MiniMax M3 Sparse Attention Enables Long-Horizon Agents
omarsar0 · x · 2026-07-07
The author points out that as long-horizon agents become multimodal, models like MiniMax M3 deserve close attention. They wrote an analysis on how sparse attention makes long-horizon agents engineeringly viable.
The core argument is that sparse attention makes long-timespan, multimodal agents much more practical regarding compute and context.
More from coding & agent
- AWS on agent prompt caching: explicit + implicit patterns to slash token costs — AWS ML Blog · 2026-10-27
- He gave his AI agent a phone number and email — then hired it as social media manager — heyshrutimishra · 2026-10-27
- Firecrawl launches Alexandria company-data API and announces $75M Series B — devdigest · 2026-10-27
- 'Let an agent run wild on vague goals and it makes your code worse 9 out of 10 times' — eptwts · 2026-10-27
- LangChain to demo the in-house design agent its team uses daily, this Wednesday — LangChain · 2026-10-27
- Biotech Agent Series Day 8: A Notebook Review Agent to Auto-Audit Lab Records — nlarusstone · 2026-10-27