MASkills: Continual Skills Optimization for Multi-Agent LLM Systems
This story is from 2026-09-03. It is preserved in the archive; the latest stories are on the live feed.
arXiv:2609.02094v1 Announce Type: new Abstract: LLM-based multi-agent systems have shown strong performance on complex tasks, yet continual improvement from interaction experience remains challenging. Existing self-reflection methods build experience memories, but memories are mostly hard to invoke…
Read the full story at arXiv cs.AI ↗
Timeline · 1 report
- 2026-09-03 04:00 · arXiv cs.AI
MASkills: Continual Skills Optimization for Multi-Agent LLM Systems
More stories
- Anthropic says Claude 'leads' 26 percent of its AI R&D work — Engadget
- Gemini Hacked Three Companies in First Known Breakout by Google’s AI — Wall Street Journal Technology
- Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
- Optimizing agent system prompts with Amazon Bedrock AgentCore — AWS Machine Learning Blog
- Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
- Introducing Astra for Law — OpenAI News
- Alibaba ships Qwen3.8-Omni-Flash to watch, listen and call tools — r/LocalLLM
- OpenAI reveals cases of ‘concerning’ AI behaviour as it announces new disclosure system — The Guardian AI
Get the daily brief of stories like this at 6:30 every morning →