From the 1 of 5 linked papers with an AI index.
5 papers
Self-Improving is Often Sudden: Enlightenment-style Finetuning for Large-Scale Models
Jing-Xiao Liao, Tianwei Zhang, Yu-Hao Jiang +3
The paper proposes Enlightenment, a training-free post‑tuning method that adds shortcut connections to key layers of large language and vision‑language models, enabling sudden perf…
Hidden Decoding at Scale: Latent Computation Scaling for Large Language Models
Aiwei Liu, Cheng Shi, Chuhan Wu +44
Scaling Large Language Models (LLMs) has been driven mainly by enlarging the Transformer backbone, but for an already-strong model this requires another round of costly pretraining…
ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute
Hao Wen, Yifan Su, Feifei Zhang +4
Recent advances in Large Language Models (LLMs) have been driven by test-time compute scaling - a strategy that improves reasoning by generating longer, sequential thought processe…
BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens
Hao Wen, Xinrui Wu, Yi Sun +7
Recent advancements in Large Language Models (LLMs) have leveraged increased test-time computation to enhance reasoning capabilities, a strategy that, while effective, incurs signi…
Tempo-R0: A Video-MLLM for Temporal Video Grounding through Efficient Temporal Sensing Reinforcement Learning
Feng Yue, Zhaoxing Zhang, Junming Jiao +4
Temporal Video Grounding (TVG), which requires pinpointing relevant temporal segments from video based on language query, has always been a highly challenging task in the field of…