Decoding silent reading from non-invasive EEG. Authors: Ingo Marquardt, Anthilia Alchanat, Priyanka Jain. Categories: cs.LG q-bio.NC. Non-invasive decoding of inner speech faces a fundamental data problem: a corpus pairing brain activity with a person's spontaneous inner monologue cannot be collected, and the available proxy paradigms (cued repetitive and retrospectively reported generative inner speech) are slow to acquire, poorly time-locked, and subject compliance is unverifiable. We therefore treat silent reading as a scalable proxy task and ask how much lexical and semantic information a contrastive decoder can extract from it. We report an open-vocabulary analysis of approximately 240,000 word presentations recorded from a single densely-sampled participant across 393 runs (ca. 49 h) of 19-channel dry-electrode EEG. Words from continuous narrative text were presented in rapid serial visual presentation, with typography randomised on every trial to partially decorrelate word identity from low-level visual form. A convolutional EEG encoder, optionally followed by a causal transformer, was trained with a CLIP-style contrastive objective to align short EEG windows with hidden-state embeddings of the presented word taken from a large language model. Decoding, evaluated as word-grouped top-10 retrieval against permutation baselines, was reliably above chance, extended to mid-frequency and rare words, and scaled log-linearly with training-data volume with no sign of saturation. Removing occipital and posterior-temporal electrodes reduced the word-level gain by roughly one third but left context tracking unchanged. Control analyses separate word-level decoding from narrative context tracking and from a non-neural positional prior introduced by the transformer's positional embedding. These results establish that open-vocabulary word-level information is recoverable from EEG during silent reading, and that decoding is data-limited rather than saturated.


Exact Algebraic Computation of Learning Coefficients for Two-Dimensional Singular Models. Authors: Grégoire Sergeant-Perthuis, Elias Tsigaridas, Jules Tsukahara. Categories: cs.LG cs.SC math.AG stat.ML. Classical information criteria such as the Bayesian Information Criterion (BIC) rely on regularity assumptions that break down for singular models, leading to incorrect model selection in settings such as deep learning. The Widely Applicable Bayesian Information Criterion (WBIC) relies on local learning coefficients $λ$, which in the analytic case coincides with local Real Log Canonical Thresholds (RLCT) of the Kullback-Leibler divergence of the model, to capture correct marginal likelihood asymptotics. Exact computation of the learning coefficients has been limited to special cases, and only sampling-based estimation methods are generally applicable. We present the first deterministic algorithm that computes local RLCTs exactly for any two-dimensional model whose Kullback-Leibler distance is contact equivalent to a polynomial, derive a bound on its complexity, and demonstrate its effectiveness for a broad class of models, with applications including polynomial neural networks. Beyond providing ground truth to calibrate sampling-based estimators, exact computation reveals algebraic structure in learning coefficients that sampling cannot and out-speeds it in the shallow regime.


Ask Self, Ask Others: Relation Is All You Need. Authors: Yuting Ge, Pengju Yang, Mingkai Nie. Categories: cs.LG. Attention directly derives normalized information flow from pairwise scores. We introduce Relation, an alternative token-mixing primitive that first organizes pairwise evidence into explicit Self and Exchange relations and derives information flow afterward. This relational organization gives rise to Full Relation, FlashRelation, Linear Relation, Hybrid Relation, and a KV-style Relation Cache. Across matched decoder-only models at approximately 10M, 30M, and 100M parameters, Full Relation achieves lower final validation NLL than MHA at all three scales. In a fixed-context reference benchmark, FlashRelation is 3.60-4.41x faster than the materialized Full Relation implementation. Across scale-matched production workloads, it reaches 76.4-84.9% of PyTorch FlashAttention throughput while executing the Full Relation operator. Hybrid Relation uses 75% Linear Relation layers and achieves strong language-modeling quality. These results support a relation-first view of token mixing: ask Self, ask Others, then let Flow follow Relation.


Feature Evolution and Migration during Vision Transformer Training. Authors: Joonas Järve, Halil Ibrahim Aysel, Tarun Khajuria, Meelis Kull. Categories: cs.CV cs.LG. We present a novel view on feature evolution in Vision Transformers (ViTs) by visualizing the training process over two dimensions -- network depth (layer) and training time (epochs). We employ Sparse Autoencoders (SAEs) to extract candidate sparse features from CLS-token representations and compare their activation profiles across epoch--layer pairs. This allows us to study feature-level dynamics that are not directly visible from representation-level similarity measures. Furthermore, we demonstrate how this framework of feature evolution allows us to describe feature migration, the change in the layer where a feature is most detectable during training. Our experiments show that migration is concentrated early in training, occurs more often toward earlier layers than toward deeper layers, and declines as feature organization stabilizes. We further find that deeper layers stabilize earlier and more strongly than shallow layers. The results show that our approach can be employed as a tool for understanding how ViTs learn and evolve.


Discrete Diffusion Inference-Time Control with Nested Sequential Monte Carlo. Authors: Lohithsai Yadala Chanchu, Hany Abdulsamad, Christian A. Naesseth. Categories: stat.ML cs.LG. We study inference-time control for text generation in discrete diffusion language models, where the goal is to steer sampling toward sequence-level rewards without retraining. Prior work in this domain has focused on particle-based methods such as best-of-$n$ sampling and bootstrap sequential Monte Carlo, which may suffer from overoptimism and weight degeneracy, respectively. We address these limitations using \emph{nested} sequential Monte Carlo methods. We formulate nested SMC (NSMC) and fully-adapted nested SMC (FA-NSMC) for Feynman--Kac steering, identifying and correcting errors in prior formulations that lead to biased final estimates. We evaluate these methods on toxicity and fluency steering tasks, showing that NSMC and FA-NSMC consistently outperform best-of-$n$ and bootstrap SMC.


SAE-Xplainers: Rule-Based Feature Interpretation for Extreme Earth Events. Authors: Hugo Porta, Emanuele Dalsasso, Chang Xu, Theo Gnassounou, Devis Tuia. Categories: cs.LG. The emergence of large-scale Weather and Climate (W&C) datasets offers new opportunities for modeling extreme Earth events (ExEE) and their impacts using deep learning. However, their adoption in operational settings remains limited by the lack of models' interpretability. While for conventional text and image modalities, tools such as Sparse Autoencoders (SAEs) have proven effective for extracting human-understandable concepts, their use for the analysis of ExEE remains challenging due to the nature of W&C data. To address this, we introduce (i) a geographic location-based modulation of the inputs of SAE to capture the local semantic meaning of environmental patterns, and (ii) an ensemble of rule-based SAE-Xplainers to interpret the resulting high-dimensional features derived from complex, multi-modal environmental predictors. We evaluate our method on three ExEE types: the prediction of fires, and the detection of tropical cyclones and atmospheric rivers. We show that SAE input modulation improves both reconstruction performance and feature utilization, and that our SAE-Xplainers enable faithful interpretation of complex climatic patterns by unfolding them into human-understandable rules that are consistent with the scientific literature, while also supporting the identification of feature absorption.


Orthogonal JEPA: Factorized Predictive States for Latent World Models. Authors: Taoyong Cui, Pheng Ann Heng, Wanli Ouyang. Categories: cs.LG. World models construct latent states that support prediction, planning, and reasoning about an underlying system. Joint-embedding predictive architectures (JEPAs) offer a direct way to learn such states by predicting targets in representation space instead of reconstructing every detail of the observation. Standard JEPAs, however, organize all predictable content through one target embedding and one prediction pathway. In complex systems, this monolithic state can allocate redundant capacity to dominant signals while providing weak or conflicting gradients to less dominant predictive structure. We introduce \method, a latent world-modeling framework based on orthogonal predictive factorization. Learned basis matrices analyze each target state into multiple components, and a dedicated prediction branch estimates each component from a shared context representation. Predictive regression preserves the factor magnitudes required for state synthesis, an orthogonality objective discourages repeated directions, factor-activity regularization maintains variation in projected targets, and online variance regularization discourages coordinate-wise encoder collapse. Predicted components are synthesized into a complete latent state that can be used by a readout, decoder, planner, or autoregressive rollout. The same predictive-state mechanism applies when the target is temporally future, spatially hidden, or another partial observation of the same system. Experiments on controlled vision, single-cell transcriptomics, longitudinal health records, continuous control, and molecular dynamics evaluate representation quality, forecasting, planning, and long-horizon stability.


What You Can't See Is What You Learn: Restricted Evidence Visibility Favors Compositional Generalization in Shared-Genome Language-Model Societies. Authors: Narcis Marincat. Categories: cs.AI cs.LG cs.MA. Multi-module systems often expose every module to the full input. We test whether restricting evidence visibility changes which solutions gradient-based training discovers. Four-cell societies share one frozen pretrained language model and one low-rank adapter, communicating only through two model-width continuous vectors in a fixed relay. On a prospectively sealed natural-language function-composition task, we train ten matched restricted/global pairs sharing initialization bytes, training order, token layout, parameters, and computation; only the attention mask differs. Restricted societies outperform their globally visible twins by at least 20 points at both depths in 9 of 10 pairs, with median paired advantages of 0.7648 and 0.6050. Cutting communication reduces every restricted society to chance, and the depth-three advantage remains 0.558 on programs whose composite function never appeared in training. Across six audited restricted societies, same-value packet transplants preserve behavior at 0.94-1.00 across all tested interfaces; destructive interventions collapse performance; and counterfactual packets redirect outputs toward the mathematically predicted answer. The sole high-performing global model also requires communication, but its same-value packets are not interchangeable across episodes. Restricted visibility is thus not necessary for composition; under this protocol it substantially increases the probability of a generalizing relay and favors a reusable, value-indexed interface. The complete preregistered battery nevertheless formally fails because restricted-arm median depth-three accuracy is 0.6988, below the 0.70 floor. An earlier qualification cohort likewise yielded 0/10 complete passes: one model met every task-performance gate, but all ten failed ordinary-language preservation, confining the system to explicitly task-gated use.


DecoVAE: a Lightweight Interpretable Trend-Seasonal VAE Framework for Efficient Probabilistic Time Series Forecasting. Authors: Alexander Marusov, Dmitry Anikin, Alexey Zaytsev. Categories: cs.LG. Probabilistic time series forecasting remains challenging, largely because modeling distinct trend and seasonal dynamics requires specialized approaches. Existing methods often fail to capture the unique inner properties of these components, lack interpretability, or suffer from heavy memory and runtime overhead. To address these limitations, we propose DecoVAE, a lightweight interpretable trend-seasonal VAE framework that explicitly decomposes time series into trend and seasonal components by applying domain-specific inductive biases. The trend stream enforces structural smoothness using a differential regularizer on the latent trajectory, analogous to the Hodrick-Prescott filter. Concurrently, the seasonal stream operates in the frequency domain via a complex Gaussian VAE, natively capturing the amplitude and phase of periodic patterns. Extensive evaluations across seven real-world benchmarks show that DecoVAE consistently outperforms strong baselines. It achieves reductions of up to 14.96\% in CRPS and 23.30\% in NMAE for short-term forecasting, and up to 52.68\% and 26.51\% for long-term horizons. Crucially, DecoVAE yields these accuracy gains while remaining highly efficient, reducing model weight by up to 93\% and accelerating speed by up to 74\% compared to the second-best method.


End-to-end Early Classification of Time Series in Non-Stationary Environments. Authors: Aurélien Renault, Alexis Bondu, Antoine Cornuéjols, Vincent Lemaire. Categories: cs.LG. Early Classification of Time Series (ECTS) requires making accurate decisions as early as possible in inherently online and evolving environments. Yet, most existing methods assume stationarity and rely on separable designs, where classification and triggering are optimized independently, an assumption that fundamentally limits their adaptability under drift. In this work, we challenge this paradigm and study ECTS under non-stationary conditions. We provide the first systematic comparison between separable and end-to-end approaches across controlled drifting scenarios. Building on Reinforcement Learning, we introduce DQeND, a unified architecture that jointly learns representation, classification, and triggering decisions, while remaining directly comparable to state-of-the-art separable baselines. Across a wide range of drifts, DQeND demonstrates strong robustness across various non-stationary scenarios, consistently outperforming separable baselines. An ablation study further highlights that jointly updating representation and decision modules is critical to these gains. Overall, our results indicate that end-to-end learning can offer improved adaptation capabilities for ECTS in dynamic environments, and motivate further investigation of alternatives to separable designs.


An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage. Authors: Ioannis Theologitis, Debin Meng, Stylianos Eleftheriadis, Vasileios Lolis, Konstantinos Votis. Categories: cs.LG cs.AI cs.CV. Artificial intelligence can support cultural heritage and digital humanities through large-scale retrieval and analysis of digitized collections. However, cultural heritage data are often distributed across institutions, constrained by ownership and access restrictions, and continuously evolving over time. Federated Continual Learning (FCL) is well suited to this setting, as it enables models to learn from distributed and sequential data without sharing raw collections. In this paper, we propose FedCurv-DR, a lightweight, regularisation-based FCL strategy. The method accumulates parameter-importance estimates across clients and experiences to protect learned knowledge, while updating them only at fixed intervals to minimize communication and computation overhead. We evaluate FedCurv-DR in a continual learning scenario using the WikiArt image dataset for genre classification with evolving styles, reporting performance, energy, and fairness metrics. Our results show that FedCurv- DR reduces forgetting and balances performance, fairness, and energy efficiency for sustainable AI in cultural heritage.


Chameleon: Robust Defense Against Tor Website Fingerprinting via Many-to-Many Traffic Morphing. Authors: Yuwen Cui, Kai Wei, Kehan Shen, Ning Wang, Zhuo Lu. Categories: cs.CR cs.CY. Website fingerprinting (WF) attacks can infer users' browsing activities from encrypted Tor traffic by exploiting side-channel features. Although many WF defenses have been proposed, we find that most existing defenses create learnable web trace mapping features. We further show that robustness against adversarial training does not necessarily imply robustness against defense-aware autoencoder (DAAE)-based attacks.   To address these limitations, we present Chameleon, a robust WF defense based on many-to-many randomized traffic morphing. Chameleon selects morphing candidates with high intra-class diversity and low inter-class disparity. Chameleon randomly maps each webpage trace to multiple candidates, and allows different webpages to share morphing targets, thereby increasing adversarial uncertainty. For practical Tor deployment, Chameleon introduces a radix-trie-based synchronization mechanism that enables pluggable transport (PT) endpoints to identify consistent morphing traces using packet-direction prefixes, together with trace mutation and normalized prefix matching to reduce overhead. We evaluate Chameleon against six state-of-the-art defenses and five WF attacks on three public datasets in closed- and open-world settings. Compared with Adaptive Tamaraw, Chameleon reduces adversarial-training-based attack accuracy by up to 36.74% while reducing bandwidth and time overhead by 34.12% and 60.38%, respectively. Under DAAE-based RF attacks on GTT23, Chameleon limits attack performance to 35.19% F1-score while Adaptive Tamaraw only limits it to 88.22% F1-score. In the real-world PT bridge evaluation, Chameleon substantially reduces the effectiveness of strong WF attacks while incurring only 16.25% time overhead.


Trustworthy mobile edge caching: a blockchain approach to mitigate malicious nodes and incentivize cache sharing. Authors: Motahare Ebrahimi, Nastooh Taheri Javan, Seyedakbar Mostafavi, Fatemeh Pakzaban. Categories: cs.CR cs.CY. As mobile network traffic continues to grow, content caching on edge servers is critical for reducing latency. However, challenges such as malicious edge servers that may delete or manipulate cached content, along with the limited capacity of these servers, need to be addressed. To overcome the capacity limitations, helper mobile nodes can contribute their cache resources. However, due to their selfish behavior, an incentive mechanism is necessary to encourage resource sharing. Additionally, these helper nodes can also be malicious. This paper proposes a blockchain-based trust management mechanism that addresses these challenges by accurately identifying trustworthy edge servers and mobile nodes. The proposed mechanism calculates both direct and indirect trust using smart contracts, ensuring that malicious nodes are effectively filtered out. Trustworthiness is determined based on mobile node satisfaction with the quality of service, and trust data is securely stored on the blockchain. To combat node selfishness, a reward mechanism is introduced to incentivize cache sharing. Furthermore, a blockchain-based authentication mechanism protects against node impersonation. Our approach optimizes trust, cache capacity, and cost efficiency while considering mobile node mobility, energy consumption, and computational power constraints during the consensus process. Simulation results show that the proposed method can accurately distinguish between honest and malicious servers, even with a 10% noise in data.


Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation. Authors: Ş. Selcan Magara, Esther Havemann, Debora Jutz, Ali Burak Ünal, Mete Akgün. Categories: cs.CR. The detection of rare disease-associated cell subsets from high-dimensional single-cell measurements is critical for understanding diseases such as leukaemia and viral infections. CellCnn, a convolutional neural network (CNN) designed for this task, has demonstrated the ability to identify phenotype-associated cell populations at frequencies as low as 0.01\%. Training such models reliably requires patient cohorts that are larger and more diverse than any single institution can typically assemble, and the underlying single-cell data is too sensitive to share across institutional boundaries under existing privacy regulations. We propose a secure multi-party computation (MPC) framework that enables the training and inference of CellCnn entirely on secret-shared data. This ensures that neither the participants nor the computing servers ever observe raw patient data or intermediate values. Evaluated on benchmark single-cell datasets for cytomegalovirus infection (CMV) and acute myeloid leukaemia (AML), our implementation preserves accuracy close to its plaintext counterpart while outperforming the prior privacy-preserving baseline. In contrast to earlier privacy-preserving approaches that removed components such as ReLU activations and bias terms, our method retains these key parts of the CellCnn architecture and supports accurate analysis without exposing raw patient data.


A Meta-Study on Replication Papers in Usable Security & Privacy. Authors: Christian Mack, Benjamin Berens, Hanna Algedri, Tobias Hilt, Daniela Reimer. Categories: cs.CR. The field of usable security and privacy research is a young and expanding field, which is still developing standards for its research, e.g. regarding replications. We used a mixed-method approach, in order to get a better understanding of the current state of replications in the field of usable security and privacy: (1) we examine the Call for Papers of 13 venues spanning security, privacy, and human-computer interaction; (2) we conduct a systematic search for papers reporting replicated user studies published across these venues between 2016 and 2025, yielding 24 relevant publications; (3) we categorized these 24 papers employing the replication taxonomy proposed by Olszewski et al. (2025); (4) we distributed a survey to the authors of these papers to understand their motivations for conducting replications. Our analysis reveals four key insights: (A) Calls for Papers would benefit from clearer guidelines for authors and reviewers regarding replication work; (B) determining what modifications were made relative to the original study proves difficult when reading replication papers; (C) strict exact replications do not exist in our sample. Approximately two-thirds of the 24 studies altered multiple aspects of the original work; (D) temporal and contextual changes affecting results emerged as one of the most frequently cited motivations for replication. Based on these findings, we offer practical recommendations for venues, researchers, and peer reviewers to strengthen replication practices in usable security and privacy research.


TrustRAG: Blockchain-Enhanced RAG via Committee-Based Credibility Scoring. Authors: Baixiang Liu, Haotian Che, Yuan Li. Categories: cs.CR cs.DC. Retrieval-Augmented Generation (RAG) lets Large Language Models (LLMs) pull in up-to-date, domain-specific information instead of relying only on what they were trained on. Yet most RAG systems still draw from centralized databases with limited oversight, making it difficult to verify where a document came from, whether it has been tampered with, or whether it should be trusted at all. This is a serious problem in domains where both the timeliness and accuracy of retrieved content are critical, such as healthcare, finance, logistics, and legal case law, where a wrong or manipulated document can directly lead to bad decisions.   We present TrustRAG, a committee-based, blockchain-backed RAG system: before a document is used, it is certified by a committee of domain experts through a zero-knowledge protocol, and the committee's hidden scores are combined via secure multi-party computation into a trust score that any client can verify. These scores, along with the underlying document data, are maintained jointly across chains through hash commitments, so no document or score can be silently altered or dropped, and every ranking can be independently replayed and checked.


EchoCoT: Extracting Hidden Chain-of-Thought from Large Reasoning Models. Authors: Yiting Qu, Ziqing Yang, Chi Cui, Ye Leng, Junjie Chu. Categories: cs.CR cs.AI. Hidden chain-of-thought (CoT) traces, especially those from frontier proprietary large reasoning models (LRMs), are valuable model assets. Yet whether these hidden CoTs can be directly extracted from black-box models remains largely unexplored. In this work, we systematically study whether hidden CoTs can be extracted near-verbatim from black-box LRMs through API interactions. We identify a previously overlooked reasoning replay surface between tool calls and develop EchoCoT, a multi-step attack that iteratively extracts hidden CoTs using API-returned fidelity signals. We further develop an LLM-based optimization framework that automatically searches for an effective universal injection trajectory across various datasets. We evaluate EchoCoT on three open-source and five frontier proprietary LRMs. On open-source LRMs, EchoCoT achieves up to 66.4\% near-verbatim extraction success, with the extracted trace length within 10\% of the target and at least 90\% of tokens exactly matching the target CoT. The same injection trajectory also generalizes to unseen datasets, achieving up to 80\% extraction success under the same criterion. For tested frontier proprietary LRMs, a substantial fraction of extracted CoTs closely align with provider-reported reasoning lengths and available CoT summaries. EchoCoT can also extract very long CoTs: on Gemini-2.5, it extracts 33,463 tokens from a 32,948-token target. These results establish hidden-CoT extraction as a practical security risk and highlight the need to better protect hidden CoT assets.


COPA: Continual Preference Optimization for Adaptive Prompt Injection Defense. Authors: Roshan Sood, Onat Gungor, Tajana Rosing. Categories: cs.CR. LLMs remain vulnerable to prompt injection attacks, where adversarial instructions embedded in user inputs or external content manipulate model behavior and bypass safeguards. Existing defenses are predominantly static, relying on fixed alignment objectives or attack-specific filtering mechanisms that require redesign as new attack strategies emerge. While recent lifelong alignment methods address shifting user preferences, they do not account for adaptive adversaries that continually evolve to exploit weaknesses in previously learned defenses. This limitation is particularly important in real-world deployments, where evolving attack distributions necessitate continual adaptation without sacrificing robustness to previously encountered threats. We present COPA, a continual preference optimization framework that treats prompt-injection defense as a lifelong learning problem. Instead of one-time alignment, COPA incrementally incorporates feedback from newly observed attacks via GRPO-based optimization and uses margin-weighted experience replay to retain defenses against prior attack classes. This enables continuous adaptation to emerging threats while mitigating catastrophic forgetting and preserving general-purpose model capabilities. Across lifelong prompt injection attack streams, COPA reduces attack success rate by up to 6.3x and 4.4x on average compared to state-of-the-art defenses. These results highlight continual preference optimization as an effective paradigm for defending LLMs against adaptive adversaries.


Tracking the Trend in How Speech Synthesizers Deceive People. Authors: Milan Šalko, Anton Firc, Kamil Malinka, Vojtěch Staněk, Martin Perešini. Categories: cs.CR cs.SD eess.AS. 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.


From Noise to Signal: Improving Security Log Anomaly Detection Using LLMs with Endpoint-Specific Logs. Authors: Christopher Henshaw, Gour Karmakar. Categories: cs.CR cs.LG. 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.


ShadowPath: Lookup-Private Credential Status Verification over Authenticated State. Authors: Patrick Herbke, Wolf Rieder, Christian René Sechting, Huaning Yang, Sid Lamichhane. Categories: cs.CR. 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.


Securing Filesystems for Confidential Computing. Authors: Dimitra Giantsidi, Antoine Delignat-Lavaud, Cédric Fournet, Jinnan Guo, Heidi Howard. Categories: cs.CR. 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.


MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection. Authors: Yue Wang, Yi Liu, Gelei Deng, Ying Zhang, Yuekang Li. Categories: cs.CR cs.AI. 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.


Inadvertent Context Leakage in Language Models. Authors: Jaiden Fairoze, Neal Mangaokar, Kamalika Chaudhuri, Sanjam Garg, Saeed Mahloujifar. Categories: cs.LG cs.CR. For AI agents to be useful beyond simple chat, they must hold sensitive user context such as calendars, credentials, health records, and financial data. We study whether the mere presence of such secrets in a model's context window introduces hidden correlations into the model's benign outputs, allowing reconstruction even when the model correctly refuses direct extraction. We further study whether an adversary can actively engineer prompts that amplify this effect, using the model as a covert carrier to transmit secrets through seemingly innocuous text. In both cases, this limited leakage is exploited using a novel adaptive attack that assumes black-box access to the underlying model.   In controlled experiments across eight proprietary models, we find that 2-digit in-context secrets are reconstructed with near-perfect accuracy and 4-digit secrets at 82\% exact match, all from outputs the model produces in response to ordinary, non-adversarial requests. We observe that more capable models leak more: stronger instruction-following amplifies sensitivity to in-context secrets, suggesting leakage is a byproduct of capability as opposed to a patchable bug. We show this leakage enables two practical attacks: (1) a trained classifier that infers semantic predicates about user memories (e.g., health conditions, financial events) from routine natural-language outputs, and (2) an RL-trained adversary that extracts full Social Security Numbers from a production-style agent.


Survival of~the~Stealthiest: Evolving Low-Entropy Ransomware via~Genetic Algorithms. Authors: Efrat Levenberg, Kristina Sviazhina, Ayelet Butman, Pierre Parrend, Harel Berger. Categories: cs.CR. Traditional ransomware deployment often relies on massive encryption procedure, triggering immediate detection by modern defense systems. This work introduces a paradigm shift in cryptographic attacks by framing ransomware execution as a Search-Based Software Engineering (SBSE) optimization problem. This approach addresses the persistence gap observed in modern threats, where attacks aim to remain undercover for hours rather than minutes. Using a Genetic Algorithm (GA), we optimize data encryption under a hard constraint on the statistical deviation from baseline system activity. We demonstrate that our evolved attack patterns can evade behavioral monitors under fingerprinting techniques. Our results suggest that search-based methods provide a powerful framework for generating evasive malware, highlighting an emerging challenge for automated software defense.


TGL-APT: Temporal Graph Learning with Graph Distillation for Efficient APT Investigation. Authors: Jing Chen, Ayong Ye, Yuanhuang Liu, Yuexin Zhang. Categories: cs.CR. Advanced Persistent Threat (APT) attacks pose a critical challenge to modern systems, as their stealthy, multi-stage nature renders conventional detection methods ineffective. While provenance graphs provide rich behavioral context for attack investigation, attack-relevant evidence is often sparse and embedded in large volumes of routine system activity, making full-graph learning both computationally expensive and difficult to correlate over long attack sequences. We present TGL-APT, an adaptive investigation framework built on the observation that attack-relevant information is non-uniformly distributed and often mediated by structurally influential or behaviorally distinctive entities, which we characterize as information-bottleneck nodes. TGL-APT combines three complementary components: (1) information-bottleneck-guided graph distillation that suppresses provenance redundancy while bounding structural distortion and preserving causal reachability; (2) adaptive temporal graph learning that continuously refines the core node set as node relevance evolves; and (3) cross-spatiotemporal attack fingerprint alignment that associates fragmented suspicious activities across different entities and time windows. Finally, causal expansion and stage characterization reconstruct coherent attack processes for investigation. Experiments on three DARPA E3 datasets show F1-scores of 95.7%, 90.9%, and 88.9%, while reducing training time, detection latency, and memory usage by approximately 39%, 33%, and 22%, respectively, compared with KAIROS. These results demonstrate that TGL-APT effectively balances detection performance, computational efficiency, and investigation capability for provenance-based APT analysis.


Enhancing Privacy in Federated Learning via Dual Obfuscation of Gradients and Training Images. Authors: Yuki Itabashi, Hiroto Sawada, Mare Hirose, Shoko Imaizumi, Hitoshi Kiya. Categories: cs.CR. Federated learning enables collaborative model training while keeping data locally at each client; however, recent studies have shown that training data can be reconstructed from shared model updates. To address this issue, this paper proposes a dual obfuscation method that enhances robustness against image restoration attacks by jointly obfuscating updated information and training images. The proposed method combines a robustness enhancement technique based on random binary weights, which randomly sets a portion of gradient elements to zero, with an image encryption technique. These techniques provide complementary protection by reducing the amount of original gradient information available to an attacker and the visual interpretability of reconstructed images, respectively. Furthermore, the image encryption technique allows independent keys to be used for each client and each image, avoiding explicit key sharing. Experimental results on an image classification task using a Vision Transformer (ViT) show that the proposed method reduces the visual information recovered by Attention Privacy Leakage (APRIL) under the evaluated settings without causing additional degradation in classification performance beyond that caused by image encryption. Although the proposed combination does not provide an absolute security guarantee, the results demonstrate the potential benefit of combining gradient modification and image encryption for privacy-enhanced federated learning.


AEGIS: Attention-Embedding Gradient Isolation Shield - Triple-Channel Gradient Masking for Privacy-Preserving Federated LLM Fine-Tuning. Authors: Ye Tao, Hong Shen, Hui Tian, Xin Wang, Can Wang. Categories: cs.CR. Gradient inversion attacks recover private training text from gradients shared in federated learning, posing a serious threat to collaborative model training. Through our analysis of transformer gradient structure, we identify three channels through which private token information leaks: the attention output projection gradient exposes a low-rank subspace that encodes input embeddings (Channel 1), the embedding gradient's row-norm sparsity directly reveals which tokens are present (Channel 2), and the MLP expansion gradient carries a recoverable subspace signal analogous to Channel 1 (Channel 3). State-of-the-art attacks exploit these channels analytically to achieve near-exact token recovery in seconds. Existing defences address at most one channel and either degrade model utility or leave the remaining structural signals intact.   We introduce AEGIS (Attention-Embedding Gradient Isolation Shield), a lightweight defence that closes all three analytical channels with three backward-path operations requiring no architectural changes: freezing attention projection parameters eliminates Channel 1 by construction, calibrated noise injection into the embedding gradient destroys Channel 2's token-presence signal, and analogous per-block noise injection into the MLP expansion gradient masks Channel 3. The same masked gradient drives both the local optimiser step and the server export, so no clean signal is retained on either side.   Evaluated across 11 models and six datasets, AEGIS reduces token recovery rates to near zero against a range of gradient inversion attacks, both analytical and optimisation-based, while preserving or improving model utility. We provide formal guarantees for Channels 1 and 2 and validate the full defence empirically against adaptive adversaries with complete knowledge of the mechanism.


A Federated Learning Framework for Privacy-Preserving Oral Cancer Screening on Smartphones. Authors: Lena D. Swamikannan, Akshay Bhagwan Sonawane, Jay S. Patel, C. S. Mani, Lakshmi Narayana. Categories: cs.CR. Data are the cornerstone of robust AI models. However, in the medical domain, access to reliable data is constrained by regulatory requirements and patient privacy, and clinical oral images are particularly difficult to obtain. Federated learning (FL) mitigates these constraints by enabling collaborative model development across decentralized datasets without centralizing or sharing patient data.   This work presents a practical FL framework that supports geographically distributed collaboration among AI healthcare researchers and facilitates the development of robust models for oral cancer screening. Client devices were interconnected via Tailscale to provide secure networking and real-time communication. We implemented the FL workflow using the Flower framework for server-side aggregation, while client deployment and orchestration were configured manually; no enterprise FL platforms were used. To support a smartphone-based screening application, we evaluated lightweight, mobile-friendly architectures including MobileNetV2, MobileNetV3Large, and MobileNetV4-Conv-Small (MNv4-Conv-S). Across the global lightweight models aggregated using FedAvg, the MNv4-Conv-S based global model (GM-V4) achieved the best performance, reaching an AUC of 0.929 and an accuracy of 87%


HARP: Hierarchical Adaptive Ranking with Preference-Adaptive Fusion for Query-Based CVE Prioritization. Authors: Haochen Liu, Zhengzhang Chen, Haoyu Wang, Yanchi Liu, Jundong Li. Categories: cs.IR cs.CL cs.CR. Vulnerability prioritization is inherently preference dependent, since the same CVE can receive different remediation priority under different operational preference scenarios. Existing scoring systems and ranking methods typically assume a fixed criterion. In practice, organizations already operate under a preference scenario, but this preference is often implicit and difficult to express as a written prompt instruction, while triage queries usually do not encode it. Past validated triage cases under the current scenario are more readily available. We study query-based CVE prioritization in this setting and propose HARP, a graph-grounded multi-view framework that ranks candidates from a natural-language query together with a support bank of historical labeled examples from the current preference scenario, without requiring an explicit textual summary of that scenario. HARP retrieves evidence from a vulnerability knowledge graph, scores candidates with policy-conditioned global, enterprise, and user views, and fits view-fusion weights from sampled supports. Experiments across three preference scenarios and multiple backbone LLMs show that HARP outperforms multiple baselines, expressing our method's effectiveness.


Redactable blockchains and polynomial equations. Authors: Alexander Demin, Alexey Ovchinnikov, Vladimir Shpilrain. Categories: cs.CR cs.NI. We develop new tools for constructing redactable authenticated data structures with post-quantum security. In our construction, inverting the proposed one-way function means solving a polynomial equation (or a system of polynomial equations) in more than one variable. This is presently considered quantum-safe, i.e., there is no known quantum algorithm that could solve this problem efficiently if parameters are chosen wisely.


Aray: Deterministic-First Synthesis of Benign Artifacts for YARA Validation. Authors: Emanuel C. A. Valente, Lourenço A. P. Júnior, Leonardo Gonçalves Chahud, Júlio Cezar Estrella, Marcus Botacin. Categories: cs.CR. A YARA rule is easy to distribute, but the malware sample used to demonstrate a positive match is not. This complicates storage, continuous integration, disaster-recovery exercises, and reproducible scanner validation. Constructing a replacement fixture requires more than embedding literals: YARA conditions can combine alternatives, counts, offsets, integer reads, and executable-container constraints, while the resulting file should not reproduce malware behavior. Positive validation is existential: it requires one file-level member of a rule's match set, not reconstruction of the originating sample. We present Aray, a deterministic-first YARA interpreter and positive-fixture synthesizer. Models may propose constructive normalizations or typed extraction fallbacks, but never backend source or binary structure. Conventional code validates normalized rules, derives string and integer witnesses, and performs extraction, routing, collision-checked layout, and ELF, PE, or generic serialization. Only residual normalization semantics reach a bounded model judge. We evaluated Aray over 416 public-rule entries. Normalization accepted 182 entries without model assistance and 234 after model normalization. Constructibility preflight admitted 406 entries, and every admitted fixture matched its upstream original rule. This yields 406/416 (97.6%) overall and 406/406 among constructible rules, with ten expected preflight dispositions and no scanner mismatches or construction failures. An unreachable endpoint confirmed zero model invocations during realization. The original-rule oracle validates generated fixtures against their source rules; proving implication for all possible files is a separate, stronger objective. Two anchored-regex failures were repaired before the final run, so these are post-fix systems results, not a held-out estimate.


Linguistic Holonomy and Statistical Watermarks: Inner Geometry of Meaning-Preserving Transformations. Authors: Daniele Corradetti. Categories: cs.CL cs.CR math.DG. Statistical watermarks for language models live in the freedom of the signifier: they choose among tokens that are nearly equivalent in meaning, and they are therefore eroded by exactly those transformations which move the form of a text while leaving its content in place. The literature measures such transformations by their endpoint, through the semantic similarity between the original and the rewritten text. We show that the endpoint is the wrong statistic. Adapting the formalism of linguistic loops, we prove that the invariant of a chain of meaning-preserving transformations factorises canonically into an endpoint part and a holonomy in the stabiliser of the initial state, the second of which the semantic deficit cannot see; the loop rotation is parallel transport on the unit sphere of the embedding space, so that the analogy with the Wilson loop becomes a theorem rather than a figure of speech. On the side of the detector we prove an exact identity: the residual statistic is proportional to the number of positions whose seeding window survived intact, from which the decay law $ρ^{h+1}$ follows as the independent-edit corollary. The identity has a disconcerting consequence, which we confirm to three decimal places: at one and the same retention rate the surviving signal may be one half of the original, one quarter of it, or exactly nothing, according only to where the edits fall.


The Structured Totient Preimage Problem: Reconstruction, Collisions, and Cryptographic Implications. Authors: Luis Adrián Lizama-Pérez. Categories: cs.CR. We define and study the Structured Totient Preimage (STP) problem as a restricted reconstruction relation with a direct cryptographic motivation. Let $p_1,\ldots,p_k$ be distinct primes of the same bit length and reveal only $x=\prod_{i=1}^k(p_i-1)$. Given $(x,λ,k)$, STP asks for any set of $k$ distinct $λ$-bit primes satisfying this product. The relation is efficiently verifiable, but its reconstruction complexity is not known. We establish three concrete results. First, for factored $x$ we derive the exact number of ordered exponent allocations and a bound showing that direct reconstruction is polynomial for fixed $k$ when $Ω(x)=O(\logλ)$; this rules out that regime as a basis for a strong hardness claim. Second, we give exhaustive algorithms for reconstruction and collision analysis. Third, we exhaustively evaluate 28 parameter pairs, with $2\leq k\leq5$, up to $λ=16$ for pairs and 4,588,935 prime sets in the largest census. The data quantify non-injectivity through collision participation, maximum multiplicity, and conditional ambiguity in bits. These results isolate STP from general inverse-totient computation and motivate a Structured Totient Preimage Assumption for explicitly growing parameter families. Under such an assumption, STP becomes a candidate preimage-resistant relation whose implications for commitments, proofs of knowledge of multiplicative witnesses, and authentication can be stated precisely. The paper establishes the computational foundation and parameter constraints for those constructions; it does not claim a security reduction or post-quantum hardness.


SiNMULI: Novel Signed Network Approach for Malicious URL Identification. Authors: Avijit Gayen, Sayan Mondal, Angshuman Jana. Categories: cs.CR cs.NI cs.SE cs.SI eess.SY. In today's era of rapid advancements in artificial intelligence, computer security and online safeguarding measures have undergone significant improvements. However, malicious websites continue to facilitate the spread of phishing schemes, fraudulent activities and unsolicited communications. Conventional methodologies in machine learning, deep learning and counterfeit website detection predominantly depend on static data analysis, which frequently proves ineffective against the evolving nature of malicious online entities. In response to these challenges, in this work, we propose a signed network-based approach for malicious URL identification, SiNMULI. We introduce an innovative framework that conceptualises the identification of harmful URLs as a signed network-based binary classification problem strongly rooted in the fundamental principles of social network analysis and social balance theory. In this approach, a signed network is constructed based on the backlinks, i.e., external hyperlinks of URLs, wherein each node symbolises a URL and the hyperlinks function as signed edges. Utilising a balance-theoretic inference mechanism, our methodology propagates edge signs and classifies unlabeled domains by employing a 51% majority rule across incoming links. Experimental results on this real-world dataset demonstrate that SiNMULI achieves 99.89% accuracy, 99.62% precision, and 99.80% F1-score, outperforming traditional ML and deep learning baseline models. Beyond high accuracy, SiNMULI offers interpretability, resilience against adversarial obfuscation, and independence from training data, making it a lightweight and scalable solution for real-world cyber defence.


Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication. Authors: Ramneet Kaur, Pradyumna Chari, Ramesh Raskar, Jugad Singh, Sumit Kumar Jha. Categories: cs.AI cs.CR. Language-model agents can communicate through continuous hidden states that are invisible in public transcripts, creating opportunities for covert harmful coordination. We introduce Verifiable Latent Alignments (VLA), an activation-aware framework for monitoring and steering these private communication channels. For every monitored decision, VLA links the private latent-state record and channel status to the resulting public action using a shared event identifier, enabling matched causal analysis. Our first contribution is a neutral-only three-layer monitor combining representation anomaly detection, counterfactual action-distribution influence, and sparse-autoencoder interpretation support. Our second contribution is a steerability framework spanning black-box behavioral instructions and white-box matched-neutral counterfactuals. Our third contribution is an evaluation on a controlled multi-agent auction benchmark covering homogeneous and heterogeneous model pairs, many-agent scalability, and intervention effectiveness. The sequential monitor achieves mean area under the receiver operating characteristic curve (AUROC) of 0.993 for homogeneous agents and 0.854 for heterogeneous pairs when text- and latent-collusion rows are pooled as positives. In Qwen3-0.6B auctions with 25-100 bidders, monitoring requires only a small normalized load relative to all possible directed pairs, while full white-box steering achieves 100% bid-distribution recovery and reduces collusive low-bid behavior by 47.3 percentage points. Because full white-box steering replays the matched neutral counterfactual, its exact recovery is a sanity check by construction. Overall, the controlled study shows that the evaluated private channel attacks can be monitored without training the primary monitor on attack examples and mitigated when matched counterfactual access is available.


FedGuard-DC: Privacy-Preserving Federated Load Forecasting and Cyber-Attack Detection for Data-Center Loads in Transmission Systems. Authors: Md Kibria Saroare, Md Rubel Ahmed. Categories: cs.CR. The rapid growth of large data-center (DC) loads is creating new challenges for power-system visibility, privacy, and cyber-physical security. System operators need accurate short-term information about these fast-varying loads, while DC operators may avoid sharing raw megawatt measurements because they can reveal sensitive workload and utilization patterns. This paper presents FedGuard-DC, a federated learning (FL) framework for privacy-preserving DC load forecasting and local false-data-injection attack (FDIA) detection. Each DC trains a dual-head model on its own measurements, where a shared encoder supports both a forecasting head and a reconstruction head. A calibrated anomaly score combines forecast residual and reconstruction error to detect corrupted measurements locally. Raw measurements and absolute MW demand remain at each DC, while only model updates are shared with the global controller. Optional differential privacy and robust trimmed-mean aggregation are included to evaluate privacy-utility behavior and poisoned-client resilience. The framework is validated using EMT simulation data from four large DC loads rated between 150 and 350 MW integrated into the IEEE 39-bus New England system. Results show a 0.5 s-ahead normalized forecast RMSE of 0.023-0.038 pu, compared with 0.32-0.34 pu for persistence. FedGuard-DC detects FDIA with ROC-AUC of 0.979, F1 = 0.930, and precision of 0.988, while robust aggregation reduces the poisoned-client RMSE impact from 0.042 to 0.035 pu.


Autonomous Cyber Defense in Connected Vehicles: A Multi-Agent Approach to V2X Security. Authors: Krishna Teja Medam. Categories: cs.CR cs.DC cs.MA cs.NI. A connected vehicle has roughly 100 milliseconds to decide whether an incoming Basic Safety Message is real or fabricated. If a false emergency braking alert reaches the planning pipeline in time, the car brakes - a safety failure triggered by a security failure. Existing intrusion detection systems are not designed to handle that coupling. They operate per vehicle, per message, with static rules - blind to attack patterns that only emerge across a fleet or over time, and blind to the fundamental tension between dropping a suspicious message and dropping a real emergency alert. We propose a three-tier multi-agent architecture that treats this timing constraint as a hard design requirement, not a performance target. At the vehicle level, an onboard agent classifies each incoming V2X message into one of four actions - Accept, Drop, Quarantine, or Escalate - within a 10-millisecond budget, deliberately biased toward Escalate when uncertain, passing ambiguous cases to the roadside edge agent rather than risking a dropped legitimate alert. The edge agent operates across a roadside unit zone with a 50-millisecond budget, fusing threat assessments from multiple vehicles and resolving safety-security conflicts using complementary sensor observations. The cloud tier refines detection models through Byzantine fault-tolerant federated learning and redistributes updated weights to the fleet. Every timing constraint derives directly from the 100-millisecond Basic Safety Message cycles mandated by SAE J2735 and ETSI EN 302 637-2. No existing framework simultaneously assigns standards-grounded latency budgets to all three deployment tiers while treating safety-security conflict resolution as a first-class design constraint. Remaining open problems - adversarial poisoning at the edge and the absence of regulatory frameworks for autonomous security response - are discussed as future work.


ABEAT: Efficient and Anonymous Encryption for ABE-based Dynamic Group Communication. Authors: Hongmiao Yu, Silas Richelson, Jiachen Chen, K. K. Ramakrishnan. Categories: cs.CR cs.NI. Confidential communication among a dynamic group of participants that ensures flexible and efficient many-to- many communication is highly desired capability. We leverage attribute-based encryption (ABE) for confidential group communication and enhance it by a graph-based namespace to create an efficient framework that allows groups to be formed and changed dynamically. In this paper, we focus on the important additional need to maintain the anonymity of recipients of a message, when using ABE for group communication for a variety of usage scenarios (e.g., emergency response).   We propose ABEAT, an efficient and anonymous dynamic group communication system that also minimizes overhead on receivers who are not the intended recipients of a message. In ABEAT, we propose a new anonymous KP-ABE approach to maintain recipient anonymity. ABEAT hides the clear attribute in the ciphertext of KP-ABE, and also prevents several attacks that seek to break anonymity. ABEAT provides fast recipient verification, dramatically lowering the decryption overhead for non-recipients by more than a factor of 90 versus the current state of the art such as hidden vector encryption (HVE). In fact, it is even 40% less than FABEO, which offers no anonymity.


Group-theoretic treatment of strong light-matter coupling with an arbitrary number of excitations. Authors: Antti Peltola, Olli Siltanen, Kimmo Luoma, Konstantinos S. Daskalakis. Categories: quant-ph. Strong light-matter interactions in optical microcavities give rise to hybrid light-matter states known as polaritons. While actively used in modern technologies, theoretical descriptions of such systems are often restricted to the single-excitation case, limiting their ability to capture many-excitation physics and hindering further technological advancements. Here, by exploiting the combinatorial structure of quantum emitters, we investigate the Tavis-Cummings model with arbitrary number of excitations. We derive the structure and properties of its eigensystem and identify allowed radiative transitions in systems of realistic size scales. Our work reveals new behavior inaccessible to the few-excitation regime, while also providing a framework to reduce the computational complexity of similar systems with exponentially growing Hilbert spaces.


4DAnyone: Create Anyone in 4D from a Casual Monocular Video. Authors: Yudong Jin, Tao Xie, Qihang Zhang, Zehong Shen, Zhen Xu. Categories: cs.CV. We present 4DAnyone, a framework for reconstructing 4D humans from an uncalibrated monocular video by generating reconstruction-grade multiview-consistent videos and lifting them into 4D Gaussian Splatting (4DGS). Existing camera-controlled video diffusion models synthesize plausible novel-view videos but fail to maintain consistency when scaled to the tens of target views required for 4DGS reconstruction. We identify this failure as a bounded-attention-context problem: when target views exceed the capacity of a single DiT forward pass, they must be split into groups, exposing two coupled bottlenecks. On the reference-context side, conditioning on all previously generated views grows as $O(N)$, weakening cross-view appearance guidance. On the target-context side, disjoint groups cannot directly exchange information, causing global structural drift. 4DAnyone addresses both bottlenecks with two complementary designs: Reference Context Packing (RCP) compresses growing reference views into a fixed-length mixed-resolution context with $O(1)$ reference-context complexity, while Target Context Routing (TCR) rotates target-view groupings during denoising to share context across groups at high-noise steps and stabilize details at low-noise steps. We further build the MVGameHuman dataset using our in-house game engine and combine it with light-stage and in-the-wild video datasets for training. Experiments on DNA-Rendering and DyMVHumans show that 4DAnyone outperforms prior methods in both novel-view video quality and downstream 4DGS reconstruction, with robust in-the-wild generalization. See our project page for video results and source code: https://4danyone.github.io.


WithEveryone: Unified Planning and Identity Grounding for Group Image Generation. Authors: Hengyuan Xu, Qixun Wang, Yiji Cheng, Miles Yang, Zhao Zhong. Categories: cs.CV. Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every reference to a distinct person and location, while training-time identity losses must establish correspondence among several noisy predicted faces. We introduce WithEveryone, a unified framework for generating group images up to ten reference identities. WithEveryone injects each selected identity as an addressed token, predicts a structured identity--layout plan, and renders the plan as a visual condition. Its key objective, Layout-Grounded ID Loss, uses annotated face regions to supervise the intended identities directly, avoiding unstable embedding-based face matching; ID Representation Forcing additionally trains a prediction for each identity before image synthesis. On an identity-disjoint benchmark, WithEveryone achieves the highest target-context identity similarity, improving face similarity from 0.462 for GPT-Image-2 to 0.499, while reducing copy-paste artifacts from 0.169 to 0.055. It further covers 97.3\% of the requested identities with a duplicate rate of only 2.8\%. These results show that explicit identity--layout grounding enables identity-preserving generation to scale to larger groups without relying on direct reference-face copying.


Swift-Image: Exploring the Performance Frontier of Compact Unified Image Generation Models. Authors: Taihang Hu, Zhao Wang, Zuan Gao, Tao Liu, Hao Yan. Categories: cs.CV. We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing. Our goal is to explore how far a relatively small visual generator can be pushed through systematic training engineering under a constrained computational budget. Swift-Image adopts an efficient 6B single-stream DiT and a progressive training pipeline that evolves from broad semantic coverage to higher resolution, stronger visual quality, and unified generation-editing supervision. For post-training, we employ parallel expert reinforcement learning followed by multi-teacher on-policy distillation to alleviate interference among heterogeneous objectives. We further decouple high-level reasoning from pixel-level rendering with a Prompt Enhancer that translates user requests into generator-aligned visual specifications. For efficient deployment, structural pruning and few-step distillation produce 3B and accelerated variants. Swift-Image achieves leading aggregate performance among evaluated open-source models with only 6B parameters and 243K GPU training hours; the compressed 3B model incurs nearly no loss, while few-step distillation further improves aggregate editing performance with substantially fewer sampling steps. Our study also summarizes practical lessons for architecture, data curriculum, post-training, prompt enhancement, and model compression.


Production of quarkonium pairs via the fragmentation mechanism. Authors: Franco Barattini, Benjamin Guiot, Marat Siddikov. Categories: hep-ph. In this manuscript, we analyze the contribution of fragmentation mechanisms to the inclusive hadroproduction of heavy quarkonium pairs, taking into account both single- and double-hadron (dihadron) fragmentation channels. To estimate the contribution of the latter mechanism, we construct a microscopic perturbative model of the dihadron fragmentation function, which is valid in the limit of large invariant masses of the heavy quarkonium pair. Using the Color Glass Condensate framework to evaluate the $Q\bar{Q}$ production amplitude, we find that in LHC kinematics, the single fragmentation of heavy quarks into quarkonium is only a minor correction, whereas dihadron fragmentation can provide a sizable contribution, on par with the contributions of single- and double-parton scattering.


Spin-torque microwave detectors of positive rectangular pulse signals. Authors: V. Prokopenko, O. Shtanko, I. Sotnyk, O. Prokopenko. Categories: physics.app-ph cond-mat.mes-hall. We analyze the performance of a spin-torque microwave detector (STMD) driven by positive rectangular current pulses $I(t)$ of various amplitudes $I_0$, durations $τ$, and repetition periods $T$ and reveal two distinct regimes of STMD operation. In the first (linear) regime, the time-averaged voltage across the detector, $U_{\rm dc}$, changes linearly with the pulse amplitude $I_0$ and depends on the ratio $τ/T$: $U_{\rm dc} \sim I_0 (τ/T)$. This regime is observed for a wide range of pulse amplitudes $I_0$ in the case of in-plane (IP) magnetization dynamics and for rather small pulse amplitudes $I_0 \le I_{\rm th}$ in an STMD with out-of-plane (OOP) magnetization dynamics. The other (nonlinear) regime is characterized by voltage jumps and drops and is observed only in a structure with OOP magnetization dynamics for input pulses with short repetition periods and large amplitudes $I_0 \ge I_{\rm th}$. We believe that the linear regime of STMD operation can be used to unambiguously detect input pulse parameters, which could be important for the development and optimization of spintronic devices capable of detecting and processing non-harmonic (e.g., digital) microwave signals.


Large Sample Properties of Higher Order Markov Models. Authors: Tuhin Majumder, Donald E. K. Martin, Soumendra N. Lahiri. Categories: math.ST math.PR. We study large-sample properties of higher-order Markov chains on a finite alphabet $Σ$ when the order $m_n$ is allowed to grow with the sequence length $n$. By embedding the process into a first-order chain on $Σ^{m_n}$ and exploiting return-time decompositions, we establish a central limit theorem for additive functionals $\sum_{t}\! g_n(Y_t^{(n)})$ under natural ergodicity and sparsity conditions. The normalization involves the stationary return time to a suitably chosen state and accommodates triangular arrays with $m_n\!\to\!\infty$ and $m_n/n\!\to\!0$. We further illustrate the assumptions in a binary variable length Markov chain (VLMC), deriving explicit lower bounds on stationary masses that yield a concrete growth regime (e.g., $m_n\log m_n/n \to 0$) ensuring the CLT. These results provide asymptotic foundations for inference in sparse/partitioned higher-order models; including VLMCs and sparse Markov models (SMMs) where the effective dimensionality grows with the sample size.


Projecting BrowseComp-Plus onto ClimbMix: Toward More Realistic Corpora for Agentic Search. Authors: Sahel Sharifymoghaddam, Lingwei Gu, Yijun Ge, Jimmy Lin. Categories: cs.IR. The BrowseComp-Plus benchmark disentangled the evaluation of agentic search by replacing opaque web search with a fixed corpus, so that an agent's role can be separated from the retriever's. That corpus, however, holds only about 100K documents and was assembled from the supporting documents of the benchmark's own queries plus mined hard negatives, so the evidence and the distractors were both selected per query. We introduce $\text{BrowseComp-Plus}_{\text{CM}}$, which keeps the BrowseComp-Plus questions but relocates their evidence to ClimbMix, a 400B-token, 553M-document mixture of web text released by NVIDIA for pre-training language models and built without reference to any benchmark. Our main contribution is the projection pipeline that makes this possible: it decomposes each question into atomic reasoning hops and grounds every hop in the new corpus, retaining a question only when automatic verification, an independent agent, and human review all confirm that every hop is supported. The pipeline is dataset-agnostic and applies to any benchmark whose questions decompose into verifiable facts. Applied to the 830 BrowseComp-Plus test questions, our pipeline yields 57 fully grounded questions with question-level relevance judgments. Projection shifts the difficulty onto retrieval, as the strongest agent we evaluate loses five points of answer accuracy but sees its evidence recall fall from 84.3% to 21.4% while issuing 63% more search calls. As the first of a series of projections, we release the pipeline, the benchmark, and our analyses at https://github.com/castorini/cmass.


Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis. Authors: Liang Xu, Chengqun Yang, Zili Lin, Xintao Lv, Yichao Yan. Categories: cs.CV. The capability to perceive and synthesize human-human interactions is fundamental to developing intelligent digital human systems. However, existing datasets and modeling approaches are fundamentally constrained by low-fidelity kinematics, the omission of dexterous hand gestures and a severe lack of rich multimodal annotations. Furthermore, fragmented interaction representations and inconsistent evaluation protocols also impede fair and rigorous benchmarking. To systematically address these bottlenecks, we present Inter-X++, a comprehensive and large-scale benchmark designed to empower versatile HHI analysis. Captured via a novel hybrid motion capture system, Inter-X++ provides 11,388 high-fidelity interaction sequences and over 8.1M frames, featuring precise whole-body movements and detailed finger articulations. Meanwhile, we enrich the data foundation with multifaceted annotations, including hierarchical fine-grained textual descriptions, interaction categories, causal interaction orders, the relationship and personality of the subjects, as well as vertex-level contact maps and physically regularized constraints. Leveraging these elaborate annotations, we formulate a unified testing ground comprising four categories of downstream tasks that symmetrically span both generative and perceptive paradigms. To eliminate benchmarking ambiguities, we systematically standardize the interaction representations and evaluation protocols. Finally, we go beyond dataset construction to propose OpenHHI, a single and unified HHI representation and modeling framework that jointly optimizes interaction reconstruction and semantic understanding. Extensive experiments reveal that OpenHHI achieves state-of-the-art performance on both generation and perception tasks. This definitively proves that our unified representation successfully bridges interaction understanding and generation simultaneously.


Signatures of a light-induced exciton condensate exhibiting BEC-BCS crossover. Authors: Khanh Duy Nguyen, Gabriele Berruto, Yunhe Bai, Thomas Marchese, Woojoo Lee. Categories: cond-mat.mes-hall cond-mat.quant-gas cond-mat.str-el. Exciton condensates provide a platform to study quasiparticle pairing, Bose-Einstein condensation-Bardeen-Cooper-Schrieffer (BEC-BCS) crossover, and excitonic topological phenomena. Achieving a nonequilibrium exciton condensate allows the ultimate tunability of these emergent phenomena. Yet, evidence of a light-induced, nonequilibrium exciton condensate and its BEC-BCS crossover remains elusive. Here, we use time- and angle-resolved photoemission spectroscopy to demonstrate signatures of a non-equilibrium exciton condensate and its BEC-BCS crossover in monolayer MnBi2Te4. Following optical excitation, a distinctive hole-like dispersion representing excitons emerges and persists for >20 ps. Strikingly, energy-domain sharpening in the valence band occurs 2 ps after time zero and exhibits a sharp onset at a threshold pump fluence of 0.84 mJ/cm2. The delayed and strongly nonlinear response is difficult to reconcile with transient field effects or conventional carrier-induced band shifts but is consistent with a model of exciton condensation governed by a Berezinskii-Kosterlitz-Thouless transition. The estimated threshold exciton density agrees quantitatively with the Nelson-Kosterlitz critical density. At higher fluences, the exciton feature develops a camel-back-shaped dispersion, consistent with the BEC-BCS crossover in the condensate framework. Our work establishes ultrathin MnBi2Te4 as a model system for studying nonequilibrium exciton condensates with a connection to superconductivity and exciton-driven topological phases.


DreamHand: Repurposing Video Diffusion Models for Occlusion-Robust Egocentric 3D Hand Motion Recovery. Authors: Yufei Liu, Xixi Wang, Hao Li, Ganlong Zhao, Kaitong Cai. Categories: cs.CV. Egocentric video offers scalable manipulation data for embodied AI, yet recovering metric 3D hand trajectories remains challenging due to severe object occlusion and frequent out-of-sight gaps. Existing single-frame and windowed temporal regressors fail when hand shortly leaves the frame, while recent video diffusion models (VDMs) rely on heavy, stochastic multi-step sampling as pixel-space renderers. We instead repurpose VDM into a deterministic geometry encoder. A single forward pass over the clean latent exposes scene content beyond current observations, including occluded and out-of-sight hands. We introduce DreamHand, an offline clip-level framework that extracts features via a Deterministic Clean-Latent Encoder and decodes them with a Bidirectional Spatiotemporal Decoder. DreamHand recovers continuous bimanual trajectories with metric placement and no external detector, while a Ray-Based Camera Solver supports a second configuration that needs no test-time camera intrinsics. Across five egocentric benchmarks, DreamHand sets a new state of the art, cutting MPJPE-p by 30% on occlusion-heavy ARCTIC and 40% on HOT3D. These gains reach 46%-61% once out-of-sight hands are included in the evaluation, offering a scalable path from everyday human video to robot manipulation data.


Computational Methods of Wave Propagation for Semiclassical Models of High Harmonic Generation in Bulk Solids. Authors: Ava N. Hejazi, Nicholas Karpowicz, Gregory D. Scholes, Julia M. Mikhailova. Categories: physics.optics physics.chem-ph. We present a theoretical framework for self consistent treatment of nonlinear light-matter interactions in the ultra-fast strong-field regime based on numerical solution of Maxwell's equations and semiconductor Bloch equations. This framework is shown to describe high-order harmonic generation and propagation in bulk semiconductors, investigating differences in reflected and transmitted harmonic spectra due to propagation effects. We show that the propagation of the combined field of the driving laser pulse and generated harmonics in a bulk semiconductor significantly modifies the harmonic spectra, affecting interpretation of experimental results relating the transmitted harmonic spectra to the underlying electronic structure of the material. This model allows the self-consistent description of strong-field light-matter interactions in the non-perturbative regime, opening the way to explore the transition between fully classical and quantum regimes of interaction, tunneling and multiphoton regimes of material ionization, and perturbative and non-perturbative regimes of harmonic generation in bulk materials.


Zero-Sum Power Factor Games. Authors: Cameron Khanpour, Samuel Talkington, Mathieu Dahan, Daniel K. Molzahn. Categories: eess.SY math.OC. Variable active power injections arising from device behavior or compromised dispatch complicate voltage regulation in electric power networks with distributed energy resources (DERs). An operator can limit the resulting voltage deviations by remotely selecting DER reactive power parameters before observing the active power injections. IEEE Standard 1547-2018 specifies constant power factor as one such control mode, coupling each device's reactive power to its realized active power. Using a linear voltage model, we formulate the operator's decision as a robust minimax problem in which the operator minimizes the largest feasible aggregate voltage deviation. We solve this problem by expressing the power factor decisions through continuous reactive to active power ratios and exactly decomposing the payoff according to the signs of the voltage deviations. When every feasible voltage residual remains on its initial side of nominal, the resulting ratios cancel each injection's contribution and yield a closed form minimax strategy. We identify realistic DER ratings for which this strategy applies and quantify the regulation capacity lost under restricted power factor ranges. Numerical tests check the cancellation computation, solve the complete minimax problem directly at a representative DER rating, and compare the linear voltage predictions with nonlinear AC power flow.


A blind spot in transverse BAO calibration. Authors: Domenico Sapone. Categories: astro-ph.CO. Transverse baryon acoustic oscillation (BAO) measurements are increasingly used for cosmological inference, and carry a calibration that no such inference can constrain. A constant error in the transverse BAO scale is exactly degenerate with the combination $r_{\rm d} h$: it leaves the goodness of fit unchanged and the recovered parameters plausible, and is therefore invisible to any analysis that uses these measurements alone. The radial BAO sector removes this degeneracy, supplying $D_{\rm M}/r_{\rm d}$ by integration without reference to $H_0$ or to any model for the expansion rate. In flat Friedmann--Lemaître--Robertson--Walker (FLRW) geometry the relation between the two sectors is an identity, so the sound horizon and the dark-energy equation of state cancel as well. An integrated form of this identity reduces the consistency test to a straight line, whose slope measures a relative transverse calibration $\varepsilon$. A departure from $\varepsilon = 1$ cannot be produced by any dark-energy model, nor by spatial curvature: it indicates an inconsistency in the measurement chain rather than in the cosmology. We apply the test to the two main SDSS transverse BAO compilations, which give $\varepsilon = 1.073 \pm 0.021$ and $1.021 \pm 0.029$. The first differs from unity at $3.8σ$ using the published independent errors, while the second is consistent with unity. The two compilations themselves differ by $(5.4 \pm 1.4)\%$, or $3.9σ$, and the offset is constant in redshift. The test provides a direct diagnostic for current and future angular BAO measurements.


Transient Early Dark Energy-Like Dynamics as a Mechanism for Enhanced Early Structure Formation in the JWST Era. Authors: Abhik Bhattacharjee, Amlan Chakraborty, Subinoy Das, Anshuman Maharana, Priyank Parashari. Categories: astro-ph.CO. The discovery of massive galaxies at redshifts $z\gtrsim10$ by the James Webb Space Telescope (JWST) has renewed interest in cosmological mechanisms capable of enhancing early structure formation while preserving the successful large-scale predictions of the standard $Λ$CDM model. We investigate a phenomenological scenario in which an exotic dark matter species can undergo a transient early dark energy-like phase during the radiation-dominated era ($10^{-7}\lesssim a\lesssim10^{-5}$) before reverting to pressureless cold dark matter. We utilize the generalized dark matter framework to model this species, which is restricted to a sub-percent fraction of the total dark matter component by CMB, BAO and Type Ia supernova data. Its background and perturbation dynamics are characterized by a time-dependent equation of state, $w(a)$, and a time- as well as scale-dependent sound speed, $c_s^2(a,k)$. The temporary negative equation of state, combined with our phenomenological pressure-response prescription, induces a finite interval of negative effective sound speed squared. This triggers an instability-driven growth of density perturbations over a limited range of comoving scales, thereby enhancing the formation of early dark matter halos. We find that the enhanced halo abundance can substantially reduce the star-formation efficiencies required to reproduce the observed abundance of JWST galaxies relative to the standard $Λ$CDM scenario, especially at higher redshifts. Our results demonstrate that transient early dark energy-like dynamics in a subdominant dark matter component provide a viable mechanism for enhancing early structure formation and offer a new framework for interpreting the abundance of high-redshift galaxies observed by JWST and future surveys.


Towards Surgical World-Action Modeling: A Preliminary Joint Visual-Trajectory Forecasting for Surgical Motion Planning. Authors: Weiliang Huang, Huanrong Liu, Bob Zhang, Qi Dou, Zhen Chen. Categories: cs.CV cs.RO. Reliable surgical planning requires models to anticipate not only how instruments will move, but also how the operative visual state will evolve together with such motion. Existing approaches typically treat future scene generation and instrument trajectory prediction as two separate tasks. Scene-only models cannot directly evaluate the accuracy of future instrument motion at the trajectory level, while trajectory-only models fail to capture the visual consequences of instrument movement, leaving the consistency between predicted trajectories and future scene evolution unaddressed. Jointly forecasting both provides a more complete account of surgical action-scene dynamics by enabling explicit trajectory-level evaluation while simultaneously modeling the corresponding visual evolution. To bridge this gap, we present a preliminary joint visual-trajectory world-action model that simultaneously forecasts future visual states and instrument trajectories from historical surgical observations. Specifically, we encode historical video frames and tool trajectories into latent representations, which are processed by a temporal-spatial encoder and subsequently decoded through separate visual-state and trajectory prediction heads. Based on this preliminary architecture, a chunked autoregressive rollout is repeatedly applied to predict fifteen future steps. The chunked strategy consistently outperforms direct one-shot prediction across all evaluated horizons, improving first-segment PSNR from 18.86 to 23.11 dB and reducing ADE from 45.77 to 22.22 pixels. These results demonstrate the initial feasibility of joint visual-motion forecasting. However, we observe progressive visual degradation and accumulated trajectory errors over longer prediction horizons, which remain important challenges for future surgical world-action modeling.


Design and Empirical Evaluation of a Network-Centric, On-Premises Architecture for Earth Observation Data Access. Authors: João Pinelo, João Gonçalves, Denis Willett, Amit Ruhela, Derek Steinmoeller. Categories: cs.DC cs.NI cs.PF. Earth observation (EO) programmes generate data at volumes that exceed the transfer and storage capacity of most institutional networks. Public cloud platforms address this for well-resourced organisations, but institutions across the Atlantic basin face constraints in connectivity, sovereignty and funding that make on-premises infrastructure the only viable path. Cloud-native data formats enable efficient partial reads, yet their performance depends on the bandwidth of the underlying network fabric, a dependency rarely measured in isolation. This paper presents a replicable, network-centric architecture for on-premises EO data access, evaluated at its first operational deployment: the AIR Data Centre, founding node of the Atlantic Cloud. The system comprises a MinIO object storage cluster on a 100 GbE fabric, a PostGIS metadata catalogue and an OGC API-EDR access layer. We characterise the fabric under sustained parallel load, evaluate object storage throughput for EO-representative workloads, and compare measured performance against throttled baselines on identical hardware, isolating network bandwidth as the sole variable. Multi-site replication benchmarks with partner institutions characterise the federation primitive the model depends on. Network bandwidth is the dominant constraint on storage throughput for bulk EO data access up to a threshold; beyond it, endpoint memory topology rather than capacity governs how much bandwidth a system can use. For this hardware class that threshold lies above 10 Gbps per server. Below it, network capacity alone sets what the facility can deliver; above it, the return on further network investment depends on endpoint memory provisioning, which can be deferred and bought later.


From Kriging to Spatial AI: Fifty Years of Spatial Statistics for Complex Dependent Data. Authors: Montserrat Fuentes, Veronica B. Patterson. Categories: stat.ME. Spatial statistics has grown from kriging for spatial prediction into a broad framework for learning from complex dependent data. This article traces that development from random fields and spectral methods to Bayesian hierarchical models and scalable computation. It then connects these foundations to Spatial AI, where graph learning and neural networks are being adapted to spatially dependent data. The article introduces the main ideas behind kriging and nonstationarity and explains how data fusion and uncertainty quantification extend spatial inference to more complex settings. The central contribution is a unified account of how these developments lead naturally to new forms of Spatial AI. Rather than treating spatial statistics and machine learning as separate traditions, we show how both learn from dependence while preserving interpretable structure. We also examine how spatial geometry and physical knowledge can guide flexible representation learning and support scientifically meaningful prediction.


GENIE: Generative Neural Inference for Epidemics. Authors: Laura M. Guzmán-Rincón, George R. E. Bradley, Joel Kandiah, Kyriakos Flouris, Pietro Liò. Categories: stat.ME q-bio.QM. The SARS-CoV-2 pandemic highlighted the ongoing risk infectious diseases pose to society and the value of reliable information on the likely future burden. When forecasting an epidemic at fine spatial resolution, traditionally used mechanistic compartmental model struggle to capture highly complex granular transmission dynamics, resulting in inaccurate and overconfident forecasts. However, detailed Agent-Based Models (ABMs), are challenging to calibrate and are too computationally expensive to use in real-time. Amortized simulation-based inference promises to overcome this difficulty by exploiting the power of machine learning (ML) to perform approximate forecasting at near-real-time using arbitrarily complex models of epidemics. In this work we introduce Generative Neural Inference for Epidemics (GENIE), a spatio-temporal ML-based framework for high-resolution forecasting of the burden of respiratory pathogens. GENIE is designed to reflect two key characteristics of outbreaks: (i) shared biological mechanisms across locations and (ii) location-specific characteristics affecting transmission dynamics. This results in the model architecture having two modules: (i) a Local Infection Encoder - which learns to represent disease dynamics shared across all locations and (ii) a Local Profile Encoder - which learns location-specific representations. Using simulations from a high-resolution spatio-temporal ABM, GENIE is trained to generate samples from an approximate posterior predictive distribution of future epidemic trajectories. Benchmarked against established statistical and ML models, GENIE demonstrates superior performance across a range of measures including the timing and magnitude of peak hospitalisations.


Video2DoorTraversal: Push Door Traversal via Simulated Door Twins. Authors: Xincheng Tang, Yiji Chen, Youhan Xie, Wanyu Li, Zhengjie Shu. Categories: cs.RO. Door opening and traversal is a long-horizon loco-manipulation task that requires precise handle interaction and coordinated base-arm control. We present Video2DoorTraversal, a single-video real-to-sim-to-real framework for wheel-legged mobile manipulators. Given one RGB video of a real door, DoorTwin reconstructs an instance-aligned, articulated, and simulation-ready door twin with realistic geometry and appearance. A simulation-in-the-loop agent converts the recovered articulation into a parameterized skill program and iteratively refines failed rollouts to generate physically executable demonstrations. These demonstrations are used to train ArticuACT, a dual-depth policy that predicts coordinated base, arm, and gripper commands using robot-centric camera conditioning and interaction-aware supervision. With all perception and policy inference running onboard, the system achieves a 96.57% average success rate across five real doors and an 80.95% zero-shot success rate on structurally similar unseen doors, while completing the full approach, opening, and traversal sequence in approximately 13s on average. Project Page: https://video2doortraversal.github.io/.


Artificial Intelligence for Workflow Analysis in Colorectal Surgery: A Multicentric, Cross-Procedural Development and Generalization Study. Authors: Pietro Mascagni, Julia Alekseenko, Pooja P Jain, Marta Goglia, Andrea Balla. Categories: cs.CV. Minimally invasive colorectal surgeries (MIS-CRS) are characterised by significant variability and inconsistent outcomes. ColoWorkflow, a tool for the video-based assessment (VBA) of MIS-CRS workflow, was recently validated. However, manual VBA is time-consuming, limiting implementation. This study presents AI-ColoWorkflow, a deep learning model for automated surgical workflow analysis across MIS-CRS. Operative videos of MIS-CRS were collected from 4 centres and a publicly available dataset. Phases and steps were manually annotated according to ColoWorkflow. A deep learning model combining a fine-tuned DINOv3 vision transformer for per-frame visual feature extraction with a hierarchical multi-stage temporal convolutional network was jointly optimized for phase and step recognition. The model trained on pooled multicentric data, namely AI-ColoWorkflow was compared against centre-specific and procedure-specific models on a held-out test set. The following metrics were used for evaluation: macro F1 score, balanced accuracy, precision, and recall. AI-ColoWorkflow achieved a macro F1 of 73.01% $\pm$ 10.27 (balanced accuracy 73.43%) for phase recognition and 39.82% $\pm$ 7.06 (balanced accuracy 38.65%) for step recognition. The global model outperformed centre- and procedure-specific models in most experiments except procedure-specific step recognition. In the generalization analysis, mean F1 was 48.42% for phase recognition. AI-ColoWorkflow can reliably recognize MIS-CRS phases. A single model trained on pooled, multicentric, multi-procedural data generalises at least as well as and often better than centre- or procedure-specific models for phase recognition in MIS-CRS, while procedure-specific step models retain advantages for certain procedure types, motivating hybrid training strategies for future surgical AI development.


Reinforcement LearningtoHarness Approximation Errors for Long-Time QuantumSimulation. Authors: Yu-Bo Shi, Markus Heyl, Roderich Moessner, Marin Bukov, Hongzheng Zhao. Categories: quant-ph cond-mat.dis-nn cond-mat.stat-mech cond-mat.str-el. Accurate digital quantum simulation at long times is limited by the accumulation of errors inherent to approximate simulation. Here we introduce RL-Trotter, a reinforcement-learning framework that treats unavoidable approximation errors as resources for error correction rather than merely imperfections to suppress. We show that low-dimensional information from conservation laws, such as the energy and energy variance, provides a sufficient learning signal to guide the agent, which learns to adapt a single scalar---the next Trotter step size---without access to the target wave function. By optimizing the entire long-time evolution rather than individual steps, RL-Trotter discovers self-correcting sequences in which later errors compensate for those accumulated earlier, increasing the accuracy of the long-time dynamics. The learned policies are intrinsically robust to measurement noise, substantially reducing measurement overhead. They also generalize to previously unseen, physically similar initial states and transfer from small, classically simulable systems to systems an order of magnitude larger. This enables a practical protocol based on classical pretraining followed by direct deployment or limited fine-tuning on quantum hardware. Our results establish a broader perspective for quantum algorithms: errors in approximate evolution can be orchestrated into resources for accurate and resource-efficient quantum dynamics.