Today’s Open Source (2025-11-3): Kuaishou and Nanjing University Lab Co-Develop HiPO for Hybrid Strategy Optimization in LLM Dynamic Inference, Dual-Mode Switching Balances Accuracy and Efficiency
🏆 Foundational Models
① Project: HiPO
HiPO-8B is a novel reinforcement learning framework based on Hybrid Policy Optimization, enabling dynamic reasoning capabilities in large language models (LLMs).
Key Highlights:
- Developed by KwaiKAT team at Kuaishou in collaboration with NJU-LINK Laboratory (Nanjing University) and ARiSE Laboratory.
- Features “think-on” and “think-off” mode switching to balance reasoning accuracy and efficiency.
- Incorporates:
- Hybrid data pipeline for categorizing queries by difficulty.
- Hybrid reward system combining mode rewards and bias adjustment to prevent over-reasoning.
🔗 One-click bookmark:
https://sota.jiqizhixin.com/project/hipo3
---
② Project: MiniMax-M2
MiniMax-M2 is a compact, fast, cost-efficient MoE model optimized for coding and Agent workflows.
Key Highlights:
- 230B total parameters, 10B active parameters.
- Strong general intelligence while excelling in coding and Agent tasks.
- End-to-end tool usage capabilities for scalable deployment.
🔗 One-click bookmark:
https://sota.jiqizhixin.com/project/minimax-m2-gguf2
---
🛠️ Frameworks, Platforms & Essential Tools
① Project: InstanceAssemble
InstanceAssemble is a lightweight layout-to-image generation framework enabling precise spatial control.
Key Highlights:
- Introduces DenseLayout and Layout Grounding Score (LGS).
- Achieves state-of-the-art performance on sparse and dense layouts.
🔗 One-click bookmark:
https://sota.jiqizhixin.com/project/instanceassemble
---
② Project: ReasonMed
ReasonMed is a multi-Agent generated dataset designed to enhance medical reasoning capabilities.
Key Highlights:
- Includes tools for generating, verifying, optimizing, ranking, summarizing, and evaluating Chain-of-Thought (CoT) responses.
- Supports research and assessment in clinical decision-making.
🔗 One-click bookmark:
https://sota.jiqizhixin.com/project/reasonmed
---
③ Project: UniLIP
UniLiP improves CLIP-based multimodal methods via two-stage self-distillation and a dual-conditional architecture.
Key Highlights:
- Balances understanding and reconstruction.
- Excels in instruction-following and edit fidelity benchmarks.
🔗 One-click bookmark:
https://sota.jiqizhixin.com/project/unilip
---
🤖 Agent Development
① Project: live-trade-bench
Live Trade Bench is a real-time evaluation platform for LLM-based trading agents.
Key Highlights:
- Built with FastAPI for running, monitoring, and benchmarking AI trading agents.
- Supports multiple markets while avoiding backtesting overfitting.
- Features:
- Concurrent operation of multiple agents.
- Coverage of stock and prediction markets.
- Automated price updates, news feeds, and social sentiment analysis.
- Open RESTful API for external integration.
🔗 One-click bookmark:
https://sota.jiqizhixin.com/project/live-trade-bench
---
💡 Cross-platform Monetization for AI Creators
For creators working in LLM frameworks, multimodal systems, or Agent architectures, efficient monetization is crucial.
Platforms like AiToEarn官网 offer:
- Open-source global AI content monetization.
- Integrated content generation, cross-platform publishing, analytics, and model ranking.
- Simultaneous publishing to:
- Douyin
- Kwai
- Bilibili
- Rednote (Xiaohongshu)
- Facebook, Instagram, LinkedIn, Threads
- YouTube, Pinterest, X (Twitter)
> With AiToEarn, cutting-edge AI projects like those above can be transformed into multi-platform revenue-generating content.
---
Do you want me to add a table summarizing all the projects with key attributes so the document becomes an easy-to-read quick reference? That could make it even more helpful for readers.
