Cryptography and Security
GNSS Spoofing in Mobile Devices: A Survey on Impact and Countermeasures
Robert Argo, Andrea Nardin, Alex Minetto et al. (+1 more)
Smartphones rely on Global
Navigation Satellite System (GNSS)-based positioning for many of the functions they execute everyday. The GNSS receivers embedded in smartphones are susceptible to anthropogenic
radio frequency interference attacks in the forms of jamming and spoofing due to the low-power and open-architecture signals they receive from the satellite constellations. While jamming is a practice that denies a GNSS receiver the ability to form a position, velocity, and time (PVT) solution, spoofing represents a more insidious threat by using forged satellite signals that aim at causing the victim receiver to compute a false PVT solution. The ubiquity of smartphones and the sensitive geolocation data they hold make them a primary target for malicious spoofing. However, their hardware constraints and the lack of deep visibility into the GNSS receiver processing chain create significant hurdles for effective countermeasures. Existing surveys comprehensively explore general spoofing countermeasures but fail to address these mobile-specific limitations. This article fills that gap with a novel survey focused on techniques viable within the unique constraints of smartphone architectures. Specifically, we establish a taxonomy for defining GNSS spoofing attack effects and countermeasures, provide a historical review of smartphone vulnerability characterization, and provide an overview of techniques proposed to detect and counteract smartphone spoofing threats, offering a comparative framework to weigh their respective pros and cons on mobile platforms.
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All authors Robert Argo, Andrea Nardin, Alex Minetto, Pau Closas
Sound
AudioJev: Direct Audio Decisions with Order-Calibrated Probabilities
Sihan Lv, Zhen Li, Zhiqi Cao et al. (+4 more)
Audio decisions often depend on evidence that a transcript does not preserve, while their probability estimates can depend on how answer options are ordered. AudioJev maps a waveform, question and supplied alternatives directly to a candidate distribution through one shared full-parameter model. We define order calibration as preserving an answer's probability under meaning-preserving option permutations. Random-derangement SKL training pairs each question with a reordered view in which every alternative changes position, supervises both answers, and aligns the two distributions before applying a symmetric KL penalty. Inference retains a single candidate-scoring forward, with no calibration head or order ensemble. Across three training seeds, AudioJev reaches 68.88%/55.33% mean accuracy on complete MMAU/MMAR and reduces random-order SKL by 43.8%/60.5% relative to the single-view removal ablation. The same model handles intent, environmental sound, note properties,
speech activity and conversational transitions. Paired removal ablations and multi-order evaluation measure predictive accuracy and probability stability together, establishing a direct audio interface whose calibration objective acts on candidate meaning rather than presentation position. Inference code and model weights are available at https://github.com/SihanLv/AudioJev-Inference and https://huggingface.co/shlv/AudioJev.
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All authors Sihan Lv, Zhen Li, Zhiqi Cao, Jinshan Zhang, Ying Li, Meng Xi, Jianwei Yin
Computer Vision
ODDR: One-Step Deshadow Diffusion via Reward Guidance
Junseong Shin, Kijun Kim, Minseong Kim et al. (+2 more)
Recent advances in deep learning for shadow removal have significantly enhanced image quality and realism. However, most approaches rely on real-world paired
datasets, which are costly to collect and often limited in scene diversity, leading to limited generalization. To address these limitations, we propose One-step Deshadow
Diffusion via Reward guidance (ODDR), a new framework that achieves efficient and high-fidelity shadow removal without relying on real-world paired supervision. Our method begins with One-step Deshadow
Diffusion (ODD), a baseline model trained on synthetic shadow data for efficient one-step shadow-free reconstruction. We further adapt ODD into ODDR using ShadowReward. In contrast to traditional, annotation-heavy approaches, ShadowReward is the first reward model for shadow removal trained entirely without human annotation. It learns to mimic human perceptual judgments by ranking synthetically generated images with controlled degradations, such as texture distortion and boundary artifacts. This reward-guided fine-tuning enables ODDR to close the synthetic-to-real domain gap. Extensive experiments show that ODD achieves strong performance without relying on real-world paired supervision, and ODDR further improves the results, narrowing the gap to fully supervised methods trained on real-world paired data while maintaining higher computational efficiency as a single-step model.
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All authors Junseong Shin, Kijun Kim, Minseong Kim, Dongjin Kim, Tae Hyun Kim
Robotics
Interaction-Stiffness-Guided Basis Allocation in Dynamic Movement Primitives for Efficient Skill Transfer
Chan Xu, Silu Chen, Dehao Wang et al. (+6 more)
Dynamic Movement Primitives (DMPs) provide a compact and stable formulation for trajectory representation and generalization in
robot skill learning. However, their predefined basis layout limits the allocation of approximation capacity according to stage-dependent precision requirements. To address this issue, this article proposes Stage-Criticality-Guided Dynamic Movement Primitives (SC-DMPs) with adaptive basis allocation for precision-critical skill learning. Operator-
robot interaction stiffness and a trajectory-consistency cue derived from cross-demonstration task-space variability are integrated to construct a stage-criticality index. Guided by this index, basis centers are redistributed in normalized time through inverse cumulative criticality and mapped to the canonical phase domain, while their bandwidths are refined to adjust local approximation support. This enables denser and more flexible representation at high-criticality stages while retaining sparser allocation elsewhere. Experiments on handwriting trajectories and three real-
robot tasks show that the inferred criticality is concentrated in geometrically demanding and task-constrained regions. Comparisons with DMPs, ProMPs, ProDMP, GP-MP, and KMP demonstrate improved trajectory reproduction, endpoint generalization, and task-critical accuracy while retaining a compact model and the stable structure of classical DMPs.
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All authors Chan Xu, Silu Chen, Dehao Wang, Xiyu Chen, Dexin Jiang, Chi Zhang, Guilin Yang, Chenguang Yang, Zaojun Fang
Computer Vision
Dyna3: VLM-Guided Training-Free 4D Reconstruction via Depth Foundation Models
Xinhao Xiang, Weiyang Li, Zhijie Zheng et al. (+2 more)
Recent depth foundation models like Depth Anything 3 (DA3) achieve remarkable multi-view depth estimation but assume static 3D scenes, limiting their applicability to real-world dynamic environments. Existing training-free 4D methods like Easi3R and VGGT4D rely on correspondence-trained backbones whose
attention encodes cross-frame matching, a property absent in depth-only models like DA3. We present Dyna3, a training-free framework that extends DA3 for 4D dynamic scene reconstruction without any fine-tuning. Our key insight is that DA3's cross-view features, though trained only for depth consistency, implicitly encode motion-discriminative signals when combined with best-match feature search across frames. Its static surfaces find consistent matches globally, while dynamic objects cannot. We further adopt
vision-
language models (VLM) to automatically generate scene-specific semantic
prompts for SAM 3, enabling precise instance-level
segmentation that distinguishes which objects move from what objects exist. For reconstruction, we decouple the scene into a cross-frame aligned static background and per-frame dynamic point clouds. Experiments on four
datasets demonstrate that Dyna3 surpasses correspondence-trained methods with +5.5pp J-Mean over state-of-the-art VGGT4D on dynamic object
segmentation, while achieving up to 13x faster pose estimation and 3x faster 4D reconstruction with 4 to 8x lower
memory. Dyna3 could therefore enable much denser temporal sampling that prior methods cannot support.
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All authors Xinhao Xiang, Weiyang Li, Zhijie Zheng, Abhijeet Rastogi, Jiawei Zhang
Machine Learning
Model validation in machine learning: A scenario-based guide from hold-out splits to nested group cross-validation in biomedical and applied research
Mehmet Baygin, Sengul Dogan and Turker Tuncer
Model validation estimates the performance of a complete learning procedure on new data. However, an invalid split can produce an optimistic and stable result. This tutorial reviews hold-out validation, train/validation/test designs, repeated random subsampling, k-fold and repeated stratified cross-validation, leave-one-out and leave-p-out schemes, group-aware validation, and nested group cross-validation. General machine-learning principles are linked to
EEG epochs, paired-eye OCT images, repeated clinical measurements, and multicenter data. Eight controlled scenarios compare flawed and leakage-safe designs: seven use locked confusion
matrices with auditable metrics, and one uses a reproducible repeated-study simulation. The scenarios cover global feature selection, normalization leakage, dependent records, center mixing, repeated test-set use, and
estimator instability. Bias, variance, metric aggregation, uncertainty, and computational cost are also examined. A data-size
matrix, a decision tree, and reporting checklists are provided. Reproducible MATLAB templates and scikit-learn counterparts are included. The results show that no validation method is universally best. The independent unit must match the intended deployment target. Every data-dependent operation must also exclude the observations used for performance estimation.
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Computer Vision
ShelfChange3D: Object-Level 3D Change Detection for Retail Shelf Monitoring
Lingyi Zhou, Yunke Wang, Mengyu Zheng et al. (+3 more)
Reliable shelf monitoring is an important capability for retail automation, yet existing out-of-stock detection methods mainly operate in image space and lack metric 3D localization for downstream robotic systems. We formulate shelf monitoring as object-level 3D change detection: given two RGB-D observations captured at different times, the goal is to identify changed products and localize each change with a 3D bounding box. To support this task, we introduce ShelfChange3D, comprising 145K synthetic and 5K real-world paired RGB-D observations with object-level 3D change annotations. We further propose ChangeBox, an end-to-end framework that jointly reasons over paired observations and predicts object-level 3D change boxes. To improve localization accuracy, we introduce a geometry-based refinement stage that exploits depth and
gravity prior to estimate relative pose and refine predicted boxes. Experiments show that ChangeBox outperforms existing change detection baselines, with further gains from refinement and effective transfer from synthetic to real-world observations.
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All authors Lingyi Zhou, Yunke Wang, Mengyu Zheng, Wenbo Wang, Zijian Wang, Chang Xu
Artificial Intelligence
Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics
Antonio Emanuele Cinà, Giovanni Scodeller, Cecilia Caterina Pasquale et al. (+5 more)
The rapid
evolution of Intelligent Transportation Systems and Logistics (ITS&L) has become a cornerstone of the modern social economy, relying heavily on the integration of Data, Artificial Intelligence (AI), and, more specifically, Machine Learning (ML). This paper provides a comprehensive review of Trustworthy Data and Machine Learning Operations (DataOps and MLOps) in the ITS&L domain, underscoring their importance in improving efficiency, reliability, and decision-making precision within transportation and logistics services. We begin by identifying gaps in current literature, offering clear context for our contribution. Subsequently, we explore the complexities of DataOps and MLOps, discussing their necessity, key components, available tools, practical insights, and case studies relevant to ITS&L. Additionally, we address the critical issue of Trustworthiness in AI applications, examining methods and tools designed to strengthen confidence in AI systems - especially in real-world ITS&L scenarios. The paper concludes with a discussion of persisting challenges and future prospects in this rapidly advancing field, aiming to serve as a vital resource for researchers, industry practitioners, and policy makers. Overall, this work not only establishes a foundational understanding of DataOps and MLOps in ITS&L but also charts a path for further research and innovation in developing more efficient, sustainable, and trustworthy intelligent transportation and logistics systems.
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All authors Antonio Emanuele Cinà, Giovanni Scodeller, Cecilia Caterina Pasquale, Silvia Siri, Davide Anguita, Fabio Roli, Simona Sacone, Luca Oneto
Computer Vision
PickMoment: Continuous-Time Single-Image-to-Video via Learning Deblurring and Blur-to-Video
Junseong Shin, Hyeonsu Jo, Daehyun Kim et al. (+1 more)
Motion blur arises from the temporal integration of a continuous sharp signal over a finite exposure window, yet existing learning-based methods sidestep this physical model and predict only the sharp signal itself: most single-image deblurring methods recover a single frame at the exposure center, while blur-to-
video methods predict a fixed set of frames. We introduce PickMoment, a continuous-time reformulation that directly learns the interval-mean blur over arbitrary sub-intervals of the exposure with a single deterministic model. Drawing an analogy to MeanFlow's average-velocity formulation, we train the model with three supervisions derived from the blur integral: an empirical reconstruction loss from available subframes, an additivity loss that enforces self-consistency across overlapping sub-intervals, and a sharp-frame loss anchored at the zero-interval limit. A single trained model unifies single-image deblurring, blur-to-
video generation, and continuous-time pick-a-moment recovery as different queries to the same network, with no separate training for each task. Our PickMoment achieves state-of-the-art performance among generative-based deblurring methods on GoPro and HIDE while competitive against restoration-based methods on RealBlur, and the highest per-frame fidelity on GoPro-7 blur-to-
video, all in a single forward pass without iterative sampling.
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All authors Junseong Shin, Hyeonsu Jo, Daehyun Kim, Tae Hyun Kim
Artificial Intelligence
SCOPE-AD: Sequential cost-aware ordinal-belief planning with energy-based models for diagnostic agents
Ziwen Yu, Ivan Koychev, Elizabeth Coulthard et al. (+7 more)
Alzheimer's disease (AD) diagnosis requires sequential evidence acquisition under heterogeneous test costs and patient burden. Fixed-modality predictors do not jointly decide which test to acquire or when the available evidence is sufficient for diagnosis. We propose SCOPE-AD (Sequential Cost-Aware Ordinal-Belief Planning with Energy-Based Models for Diagnostic
Agents) for cost-aware classification of cognitively normal (CN), mild cognitive impairment (MCI), and AD cases. A mask-aware ordinal model represents uncertainty along the ordered CN–MCI–AD continuum. Retrospective training records provide sampled Bellman targets for an energy-based teacher, whose action distributions are distilled into a Qwen policy. At deployment, the
agent selects acquisition or diagnosis actions under availability and budget constraints without access to unacquired values. After each acquisition, the evidence and ordinal belief are updated before the next decision. On ADNI, SCOPE-AD achieves 77.70% Macro-F1 at an average acquisition cost of \$50.46, exceeding the strongest evaluated baseline by 9.34 percentage points. Full-modality evaluation raises Macro-F1 by only 1.89 points while increasing acquisition cost by 116.7 times. These results support selective acquisition for cost-effective diagnosis.
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All authors Ziwen Yu, Ivan Koychev, Elizabeth Coulthard, Ting Zhou, Bolin Chen, Dian Hong, Zinuo You, Yujiao Wang, Anthony Mulholland, Qiang Liu
Computation and Language
Know When to Hold 'em: Correct-Token Retention in Uniform-State Diffusion Language Models
Mojtaba Nafez and James Henderson
Uniform-state
diffusion models (USDMs) can revise any
token at any denoising step, which lets them correct their own mistakes, a key advantage over masked
diffusion. Self-correction, however, requires both revising incorrect
tokens and retaining correct ones, and we show that current USDMs lack the latter. Even under greedy-tail decoding, state-of-the-art USDMs (DUO, UDLM, and uniform-noise SEDD) keep revising 173–270 of 512 positions at every step, and these large, uncoordinated edits collapse sample diversity. A random-
token corruption experiment traces this deficit to the models themselves: they reconstruct clean and corrupted
tokens with nearly identical accuracy, even though clean
tokens are easier targets. A decomposition of the validation NELBO shows that training barely rewards retention: incorrect predictions are heavily penalized at corrupted positions but almost free at clean ones. We propose Correct-
Token Retention Regularization (CTR-Reg), a simple but effective auxiliary loss that trains the model to retain
tokens left unperturbed by the forward process and requires no change to the sampler. CTR-Reg improves clean-
token accuracy by 26.5 percentage points on average across six
benchmarks, while leaving corrupted-
token accuracy virtually unchanged, and its per-step revisions converge to only 3–11 positions. With just five greedy-tail steps, generative perplexity more than halves under CTR-Reg for all three models while diversity is preserved, and these gains hold across sampling budgets. Our results identify correct-
token retention as a key missing ingredient for self-correcting
diffusion language models, and demonstrate an effective fix.
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Machine Learning
Not All Is Lost: Repairing Lossy User Preference States of Personalization Encoders
Parthiv Chatterjee, Dhiraj Golhar, Ummesalma Diwan et al. (+3 more)
Personalization encoders compress evolving interaction histories into preference states used to rank items or condition text generation. A task head operating only on this state can miss useful evidence that remains in the frozen encoder's cached representations for individual timesteps. We study this recoverability gap and propose REPAIR, which compares cached representations with the current preference state in a compact learned coordinate space. It resolves corrective evidence over extended history, recent interactions, and localized bursts. It then selects which patterns at which timesteps contribute and adds their aggregate correction to the state before the task head. Encoder-host repair reuses representations from the existing forward computation without re-encoding the history. Across MovieLens, PENS, MIND, and Amazon Reviews 2023, training only REPAIR improves MRR and nDCG@10 for all twelve representative recommendation hosts while both encoder and task head remain frozen. Head-only finetuning of the same hosts yields smaller gains. For example, Mamba4Rec on MovieLens gains 3.96 MRR points, compared with 0.19 from head-only finetuning. Rank and temporal diagnostics support a compact, host-dependent corrective structure. In personalized generation, IMPerSumm improves the two reported weighted PerSEval variants, which assess responsiveness to user preference, by up to 25.23%. These results support post-compression state correction and distinguish the availability of preference evidence from its downstream use.
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All authors Parthiv Chatterjee, Dhiraj Golhar, Ummesalma Diwan, Sourish Dasgupta, Manjunath Joshi, Tanmoy Chakraborty
Machine Learning
IQS-BO: In-Context Query Selection for Bayesian Optimisation
Luca Geminiani and Nadja Klein
Bayesian Optimisation (BO) is a powerful framework for the optimisation of expensive black-box functions, but typically requires refitting a surrogate and maximising an acquisition function at every evaluation step. In-context approaches based on Prior-data Fitted Networks (PFNs) amortise part of this cost by pre-training
transformers on functions drawn from synthetic priors. PFNs4BO amortises the surrogate but still relies on a numerically maximised acquisition function, while FIBO performs BO fully in-context by sampling optimiser locations from a learned density, which fixes the decision rule and admits no surrogate. Learned acquisition functions score a finite candidate set with a trained network, but, lacking a label for the query, learn the score by reinforcement learning on previously solved tasks. We propose IQS-BO, a PFN that learns the query decision by supervised learning on synthetic priors. In a single forward pass, IQS-BO predicts the probability that each candidate maximises the objective over the set, and we show that the minimiser of its objective is the posterior probability of this event. The model can be pre-trained without a surrogate for fully in-context BO, or take the predictions of a fixed probabilistic surrogate as additional input, amortising only the decision step. Our method proposes queries at a fraction of the cost of acquisition-based methods, while either matching or outperforming standard BO with Gaussian processes (GPs) and available in-context methods on synthetic and real-world
benchmarks. Finally, we propose a mixture prior for pre-training PFNs which combines samples from GPs with functions exhibiting warped inputs, isolated narrow optima, or plateaus that are poorly modeled by stationary kernels common in GP surrogates. We show that pre-training on this prior can lead to improved optimisation performance.
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Distributed Computing
RapidMoE: Exploiting Cross-Asymmetry via Adaptive Residual Offloading for Large-Scale MoE Inference
Wenxun Wang, Likai Ma, Zongle Huang et al. (+2 more)
The widespread adoption of Mixture-of-Experts (MoE) has created a growing need for deployment on heterogeneous platforms. However, it exposes a fundamental mismatch between the algorithmic demands of large-scale MoE and the disparate characteristics of hardware.Existing CPU-GPU hybrid inference systems fail to resolve this as they either encounter PCIe bandwidth bottlenecks when loading experts to GPUs, or rely heavily on CPU computation. Consequently, this leads to low resource utilization and inevitable violations of fixed latency budgets as parameters scale. In this paper, we identify and exploit Cross-Asymmetry–a structural
alignment between the algorithmic workload skew of MoE routing and the physical disparity of heterogeneous hardware. To this end, we introduce RapidMoE, a residual offloading system for efficient large-scale MoE inference. We propose how RapidMoE leverages a residual-split framework to enable offloading paradigm shift from expert-level to bit-level, which unfolds across three key dimensions: (1) data representation, enabling compact and decoupled storage; (2) routing strategy, partitioning computation into dual paths aligned with hardware capabilities; (3) execution parallelism, scheduling a balanced storage-compute workload across devices. We further employ a novel Unified Multi-Level Importance Arbitration to adaptively adjust the critical expert set at runtime, ensuring the accuracy-latency Pareto frontier. These innovations exploit inherent cross-asymmetry, fundamentally breaking the
algorithm-hardware misalignment. Experimental results show that RapidMoE achieves up to 3.5x speedup in decoding and 2.1x speedup in prefill compared to state-of-the-art (SOTA) offloading systems.
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All authors Wenxun Wang, Likai Ma, Zongle Huang, Chen Tang, Yongpan Liu
Cryptography and Security
Autonomous OSS Threat Detection via Taxonomy-Aligned LLMs
Md. Robiul Islam Niloy
Open-source software (OSS) ecosystems face escalating supply chain threats, including typosquatting, dependency confusion, Trojan Source obfuscation, malicious build injection, and CI/CD pipeline poisoning. Sonatype documented over 1.2 million cumulative malicious packages across major registries, while signature-based and rule-based detection approaches lack the semantic understanding needed to generalize across evolving attacks. This paper proposes a taxonomy-aligned large
language model framework for automated detection and multi-class classification of OSS supply chain threats. We introduce an AV-xxx taxonomy covering five semantically distinct attack categories and curate 999 verified real-world incidents from GitHub Security Advisories, CISA alerts, government security databases, and commercial threat intelligence reports, spanning 2018 to 2026 across eight package ecosystems. Using taxonomy-aligned prompting with GPT-4, the framework achieves 97% accuracy and 97% macro F1 across all five categories. Evaluation against eight methods five traditional machine learning baselines, one zero-shot open-source LLM, and two fine-tuned neural models reveals that fine-tuned Llama 3.1 8B (70.5%) and SecRoBERTa (77.5%) underperform a simple TF-IDF classifier (82.3%), while Mistral 7B without taxonomy
alignment achieves 65.7%. These findings highlight taxonomy-aligned prompting over model scale, domain pretraining, or fine-tuning as the critical performance factor. Cross-ecosystem analysis shows varying classification difficulty, with npm achieving 65.5% baseline accuracy versus 100% in smaller ecosystems. GitHub Actions exhibits a substantial accuracy F1 divergence (82.4% accuracy, 0.367 macro F1), indicating systematic class imbalance and highlighting ecosystems where LLM-based contextual
reasoning offers the greatest benefit. Our
dataset, scripts, and code are publicly available in Github.
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Artificial Intelligence
Feedback Without the Wait: Piloting a Generative AI Practice Platform in a Large Maths Class
Lili Chen, Gavin Buskes, Yuxin Ren et al. (+1 more)
Timely and specific feedback is one of the strongest influences on student learning, yet it is difficult to sustain in large electrical engineering classes where the ratio of students to demonstrators is high and a learner who is stuck may wait days to find out why an approach was wrong. Generative Artificial Intelligence (GenAI) offers a way to scale conversational feedback, but using it to grade assessed work raises trust and accountability concerns, and keeping a human in the loop to assure its judgements reintroduces the very delay that erodes the value of feedback. The result is a tension between the immediacy that makes feedback so impactful and the human oversight that makes it trustworthy. In this work, we set out to resolve that tension in practice by designing and piloting a GenAI practice platform that delivers immediate, scaffolded feedback during self-directed practice. This relocates human oversight from real-time grading to the upfront verification of solutions. Our goal was to understand how students engaged with the tool, how they perceived the value and reliability of its feedback, and what lessons transfer to other engineering subjects.
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All authors Lili Chen, Gavin Buskes, Yuxin Ren, Chin Tong Leong
Robotics
PROMO: Preference-conditioned Multi-Objective Reinforcement Learning for Quadrupedal Robots
Amr Mousa, Rifny Rachman, Neil Karavis et al. (+2 more)
Quadrupedal locomotion requires balancing conflicting objectives such as command tracking, stability, and energy efficiency, yet conventional reinforcement learning (RL) hardcodes these priorities into a fixed scalar reward at training time. We present PROMO (Preference-Conditioned Multi-Objective Reinforcement Learning), a semantic multi-objective approach that makes this trade-off an explicit runtime input to a single locomotion policy. PROMO conditions the policy on deployment facing preferences while keeping embodiment-specific locomotion priors fixed, thereby separating operator intent from reward shaping terms required for viable gait generation. Compared with fixed-objective controllers, multi-objective baselines, and independently trained specialists, PROMO achieves objective specialization and robustness from a single deployable policy. Across 100 sampled preferences in simulation, 67 behaviors are non-dominated under exact Pareto dominance, with a mean preference-objective correlation of 0.843, demonstrating broad Pareto coverage and predictable preference response. The same policy transfers zero-shot to a Unitree Go2, where preference changes alone reduce specific energy by up to 30.4%, position error by 38.7%, and peak body-attitude deviation by 59.0% relative to the balanced preference. These results establish preference-conditioned multi-objective RL as a practical runtime interface for adaptive legged locomotion, extending its role beyond offline Pareto-set construction. Open-source code and
videos are available at https://amrmousa.com/promo/.
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All authors Amr Mousa, Rifny Rachman, Neil Karavis, Michele Caprio, Richard Allmendinger
Robotics
ColoACT: Multi-Cue Action Chunking for Smooth Autonomous Colon Navigation on a Self-Propelled Endoscopic Robot
Jian Hu, Shujing He, Leixin Chang et al. (+4 more)
Autonomous colonoscopic
navigation can reduce operator burden and the risk of loop formation or tissue trauma, but remains challenging due to deformable anatomy, weak-texture and specular endoscopic visuals, and contact-rich viscoelastic interactions. Existing methods either rely on geometry-driven pipelines, which are efficient and interpretable yet brittle due to manually engineered features and switching logic, or adopt learning-based policies, whose inferred depth/geometry can become temporally inconsistent or overly smooth under weak texture and specular highlights while simulation-trained variants (e.g., deep reinforcement learning) may further suffer from a sim-to-real gap. We propose ColoACT, an autonomous
navigation system that integrates an RGB-D-E based Action Chunking
Transformer policy (ColoACT policy) for a compact self-propelled Bevel-Gear-Based Endoscopic
Robot (BGER). The ColoACT policy augments RGB with estimated relative depth and a gradient-based pseudo-elevation map to enhance fold-ridge saliency and other high-frequency
geometric cues, and enables smooth continuous control of the BGER by predicting overlapping action chunks and fusing them via temporal ensembling. In different ex-vivo porcine colons (approximately 60 cm), our system achieves success rates of 85.4% and 72.5% in straight and curved segments, respectively, and achieves 70% success in 90-degree turns and 60% in double-bend sequences, with feasibility further demonstrated in challenging triple-bend segments. The project page is available at: https://Adamhu1.github.io/ColoACT/.
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All authors Jian Hu, Shujing He, Leixin Chang, Zongze Li, Ding Huang, Chaoyang Shi, Chengzhi Hu
Computation and Language
Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems
Yiqiao Jin, Yiyang Wang, Lucheng Fu et al. (+8 more)
Scientific progress emerges from a longitudinal ecosystem in which researchers, institutions, funding agencies, collaboration networks, and the scientific literature co-evolve. As AI becomes increasingly involved throughout the scientific research cycle, understanding these interconnected and evolving processes becomes increasingly important. We introduce SciUtopia, a persistent, closed-loop LLM-
agent simulation framework for studying academic research ecosystems. SciUtopia models interconnected scientific processes such as research-direction choice, collaboration, submission, peer review, resubmission, citation, funding, and researcher attrition, while maintaining evolving states across simulated years. Its configurable institutional mechanisms and information channels provide a controlled testbed for matched counterfactual experiments and targeted interventions. Across 61 simulation worlds, SciUtopia simulates over 40,000 researchers from 8,000 institutions, producing around 400,000 publication decisions and 1.2 million LLM-generated peer reviews. Using these longitudinal simulations, we find that rejection-driven resubmission substantially amplifies reviewer burden beyond population growth alone, cautious exploration balances citation impact with career success and long-term topic diversity, and resource inequality can emerge even without detectable cumulative advantage from narrowly winning early funding. Code is available at https://github.com/Ahren09/ScienceUtopia.
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All authors Yiqiao Jin, Yiyang Wang, Lucheng Fu, Bing He, Siheng Xiong, Yijia Xiao, B. Aditya Prakash, Josiah Hester, Srijan Kumar, James Evans, Jindong Wang
Artificial Intelligence
DeFA: Dependency-Guided Failure Attribution for LLM Agents
Bo Deng, Xinlei Zheng, Yi Wei et al. (+6 more)
Errors in LLM
agent executions and their visible consequences can be separated by many steps, making decisive-error localization a matter of understanding both step content and step dependencies. We introduce DeFA, a dependency-guided framework for
agent failure attribution. DeFA first combines protocol relations and semantic dependencies into an event dependency graph spanning the trajectory. It then identifies events that may violate task requirements and traces their sources and subsequent effects to construct a failure propagation graph. Finally, DeFA uses step evidence and the steps' roles in failure propagation to identify the decisive error, responsible
agent, and error category. To support long trajectories, DeFA partitions executions into segments and combines the current segment's detailed content with summaries of the other segments, giving local diagnosis access to global execution context. Across Who and When and the Who and When Pro text subset, DeFA achieves the highest responsible-
agent and exact step accuracy with all evaluated backbones, and the highest failure-mode accuracy among taxonomy-aligned methods on Pro. Further experiments on image and
video trajectories demonstrate its applicability to
multimodal failure attribution. Ablations support the contributions of
segmentation, the event dependency graph, and the failure propagation graph. Using DeFA's diagnostic feedback for skill
evolution in Trace2Skill improves downstream task accuracy by 6-15 percentage points over the native pipeline, showing that the diagnoses can also support
agent improvement on subsequent tasks.
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All authors Bo Deng, Xinlei Zheng, Yi Wei, Kang Zhou, Chongyang Tao, Renzhao Liang, Xuanren Chen, Lifan Guo, Chi Zhang
Machine Learning
Context-Aware Error Mitigation Orchestration for Hybrid Quantum Reinforcement Learning on NISQ Systems
Bisma Majid, Shabir Ahmed Sofi and Mir Mohammad Yousuf
Quantum Reinforcement Learning (QRL) integrates reinforcement learning with parameterized
quantum circuits and is a promising approach to combinatorial optimization. On Noisy Intermediate-Scale
Quantum (NISQ) devices, however, decoherence, gate imperfections, and measurement errors reduce policy quality and make learning less reliable. Existing error mitigation techniques are generally applied as fixed corrections that do not adapt to changing noise conditions or to the evolving state of training. This work presents Adaptive Policy-Guided Error Mitigation (APGEM) as a context-aware orchestration layer of the hybrid
quantum-classical training loop that dynamically selects the most suitable mitigation strategy during QRL training. APGEM evaluates Zero-Noise Extrapolation (ZNE), Probabilistic Error Cancellation (PEC), Clifford Data Regression (CDR), and Readout Error Mitigation (REM) using policy-level indicators, including
quantum-state fidelity, policy
entropy, cumulative reward, and approximation ratio, and integrates the selected strategy directly into the reinforcement learning loop. The framework is evaluated on the Capacitated Vehicle Routing Problem (CVRP), a representative NP-hard problem in urban logistics, under a range of NISQ noise models and noise levels. APGEM consistently outperforms conventional static mitigation methods, reaches approximately 94% of the utility of an oracle strategy, maintains higher
quantum-state fidelity as noise increases, and produces more stable learning behaviour throughout training. Ablation studies show that the framework learns context-aware mitigation policies that adapt to different noise environments and
circuit execution conditions. These findings demonstrate that integrating adaptive error mitigation into the learning process substantially improves the robustness and reliability of QRL on NISQ hardware.
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Cryptography and Security
A Resource-Aware Behavior Reconstruction and Hierarchical Semantic Learning Framework for Host Intrusion Detection
Youli Tao, Rui Tang, Hao Ren et al. (+4 more)
System calls (syscalls) record key interactions between running programs and the operating system kernel, providing fine-grained and minimally intrusive data for host-based intrusion detection systems (HIDS) deployed in cloud and other modern computing environments. However, existing methods often model syscalls in their original execution order, where sequences from different processes are interleaved, making informative patterns difficult to extract and raising two questions: whether raw syscall sequences can be reorganized in a way that yields more discriminative representations, and how complex attack patterns can be effectively learned from the reorganized sequences. We propose ReSHID, a resource-aware behavior reconstruction and hierarchical semantic learning framework for host intrusion detection. It reconstructs semantically continuous sequences by leveraging syscall semantic invariants to cast subject identity and relationship resolution across PID namespaces as a bipartite matching problem and tracking file descriptor (FD) lifecycles to associate descriptors referring to the same resource. Additionally, features extracted from these sequences are organized into a lightweight subject behavior graph incorporating inter-subject relationships, where GATv2 captures key coordination patterns to model complex attacks involving multiple subjects. Experimental results show that sequence reconstruction combined with the detection method can improve HIDS performance. Even with a lightweight linear classifier, the proposed method achieves the best results among all compared methods in terms of F1-score (98.64%), ROC-AUC (99.80%), and PR-AUC (98.10%), while reducing the number of n-gram features by approximately 75.2% and 44.1% compared with the raw sequences and MGFE, respectively.
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All authors Youli Tao, Rui Tang, Hao Ren, Chengsheng Zhou, Dengzhe Wang, Shuyu Jiang, Xingshu Chen
Artificial Intelligence
Revision-Aware Independent Agent Graphs for Dynamic Reasoning
Yan Luo, Selim-Antoine Lali, Jeremy Moebel et al. (+3 more)
Conventional
reasoning protocols present a fixed, preselected task, so they cannot test whether an
agent propagates relevant updates, preserves unaffected work, or reconstructs a historical task binding. We therefore study dynamic task routing, in which an event stream revises task bindings and a system must select the document version valid at each query time before solving it. To study this problem, we repurpose six widely used
benchmarks: MMLU, MMLU-Pro, MedMCQA, MATH, GPQA, and HumanEval into 31{,}119 dynamic episodes comprising 373{,}428 temporally categorized queries. This setting exposes a central trade-off: recomputing after every event wastes work, whereas unguarded reuse returns stale conclusions. We introduce the Revision-Aware Independent
Agent Graph (RIAG), a bounded multi-
agent policy that separates deterministic temporal resolution from task
reasoning. RIAG caches solutions by immutable document identity, starts each fresh task with two unexposed attempts, and conditionally invokes audit and repair, using at most four calls per document version. On this collection, homogeneous RIAG achieves 54.24% joint routing-and-answer accuracy at 0.62 calls/query, compared with 32.22% at 18.00 calls/query for the strongest comparison method; heterogeneous RIAG reaches 49.78% at 0.63 calls/query.
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All authors Yan Luo, Selim-Antoine Lali, Jeremy Moebel, Iliass Khoutaibi, Ahmadou Aidara, Mengyu Wang
Programming Languages
Augmenting Rewrite Rule Sets via Knuth-Bendix Completion
Michael Schifferer, Marcel Ullrich and Sebastian Hack
Equality Saturation (EqSat) is a powerful technique for program optimization, systematically exploring the search space of candidate programs to overcome the phase ordering problem. However, the feasibility and performance of EqSat rely heavily on the specific rewrite rules used to derive equivalent programs. These rule sets are typically handcrafted, requiring extensive domain expertise and carrying the risk of missing valuable transformations. In this work, we evaluate Knuth-Bendix Completion (KBC) as a method for automatically generating and augmenting rewrite rules for EqSat. Our experiments show faster optimization as well as reaching better terms with previously missed optimizations.
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Computation and Language
Right Answers, Wrong States: Hidden Information Failures in Multi-Agent Collaboration
Herun Wan, Jiaying Wu, Minnan Luo et al. (+4 more)
Multi-
agent systems are often judged by whether they reach the correct answer. This can miss a distinct failure: collaboration may leave behind a corrupted information state even when the immediate decision is correct. We call this an off-query failure. To study this failure in collaborative decision support, we introduce OffQuery, which separately evaluates evidence verification (T1), shared-state reconstruction (T2), and task resolution (T3) in two representative high-stakes settings: healthcare and disaster response. Across GPT, Gemini, and Qwen models, standard collaboration shows much stronger task performance than state reliability. Averaged over 21 model–setting combinations, task resolution reaches 64.7%, while evidence verification and state reconstruction reach only 14.3% and 43.1%. We trace this gap to selective information use: current queries often bypass corrupted facts, which become consequential when later tasks require them. We further introduce ReGround, which resolves conflicting evidence, verifies shared facts, reconstructs a trusted state, and reasons over that state. Across seven models from three families, ReGround improves all three capabilities in every evaluated setting, with average relative gains of 309.0%, 82.9%, and 17.6% on T1, T2, and T3. Reliable collaboration therefore requires both a correct decision and a reliable shared state for future
reasoning.
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All authors Herun Wan, Jiaying Wu, Minnan Luo, Zihan Ma, Fanxiao Li, Nancy F. Chen, Min-Yen Kan
Computer Vision
When the Judge Acts: Auditing VLM-Guided Image Selection on Culturally Situated Prompts
Huichan Seo
Vision-
language models (VLMs) increasingly act as judges that pick the best of several generated images, so their choices decide what users see. Such judges are usually validated by score agreement with human ratings, not by the images they return. We audit VLM judges as decision-makers: on 300 culturally situated
prompts, we compare the returned image with human ratings the judge never sees and with random choice from the same candidates, and repeat every decision with the candidates reordered. A 4B-parameter judge barely beats random and falls short of a CLIP similarity baseline. It picks the first image shown in 49% of calls (chance: 28%), and reordering changes its choice on 60% of
prompts. For this judge, agreement across orders is informative: decisions that survive reordering are much better than random, whereas agreement with a weaker second judge keeps the wrong ones. An 8B judge shows almost no position bias and outperforms CLIP, yet for it the same filter mostly discards good decisions. Agreement helps only when it targets the judge's failure mode, so filters must be re-audited whenever the judge changes. The 4B judge's slight rise in stereotype ratings is no longer detectable after aggregating across orders or with the larger judge.
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Computation and Language
Evaluating the Robustness of Japanese LLMs to IME-Related and Typographical Errors
Ryota Mibayashi and Hiroaki Ohshima
Large
language models (LLMs) have achieved strong performance across various natural
language processing tasks. However, their robustness to typographical errors remains underexplored, particularly in Japanese, where text input involves multiple writing systems and IME-based conversion. In this study, we evaluate the robustness of Japanese LLMs against realistic Japanese-specific typos. We introduce five typo categories: Character Transposition, Character Replacement, Homophone Conversion, Japanese IME Conversion, and Full-Width Conversion. These perturbations are applied to three Japanese
benchmark datasets (JMMLU, JCommonsenseQA, and JamC-QA), and eleven Japanese and
multilingual LLMs are evaluated. The results show that Character Transposition and Character Replacement typos consistently reduce accuracy across
benchmarks, whereas IME Conversion, Full-Width Conversion, and Homophone Conversion have relatively limited impact. These findings reveal that current Japanese LLMs remain vulnerable to realistic Japanese typing errors, particularly those that substantially distort the original input, highlighting the importance of robustness evaluation in practical input environments.
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Discrete Mathematics · Cross-listed from math.CO
A proof of Lehmer's permutation conjecture for neighbor-swap graphs
Tom Verhoeff
In 1965, D. H. Lehmer conjectured that the permutations of every multiset admit an imperfect
Hamiltonian traversal by adjacent swaps: a walk in the neighbor-swap graph that visits every word, with some words visited twice in order to reach a neighbor and return. The question is posed as an unsolved research problem in Knuth's Art of Computer Programming. Verhoeff (2017) chose the stutter words, in which every domino is a double, as the words to be reached this way, and reformulated the
conjecture as the Hamiltonicity of the graph $N(S)$ on the non-stutter words, with two exceptional families — binary signatures with an odd multiplicity, and the permutations of $(2k,1,1)$ — that admit a
Hamiltonian path but no cycle. This article proves the reformulated
conjecture, and with it Lehmer's
conjecture. The key structure is a partition of the words into hypercubes: the swaps inside dominoes turn each class of words with the same domino contents into a hypercube, and the stutters are exactly the $0$-dimensional classes. When every multiplicity is even,
Hamiltonian cycles of the hypercubes are glued along a spanning tree, with no finite check. The case of exactly one odd multiplicity reduces to the all-even case and to a
theorem of Stachowiak (1992), the one inherited Hamiltonicity input, which also settles two or more odd multiplicities. The only finite ingredients are two explicit cycles, of 28 and 84 words. Every construction is implemented in Python and checked against brute-force graphs, and the proof is formalized in Lean 4 over Mathlib.
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Machine Learning
Mixture-Trained Merging for Unified Multi-Objective Models
SeongHyeon Kim, Chaeyun Jang, Seungyoo Lee et al. (+4 more)
Unified
language models are increasingly expected to combine heterogeneous capabilities, such as mathematics, code, instruction following, and controllable thinking behavior, within a single set of parameters. A common solution is sequential post-training on multiple objectives, but this entangles all objectives along one optimization trajectory and makes the final model highly sensitive to training order, data ratios, schedules, and stopping criteria. Weight-space merging offers a modular alternative, but naive merging of single-objective experts often fails: domain capabilities degrade sharply, or think/non-think modes collapse into one dominant behavior. We attribute both failures to incompatible weight-space geometry: experts trained on single objectives drift to distant regions of parameter space, placing their interpolations outside any shared low-loss basin. We propose Mixture-Trained Merging (MTM), which trains each branch on an objective-biased data mixture rather than a single objective, exposing it to cross-objective interactions and making branches compatible at merge time. MTM uses merged-model evaluations as a low-cost signal for selecting branch mixtures, avoiding expensive data-mixture ablations. The procedure is iterative: each round promotes the base model using globally selected merge coefficients and refines each branch mixture using domain-preferred coefficients under constraints that preserve other objectives. To scale beyond simplex grid search, MTM uses qNEHVI-based multi-objective
Bayesian optimization. Across code, mathematics, instruction following, and think/non-think control, MTM outperforms naive merging and preserves behavioral separation where single-objective merging collapses, suggesting that effective unified models require branches trained to be mergeable.
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All authors SeongHyeon Kim, Chaeyun Jang, Seungyoo Lee, Jiyeon Ham, Yunju Bak, Boseop Kim, Juho Lee
Artificial Intelligence
Learning to Ask: Information Acquisition for SLM-LLM Collaboration, under a budget
Yongjun Kim, Xiaoxiao Li and Jaeho Lee
Collaboration between a small
language model (SLM) and a large
language model (LLM) offers an opportunity to combine the efficiency of smaller models with the strong
reasoning capabilities of larger ones. Existing approaches primarily frame such collaboration as a computation allocation problem, determining which model should handle each portion of the
reasoning process. In black-box API-based settings, however, this paradigm can be inefficient due to coarse-grained delegation or repeated transmission of context across model switches. In this work, we instead formulate SLM-LLM collaboration as an information acquisition problem, under an API budget constraint. The SLM remains the primary reasoner and selectively queries a black-box LLM advisor only when needed, issuing targeted queries rather than delegating the
reasoning process itself. To realize this strategy, we develop a three-stage RLVR framework that learns whether to call the advisor, how to formulate useful queries, and how to integrate the collaboration into the
reasoning process by jointly refining advisor invocation and information use. Across mathematical
reasoning and coding tasks, our approach improves the performance–cost tradeoff over existing collaboration baselines and, in some settings, matches or exceeds oracle problem-level routing. Finally, we show that our strategy can transfer to other advisor model families, without further training.
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