5 papers
Relative Kinetic Utility for Reasoning-Aware Structural Pruning in Large Language Models
Tianhao Qian
Chain-of-Thought (CoT) prompting symbolized a huge improvement of reasoning capabilities of Large Language Models (LLMs). However, scaling up test-time computation yields extensive…
PAC-MCTS: Bias-Aware Pruning for Robust LLM-Guided Search and Planning
Tianhao Qian
As search depth increases in autonomous reasoning and embodied planning, candidate action spaces expand exponentially, often exhausting computational budgets. While heuristic pruni…
Resource-constrained Amazons chess decision framework integrating large language models and graph attention
Tianhao Qian, Zhuoxuan Li, Jinde Cao +2
Artificial intelligence has advanced significantly through the development of intelligent game-playing systems, providing rigorous testbeds for decision-making, strategic planning,…
Alternating Gradient Flow Utility: A Unified Metric for Structural Pruning and Dynamic Routing in Deep Networks
Tianhao Qian, Zhuoxuan Li, Jinde Cao +2
Efficient deep learning traditionally relies on static heuristics like weight magnitude or activation awareness (e.g., Wanda, RIA). While successful in unstructured settings, we ob…
Large Language Model for Discrete Optimization Problems: Evaluation and Step-by-step Reasoning
Tianhao Qian, Guilin Qi, Z. Y. Wu +3
This work investigated the capabilities of different models, including the Llama-3 series of models and CHATGPT, with different forms of expression in solving discrete optimization…