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Researcher profile

Yue Li

Yue Li contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputation and Languagecond-mat.mtrl-sciGraphicsHuman-Computer InteractionMultimediaphysics.chem-phProgramming Languages

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Published work

5 published item(s)

preprint2026arXiv

A non-equilibrium strategy for the general synthesis of single-atom catalysts

Single-atom catalysts (SACs) maximize atom efficiency and exhibit unique electronic structures, yet realizing precise and scalable atomic dispersion remains a key challenge. Here, we report a non-equilibrium strategy for the scalable synthesis of SACs via ion implantation, enabling precise stabilization of metal atoms on diverse supports. Using an industrial-grade ion source, wafer-scale ion implantation with milliampere-level beam currents enables high-throughput fabrication of SACs, while the synergistic energy-mass effects stabilize isolated metal atoms in situ. A library of 36 SACs was constructed, and the resulting Pt/MoS2 exhibits outstanding hydrogen evolution performance with an overpotential of only 26 mV at 10 mA cm-2 and exceptional long-term stability, surpassing commercial Pt/C. This work demonstrates ion implantation as a versatile platform bridging fundamental SACs design and scalable manufacturing, providing new opportunities for high-performance catalysts in energy conversion applications.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Agent Bain vs. Agent McKinsey: A New Text-to-SQL Benchmark for the Business Domain

Text-to-SQL benchmarks have traditionally only tested simple data access as a translation task of natural language to SQL queries. But in reality, users tend to ask diverse questions that require more complex responses including data-driven predictions or recommendations. Using the business domain as a motivating example, we introduce CORGI, a new benchmark that expands text-to-SQL to reflect practical database queries encountered by end users. CORGI is composed of synthetic databases inspired by enterprises such as DoorDash, Airbnb, and Lululemon. It provides questions across four increasingly complicated categories of business queries: descriptive, explanatory, predictive, and recommendational. This challenge calls for causal reasoning, temporal forecasting, and strategic recommendation, reflecting multi-level and multi-step agentic intelligence. We find that LLM performance degrades on higher-level questions as question complexity increases. CORGI also introduces and encourages the text-to-SQL community to consider new automatic methods for evaluating open-ended, qualitative responses in data access tasks. Our experiments show that LLMs exhibit an average 33.12% lower success execution rate (SER) on CORGI compared to existing benchmarks such as BIRD, highlighting the substantially higher complexity of real-world business needs. We release the CORGI dataset, an evaluation framework, and a submission website to support future research.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Qihe: A General-Purpose Static Analysis Framework for Verilog

In the past decades, static analysis has thrived in software, facilitating applications in bug detection, security, and program understanding. These advanced analyses are largely underpinned by general-purpose static analysis frameworks, which offer essential infrastructure to streamline their development. Conversely, hardware lacks such a framework, which overshadows the promising opportunities for sophisticated static analysis in hardware, hindering achievements akin to those witnessed in software. We thus introduce Qihe, the first general-purpose static analysis framework for Verilog -- a highly challenging endeavor given the absence of precedents in hardware. Qihe features an analysis-oriented front end, a Verilog-specific IR, and a suite of diverse fundamental analyses that capture essential hardware-specific characteristics -- such as bit-vector arithmetic, register synchronization, and digital component concurrency -- and enable the examination of intricate hardware data and control flows. These fundamental analyses are designed to support a wide array of hardware analysis clients. To validate Qihe's utility, we further developed a set of clients spanning bug detection, security, and program understanding. Our preliminary experimental results are highly promising; for example, Qihe uncovered 9 previously unknown bugs in popular real-world hardware projects (averaging 1.5K+ GitHub stars), all of which were confirmed by developers; moreover, Qihe successfully identified 18 bugs beyond the capabilities of existing static analyses for Verilog bug detection (i.e., linters), and detected 16 vulnerabilities in real-world hardware programs. By open-sourcing Qihe, which comprises over 100K lines of code, we aim to inspire further innovation and applications of sophisticated static analysis for hardware, aspiring to foster a similarly vibrant ecosystem that software analysis enjoys.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Sketch Then Paint: Hierarchical Reinforcement Learning for Diffusion Multi-Modal Large Language Models

Diffusion Multi-Modal Large Language Models (dMLLMs) are powerful for image generation, but optimizing them through reinforcement learning (RL) remains a major challenge. One primary difficulty is that a single image can be generated through many different unmasking sequences, which makes calculating importance ratios often intractable. Additionally, existing methods tend to ignore the hierarchical generation process of dMLLMs, where early tokens define the global layout and later tokens focus on local details. By assigning uniform rewards to all tokens, these current methods fail to reflect the actual contribution of each token to the final image. To address these issues, we propose Hierarchical Token GRPO (HT-GRPO), which integrates this hierarchy directly into the policy optimization process. Our approach features a Sketch-Then-Paint training scheme that organizes updates into three distinct stages: global, structure, and refinement. We also use a prompt-conditioned estimator to calculate importance ratios starting from a fully masked state. Furthermore, we introduce a Hierarchical Credit Assignment mechanism that prioritizes key structural tokens to ensure accurate reward propagation. Experiments using two popular dMLLM backbones, MMaDA and Lumina-DiMOO, demonstrate that HT-GRPO achieves substantial gains on the GenEval and DPG benchmarks. Evaluations across six additional metrics confirm significant improvements in image quality, aesthetics, and human preference.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

The perceptual gap between video see-through displays and natural human vision

Video see-through (VST) technology aims to seamlessly blend virtual and physical worlds by reconstructing reality through cameras. While manufacturers promise perceptual fidelity, it remains unclear how close these systems are to replicating natural human vision across varying environmental conditions. In this work, we quantify the perceptual gap between the human eye and different popular VST headsets (Apple Vision Pro, Meta Quest 3, Quest Pro) using psychophysical measures of visual acuity, contrast sensitivity, and color vision. We show that despite hardware advancements, all tested VST systems fail to match the dynamic range and adaptability of the naked eye. While high-end devices approach human performance in ideal lighting, they exhibit significant degradation in low-light conditions, particularly in contrast sensitivity and acuity. Our results map the physiological limitations of digital reality reconstruction, establishing a specific perceptual gap that defines the roadmap for achieving indistinguishable VST experiences.

0 citations0 reviewsNo code linkedOpen access