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01.
medRxiv (Medicine) 2026-06-23

Intellectual Property Literacy, Innovation Readiness and Innovation Practice in Syria's Pharmaceutical Sector: A Cross-Sectional Study

Background Innovation in pharmaceutical sectors operating under resource and institutional constraints may depend not only on knowledge and attitudes but also on the conditions that enable innovation-related activities to occur. This study examined the relationships among intellectual property (IP) literacy, innovation attitudes, innovation readiness, and reported innovation practice among pharmaceutical professionals in Syria. Methods A cross-sectional survey was conducted among 303 pharmaceutical professionals between March and April 2026. Four composite indices were constructed to assess IP literacy, innovation attitudes, innovation readiness, and innovation practice. Descriptive statistics, correlation analyses, group comparisons, and multivariable regression models were used to characterize patterns of association among study domains. The analysis was designed to identify empirical patterns rather than infer causal relationships. Results Innovation attitudes were comparatively high (73.56/100), whereas innovation readiness (17.00/100) and innovation practice (12.65/100) were substantially lower. IP literacy was positively associated with innovation readiness (r = 0.384, p < 0.001) and innovation practice (r = 0.205, p < 0.001). In contrast, innovation attitudes were not significantly associated with reported innovation practice (p = 0.332). Regression analyses indicated that the inclusion of innovation readiness improved model fit beyond specifications based on knowledge and attitudes alone ({Delta}R{superscript 2} = 0.058, p = 0.028). Significant differences in readiness and practice were observed across professional groups (p < 0.001), whereas knowledge and attitudes showed limited variation. Conclusions High levels of innovation-related knowledge and positive attitudes did not correspond to high levels of reported innovation practice in this setting. The findings suggest that innovation readiness may capture enabling conditions that are not reflected by knowledge or attitudinal measures alone. These results support the value of examining contextual and institutional factors when assessing innovation capacity in resource-constrained pharmaceutical systems. Given the substantial gap observed between innovation attitudes and innovation practice, educational strategies may represent one avenue for strengthening innovation readiness. In the Syrian context, strengthening innovation-oriented education and university-industry engagement may help cultivate innovation competencies and support the translation of research into practical applications.

02.
medRxiv (Medicine) 2026-06-22

Symptom-based phenotype discovery in motor neuron disease using natural language processing of electronic health records

Background: Motor neuron disease (MND) is a fatal neurodegenerative condition with significant clinical heterogeneity that is incompletely captured by existing phenotype classifications based on onset site. Electronic health records (EHRs) contain detailed symptom documentation in clinical narratives that may enable data-driven discovery of clinically meaningful patient subgroups. Methods: We developed a natural language processing (NLP) pipeline using MedCAT to extract symptoms from clinical notes of 2,361 people with a confirmed diagnosis of MND at a tertiary neurology center. MND cohort confirmation used three complementary methods: clinic attendance records, text-based diagnosis detection, and NLP extraction with negation detection. Extracted symptoms were filtered to Unified Medical Language System semantic type T184 (Sign or Symptom) with removal of negated concepts. Patients were clustered using latent class analysis on binary symptom profiles. Survival differences were assessed using Kaplan-Meier analysis, log-rank tests, and Cox proportional hazards regression. Results: From the first clinical notes, we identified four clusters of symptoms among 872 patients and 76 symptoms: Motor-Bulbar (n=373), Motor-Tremor (n=154), Sensory-Pain (n=222), and Motor-Respiratory (n=123). When extended to all clinical notes (n=2,065; 184 symptoms), these reorganized into three clusters: Autonomic-Respiratory (n=472), Nocturnal-Respiratory (n=338), and Classic Motor (n=1,255). Survival differences were significant across all clusters in both the first notes and all notes analyses (log-rank p < 0.001). Conclusions: NLP-based symptom extraction from EHRs identifies clinically meaningful MND subgroups that extend beyond traditional onset-site classifications. Autonomic-respiratory symptom burden is associated with poorer survival while a newly identified Sensory-Pain subtype with a better prognosis. These data-driven phenotypes may improve prognostication and inform targeted supportive care.

03.
arXiv (CS.CV) 2026-06-12

VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models

Semantic 3D occupancy provides a voxelized world state for autonomous driving and robot decision making, but object and rare-class errors can affect free-space interpretation, collision checking, and temporal state propagation. We show that a common VLM strategy, aligning 3D voxel or object features with crop-caption embeddings, improves text-space similarity without reliably improving closed-set occupancy mIoU. Motivated by this mismatch, we propose VISA, a training-time semantic auditing approach for existing occupancy world models. VISA queries an offline VLM on a representative crop of each physical object instance, obtains a structured audit with class hypotheses, plausible confusions, reliability, attributes, and evidence, and propagates it along the object track. The audit is grounded to matched 3D object voxels and distilled into semantic logits through reliability-weighted taxonomy, attribute-factor, and scene-level audit graph losses, while inference remains unchanged and requires no VLM. On nuScenes, averaged across three runs, VISA improves OccWorld from 19.06 to 20.05 mIoU and GaussianWorld from 21.36 to 21.91 mIoU; on GaussianWorld, object mIoU improves from 18.18 to 19.16 and rare-class mIoU from 15.60 to 16.79. These results suggest that VLMs are better suited to closed-set occupancy as reliability-aware semantic auditors than as generic caption-embedding targets.

04.
arXiv (CS.CL) 2026-06-19

Benchmarking Local LLMs for Natural-Language-to-SQL Querying in Biopharmaceutical Manufacturing: An Empirical Benchmark on Consumer-Grade Hardware

Biopharmaceutical manufacturing organizations operate under regulatory frameworks such as FDA guidance, EU Good Manufacturing Practice (GMP), and the EU AI Act, which can restrict the use of cloud-based artificial intelligence systems. Locally deployed large language models (LLMs) offer a privacy-preserving alternative, but their suitability for pharmaceutical manufacturing tasks remains underexplored. This study evaluates four open-source LLMs (Qwen 2.5 Coder 7B, Llama 3.1 8B, Mistral 7B, and Meditron 7B) deployed locally via Ollama for natural-language-to-SQL generation over a pharmaceutical manufacturing database. A FastAPI-based evaluation platform, PharmaBatchDB AI, was developed using a synthetic Microsoft SQL Server database containing approximately 63,000 records across Batch, Manufacturing Execution System (MES), and Clean-In-Place (CIP) modules. Models were benchmarked on 60 domain-specific natural-language questions using metrics including SQL extraction rate, SQL compliance, factual consistency, ROUGE-L, hallucination rate, throughput, and latency. Qwen 2.5 Coder 7B, Llama 3.1 8B, and Mistral 7B generated SQL for all evaluation tasks, while Meditron 7B failed on nearly all tasks due to context-window limitations and poor SQL generation capability. Llama 3.1 8B achieved the highest SQL compliance, whereas Qwen 2.5 Coder 7B achieved the strongest overall text similarity and factual consistency. Performance differences between the two leading models were not statistically significant. The results show that code-tuned general-purpose LLMs outperform a domain-specific biomedical model on structured query generation for pharmaceutical manufacturing data. Although fully local, GxP-aligned NLQ systems are feasible on consumer hardware, current performance levels still require human oversight and downstream validation for regulated use.

05.
arXiv (CS.CL) 2026-06-17

Environment-Grounded Automated Prompt Optimization for LLM Game Agents

LLM agents in interactive environments are highly sensitive to their prompts, yet prompt engineering remains a manual, task-specific process. We introduce an automated prompt optimization framework for LLM agents that decomposes the observation-to-action pipeline into a goal-conditioned descriptor agent and an action selection agent, and iteratively refines each module's prompt through an LLM-driven evolutionary loop guided by environment returns. We propose a behavior analyzer to attribute episode outcomes to specific prompt components, and a mutator to propose targeted revisions to the prompt, before validating them through environment rollouts. We evaluate on all five BabyAI tasks in the BALROG benchmark, comparing our pipeline against BALROG's RobustCoTAgent under both plain and guided prompt initializations. Optimization improves performance consistently across tasks and conditions, without requiring updates to the model weights. On PutNext, a multi-step coordination task where the RobustCoTAgent achieves 0% success, our framework reaches up to 72.5% success rate using the same underlying LLM with optimized prompts. These results suggest that a multi-agent framework, combined with automatic prompt optimization, enhances LLMs without the need for fine-tuning or extensive human supervision.

06.
arXiv (CS.LG) 2026-06-24

Dirac-Frenkel dynamics with inertia for nonlinearly parametrized solutions of evolution problems

arXiv:2606.24769v1 Announce Type: cross Abstract: Even when Dirac-Frenkel dynamics determine a well-defined evolution in function space, the corresponding parameter dynamics can be non-unique or ill-conditioned for redundant nonlinear parametrizations such as neural networks or mixture models. We propose to add inertia to the Dirac-Frenkel dynamics and show that this allows useful parameter velocity information to persist from the past trajectory in directions that are weakly informed, while well-informed parameter velocity directions continue to follow the Dirac-Frenkel dynamics. We prove that the inertial formulation yields well-posed parameter dynamics and provide a posteriori error bounds. After time discretization, the method requires the solution of the same type of regularized linear least-squares problem as standard Dirac-Frenkel dynamics, but with the previous velocity appearing as an anchor. Numerical experiments demonstrate the increased robustness obtained with inertia.

07.
arXiv (CS.CL) 2026-06-16

Do LLMs Reliably Identify Correct Information Units in Aphasic Discourse?

Correct Information Units (CIUs) are central to discourse assessment in aphasia because they quantify communicative informativeness rather than linguistic form alone. However, CIU scoring is time intensive and requires trained raters. This study examined whether instruction-tuned large language models (LLMs) can reliably perform token-level CIU classification from aphasic discourse transcripts. Sixteen picture-description transcripts elicited with the Cat Rescue stimulus were annotated for CIU status according to Nicholas and Brookshire (1993). The sample spanned four severity strata: control, mild, moderate, and severe aphasia. Four publicly available instruction-tuned LLMs were benchmarked under zero-shot and two few-shot prompting conditions across five stratified random seeds. Performance was evaluated against consensus human labels using accuracy, precision, recall, F1, and Cohen's kappa. Zero-shot prompting was insufficient across models. In contrast, few-shot prompting yielded substantial gains and produced competitive performance for three viable models. Mean few-shot F1 scores ranged from 0.776 to 0.817 across Llama-3.1-8B, Qwen2.5-7B, and Mistral-7B, with no significant differences between fixed global and per-chunk local example selection. Phi-3-mini was unstable and did not yield reliable performance. Viable models showed high recall but lower precision, suggesting systematic over-classification of tokens as CIUs. Performance also varied by discourse severity, with the weakest results in more severe aphasia. Few-shot LLM prompting can support automated CIU identification without gradient-based task training, but agreement with human annotation remains insufficient for fully autonomous use. These findings support LLM-based CIU scoring as a promising human-in-the-loop component of discourse assessment systems.

08.
arXiv (CS.CL) 2026-06-15

DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation

Lawyer-client consultation is a critical starting point for legal services. Effective legal assistance hinges on eliciting sufficient and truthful information from clients in order to devise strategies that best protect their interests. This task requires Large Language Models (LLMs) not only to perform robust legal reasoning, but also to strategically elicit material facts through multi-turn interactions and effectively guide clients with diverse personalities. Yet existing legal benchmarks overlook this interactive capability. To fill this gap, we introduce DLawBench, a diagnostic benchmark for real-world legal consultation. Drawing on realistic client behavior, we characterize lawyer-client interactions into four types: Cooperative, Dependent, Withdrawn, and Adversarial. Using dialogues grounded in real cases, DLawBench evaluates whether LLMs can effectively conduct legal consultation under realistic conditions. DLawBench comprises 461 cases from Chinese and U.S. law, 5,532 paired fact entries, 3,411 inquiry rubrics, and 3,348 issue-resolution rubrics, and evaluates 26 representative LLMs. Systematic experiments show substantial headroom: the best-performing model, GPT-5.5, achieves only 0.562 on consultation-grounded legal reasoning. More importantly, DLawBench exposes both sycophancy in legal consultation and a paradox: models perform worse when clients need guidance most.

09.
arXiv (CS.CL) 2026-06-16

Understanding LLM Reasoning for Abstractive Summarization

Reasoning has substantially improved Large Language Models (LLMs) on analytical tasks such as mathematics and code generation, but its value for abstractive summarization remains unclear. To address this gap, we adapt general reasoning strategies to the summarization setting and conduct a large-scale comparative study of 8 reasoning strategies and 3 Large Reasoning Models (LRMs) across 8 diverse datasets, evaluating both summary quality and factual faithfulness. Our results show that reasoning is not a universal solution and its effectiveness depends strongly on the strategy and the summarization setting. In particular, we find a trade-off between summary quality and factual faithfulness. Explicit reasoning strategies often improve reference-based quality, but may weaken factual grounding, whereas implicit reasoning in LRMs shows the opposite tendency. We further find that increasing an LRM's internal reasoning budget does not reliably improve summarization and can even reduce factual consistency. These findings suggest that, for summarization, more reasoning is not always better. Effective reasoning should preserve faithful compression rather than induce over-elaboration. Our source code is publicly available.

10.
arXiv (quant-ph) 2026-06-17

Effects of Josephson Junction Non-idealities on Adiabatic Quantum Flux Parametron Circuits

arXiv:2606.17338v1 Announce Type: new Abstract: Adiabatic quantum flux parametron (AQFP) gate is a promising approach to scale up the cryogenic microwave electronics for superconducting qubit multiplexed control. However, the performance of these circuits depends on the quality of the Josephson junctions which are ideally superconductor-insulator-superconductor (SIS) type following the ideal sinusoidal relation between current and quantum phase. We demonstrate how the non-sinusoidal current-phase relation in Superconductor-Normal metal-Superconductor (SNS) and weak link (WL) junctions affects the speed, delay, and margin of the AQFP gates. The JJ models are defined in the Keysight ADS simulator using symbolically defined device (SDD) method.

11.
arXiv (CS.AI) 2026-06-24

Critique of Agent Model

arXiv:2606.23991v1 Announce Type: new Abstract: What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'', and other ``agentic" tools that promise to drive up productivity, and at the same time, ``existential" concerns such as AI escaping human control with destructive power under a speculative ``machine agency" against humans, it has become essential to clarify where automation ends and agency begins, both for building capable systems and for understanding whether and what to fear. Drawing on Descartes' grounding of agency in independent thought, and on portrayals of autonomous beings in science fiction, we survey the current landscape of AI agents, and analyze agent architectures along five dimensions: goal, identity, decision-making, self-regulation, and learning. Specifically, we argue that genuine agency requires these structures to be internalized within the system itself rather than assembled through external scaffolding. This distinction between agentic systems, whose competence resides in engineered workflows, and agentive systems, whose capabilities (including social interaction) arise endogenously, defines the boundary between systems designed for prescribed tasks, and those capable of operating in the open world with true autonomy. Building on this analysis, we propose the Goal-Identity-Configurator (GIC) architecture for a general-purpose agent model, combining hierarchical goal decomposition, identity evolution, simulative reasoning grounded in a separately trained world model, learned self-regulation, and self-directed learning from both real and simulated experience. Furthermore, we share insight on the auditability, controllability, and safety of agentive systems that possess greater autonomy and ``agency", but remain under human oversight.

12.
arXiv (CS.CV) 2026-06-24

Hybrid Event Frame Sensors: Modeling, Calibration, and Simulation

Hybrid event-frame sensors integrate an Event Vision Sensor (EVS) and an Active Pixel Sensor (APS) within a single chip, combining the high dynamic range and low latency of the EVS with the rich spatial intensity information from the APS. While this tight integration offers compact and temporally precise imaging, the complex circuit architecture introduces nontrivial noise patterns that remain poorly understood and unmodeled. In this work, we present the first unified statistics-based imaging noise model that jointly describes the noise behavior of APS and EVS pixels. Our formulation explicitly incorporates photon shot noise, dark current noise, fixed-pattern noise, and quantization noise, and links EVS noise to illumination level and dark current. Based on this formulation, we further develop a calibration pipeline to estimate noise parameters from real data and provide a detailed analysis of both APS and EVS noise behaviors. Finally, we propose H-ESIM, a statistically grounded simulator that generates RAW frames and events under realistic jointly calibrated noise statistics. Experiments on two hybrid sensors validate our model across multiple imaging tasks, including video frame interpolation and deblurring, demonstrating strong transfer from simulation to real data.

13.
arXiv (CS.AI) 2026-06-12

A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction

arXiv:2606.12673v1 Announce Type: cross Abstract: Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data. However, existing methods often depend on dataset-specific feature semantics and structural patterns, which limits their ability to generalize across different domains. To address this challenge, we propose AlignGAD, a zero-shot generalized graph anomaly detection framework. Our framework is built upon three key components: a Global Unification Module that aligns heterogeneous node features and normalizes graph signals in the spectral domain; a Clustering Module that constructs cluster-aware graph views to capture group-level abnormal patterns; and a Node Discrepancy Scoring Module that measures reconstruction discrepancy and aggregates anomaly evidence from different graph views. Experiments on multiple real-world datasets demonstrate the effectiveness of AlignGAD under the zero-shot GAD setting.

14.
arXiv (CS.CV) 2026-06-19

LooseControlVideo: Directorial Video Control using Spatial Blocking

Precise 3D spatial orchestration in text-to-video generation remains a significant challenge, particularly for multi-object scenes where semantic layout and temporal dynamics are often entangled. While existing depth-conditioned models achieve good structural fidelity, they necessitate dense, frame-accurate guidance that is labor-intensive to author for dynamic events involving deformable objects. We present LooseControlVideo, a framework that enables intuitive and expressive control by using sparse, oriented 3D boxes as a "blocking" proxy. This allows users to author high-level layout and trajectory while leveraging a video generative model to generate realistic occlusions, dynamics and interactions. We achieve this by fine-tuning a Wan 2.2 backbone on a video dataset annotated with DNOCS, a novel encoding for 3D size, orientation and depth-ordered occlusions. Furthermore, our method allows for localized refinement, such as adjusting a jump trajectory or adding an interaction, with minimal disruption to the global scene context. Extensive evaluations on the nuScenes, HO-3D, and BEHAVE benchmarks demonstrate that LooseControlVideo significantly outperforms existing 2D-box and flow-based baselines. Our findings indicate a 1.2x to 3x improvement in Trajectory Error; 2x improvement in Rigid Motion Consistency; and a 1.5x to 2x increase in Occlusion Accuracy over current state-of-the-art layout-conditioned models, demonstrating that oriented 3D primitives provide good geometric prior for complex, multi-agent video authoring.

15.
arXiv (CS.CV) 2026-06-24

TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents

Automatic tooth segmentation and identification from intra-oral scanned 3D models are fundamental problems in digital dentistry, yet most existing approaches rely on task-specific 3D neural networks trained with densely annotated datasets, resulting in high annotation cost and limited generalization to scans from unseen sources. Thus, we propose TSegAgent, which addresses these challenges by reformulating dental analysis as a zero-shot geometric reasoning problem rather than a purely data-driven recognition task. The key idea is to combine the representational capacity of general-purpose foundation models with explicit geometric inductive biases derived from dental anatomy. Instead of learning dental-specific features, the proposed framework leverages multi-view visual abstraction and geometry-grounded reasoning to infer tooth instances and identities without task-specific training. By explicitly encoding structural constraints such as dental arch organization and volumetric relationships, the method reduces uncertainty in ambiguous cases and mitigates overfitting to particular shape distributions. Experimental results demonstrate that this reasoning-oriented formulation enables accurate and reliable tooth segmentation and identification with low computational and annotation cost, while exhibiting strong generalization across diverse and previously unseen dental scans.

16.
arXiv (math.PR) 2026-06-19

Theory of uncertain probability: can we derive the probability density function of uncertain random experiments with continuously changing conditions?

作者:

arXiv:2606.20169v1 Announce Type: new Abstract: This paper aims to explore the formation mechanism of probability distribution in situations where the differences among random experiments are distinguishable, and these differences continue to evolve along with the dynamic changes in conditions and their mechanisms of action. To this end, we are motivated to devise a new theoretical system – theory of uncertain probability (TUP) with Kolmogorov's system and nonlinear theories as special cases. TUP develops a novel model that integrates probability and uncertainty as well as the known and unknown to more accurately depict numerous typical random phenomena under more realistic assumptions, and thus provides appropriate tools for greater variety of real needs. It also allows for pioneering interpretation of the causal mechanisms underlying many important distributional characteristics and incorporation of pathwise property to distribution model.

17.
arXiv (CS.LG) 2026-06-16

DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising

arXiv:2606.15359v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for planning and control by learning multimodal distributions over actions and trajectories. Yet reliable inference-time safety enforcement remains a key barrier to their deployment in safety-critical tasks. Existing approaches typically project each denoising iterate onto the feasible set, even though constraints are defined only on the final clean trajectory. Enforcing feasibility on noisy intermediate samples can therefore overconstrain the sampling dynamics, substantially degrading sample quality. To address this limitation, we introduce DiRecT (Diffusion-based planning via Receding-horizon denoising with Terminal constraints), a training-free algorithm for constrained sampling from diffusion models via stochastic optimal control (SOC). DiRecT enforces constraints only on the final clean sample, avoiding unnecessary restrictions on the intermediate denoising dynamics. Inspired by model predictive control, we derive a principled receding-horizon surrogate for the otherwise intractable constrained SOC formulation, yielding an efficient algorithm that cleanly separates stochastic denoising from constraint satisfaction, progressively steering samples toward feasible final trajectories without distorting the learned diffusion dynamics. Furthermore, DiRecT is highly flexible: it can leverage off-the-shelf or domain-specific optimizers, incorporate priors over environment dynamics, and optimize additional soft rewards. Extensive experiments on safe planning benchmarks demonstrate that DiRecT substantially improves deployment safety and task performance over existing diffusion-based planning baselines.

18.
arXiv (CS.AI) 2026-06-12

Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence

arXiv:2606.12441v1 Announce Type: cross Abstract: The four dominant learning theories of behaviorism, cognitivism, constructivism, and connectivism show significant conceptual limitations as generative artificial intelligence (AI) proliferates in educational settings. These frameworks were formulated before the emergence of AI systems capable of generating, synthesizing, and reasoning about knowledge. This article critically examines each learning theory and identifies assumptions challenged by generative AI's affordances. Drawing on research in distributed cognition, extended mind, human-AI collaboration, AI literacy, cognitive offloading, and metacognition, the article proposes Generativism as a learning theory for the generative AI age. Generativism posits that learning increasingly occurs through the iterative co-construction of knowledge between human learners and AI systems. The proposed framework is organized around four principles: epistemic partnership, distributed agency, generative literacy, and adaptive metacognition. The framework offers a foundation for rethinking instructional design, learning, assessment, and expertise development in contexts where generative AI plays an integral role in cognition.

19.
bioRxiv (Bioinfo) 2026-06-12

Systematic functional annotation of thousands of BAHD acyltransferases in plant genomes using Protein Language Model and phylogenomic tools

The functional annotation of plant genes lags significantly behind their genomic annotation. Closing this gap requires thorough cataloging of reported protein activities alongside predictive methods that scale beyond sequence-similarity inference. Focusing on the BAHD acyltransferase enzyme family as a model, we assembled FuncZymeDB-BAHD, a large database of 2,705 LLM-retrieved and curated enzyme-acceptor-donor activities covering 336 BAHDs from 156 plant species, a 2-to-6-fold expansion over Swiss-Prot and prior compilations. We further developed FuncPred-OG, which maps queries to orthologous groups and previously characterized enzymes in FuncZymeDB-BAHD, returning hits with high evidence provenance. FuncPred-OG enabled functional prediction of over half of BAHDs across 85 plant proteomes, of which five novel predictions were validated via in vitro assays and recent studies. For the remaining BAHDs without FuncPred-OG annotation, we developed FuncPred-AI, where logistic-regression classifiers trained on protein language model embeddings achieved high Area-Under-the-Precision-Recall-curve (AUPR) scores and correct-hit rates up to 93%. FuncPred-AI yielded >1 probable donor/acceptor annotation for 99.9% (8894/8897) of BAHDs in our pan-plant dataset. Finally, the FuncPred workflow and datasets were deployed on a web portal for broader utilization, potentially reducing experimentalist efforts for selecting candidates from days to minutes. Overall, this framework provides a generalizable template for functional annotation of entire enzyme families.

20.
arXiv (CS.CL) 2026-06-17

SuCo: Sufficiency-guided Continuous Adaptive Reasoning

Despite remarkable performance on complex tasks, Large Reasoning Models (LRMs) often generate excessively long Chain-of-Thoughts (CoT), inflating computational costs even for simple queries. Existing efforts to mitigate this inefficiency typically rely on discrete reasoning modes or fixed budget tiers, lacking a principled criterion of when reasoning is sufficient. In this work, we introduce Minimal Sufficient CoT (MSC), defined as the shortest prefix of a CoT trajectory which is adequate for producing the correct answer. We empirically show that MSC not only reduces reasoning tokens, but also improves accuracy across difficulty levels. Building on MSC, we propose Sufficiency-guided Continuous Adaptive Reasoning (SuCo), a two-stage training framework for autonomous reasoning control along a continuous spectrum. In stage 1, MSC-Aligned Fine-Tuning (MFT) constructs MSC data using problem-adaptive sufficiency thresholds that naturally scale with question difficulty, then fine-tunes the model to internalize concise yet sufficient reasoning patterns. In stage 2, Sufficiency-Aware Policy Optimization (SAPO) further optimizes the model through reinforcement learning with dynamic complexity tracking and sufficiency-aware rewards that penalize both over- and under-thinking. Extensive experiments across mathematics, code, and science benchmarks show that SuCo consistently achieves improvements in both accuracy and reasoning efficiency.

21.
arXiv (quant-ph) 2026-06-24

Thermodynamics of quantum processes: An operational framework for free energy and reversible athermality

arXiv:2510.12790v4 Announce Type: replace Abstract: We explore the thermodynamics of quantum processes (quantum channels) by axiomatically introducing the free energy for channels, defined via the quantum relative entropy with an absolutely thermal channel whose fixed output is in equilibrium with a thermal reservoir. This definition finds strong support through its operational interpretations in designated quantum information and thermodynamic tasks. We construct a resource theory of athermality for quantum processes, where free operations are Gibbs preserving superchannels and golden units are unitary channels with respect to absolutely thermal channel having fully degenerate output Hamiltonian. We exactly characterize the one-shot distillation and formation of quantum channels using hypothesis-testing and max-relative entropy with respect to the absolutely thermal channel. These rates converge asymptotically to the channel free energy (up to a multiplicative factor of half the inverse temperature), establishing its operational meaning and proving the asymptotic reversibility of the athermality. We show the direct relation between the resource theory of athermality and quantum information tasks such as private randomness and purity distillation, and thermodynamic tasks of erasure and work extraction. Our work connects the core thermodynamic concepts of free energy, energy, entropy, and maximal extractable work of quantum processes to their information processing capabilities.

22.
arXiv (CS.LG) 2026-06-15

Attention-Based Estimation of the Individual Treatment Benefit Probability under Dose Variation

arXiv:2606.13821v1 Announce Type: new Abstract: Estimating the probability that a treatment outperforms a control for an individual patient, called the Individual Probability of Treatment Benefit (IPTB), offers a clinically intuitive alternative to population-average metrics. However, existing methods for IPTB estimation are largely confined to binary treatment settings, despite the prevalence of dose-varying interventions in clinical practice. We propose a general framework for IPTB estimation with ordinal outcomes under discrete dose assignments, called Dose-AIPTB (Dose Attention-based IPTB). Our approach recasts the problem as binary classification over the unobserved sign of the individual treatment effect, constructing pseudo-labels from covariate-similar pairwise comparisons and aggregating them via attention mechanisms or Nadaraya-Watson kernel regression. This formulation naturally accommodates multiple discrete dose levels, extending beyond the binary treatment paradigm. Through numerical experiments on real-world and synthetic data under covariate shift, varying sample sizes, and heterogeneous outcomes, we demonstrate that attention-based aggregation consistently outperforms kernel alternatives. The framework provides a foundation for personalized dose selection grounded in individual-level benefit probabilities. Codes implementing the model are publicly available at https://github.com/NTAILab/AIPTBDose.

23.
arXiv (CS.LG) 2026-06-18

SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering

arXiv:2606.19255v1 Announce Type: new Abstract: Time series anomaly detection plays a crucial role in a wide range of real-world applications. Reconstruction-based methods have become the mainstream paradigm, but they suffer from over-generalization and under-generalization problems, which are challenging to balance. To address this, we introduce multi-scale clustering to enhance reconstruction-based methods. At the representation level, we integrate the cluster center representations of normal patterns to constrain the model to target representative normal patterns for reconstruction, preventing dominance of powerful capacity and representation capability. At the anomaly criterion level, we derive anomaly confidence score based on cluster membership probability and combine it with reconstruction error, providing dual criteria for detection. Furthermore, the effectiveness of the cluster center representations and anomaly confidence score depends on the clustering performance. Accordingly, we extract neighborhood-centered representations for multi-view clustering to improve clustering performance. Extensive experiments on multiple real-world datasets from diverse application domains demonstrate the state-of-the-art performance of SCAN.

24.
arXiv (CS.LG) 2026-06-24

NeuroSonic: Conditional Flow Matching for EEG-to-Speech Reconstruction

arXiv:2606.24087v1 Announce Type: new Abstract: Reconstructing continuous speech from scalp electroencephalography (EEG) remains fundamentally challenging. EEG provides a weak, spatially diffuse, and highly variable measurement of distributed cortical activity, whereas speech is organized as a coherent acoustic trajectory with strong harmonic and temporal structure. The resulting mismatch makes waveform regression unstable and causes stochastic multi-step generation to be sensitive to artifact-dependent conditioning and subject variability. We introduce NeuroSonic, a conditional flow-matching framework for EEG-to-speech reconstruction. Instead of predicting waveforms directly or refining them through stochastic denoising, NeuroSonic learns a deterministic probability-flow velocity field that transports a noise-corrupted acoustic state toward clean speech under EEG conditioning. EEG and audio are embedded into a shared token space and processed by a time-conditioned gated Transformer that parameterizes the transport ordinary differential equation. This formulation models trajectory evolution explicitly while avoiding iterative stochastic sampling. We evaluate NeuroSonic on the CineBrain and EAV benchmarks under cross-subject evaluation. Across both datasets, the proposed method improves distributional realism, spectral fidelity, and perceptual quality over representative GAN-, diffusion-, and mean-flow baselines, with up to a 26.3\% gain in overall perceptual quality. The performance gap is most evident in artifact-heavy segments, where conditioning variability is strongest. These findings indicate that deterministic conditional transport provides a stable and effective formulation for EEG-driven speech reconstruction. Code is available at https://github.com/Y-Research-SBU/NeuroSonic/ .

25.
arXiv (CS.LG) 2026-06-15

Multi-fidelity aerodynamic data fusion by autoencoder transfer learning

arXiv:2512.13069v2 Announce Type: replace Abstract: Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling. This limitation motivates the development of multi-fidelity strategies that leverage inexpensive low-fidelity information without compromising accuracy. Addressing this challenge, this work presents a multi-fidelity deep learning framework that combines autoencoder-based transfer learning with a newly developed Multi-Split Conformal Prediction (MSCP) strategy to achieve uncertainty-aware aerodynamic data fusion under extreme data scarcity. The methodology leverages abundant Low-Fidelity (LF) data to learn a compact latent physics representation, which acts as a frozen knowledge base for a decoder that is subsequently fine-tuned using scarce HF samples. Tested on surface-pressure distributions for NACA airfoils (2D) and a transonic wing (3D) databases, the model successfully corrects LF deviations and achieves high-accuracy pressure predictions using minimal HF training data. Furthermore, the MSCP framework produces robust, actionable uncertainty bands with pointwise coverage exceeding 95%. By combining extreme data efficiency with uncertainty quantification, this work offers a scalable and reliable solution for aerodynamic regression in data-scarce environments.