
Regularizes prompt optimization to limit prompt bloat and narrow, sample-specific rules. Combines gradient purification, semantic edit regularization, and regularization-guided updates to improve out-of-distribution generalization.
May 20, 2026

A unified study of LLM self-distillation that combines teacher agreement, EMA stabilization, contrastive learning, feature matching, and divergence clipping to improve adaptation without stronger external teachers.
May 7, 2026

A position paper arguing that consistency across views, modalities, and prompts should be the priority research target for unified multimodal models.
Feb 3, 2026
An efficient approach to probing LLM knowledge that adapts pre-trained embeddings to query model knowledge with substantially reduced compute.
Aug 8, 2025

We propose a framework for developing topology-aware Multi-Agent Systems (MAS), emphasizing agent selection, structure profiling, and topology synthesis, to enhance coordination and efficiency in complex task.
Jul 4, 2025
A survey of multimodal foundation model evaluation from a hierarchical perspective, covering a broad range of applications.
May 1, 2025
Studies how rewriting YouTube video titles when sharing them on Reddit affects engagement, using controlled comparisons to isolate the effects of language and community context.
Apr 26, 2025
A methodology-centered survey of LLM agents, connecting agent architectures, collaboration, and evolution with evaluation, tools, applications, and open challenges.
Mar 27, 2025