activity
20242026
most citedBuffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

8 citations · 19 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CL2026

JEPA-Anything: Learning Predictive Models across Different Worlds

Taoyong Cui, Zhongyao Wang, Xinyue Xu +10

World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning…

cs.AI2026

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

Shuhan Xue, Jianyuan Zhong, Ziyuan Nan +10

We introduce and release ScienceBuddy, an interactive scientific research workspace that brings continually improving scientific agents into researchers' everyday workflows. Scienc…

cs.AI2026

Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses

Zhaochen Yu, Yingcheng Wu, Zhenfei Yin +5

Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We introduce Recuris, a recursive…

cs.CL2025

Demystifying Reinforcement Learning in Agentic Reasoning

Zhaochen Yu, Ling Yang, Jiaru Zou +2

Recently, the emergence of agentic RL has showcased that RL could also effectively improve the agentic reasoning ability of LLMs, yet the key design principles and optimal practice…

cs.CL2025★ 2 cited

ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates

Ling Yang, Zhaochen Yu, Bin Cui +1

We present that hierarchical LLM reasoning via scaling thought templates can effectively optimize the reasoning search space and outperform the mathematical reasoning capabilities…

cs.CL2024★ 1 cited

SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction

Ling Yang, Zhaochen Yu, Tianjun Zhang +4

Large language models (LLMs) like GPT-4, DeepSeek-R1, and ReasonFlux have shown significant improvements in various reasoning tasks. However, smaller LLMs still struggle with compl…

cs.CV2024★ 1 cited

VideoTetris: Towards Compositional Text-to-Video Generation

Ye Tian, Ling Yang, Haotian Yang +9

Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scen…

cs.CL2024★ 8 cited

Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Ling Yang, Zhaochen Yu, Tianjun Zhang +5

We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs).…

cs.CV2024★ 1 cited

Contextualized Diffusion Models for Text-Guided Image and Video Generation

Ling Yang, Zhilong Zhang, Zhaochen Yu +4

Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion m…

cs.CV2024★ 2 cited

RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models

Xinchen Zhang, Ling Yang, Yaqi Cai +8

Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositio…

cs.CV2024★ 4 cited

Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs

Ling Yang, Zhaochen Yu, Chenlin Meng +3

Diffusion models have exhibit exceptional performance in text-to-image generation and editing. However, existing methods often face challenges when handling complex text prompts th…