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Wen Zhang

Publications and source records attributed to Wen Zhang.

At least 37 records · Page 2Linked to original sources
arXiv2026

NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning

OWL ontologies provide a formal knowledge representation framework that enables semantic reasoning, and have been widely adopted across domains such as healthcare and bioinformatics. In practice, however, real-world ontologies are often incomplete, which pose challenges for reasoning. In this work, we focus on a fundamental subsumption reasoning problem: given an incomplete ontology and a candidate (non-entailed) subsumption, determine whether the subsumption is semantically plausible and, if so, providing a logically sound explanation containing potential missing axioms. This task unifies subsumption verification with ontology abduction, and generalizes the latter by removing the need for a predefined candidate set of missing axioms. To address this subsumption reasoning problem, we propose NeurOWL, an end-to-end neuro-symbolic framework that jointly performs verification and abduction, leveraging both formally defined semantics and textual semantics through Large Language Models and ontology embeddings. We evaluate NeurOWL on real-world ontologies across multiple domains, demonstrating strong and robust performance across different domains.

cs.AI↗
arXiv2026

AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching

Ontology matching (OM) has traditionally been formulated as either equivalence discovery or subsumption matching. The existing OM systems identify only one type of semantic correspondence and cannot simultaneously discover equivalence and subsumption mappings. In this paper, we introduce Hybrid Ontology Matching (HOM), a new OM task that unifies equivalence and subsumption discovery, and accordingly propose a Large Language Model (LLM)-based multi-agent OM framework AgentMap that is implemented by a series of interdependent semantic decisions. Given a concept in the source ontology, AgentMap integrates semantic retrieval, hierarchical search, and collaborative multi-agent LLM reasoning to progressively explore the target ontology, identifying either the equivalent concept, if one exists, or the most fine-grained subsumer. We further extend four OM datasets for a HOM benchmark and evaluate AgentMap under hybrid, equivalence-only, and subsumption-only settings. Experimental results show that AgentMap achieves promising performance on the hybrid setting, and at the same time outperforms equivalence matching and subsumption matching baselines on the equivalence-only and subsumption-only settings, respectively.

cs.AI↗
arXiv2026

OmniPhys: Knowledge-Graph-Driven Benchmarking and Collective Optimization for Physical Commonsense in Text-to-Image Generation

While text-to-image models exhibit remarkable visual fidelity, they frequently violate fundamental physical commonsense. Existing benchmarks often rely on coarse-grained descriptions, failing to diagnose the mastery of specific physical principles. Moreover, the high stochasticity of generative processes causes current prompt optimization methods to suffer from gradient hallucinations, where optimizers are misled by transient visual artifacts rather than systemic flaws. To address these challenges, we introduce OmniPhys, a rigorous benchmark of 1,551 samples grounded in a Physical Knowledge Graph. By aligning PhET simulations with standard curricula, OmniPhys operationalizes a knowledge-to-scenario pipeline that performs diagnostic stress tests via a dual-path verification protocol. We further propose OmniPrompt, an iterative framework that treats physical alignment as a discrete optimization problem. For each query, OmniPrompt aggregates K stochastic images into a per-query feedback buffer. Across training, it further merges feedback from batches of B queries before each meta-policy update, filtering seed and query-local noise. Evaluations across 12 representative text-to-image models reveal universal physical bottlenecks. Results demonstrate that OmniPrompt significantly enhances physical consistency across diverse backbones, proving the transferability and efficacy of our evolved meta-policies. The code and data are available at https://github.com/zjukg/OmniPhys

cs.CV↗
arXiv2026

Memory Layer: Train the In-Model Cache for Recommendation Models

Early ranking stages in recommendation systems precompute item embeddings and cache them in-model for scoring within strict latency constraints. Because this cache exists only at serving time, outside the training loop, training and serving use different item representations, a structural discrepancy that limits quality and adds operational fragility. We show that co-designing the training and serving paths removes this representation discrepancy at its source. We introduce the memory layer, an in-model key-value embedding cache co-trained with the model: the item tower writes embeddings during training and the model reads them at serving, one source of truth for item representations by construction. Always-on embeddings cover items not yet cached, so every item receives a prediction, and the design consolidates three separate trainer-to-predictor update paths into a single self-contained pipeline. Deployed in production on Instagram Reels, the memory layer raises prediction coverage from 96% to 100%, improves embedding freshness from $O(5\text{ min})$ to $O(20\text{ s})$, and narrows the training-serving Normalized Entropy (NE) gap by up to 86%, yielding over $2\times$ recall for the freshest content and a 5-6% cold start engagement lift. Because embeddings are produced during training, the system needs no separate bulk-evaluation or publish-time recomputation, cutting training-and-publish computational cost by 30% at neutral serving computational cost.

cs.IR↗
arXiv2026

Extracting Database Access-Control Policies From Web Applications

To safeguard sensitive user data, web developers typically rely on implicit access-control policies, which they implement using access checks and query filters. This ad hoc approach is error-prone as these scattered checks and filters are easy to misplace or misspecify, and the lack of an explicit policy precludes external access-control enforcement. More critically, it is difficult for humans to discern what policy is embedded in application code (i.e., what data the application may access) -- an issue that worsens as development teams evolve. This paper tackles policy extraction: the task of extracting the access-control policy embedded in an application by summarizing its data queries. An extracted policy, once vetted for errors, can stand alone as a specification for the application's data access, and can be enforced to ensure compliance as code changes over time. We introduce Ote, a policy extractor for Ruby on Rails web applications. Ote uses concolic execution to explore execution paths through the application, generating traces of SQL queries and conditions that trigger them. It then merges and simplifies these traces into a final policy that aligns with the observed behaviors. We applied Ote to three real-world applications and compared extracted policies to handwritten ones, revealing several errors in the latter.

cs.SE↗
arXiv2026

WorkDrive: Roadwork Chain of Causation for Autonomous Driving

Autonomous driving vision-language models (VLMs) struggle in roadwork zones, where familiar visual cues such as lane markings and permanent signs are altered or absent, and temporary devices such as cones and barriers redefine the drivable corridor. VLMs can detect these objects, but without explicit guidance they anchor their reasoning on familiar elements from pre-training and fail to connect work-zone observations to correct planning decisions. We propose WorkDrive, a framework that constructs perception-grounded causal reasoning for work zones and aligns it with trajectory prediction. An automated multitask perception pipeline extracts structured scene facts and injects them into a Chain-of-Causation (CoC) annotation pipeline, redirecting the annotator's attention to domain-specific elements. The resulting reasoning labels are used for supervised fine-tuning, followed by reinforcement learning with a single reward: consistency between lateral meta-actions and the predicted trajectory. On ROADWork, the largest public work-zone dataset, the proposed roadwork CoC reduces trajectory average displacement error (ADE) by 9.0\%, and consistency-based GRPO yields a further 3.0\%, achieving progressive improvement over the trajectory-only baseline. Code and data will be publicly released.

cs.CV↗
arXiv2026

Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis

Multimodal survival analysis aims to improve cancer prognosis using heterogeneous biomedical data, such as histopathology images and genomic profiles. A common strategy is to align representations across modalities so that shared signals can be captured. However, strong cross-modal alignment can also remove modality-specific evidence that is critical for survival prediction. In this paper, we revisit multimodal survival learning from a simple observation: effective models should first discover shared patterns across modalities, and then preserve modality-specific signals. This motivates a representation learning principle that we refer to as Together Then Apart. Based on this idea, we propose TTA, a framework that balances cross-modal alignment and representation distinctiveness. TTA first performs prototype-based alignment to capture shared survival-related structures between modalities. It then encourages modality-specific distinctiveness through an anchor-guided contrastive objective. To further account for modality imbalance and noisy correspondences, we model cross-modal interactions using unbalanced optimal transport. We evaluate the proposed approach on multiple TCGA cancer cohorts with paired histopathology and genomic data. TTA consistently improves survival prediction over recent multimodal survival models. Moreover, the learned prototype structures reveal interpretable cross-modal patterns associated with clinical outcomes.

cs.CV↗
arXiv2026

Scaling LLM Knowledge Boundaries via Distribution-Optimized Synthesis

Knowledge injection via synthetic data is crucial for enhancing Large Language Models (LLMs). However, current synthesis methods simply stop at preset token counts or fixed data ratios, lacking awareness of knowledge distribution. This results in some domains being sparse while others are redundant, limiting LLM knowledge boundaries. We revisit knowledge injection from a distribution perspective and hypothesize that an optimal knowledge distribution exists to maximize knowledge boundary expansion. We propose KDoS (Knowledge Distribution-optimized Synthesis), a framework that introduces knowledge density to drive synthesis through a three-stage feedback mechanism, shifting from blind generation to distribution-optimized synthesis. We construct Wikipedia-based synthetic data with varying knowledge distributions and conduct experiments on models from 0.6B to 16B (Qwen, Ling, LLaMA) and data scales from 1B to 5B tokens. Our key findings are: (1) an optimal knowledge distribution consistently maximizes boundary expansion; (2) this distribution is stable across backbones and scales; (3) KDoS outperforms baselines across six knowledge benchmarks. Our work offers a new perspective and practical framework for synthetic data-driven knowledge injection.

cs.CL↗
arXiv2026

Time-Unconditional Generative Speech Enhancement via Autonomous Rectified Flow

Most generative speech enhancement methods rely on explicit time-step embeddings for temporal conditioning. In this paper, we propose the Autonomous Rectified Flow framework, which challenges the necessity of such conditioning. Using a linear interpolation path, we show that the target vector field is inherently time-invariant. We further introduce a time-unconditional network that eliminates explicit time-step information and infers the denoising direction solely from the spatial relationship between the current state and the noisy observation. Predicting this target vector field is equivalent to modeling the noise distribution. By avoiding overfitting to temporal trajectories, the proposed autonomous design significantly improves generation quality, robustness, and inference efficiency.

eess.AS↗
arXiv2026

Single-photon time-stretch computational ghost spectroscopy

Time-stretch spectroscopy is powerful for capturing transient spectral phenomena but remains fundamentally limited by detector bandwidth or timing jitter, especially under photon-starved conditions. Here, we devise and implement single-photon time-stretch computational ghost spectroscopy, which integrates dispersive wavelength-to-time mapping with programmable temporal encoding and correlation-based reconstruction to overcome these detection limitations. Specifically, temporally stretched ultrashort pulses are modulated by predefined encoding patterns and detected by a low-bandwidth detector, allowing reconstruction of near-infrared spectra with 450 resolvable channels across 1530-1590 nm without direct high-speed waveform acquisition. By further incorporating compressive sensing, accurate spectral recovery is achieved at sub-Nyquist sampling rates, substantially reducing acquisition requirements to facilitate high-speed operation at 210 kHz. In the single-photon regime, computational ghost reconstruction effectively suppresses the intrinsic detector timing jitter, yielding high-fidelity spectra at illumination fluxes down to 0.01 photons/pulse. By jointly enabling broadband coverage, high spectral resolution, high acquisition speed, and single-photon sensitivity, this approach establishes a computation-enhanced paradigm for time-stretch spectroscopy and provides a versatile platform for ultrafast and photon-efficient spectroscopic applications.

physics.optics↗
arXiv2026

PRISM: Prosody-Integrated Multi-Agent Reasoning Framework for Empathetic Spoken Dialogue

Empathetic spoken dialogue systems require not only semantically appropriate responses but also emotionally aligned prosodic expression. However, cascade pipelines often discard acoustic cues during speech-to-text conversion, while end-to-end speech models lack interpretable control over emotion and knowledge integration. To address these challenges, we propose PRISM, a multi-agent framework for empathetic spoken dialogue that decouples speech perception, response generation, and speech synthesis into coordinated components. PRISM introduces a prosody-to-language translation mechanism to stabilize large language model reasoning and enables on-demand invocation of external knowledge tools for empathetic dialogue generation. Experimental results demonstrate that PRISM achieves consistent improvements in empathy, prosodic appropriateness, and text response generation quality across objective and subjective metrics. Our code is available at: https://github.com/Bxzfrm/PRISM.

cs.CL↗
arXiv2026

Engineering Robustness into Personal Agents with the AI Workflow Store

The dominant paradigm for AI agents is an "on-the-fly" loop in which agents synthesize plans and execute actions within seconds or minutes in response to user prompts. We argue that this paradigm short-circuits disciplined software engineering (SE) processes -- iterative design, rigorous testing, adversarial evaluation, staged deployment, and more -- that have delivered the (relatively) reliable and secure systems we use today. By focusing on rapid, real-time synthesis, are AI agents effectively delivering users improvised prototypes rather than systems fit for high-stakes scenarios in which users may unwittingly apply them? This paper argues for the need to integrate rigorous SE processes into the agentic loop to produce production-grade, hardened, and deterministically-constrained agent *workflows* that substantially outperform the potentially brittle and vulnerable results of on-the-fly synthesis. Doing so may require extra compute and time, and if so, we must amortize the cost of rigor through reuse across a broad user community. We envision an *AI Workflow Store* that consists of hardened and reusable workflows that agents can invoke with far greater reliability and security than improvised tool chains. We outline the research challenges of this vision, which stem from a broader flexibility-robustness tension that we argue requires moving beyond the ``on-the-fly'' paradigm to navigate effectively.

cs.CR↗
arXiv2026

Symbolic and Abstractive Reasoning with Complex Visual Queries

Understanding and reasoning over abstract visual content remains a challenge for current multi-modal large language models (MLLMs). In this paper, we explore a novel abstract data type termed complex visual query (CVQ), designed to probe symbolic and abstractive reasoning, which is a critical yet underexplored dimension of human-like neuro-symbolic reasoning for MLLMs. We present a comprehensive investigation from three perspectives: \textbf{Data $\times$ Paradigm $\times$ Exploration}. Specifically, we propose a scalable pipeline for synthesizing CVQs grounded in large-scale multi-modal knowledge graphs, generating a diverse dataset encompassing 14 distinct query types via systematic combinations of first-order logic operators. We further introduce a two-stage training framework that progressively equips MLLMs with robust visual reasoning capabilities. We conduct extensive experiments to rigorously evaluate MLLMs across multiple dimensions, including reasoning performance on CVQs, as well as cross-task and cross-scenario generalization. We believe our work opens new perspectives and avenues for advancing the reasoning frontiers of MLLMs.

cs.CL↗
arXiv2026

CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning

Real-world scenarios involve massive heterogeneous structured data (e.g., tables, knowledge graphs), making effective reasoning over such diverse data increasingly important. Unified structured data question answering has emerged as a prominent research trend, aiming to answer natural language questions across different structured data types within a single framework. However, existing unified methods share a common limitation: they rely on a set of predefined functions, which restricts their ability to perform complex reasoning beyond these predefined operations. To overcome this fundamental limitation, we propose CRAFTQA, a novel adaptive code-driven framework comprising two core modules, CodeSTEP and CRAFT. The CodeSTEP module is a paradigm that generates a complete executable Python code sequence, which contains step-by-step code-based reasoning operations based on the question. The CRAFT module dynamically generates custom code functions for operations beyond the predefined function set, and seamlessly integrates with CodeSTEP to significantly enhance flexibility in handling complex reasoning. Comprehensive experiments on multiple structured datasets demonstrate that CRAFTQA achieves remarkable improvements in complex reasoning scenarios compared to existing unified methods.

cs.CL↗
arXiv2026

Highly-sensitive mid-infrared upconversion detection based on external-cavity pump enhancement

Sensitive mid-infrared (MIR) detection is highly demanded in various applications, ranging from remote sensing, infrared surveillance, environmental monitoring to industrial inspection. Among others, upconversion infrared detectors have recently attracted increasing attention due to the advantageous features of high sensitivity, fast response, and room-temperature operation. However, it remains challenging to realize high-performance passive MIR sensing due to the stringent requirement of high-power continuous-wave pumping. Here, we propose and implement a high-efficiency and low-noise MIR upconversion detection system based on pumping enhancement via a low-loss optical cavity. Specifically, a single-longitudinal-mode pump at 1064 nm is significantly enhanced by a factor of 36, thus allowing for a peak conversion efficiency of up to 22\% at an intra-cavity average power of 55 W. The corresponding noise equivalent power is achieved as low as 0.3 fW/Hz$^{1/2}$, which indicates at least a ten-fold improvement over previous results. Notably, the involved single-frequency pumping would facilitate high-fidelity spectral mapping, which is particularly attractive for high-precision MIR upconversion spectroscopy in photon-starved scenarios.

physics.optics↗
arXiv2026

ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering

Table serialization remains a critical bottleneck for Large Language Models (LLMs) in complex table question answering, hindered by challenges such as structural neglect, representation gaps, and reasoning opacity. Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility, while current tree-based approaches suffer from limited semantic adaptability. To address these limitations, we propose ASTRA (Adaptive Semantic Tree Reasoning Architecture) including two main modules, AdaSTR and DuTR. First, we introduce AdaSTR, which leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees. This serialization explicitly models hierarchical dependencies and employs an adaptive mechanism to optimize construction strategies based on table scale. Second, building on this structure, we present DuTR, a dual-mode reasoning framework that integrates tree-search-based textual navigation for linguistic alignment and symbolic code execution for precise verification. Experiments on complex table benchmarks demonstrate that our method achieves state-of-the-art (SOTA) performance.

cs.CL↗
arXiv2026

Mid-infrared single-photon computational temporal ghost imaging

The capture of transient optical waveforms is critical to reveal dynamical phenomena in various fields. However, fast and sensitive mid-infrared (MIR) measurements are typically limited by processing bandwidth and detection sensitivity of conventional infrared detectors. Here, we propose and implement a computational temporal ghost imaging system, which favors high-speed and high-sensitivity characterization of MIR temporal objects. The core process relies on high-fidelity nonlinear optical transduction for facilitating both the programmable structured illumination and frequency upconversion detection based on the high-performance near-infrared light modulator and detector, respectively. Consequently, the correlation between the recorded integral upconversion intensity and the designated encoding patterns allows one to reconstruct the MIR profiles with a temporal resolution of 80 ps, well beyond the intrinsic bandwidth or timing jitter of the involved detectors. Moreover, a record-high detection sensitivity is manifested by recovering single-photon MIR waveforms with an incident flux below 0.1 photon/bit. Additionally, faithful reconstructions at sub-Nyquist sampling rates are demonstrated using the compressive sensing algorithm, which can reduce the data acquisition time by over 90\%. The presented paradigm features high timing precision, single-photon sensitivity, and efficient data sampling, which could be extended into far-infrared or terahertz regions to address pressing demands in fast and sensitive sensing.

physics.optics↗
arXiv2026

Wide-field mid-infrared cavity-enhanced upconversion imaging

Mid-infrared (MIR) spectral imaging enables precise target identification and analysis by capturing rich chemical fingerprints, which calls for high-sensitivity broadband MIR imagers at room temperature. Here, we devise and implement a continuous-wave pumping MIR upconversion imaging system based on external-cavity enhancement, which favors a large field of view, a low cavity loss, and a high spectral resolution. The involved optical cavity is constructed in an integrated fashion by utilizing one crystal facet as a cavity mirror, which allows a 43-fold power enhancement for the single-longitudinal-mode pump at 1064 nm. In combination with the chirped-poled crystal design, high-fidelity and wide-field spectral imaging mapping is permitted to facilitate an acceptance angle up to 28.5$^\circ$ over a spectral coverage of 2.5--5 $μ$m. Moreover, a thermal locking approach is used to stabilize the cavity at the high-power operation, eliminating active feedback and ensuring long-term stability. A proof-of-principle demonstration is presented to showcase real-time observation of CO$_{2}$ gas injection dynamics. The implemented MIR upconversion imager features wide-field operation, high detection sensitivity, and compact footprint, which would benefit subsequent applications including environment monitoring, gas leakage inspection, and medical diagnostics.

physics.optics↗