6 citations · 9 across the 6 of their papers we have counts for
6 papers
MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games
Yunfei Xie, Kevin Wang, Bobby Cheng +9
Multi-turn, multi-agent LLM game evaluations often exhibit substantial run-to-run variance. In long-horizon interactions, small early deviations compound across turns and are ampli…
Neurosymbolic LoRA: Why and When to Tune Weights vs. Rewrite Prompts
Kevin Wang, Neel P. Bhatt, Cong Liu +7
Large language models (LLMs) can be adapted either through numerical updates that alter model parameters or symbolic manipulations that work on discrete prompts or logical constrai…
On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability
Kevin Wang, Junbo Li, Neel P. Bhatt +4
Recent advancements in Large Language Models (LLMs) have showcased their ability to perform complex reasoning tasks, but their effectiveness in planning remains underexplored. In t…
Expressive Gaussian Human Avatars from Monocular RGB Video
Hezhen Hu, Zhiwen Fan, Tianhao Wu +4
Nuanced expressiveness, particularly through fine-grained hand and facial expressions, is pivotal for enhancing the realism and vitality of digital human representations. In this w…
Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation
Wenqing Zheng, S P Sharan, Ajay Kumar Jaiswal +4
For a complicated algorithm, its implementation by a human programmer usually starts with outlining a rough control flow followed by iterative enrichments, eventually yielding care…
Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search
Wenqing Zheng, S P Sharan, Zhiwen Fan +3
Learning efficient and interpretable policies has been a challenging task in reinforcement learning (RL), particularly in the visual RL setting with complex scenes. While neural ne…