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STAGE 04 · 02

小方向 小方向

Subdirections

方法族趨勢來源於 field-state-*.json（機械化，零 LLM）。加速度 = (近 7d − 前 7d) / max(前值, 1)。

方法族趋势来源于 field-state-*.json（机械化，零 LLM）。加速度 = (近 7d − 前 7d) / max(前值, 1)。

速度榜 · 前 8 名 速度榜 · 前 8 名 / Velocity Top 8

  1. 01 AI Context Engineering +500%↑ 3/0
  2. 02 AI Agent Safety +300% 3/1
  3. 03 AI Explicit Reasoning (CoT / Search) +200% 4/2
  4. 04 VLA VLA Generalist +131% 72/48
  5. 05 VLA VLA Post-Training RL +129% 31/12
  6. 06 AI Frontier Models +122% 11/9
  7. 07 AI MCP (Model Context Protocol) +100% 7/7
  8. 08 VLA VLA Generalist +95% 23/15
新冒頭新冒头 · EMERGING · prior volume = 0

VLA · 15

+129%
31/12
VLA

VLA Post-Training RL vla.policy.robot_rl.vla_post_training_rl

頂尖實驗室：顶尖实验室： Stanford UniversitySiemens

No evidence papers matched in the last 90 days. This family is tracked mechanically — when the threshold fires, papers will appear here.

+79%
19/8
VLA

Flow Matching vla.action.flow_matching

頂尖實驗室：顶尖实验室： LIBERO TeamShanghai Jiao Tong UniversityThe Hong Kong University of Science and Technology (Guangzhou)
佐證論文佐证论文 Coupling Execution Horizon and Sampling Depth in Flow-Matching VLA Policies VisFlow: Interpretable Flow Matching for Visibility Reconstruction, Characterization, and Recognition Trajectory Matching: Discrete Flow Matching with Structured Actions
+66%
16/5
VLA

Imitation Learning vla.policy.imitation_learning

頂尖實驗室：顶尖实验室： HKUST (GZ)Tsinghua UniversityUniversity of California, Berkeley
佐證論文佐证论文 DiscoDemo: Discovering Efficient and Diverse Robot Demonstrations for Imitation Learning Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation Pedicel-Targeted Strawberry Harvesting via Imitation Learning on a Low-Cost Teleoperated Robotic Arm
+46%
11/11
VLA

Dexterous Manipulation vla.manipulation.dexterous_manipulation

頂尖實驗室：顶尖实验室： Robotics Institute, Beihang University, Beijing, China
佐證論文佐证论文 Learning Cooperative Bimanual Dexterous Grasp of Large Objects with Tactile Sensing ZeroDex: Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale
+29%
7/4
VLA

Cross-Embodiment Foundation Models vla.foundation.cross_embodiment_fm

頂尖實驗室：顶尖实验室： StanfordThe Hong Kong University of Science and Technology (Guangzhou)CMU
佐證論文佐证论文 HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation Multi-View Latent Action Pretraining for Cross-Embodiment Robot Learning from Videos T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale
+25%
6/3
VLA

Mobile Manipulation vla.manipulation.mobile_manipulation

頂尖實驗室：顶尖实验室： Institute for AI Industry Research (AIR), Tsinghua UniversityBeihang UniversityShenzhen Campus of Sun Yat-sen University
佐證論文佐证论文 SimVLA: Zero-Shot Sim-to-Real VLA Learning for Mobile Manipulation VOMMI: Collecting and Leveraging Portable Demonstrations for Mobile Manipulation MobiAgent: Dual-Loop Recursive Policy Self-Improvement for Long-Horizon Mobile Manipulation
+12%
3/1
VLA

Diffusion Policy vla.action.diffusion_policy

頂尖實驗室：顶尖实验室： Shanghai Jiao Tong UniversityTsinghua Shenzhen International Graduate School, Tsinghua UniversitySchool of Artificial Intelligence, University of Chinese Academy of Sciences
佐證論文佐证论文 Feedback World Model Enables Precise Guidance of Diffusion Policy CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance Immiscible Diffusion Policy: Preserving Multimodal Robot Actions through Label-Free Noise Assignment

AI App · 11

+55%
6/11
AI App

Agent Infra ai.infra.agent_infra

No evidence papers matched in the last 90 days. This family is tracked mechanically — when the threshold fires, papers will appear here.

+200%
4/2
AI App

Explicit Reasoning (CoT / Search) ai.reasoning.reasoning_planning

No evidence papers matched in the last 90 days. This family is tracked mechanically — when the threshold fires, papers will appear here.

+999%
3/0
AI App

Context Engineering ai.context.context_engineering

No evidence papers matched in the last 90 days. This family is tracked mechanically — when the threshold fires, papers will appear here.

+300%
3/1
AI App

Agent Safety ai.safety.agent_safety

No evidence papers matched in the last 90 days. This family is tracked mechanically — when the threshold fires, papers will appear here.

+999%
1/0
AI App

Agent Eval ai.eval.agent_eval

No evidence papers matched in the last 90 days. This family is tracked mechanically — when the threshold fires, papers will appear here.

+999%
1/0
AI App

Voice & Multimodal ai.frontier.voice_multimodal

No evidence papers matched in the last 90 days. This family is tracked mechanically — when the threshold fires, papers will appear here.

+100%
1/1
AI App

Vertical Agents ai.vertical.vertical_agent

No evidence papers matched in the last 90 days. This family is tracked mechanically — when the threshold fires, papers will appear here.