Release4d ago
SenseTime open-sources SenseNova-Loop-9B-Preview, a 9B looped Transformer with about 14.6B inference parameters for long-context and agentic work
On Oct 30, SenseTime released SenseNova-Loop-9B-Preview (Apache 2.0) on its official X account and Hugging Face. It runs layers 4 to 27 of a 32-layer decoder twice with the same weights, so each forward pass executes 56 layer computations while storing about 9B parameters, about 14.6B at inference, and it uses two-stream mHC (manifold-constrained hyper-connections) to manage the residual streams. It takes text and images, with training sequences up to 262,144 tokens. SenseTime charts show it beating Qwen3.5 and Qwen3.6 peers on BIRD-Dev, WorkBuddy Bench, and other agent and data tasks, while it still trails on Terminal Bench 2.1; the technical report is not out yet. The official launch post drew about 910 likes.
Release6d ago
Liquid AI releases LFM3, six open-weight ~2B models spanning text, vision, audio, omni and decision tasks
On Oct 27 Liquid AI released LFM3, a new generation of foundation models built on a tri-hybrid architecture of gated short convolutions, sliding-window attention and global attention, pre-trained on 22T tokens. The first six models all branch from LFM3-1.7B-Base at around 2B parameters, with weights on Hugging Face under the LFM Open License v1.0. Liquid says the text model LFM3-1.7B needs about 1 GB at 4-bit, supports 256K context and beats models several times its size on several agent benchmarks.
Benchmark12d ago
Artificial Analysis Benchmarks DeepSeek Search API: $0.75/1K Searches Delivers +36 Point Score Lift
Independent AI evaluation platform Artificial Analysis (@ArtificialAnlys) officially benchmarked DeepSeek's web search API (DeepSeek Search). Augmenting the base model with DeepSeek Search boosted its Search Index score from 33 to 69—an impressive +36 point leap (ranking 17th among 27 tested search APIs). DeepSeek Search established a market-leading price-performance ratio, priced at just $0.75 per 1,000 searches (~$0.032 per completed task), making it the most cost-effective paid search API evaluated. It recorded 70% accuracy on AA-Omniscience and 63% on BrowseComp, highlighting an optimal balance between factual grounding and operational economics.
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