A 1.7B model beating Qwen3.5-9B on agent benchmarks.
Liquid AI's LFM3-1.7B, open weights:
- 256K context
- 1.6 GB KV cache at 128K
- Fine-tunes on one GPU
Liquid's own evals, so test it on your tasks.
Which agent job would you move to a 2B local model?
xitter.cf/liquidai/status/211512…
Replying to @liquidai
𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 powers local text agents with a 256K-token context window. Fine-tune it on a single GPU for your domain, harness and tools, then run it on your own hardware without per-token API fees.
Post-trained in agent harnesses such as Hermes Agent, Pi, and OpenClaw, it can triage your inbox, manage your calendar, research the web, send you morning briefings on WhatsApp, and find the right tool among thousands of options.
In our evaluations, it outperforms Qwen3.5-9B on WebArena-Verified and AutomationBench, which test multi-step tasks in browsers and business applications.
At 128K tokens, its KV cache occupies 1.6 GB, compared with 4.3 GB for Qwen3.5-4B. That leaves more memory for your application as task histories grow.
It also scales to server workloads. In our serving tests, p95 request latency remains stable under heavy traffic, supporting latency-sensitive applications such as online fraud detection.
To demonstrate that scale, we ran more than 300,000 LFM3-1.7B agents in parallel to investigate the Enron email archive.
> 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 model card: huggingface.co/LiquidAI/LFM3…
4/
Plain RAG beat fancy agent memory in 90 of 99 matchups.
The Memory Tax paper tested 11 memory systems:
- On LongMemEval, none beat plain retrieval
- Memory rewrites history and loses facts
- Fix: keep a path back to the raw transcript
Has a memory layer ever paid off for you?
The longest proof in math history is now machine-checked.
Axiom's agent swarms put the finite simple groups classification in Lean:
- ~15,000 pages
- 43.1M lines in 24 days
- 34 real issues in the proof fixed
Agents scale when a checker can say no.
xitter.cf/axiommathai/status/211…
8/ Cursor added Annotate to design mode. Draw on the running page, edit elements and check other screen sizes, then hand it all to the agent as a visual prompt.
xitter.cf/cursor_ai/status/21151…





