Black-Box Inference of LLM Architectural Properties with Restrictive API Access
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arXiv:2607.01313v1 Announce Type: new Abstract: In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures. However, prior work has shown that given limited API access to an LLM (namely, top-$k$ logits and/or a logit bias function), one can recover certain architectural details of an LLM, such as the hidden dimension of the feed-forward network. Perhaps in response to these results, most commercial LLM providers have restricted their APIs to…
1Key Takeaways
- arXiv:2607.01313v1 Announce Type: new Abstract: In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures.
- However, prior work has shown that given limited API access to an LLM (namely, top-$k$ logits and/or a logit bias function), one can recover certain architectural details of an LLM, such as the hidden dimension of the feed-forward network.
- Perhaps in response to these results, most commercial LLM providers have restricted their APIs to….
2AIWedia Score
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv ML reports that arXiv:2607.01313v1 Announce Type: new Abstract: In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures.
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