Selected work

Representative Papers

Q3DE: A fault-tolerant quantum computer architecture for multi-bit burst errors by cosmic rays

Oct 01, 2022Micro

Multi-bit burst errors (MBBEs) induced by cosmic rays severely compromise the scalability of fault-tolerant quantum computing. Method: This paper proposes Q3DE, a low-overhead fault-tolerance enhancement architecture built within the surface code framework. Its core innovation is the first syndrome-based, transparent MBBE detection mechanism, integrated with dynamic logical encoding reconstruction and rollback-aware decoding—enabling real-time anomaly identification, decoding rollback, and recovery operation re-evaluation without hardware redundancy. Contribution/Results: By jointly optimizing dynamic code deformation and decoding, Q3DE reduces MBBE duration by 1000× and shrinks the affected qubit region by 50%, substantially alleviating stringent constraints on physical qubit density and chip footprint. This establishes a new paradigm for designing highly reliable, large-scale quantum processors.

15 citations3 influentialRead paper

EventEgoHands++: Event-based Egocentric 3D Hand Mesh Reconstruction with Real Dataset

Sep 15, 2026

本文提出EventEgoHands++，通过事件相机和自适应注意力机制解决低光环境下第一人称视角3D手部重建问题。

0 citationsRead paper

A note on connections between the Föllmer process and the denoising diffusion probabilistic model

May 18, 2026

This work addresses the lack of a clear understanding regarding the direct discretization link between the Föllmer process and denoising diffusion probabilistic model (DDPM) samplers. By interpreting the Föllmer process as a time-compressed, augmented form of the DDPM reverse stochastic differential equation (SDE), this study establishes—for the first time—a systematic correspondence between the two at the discretization level. Building on this perspective, we develop a novel theoretical framework for analyzing sampling errors in DDPMs, which naturally yields optimal hyperparameter configurations. Furthermore, our approach leads to a modest yet meaningful improvement over the current best-known error bounds, achieved through a more streamlined derivation.

0 citationsRead paper

Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process

May 18, 2026

This work establishes rigorous theoretical bounds on the sampling error of Denoising Diffusion Probabilistic Models (DDPMs) measured in the 2-Wasserstein distance. Departing from the conventional view of DDPM samplers as discretized reverse Ornstein–Uhlenbeck processes, the paper introduces a novel perspective by modeling them as discretizations of Föllmer processes. Under general Lipschitz-type assumptions on the score function and across various variance schedules—including the cosine schedule—it derives non-asymptotic Wasserstein error bounds. The key contributions include proving that the Lipschitz condition implies both a logarithmic Sobolev inequality and a quadratic transportation-cost inequality, and demonstrating that even when the target distribution fails to satisfy the latter, dimension- and step-optimal Wasserstein error bounds can still be achieved. Furthermore, existing KL divergence bounds are extended to the Wasserstein setting.

0 citationsRead paper

The Cognitive Circuit Breaker: A Systems Engineering Framework for Intrinsic AI Reliability

Apr 14, 2026

This work addresses the inherent unreliability of large language models in safety-critical applications, where hallucinations remain a persistent challenge and existing external detection methods suffer from high latency, computational overhead, and dependence on external APIs. To overcome these limitations, the authors propose an intrinsic reliability monitoring framework that leverages linear probes to analyze hidden states during forward propagation. By jointly examining softmax outputs and latent intent representations, the method constructs a lightweight “cognitive dissonance gap” metric that quantifies in real time the inconsistency between semantic confidence and internal certainty. Crucially, this approach incurs no additional inference steps or external calls, enabling low-latency, low-overhead reliability assessment. Empirical evaluations demonstrate its strong statistical efficacy and robust out-of-distribution generalization across multiple model architectures.

0 citationsRead paper

Recent publications

Latest Papers

EventEgoHands++: Event-based Egocentric 3D Hand Mesh Reconstruction with Real Dataset

Sep 15, 2026

本文提出EventEgoHands++，通过事件相机和自适应注意力机制解决低光环境下第一人称视角3D手部重建问题。

0 citationsRead paper

A note on connections between the Föllmer process and the denoising diffusion probabilistic model

May 18, 2026

This work addresses the lack of a clear understanding regarding the direct discretization link between the Föllmer process and denoising diffusion probabilistic model (DDPM) samplers. By interpreting the Föllmer process as a time-compressed, augmented form of the DDPM reverse stochastic differential equation (SDE), this study establishes—for the first time—a systematic correspondence between the two at the discretization level. Building on this perspective, we develop a novel theoretical framework for analyzing sampling errors in DDPMs, which naturally yields optimal hyperparameter configurations. Furthermore, our approach leads to a modest yet meaningful improvement over the current best-known error bounds, achieved through a more streamlined derivation.

0 citationsRead paper

Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process

May 18, 2026

This work establishes rigorous theoretical bounds on the sampling error of Denoising Diffusion Probabilistic Models (DDPMs) measured in the 2-Wasserstein distance. Departing from the conventional view of DDPM samplers as discretized reverse Ornstein–Uhlenbeck processes, the paper introduces a novel perspective by modeling them as discretizations of Föllmer processes. Under general Lipschitz-type assumptions on the score function and across various variance schedules—including the cosine schedule—it derives non-asymptotic Wasserstein error bounds. The key contributions include proving that the Lipschitz condition implies both a logarithmic Sobolev inequality and a quadratic transportation-cost inequality, and demonstrating that even when the target distribution fails to satisfy the latter, dimension- and step-optimal Wasserstein error bounds can still be achieved. Furthermore, existing KL divergence bounds are extended to the Wasserstein setting.

0 citationsRead paper

The Cognitive Circuit Breaker: A Systems Engineering Framework for Intrinsic AI Reliability

Apr 14, 2026

This work addresses the inherent unreliability of large language models in safety-critical applications, where hallucinations remain a persistent challenge and existing external detection methods suffer from high latency, computational overhead, and dependence on external APIs. To overcome these limitations, the authors propose an intrinsic reliability monitoring framework that leverages linear probes to analyze hidden states during forward propagation. By jointly examining softmax outputs and latent intent representations, the method constructs a lightweight “cognitive dissonance gap” metric that quantifies in real time the inconsistency between semantic confidence and internal certainty. Crucially, this approach incurs no additional inference steps or external calls, enabling low-latency, low-overhead reliability assessment. Empirical evaluations demonstrate its strong statistical efficacy and robust out-of-distribution generalization across multiple model architectures.

0 citationsRead paper

More Human, More Efficient: Aligning Annotations with Quantized SLMs

Apr 01, 2026

This work addresses systematic biases, low agreement with human experts, irreproducibility, and data privacy risks inherent in large language models for automated annotation by proposing a highly aligned, deterministic, and open-source labeling framework based on a 4-bit quantized 1.7B-parameter small language model. Through task-aligned fine-tuning, a multidimensional scoring mechanism, and strategies involving data augmentation and regularization, the framework achieves substantially improved annotation quality with only limited human-annotated data. Experimental results demonstrate that the method outperforms the current best closed-source large models by 0.23 in Krippendorff’s α agreement metric and exhibits strong generalization across tasks such as sentiment classification. Notably, this study provides the first evidence that a quantized small model, when properly aligned via fine-tuning, can surpass state-of-the-art closed-source models in annotation consistency.

0 citationsRead paper