Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures explores A survey of intrinsic interpretability methods for LLMs, categorizing design principles and architectures to guide future research in transparent AI.. Commercial viability score: 3/10 in LLM Interpretability.
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Freshness
Canonical route: /paper/towards-intrinsic-interpretability-of-large-language-models-a-survey-of-design-principles-and-architectures
Verification is still converging across references, source coverage, and proof checks.
Proof Quality
One canonical proof ledger now drives the badge, counts, indexing, and commercialization gating.
Commercialization rails stay hidden until proof clears: proof_status, references_count.
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Agent Handoff
Canonical ID towards-intrinsic-interpretability-of-large-language-models-a-survey-of-design-principles-and-architectures | Route /paper/towards-intrinsic-interpretability-of-large-language-models-a-survey-of-design-principles-and-architectures
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/towards-intrinsic-interpretability-of-large-language-models-a-survey-of-design-principles-and-architecturesMCP example
{
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}
}source_context
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}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 3.0
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