Elorian发布Glimpse和DecoyCount基准
Elorian发布其首个视觉推理模型Glimpse，并与Mercor联合推出DecoyCount基准。该基准用于检验模型是真正计数物体，还是依赖视觉捷径和替换启发式。配套论文称，所有参测前沿模型的准确率均低于50%，其中GPT-6 Astra以43.17%居首。Elorian CEO Andrew Dai表示，Glimpse在基准包含的1660个测试项上取得50.48%的内部测试成绩，但该模型和成绩均未纳入论文。他表示，评测图像和文本未用于Glimpse训练，污染检查也未在所检查的训练数据中发现重叠。Dai还表示，Glimpse的成绩来自一次不带智能体或工具框架的单独运行，而论文中的六个模型结果均为三次运行的平均值。Elorian将Glimpse描述为在推理过程中生成和编辑图像的视觉思考系统，并以此区分其方法与世界模型。
DecoyCount提供了一个专项测试，用于检验模型是依据可见物体准确计数，还是依赖习得的线索。其结果还显示出评测协议差异：论文对六个模型采用三次运行平均值，而Glimpse所称的50.48%成绩来自一次内部运行。
This month Elorian launched its first visual reasoning model, Glimpse. It’s being promoted as “a new category of visual thinking model: a system that reasons about the physical world from what it sees.”
So how does Glimpse compare to both frontier models and world models in vision tests? A new benchmark called DecoyCount, co-released by Elorian,provides some answers.
We quizzed CEO Andrew Dai on the benchmark, and also talked about Elorian’s positioning in a crowded models landscape.
DecoyCount results
Elorian emerged from stealth in April with $55 million in seed funding, co-led by Striker Venture Partners, Menlo Ventures and Altimeter Capital.Now, in partnership with AI data company Mercor, Elorian is announcing the DecoyCount benchmark, which tests whether language models genuinely count objects or rely on visual shortcuts and substitution heuristics.
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