Paper

Toward Open Weight Models Without Risks: Separating Public and Private Capabilities in LLMs

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arXiv:2606. 21638v1 Announce Type: new Abstract: Open-weight Large Language Models (LLMs) enable scientific progress and broad deployment.

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Authors: Charbel El Feghali, Arkil Patel, Nicholas Meade, Spandana Gella, Verna Dankers, Siva Reddy
Released: 2026-06-23 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Toward Open Weight Models Without Risks: Separating Public and Private Capabilities in LLMs overlaps with clean result "The marker is a representational handle, not a behavioural one — sharing it between a villain persona and the assistant transfers no misalignment (HIGH confidence)", experiment "Implement Chen et al. persona-vector extraction recipe and compare to project's centroid-difference recipe", experiment "Add C2 control arm (donor sees marker_B without marker_A) to disambiguate paired-marker binding from marker_B leaking alone". Matching terms: rate, extraction, control, without, capability, language, model. Source: arxiv cs.CR (Cryptography and Security).
arXiv: 2606.21638

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