Science & Mathematics

Terminally ill patients generate extensive digital health data through hospital portals, physiological monitoring devices, consumer health applications, and end-of-life care platforms. Yet their ability to control this data is systematically undermined by the structural conditions of terminal care. Through semi-structured in-depth interviews with 21 terminally ill patients, 11 family caregivers, and 8 physicians recruited from tertiary hospitals, this study examines the digital privacy circumstances and subjective attitudes of patients in end-of-life contexts. Thematic analysis reveals five structural dimensions of digital privacy challenge: continuous bodily digitization without meaningful informed consent; cognitive decline driving involuntary proxy control of accounts; inferential data generation exceeding patients' epistemic boundaries; dissolution of individual privacy boundaries amid relational network restructuring; and the absence of institutional governance over post-mortem data. Patient privacy attitudes span three profiles: fundamental absence of privacy awareness, sustained active management under severe tool constraints, and perception of problems followed by resigned abandonment. Interviews with caregivers and physicians reveal benevolent substitution in the name of care needs, expansion of access beyond the initial task, and unresolved boundaries under absent institutional guidance. These findings reveal a compounding privacy vulnerability unique to end-of-life contexts and call for privacy-by-design principles, granular permission mechanisms, and institutional governance frameworks tailored to the needs of dying patients.

The construction industry increasingly relies on software built from third-party components, yet the dependency composition and known-vulnerability exposure of construction-related software remain poorly analyzed. In this study, we conduct an empirical, cross-ecosystem investigation of 904 construction-related GitHub repositories spanning six construction functions and a multifunction category. Using commit-pinned repository snapshots and OSV-Scanner, we examine dependency assessability, known-vulnerability prevalence, severity, weakness classifications, and shared upstream exposure. Our results reveal that only 494 repositories (54.6%) completed vulnerability assessment, revealing substantial gaps in dependency visibility. Among successfully scanned repositories, 395 (80.0%) contained at least one dependency version associated with a published vulnerability advisory, and 97.2% of affected repositories contained a High or Critical severity risk. Additionally, Common Weakness Enumeration (CWE) mapping identified recurring resource-consumption, path-traversal, information-exposure, and code-injection weaknesses. Vulnerability burden was uneven, with a median of 50 distinct advisory clusters per affected repository, while just two affected upstream package families collectively reached ~70% of affected repositories. These findings establish an empirical baseline for dependency assessability and known-vulnerability exposure in construction-related open-source software. Our findings underscore the importance of documenting assessment coverage, interpreting advisory matches in their deployment context, and examining shared upstream dependencies when evaluating construction software supply-chain security.

AI/ML models are useful for the prediction of materials' properties. Recently, there has been great progress in developing Neural Network models to predict the properties of solids, molecules, and crystals, avoiding expensive and time-consuming laboratory testing. This study presents a novel AI/ML-based multi-modal approach to predict material magnetic ordering using material crystal structure information as input to a Graph Neural Network model (GNN) and semantic text descriptions of the material's properties and structure as input to a Large Language Model (LLM), before performing information fusion of the GNN and LLM outputs to predict material magnetic ordering in four classes (Non-magnetic, Ferromagnetic, Antiferromagnetic, Ferrimagnetic). The model is trained, validated, and tested on a dataset which is a combination of Materials Project dataset, Magndata dataset, and a set of uranium compounds with magnetic ordering determined in laboratory testing, with an 80/10/10 (training/validation/testing) data split. The proposed GNN and LLM multi-modal approach achieves better overall accuracy than both the standalone GNN and LLM approaches with three fusion strategies.

Recovering from failures in long-horizon, stateful language agents is challenging because the action introducing a defect, the symptom exposing it, and the persistent state requiring repair frequently diverge. Existing methods largely focus on retrospective failure attribution, leaving sequential diagnosis and state repair poorly integrated under constrained budgets. We introduce TRACE-FIX, a unified recovery framework centered on versioned residual task state. Trace transforms execution logs into a structured dependency-linked ledger, prioritizing unresolved task requirements rather than salient surface errors. Fix formulates recovery as budgeted planning over admissible actions, incorporating outcome forecasting and explicit repair-reserve constraints to prevent premature budget exhaustion. To rigorously decouple localization accuracy from downstream repair capability, we present LongH-Debug, evaluating root-cause attribution, verified-fault recovery, and end-to-end closed-loop execution independently. Our empirical analysis demonstrates that structured residual tracking substantially improves causal localization over raw-trace baselines, while budget-aware sequential planning converts verified fault signals into more reliable task recovery without destructive trial-and-error. Code is available at https://github.com/toffeecat/tracedp.

Automated fact-checking (AFC) systems powered by large language model (LLM) agents still face challenges in evidence retrieval and claim verification. Existing approaches improve their capabilities through computationally costly post-training or human-in-the-loop feedback that is difficult to scale. To transfer professional fact-checking expertise to LLM agents efficiently, we distill publicly available guidelines published by Agence France-Presse (AFP) Fact Check into a reusable, modular Professional Fact-Checking Skill (PFC-Skill). We then present SkillCheck, the first AFC system that gives LLM agents dynamic access to PFC-Skill to guide evidence retrieval and claim verification at inference time. Extensive experiments demonstrate that SkillCheck improves upon skill-free DEFAME baselines across multiple LLM backbones, with gains of 2.2--4.8 pp. in Accuracy and 1.5--6.3 pp. in Macro-F1 on the AVeriTeC benchmark. Notably, SkillCheck with GPT-4o-Mini exceeds DEFAME with GPT-4o by 3.4 pp. in Accuracy while cutting inference cost by roughly 61%. The performance gains also extend to open-web retrieval. Ablations show that incorporating the skill during evidence retrieval provides most of the gains, while incorporation into both stages yields the best overall performance. Case studies show that PFC-Skill enables more targeted search and closer scrutiny of evidence. To conclude, our work shows that LLM agents can "stand on the shoulders of giants" by drawing on the best practices of professional fact-checkers, substantially enhancing AFC performance without extra training or continuous human supervision.

Deep research agents investigate complex questions through iterative search, source examination, and evidence synthesis. A conventional workflow alternates reasoning with individual search and reading actions. Search as Code introduces a new paradigm: agents write programs that compose search, source reading, reranking, model calls, and local processing, returning selected observations for further reasoning. To support this workflow in existing coding agents, we present OpenSaC, an open-source Python library with a companion Search as Code skill. Configurable capability interfaces let agents combine these operations while retaining evidence for inspection and reuse. Compared with standard Codex using its built-in web search, the OpenSaC configuration uses less execution time and fewer model tokens on BrowseComp and WideSearch, with higher accuracy on BrowseComp and slightly lower row F1 on WideSearch. We aim to lower the barrier to adopting Search as Code and provide an open foundation for community development and transparent research on deep research agents.

Hyperspectral image classification (HSIC) relies on static spatial-spectral representations learned from labeled samples, limiting its ability to model structured dependencies in the latent representation space. To address this limitation, this work proposes Stochastic Latent World Modeling (LWM), a trajectory-based representation-learning framework that enriches classification through stochastic latent-state prediction. Given a hyperspectral patch, LWM tokenizes and encodes spectral and spatial information separately using dedicated transformer branches, then fuses their outputs into a unified representation. This representation is further organized into an environment state that captures center, neighborhood, spectral, and global contextual information. A stochastic latent transition module then predicts multiple future latent states through Gaussian parameterization and reparameterization, with recurrent refinement used to model dependencies across successive states. The resulting latent trajectory is adaptively aggregated by a rollout planner to construct a planned representation, which is integrated with the base representation for final classification. By extending static spatial-spectral encoding with stochastic multi-step latent-state modeling, LWM enables the classifier to exploit structured dependencies in the representation space and provides richer features for HSIC. Across five benchmark datasets, LWM achieves overall accuracies ranging from 89.21% to 99.52%, demonstrating consistent classification performance under limited-label settings.

We study the problem of non-stationary bandits without knowledge of the environmental changes. We propose Detection Augmented Learning (DAL) a black-box paradigm that comprises three modular components: a bandit algorithm, a change detector, and a forced exploration schedule. This modular design enables its applicability across a broad spectrum of bandit problems, from classical to highly structured settings. First, we provide theoretical insights into DAL's strong empirical performance: a single regret bound shows that DAL transfers the regret of its stationary input to the piecewise stationary setting, attaining or improving the best known bounds in every setting considered. We further identify conditions under which DAL performs well in drifting environments. Then, we showcase DAL's practical performance and versatility through extensive experimentation in which DAL consistently surpasses all state-of-the-art methods, both prior-free and prior-based, across diverse non-stationary scenarios in nine bandit settings. Finally, we investigate the effectiveness of the forced exploration and show that a bandit-driven strategy improves performance and shortens detection delay.

System One models produce probabilistic judgments that software can consume directly, making reliable confidence as important as answer accuracy. Yet joint training across text, images, and video remains underexplored, and the contributions of supervised fine-tuning (SFT) and reinforcement learning for calibrated decisions (RLCD) are difficult to separate. We introduce UniJev, a trimodal framework that scores candidate answers with a shared head under joint supervised training across text, images, and video. Supervised decision models improve accuracy over their backbone readouts across all three modalities, while the existing RLCD algorithms change accuracy little and have inconsistent effects on visual calibration across recipes. These findings motivate a separate supervised objective for confidence calibration. We introduce Cora (Confidence--Outcome Reliability Alignment), which groups current predictions by confidence and optimizes each prediction's confidence toward the empirical accuracy of its bin. At both 2B and 9B, Cora lowers image and video expected calibration error (ECE) while preserving accuracy, without a reinforcement stage. We release our code at https://github.com/wjnwjnwj/UniJev.

Despite recent advances in policy learning, long-horizon manipulation remains difficult because learned policies must avoid compounding errors while preserving future feasibility. While search-based planning can explicitly reason over such future consequences, it quickly becomes expensive in high-dimensional continuous action spaces. This raises a central question: how can long-horizon reasoning from search be transferred into policies in continuous action spaces? To this end, we propose DISCO: Distilled Tree Search over Continuous Actions. DISCO uses progressive-widening MCTS in physics simulation to generate long-horizon plans. During search, a learned policy proposes continuous actions, while a learned value function scores actions by their expected progress toward task completion. Using GPU-parallelized simulation, DISCO periodically distills MCTS-generated plans into the policy-value model, improving the action proposals and value estimates used in subsequent searches. Finally, the learned policy-value model can be directly deployed without the need for simulator-based search at test time. Across simulated and real-world rearrangement tasks, DISCO outperforms behavior cloning, reinforcement learning, and search-based baselines.

Can language-model user proxies reproduce human reliance on an AI answer across contexts? Published simulated-user studies do not establish validity for this partial-answer decision, and Rel-A.I.'s released human record omits trial-level choices. We audit its 90 printed questions and 15 domain-by-marker reliance rates using sampled decisions and counterbalanced A/B probabilities from open models through 72B parameters. A matched crossover varies the printed agent specialty within each question, separating question content, label averages, and their interaction. Mistral Small's strong-marker math-minus-law label marginal is -0.4 percentage points, yet matching the agent label to the question domain raises reliance by 13.3 points on average across the five domains. The best A/B proxy has 22.5-point absolute-rate error and 8.3-point domain-effect error against the human table. A two-sample independent-binomial human replication has 3.5-point expected domain-effect error under the stated working model. The interaction is model dependent, and inference layout changes identical-prompt probabilities. This single-study audit does not establish proxy validity for human effect sizes or general directional screening.

We introduce FormBench, a platform for testing the ability of models to retrieve precise chemical information from technical documents. FormBench contains 55,352 queries and two retrieval corpus variants each with ~1M passages: FormBench-Random (C0), with randomly sampled distractors, and FormBench-Structured (C1), with structurally proximate distractors. Each query is paired with an anchor passage, a hard negative, and a soft negative - derived from a materials ontology through entity overlap scoring, providing a deeper graded signal on a model's discriminatory power. We evaluate 10 model configurations spanning commercial API encoders (OpenAI, Voyage), open-source encoders (Nomic, GTE-large, Qwen3-Embed), and their task-adaptive fine-tuned (TAPT) variants. We find: (1) commercial APIs score very poorly across all configurations (OpenAI-3-large: 0.009 on C1, 0.213 on C0); (2) TAPT on FormBench triplets lifts Qwen3-Embed to 0.4085 on C1, 0.4835 on C0; (3) BM25 remains competitive (0.375 on C1, 0.467 on C0), exceeded only by TAPT-Qwen3; (4) corpus size has a significant impact on retrieval behavior, as demonstrated through a corpus-size ablation; (5) the API failure traces to within-domain geometric collapse; (6) APIs outperform even the TAPT variants on classification and clustering tasks. FormBench is released in BEIR-compatible format at huggingface.co/datasets/vinven7/FormBench under CC-BY-4.0.

Reflection lets LLM agents improve from their own attempts, but the memory it builds is local to a single task, so an agent reflecting on its own tends to revisit the same explanation for failure. Cross-task reflective memory addresses this in two forms: retrieved memory keeps past experience explicit and looks up related cases, which is specific but bounded by the bank, and parametric memory internalizes experience into parameters, which generalizes but is grounded in nothing beyond the task description. When both are used, existing agents combine them additively, feeding their outputs to the solver side by side, so retrieved experience never changes what the learned memory produces. We introduce CoMem (Compounding Memory), which makes retrieved experience an input to the learned memory itself, at training and at inference. The agent builds a bank of its own successful and failed reflections, learns from retrieved cases to write failure hypotheses, candidate explanations of how an attempt is likely to go wrong, and at test time reasons from newly retrieved cases to guide each attempt. Varying the retrieved cases gives the memory different perspectives, yielding reflective diversity that is not merely sampled but also grounded in retrieved experience. Across two backbones and five benchmarks in math, code, and multi-hop QA, CoMem achieves strong overall performance. Additional evaluations show that it generalizes to out-of-distribution tasks and enables smaller models to provide effective guidance to stronger solvers.

Large language models acquire broad knowledge from web-scale corpora during pretraining, including knowledge that could be hazardous if misused. Removing such knowledge from the model itself without degrading general capability is desirable but difficult, as it is deeply entangled with general capability within the same parameters. Existing safety alignment and unlearning methods typically adjust these shared parameters, tending to suppress the expression of hazardous knowledge rather than remove it. As a result, the knowledge remains in the model and is readily recoverable by subsequent fine-tuning. To address this limitation, we propose REX (Relocating into Removable EXperts), a knowledge relocation method that separates hazardous knowledge from general capability at the architectural level. Exploiting the modular structure of Mixture-of-Experts models, REX expands the model with a set of new, physically removable experts designated to hold hazardous knowledge. Through expert expansion, selective gradient masking and routing differentiation, it relocates hazardous knowledge out of the original experts and concentrates it in the new ones. Excising these new experts yields a model suitable for public release. This removal holds consistently across Intern-S2, Qwen3.5-35B-A3B and Gemma-4-26B-A4B; on Qwen3.5-35B-A3B, for instance, excision lowers WMDP-Bio accuracy by 48.4 points while MMLU changes by less than 3 points. The excised model matches the strongest of nine unlearning baselines in removing hazardous knowledge, and it is the only one that preserves this removal under subsequent fine-tuning. After 250M tokens of standard fine-tuning on Qwen3.5, the best baseline rebounds on WMDP-Bio from 44.1% to 78.3%, whereas REX rises only from 32.4% to 42.8%.

As neuromorphic systems scale to thousands of near-memory cores, aging-induced permanent faults in neuron-update datapaths become an important obstacle to long-term deployment. Existing online recovery methods typically retire a faulty core and migrate its neurons, along with their weights and membrane states, to healthy cores. This whole-core treatment incurs high recovery traffic and makes recovery depend on scarce destinations; at high utilization, there may be enough surviving compute capacity but insufficient free storage to copy the affected neuron context. This paper presents SpikeRelay, an online recovery framework for diagnosed compute-unit faults. SpikeRelay exploits the fact that, in many neuromorphic cores, the compute datapath, local SRAM, and router are distinct modules: a compute fault need not destroy stored weights and states or disconnect the core from the NoC. Instead of retiring a faulty core and migrating its neuron data, SpikeRelay converts the faulty core into a storage-and-forwarding node while offloading neuron-update computation to a healthy rescue core. This decoupling of storage and computation eliminates recovery-time data migration and greatly reduces spare-SRAM requirements. Consequently, SpikeRelay sustains 100\% recovery at 92.2\% SRAM utilization and a 50\% fault rate, whereas the best baseline achieves only 22.1\%.