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Yifei Sun

Publications and source records attributed to Yifei Sun.

At least 19 recordsLinked to original sources
arXiv2026

Diagnosis by Composition: Detecting and Localizing Unseen Diseases by Composing Learned Radiological Findings

Deep models for radiological diagnosis are typically trained to predict disease labels directly from images, which limits them to diseases seen during training and makes their decisions hard to interpret in radiologists' terms. Radiology, however, proceeds from visual findings to disease through published guidelines that make this decision rule explicit, interpretable, and reproducible. We introduce diagnosis by composition (DbC), a representation learning framework that encodes this findings-first logic. DbC learns a vocabulary of radiological findings by aligning image features with the text embeddings of the findings, each finding represented as a dense map over the scan. A disease is defined by a set of required findings derived from its guidelines, and its diagnosis map is a fixed, parameter-free composition of the corresponding finding maps. Because the finding-to-disease mapping is derived from guidelines rather than learned, DbC is interpretable and auditable by design, can extend to new diseases by specifying their findings without retraining, and can diagnose unseen diseases by composing their findings. On multi-phase liver CT, DbC detects and localizes unseen diseases, rare diseases from a different institution, and an external cohort significantly better than all comparators, including models that align images with disease names. DbC also improves detection and localization in the fully supervised setting and remains superior across training-set sizes. Although demonstrated on liver CT, DbC applies wherever radiological guidelines define diseases through findings.

cs.CV↗
arXiv2026

Solver-Integrated Adversarial Attacking and Training of Neural Operators

Neural operators are widely used as fast surrogates for numerical PDE solvers, mapping input functions to solution functions. However, their generalizability and robustness are not yet clearly defined in the operator-learning setting, which differs from traditional adversarial robustness definitions. This paper studies the generalizability and robustness of a learned neural operator from a solver-integrated perspective, addressing the challenge that the output of a learned operator and a numerical solver tends to change in tandem under input perturbation. First, we formalize the definition of generalization and robustness through a model-solver error operator, identifying fixed-input model-solver loss as generalization metric, and norm-bounded adversarial attack loss increase and Jacobian-error function norm as robustness metric. Second, we identify the solver-integrated adversarial attack as appropriate for PDE operator learning and show why model-only or fixed-ground-truth attacks can be insufficient when the solver output also changes with the input. Third, we develop solver-integrated adversarial training methods for neural operators. Experiments on representative PDE benchmarks show that this solver-integrated adversarial training clearly improves both generalizability and robustness. Deeper solver integration yields more effective attacks, more informative samples, and more efficient training than less integrated alternatives. These results provide a general framework for robust operator training and automatic sample selection without heavy manual intervention. More broadly, the formulation applies to adversarial regression whenever a ground-truth oracle can evaluate, and ideally differentiate, the true input-output map; PDE operator learning is one such case.

cs.LG↗
arXiv2026

WebViSE: Scaling Visual Design Intelligence from the Open Web for LLM Website Generation

Large language models have made remarkable strides in website development, enabling complete and interactive web experiences to be built from natural-language instructions with far less effort. Yet, non-expert users often struggle to articulate sophisticated visual and interaction designs in natural language. Meanwhile, the open web already contains a vast reservoir of such design knowledge, embodied in mature website implementations. To this end, we introduce Website-derived Visual Skill Extraction (WebViSE), a framework that automatically abstracts these implementations into reusable Visual Skills and builds a structured Skill library. To scale the library, WebViSE organizes its Skills in a multi-parent DAG and jointly evolves Skill descriptions and DAG structure using Skill-conditioned Potential Queries, enabling efficient matching between diverse user instructions and relevant Skills. The auto-evolved structure outperforms non-hierarchical LLM retrieval baselines at only 39.09% of the estimated retrieval cost with Qwen3.8-Max. For any models, WebViSE can be directly injected as plug-and-play guidance, enabling diverse models to produce more visually compelling and aesthetically refined web pages. For open-source models, WebViSE can be used to build a data-synthesis pipeline that internalizes visual design intelligence into model weights. Experiments across multiple web development benchmarks and human evaluation consistently show that WebViSE improves the visual quality and interaction fidelity of artifacts. Overall, our work establishes an automated path for extracting, organizing, and transferring design intelligence from the open web to website-building models. We release all extracted Visual Skills, the associated Skill DAG, and our code at https://github.com/YifeiSun-0710/WebViSE.

cs.AI↗
arXiv2026

DeBoneDiT: Depth-Driven Conditional Bridge Diffusion Transformers for Bone Suppression in Chest X-Ray Images

Chest X-Ray (CXR) is a primary modality for diagnosing pulmonary diseases, yet the overlap of bony structures often obscures critical radiographic findings, increasing the risk of diagnostic uncertainty. While Dual-Energy Subtraction (DES) is the clinical benchmark for bone suppression, its widespread adoption is primarily hindered by specialized hardware requirements and increased radiation exposure. Recently, diffusion-based generative models have shown promise for this task, yet they are still constrained by reconstruction-biased latent compression, prior-induced domain gap, and a scarcity of high-quality paired datasets. To address these challenges, we propose DeBoneDiT, a depth-driven conditional bridge diffusion Transformer architecture for efficient high-resolution bone suppression. We first introduce Depth Auto-Encoder (DAE), a structure-aware vision tokenizer for latent compression. By incorporating a multi-level depth feature loss derived from the pretrained Depth Anything V2 encoder, DAE preserves both visual fidelity and structural consistency. Second, we formulate bone suppression as a source-conditioned Brownian bridge diffusion process, leveraging CXRs as both the diffusion prior and structural guidance to narrow the source-to-target domain gap and maintain anatomical plausibility. Furthermore, we have constructed and released SZCH-X-Rays, the largest publicly available high-quality paired dataset for bone suppression to date, comprising 741 pairs of CXR and DES soft tissue images. Extensive experiments and downstream evaluation demonstrate that DeBoneDiT achieves superior bone suppression performance with reduced computational overhead.

cs.CV↗
arXiv2026

Beyond Answer Sharing: TrueMAD for Targeted Peer Engagement in Multi-Agent Debate

Multi-agent debate (MAD) aims to improve large language model reasoning by exposing agents to alternative solutions and enabling iterative revision. However, standard broadcast-style MAD makes peer answers visible without requiring agents to engage with a particular peer claim. As a result, reliable reasoning may be suppressed by conflicting or majority responses, limiting achievable performance, while erroneous reasoning may persist and degrade final predictions without actionable feedback. We propose TrueMAD, a backbone-agnostic test-time plug-in that introduces one-to-one, free-form peer advice between answer broadcasting and self-revision. Each source agent responds to a target's current reasoning through support, rebuttal, refinement, or further questions, while the target retains control over how to revise. The same mechanism can augment standard MAD and its improved variants without parameter updates. Across AIME, GPQA-Diamond, and TruthfulQA with Qwen3-30B-A3B, TrueMAD improves agent-level and strict-majority accuracy over MAD by 3.62 and 3.52 percentage points on average, while TrueDMAD improves DMAD by 4.72 and 4.06 points, respectively. Analyses at the utterance, agent-transition, and group levels further show more peer-specific deliberation, more correction-oriented revisions, and more favorable consensus dynamics.

cs.AI↗
arXiv2026

Transportable inference using target population summary statistics under covariate shift

Transporting findings from a study population to a target population is central to evidence-based decision-making in real-world settings. Most existing methods require individual-level data from both populations to account for covariate shift. However, privacy regulations and data-sharing constraints often preclude access to such data from the target population, leaving only covariate summaries available for analysis. In this paper, we develop transportability methods that enable valid inference using source individual-level data and target covariate summaries. First, we apply entropy balancing to transportability, enabling source individual-level data to be adjusted to match the target covariate moments. We establish asymptotic normality for the entropy balancing estimator and propose a variance estimator to account for uncertainty in covariate summaries. Second, we develop a new transportability method that allows more flexible modeling of covariate shift, thereby accounting for covariate shift and uncertainty in covariate summaries simultaneously. Asymptotic normality for the proposed estimator is established and its asymptotic variance is consistently estimated. The proposed method offers greater flexibility in accounting for covariate shift and thus permits consistent estimation and valid inference when the validity of entropy balancing is not guaranteed. The proposed methods are evaluated by simulations and illustrated with an analysis of Surveillance, Epidemiology, and End Results breast cancer data.

stat.ME↗
arXiv2026

Self-Evolving Spatial Reasoning in Vision Language Models via Geometric Logic Consistency

Vision-Language Models (VLMs) have made striking progress, yet their spatial reasoning remains fragile. Models that answer an original input correctly can still fail under valid transformations with predictable answer mappings, revealing a gap between instance-level correctness and robust spatial reasoning. To address this, we propose Spatial Alignment via Geometric Evolution (SAGE), a self-evolving framework that improves robust spatial reasoning through geometric and linguistic duality operations. SAGE incorporates duality consistency into GRPO training, encouraging models to produce coherent answers across original and transformed inputs. SAGE co-evolves duality generation and solution, allowing the model to continually expose and address its own reasoning weaknesses. A dynamic operation pool identifies challenging operations and retires mastered ones, keeping training focused on informative duality signals. SAGE is model-agnostic, data-efficient compared to prior post-training methods, and can be applied as a lightweight adaptation stage to any existing VLM. Experiments on video and spatial reasoning benchmarks demonstrate consistent improvements over strong baselines and enhanced generalization to unseen data.

cs.CV↗
arXiv2026

Learning in the Transverse Subspace: A Minimal Representation for Divergence-Free Operator Learning

Divergence-free vector fields are fundamental state variables in incompressible flows and many PDE systems. Redundant parameterizations, including Neural Conservation Law (NCL) potentials, map multiple auxiliary representations to the same physical field. Our experiments show that this redundancy can reduce static representation-fitting error by enlarging the set of equivalent solutions, but the resulting many-to-one mapping does not provide a unique state for operator learning. We introduce a minimal representation that encodes a real \(D\)-component divergence-free vector field on a \(D\)-dimensional domain as a real \((D-1)\)-component field on the same domain. Exploiting the transverse structure imposed by incompressibility in Fourier space, we use a Householder orthogonal transformation to construct the reduced coordinates directly. For periodic and closed impermeable fields, the transform is invertible, isometric, and angle-preserving. For open nonperiodic flows, Fourier extension constructs a compatible periodic field, and a minimum-energy rule selects a unique reduced representation. Neural operators then learn temporal evolution entirely in this reduced space. At inference, the predicted \((D-1)\)-component field is decoded directly into a physical divergence-free \(D\)-component field, without predicting an ambient field or applying post-hoc projection. Experiments on static fitting and temporal prediction reveal a task-dependent trade-off: redundancy facilitates static optimization, whereas unique invertible coordinates provide a well-defined state for temporal dynamics. By removing unconstrained longitudinal or null directions from the learned state space, the proposed formulation achieves lower prediction error and greater robustness while preserving divergence freedom by construction.

cs.LG↗
arXiv2026

Strategic Disclosure and the Design of Attention

An institution that cannot compel disclosure can still allocate exposure. We study two opposed advocates who select verifiable observations and a receiver who averages the selected record without correcting for selection. The institution commits exposure before the state and evidence are known; each withheld item's assigned weight expires. With commonly known pools and strictly opposed monotone preferences, all finite-game Nash equilibria produce one action and common cutoff. In large pools with a vanishing anchor, directional tail moments determine the implementing exposure share. Under common evidence laws, equal exposure implements every state. The institutional restriction matters: full within-camp reallocation induces extreme selection and, on a fixed support, eliminates state responsiveness. In a finite-state common-law benchmark, small reallocation separates disclosure cutoffs and changes implementing shares according to favorable-tail frequencies. Variation in these frequencies across states creates a positive local welfare cost even after exposure is optimized. Ex ante results bound the cost of commitment and identify information sufficient for limiting implementation. Separately, under linear payoffs, uniform item weights and continuous evidence laws on a compact state space, all Bayesian equilibria converge to the baseline outcome when advocates observe only their own samples and learn the state. Finite-pool results characterize a precision cost under common laws, balanced exposure, uniform weights and equal limiting camp sizes, and first corrections to exact risk-minimizing shares under additional regularity.

econ.TH↗
arXiv2026

RAT: RunAnyThing via Fully Automated Environment Configuration

Automating repository-level software engineering tasks is a foundational challenge for autonomous code agents, largely due to the difficulty of configuring executable environments. However, manual configuration remains a labor-intensive bottleneck, necessitating a transition toward fully automated environment configuration. Existing approaches often rely on pre-defined artifacts or are restricted to specific programming languages, limiting their applicability to diverse real-world repositories. In this paper, we first propose RAT (RunAnyThing), a modular and extensible agent framework for fully automated configuration across programming languages on arbitrary repositories. RAT adopts a multi-stage pipeline that integrates language-aware abstraction, image initialization, specialized configuration toolset, and robust sandbox. Furthermore, to enable rigorous evaluation, we propose RATBench, a benchmark reflects the comprehensive coverage of real-world repositories. Extensive experiments demonstrate that RAT achieves state-of-the-art performance, improving Environment Setup Success Rate (ESSR) by an average of 36.1% over strong baselines.

cs.SE↗
arXiv2026

PTCG-Bench: Can LLM Agents Master Pokémon Trading Card Game?

Given a strategically complex board game, human players can quickly learn to devise strategies after playing a few rounds. Autonomous agents require similar capabilities in realistic interactive environments, yet existing agent benchmarks often fail to fully capture such strategic and evolving decision-making scenarios. We present PTCG-Bench, a benchmark built on the Pok'{e}mon Trading Card Game (PTCG) that evaluates LLM agents at two complementary levels: (1) their decision-making performance within a single complex environment, and (2) their ability to self-evolving through accumulated experience. We further include a modular harness ablation to better interpret agent performance without conflating it with model capability. Our experiments show that, although LLM agents can achieve non-trivial gameplay performance, sustained and stable self-evolution remains challenging, and performance is sensitive to harness design. We hope that PTCG-Bench will facilitate future research on harness-aware and self-evolving agents in realistic interactive environments.

cs.AI↗
arXiv2026

Nonparametric Estimation of Event-Free Survival for Data with Left-Truncated Death and Intermittently Assessed Nonfatal Events

In certain clinical settings, patients diagnosed with a disease of interest are at risk for death as well as a serious nonfatal event, and interest lies in event-free survival (EFS), a composite endpoint defined as the time from disease onset until the earlier of the nonfatal event and death. Component-wise censoring of EFS arises when each component is subject to a different censoring mechanism. For example, the nonfatal event may be interval censored between assessments, and death is right-censored. Further, in studies where individuals enroll after disease onset (including prevalent cohort studies), EFS is left truncated. Methods to estimate EFS probability with left-truncated and right-censored data are available in the literature, but they cannot handle component-wise censoring. We propose a kernel smoothing method to non-parametrically estimate EFS in this setting. Our method can also estimate and test for differences in the restricted mean event-free survival time, and can leverage two types of supplemental data that may be available: data from participants followed for death only (not followed for the nonfatal event), and incident cohort data, which arises when there is no delay between disease onset and study enrollment. We assess the proposed method using simulations and demonstrate the method using data from the Atherosclerosis Risk in Communities (ARIC) Study to estimate dementia-free survival probability following a myocardial infarction.

stat.ME↗
arXiv2026

Simulation-to-Real First-Break Segmentation for Efficient Inversion in Musculoskeletal Ultrasound Tomography

Full-waveform inversion (FWI) is a promising strategy for quantitative musculoskeletal ultrasound computed tomography (USCT), but bone-related scattering, attenuation, and signal degradation make it highly sensitive to the accuracy of the initial acoustic-property distributions and prone to cycle skipping. First-arrival traveltimes provide important kinematic information for initial-model construction, yet conventional trace-wise picking is unreliable when arrivals are weak, spatially heterogeneous, or buried in system noise. We propose a learning-assisted reconstruction pipeline that combines segmentation-based first-arrival extraction with hybrid full-waveform inversion (HFWI), which incorporates Rytov-approximation-based traveltime information together with waveform fitting during the early inversion stage. A lightweight 2D U-Net treats the first-arrival trajectory across receiver channels as a first-break segmentation target and exploits its spatial continuity rather than processing each trace independently. To address both limited manual annotations and the simulation-to-real gap, the network is pretrained on task-specific simulations augmented with real system-noise recordings, followed by stage-wise training with progressively increased signal degradation and decoder-only fine-tuning using limited weakly labeled experimental data. The method is evaluated on in vitro phantom, ex vivo bovine-limb, and in vivo human-thigh datasets. Compared with conventional STA/LTA picking, the proposed network yields more spatially coherent first-arrival trajectories, lower mean extraction errors, and processes a full-matrix-capture dataset within seconds. When integrated into HFWI, the extracted arrivals improve initial-model construction and lead to stable subsequent FWI reconstructions, including challenging cases with estimated local first-arrival SNRs below 3 dB.

eess.IV↗
arXiv2026

MIND: Multimodal Intent-Driven Network via Diffusion Transformers for Medical Image Fusion

Medical image fusion aims to integrate complementary information from diverse imaging modalities to support clinical diagnosis. Existing methods typically apply uniform fusion rules globally, lacking a deep understanding of diagnostic intents and pathological structures. To address these limitations, we propose MIND, a Multimodal Intent-Driven Network via Diffusion Transformers (DiTs) for medical image fusion. Specifically, we utilize BioMedGPT to generate intent-driven fusion texts from source images, guiding the fusion process with pathology-aware diagnostic intents. To combat the loss of 2D spatial continuity caused by 1D sequence flattening in DiTs, we design a Multi-scale Latent Adapter. This module explicitly extracts source image features before serialization, injecting them into the network via strict dimensional alignment to effectively supplement image features. To resolve the semantic shift caused by decoupling image outputs from diagnostic intents, we design a medical semantic consistency loss. This loss ensures deep semantic locking between fused images and fusion texts while maintaining the stability of the underlying physical manifold reconstruction. Comprehensive experiments on the Harvard, BraTS, and GFP datasets reveal that MIND delivers superior fusion quality, significantly improves downstream brain tumor segmentation accuracy, and enables flexible interactive fusion, holding significant promise for intent-driven intelligent clinical decision support systems.

cs.CV↗
arXiv2026

Universal mapping of drop impact spreading from wetting to Leidenfrost regimes

We establish a universal mapping between the maximum spreading of a drop under wetting and Leidenfrost impact conditions from an energy-dissipation perspective, with the latter featuring a stable vapor film between the drop and the substrate. Experiments and direct numerical simulations demonstrate that the mapping remains valid over two to three orders of magnitude in the Ohnesorge and Weber numbers. This framework provides a simple route for converting established predictions for wetting impacts into their Leidenfrost counterparts, with implications for predicting and controlling droplet impact.

physics.flu-dyn↗
arXiv2026

Multi-Label Node Classification with Label Influence Propagation

Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI networks with multiple functions and users in social or e-commerce networks exhibiting diverse interests. Tackling multi-label node classification (MLNC) on graphs has led to the development of various approaches. Some methods leverage graph neural networks (GNNs) to exploit label co-occurrence correlations, while others incorporate label embeddings to capture label proximity. However, these approaches fail to account for the intricate influences between labels in non-Euclidean graph data. To address this issue, we decompose the message passing process in GNNs into two operations: propagation and transformation. We then conduct a comprehensive analysis and quantification of the influence correlations between labels in each operation. Building on these insights, we propose a novel model, Label Influence Propagation (LIP). Specifically, we construct a label influence graph based on the integrated label correlations. Then, we propagate high-order influences through this graph, dynamically adjusting the learning process by amplifying labels with positive contributions and mitigating those with negative influence. Finally, our framework is evaluated on comprehensive benchmark datasets, consistently outperforming SOTA methods across various settings, demonstrating its effectiveness on MLNC tasks.

cs.LG↗
arXiv2026

Two-Stage Cross-Domain Cervical Abnormality Screening with Cytopathological Image Synthesis and Knowledge Distillation

Cross-domain diagnosis remains a major challenge in cervical cell pathology due to pronounced domain shifts across institutions and the subtle visual differences among disease stages, which jointly impair model generalization. To address these issues, this paper proposes a two-stage framework for cross-domain cervical cell detection. In the first stage, we propose the Spatially-Continuous Unpaired Neural Schrödinger Bridge (SC-UNSB), which constructs a synthetic intermediate domain to mitigate cross-domain distribution shifts by modeling image translation as an entropy-regularized optimal transport process. In the second stage, we propose a dual-level feature alignment strategy within a knowledge distillation, which progressively aligns shallow structural features and deep semantic representations to facilitate the transfer of domain-invariant knowledge from the source to the target model. Experimental results demonstrate that the proposed method effectively mitigates domain shift and category ambiguity, improving the cross-domain detection performance.

cs.CV↗
arXiv2026

Handling Feature Heterogeneity with Learnable Graph Patches

In recent years, the rapid development of foundation models and graph pre-training technologies has spurred increasing interest in constructing a universal pre-trained graph model or Graph Foundation Model (GFM). However, a significant challenge is that existing models are unable to address feature heterogeneity in graph data without textual information, which hinders the transferability of graph models across different datasets. To bridge this gap, we propose the concept of learnable graph patches, which we regard as the smallest semantic units of any graph data. We decompose the graph into learnable graph patches by unfolding the node features and constructing corresponding patch structures separately. We then design a framework that mines transferable information from graph data across domains. Specifically, after extracting graph patches, we propose a patch encoder to extract knowledge from each unit and a patch aggregator to learn how the units are combined into a whole. Due to its domain-agnostic nature, the model can be applied to downstream data across different domains. Furthermore, we analyze the connection between our method and existing graph models, as well as the transferability of the node embeddings it generates. Empirically, our method not only achieves the capability to use multi-domain graphs for pre-training, but also shows enhanced performance across various downstream datasets and tasks. Moreover, we observe consistent improvement in downstream performance as the volume of pre-training data increases.

cs.LG↗