Artificial Intelligence
Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning
Zhiquan Huang, Jiazhe Wang, Linjing Xue et al. (+3 more)
Multivariate time series
forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based
forecasting methods generally fall into three categories: fixed partitioning, multi-scale partitioning, and extendable partitioning. Fixed partitioning often breaks meaningful temporal boundaries, multi-scale partitioning may introduce redundant representations across scales, and extendable partitioning improves flexibility but still lacks an explicit mechanism for organizing semantic structure and modeling interactions among heterogeneous temporal patterns. To address these limitations, we propose SCPaT, a
Transformer based framework built on semantic structured partitioning. SCPaT first decomposes input sequences into semantically consistent units through adaptive semantic unit generation, then constructs a dynamic semantic graph to model directed dependencies among these units and organize them into higher order semantic blocks. Based on these structured representations, an importance aware routing mechanism adaptively dispatches different semantic blocks to different experts for customized modeling. Extensive experiments on 12 real world
datasets demonstrate the effectiveness of SCPaT.
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All authors Zhiquan Huang, Jiazhe Wang, Linjing Xue, Ming Liu, Meiwen Li, Ruijuan Zheng
Computer Vision
Flow Matching Meets 3D Curvilinear Structure Segmentation in Medical Imaging
Sidi Mohamed Sid'El Moctar, Nicolas Vitry and Hélène Bouvrais
Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex
topology, severe class imbalance, weak contrast, and large variations in structure morphology. While deep learning approaches for 3D curvilinear
segmentation have been proposed, they are often tailored to specific anatomies or modalities, limiting generalization across clinical settings and leaving room for improvement. Recent generative models have shown the benefits of iterative prediction for structured
segmentation tasks, yet
diffusion-based methods suffer from computationally expensive sampling, hindering their use on high-resolution 3D volumes. We present 3D-CurvSegFlow, a flow matching-based model for 3D curvilinear structure
segmentation. The model learns a continuous transformation from a simple source distribution to the target vascular representation, enabling progressive refinement of complex curvilinear geometries with efficient inference. We evaluate our method on Three public challenging
datasets covering distinct anatomies and modalities: portal vein, cerebral vessel, and coronary arteries. Using a common architecture and training strategy across all tasks, our method outperforms general-purpose and vessel-specific approaches, with strong preservation of thin branches and vascular continuity. This work not only advances the state-of-the-art in 3D curvilinear
segmentation but also opens new avenues for efficient, generalizable, and clinically applicable methods in medical image analysis.
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Machine Learning
G-MARK: Grounded Multi-Agent Reasoning for Cooperative Driving via Knowledge Graphs
Bhavya Gupta, Onat Gungor and Tajana Rosing
Autonomous driving systems must operate under partial observability, where safety-critical objects may be occluded or visible only to neighboring connected vehicles. Vehicle-to-vehicle cooperation can reduce this uncertainty, but existing cooperative driving methods often compress multi-
agent evidence into latent features or hidden
multimodal states. As a result, they obscure which
agent observed each object, whether the object is visible to the ego vehicle, and how conflicting evidence affects downstream decisions. We propose G-MARK, a grounded multi-
agent reasoning framework that converts cooperative object-centric observations into explicit provenance-aware knowledge graphs (KGs). The resulting KGs preserve object hypotheses together with their source attribution, ego-versus-partner visibility, uncertainty, conflicts, spatial relations, and planning-relevant context. G-MARK then derives a shared feature representation from these KGs, enabling lightweight task heads to support object
reasoning, motion prediction, control selection, and trajectory
forecasting. Compared with the state-of-the-art baseline, GMARK improves occlusion
reasoning accuracy by 42.2%, reduces control-selection error by 13.1%, and achieves comparable trajectory-planning accuracy with a 25.6x smaller structured communication payload. Our code is available at https://github.com/bhavyagupta98/g-mark.
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Software Engineering
Evaluating Automated Testing on an Open-Source Web Application Using Cypress
Quoc-Binh Nguyen, Truc-Ly Phan Nguyen, Ngoc Hong Tran et al. (+1 more)
End-to-end automated testing is increasingly used in web software development to ensure system quality and shorten response times during development. However, the true effectiveness of automated testing depends on many factors including execution time, stability of test results, and maintainability of the test suite as the application continues to evolve. In this paper, we evaluate the effectiveness of end-to-end automated testing using the Cypress framework for an open-source web application. We deployed the experiment with 27 test cases. The test's effectiveness is measured by execution speed, reliability, and maintainability. The experimental results show that the Cypress-based end-to-end test suite has short and stable execution times. It suits frequent runs during software development. The majority of test cases achieved consistent results across multiple runs, while flakiness only occurred in a few tests which involve complex interactive functions. Furthermore, the study highlights the impact of element locator strategies and Page Object Model (POM) architecture on test suite maintainability, demonstrating that resilient data-cy attributes significantly reduce maintenance overhead when UI changes occur.
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All authors Quoc-Binh Nguyen, Truc-Ly Phan Nguyen, Ngoc Hong Tran, Dung Hai Dinh
Cryptography and Security
Tracking the Trend in How Speech Synthesizers Deceive People
Milan Šalko, Anton Firc, Kamil Malinka et al. (+4 more)
Advances in
speech synthesis have made deepfake audio highly realistic. Earlier studies reported 70-80% human detection accuracy, but relied primarily on older synthesizers. We compare human detection for three selected voice synthesis tools released in 2019, 2022, and 2024 with 82 IT professionals, and
benchmark humans against six pretrained detectors on the same material. For fully synthetic
speech (full spoofs), the F1 score drops from about 90% for RTVC and YourTTS to 48% for ElevenLabs, although listeners were explicitly warned that deepfakes were present. For partial spoofing, where only one sentence of an utterance is altered, strict accuracy falls to 9%, and listeners classify the synthetic sentence as bona fide 77% of the time. Humans and detectors fail in complementary ways, and neither reliably localizes short manipulations. Additionally, listeners increasingly mislabel bona fide
speech as fake, eroding trust in unmanipulated audio. These findings show that human perception alone is unreliable for the selected modern and partial-spoof conditions and motivate procedural verification, provenance, watermarking, and segment-level detection.
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All authors Milan Šalko, Anton Firc, Kamil Malinka, Vojtěch Staněk, Martin Perešini, Filip Pleško, Jakub Reš
Computation and Language
Natural Language Code Retrieval for 1C:Enterprise: An Open Benchmark and Efficient Bi-Encoder
Konstantin Chesnokov and Chingiz Mingazov
Natural
language code
retrieval is a rapidly evolving task in computer science. However, the 1C:Enterprise ecosystem combines Russian syntax with highly domain-specific terminology, for which open
datasets and specialized models have been virtually non-existent. We present a comprehensive pipeline for 1C code
retrieval: an open
benchmark of 3,413 real-world, PII-scrubbed query-code pairs, a reproducible evaluation harness, and a specialized bi-encoder. To overcome scarce labeled data, we fine-tune on 784,057 synthetic triplets generated by google/gemma-4-26B-A4B-it from public code repositories, using Matryoshka Representation Learning (MRL) and a
privacy-aware tokenizer. Because the
benchmark subsets differ in size, we report balanced-subset macro, query-weighted micro, and forum-only results. Our model reaches 0.5992 balanced macro nDCG@10, 0.5044 micro, and 0.4617 on forum, versus 0.4932 macro for the baseline architecture and 0.5404 for google/embeddinggemma-300m. Removing every
benchmark example flagged by the conservative exact/13-gram overlap audit leaves 0.6011 balanced macro (0.5010 micro), indicating that detected train-
benchmark overlap does not explain the headline result. MRL truncation to 256 dimensions preserves 99.9% of
retrieval quality while reducing dense-index storage and exact similarity arithmetic by a factor of three.
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Robotics
MILD: Tractable Terrain Modeling for Learning Improved Bipedal Locomotion on Deformable Surfaces
Zeren Luo, Jiahui Zhang, Zhe Xu et al. (+6 more)
Enabling
robots to walk on yielding terrain is vital for applications ranging from disaster response to planetary exploration. While bipedal
robots hold immense potential, their locomotion on deformable surfaces remains limited as current simulators fail to capture the spatiotemporal heterogeneity of such yielding substrates. We present MILD, featuring a physics-grounded discrete-element contact solver that accurately simulates spatially varying foot-terrain interactions. Complementing this model, we train a terrain-aware locomotion controller via deep reinforcement learning with latent modulation and proprioceptive estimation. Quantitative comparisons against state-of-the-art methods show our approach generates more diverse and realistic contact scenarios during training, resulting in controllers that exhibit natural adaptation on real deformable surfaces. Through hardware experiments, we demonstrate the system's capability for online terrain identification and adaptation across a wide range of surface stiffness.
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All authors Zeren Luo, Jiahui Zhang, Zhe Xu, Wanyue Li, Xinqi Li, Xuechao Chen, Zhangguo Yu, Annan Tang, Peng Lu
Hardware Architecture
Experimental Verification of Fast Voltage Droop Correction Circuits
Shreyas Srinivas, Ian W. Jones, Carsten Schulze et al. (+2 more)
Due to the trend towards minimizing guard bands for energy saving purposes, voltage droops are a key limiting factor for the operational frequency of today's VLSI
circuits. Adapting clock frequencies dynamically presents the challenge of metastability in the device that detects and stores the existence of voltage droops. We present an implementation of a fast all-digital
circuit for adaptive response to droops using the IHP 130 nm process. The description of the design is presented in an accompanying paper. We experimentally validate the functionality of the design on a test chip.
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All authors Shreyas Srinivas, Ian W. Jones, Carsten Schulze, Milos Krstic, Christoph Lenzen
Artificial Intelligence
Learning Early-to-Final Solution Consistency for MILP Acceleration
Guanlin Li, Chengrui Gao, Chenguang Wang et al. (+6 more)
Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making. Owing to their NP-hardness, however, modern solvers may struggle to find high-quality solutions for challenging MILP instances within practical time limits. Recent learning-based approaches seek to accelerate MILP solving by directly predicting high-quality solutions from static instance-level features, such as variable-constraint bipartite graphs. Yet accurate solution prediction from instance features alone is difficult, and these methods largely overlook the information revealed during the solver's search process. In this paper, we find that solutions produced at the early search stage of MILP solvers, which are computationally cheap to obtain, are often structurally close to the solutions found after full-budget search. Motivated by this observation, we propose a new solver-informed paradigm that shifts the learning target from variable assignment to early-to-final consistency: for each variable, we predict whether its early-stage assignment should persist in full-budget solutions. The predicted consistency naturally guides downstream search, for instance by fixing the assignments deemed consistent. At inference time, we further ensemble consistency predictions across multiple early-stage solutions to improve robustness. Experiments across four MILP
benchmarks show our method improves prediction-guided search across diverse downstream pipelines. With Gurobi, our proposed method reduces the primal gap by 56.9% on average and closes it completely on combinatorial auction instances. Besides, we transferred the Gurobi-trained model zero-shot to SCIP without adaptation, achieving a 36.4% average gap reduction across
benchmarks.
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All authors Guanlin Li, Chengrui Gao, Chenguang Wang, Haopu Shang, Zherong Zhang, Ke Xue, Jixiang Lu, Weiyong Yang, Chao Qian
Data Structures and Algorithms
New Complexity Results for Fair Repetitive Scheduling
Moran Koren, Michael L. Pinedo and Dvir Shabtay
We revisit the problem of finding fair solutions to repetitive scheduling problems with a single machine. In this problem, we are given a set of $n$ clients and a planning horizon consisting of $q$ periods (days). Each day, every client submits a single job that must be processed by the machine. The objective is to construct a set of $q$ schedules, one for each day, such that the quality of service (QoS) received by each client meets a predefined threshold. The QoS measure may be any standard scheduling criterion, such as the total waiting time or total completion time of a client's jobs over the entire planning horizon. This problem has been studied in the literature, with previous works providing complexity classifications and approximation
algorithms for various QoS measures. Nevertheless, several important questions remain open. In this paper, we resolve three of these questions and identify several additional directions for future research.
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Human-Computer Interaction
Designing Human-mediated AI Guidance: Ready Together for Personalized Family Emergency Preparedness
Nini Kurashvili, Yana Ivanchenko, Greta Schiavo et al. (+2 more)
Artificial intelligence (AI) systems are increasingly used across domains to provide personalized information, recommendations, and decision support. However, in some contexts, AI-generated information may not be suitable for direct delivery to the final recipient. Instead, it may need to be interpreted, adapted, and communicated by a human who understands the recipient's needs, emotional state, and situational context. Human-AI interaction research has given less
attention to situations in which a more knowledgeable human acts as an intermediary between an AI system and a less experienced or less informed recipient. We introduce the human-mediated AI guidance framework and explore it through Ready Together, an AI-supported family emergency preparedness system in which parents mediate AI-generated content for their children. The system is designed to provide personalized guidance and support parents in making emergency preparedness more interactive and understandable through guided activities and family-centered learning. The system design was informed by a qualitative, design-oriented research process involving semi-structured interviews and co-design activities. Findings identified challenges in family emergency preparedness, including difficulty discussing emergencies with children, uncertainty about providing appropriate explanations, and a preference for interactive learning activities. These findings informed the design of an interactive prototype, subsequently evaluated through a pilot study and a heuristic evaluation. Participants responded positively to the personalized recommendations and practical activities. Preliminary findings suggest that human-mediated AI guidance may support context-sensitive family preparedness while preserving parents' responsibility for interpreting, adapting, and communicating AI-generated information.
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All authors Nini Kurashvili, Yana Ivanchenko, Greta Schiavo, Cansu Koyuturk, Dimitri Ognibene
Computation and Language
Dynamic Gated Cross-Modal Fusion with Sarcastic-aware Contrastive Regularization for Multimodal Sarcasm Detection
Hao Guo, Subin Huang, Junjie Chen et al. (+3 more)
Multimodal sarcasm detection aims to identify sarcastic intent from
multimodal content, where inconsistencies between literal meaning and contextual cues often signal irony. This task has attracted increasing research
attention. However, accurate detection remains challenging due to instance-dependent modality contributions and misleading semantic consistency, where surface-level
alignment masks underlying contradictory intent. Existing methods often rely on fixed fusion strategies and treat sarcasm as generic cross-modal mismatch, limiting their ability to capture subtle sarcasm cues and instance-specific modality interactions. To address these challenges, we propose a novel MSD framework that integrates Dynamic Gated Cross-Modal Fusion with Sarcastic-aware Contrastive Regularization (SaCR). Specifically, a bidirectional gated interaction module performs cross-modal feature filtering and adaptively calibrates textual and visual contributions at the instance level. A dynamic fusion gate further balances modality importance to generate more robust
multimodal representations. Furthermore, SaCR is introduced as a label-aware contrastive regularization objective that encourages semantic consistency for non-sarcastic samples while suppressing misleading consistency in sarcastic cases. The proposed framework is trained end-to-end with a multi-objective learning strategy that jointly optimizes
multimodal classification and auxiliary unimodal supervision. Extensive experiments on MMSD and MMSD2.0 demonstrate that the proposed method consistently outperforms strong baselines.
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All authors Hao Guo, Subin Huang, Junjie Chen, Zhifa Geng, Sanmin Liu, Chao Kong
Cryptography and Security
From Noise to Signal: Improving Security Log Anomaly Detection Using LLMs with Endpoint-Specific Logs
Christopher Henshaw and Gour Karmakar
Existing approaches to anomalous behaviour log detection, such as Wazuh rely primarily on predefined detection rules, while statistical anomaly detection approaches such as OpenSearch identify deviations from previously observed behavioural patterns. Recent research has investigated LLMs for log anomaly detection because of their ability to interpret semantic and contextual information. However, LLM-based approaches can be affected by
prompt construction, noisy log data, and reliance on generic
datasets that may lack endpoint-specific authentication behaviours. To address these limitations, this study develops a standardised instruction-based LLM classification framework for detecting anomalous authentication behaviours, including borderline cases. A controlled cybersecurity testbed was developed to generate endpoint-specific authentication data, producing a curated
dataset comprising normal, borderline, and anomalous behavioural scenarios. Three instruction-tuned LLMs, Meta Llama 3.1 8B Instruct, Qwen 2.5 7B Instruct, and GPT-OSS 20B, were evaluated against Wazuh rule-based detection and OpenSearch Anomaly Detection using a common ground-truth severity framework. Meta Llama 3.1 8B Instruct achieved the strongest overall end-to-end detection performance, with an accuracy of 89.3%, recall of 88.2%, F1-score of 91.8%, and false negative rate of 11.8%. In comparison, Wazuh achieved an accuracy of 52.0% and false negative rate of 68.6%, while OpenSearch achieved an accuracy of 49.3% and false negative rate of 74.5%. Meta Llama also detected 80% of the borderline anomalous scenarios, compared with 20% for Wazuh and 15% for OpenSearch. Qwen achieved lower overall detection performance than Meta Llama but recorded the lowest average inference latency and 100% structured-response validity. GPT-OSS demonstrated strong classification performance when valid responses were produced.
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Cryptography and Security
ShadowPath: Lookup-Private Credential Status Verification over Authenticated State
Patrick Herbke, Wolf Rieder, Christian René Sechting et al. (+4 more)
Verifiable credentials let holders present digitally signed claims without requiring the issuer to participate in every presentation. Revocation complicates this
privacy model because a verifier must determine whether a credential remains valid. Existing status checks may expose recurring identifiers, registry positions, or request metadata. Such information can serve as stable handles to link separate presentations. ShadowPath moves the credential status lookup to the holder. For each presentation, the holder proves, in zero-knowledge, that the credential has not been revoked under the verifier-selected registry root. The verifier learns the status result but not observable metadata. To the best of our knowledge, we provide the first evaluation of Verkle trees for credential revocation and compare them with sparse Merkle trees to assess their applicability in real world applications. The comparison tests whether reducing path depth with Verkle trees offsets the higher cost of KZG-based authentication. Across 30 desktop trials, median Groth16 proving took 371.6ms with sparse Merkle and 2.11s with Verkle. Verification took 3.70ms and 7.55ms, respectively. Groth16 Verkle proving took about 3s on both primary mobile devices. The results show that shorter authenticated paths do not necessarily yield cheaper zero-knowledge proofs. With fresh session randomness, verifier-visible status data do not reveal whether two presentations use the same credential under the stated assumption of session-value independence. This guarantee excludes issuer-verifier collusion and synchronization traffic.
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All authors Patrick Herbke, Wolf Rieder, Christian René Sechting, Huaning Yang, Sid Lamichhane, Philip Raschke, Axel Küpper
Sound
Towards Quantifying Benchmark Optimization in ASR Models
Theo Lebryk, David Ayllon, Alice Baird et al. (+3 more)
Public
benchmarks are important measures of Automatic
Speech Recognition (ASR) model capabilities. However, by nature of being public, there is risk of models being optimized for these
benchmarks in ways that do not generalize well to real-world data. We present a methodology for quantifying
benchmark optimization, focusing on cases where the audio underdetermines the reference transcript. We identify three families of behavioral probes that reveal models' capabilities of reproducing
benchmark reference spans despite underdetermined audio: reference disagreement, masked-number recovery, and orthographic switching. We find that the highest-scoring open source models output verbatim reference transcript spans even when the relevant audio is contradictory, masked, or ambiguous. Using a variety of mechanistic probes, we show that models respond to narrow acoustic cues to override the faithful representation of the audio in favor of a
benchmark-optimized policy. We show the
benchmark-optimized behavior can be causally manipulated via low-rank linear steering or simply appending audio to the end of a segment in some cases. Overall, our results indicate that high-performing models exhibit
benchmark-conditioned behaviors that can inflate
benchmark performance without reflecting improved general-purpose transcription ability.
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All authors Theo Lebryk, David Ayllon, Alice Baird, Jakub Piotr Cłapa, Jens Madsen, Panagiotis Tzirakis
Distributed Computing
ParaWeb: Parallel Programming Patterns for Web Development
Suejb Memeti
Modern web applications increasingly require computationally intensive processing, yet JavaScript, the dominant
language of the web, has traditionally been limited to a single-threaded execution model. Node.js Worker Threads and browser Web Workers provide low-level mechanisms for parallel execution, but developers lack high-level abstractions that capture recurring parallel structures as reusable patterns. In this paper, we present ParaWeb, a TypeScript library that implements ten parallel programming patterns for server-side Node.js, client-side browser environments, and WebGPU compute shaders. ParaWeb provides three implementation variants for each pattern: a message-passing (MP) variant based on structured cloning via postMessage, a shared-buffer (Shared) variant that uses SharedArrayBuffer with typed array views, and a GPU variant that uses WebGPU compute shaders for hardware-accelerated execution. We describe the architecture, design decisions, and pattern-specific implementation strategies, and we evaluate the performance of all thirty implementations across three data sizes. Experimental evaluation results show that the CPU-based variants achieve speedups of up to 11.6x with 16 threads for compute-bound patterns, while the GPU variants reach speedups of up to 260x for compute-bound patterns with high arithmetic intensity such as Farm, Scatter, Reduce, and Map. A case study on five image-convolution filters further shows that GPU acceleration reaches up to 414x speedup over single-threaded CPU on non-separable kernels, with consistent scaling across 1024x1024$, 2048x2048$, and 4K images.
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Artificial Intelligence
A Strong Linear Baseline for Whole-Heart Cardiac Shape Completion on CT, with an Open Eleven-Structure Statistical Shape Model
Matej Gazda, Jakub Gazda, Juraj Gazda et al. (+1 more)
Public cardiac cohorts annotate different subsets of the heart, so shapes from separate sources cannot be pooled without shared correspondence. Among released cardiac shape resources, none we identified carries the atrial appendage, pulmonary veins, and caval stumps as separate blocks in one mesh. Completion
benchmarks also compare deep models against a least-squares projection onto shape modes, not the conditional
estimator the same fitted model implies. We release an eleven- structure cardiac computed-
tomography (CT) statistical shape model, built from 383 automatically labelled cases in 11 571-vertex correspondence, and compare completion
estimators under one frozen internal split and endpoint. On a 76-case internal list held out from fitting, a closed-form conditional-Gaussian
estimator reconstructed the missing non-chamber structures at 3.717 mm mean per-vertex error, averaged equally over one, three, five, and nine observed structures. A five-refit mask-conditioned graph variational autoencoder reached 5.248 mm and nearest-neighbour
retrieval 8.931 mm. The paired difference was 1.531 mm (95% confidence interval 1.384 to 1.711), and the ordering held in a raw-coordinate sensitivity arm. Expert manual labels exist for 58 external CT cases, but our registered reference is close enough to score only five structures. There the closed-form
estimator again had lower average surface distance, 95th-percentile Hausdorff distance, and Chamfer error for both completed atria. On a second public
benchmark of 20 cases the reference was close enough for three of four completed structures, and the same ordering held there. Four structures have no expert reference. The released model and its completion operator support cohort-unification research on aligned CT, not clinical use.
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All authors Matej Gazda, Jakub Gazda, Juraj Gazda, Peter Drotar
Cryptography and Security
Securing Filesystems for Confidential Computing
Dimitra Giantsidi, Antoine Delignat-Lavaud, Cédric Fournet et al. (+5 more)
Confidential computing protects applications inside Trusted Execution Environments (TEEs), but it leaves storage vulnerable. Even with disk encryption, a malicious cloud provider can roll back, replay, fork, or tamper with disk state, breaking the integrity and freshness guarantees required by stateful applications. Existing solutions either assume trusted storage, incur high overheads, or push integrity logic into applications. We present ShieldFS, a POSIX-compliant filesystem that provides end-to-end integrity and freshness for persistent storage in the confidential-computing threat model without requiring application changes. ShieldFS represents permissible filesystem states using succinct cryptographic commitments, maintained inside TEEs and replicated in a lightweight trusted registry. On-disk data structures, including a write-ahead log and a storage pool, are authenticated using hash chains and an embedded Merkle tree. ShieldFS utilizes transactions and copy-on-write to update persistent filesystem state and commitments atomically. The commitments are verified during reads, ensuring that rollback, replay, and equivocation attacks are detected even when the entire I/O stack is untrusted. We implement the design by extending ZFS, yielding ShieldZFS. Evaluation with standard filesystem
benchmarks and real-world workloads shows that ShieldZFS provides strong integrity and freshness guarantees with performance comparable to state-of-the-art filesystems.
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All authors Dimitra Giantsidi, Antoine Delignat-Lavaud, Cédric Fournet, Jinnan Guo, Heidi Howard, Tianjiao Huang, Kapil Vaswani, Stavros Volos
Machine Learning
Auditing Recorded Predictive Lead Service-Line Classifications Against Physical Verification: A Statewide Study of New York
Muhammad Sarmad Sohail
Under the US Lead and Copper Rule Revisions, a utility may determine a service line's material with a predictive model instead of inspecting it. New York State publishes, per address, which method was used. Almost no address carries both a model classification and a physical verification, so the check is between populations within a utility rather than paired addresses. We screen all 153 New York localities that classified at least 100 addresses this way. Seventy-five (49%), covering 125,990 addresses or 57% of those screened, record one value. Zero variance alone is not misconduct: 68 of the 75 match their own verification or have too little to test. Seven are contradicted by their own crews, six beyond any sampling explanation. Five are boroughs of New York City, which file as one system; one is East Rochester, 550 km away. New York City is the largest case: a predictive model is the recorded basis for 43,215 addresses, and on all of them the recorded material is "Known Other". The city records "Unknown" on 121,779 addresses, 1,880 already excavated, and lead on 120,692. In the model bucket both counts are zero, and the 95% upper bound on the rate is 0.0085%. Across the rest of New York the same method records lead or the hedge "Unknown but could be lead" on 12.21% of 176,888 addresses, a comparison whose weaknesses we report. The model-cleared population is newer, median year built 1984 against 1930, and construction era accounts for about a third of the gap and not the rest: holding era fixed, records-based classification finds lead at 4.3-31.9%, physical verification at 1.5-14.5%, the model in no era. Six era-aware
estimators place the expected lead lines among them at 1,150-1,450. Two findings need no comparison: 7,782 of these addresses are in pre-1940 buildings, and the archived 2025 snapshot shows the public-side determination was copied from a customer-side model output.
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Computation and Language
Learning how to Forget: Fine-tuning for Long-Context Sparse Attention
Matthias Seeger, Zeyu Zhang, Vihang Patil et al. (+2 more)
A lot of prior work addressed key-value (KV) cache selection and compression by sparse
attention to enable long-context inference for
transformer language models without excessive hardware budgets. We provide a new method for fine-tuning models with sparse
attention. It works for any KV cache policy, runs on a moderate hardware budget (e.g., a single Nvidia A100 GPU with 40 GB RAM), and allows the model to co-adapt with the policy, often outperforming models trained with exact
attention (sequence parallelism). We also provide an efficient implementation of H2O sparse
attention (the leading policy in our experiments) with dedicated scaled dot product
attention kernel support. KeysAndValues (https://github.com/awslabs/keys_values), a new open source library for long-context inference and fine-tuning, provides easy-to-use and performant code for all methods discussed here.
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All authors Matthias Seeger, Zeyu Zhang, Vihang Patil, Konstantinos Benidis, Sebastian Schelter
Sound
Unified Music Identification for Tracks and Versions
R. Oguz Araz, Joan Serrà, Yuki Mitsufuji et al. (+2 more)
Given a music database, track identification (TI) retrieves the exact track matching an audio excerpt, whereas version identification (VI) retrieves its musical versions. Traditionally, the two tasks have been addressed separately. However, as every track is its own closest version, we investigate whether VI can subsume TI. This requires VI systems to be robust to both signal manipulation and audio degradation. We therefore propose a unified
benchmark that evaluates accuracy and robustness on each task. Comparing seven existing models on this
benchmark, we show that none of them are both accurate and robust on both tasks. We then train a baseline model targeting both tasks and show that a unified system is possible with 10 s TI queries. Lastly, we characterize the two
retrieval constraints that limit our model's TI performance. We envision extending this unification to other music identification tasks.
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All authors R. Oguz Araz, Joan Serrà, Yuki Mitsufuji, Xavier Serra, Dmitry Bogdanov
Discrete Mathematics · Cross-listed from math.CO
How many cherry-picking sequences are needed to reduce all subtrees of a phylogenetic tree?
Bálint Kollmann, Yukihiro Murakami and Takatora Suzuki
Phylogenetic networks are graphs that represent the evolutionary history of species. Recently, the class of orchard phylogenetic networks, which can be reduced by so-called cherry-picking sequences, has gained
attention for its computational and biological aspects. In this paper, we study a fundamental question on orchards and their cherry-picking sequences by considering the CoveringNumber problem: given an orchard network $N$, how many cherry-picking sequences are needed to reduce all subnetworks of $N$? We initiate this study by considering the problem for trees. We then show that the covering number can be computed for binary trees recursively using a similar but more fine-grained notion of survival covering number. We also give a recursive formula for the survival covering number of non-binary trees. However, computing the covering number for non-binary trees appears to be considerably more challenging. For this case, we show that the covering number of
star trees (whose root is adjacent to all leaves) is equivalent to the so-called SubsetConnectivity problem, which we introduce in this paper. Finally, we show that if there is no restriction on the sequence length, a single sequence of minimum length $\binom{n}{2}$ suffices to reduce all subtrees of a tree on $n$ leaves.
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Machine Learning
Multi-Source Wasserstein Distributionally Robust Graph Learning
Chuansen Peng, Yifan Xia, Jinshan Zhong et al. (+1 more)
Reconstructing complex network
topologies from data is a fundamental challenge in cybernetics and graph signal processing, with applications in neuroscience, sensor, and social networks. In practice, target-domain samples are scarce while heterogeneous source-domain data are abundant. Fusing these sources is challenging: Euclidean averaging works for homogeneous sources but degrades sharply as inter-source divergence grows, collapsing distinct geometries into an inflated, biased consensus. We exploit the Wasserstein metric's distribution-preserving properties to counter heterogeneity while preserving each source's intrinsic geometry. We propose MS-WDRO, a multi-source Wasserstein distributionally robust graph learning framework that fuses heterogeneous sources via their weighted Wasserstein barycenter, a geometrically principled nominal distribution, then builds an ambiguity ball around it to hedge residual uncertainty. Minimizing worst-case risk yields a tractable regularized
Laplacian estimator solved efficiently via a provably convergent ADMM scheme. We establish non-asymptotic guarantees: a finite-sample concentration bound for the empirical barycenter, a pooling bias lower bound proving naive aggregation is suboptimal, and an out-of-sample excess risk bound decaying at a parametric rate with only logarithmic dependence on source count. To calibrate hyperparameters governing robustness, sparsity, and source fusion, we unroll the solver into a differentiable architecture trained end-to-end, achieving data-adaptive calibration beyond cross-validation while retaining interpretability. Experiments on synthetic
benchmarks and the multi-site ABIDE~I neuroimaging
dataset show MS-WDRO consistently outperforms seven baselines in graph recovery, sample efficiency, and downstream diagnostic utility, with the largest gains in the sample-scarce regime.
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All authors Chuansen Peng, Yifan Xia, Jinshan Zhong, Xiaojing Shen
Computational Geometry
Polyomino Nets Covering Three Different Boxes of Area 106 and Related Results
Erik D. Demaine, Jenny Diomidova, Nicole Jacobus et al. (+5 more)
We present new results for polyomino nets that fold into 2 and 3 different cuboids through a computer search. The main result is the finding of 40 nets that fold into all three different cuboids with a surface area of 106. The secondary results are the finding of infinite families of nets that fold into three cuboid shapes, and the calculation of the number of common nets between smaller cuboids. The
algorithms used to make the searches feasible will also be explained. The
algorithms include taking advantage of some hidden structures in the nets that fold into the Nx1x1 cuboids, taking advantage of how a lot of nets fold into cuboid shapes in a 'striped' way, and using a variant of Redelmeier's
algorithm. The paper ends with open questions that encourage the reader to broaden our collective understanding of the subject of creating polyomino nets.
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All authors Erik D. Demaine, Jenny Diomidova, Nicole Jacobus, Landon Kryger, Matthew T. Parker, Michael Tardibuono, Ryuhei Uehara, Hanyu Alice Zhang
Information Theory
A Layered Simplex Architecture for Large Alphabets
Meir Feder, Yaniv Fogel and Ruediger Urbanke
A learning architecture induces a prior over its expressible models. Recent work links architectural success to a broad complexity spectrum: model probabilities spanning many orders of magnitude. Understanding how depth shapes these priors and prediction may help explain the success of neural networks and
transformers. We study this connection in large-alphabet prediction through the *layered simplex architecture* (LSA), which multiplies independent uniform simplex vectors coordinate-wise and normalizes. At depth zero, the prior is concentrated on the uniform distribution; at depth one, the
Bayesian predictor is Laplace's rule. Each additional layer shifts prior mass toward more concentrated distributions. We derive exact
Bayesian marginal likelihood and regret expressions using reusable depth-specific kernels. Mixing over depths up to a cutoff logarithmic in alphabet size yields an explicit predictor with rigorous guarantees. For targets uniform on a specified sparse support, the mixture's prior complexity is asymptotic to the support-description cost at every fixed positive KL resolution, establishing a broad complexity spectrum. We also prove a regret lower bound for every label-invariant predictor: the expected description length of the discovered symbol set minus the source's uncertainty about its identity given its size. For Zipf sources in a growing-alphabet regime, the depth mixture attains this bound to leading order. Thus, the symbol-discovery rate determines the regret exponent. Although introduced to study depth, the LSA predictor competes with established large-alphabet methods, including Good–Turing, Ristad's law and an adaptive Dirichlet mixture, while substantially outperforming classical add-constant rules on concentrated sources.
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Artificial Intelligence
Spike-based Belief Propagation in Nonlinear Dynamical Systems
Sepideh Adamiat, Hongye Wang, Wouter M. Kouw et al. (+1 more)
This paper presents a
Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control.
Bayesian inference is widely regarded as a core computational principle of
brain function, providing a normative framework for perception, decision-making, and learning under uncertainty. By combining a biologically inspired spiking neural model with
Bayesian inference principles, we propose a
brain-like control
algorithm capable of operating in uncertain environments. We use the mountain car parking problem as a
benchmark with non-linear dynamics. Our results demonstrate that the proposed controller can successfully update states in real time and generate goal-directed action plans through spike-driven dynamics. The results highlight the proposed model's potential as a bridge between computational neuroscience and probabilistic control theory.
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All authors Sepideh Adamiat, Hongye Wang, Wouter M. Kouw, Bert de Vries
Machine Learning
PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening
Jia-Qi Lin, Yinghua Yao, Chang-Dong Wang et al. (+2 more)
Accurately ranking active ligands for a target
protein pocket from massive chemical libraries remains a central challenge in virtual screening. DrugCLIP and its recent extensions substantially accelerate this process by encoding
protein pockets and molecules into a shared
embedding space. Despite this progress, further performance improvements typically require retraining the entire model, incurring substantial computational overhead and making target-specific customization inefficient. In this work, we formulate the specialization of pretrained virtual screening models to individual pockets as a test-time adaptation problem and propose PETA, a parameter-efficient framework that directly adapts pretrained model at test time. Given a target pocket, PETA constructs pocket-specific negatives through molecular
diffusion and chemical validity filtering, and further moves them toward the reference ligand retrieved from structural databases via
embedding-space mixup to create more challenging ranking tasks. A ranking objective then places greater emphasis on suppressing high-scoring invalid candidates that could contaminate the top-ranked screening results, providing structured supervision for lightweight adaptation. Experiments across diverse
benchmarks demonstrate that this lightweight, pocket-specific adaptation outperforms both pretrained and fully retrained baselines while updating only the LayerNorm parameters, which account for approximately $0.03\%$ of the full model.
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All authors Jia-Qi Lin, Yinghua Yao, Chang-Dong Wang, Yew-Soon Ong, Yuangang Pan
Machine Learning · Cross-listed from stat.ME
Where Does the Union Bound Go? Best-Arm Identification and Strong FWER Control
Rianne de Heide
In fixed-confidence best-arm identification, proofs often use a union bound across the competing arms. From a multiple-testing point of view this can look puzzling: if the best arm is unique, only one hypothesis of the form “arm $i$ is best” can be true. Why then should there be a Bonferroni-type factor of $K-1$? The answer is that there are two natural ways to orient the hypotheses. In one orientation, best-arm identification is literally a strong familywise-error-rate (FWER) problem with $K-1$ true nulls. In the opposite orientation, exactly one null is true, but a pairwise implementation can falsely reject that one null through any of $K-1$ comparisons. Thus the multiplicity has not disappeared; it just pops up in different places. This note makes the equivalence explicit in the terminology of both communities.
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Artificial Intelligence
Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis
Zijiao Chen, Nicholas Lu, Xinhui Li et al. (+14 more)
AI
agents can execute scientific analyses, but an analytic output becomes a defensible claim only after alternatives are weighed and the claim is limited to what the evidence supports.
Agents may reproduce failures including selective analysis, premature declarations of success and optimization of imperfect criteria. We present
Brain Researcher, an agentic research harness operating in a neuroimaging researcher's computational environment under rules for admissible analyses, required checks and claim scope. In
benchmarks,
Brain Researcher increased first-choice tool-selection accuracy across seven models by 70.2 percentage points (23.3% without it versus 93.6% with it) and verifiable grounding from 4.6% to 22.0%. In collaborator-led and self-evolving studies, multiverse analyses exposed analytic-choice sensitivity, and scientific review classified claims as accepted, qualified, revised, blocked, rejected or deferred. By linking decisions to evidence and provenance,
Brain Researcher embeds methodological judgment within the workflow, not after it.
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All authors Zijiao Chen, Nicholas Lu, Xinhui Li, Jocelyn A. Ricard, Ce Ju, Huan H. Wang, Christian Kindermann, Jeanette A. Mumford, Steven Dillmann, James Kent, Alejandro de la Vega, Sanmi Koyejo, Vince D. Calhoun, Joshua W. Buckholtz, Juan Helen Zhou, Steffen Bollmann, Russell A. Poldrack
Cryptography and Security
MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection
Yue Wang, Yi Liu, Gelei Deng et al. (+4 more)
Agent Skills extend LLM
agents with reusable instruction packages that may also include scripts, resources, and service configuration. This creates a direct distribution channel for malicious behavior, yet existing malicious-Skill
datasets are fragmented across sources, artifact formats, evidence regimes, and benign coverage; duplicated and structurally related content further complicates direct aggregation and evaluation. We present MaliciousSkillBench, a comprehensive
benchmark for malicious
Agent Skill detection. We consolidate 13 public sources, 11 of which contribute Core malicious artifacts, and reduce 8,414 raw malicious records to 7,539 normalized-unique identities in 4,588 operational structural families. After conservative cross-label conflict exclusion, the primary
benchmark contains 9,740 Skills: 7,505 malicious and 2,235 benign. To characterize its coverage, we harmonize 11 attack categories for 4,983 malicious identities with supported source-native mappings and find substantial differences in threat composition across sources. We then evaluate three learned text detectors and three off-the-shelf Skill scanners. Learned detectors achieve 0.882-0.932 Random Macro-F1 but only 0.653-0.665 under Source-Disjoint evaluation; the strongest word TF-IDF SVM scores 0.932/0.916/0.665 on Random/structural-disjoint/Source-Disjoint while retaining 95.6% malicious recall but producing 62.4% benign FPR on held-out sources. Off-the-shelf scanners occupy different but also unsatisfactory operating regimes, reducing false positives only at the cost of sharply lower malicious recall. Together, these results show that reliable malicious-Skill detection requires both broader cross-source
benchmark coverage and evaluation that jointly measures attack detection and benign over-flagging.
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All authors Yue Wang, Yi Liu, Gelei Deng, Ying Zhang, Yuekang Li, Zhenyu Chen, Leo Zhang