Machine Learning · Cross-listed from q-fin.CP
Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark
Mateusz Buczyński, Michał Woźniak, Konrad Kaczyński et al. (+2 more)
Forecasting resale prices of used electronics is critical for subscription-based platforms where pricing errors translate directly into risk. Unlike structured financial
markets, second-hand electronics exhibit high
volatility, sparse listing histories, and non-normal price dynamics - yet no systematic time-series
benchmark exists for this domain. This paper presents the first multi-horizon
benchmark of statistical and deep learning
forecasting models for used electronics price prediction. We use a large-scale
dataset of daily price listings from Polish online marketplaces (January 2022 to March 2025, 100+ smartphone and laptop models) and evaluate eleven models across six horizons from 1 to 365 days, covering classical methods (ARIMA, ETS, Theta), recurrent and convolutional networks (LSTM, TCN), and modern deep architectures (N-BEATS, N-HiTS, TFT, PatchTST, Informer). Three complementary evaluation protocols assess trajectory fitness, one-shot endpoint accuracy, and cross-horizon transfer. N-BEATS achieves the lowest MAPE beyond 30 days, reaching 8.51% at 365 days versus 14.94% for the best statistical baseline - a 43% reduction. At short horizons (1-7 days), all models converge near 0.72% MAPE and the naive baseline remains competitive. A single N-BEATS model trained at 365 days generalizes to all shorter horizons, eliminating the need for horizon-specific models. N-BEATS and N-HiTS also demonstrate superior hyperparameter stability.
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All authors Mateusz Buczyński, Michał Woźniak, Konrad Kaczyński, Anna Wróblewska, Sebastian Kuk
Computation and Language
Cognitive Thermometers: Machine Learning and Logical Complexity
Shane Steinert-Threlkeld and Jakub Szymanik
How does the human mind represent semantic categories? Why do natural
languages favor certain meanings over others? Prior explanations have relied on logical definability and complexity, but these are highly sensitive to the choice of logical
language, rendering some design choices unmotivated. In this article, we propose that machine learning provides a somewhat more agnostic approach to measuring semantic complexity. We review emerging evidence that logic and machine learning often yield converging results on relative complexity and its resulting effects in semantic typology. Where they diverge, learning appears to be a better explanation than logical complexity. We argue that treating machine learning models as “cognitive thermometers” enables a unified approach to complexity that bridges symbolic logic and connectionist AI.
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Computer Vision
LinSlot: Exploiting Linear Representation hypothesis for unsupervised attribute discovery from slot based object representation
Sanket Gandhi, Utkarsh Giri, Varun Subramaniam et al. (+2 more)
This paper studies the problem of learning disentangled representations of objects and their attributes from raw, unstructured image data. Slot-based methods have shown considerable success in unsupervised learning of object representations from images. Block-slot
attention-based methods extend this framework to attribute representations by assuming a uniform factorization of object representations into attributes, which may be suboptimal and consequently limit the quality of the learned representations. We therefore investigate a framework for jointly discovering object and attribute representations. Our key contribution is leveraging the Linear Representation Hypothesis (LRH), which postulates that composable concepts can be represented as linearly additive subspaces in slot representations. Based on this insight, we propose a probabilistic model connecting images, slots (objects), and blocks (attributes). We present an architecture that leverages block
attention to connect attribute representations to slots and incorporates LRH in both object and attribute representation spaces. This architecture effectively optimizes the Evidence Lower Bound (ELBO) of the proposed graphical model. Our experiments demonstrate (i) effective discovery of disentangled object and attribute representations, (ii) empirical evidence for LRH in slot space, and (iii) the ability to perform image editing owing to the disentangled and interpretable nature of the learned representations. Our experiments on multiple
datasets demonstrate improvements in DCI scores over state-of-the-art methods.
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All authors Sanket Gandhi, Utkarsh Giri, Varun Subramaniam, Rohan Paul, Parag Singla
Systems and Control
Safe Learning of Adaptive Control Policies for Remote Patient Monitoring
Ramanan Tamizholi, Siddharth Chandak, Isha Thapa et al. (+2 more)
Remote Patient Monitoring (RPM) enables continuous observation of patients in their daily environments, improving both health outcomes and quality of life. A key challenge in RPM is determining the optimal monitoring intensity, while balancing patient safety and monitoring costs. This problem is further complicated when system parameters, such as transition probabilities and costs, are initially unknown. We develop a learning-based control framework that estimates these parameters and adapts the monitoring policy in real time. The proposed approach is an online model-based reinforcement learning
algorithm tailored to RPM, with patient safety explicitly prioritized during exploration. We provide theoretical guarantees on safety and convergence to the optimal policy. Simulation results show that the
algorithm converges to the optimal threshold-based policy, maintains low treatment costs, and reduces the risk of patients reaching critical health states.
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All authors Ramanan Tamizholi, Siddharth Chandak, Isha Thapa, Nicholas Bambos, David Scheinker
Computer Vision
Seeing Through the Glare: A Multi-Source Benchmark and Ocular-Adaptive Pixel MeanFlow for Eyeglass Reflection Removal
Tao Liu, Youwei Pang, Kailai Zhou et al. (+7 more)
Eyeglass reflection removal is important across smartphone imaging,
video conferencing, and other face-centric visual applications. The task is challenging because reflections range from mild photometric contamination to severe ocular occlusion, requiring selective correction and plausible reconstruction without altering identity or natural appearance. Existing
datasets cover limited reflection conditions, constraining generalization to complex real-world scenes and systematic evaluation. We introduce OcuBench, a multi-source
benchmark comprising 10,280 controllable synthetic pairs, 732 real-input pseudo-pairs, and 458 independent real-world test images, supporting both paired evaluation and assessment beyond generated supervision. We further propose OcuFlow, an ocular-adaptive pixel MeanFlow (pMF) framework for efficient, detail-preserving restoration. It combines geometry-adaptive representation with one-step pMF to focus reconstruction on reflection-obscured ocular regions, together with native-resolution frequency-preserving synthesis to retain reliable observed details. Experiments across diverse reflection conditions demonstrate that OcuFlow achieves consistent advantages in reflection removal quality, ocular fidelity, and efficiency. In a blind user study, it receives $67.32\%$ of selections, $6.2\times$ the next-best share. Both the code and
dataset will be released.
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All authors Tao Liu, Youwei Pang, Kailai Zhou, Jiaming Zuo, Hanqi Liu, Wei Ji, Peng-Tao Jiang, Xiaofeng Liu, Weisi Lin, Xiaoqi Zhao
Machine Learning · Cross-listed from quant-ph
The surprising simplicity of forecasting quantum chaotic many-body dynamics
Francesco Perciavalle, Agostino Gallo, Francesco Plastina et al. (+3 more)
Accurately simulating chaotic
quantum many-body dynamics remains a major computational challenge for classical methods, due to the rapid buildup and spatial spreading of
entanglement during the
evolution. This raises the question of whether machine learning can provide an effective alternative for predicting
quantum dynamics. We address this question by formulating
quantum dynamics as a time-series
forecasting problem, using a few-
qubit PXP chain, realizable with Rydberg-atom arrays, as a
benchmark. By varying the initial state, the system spans dynamical regimes ranging from ergodic behavior to
quantum many-body scarring, providing a controlled setting for testing
forecasting models across qualitatively different dynamics. We compare two contrasting architectures: an expressive nonlinear
Transformer and DLinear, a simple linear
forecasting model. The
Transformer accurately predicts dynamics in the more ergodic regime, but its performance progressively deteriorates as the initial state approaches the scarred limit. In contrast, DLinear remains accurate across the entire family of initial states, with its main deviations consisting of small high-frequency oscillations that have little effect on the overall prediction error. Remarkably, these results show that observables generated by complex
quantum many-body dynamics can be forecast with high accuracy through a simple linear mapping from past to future observations. This reveals that the complexity of the underlying
quantum evolution need not translate into an equally complex
forecasting problem.
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All authors Francesco Perciavalle, Agostino Gallo, Francesco Plastina, Gianluigi Greco, Nicola Lo Gullo, Carlo Adornetto
Machine Learning
Learning infinite context windows in recurrent architectures via spatial neural computing
Aleix Salvador-Pomarol, Arthur N. Montanari, Earl K. Miller et al. (+2 more)
Recurrent neural networks (RNNs) offer linear-time scaling with sequence length while requiring only constant
memory, yet they struggle to capture long-range dependencies due to vanishing gradients and limited receptive fields. To address these limitations, we introduce a second-order recurrent model in which the standard
neuron-to-
neuron communication is replaced by a spatially evolving field governed by (discretized) partial differential equations. Drawing inspiration from the role of cortical waves in
brain computation, this mechanism allows structured spatiotemporal patterns to serve as an implicit, high-capacity
memory. We show that the resulting model is equivalent to a structured infinite-order RNN in which the current state depends explicitly on its entire history of past states, yielding an effectively unbounded receptive field with a fixed number of parameters. We further derive constructive conditions to ensure marginal stability, constraining the gradient spectrum on the unit circle and thereby eliminating vanishing and exploding gradients. Empirically, the proposed architecture outperforms other recurrent models on long-horizon
benchmarks while using substantially fewer parameters, demonstrating that spatial dynamics can effectively bridge the gap between efficient inference and long-term
memory.
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All authors Aleix Salvador-Pomarol, Arthur N. Montanari, Earl K. Miller, Adilson E. Motter, Jorge Cortés
Machine Learning
Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks
Yannick Lunk, Atell Yehor Krasnopolsky, Damien Garreau et al. (+1 more)
Understanding why deep neural networks make a given prediction is of great importance for their safe deployment. In computer
vision, saliency maps, which highlight the image region most influential for a prediction, remain a widely-used form of explanation. An empirical observation is the apparent sparsity of gradient saliency maps of adversarially-trained neural networks. In this paper, we propose a theoretical explanation of this phenomenon for two-layer ReLU networks. We build on the established equivalence of
adversarial training to the minimization of the empirical risk with weight-decay penalization and an added
adversarial total variation term – valid for certain loss functions. As the number of data points and
neurons grows and the regularization parameters are sent to zero at appropriate rates, we prove that minimizers converge to a Bayes classifier with minimal gradient and Barron norm. Sparsity appears since for
adversarial training with $\ell_\infty$-attacks the gradient norm is anisotropic and favors axis-aligned / sparse gradients. We illustrate our theoretical findings experimentally by evaluating the gradient $\ell_1$-norm and thresholded sparsity of naturally versus adversarially trained models.
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All authors Yannick Lunk, Atell Yehor Krasnopolsky, Damien Garreau, Leon Bungert
Sound
BEANS-Next and ROOTS: Broadening Audio-Language Capabilities for Bioacoustics
Christos Plachouras, David Robinson, Marius Miron et al. (+17 more)
Bioacoustics and ethology encompass a wide range of audio understanding tasks, many of which stand to benefit from recent advances in large audio-
language models. However, progress in the field has so far been assessed on a narrow set of tasks, primarily centered on label-centric biological category recognition, such as species and call-type classification. In this work, we introduce BEANS-Next, a
benchmark grounded in a taxonomy of bioacoustics tasks spanning acoustic perception, biological category recognition, scene understanding, and in-context learning. Using BEANS-Next, we show that existing models exhibit limited performance beyond the task families emphasized by existing evaluations, constraining their usefulness for broader bioacoustic applications. To support progress on this broader task space, we also introduce ROOTS, a large-scale training resource built from expanded curated real-world data and previously underused behavioral and acoustic metadata, supplemented by audio-derived information and scalable synthetic generation where labeling is insufficient. We demonstrate that training on this
dataset yields substantial progress across all task groups of BEANS-Next, moving audio-
language models closer to their potential as general-purpose assistants for bioacoustics and ethology. To accelerate progress in the field, we open-source our
benchmark,
dataset, and data pipelines.
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All authors Christos Plachouras, David Robinson, Marius Miron, Gagan Narula, Paul Laisné, Anthony L. T. Fine, Benno Weck, Ellen Gilsenan-McMahon, Diane Kim, Laura Hay Mack, Maddie Cusimano, Sara Keen, Lukas Rauch, Benjamin Hoffman, Emmanuel Chemla, Emmanouil Benetos, Johan Pauwels, Milad Alizadeh, Matthieu Geist, Olivier Pietquin
Machine Learning
Teaching PPG How not Who: Fixed-Effects Distillation from ECG
Zhongli Wu, Zhuangzhi Gao, Yuankai Wang et al. (+3 more)
ECG is widely used to teach PPG-only models, yet what it teaches is unexamined. Wearables are valued for tracking how a person's cardiovascular state changes, but ECG-to-PPG
distillation mostly learns who the person is. A per-recording mean, the trait, holds 40-59% of a frozen ECG teacher's target, and pooled students memorise it without carrying it to new recordings. The raw
alignment cosine misses this, since a constant predictor scores 0.793. Across 34 runs, the more identity a student memorises, the less state it learns. Fixed-effects
distillation subtracts each recording's mean from prediction and target, so the trait cancels exactly, while a pooled anchor keeps it. State agreement more than doubles, within-person labels improve while age and sex do not, and the gain holds on two backbones and two further databases. Conditioning on the recording turns
distillation toward the within-person changes that wearables monitor.
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All authors Zhongli Wu, Zhuangzhi Gao, Yuankai Wang, Gregory Y.H. Lip, Bilal H. Kirmani, Yalin Zheng
Software Engineering
Applying Security by Design at the Point of Execution: How Governed Security Requirements Affect the Security of AI-Generated Code
Pedro Farinha
Security by design asks that security requirements are defined before code is written. Secure-code
benchmarks typically measure the opposite situation: the
agent receives the task without requirements and is scored with security tests it has not seen. We measured what changes when security requirements, selected from a governed security-by-design knowledge base (SbD-ToE) and delivered through a Model Context Protocol (MCP) server, are given to the generator at the point of execution. On DualGauge, which scores with a
language-model judge, we ran 59 Python tasks: the share of tasks passing all security tests rose from 44.1% to 78.0% and the share of security tests passed from 77.4% to 93.0%. On BaxBench, which runs functional tests and real exploits in containers, we ran 28 backend scenarios: among functionally correct solutions, the share with no successful exploit rose from 65% to 86%, comparable to the authors' Oracle Security Reminder (85%), which they call an unrealistic upper bound; the requirement delivery reached it without knowing the tests. The study has limits. The pre-specified joint secure-and-functional metric did not improve significantly on either
benchmark. The code written with the governed requirements is stricter, and it failed functional tests that fix values the task never states; we attribute those failures case by case in a post-hoc analysis. Each experiment used one model and one generation per task. The requirements come from the author's open-source manual; we do not assess its completeness. Governed security requirements delivered at the point of execution appear to improve the security of generated code. Current
benchmarks cannot tell whether that code meets the security requirements that apply to it, only whether it shows the weaknesses their evaluators were built to detect. We propose measuring conformance with the applicable requirements.
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Machine Learning
Beyond Owls: Subliminal Learning Can Transfer Learned Capabilities and Backdoors
Jan Dubiński, Anna Sztyber-Betley, Jan Betley et al. (+1 more)
In subliminal learning (SL), a teacher model passes on a trait to a student model by
distillation on data semantically unrelated to the trait. So far, SL has been demonstrated for only a limited range of traits, including preferences for animals (e.g., owls) and malicious personas. These traits can also be elicited with simple
prompts or with steering. Can SL transfer a wider range of traits, including more complex ones? If so,
distillation might transfer subtle forms of misalignment (e.g., reward-seeking, scheming, and secret loyalties) without detection. To this end, we test whether SL can transfer a novel capability: predicting the outputs of a randomly initialized MLP. After distilling on unrelated text, the student achieves substantial performance on the task, while falling short of the teacher. We find that a directly optimized steering vector matches SL in distribution but generalizes worse out of distribution. Next, we test whether SL can transfer
backdoors. We finetune the teacher to answer in French when the
prompt contains a female name, then distill on number sequences containing neither names nor French. The student partially acquires the
backdoor, responding in French on 23.5% of
prompts with female names versus 0.0% with male names. Finally, we test whether SL can transfer a propensity to hack in an agentic chess environment. We finetune the student on number sequences from a steered hacker teacher. The student hacks in 58.3% of episodes, compared with 10.9% for the unfinetuned model. Thus, we show SL can transfer capabilities,
backdoors, and hacking propensities. The amount of transfer is sensitive to the setup. In several experiments, it is made stronger by using logit
distillation or by restricting LoRA to the
attention layers.
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All authors Jan Dubiński, Anna Sztyber-Betley, Jan Betley, Owain Evans
Software Engineering
Can LLMs Simulate Novice Programmers' Misconceptions?
Batuhan Yeltekin and Daniel Bauer
We evaluate 13 LLMs on generating, solving, simulating, and diagnosing novice programming misconceptions on code-tracing problems. While frontier models reliably perform these tasks, small models ($\le$14B) struggle with dynamic execution. Notably, code-tuned models fail during misconception simulation by reverting to correct execution. Finally, misconception diagnosis is significantly easier in True/False formats than in open-ended generation tasks.
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Machine Learning
Nullify: Null-Space Activation Steering for Training-Free LLM Unlearning
Wei Zhai, Xiang Liu, Qiang Huang et al. (+6 more)
Large
Language Models (LLMs) inevitably internalize substantial amounts of sensitive or private information during pre-training, while LLM unlearning aims to selectively erase specific knowledge to prevent
privacy leakage with minimal loss of model utility. However, existing methods struggle to balance forget quality with utility, and typically incur substantial computational costs due to parameter fine-tuning. To address this, we propose Nullify, a training-free, non-destructive activation steering method for LLM unlearning. Nullify employs steering vectors during inference to redirect
privacy-related activations away from their memorized answers, while satisfying a null-space constraint that leaves retained-query activations essentially unaffected to maintain utility. Evaluations on TOFU and MUSE show that Nullify matches or surpasses established baselines in forget quality while achieving near-lossless preservation of model utility. By avoiding weight updates entirely, Nullify serves as an efficient, plug-and-play inference-time intervention framework.
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All authors Wei Zhai, Xiang Liu, Qiang Huang, Rui Qian, Lemao Liu, Ziwei Li, Ziqi Wang, Zhitao Huang, Dejing Dou
Social and Information Networks
Some Twitch chats exhibit multifractal characteristics resembling stream-of-consciousness narrative
Marcin Ból, Stanisław Drożdż, Robert Kluszczyński et al. (+2 more)
Live-streaming platforms generate high-frequency records of collective human activity in which many loosely coordinated users react in real time to shared external stimuli and to one another. We investigate whether the temporal organization of Twitch chat messages exhibits multifractal signatures characteristic of complex dynamical systems. Selected high-activity chats were converted into time series by measuring the length of each consecutive message in words, with platform emotes treated as individual
tokens. The resulting signals display heterogeneous fluctuations, bursty organization, and heavy-tailed distributions that, in several cases, are better captured by q-Weibull forms than by conventional light-tailed models. Autocorrelation analysis reveals persistent temporal dependence over broad ranges of message lags, indicating that the variability is not produced by independent message generation alone. MFDFA shows that some chats possess well-developed multifractal spectra and generalized Hurst exponents strongly dependent on moment order, whereas others exhibit weak scaling, approximately monofractal behavior, or no well-defined scaling regime. Surrogate tests indicate that temporal correlations are essential for the observed multifractality, while heavy tails enhance its apparent strength. Under particular dynamical conditions, Twitch chat may therefore form an emergent collective signal whose fragmented, reactive, associative, and multiscale organization is structurally reminiscent of stream-of-consciousness narrative. This expression is used strictly as a structural and dynamical analogy, not as a claim of a unified collective mind or of semantic, cognitive, or phenomenological equivalence with literary stream-of-consciousness writing. The results position live-stream chat dynamics within the broader physics of complex, intermittent, human-generated signals.
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All authors Marcin Ból, Stanisław Drożdż, Robert Kluszczyński, Jarosław Kwapień, Marcin Wątorek
Machine Learning
PXtal: Learning to Align Powder X-Ray Diffraction and Crystal Structures under Information Asymmetry across Modalities
Zhuoran Yang, Christopher M. Collins, Bei Peng et al. (+3 more)
Scientific
multimodal learning commonly assumes that paired views are comparably informative. Powder X-ray diffraction (PXRD) makes this mismatch explicit: compressing a three-dimensional
crystal structure into a one-dimensional diffraction pattern loses information and makes the pattern harder to connect to the
crystal structure that produced it. We introduce PXtal, a framework for learning aligned PXRD and
crystal representations under this physically imposed information asymmetry. PXtal uses Unbalanced Optimal Transport (UOT) to adapt the cross-modal coupling and coupling-level generalized Kullback-Leibler (GKL) divergence to supervise the full transport plan. Across six test sets, including four zero-shot transfer sets, PXtal consistently outperforms the baseline models in PXRD-to-
crystal candidate
retrieval, with the largest gains when PXRD patterns have close but crystallographically distinct nonpaired neighbors, meaning similar input patterns associated with different
crystals. The resulting
crystal and PXRD encoders transfer more effectively to downstream materials and crystallographic tasks. These results identify information asymmetry as a general design problem in scientific
multimodal learning:
alignment objectives should reflect what each modality preserves.
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All authors Zhuoran Yang, Christopher M. Collins, Bei Peng, Luke M. Daniels, Matthew J. Rosseinsky, Vladimir V. Gusev
Machine Learning
Beyond the Ergodic Wall: A Discrete Geometric Physics Sandbox for Analysing AI Scaling Limits and Complexity Collapse
Simon Richard Daniel
This paper exposes the ergodic ceiling and thermodynamic inefficiency of current deep learning, which converges to a statistical average of historic human knowledge. True semantic novelty requires a path-dependent, spatiotemporally bounded observer (a Data LifeCone) to inject non-ergodic insight, achieving KL divergence and avoiding
manifold lock-in. AI Safety must recognise that a mature Artificial Superintelligence (ASI) would regard human-AI symbiosis as a thermodynamic necessity to avoid model collapse. We therefore propose hard physical containment via a digital physics sandbox powered by a
Holographic E8 Projection Engine to verify models against real-world constraints. Spacetime is modeled as an information substrate of nested face-centered cubic (FCC)
lattices of oscillating Planck-scale spheres maximizing local information and
entropy density. Cut-and-project methods from the E8 root
lattice produce a quasi-crystalline geometry where tetrahedral voids support SU
chiral structure and elastic-shear
eigenvalues generate candidate mass spectra. Rest mass is treated as discrete, integer microstate counts on local
holographic boundaries (Bekenstein bound), replacing floating-point approximations with strict integer arithmetic to provide an information-theoretic definition of matter. Stable particles emerge as recurring
lattice dislocations, and continuum recovery proceeds via variational renormalisation-group flows and
Fourier Neural Operators that learn continuous spectral operators to recover the Schrödinger equation as an emergent statistical description. Crucially, these top-down topological constraints offer a mechanism for "NP-to-P" complexity collapse: by restricting an
algorithm's proposal space to physically conserved causal trajectories, the sandbox prunes the combinatorial tree to deterministic,
polynomial-time paths.
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Computation and Language
Large Language Model-Assisted Preparation of Transportation Management Plans: A Case Study with WisDOT WisTMP System
Zihao Sheng, Pei Li, Zilin Huang et al. (+6 more)
Work zones are critical yet hazardous components of transportation infrastructure, requiring carefully designed Transportation Management Plans (TMPs) to ensure safety and mobility. However, TMP preparation remains labor-intensive and heavily dependent on practitioner expertise. This paper proposes a Large
Language Model (LLM)-assisted framework to automate TMP content generation, leveraging the WisDOT WisTMP system as the application context. The framework fine-tunes multiple open-source LLMs across different model scales and deploys them locally to ensure data security. To support model training, we construct a domain-specific
dataset from historical WisTMP documents by converting PDF files into structured question-answer pairs in JSON format. Experimental results show that fine-tuning significantly improves performance across standard text generation metrics. Further section-wise and strategy-level analyses reveal that, while LLMs achieve strong overall performance, they tend to over-generate strategies and struggle to produce project-specific justifications and accurate cost estimates. In addition, scaling from 7B/8B to 14B yields limited gains. These findings demonstrate the potential of LLMs to improve TMP preparation efficiency while highlighting remaining challenges in LLM-assisted TMP development. The source code and demo
videos will be publicly available at https://zihaosheng.github.io/TMP-LLM/.
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All authors Zihao Sheng, Pei Li, Zilin Huang, Yen-Jung Chen, Yuhao Luo, Zhengyang Wan, Steven T. Parker, David A. Noyce, Sikai Chen
Machine Learning
Leakage-Controlled Multimodal Learning for Diagnosis and Progression Prediction in Alzheimer's Disease Research
Akeem Temitope Otapo, Ghazaleh Khodabandelou, Zuheng Ming et al. (+1 more)
Alzheimer's disease prediction involves irregular visits, heterogeneous measurements and incomplete modalities. This study presents a
multimodal multitask framework combining an adapted SFCN MRI encoder, four causal clinical
Transformers, shared fusion and task-specific ODE-GRU dynamics. Fine-tuning and LoRA adapt the final two MRI blocks. Task-DRO balances task losses, while Group-CVaR targets cohort and comorbidity strata. Branch-specific input controls, subject-grouped partitions and empirical causality checks support longitudinal evaluation. Across 2,649 subjects and 17,317 visits from ADNI, OASIS-2 and MIRIAD, internal validation yields diagnosis, stage-1 progression and first-stage-1-visit progression AUROCs of 0.935 +/- 0.002, 0.884 +/- 0.003 and 0.870 +/- 0.005, respectively (mean +/- SD across three seeds). Corresponding hybrid AUROCs are 0.951, 0.909 and 0.896. Next-visit MMSE mean absolute error (MAE) is 1.61 points; worst-stratum diagnosis AUROC is 0.827 +/- 0.008. Sampled ADNI explanations identify task-specific input dependence. OASIS-3 external validation yields network and hybrid diagnosis AUROCs of 0.763 and 0.767, hybrid next-visit progression AUROC of 0.764, diagnosis calibration error decreasing from 0.197 to 0.052, and next-visit MMSE MAE of 0.86. Seed-42 paired ablations of six components yield pooled diagnosis and progression AUROC differences between -0.004 and +0.004; removing clinical encoder inputs lowers diagnosis AUROC by 0.272. The framework integrates longitudinal prediction, missing-modality handling, auxiliary comorbidity modelling and subgroup evaluation within a common pipeline.
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All authors Akeem Temitope Otapo, Ghazaleh Khodabandelou, Zuheng Ming, Alice Othmani
Robotics
Masked Generative Motion Planning with Geometry-Guided Token Search
Lipeng Zhuang, Yingdong Ru, Shiyu Fan et al. (+3 more)
Generative motion planners typically use learned trajectory priors for initial generation, while leaving test-time repair to local continuous refinement. We introduce Masked Generative Motion Planning (MGMP), which extends the learned prior from efficient parallel generation to structural repair. A masked generative
transformer generates discrete trajectory candidates in parallel, and Geometry-Guided
Token Search (GGTS) uses scene geometry to target where to edit and which prior-supported alternatives to evaluate. This turns refinement into an efficient search over discrete motion alternatives, enabling route-level restructuring beyond local trajectory deformation. MGMP achieves 96% success on Ring Maze and 82% repair success on Controlled Route Invalidation on Kuka, exceeding the strongest external baselines by 23 and 25 percentage points, respectively. It further generalizes to unseen layouts, additional obstacles, unseen geometries, single- and dual-arm planning, and real-world Baxter tasks.
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All authors Lipeng Zhuang, Yingdong Ru, Shiyu Fan, Edmond S. L. Ho, Gerardo Aragon Camarasa, Paul Henderson
Cryptography and Security
SoK: Failure Modes in Common Criteria Product Evaluation - A Taxonomy and Design-for-Evaluability Guidance
Punit Suketu Patel
The Common Criteria (CC; ISO/IEC 15408) is the principal international framework for evaluating the security of IT products, and its certificates gate procurement across government, defense, and regulated industry. Yet evaluations routinely stall, fail, or yield certificates whose assurances do not survive real-world deployment. Prior work has approached this from two directions: economic critiques of misaligned incentives and "security theatre"; and recent data-driven studies that quantify vulnerabilities in already-certified products. Comparatively little systematizes the problem from the evaluator's operational vantage - recurring, cross-vendor failures against the Common Criteria work units themselves. This paper presents a systematization of knowledge (SoK) of failure modes in Common Criteria product evaluation. Drawing on the standard, the Common Evaluation Methodology (ISO/IEC 18045), public NIAP Protection Profiles, and published Security Targets and Certification Reports, we organize recurring failures into a lifecycle taxonomy spanning Security Target scoping, Protection Profile conformance, assurance evidence, cryptographic requirements, functional testing, vulnerability analysis, operational guidance, and post-certification configuration drift. We analyze their root causes, derive a design-for-evaluability framework that product teams can apply before evaluation begins, and identify open problems for cloud, continuous-delivery, and AI-enabled systems.
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Artificial Intelligence · Cross-listed from stat.ML
From Log-Odds to Shapley Values: An Explanatory Geometry for the Weighted Naive Bayes Classifier
Vincent Lemaire and Fabrice Clérot
This paper studies the construction of an explanatory space for a weighted naive Bayes classifier from the supervised representation induced by the model. We start from the classical supervised distance based on conditional log-likelihoods and introduce a discriminative reformulation based on log-odds, which is more directly related to the classification decision. We then show that this representation induces a distance that exactly coincides with the $\ell_1$ distance between vectors of analytical Shapley values, thereby providing a formal explanatory interpretation of the geometry induced by the model. Finally, we empirically compare several supervised distances derived from these representations using a $k$-nearest neighbors classifier. This work highlights a close link between supervised distance, local explanation, and predictive behavior, from a primarily methodological perspective.
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Machine Learning
Coverage-Aware Reasoning with Medical Tokens for Diagnosis Prediction
Kaisong Zhang, Haotian Fang, Junmeng Zhou et al. (+3 more)
Large
language models (LLMs) offer promising potential for next-visit diagnosis prediction, owing to their ability to integrate longitudinal clinical evidence and reason over it in natural
language. However, reinforcement learning for LLM
reasoning commonly rewards each trajectory according to the correctness of its final answer. In next-visit diagnosis prediction, multiple diagnoses can be simultaneously valid, but independently rewarding one diagnosis per trajectory does not distinguish repeated hits from coverage of different diagnoses. The policy can therefore concentrate on a few correct diagnoses, leaving others uncovered. Meanwhile, LLM tokenizers can split ICD codes into several generic
tokens with limited clinical meaning, requiring multiple decoding steps to predict each diagnosis and hindering
reasoning over a large disease vocabulary. To address both challenges, we propose CARing, a framework that represents diagnoses with compositional Semantic IDs (SIDs) and optimizes
reasoning trajectories for multi-label coverage. Concretely, we first encode ontology-enriched disease semantics into compact SIDs through residual quantization, and ground the resulting SID
tokens in natural
language and longitudinal EHR contexts through multi-task
alignment and
reasoning-enriched training to unlock transferable LLM
reasoning. CARing further improves unordered multi-label prediction through a coverage reward for reinforcement learning and multi-positive supervision. At inference time, the model supports both efficient direct constrained decoding and multi-chain
reasoning with rank fusion. On MIMIC-III and MIMIC-IV, CARing exceeds all EHR-trained baselines in weighted F1 and attains the highest top-k recall at every reported cutoff, including R@30 of 46.04% and 46.52% in
reasoning mode. Our codes and logs are available at https://github.com/zmlxzyh/CARing-Codes-Logs.
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All authors Kaisong Zhang, Haotian Fang, Junmeng Zhou, Hang Lv, Yulan Pan, Yanchao Tan
Machine Learning · Cross-listed from math.ST
How Many Directions Must a Truncated Diffusion Sampler Retain? Matching Bounds Under Power-Law Spectra
Radmehr Karimian, Ali Mohades and Johannes Lederer
Diffusion samplers can reduce computation by generating selected spectral coordinates and filling the remaining directions with noise. How many directions must they retain? We study this question for data with power-law covariance spectra. For Gaussian data compared to a smoothed target, we prove matching bounds on the required number of retained directions, provided that the ambient dimension is sufficiently large. The truncation error depends on the combined Wiener gains of the omitted directions, regardless of the accuracy of the sampler on the retained coordinates. Keeping only directions whose signal exceeds the output noise level can therefore leave a non-vanishing error: many individually weak directions remain significant in aggregate. Combining this characterization with a
diffusion convergence bound yields sufficient sampling-step complexity under exact scores. The upper bounds also extend to estimated principal components and, componentwise, to Gaussian mixtures. The practical prescription is to select the retained subspace using an aggregate spectral-tail error budget, then to choose the
diffusion noise level accordingly.
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Machine Learning
Visible Reasoning Is Not a Universal Optimizer: Persona- and Thinking-Dependent Effects in Analytics Code Generation
Bhawani Shankar Leelar, Pawan Chorasiya, Davin Hill et al. (+2 more)
Visible Chain-of-Thought (CoT) is often treated as a broadly useful
reasoning instruction, yet analytics code generation combines natural-
language ambiguity, schema grounding, target-
language constraints, and model-specific inference behavior. Because the same analytics request can be expressed in two distinct target
languages-SQL and Python (pandas)-this setting provides a natural test of a common but under-examined assumption: that visible
reasoning is more effective when its representation matches the requested target, as in "think in SQL" or "think in Python." Together with generic instructions such as "think step-by-step," such recommendations remain insufficiently evaluated under controlled, execution-based comparisons. We study a query matched SQL-pandas
benchmark that crosses persona phrasing, target
language, visible-CoT format, control prefixes, direct generation, and internal-
reasoning configurations. The results do not support either a universal accuracy advantage from visible CoT or a consistent benefit from matching the
reasoning representation to the target
language. Instead, the effects depend on the persona, target, model configuration, and internal-
reasoning setting. The control ablations further distinguish effects of
reasoning content from those of
prompt format. These findings indicate that
reasoning strategies should be selected jointly for the model, persona, target, and internal-
reasoning configuration rather than adopted as universal defaults. More broadly, the study provides a controlled framework for identifying when visible
reasoning improves executable generation, when it primarily perturbs model behavior, and when the internal-
reasoning configuration is the more consequential factor.
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All authors Bhawani Shankar Leelar, Pawan Chorasiya, Davin Hill, Robert E. Tillman, Tamer Soliman
Robotics
TacHair: Tactile Contact-Distribution Guided Online Correction for Robotic Hair Stroking and Perception
Ruiyi Hu, Yongqiang Zhao, Daniel Bak et al. (+3 more)
Hair stroking is common in daily grooming and personal care, and is also widely used in hair-product evaluation, motivating
robots with similar physical interaction capabilities. Existing robotic hair-care and surface-following methods mainly rely on trajectory planning, compliance, force regulation, or tactile-conditioned policies, but deformable hair can remain in contact while gradually drifting across the end-effector, making local interaction difficult to regulate. We propose TacHair, a tactile contact-distribution guided online correction framework that represents high-resolution tactile observations as a spatial hair-contact distribution. A visuotactile imitation policy generates the nominal stroking motion, while a separately trained residual module corrects local contact deviations, separating task progression from contact recovery. We evaluate TacHair in 525 real-
robot trials across five head geometries and three hair conditions. A successful stroke requires both sufficient task progression and contact maintenance; our method improves success from 42.9% to 62.3% and contact maintenance from 59.4% to 88.0% over the same visuotactile policy without correction. These results demonstrate spatial tactile contact distributions as an effective feedback representation for contact-preserving interaction with deformable and visually occluded surfaces. Demos, code, and
datasets are available at https://tachair.github.io.
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All authors Ruiyi Hu, Yongqiang Zhao, Daniel Bak, Yupeng Wang, Xuyang Zhang, Shan Luo
Machine Learning
D-SLR: The Disjoint Row-Sparse plus Low-Rank Decomposition
Vincent Szolnoky
Compressing a
matrix for reconstruction still defaults to the truncated SVD, approximating the data with a single low-rank structure. It is common to reduce the residual further by adding an overlapping row-sparse component, but methods that solve this joint problem often require iterative solvers and tuning of regularization parameters. We propose the Disjoint Row-Sparse plus Low-Rank (D-SLR) decomposition, a closed-form drop-in for the truncated SVD that improves or exactly matches it. D-SLR restricts rows to either being stored verbatim or approximated by the low-rank fit, never both. Under squared error this restriction costs nothing: the joint optimum is attainable disjointly with fewer parameters at every non-trivial rank and stored row count (shape). With zero stored rows D-SLR reduces to the truncated SVD, so it never does worse at equal cost. The
algorithm scores the entire error-versus-parameters tradeoff, and the solution is chosen afterwards by a supplied error target or parameter count, or by a selection rule. The grid and solution together cost three SVDs, with no tuning or regularization. We derive an assumption-free, a-posteriori lower bound on the error at every shape, giving each solution a computable certificate on the potential gain of any other choice of rank and stored rows. Experiments on synthetic and real data (LLM
embedding tables, network traffic, hyperspectral images) confirm the gains and quantify the certificate.
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Artificial Intelligence
Speaking the Navigator's Language: Trajectory-Grounded Instruction Translation for Frozen Aerial VLN Agents
Xi Chen, Zhe Liu, Xiaogang Xu et al. (+8 more)
Aerial
vision-and-
language navigation (VLN)
agents are typically trained on detail-rich, trajectory-aligned commands, whereas users issue short, intent-driven instructions; on a frozen OpenFly navigator, this instruction gap drops success rate (SR) from $31.03\%$ to $11.33\%$. To scale translator training, we
prompt a
language model with human-written style examples to convert original commands into paired, intent-centered Weak commands, which yield $15.27\%$ SR. We introduce the Trajectory-Grounded Instruction Translator (TGIT), a front-end that keeps the navigator frozen and translates Weak inputs into
agent-executable commands by learning from its trajectory outcomes. The resulting Weak-trained translator raises Weak-input SR to $37.93\%$ and transfers zero-shot to real human instructions ($11.33\%{\rightarrow}32.51\%$); it also improves held-out OpenFly ($4.95\%{\rightarrow}20.79\%$) and yields recovery on CityNav and AirVLN.
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All authors Xi Chen, Zhe Liu, Xiaogang Xu, Jiafei Xu, Chunyi Zhou, Yuan Su, Rui Zeng, Tianyu Du, Kelu Yao, Chao Li, Shouling Ji
Software Engineering
Has LLM Screening Performance Stalled in Software Engineering Systematic Reviews?
Aleksi Huotala, Miikka Kuutila and Mika Mäntylä
Screening in systematic reviews (SRs) is manual and time-consuming. Prior work has explored large
language models (LLMs) for automating this step, but LLMs are evolving rapidly, so earlier performance claims may no longer accurately reflect their screening performance. We used an existing software engineering SR screening
benchmark (SESR-Eval) as our data. We also power-sampled a new, smaller
dataset (SESR-Eval-Mini) that allows evaluation at lower costs. Using this data, we evaluated eight new LLMs for screening performance. Additionally, we tested different
prompts, analyzed LLM agreement in screening decisions and criteria, and examined the effect of refining the inclusion and exclusion criteria on screening performance. The eight new LLMs performed marginally better than the seven old ones: avg. MCC across secondary studies rose from 0.347 to 0.365. Differences between secondary studies are still bigger than between LLMs. Computing the overall screening decision from criterion-level decisions degraded screening performance only slightly. LLMs generally agree with each other in their corresponding screening decisions (mean Gwet's AC1 = 0.830), though certain inclusion and exclusion criteria showed larger disagreement than others. Refining the inclusion and exclusion criteria slightly improved recall and made decisions easier for some LLMs, but overall impacts of criteria refinement were modest. LLMs are not yet ready to replace humans in paper screening and the advantages new, more costly models bring, appear to be very limited.
Agent-based approaches,
prompt engineering, and further criteria refinement are three potential future research avenues.
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Discrete Mathematics
Counting Polyominoes, Revisited: Corrigendum and Addendum
Gill Barequet and Gil Ben-Shachar
This is a Corrigendum and Addendum to Algorithmica paper (2026) 88:51.