US vs China

What each side shipped this week: models, prices, benchmarks · 11402 items
10月22日
ModelsBuildersMulti-sourceSelected16:36
Muse Glimmer 30B: Meta's distilled agent model runs on one consumer GPU

Distilling from a large teacher into a small model specialised for long tool-use chains may matter more than raw size for local agents, a trade-off the OSWorld gap makes visible. Builders running agents on local hardware can use it as a drop-in for multi-tool MCP workflows without cloud dependency. Compared with Qwen3.6-27B, it wins on tool orchestration but loses on general OS-level tasks.

Event
rohanpaul_ai@rohanpaul_ai4 sourcesSource
ProductsUS vs ChinaSelected12:24
DeepSeek Search ranks 17th on Artificial Analysis Search Index

The headline price is low, but the benchmark shows the real cost lives in tokens: ~7k-token search results make DeepSeek's model cost the 2nd highest on the board at ~US$0.023 per task. Developers building search-augmented agents should weigh that against the weak BrowseComp score of 63%, which trails Perplexity variants and Octen at 85% to 87% on multi-hop tasks. Compared with alternatives like Exa and Firecrawl, DeepSeek wins on price, not on factual accuracy.

官方账号Artificial Analysis@ArtificialAnlysSource
PapersBuildersSelected12:03
Google Paper: OO-Harness Specialist Harnesses Outperform One Global Agent Harness

The object-oriented structure explains why single-harness tuning keeps trading one task type's gains for another's losses, and shows a router can avoid that cost on out-of-distribution tasks. Teams building agentic pipelines can adopt the pattern directly: keep the base harness as fallback and route to specialists only when runs match known failure clusters. The AutomationBench comparison against Meta-Harness gives a concrete baseline for evaluating similar routing setups.

rohanpaul_ai@rohanpaul_aiSource
PapersBuildersMulti-source11:01
Chinese content六款前沿模型实测 LIBERO-10 机器人操作基准,GPT-6 Astra 以 44% 成功率居首

研究者让 GPT-6、Claude Opus 5.5 这些模型直接操控真实机器人跑 LIBERO-10,1,800 次测试排出了名次,最强也只有 44% 成功率,想了解大模型动手能力差在哪的可以看。

Event
arXiv cs.AI@May Espinola et al. (3)8 sourcesSource