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Jie An

12 accepted papers

2026

How Powerful are LLMs in Generating Program Specifications?

ICML 2026poster

Formal verification provides strong guarantees of software correctness, but its adoption is limited by the high cost of writing precise formal specifications. While recent large language models (LLMs) have demonstrated impressive capabilities in theorem proving and verified code generation, how powe…

Cited by 0SourceScholar
2026

PRM-PBE: Process Reward Model for Reinforcement Learning in Programming-by-Example

ICML 2026poster

Programming-by-Example (PBE), as a typical few-shot inductive reasoning paradigm, aims to synthesize corresponding algorithms from a set of input-output examples. Although Large Language Models (LLMs) have demonstrated strong program synthesis potential, they still remain ineffective when handling c…

Cited by 0SourceScholar
2026

RESTL: Reinforcement Learning Guided by Multi-Aspect Rewards for Signal Temporal Logic Transformation

AAAI 2026technical

Signal Temporal Logic (STL) is a powerful formal language for specifying real-time specifications of Cyber-Physical Systems (CPS). Transforming specifications written in natural language into STL formulas automatically has attracted increasing attention. Existing rule-based methods depend heavily on

Cited by 0SourcePDFScholar
2026

Runtime Safety and Reach-avoid Prediction of Stochastic Systems via Observation-aware Barrier Functions

AAAI 2026technical

Stochastic dynamical systems have emerged as fundamental models across numerous application domains, providing powerful mathematical representations for capturing uncertain system behavior. In this paper, we address the problem of runtime safety and reach-avoid probability prediction for discrete-ti

Cited by 0SourcePDFScholar
2025

Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge

ACL 2025finding

Temporal Logic (TL), especially Signal Temporal Logic (STL), enables precise formal specification, making it widely used in cyber-physical systems such as autonomous driving and robotics. Automatically transforming NL into STL is an attractive approach to overcome the limitations of manual transform…

2025

Latent-Reframe: Enabling Camera Control for Video Diffusion Models without Training

ICCV 2025poster

Precise camera pose control is crucial for video generation with diffusion models. Existing methods require fine-tuning with additional datasets containing paired videos and camera pose annotations, which are both data-intensive and computationally costly, and may disrupt the model's distribution le…

Cited by 0SourcePDFScholar
2025

On Inductive Biases That Enable Generalization in Diffusion Transformers

NeurIPS 2025poster

Recent work studying the generalization of diffusion models with locally linear UNet-based denoisers reveals inductive biases that can be expressed via geometry-adaptive harmonic bases. For such locally linear UNets, these geometry-adaptive harmonic bases can be conveniently visualized through the e…

Cited by 0SourcecodeScholar
2025

Ouroboros-Diffusion: Exploring Consistent Content Generation in Tuning-free Long Video Diffusion

AAAI 2025technical

The first-in-first-out (FIFO) video diffusion, built on a pre-trained text-to-video model, has recently emerged as an effective approach for tuning-free long video generation. This technique maintains a queue of video frames with progressively increasing noise, continuously producing clean frames at…

Cited by 0SourcePDFScholar
2024

Bring Metric Functions into Diffusion Models

IJCAI 2024poster

We introduce a Cascaded Diffusion Model (Cas-DM) that improves a Denoising Diffusion Probabilistic Model (DDPM) by effectively incorporating additional metric functions in training. Metric functions such as the LPIPS loss have been proven highly effective in consistency models derived from the score…

2023

Make-A-Video: Text-to-Video Generation without Text-Video Data

ICLR 2023poster

We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from un…

Cited by 1412SourcePDFScholar
2023

QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity

CVPR 2023poster

The mechanism of existing style transfer algorithms is by minimizing a hybrid loss function to push the generated image toward high similarities in both content and style. However, this type of approach cannot guarantee visual fidelity, i.e., the generated artworks should be indistinguishable from r…

2021

ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows

CVPR 2021poster

Universal style transfer retains styles from reference images in content images. While existing methods have achieved state-of-the-art style transfer performance, they are not aware of the content leak phenomenon that the image content may corrupt after several rounds of stylization process. In this…

Cited by 258PDFcodeScholar