arXiv subjects

Yuxuan Zhang

Publications and source records attributed to Yuxuan Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2026

StructEval: Benchmarking LLMs' Capabilities to Generate Structural Outputs

As Large Language Models (LLMs) become integral to software development workflows, their ability to generate structured outputs has become critically important. We introduce StructEval, a comprehensive benchmark for evaluating LLMs' capabilities in producing both non-renderable (JSON, YAML, CSV) and renderable (HTML, React, SVG) structured formats. Unlike prior benchmarks, StructEval systematically evaluates structural fidelity across diverse formats through two paradigms: 1) generation tasks, producing structured output from natural language prompts, and \textbf{2)} conversion tasks, translating between structured formats. Our benchmark encompasses 18 formats and 44 types of task, with novel metrics for format adherence and structural correctness. Results reveal significant performance gaps-even state-of-the-art models like o1-mini achieve only 75.58 average score, with open-source alternatives lagging approximately 10 points behind. We find generation tasks more challenging than conversion tasks, and producing correct visual content more difficult than generating text-only structures.

cs.SE↗

arXiv2026

Achieving Optimal Sample Complexity for a Broader Class of Signals in Sparse Phase Retrieval

Sparse phase retrieval aims to recover a $k$-sparse signal from $m$ phaseless measurements. While the theoretically optimal sample complexity for successful recovery is $Ω(k \log n)$, existing algorithms can only achieve this bound for signals with specific structural assumptions, leading to a notable gap between theory and practice. To bridge this gap, we introduce an efficient initialization algorithm, termed generalized Exponential Spectral Pursuit (gESP). We prove that gESP can significantly expand the family of signals that are guaranteed to be recovered with the optimal sample complexity, thereby extending the scope of theoretical optimality to a much broader class of signals. Extensive simulations validate our theoretical findings and demonstrate that gESP consistently outperforms the state-of-the-art methods across diverse signal types.

cs.IT↗

arXiv2026

Fixturize: Bridging the Fixture Gap in Test Generation

Current Large Language Models (LLMs) have advanced automated unit test generation but face a critical limitation: they often neglect to construct the necessary test fixtures, which are the environmental setups required for a test to run. To bridge this gap, this paper proposes Fixturize, a diagnostic framework that proactively identifies fixture-dependent functions and synthesizes test fixtures accordingly through an iterative, feedback-driven process, thereby improving the quality of auto-generated test suites of existing approaches. For rigorous evaluation, the authors introduce FixtureEval, a dedicated benchmark comprising 600 curated functions across two Programming Languages (PLs), i.e., Python and Java, with explicit fixture dependency labels, enabling both the corresponding classification and generation tasks. Empirical results demonstrate that Fixturize is highly effective, achieving 88.38%-97.00% accuracy across benchmarks in identifying the dependence of test fixtures and significantly enhancing the Suite Pass rate (SuitePS) by 18.03%-42.86% on average across both PLs with the auto-generated fixtures. Owing to the maintenance of test fixtures, Fixturize further improves line/branch coverage when integrated with existing testing tools of both LLM-based and Search-based by 16.85%/24.08% and 31.54%/119.66% on average, respectively. The findings establish fixture awareness as an essential, missing component in modern auto-testing pipelines.

cs.SE↗

arXiv2026

Efficient and Flexible Differet-Radix Montgomery Modular Multiplication for Hardware Implementation

Montgomery modular multiplication is widely-used in public key cryptosystems (PKC) and affects the efficiency of upper systems directly. However, modulus is getting larger due to the increasing demand of security, which results in a heavy computing cost. High-performance implementation of Montgomery modular multiplication is urgently required to ensure the highly-efficient operations in PKC. However, existing high-speed implementations still need a large amount redundant computing to simplify the intermediate result. Supports to the redundant representation is extremely limited on Montgomery modular multiplication. In this paper, we propose an efficient parallel variant of iterative Montgomery modular multiplication, called DRMMM, that allows the quotient can be computed in multiple iterations. In this variant, terms in intermediate result and the quotient in each iteration are computed in different radix such that computation of the quotient can be pipelined. Based on proposed variant, we also design high-performance hardware implementation architecture for faster operation. In the architecture, intermediate result in every iteration is denoted as three parts to free from redundant computations. Finally, to support FPGA-based systems, we design operators based on FPGA underlying architecture for better area-time performance. The result of implementation and experiment shows that our method reduces the output latency by 38.3\% than the fastest design on FPGA.

cs.CR↗

arXiv2026

GLM-OCR Technical Report

GLM-OCR is an efficient 0.9B-parameter compact multimodal model designed for real-world document understanding. It combines a 0.4B-parameter CogViT visual encoder with a 0.5B-parameter GLM language decoder, achieving a strong balance between computational efficiency and recognition performance. To address the inefficiency of standard autoregressive decoding in deterministic OCR tasks, GLM-OCR introduces a Multi-Token Prediction (MTP) mechanism that predicts multiple tokens per step, significantly improving decoding throughput while keeping memory overhead low through shared parameters. At the system level, a two-stage pipeline is adopted: PP-DocLayout-V3 first performs layout analysis, followed by parallel region-level recognition. Extensive evaluations on public benchmarks and industrial scenarios show that GLM-OCR achieves competitive or state-of-the-art performance in document parsing, text and formula transcription, table structure recovery, and key information extraction. Its compact architecture and structured generation make it suitable for both resource-constrained edge deployment and large-scale production systems.

cs.CL↗

arXiv2026

EasyText: Controllable Diffusion Transformer for Multilingual Text Rendering

Generating accurate multilingual text with diffusion models has long been desired but remains challenging. Recent methods have made progress in rendering text in a single language, but rendering arbitrary languages is still an unexplored area. This paper introduces EasyText, a text rendering framework based on DiT (Diffusion Transformer), which connects denoising latents with multilingual character tokens encoded as character tokens. We propose character positioning encoding and position encoding interpolation techniques to achieve controllable and precise text rendering. Additionally, we construct a large-scale synthetic text image dataset with 1 million multilingual image-text annotations as well as a high-quality dataset of 20K annotated images, which are used for pretraining and fine-tuning respectively. Extensive experiments and evaluations demonstrate the effectiveness and advancement of our approach in multilingual text rendering, visual quality, and layout-aware text integration.

cs.CV↗

arXiv2026

Discovering Semantic Latent Structures in Psychological Scales: A Response-Free Pathway to Efficient Simplification

Psychological scale refinement traditionally relies on response-based methods such as factor analysis, item response theory, and network psychometrics to optimize item composition. Although rigorous, these approaches require large samples and may be constrained by data availability and cross-cultural comparability. Recent advances in natural language processing suggest that the semantic structure of questionnaire items may encode latent construct organization, offering a complementary response-free perspective. We introduce a topic-modeling framework that operationalizes semantic latent structure for scale simplification. Items are encoded using contextual sentence embeddings and grouped via density-based clustering to discover latent semantic factors without predefining their number. Class-based term weighting derives interpretable topic representations that approximate constructs and enable merging of semantically adjacent clusters. Representative items are selected using membership criteria within an integrated reduction pipeline. We benchmarked the framework across DASS, IPIP, and EPOCH, evaluating structural recovery, internal consistency, factor congruence, correlation preservation, and reduction efficiency. The proposed method recovered coherent factor-like groupings aligned with established constructs. Selected items reduced scale length by 60.5% on average while maintaining psychometric adequacy. Simplified scales showed high concordance with original factor structures and preserved inter-factor correlations, indicating that semantic latent organization provides a response-free approximation of measurement structure. Our framework formalizes semantic structure as an inspectable front-end for scale construction and reduction. To facilitate adoption, we provide a visualization-supported tool enabling one-click semantic analysis and structured simplification.

cs.CL↗

arXiv2026

DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer

Simulation is essential to the development and evaluation of autonomous robots such as self-driving vehicles. Neural reconstruction is emerging as a promising solution as it enables simulating a wide variety of scenarios from real-world data alone in an automated and scalable way. However, while methods such as NeRF and 3D Gaussian Splatting can produce visually compelling results, they often exhibit artifacts particularly when rendering novel views, and fail to realistically integrate inserted dynamic objects, especially when they were captured from different scenes. To overcome these limitations, we introduce DiffusionHarmonizer, an online generative enhancement framework that transforms renderings from such imperfect scenes into temporally consistent outputs while improving their realism. At its core is a single-step temporally-conditioned enhancer that is converted from a pretrained multi-step image diffusion model, capable of running in online simulators on a single GPU. The key to training it effectively is a custom data curation pipeline that constructs synthetic-real pairs emphasizing appearance harmonization, artifact correction, and lighting realism. The result is a scalable system that significantly elevates simulation fidelity in both research and production environments.

cs.CV↗

arXiv2026

Lang2Str: Two-Stage Crystal Structure Generation with LLMs and Continuous Flow Models

Generative models hold great promise for accelerating material discovery but are often limited by their inflexible single-stage generative process in designing valid and diverse materials. To address this, we propose a two-stage generative framework, Lang2Str, that combines the strengths of large language models (LLMs) and flow-based models for flexible and precise material generation. Our method frames the generative process as a conditional generative task, where an LLM provides high-level conditions by generating descriptions of material unit cells' geometric layouts and properties. These descriptions, informed by the LLM's extensive background knowledge, ensure reasonable structure designs. A conditioned flow model then decodes these textual conditions into precise continuous coordinates and unit cell parameters. This staged approach combines the structured reasoning of LLMs and the distribution modeling capabilities of flow models. Experimental results show that our method achieves competitive performance on \textit{ab initio} material generation and crystal structure prediction tasks, with generated structures exhibiting closer alignment to ground truth in both geometry and energy levels, surpassing state-of-the-art models. The flexibility and modularity of our framework further enable fine-grained control over the generation process, potentially leading to more efficient and customizable material design.

cs.LG↗

arXiv2026

From the Hallmarks of Cancer to the Survival System: A Paradigmatic Reconstruction of Oncological Theory through the Existential Crisis-Driven Survival (ECDS) Framework

Malignant tumors exhibit complex pathogenesis, yet classical oncological theories remain fragmented, failing to provide a unifying framework to address this complexity. This gap limits the utility and translational potential of the prevailing "confront-and-eradicate" therapeutic paradigm, constraining transformative therapeutic breakthroughs and driving the emergence of acquired and recurrent drug resistance. Here, we propose the Tumor Existential Crisis-Driven Survival (ECDS) theory, anchored in the core proposition that impairment of Existential Stability drives the compensatory hyperactivation of Survival Capacity. This framework defines three foundational constructs (Existential Stability, Survival Capacity, and Existence Threshold) and three guiding principles, unifying and integrating canonical core theories of tumorigenesis. It delineates the dynamic coupling between declining Existential Stability and escalating Survival Capacity during tumor evolution, reinterprets the hierarchical activation of the well-established 14 cancer hallmarks, elucidates the redundancy of survival signaling pathways that underpins intratumoral and intertumoral heterogeneity, and unravels the "hierarchical leap" in therapeutic resistance. By reframing tumors as "Existential Stability erosion-driven passive survival systems" rather than "intrinsically aggressive cellular aggregates", ECDS challenges prevailing dogma, uncovers tumors' intrinsic vulnerability, and establishes a robust meta-theoretical foundation for both basic cancer research and translational clinical management.

q-bio.TO↗

arXiv2026

PLAICraft: Large-Scale Time-Aligned Vision-Speech-Action Dataset for Embodied AI

Advances in deep generative modeling have made it increasingly plausible to train human-level embodied agents. Yet progress has been limited by the absence of large-scale, real-time, multi-modal, and socially interactive datasets that reflect the sensory-motor complexity of natural environments. To address this, we present PLAICraft, a novel data collection platform and dataset capturing multiplayer Minecraft interactions across five time-aligned modalities: video, game output audio, microphone input audio, mouse, and keyboard actions. Each modality is logged with millisecond time precision, enabling the study of synchronous, embodied behaviour in a rich, open-ended world. The dataset comprises over 10,000 hours of gameplay from more than 10,000 global participants. Alongside the dataset, we provide an evaluation suite for benchmarking model capabilities in object recognition, spatial awareness, language grounding, and long-term memory. PLAICraft opens a path toward training and evaluating agents that act fluently and purposefully in real time, paving the way for truly embodied artificial intelligence.

cs.LG↗

arXiv2026

Dr. Bench: A Multidimensional Evaluation for Deep Research Agents, from Answers to Reports

As an embodiment of intelligence evolution toward interconnected architectures, Deep Research Agents (DRAs) systematically exhibit the capabilities in task decomposition, cross-source retrieval, multi-stage reasoning, information integration, and structured output, which markedly enhance performance on complex and open-ended tasks. However, existing benchmarks remain deficient in evaluation dimensions, response format, and scoring mechanisms, limiting their effectiveness in assessing such agents. This paper introduces Dr. Bench, a multidimensional evaluation framework tailored to DRAs and long-form report-style responses. The benchmark comprises 214 expert-curated challenging tasks across 10 broad domains, each accompanied by manually constructed reference bundles to support composite evaluation. This framework incorporates metrics for semantic quality, topical focus, and retrieval trustworthiness, enabling a comprehensive evaluation of long reports generated by DRAs. Extensive experimentation confirms the superior performance of mainstream DRAs over web-search-tool-augmented reasoning models, yet reveals considerable scope for further improvement. This study provides a robust foundation for capability assessment, architectural refinement, and paradigm advancement of DRAs.

cs.AI↗

arXiv2026

WikiGap: Promoting Epistemic Equity by Surfacing Knowledge Gaps Between English Wikipedia and other Language Editions

With more than 11 times as many pageviews as the next largest edition, English Wikipedia dominates global knowledge access relative to other language editions. Readers are prone to assuming English Wikipedia as a superset of all language editions, leading many to prefer it even when their primary language is not English. Other language editions, however, comprise complementary facts rooted in their respective cultures and media environments, which are marginalized in English Wikipedia. While Wikipedia's user interface enables switching between language editions through its Interlanguage Link (ILL) system, it does not reveal to readers that other language editions contain valuable, complementary information. We present WikiGap, a system that surfaces complementary facts sourced from other Wikipedias within the English Wikipedia interface. Specifically, by combining a recent multilingual information-gap discovery method with a user-centered design, WikiGap enables access to complementary information from French, Russian, and Chinese Wikipedia. In a mixed-methods study (n=21), WikiGap significantly improved fact-finding accuracy, reduced task time, and received a 32-point higher usability score relative to Wikipedia's current ILL-based navigation system. Participants reported increased awareness of the availability of complementary information in non-English editions and reconsidered the completeness of English Wikipedia. WikiGap thus paves the way for improved epistemic equity across language editions.

cs.HC↗

arXiv2026

TransLibEval: Demystify Large Language Models' Capability in Third-party Library-targeted Code Translation

In recent years, Large Language Models (LLMs) have been widely studied in the code translation field on the method, class, and even repository levels. However, most of these benchmarks are limited in terms of Third-Party Library (TPL) categories and scales, making TPL-related errors hard to expose and hindering the development of targeted solutions. Considering the high dependence (over 90%) on TPLs in practical programming, demystifying and analyzing LLMs' code translation performance involving various TPLs becomes imperative. To address this gap, we construct TransLibEval, the first benchmark dedicated to library-centric code translation. It consists of 200 real-world tasks across Python, Java, and C++, each explicitly involving TPLs from diverse categories such as data processing, machine learning, and web development, with comprehensive dependency coverage and high-coverage test suites. We evaluate seven recent LLMs of commercial, general, and code-specialized families under six translation strategies of three categories: Direct, IR-guided, and Retrieval-augmented. Experimental results show a dramatic performance drop compared with library-free settings (average CA decline over 60%), while diverse strategies demonstrate heterogeneous advantages. Furthermore, we analyze 4,831 failed cases from GPT-4o, one of the State-of-the-Art (SOTA) LLMs, revealing numerous third-party reference errors that were obscured previously. These findings highlight the unique challenges of library-centric translation and provide practical guidance for improving TPL-aware code intelligence.

cs.SE↗

arXiv2026

GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

We present GLM-4.1V-Thinking, GLM-4.5V, and GLM-4.6V, a family of vision-language models (VLMs) designed to advance general-purpose multimodal understanding and reasoning. In this report, we share our key findings in the development of the reasoning-centric training framework. We first develop a capable vision foundation model with significant potential through large-scale pre-training, which arguably sets the upper bound for the final performance. We then propose Reinforcement Learning with Curriculum Sampling (RLCS) to unlock the full potential of the model, leading to comprehensive capability enhancement across a diverse range of tasks, including STEM problem solving, video understanding, content recognition, coding, grounding, GUI-based agents, and long document interpretation. In a comprehensive evaluation across 42 public benchmarks, GLM-4.5V achieves state-of-the-art performance on nearly all tasks among open-source models of similar size, and demonstrates competitive or even superior results compared to closed-source models such as Gemini-2.5-Flash on challenging tasks including Coding and GUI Agents. Meanwhile, the smaller GLM-4.1V-9B-Thinking remains highly competitive-achieving superior results to the much larger Qwen2.5-VL-72B on 29 benchmarks. We open-source both GLM-4.1V-9B-Thinking and GLM-4.5V. We further introduce the GLM-4.6V series, open-source multimodal models with native tool use and a 128K context window. A brief overview is available at https://z.ai/blog/glm-4.6v. Code, models and more information are released at https://github.com/zai-org/GLM-V.

cs.CV↗

arXiv2025

MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues

We propose MagicQuill V2, a novel system that introduces a \textbf{layered composition} paradigm to generative image editing, bridging the gap between the semantic power of diffusion models and the granular control of traditional graphics software. While diffusion transformers excel at holistic generation, their use of singular, monolithic prompts fails to disentangle distinct user intentions for content, position, and appearance. To overcome this, our method deconstructs creative intent into a stack of controllable visual cues: a content layer for what to create, a spatial layer for where to place it, a structural layer for how it is shaped, and a color layer for its palette. Our technical contributions include a specialized data generation pipeline for context-aware content integration, a unified control module to process all visual cues, and a fine-tuned spatial branch for precise local editing, including object removal. Extensive experiments validate that this layered approach effectively resolves the user intention gap, granting creators direct, intuitive control over the generative process.

cs.CV↗

arXiv2025

Circuit compression for 2D quantum dynamics

The study of out-of-equilibrium quantum many-body dynamics remains one of the most exciting research frontiers of physics, standing at the crossroads of our understanding of complex quantum phenomena and the realization of quantum advantage. Quantum algorithms for the dynamics of quantum systems typically require deep quantum circuits whose accuracy is compromised by noise and imperfections in near-term hardware. Thus, reducing the depth of such quantum circuits to shallower ones while retaining high accuracy is critical for quantum simulation. Variational quantum compilation methods offer a promising path forward, yet a core difficulty persists: ensuring that a variational ansatz $V$ faithfully approximates a target unitary $U$. Here we leverage Pauli propagation techniques to develop a strategy for compressing circuits that implement the dynamics of large two-dimensional (2D) quantum systems and beyond. As a concrete demonstration, we compress the dynamics of systems up to $30 \times 30$ qubits and achieve accuracies that surpass standard Trotterization methods by orders of magnitude at identical circuit depths. To experimentally validate our approach, we execute the compiled ansatz on Quantinuum's H1 quantum processor and observe that it tracks the system's dynamics with significantly higher fidelity than Trotterized circuits without optimization. Our circuit compression scheme brings us one step closer to a practical quantum advantage by allowing longer simulation times at reduced quantum resources and unlocks the exploration of large families of hardware-friendly ansätze.

quant-ph↗

arXiv2025

Know Your Intent: An Autonomous Multi-Perspective LLM Agent Framework for DeFi User Transaction Intent Mining

As Decentralized Finance (DeFi) develops, understanding user intent behind DeFi transactions is crucial yet challenging due to complex smart contract interactions, multifaceted on-/off-chain factors, and opaque hex logs. Existing methods lack deep semantic insight. To address this, we propose the Transaction Intent Mining (TIM) framework. TIM leverages a DeFi intent taxonomy built on grounded theory and a multi-agent Large Language Model (LLM) system to robustly infer user intents. A Meta-Level Planner dynamically coordinates domain experts to decompose multiple perspective-specific intent analyses into solvable subtasks. Question Solvers handle the tasks with multi-modal on/off-chain data. While a Cognitive Evaluator mitigates LLM hallucinations and ensures verifiability. Experiments show that TIM significantly outperforms machine learning models, single LLMs, and single Agent baselines. We also analyze core challenges in intent inference. This work helps provide a more reliable understanding of user motivations in DeFi, offering context-aware explanations for complex blockchain activity.

cs.AI↗