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Claude Fable 5.2 is the top Anthropic model on Ramp Accounting Bench.
It scores 52.2%, ahead of Opus 5.5 (50.5%). Only Gemini 4 Argon scores higher (61.1%).
It solves more tasks on the first try than Opus 5.5 (13.9% vs 10.9%) and runs 2.8x faster than Argon (6 vs 16.7 min).
It scores 52.2%, ahead of Opus 5.5 (50.5%). Only Gemini 4 Argon scores higher (61.1%).
It solves more tasks on the first try than Opus 5.5 (13.9% vs 10.9%) and runs 2.8x faster than Argon (6 vs 16.7 min).
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Reframing cartridge training as a self-play game cuts the training steps needed by more than half, just by asking better questions.
This post continues our series on cartridges, compressed KV caches of long documents. Here we focus on self-study, the method used to train them. Self-study teaches a model reading the cartridge to answer the way it would with the full document in context. Currently, its questions come from fixed, generic prompts, so many of them ask about things the cartridge already knows.
This post continues our series on cartridges, compressed KV caches of long documents. Here we focus on self-study, the method used to train them. Self-study teaches a model reading the cartridge to answer the way it would with the full document in context. Currently, its questions come from fixed, generic prompts, so many of them ask about things the cartridge already knows.
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We had early access to @NVIDIAAI Nemotron 3.5 Super VL and post-trained it for one of our highest-volume workflows: receipt extraction.
One epoch of supervised fine-tuning on 10,000 examples made it competitive with leading open and closed-source models on our receipt benchmark.
One epoch of supervised fine-tuning on 10,000 examples made it competitive with leading open and closed-source models on our receipt benchmark.
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