5 papers
Digital Metabolism: Decoupling Logic from Facts via Regenerative Unlearning -- Towards a Pure Neural Logic Core
Mengmeng Peng, Zhenyu Fang, He Sun
Large language models (LLMs) currently suffer from parameter entanglement, where general reasoning capabilities (logic) and specific factual knowledge (facts) exist in a superposit…
LLMdoctor: Token-Level Flow-Guided Preference Optimization for Efficient Test-Time Alignment of Large Language Models
Tiesunlong Shen, Rui Mao, Jin Wang +4
Aligning Large Language Models (LLMs) with human preferences is critical, yet traditional fine-tuning methods are computationally expensive and inflexible. While test-time alignmen…
Length-Aware Adversarial Training for Variable-Length Trajectories: Digital Twins for Mall Shopper Paths
He Sun, Jiwoong Shin, Ravi Dhar
We study generative modeling of \emph{variable-length trajectories} -- sequences of visited locations/items with associated timestamps -- for downstream simulation and counterfactu…
Seed-Prover: Deep and Broad Reasoning for Automated Theorem Proving
Luoxin Chen, Jinming Gu, Liankai Huang +33
LLMs have demonstrated strong mathematical reasoning abilities by leveraging reinforcement learning with long chain-of-thought, yet they continue to struggle with theorem proving d…
Breaking the Boundaries of Long-Context LLM Inference: Adaptive KV Management on a Single Commodity GPU
He Sun, Li Li, Mingjun Xiao +1
Advanced Large Language Models (LLMs) have achieved impressive performance across a wide range of complex and long-context natural language tasks. However, performing long-context…