia Zheng, Ziyi Chen, Kun Zhang, Heng Huang
Artificial Intelligence
Next Thoughts Are Distributions: Generative Autoregressive Reasoning in the Latent Space
Yang Li, Yi Wang, Shiyuan Huang et al. (+3 more)
Reasoning problems often admit multiple valid ways to proceed. Continuous
reasoning promises to move computation beyond
language tokens into a more compact latent space, but representing several plausible ways to think next remains difficult. We introduce Autoregressive Thought Flow (ATF), which models the next continuous thought as a
multimodal distribution. A causal autoregressive model performs the
reasoning computation, while a lightweight
diffusion head generates a plausible next thought from the resulting condition. The sampled thought is fed back into the model, allowing continuous
reasoning to unfold for a variable number of steps while preserving the pretrained backbone. Across mathematical
reasoning tasks, ATF improves accuracy with compact latent traces and benefits from reinforcement learning and additional test-time thinking. Multi-sample evaluation shows broader solution coverage, indicating that its
multimodal predictions capture useful diversity among
reasoning paths. Our results suggest that continuous
reasoning is more effective when multiple possible next thoughts remain available rather than being collapsed into a single prediction.
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All authors Yang Li, Yi Wang, Shiyuan Huang, Yang Liu, Hao Wang, Chengzhi Mao
Artificial Intelligence
Structured Sparse Memory for Recurrent Reasoning
Zixuan Zhao, Samuel Wheeler, Neil Getty et al. (+3 more)
Recurrent models trained from scratch have recently become competitive on ARC-style
reasoning tasks, but the usual framing around small recurrent backbones overlooks two important parts of the system: task-conditioned
memory and synthetic augmentation data. We study this regime through CHARM, a compact hybrid ARC model that combines recurrent
reasoning with structured task
memory, synthetic data, and inference-time aggregation. In existing approaches, task-conditioned
memory supplies a large hidden source of capacity, reaching more than 30x the size of the recurrent backbone. We introduce a compositional sparse
embedding (CoSE) for task conditioning that reduces learned task-
memory parameters by over 90% while improving pass@2 in controlled ARC ablations. For the recurrent backbone, recurrent depth helps only when balanced with learning horizon. Combining these ingredients, our system reaches 84% pass@2 on ARC-AGI-1 and 46.7% pass@2 on ARC-AGI-2 public evaluation. The benefits of structured
memory also generalize to unseen puzzles and other domains. Our code,
dataset, and model checkpoints are available at https://github.com/water-vapor/charm.
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All authors Zixuan Zhao, Samuel Wheeler, Neil Getty, Xiaotian Duan, Rick Stevens, Fangfang Xia
Robotics
Q-WAM: 4-Bit Quantization of World Action Models with Action-Subspace Protection
Arash Akbari, Arman Akbari, Jingwu Luo et al. (+7 more)
World Action Models (WAMs) jointly generate
video and
robot actions through iterative
diffusion and perform strongly in robotic manipulation. However, their prohibitive compute and
memory costs pose substantial deployment challenges. Post-training quantization (PTQ) can reduce these costs, but existing PTQ methods such as smoothing and rotation are insufficient to maintain the precision of action generation. To overcome this limitation, we propose Q-WAM, a new 4-bit weight-activation quantization for WAMs that preserves the actions the model generates. Specifically, we introduce the Action Observability Gramian (AOG), which measures how much rounding errors in each weighted combination of a layer's input channels change the final action through all denoising steps. We also develop Action-Subspace Protection (ASP), which keeps the few most action-sensitive channel combinations in a tiny 16-bit low-rank branch and quantizes the complementary weights and activations to 4 bits, both as dense
matrix multiplications that run efficiently on GPUs. Finally, to preserve action quality with minimal overhead, we identify the experts that matter most for the generated action by aggregating the AOG-derived action mass across the layers of each expert and apply ASP only to those experts. We evaluate Q-WAM on three WAMs, both in simulation and in real-world deployment. On the RoboTwin 2.0
benchmark, it reaches 89.6–93.0% average success rate, within 1.1 percentage points of the 16-bit models, while reducing the
memory of the quantized blocks by 3.1–3.4$\times$. Our method outperforms the strongest baseline, SVDQuant, by 2.5–8.7 percentage points. On a Unitree G1
humanoid and a bimanual UR3
robot, it improves success over SVDQuant by 12.8-17.6 percentage points.
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All authors Arash Akbari, Arman Akbari, Jingwu Luo, Yuhao Lei, Yi Gao, Weiwei Chen, Xuan Zhang, Zhenman Fang, Geng Yuan, Yanzhi Wang
Artificial Intelligence
LSTMem: Hierarchical Long Short-Term Online Memory for Large Language Models
Xianglong Shi, Ruijie Yang, Sirui Zhao et al. (+4 more)
Large
language models increasingly serve as long-horizon assistants and
agents, where they must both accumulate information across interactions and make the relevant parts available when later requests depend on them. Existing compact online
memories typically use a single persistent state both to accumulate history and to serve readout, so what the
memory stores cannot be controlled separately from what it exposes to the current computation. We propose LSTMem, an LSTM-inspired online
memory that instead equips each layer of a frozen LLM with two
matrix-valued states: a
cell state that accumulates history and a hidden state whose readouts correct the backbone's
attention. Input and forget gates control what the
cell stores, while an output gate separately controls what the
cell exposes through the hidden state. LSTMem further connects
memory across depth through forward hidden-state propagation and block-end feedback, and uses higher-layer reconstruction gradients to refine lower-layer
cell states before rebuilding hidden states from shallow to deep layers. Across
memory benchmarks on Qwen3-4B-Instruct, LSTMem consistently improves MemoryAgentBench, LoCoMo, and HotpotQA over the plain backbone. Comparisons further show that the LSTM-based
memory formulation outperforms an associative-
memory counterpart, while removing cross-layer hidden-
memory propagation degrades performance. These results demonstrate the benefits of separating
memory accumulation from
memory expression and organizing
memory hierarchically across model depth. The code is available at https://github.com/Longchentong/LSTMem.
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All authors Xianglong Shi, Ruijie Yang, Sirui Zhao, Shukang Yin, Zihao Bian, Tinghao Yi, Enhong Chen
Sound
SCISSOR: Score-Conditioned Instrument Source Separation for Orchestral Recordings
Yiheng Lu and Hao-Wen Dong
Orchestral separation recovers instrument sections from mixtures in which shared pitches, harmonics, and timbres obscure source identity. An aligned score provides instrument labels, note pitches, and activity times. A score-informed approach appends piano rolls to audio features before mask prediction. We introduce SCISSOR (Score-Conditioned Instrument Source Separation for Orchestral Recordings), which uses the score to form a frame-wise query for each source. Each query matches a shared audio representation, and a softmax over instrument and background slots jointly assigns overlapping time-frequency evidence. The queries retain instrument identity even when notes are missing from the score. After training on SynthSOD and a small set of URMP and PHENICX-Anechoic recordings, SCISSOR achieves the highest average SDR on held-out real recordings. With SynthSOD-only training, it leads on SynthSOD and zero-shot PHENICX-Anechoic, and improves on its audio-only control on zero-shot URMP. SCISSOR also degrades less under score corruption than the evaluated score-based baselines.
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Computer Vision
VehDyn: A Driving World Model Benchmark for Vehicle Dynamics
Tianyi Wang, Wangsheng Du, Jiazhou Chen et al. (+12 more)
Video world models are emerging as data engines, action planners, and generative simulators for autonomous driving, but existing
benchmarks primarily assess visual fidelity and coarse physical plausibility, providing limited evidence on whether generated driving futures obey realistic vehicle kinematics and dynamics. This limitation is further compounded by the lack of
datasets in which vehicle, road, maneuver, and speed conditions are independently controlled, and ground-truth vehicle states are recorded in synchrony with
videos. We introduce VehDyn, a driving world model
benchmark for vehicle dynamics. VehDyn is built on a CARLA-CarSim co-simulation platform where photorealistic rendering is coupled with a validated multi-body dynamics model, and it contains 10,080 configurations from a full factorial design over five vehicle types, four tire-road friction coefficients, three maneuvers, four target speeds, 14 scenes, and three illuminations, each paired with synchronized position, velocity, and attitude sequences. Built on this
dataset, VehDyn introduces a hierarchical evaluation framework that measures trajectory
alignment, kinematic consistency, and dynamic consistency, and
benchmarks 12 state-of-the-art
video world models. We further assess the
video quality using two established protocols and correlate it with the VehDyn score. Trajectory-level metrics are nearly saturated, with ten of twelve models within 20% of ground truth, while no model reaches 92% of ground truth on dynamic consistency, and visual-quality metrics are only weakly correlated with vehicle-dynamics fidelity. DrivingWorld achieves the highest VehDyn score, followed by Cosmos 3 Nano and LTX-
Video 2.5, and the VehDyn score agrees closely with human judgment. VehDyn provides a systematic foundation for developing driving world models that are physically consistent and visually realistic.
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All authors Tianyi Wang, Wangsheng Du, Jiazhou Chen, Tianyi Zeng, Xiangyu Li, Jiseop Byeon, Yujin Wang, Yiming Xu, Yangyang Wang, Bingzhao Gao, Sikai Chen, Zhaomiao Guo, Junfeng Jiao, Christian Claudel, Alexandre Bayen
Information Theory · Cross-listed from math.PR
Robust Reconstruction on Trees with a Growing Alphabet
Heon Lee
We study robust reconstruction for the \(q\)-state Potts broadcast process on a Galton-Watson tree in the growing-alphabet regime \(q\to\infty\), with boundary depth growing sufficiently quickly in relation to \(q\). We show that, in the regime \(d\lambda >1\), where \(d\) is the expected number of offspring and \(\lambda\) is the nontrivial
eigenvalue of the Potts transition
matrix, the root posterior is asymptotically unchanged by a broad class of noise channels applied independently to the boundary labels. This remains true even when the probability of retaining the true label at an individual boundary vertex tends to zero as \(q\to\infty\). More precisely, under suitable moment and noise assumptions, the noiseless and noisy root posteriors converge to one another in expected total variation, and hence have the same asymptotic Bayes-optimal reconstruction accuracy.
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