Jie Luo

The paper introduces Autonomous Agentic Data Engineering, demonstrating that LLMs can autonomously plan and optimize end-to-end data curation pipelines, leading to substantial performance gains in specialized models.

The paper demonstrates that tool-augmented agentic AI can learn from prior field experiment data to automatically generate superior, domain-specific interventions, transforming one-shot A/B testing into a cumulative learning system.

This paper introduces BigPower, a hierarchical source-level surrogate model for fine-grained module-level power estimation during CPU design using large language models and architectural hierarchy.

This paper establishes a finite combinatorial version of Gödel's incompleteness theorems in Boolean $K$-SAT, resolving assignment correlations and constructing structurally irreducible SAT/UNSAT pairs.

RecGPT-V3 is a stateful, hybrid-modal recommender system that uses a Memory Hub for user memory and a Hybrid-modal Foundation Model for joint reasoning over text tags and Semantic IDs, achieving consistent gains in user experience and commercial outcomes.

This paper introduces Re-Sonance, a real-time speech-driven AAC system for professional speaking scenarios using LLM-enhanced Whisper ASR, Qwen LLM, and CosyVoice TTS.

Proposed DDMSR framework for multi-modal sequential recommendation using graph-based feature denoising and frequency-domain sequence denoising, and multi-modal contrastive alignment objective.