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actually, here's a broader thought. since this approach only works for classification, why not make that the whole story and spin it as a positive? Call your ap

by gcr 11mo ago

actually, here's a broader thought. since this approach only works for classification, why not make that the whole story and spin it as a positive? Call your approach a "classification foundation model" (for example) and say it's a special-purpose model distilled from a larger world model. Abstract's gestalt could read like "If you don't need to be generative, then you can compress the representation way down" or "discriminative understanding takes far fewer parameters than language production" This would then set the stage for the reader to understand the limitations and why the benchmarks are set up the way they are. then the kitschy paper titles could follow from that, e.g. "extreme llama compression: when classification is all you need", or "Encoder-only models: a lightweight alternative to decoder-only GPT world models" or etc. just spitballing

anima-core 11mo ago
I appreciate this framing a lot. It is actually close to how I think about the result internally. The paper focuses on the geometric behavior of intermediate representations, and classification is the cleanest setting to study that. Generative decoding is a much harder problem, and the limitations section already makes that distinction explicit. Recasting the work as a “classification-native distilled model” or “discriminative foundation model” is a good way to signal scope without underselling the contribution. You're right that discriminative understanding requires far fewer parameters than generation, and my experiments reinforce that. This will help me get better. The goal for the next revision is exactly what you describe: make the setup clearer, emphasize the intended domain, and avoid suggestive wording that implies capabilities the method does not claim. Duly noted. Your suggestions on positioning and title direction are genuinely helpful, and I’ll incorporate some of this thinking when I prepare the academic submission. Thanks for taking the time to articulate it so clearly. I appreciate your time and your critique.

by gcr 11mo ago

anima-core 11mo ago

I appreciate this framing a lot. It is actually close to how I think about the result internally. The paper focuses on the geometric behavior of intermediate representations, and classification is the cleanest setting to study that. Generative decoding is a much harder problem, and the limitations section already makes that distinction explicit. Recasting the work as a “classification-native distilled model” or “discriminative foundation model” is a good way to signal scope without underselling the contribution. You're right that discriminative understanding requires far fewer parameters than generation, and my experiments reinforce that. This will help me get better. The goal for the next revision is exactly what you describe: make the setup clearer, emphasize the intended domain, and avoid suggestive wording that implies capabilities the method does not claim. Duly noted. Your suggestions on positioning and title direction are genuinely helpful, and I’ll incorporate some of this thinking when I prepare the academic submission. Thanks for taking the time to articulate it so clearly. I appreciate your time and your critique.