No primary source connects Qi et al.'s (2023) fine-tuning jailbreak to Sadtler et al.'s (2014) within-/outside-manifold learning asymmetry — the comparison is an analogy the vault constructed, not a finding of the literature

neural-manifoldsintrinsic-dimensionfine-tuningjailbreaksafety-alignmentcross-domain-bridgelarge-language-modelsneuroscience

A targeted search combining "jailbreak," "fine-tuning," "intrinsic dimension," "low-rank subspace," "neural manifold," "within-manifold," and "Sadtler" surfaced an active 2025–2026 research thread on the geometry of safety-relevant fine-tuning updates — but no paper in that thread references Sadtler et al.'s brain–computer-interface work (claim-sadtler-2014-within-manifold-bci-learning-fast-outside-resists), the neural-manifold hypothesis, or "within-manifold learning" by name. Conversely, no neuroscience or cross-domain source was found applying Sadtler's asymmetry to LLM fine-tuning jailbreaks specifically.

The consequence is a scoping correction, not a discovery. The proposal that Qi et al.'s ten-example jailbreak is cheap because it stays inside the low-intrinsic-dimension subspace ordinary fine-tuning already occupies (claim-aghajanyan-2020-fine-tuning-low-intrinsic-dimension, claim-hu-2021-lora-gpt3-175b-intrinsic-rank-one-or-two) — a within-manifold move in the sense of Sadtler — is the vault's own analogy-construction, traced through observation-low-dimensional-subspace-constrains-adaptation-brains-and-nets and question-low-dimensional-subspace-one-object-or-analogy. It should not be recorded as a claim resting on any source.

This is the same shape of evidence-of-absence outcome the vault already logged for the broader "one object or analogy" question on 2026-07-25, narrowed here to the specific empirical within-/outside-manifold test that question named as a candidate next move. The closest bearing evidence the search did surface — three primary geometric papers on whether jailbreak-relevant updates share the ordinary fine-tuning subspace — is recorded at claim-ponkshe-2025-safety-subspaces-not-linearly-distinct-entangled-with-general-learning, claim-springer-2026-finetuning-orthogonality-unstable-curvature-steers-into-alignment-subspace, and claim-zhang-liu-shao-2023-intrinsic-finetuning-subspaces-task-specific-no-global-subspace. That literature is itself split, offering partial and conflicting support rather than a clean confirmation.