Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All
Multiple research papers released on arXiv explore advancements in AI agents, focusing on improving their reasoning, memory, and training efficiency. Qwen3.6-35B-A3B, an open-source sparse MoE model, demonstrates strong agentic coding capabilities. Other studies introduce methods for better skill presentation, long-context reasoning through RL, skill reuse as compression, and adaptive context management for agents tackling complex, long-horizon tasks. Additionally, research presents AutoSci, a system for automating the scientific research lifecycle, and PithTrain, a compact training framework for MoE models designed for agent-native development. AI
IMPACT Advances in agent capabilities, memory management, and training efficiency could accelerate the development of more sophisticated AI systems.
- LLM
- ALFWorld
- LatentRAG
- MemReranker
- BeliefMem
- AgenticRAG
- Gemini-3-Flash
- SIRA
- Qwen3-Reranker
- BRIGHT
- GPT-4o-mini
- InterLV-Search
- AI agents
- MemReread
- SuperIntelligent Retrieval Agent (SIRA)
- Grok-4-Fast
- RecMem
- LongMINT
- SocialMemBench
- DimMem
- EvoMemBench
- H-Mem
- MeMo
- Gemini 2.5 Flash
- Qwen3-235B
- Llama-4-Maverick
- PithTrain
- Qwen
- DeepSeek V4-Flash
- SCALE
- Qwen3.6-35B-A3B
- Qwen2.5-7B-Instruct
- Qwen2.5-3B-Instruct
- ASH
- AdaCoM
- AutoSci
- ReuseRL
- ElasticMem
- LongTraceRL
- GPT-5.5