arXiv subjects

Huan He

Publications and source records attributed to Huan He.

At least 19 recordsLinked to original sources

arXiv2026

OneTrans-GSR: A Generative Slate Ranking Paradigm Unifying Reranking and Mix-Ranking with One Autoregressive Transformer

Industrial recommender systems allocate substantial computation to pointwise candidate scoring, yet separate reranking and mix-ranking stages still assemble the final exposure slate using hand-crafted rules and specialized low-capacity models. Mix-ranking combines product cards, short videos, livestreams, and advertising cards supplied by distinct upstream business channels. Although these rules and models encode valuable domain knowledge, they are inherently limited in leveraging additional data and computation. They constrain the combinatorial space of candidate slates and determine objective trade-offs through manual tuning. To enable scalable and effective slate generation, we reformulate reranking and mix-ranking as a unified learning problem whose performance can improve with computation. Specifically, we instantiate this formulation across the reranking and mix-ranking stages and propose OneTrans-GSR, a single decoder-only policy that extends our deployed OneTrans backbone to generate the exposure slate end-to-end. During training, a reward model evaluates complete slates and guides reinforcement learning toward slate-level and long-term objectives. During decoding, business constraints are explicitly enforced. Offline evaluation shows that slate quality consistently improves as reward-model and policy compute increase. Deployed in a large-scale industrial recommendation system, OneTrans-GSR mixes heterogeneous content types within a unified slate. In online A/B tests, it improves GMV/u by 2.63%, advertiser value by 2.06%, and repurchase rate by 2.03%, while reducing end-to-end latency across the reranking and mix-ranking stages by 13%. These results mark a paradigm shift in slate generation: moving beyond human-engineered heuristics toward a general learned policy creates a scalable path for further algorithmic optimization and compute-driven improvement.

cs.IR↗

arXiv2026

Foundation Models to Unlock Real-World Evidence from Nationwide Medical Claims

Real-world data increasingly inform regulatory and clinical decisions. Administrative claims, among their major sources, continuously track care and its cost across providers throughout enrollment, yet remain largely unexplored for healthcare foundation models. Here we present ReClaim, a family of generative foundation models built on 43.8 billion claims records from more than 200 million enrollees in the MarketScan Research Databases. ReClaim learns diagnoses, procedures, medications and expenditure jointly through pre-training from scratch, then specializes for downstream prediction tasks through post-training, with a training cutoff at the end of 2022. In predicting more than 1,000 diseases, ReClaim achieved a mean ROC-AUC of 0.769, outperforming disease-specific gradient-boosted models (0.663) and a transformer-based baseline (0.694), and retained this advantage when generalizing to two independent cross-domain electronic health record datasets. ReClaim also outperformed gradient-boosted models in forecasting next-year expenditure and in identifying high-need, high-cost enrollees. ReClaim maintained its advantage in disease prediction and expenditure forecasting on post-cutoff data (2023-2024). Beyond prediction, ReClaim representations used as covariates resulted in improved effect estimates compared with algorithm-derived covariates, when benchmarked against results expected from randomized controlled trials. Together, these results establish nationwide insurance claims as a scalable substrate for healthcare foundation models supporting disease prediction, expenditure forecasting and confounding adjustment in real-world evidence studies. Code is available at https://github.com/BIDS-Xu-Lab/ReClaim.

cs.AI↗

arXiv2026

Herculean: An Agentic Benchmark for Financial Intelligence

As AI agents advance, the central question is no longer whether they can solve isolated financial tasks, but whether their capabilities generalize across complete professional workflows. Existing benchmarks cannot answer this question because they evaluate either static competencies or agents within a single interaction structure. We introduce Herculean, a benchmark of end-to-end agentic financial intelligence that crosses agent architecture, backbone model, and four structurally distinct workflows: Trading, Hedging, Market Insights, and Auditing. Each workflow is implemented as an MCP skill that preserves its native state, actions, constraints, and outcome criteria, allowing intact agent systems to be evaluated under matched external conditions. Across 30 architecture--backbone configurations, we find that agent capability is not globally ordered. Workflow rankings are weakly aligned, changing the architecture frequently reverses performance under the same backbone, and each architecture concentrates failure in different forms of workflow control. These results establish that agent performance is an interaction between architecture and workflow demand, rather than an intrinsic property of the underlying model. Consequently, success on a single financial workflow provides little evidence of dependable, general financial intelligence.

cs.AI↗

arXiv2026

Amortized Data Borrowing with Exchangeability-Aware Neural Posterior Estimation

Augmenting small concurrent studies with external or historical cohorts is attractive in drug development, where enrollment is slow, follow-up is expensive, and closely related trial or real-world data are often already available. Bayesian dynamic borrowing (BDB) provides a principled framework for adaptively controlling the influence of external data, but classical implementations often depend on hand-specified priors and MCMC-based inference, which can be computationally expensive and not generalizable. In this work, we study amortized neural posterior estimation (NPE) as a flexible alternative. A single network is pretrained on simulated current/external dataset pairs spanning covariate shift, outcome drift, and joint non-exchangeability, and then returns an approximate posterior for a scalar current-study target in a single forward pass. Through simulation studies, we find that NPE is most useful under outcome drift and joint mismatch: in the harder outcome-drift regimes, it gives up to about five-fold lower absolute bias than the best classical baseline and keeps Type I error close to nominal. After pretraining, posterior summaries are obtained in about 8 ms per dataset, roughly $10^3\times$ faster than MCMC-based borrowing baselines in our timing experiment. We further analyze Alzheimer's Disease Neuroimaging Initiative (ADNI) data and show that, when mild cognitive impairment outcomes differ across cohorts, the NPE formulation recovers the later-cohort risk level in this example without claiming greater precision. Code is available at https://github.com/ChinHungScott/NPE-for-Bayesian-Dynamic-Borrowing-MLHC-.

cs.LG↗

arXiv2026

MatchMiner-AI: Open-source, Privacy-preserving Cancer Clinical Trial Matching using Artificial Intelligence

Background: Clinical trials are essential to advancing cancer treatments, but fewer than 10% of adults with cancer enroll in therapeutic trials. Open-source AI trial matching tools could democratize access to trial options. Methods: We created MatchMiner-AI, co-developed with practicing clinical oncologists and trained on synthetic electronic health record (EHR) data. It uses open-weight LLMs to summarize patient histories from unstructured EHR text and extract target populations from trial eligibility documents. Embedding and re-ranking models were distilled to retrieve and rank trial and patient suggestions. Multifaceted evaluation was performed, including retrospective quantification of distillation fidelity; applying a closed-source LLM as judge of patient summarization and matching; and evaluation of candidate matches by oncologists. Results: Across retrospective evaluations of distillation fidelity, the pipeline outperformed a baseline text-embedding model, improving mean average precision (MAP) at 20 from 0.44 (95% CI 0.44-0.45) to 0.95 (95% CI 0.95-0.96) for trial-enrolled patients and from 0.38 (95% CI 0.37-0.38) to 0.94 (95% CI 0.93-0.94) for patients who received standard of care therapies. In a 50-patient sample selected for comparison between MatchMiner-AI and a rules-based tumor genomic trial matching algorithm, MatchMiner-AI retrieved trials for all patients, as opposed to 19 patients (38%) who had tumor genomic data available. Among those 19 patients, 80% of 256 trial suggestions retrieved by MatchMiner-AI were deemed reasonable considerations by a frontier LLM, vs 53% of 113 suggestions retrieved by the rules-based approach. Conclusion: MatchMiner-AI is an open-source, open-weights, clinical trial matching AI pipeline for oncology. Synthetic training data, model weights, inference tools, and demonstration frontends are publicly available.

cs.AI↗

arXiv2026

Ghost-wave momentum bandgaps in anisotropic Floquet lattices

Momentum bandgaps, characterized by complex frequencies and non-resonant amplification effects, provide a powerful route for wave manipulation beyond conventional band theory. Here, we introduce a distinct mechanism for momentum-gap engineering in higher-dimensional Schrödinger-type Floquet lattices by exploiting the intrinsically complex wave vectors of ghost waves, with complex-frequency excitation providing an additional degree of freedom for continuously tailoring the ghost-wave branch and the associated Floquet spectrum. Furthermore, we show that the higher-dimensional Floquet band structure supports momentum bandgaps extending across the entire Brillouin zone along the propagation direction and enables amplification over a broad frequency range under arbitrarily weak modulation. When the lattice is truncated along the ghost-wave decay direction, the resulting Floquet waveguide exhibits broadband reflectionless pulse amplification. Our results establish a higher-dimensional framework for ghost-wave momentum-gap physics and reveal new opportunities for non-Bloch wave engineering in time-varying photonic systems.

physics.optics↗

arXiv2026

A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis

Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task, lacking safeguards against missed high-risk alternatives or rigorous verification of their reasoning. Here, we present AegisDx, a safety-oriented framework for hypothetico-deductive clinical reasoning. AegisDx coordinates specialized LLM components through role-specific contracts, structured intermediate outputs, evidence-retrieval interfaces, and verification gates to generate broad differential diagnoses, enforce explicit screening for dangerous "must-not-miss" conditions, verify reasoning against grounded medical evidence, and structure actionable next steps. We evaluated AegisDx across three layers. On literature-derived case reports from NEJM and JAMA, with GPT-oss-120B as the shared backbone, Top-3 diagnostic accuracy was 59.9% versus 52.1% for the standalone LLM on JAMA cases and 62.7% versus 51.4% on NEJM cases. On cases from Annals of Emergency Medicine, Top-3 accuracy was 85.7% versus 68.6%; against physician-consensus must-not-miss diagnosis sets, AegisDx captured at least one such condition among its top three diagnoses in 78.0% of cases versus 52.0%. In a blinded physician evaluation of 43 real-world emergency department notes from the Yale New Haven Health System compared against GPT-5, AegisDx improved the physician-rated composite safety score from 4.31 to 4.55 on a 5-point scale (adjusted p = 2.1x10^-4), with qualitative gains in must-not-miss identification and reasoning safety. Our findings suggest that engineering diagnostic AI as a safety-oriented reasoning framework, rather than optimizing raw predictive accuracy alone, can provide a safer, more transparent, and clinically meaningful layer of bedside decision support for acute care workflows.

cs.AI↗

arXiv2026

SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection

Task-oriented proactive dialogue agents play a pivotal role in recruitment, particularly for steering conversations towards specific business outcomes, such as acquiring social-media contacts for private-channel conversion. Although supervised fine-tuning and reinforcement learning have proven effective for training such agents, their performance is heavily constrained by the scarcity of high-quality, goal-oriented domain-specific training data. To address this challenge, we propose SimRPD, a three-stage framework for training recruitment proactive dialogue agents. First, we develop a high-fidelity user simulator to synthesize large-scale conversational data through multi-turn online dialogue. Then we introduce a multi-dimensional evaluation framework based on Chain-of-Intention (CoI) to comprehensively assess the simulator and effectively select high-quality data, incorporating both global-level and instance-level metrics. Finally, we train the recruitment proactive dialogue agent on the selected dataset. Experiments in a real-world recruitment scenario demonstrate that SimRPD outperforms existing simulator-based data selection strategies, highlighting its practical value for industrial deployment and its potential applicability to other business-oriented dialogue scenarios.

cs.AI↗

arXiv2026

Unveiling Nanoscale Surface Damage Dynamics in Swift Heavy Ion Irradiated Gallium Nitride

This work systematically unveils the nanoscale surface damage dynamics in gallium nitride by investigating the atomistic mechanisms of hillock formation. The results identify two distinct hillock morphologies dependent on electronic energy loss (Se) values. Bell-shaped hillocks form under 18.2 keV/nm Kr irradiation, whereas crater-rim hillocks with central holes emerge under 40.2 keV/nm Ta irradiation. Microstructural analysis reveals that Ga-rich hillocks are accompanied by the generation of metastable zincblende nanodomains. These nanodomains preferentially aggregate around the periphery or sidewalls of the hillocks and exhibit a high spatial correlation with screw dislocations. Further temperature-dependent studies indicate that elevated temperatures significantly enlarge the overall dimensions of the hillock structures without altering their fundamental morphologies. Notably, under Ta irradiation above 1200 K, the high temperatures drastically reduce the viscosity and surface tension of liquid gallium. This enhanced fluidity of the transient molten phase promotes the formation of penetrating nanochannels.

cond-mat.mtrl-sci↗

arXiv2026

BRAINCELL-AID: An Agentic AI Created Brain Cell Type Resource for Community Annotation

Single-cell RNA sequencing has transformed our ability to identify diverse cell types and their transcriptomic signatures. However, annotating these signatures-especially those involving poorly characterized genes-remains a major challenge. Traditional methods, such as Gene Set Enrichment Analysis (GSEA), depend on well-curated annotations and often perform poorly in these contexts. Large Language Models (LLMs) offer a promising alternative but struggle to represent complex biological knowledge within structured ontologies. To address this, we present BRAINCELL-AID (BRAINCELL-AID: https://biodataai.uth.edu/BRAINCELL-AID), a novel multi-agent AI system that integrates free-text descriptions with ontology labels to enable more accurate and robust gene set annotation. By incorporating retrieval-augmented generation (RAG), we developed a robust agentic workflow that refines predictions using relevant PubMed literature, reducing hallucinations and enhancing interpretability. Using this workflow, we achieved correct annotations for 77% of mouse gene sets among their top predictions. Applying this approach, we annotated 5,322 brain cell clusters from the comprehensive mouse brain cell atlas generated by the BRAIN Initiative Cell Census Network, enabling novel insights into brain cell function by identifying region-specific gene co-expression patterns and inferring functional roles of gene ensembles. BRAINCELL-AID also identifies Basal Ganglia-related cell types with neurologically meaningful descriptions. Hence, we create a valuable resource to support community-driven cell type annotation.

cs.AI↗

arXiv2026

Anisotropic Core-Shell Swift Heavy Ion Tracks in beta-Ga2O3

Swift heavy ion (SHI) irradiation generates nanoscale ion tracks through intense electronic excitation, yet the microscopic mechanisms governing their morphology and phase stability in low symmetry oxides remain poorly understood. Here, a multiscale atomistic simulation framework is used to investigate the formation and recovery of SHI-induced tracks in monoclinic $β$-Ga2O3 over a wide range of electronic energy losses (Se) and crystallographic orientations. A sequence of distinct structural responses is identified with increasing Se: (i) complete lattice recovery at low Se; (ii) recrystallization into a metastable $γ$-Ga2O3 phase at intermediate Se; and (iii) the formation of core-shell ion tracks at high Se, consisting of an amorphous core surrounded by a recrystallized $γ$-phase shell. Despite the essentially isotropic initial energy deposition, the final ion-track morphology exhibits pronounced crystallographic anisotropy, governed by orientation-dependent recovery dynamics. The superior recrystallization along the [010] direction is attributed to its exceptionally high elastic stiffness. Notably, SHI irradiation perpendicular to the (100) plane induces a more severe structural response at low Se ($\le$ 10 keV/nm), however, at higher Se, it yields a smaller residual ion track compared to the other orientations. The simulated ion-track sizes show excellent quantitative agreement with the available experimental measurements over a wide range of Se values. These findings establish a unified atomic-scale picture of core-shell track formation and anisotropic recovery in $β$-Ga2O3.

cond-mat.mtrl-sci↗

arXiv2026

IRIS: time-structured manifold projections

High-dimensional biomedical data, such as cell-by-gene matrices, are increasingly generated temporally. However, Manifold Learning algorithms, like t-SNE and UMAP, cannot incorporate time-ordering in their layouts, obfuscating the dynamics of cell types or other classes. As a solution, we present IRIS, a new Manifold Learning algorithm that structures layouts both chronologically and by manifold topology. IRIS can visualize a wide range of dynamic biomedical data, including scRNA-seq, comparative metagenomics, and literature.

cs.LG↗

arXiv2026

MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection

Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting signals, remains challenging. Existing methods often face difficulties with contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. To address these, we propose Retrieval-Augmented Multi-modal Multi-agent Stance Detection (MM-StanceDet), a novel multi-agent framework integrating Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, a Reasoning-Enhanced Debate stage for exploring perspectives, and Self-Reflection for robust adjudication. Extensive experiments on five datasets demonstrate MM-StanceDet significantly outperforms state-of-the-art baselines, validating the efficacy of its multi-agent architecture and structured reasoning stages in addressing complex multimodal stance challenges.

cs.AI↗

arXiv2026

Atomistic Mechanisms of Temperature-Dependent Ion Track Formation in Gallium Nitride under Swift Heavy Ion Irradiation

The radiation tolerance of gallium nitride under extreme conditions is critical for its deployment in next-generation electronic and optoelectronic devices, yet the microscopic mechanisms governing swift heavy ion induced damage at elevated temperatures remain poorly understood. Therefore, this study employs a coupled approach including the two-temperature model and molecular dynamics simulations to resolve the entire processes of ion track generation induced by swift heavy ions irradiation across a wide temperature range. A temperature-driven morphological transition of ion tracks, evolving from discontinuous segments to continuous tracks composed of isolated nanobubbles, and ultimately to fully continuous channels is observed. Under lower electronic stopping loss of 430 MeV Kr irradiation, increasing temperature significantly enhances track visibility, enlarges track radii and promotes nanobubble formation. For higher electronic stopping conditions of 1171 MeV Ta irradiation, continuous ion tracks consisting of discontinuous nanobubbles (~1.5 nm radius) emerge already at 300 K, followed by a thermally activated transition into continuous channels with further radial expansion. At the atomic scale, SHI irradiation induces decomposition of wurtzite GaN into Ga clusters and N2 molecules along the ion trajectory, with Ga-rich regions and recrystallized wurtzite phases accumulating near bubble interfaces, while N2 preferentially segregates within bubble cores. Additionally, zincblende nanodomains nucleate around ion tracks and exhibit strong spatial correlation with radiation-induced dislocation networks, particularly screw dislocations, providing potential pathways for leakage current and increased susceptibility to single-event burnout.

cond-mat.mtrl-sci↗

arXiv2026

MedViz: An Agent-based, Visual-guided Research Assistant for Navigating Biomedical Literature

Biomedical researchers face increasing challenges in navigating millions of publications in diverse domains. Traditional search engines typically return articles as ranked text lists, offering little support for global exploration or in-depth analysis. Although recent advances in generative AI and large language models have shown promise in tasks such as summarization, extraction, and question answering, their dialog-based implementations are poorly integrated with literature search workflows. To address this gap, we introduce MedViz, a visual analytics system that integrates multiple AI agents with interactive visualization to support the exploration of the large-scale biomedical literature. MedViz combines a semantic map of millions of articles with agent-driven functions for querying, summarizing, and hypothesis generation, allowing researchers to iteratively refine questions, identify trends, and uncover hidden connections. By bridging intelligent agents with interactive visualization, MedViz transforms biomedical literature search into a dynamic, exploratory process that accelerates knowledge discovery.

cs.IR↗

arXiv2026

EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records

Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language question-answering (QA) systems are evaluated solely on benchmark datasets, limiting their practical relevance. To overcome this limitation, we introduce EHRNavigator, a multi-agent framework that harnesses AI agents to perform patient-level question answering across heterogeneous and multimodal EHR data. We assessed its performance using both public benchmark and institutional datasets under realistic hospital conditions characterized by diverse schemas, temporal reasoning demands, and multimodal evidence integration. Through quantitative evaluation and clinician-validated chart review, EHRNavigator demonstrated strong generalization, achieving 86% accuracy on real-world cases while maintaining clinically acceptable response times. Overall, these findings confirm that EHRNavigator effectively bridges the gap between benchmark evaluation and clinical deployment, offering a robust, adaptive, and efficient solution for real-world EHR question answering.

cs.CL↗

arXiv2025

When Agents Trade: Live Multi-Market Trading Benchmark for LLM Agents

Although Large Language Model (LLM)-based agents are increasingly used in financial trading, it remains unclear whether they can reason and adapt in live markets, as most studies test models instead of agents, cover limited periods and assets, and rely on unverified data. To address these gaps, we introduce Agent Market Arena (AMA), the first lifelong, real-time benchmark for evaluating LLM-based trading agents across multiple markets. AMA integrates verified trading data, expert-checked news, and diverse agent architectures within a unified trading framework, enabling fair and continuous comparison under real conditions. It implements four agents, including InvestorAgent as a single-agent baseline, TradeAgent and HedgeFundAgent with different risk styles, and DeepFundAgent with memory-based reasoning, and evaluates them across GPT-4o, GPT-4.1, Claude-3.5-haiku, Claude-sonnet-4, and Gemini-2.0-flash. Live experiments on both cryptocurrency and stock markets demonstrate that agent frameworks display markedly distinct behavioral patterns, spanning from aggressive risk-taking to conservative decision-making, whereas model backbones contribute less to outcome variation. AMA thus establishes a foundation for rigorous, reproducible, and continuously evolving evaluation of financial reasoning and trading intelligence in LLM-based agents.

cs.CL↗

arXiv2025

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application

Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing evaluations of LLMs in finance remain text-only, monolingual, and largely saturated by current models. To bridge these gaps, we present MultiFinBen, the first expert-annotated multilingual (five languages) and multimodal (text, vision, audio) benchmark for evaluating LLMs in realistic financial contexts. MultiFinBen introduces two new task families: multilingual financial reasoning, which tests cross-lingual evidence integration from filings and news, and financial OCR, which extracts structured text from scanned documents containing tables and charts. Rather than aggregating all available datasets, we apply a structured, difficulty-aware selection based on advanced model performance, ensuring balanced challenge and removing redundant tasks. Evaluating 21 leading LLMs shows that even frontier multimodal models like GPT-4o achieve only 46.01% overall, stronger on vision and audio but dropping sharply in multilingual settings. These findings expose persistent limitations in multilingual, multimodal, and expert-level financial reasoning. All datasets, evaluation scripts, and leaderboards are publicly released.

cs.CL↗