Bing Li

Trans-RAG introduces a novel query-centric vector transformation technique to enable secure, efficient, and accurate cross-organizational retrieval in RAG systems without plaintext decryption.

The paper introduces Thinking as Compression (TaC), a novel paradigm showing that the inherent reasoning process of a large language model can naturally compress long context inputs, outperforming dedicated compression methods.

CLANE presents an end-to-end continual action recognition system deployed on neuromorphic hardware (Intel Loihi 2) using event cameras, achieving high accuracy with massive reductions in energy and latency compared to traditional methods.

The paper proposes formulating RAG design as an architecture search problem and introduces RAISE, a comprehensive framework and benchmark for systematically optimizing RAG hyperparameters.

The paper introduces OpenClawBench, a large-scale dataset and framework for measuring process-side anomalies in real-world agent execution trajectories, demonstrating that task success does not guarantee operational reliability.

This paper introduces 'implicit identity' as a unifying framework to survey and categorize LLM fingerprinting and watermarking techniques for verifying ownership and provenance across datasets, models, and generated content.

MobEvolve introduces an agentic self-evolving heuristic system that significantly improves human mobility generation by iteratively refining its internal logic using an LLM agent, outperforming deep generative and LLM-based methods.

The paper introduces DocFormBench, a new benchmark for content-aware document formatting, and proposes DocFormFlow, a workflow that improves formatting accuracy and efficiency by decoupling target localization from modification execution.