r/LocalLLaMAMay 15, 2026, 01:33 UTC+8

MOOSE-Star (ICML 2026): 7B Model and 108K-Paper Dataset for Scientific Hypothesis Discovery

MOOSE-Star (ICML 2026): 7B model + 108K-paper dataset for scientific hypothesis discovery

MiroMind researchers released the MOOSE-Star collection with three 7B models and TOMATO-Star, a dataset of 108,717 NCBI papers. MS-IR-7B reaches 54.37% inspiration-retrieval accuracy, uses DeepSeek-R1-Distill-Qwen-7B as its base, runs at about 14GB fp16, and supports llama.cpp, vLLM, and SGLang.

Why it matters: HKR-H/K/R all pass via the local 7B research-agent hook and concrete dataset metrics. Single Reddit source and limited lab gravity keep it below the must-write band.

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MOOSE-Star’s useful claim is not “AI discovers science.” It puts a small, runnable baseline under a noisy category. The concrete hooks are good: three 7B models, TOMATO-Star with 108,717 NCBI papers, MS-IR-7B based on DeepSeek-R1-Distill-Qwen-7B, 54.37% inspiration-retrieval accuracy, about 14GB fp16, with llama.cpp, vLLM, and SGLang support.

I like the 7B choice. Scientific hypothesis agents get muddy fast when the demo rides a closed frontier model; nobody can separate retrieval, reasoning, memorization, and prompt craft. A 14GB fp16 model lets labs run ablations instead of applauding a slide. The Reddit body is blocked by 403, so training details and benchmark construction are not visible here. I would be careful with the 54.37% number until we see negatives, time splits, and leakage controls.

Source: r/LocalLLaMA · reddit.com
Published: May 15, 2026, 01:33 UTC+8 · May 15, 2026