Bounded definition
A source-bounded measurement record for superposition geometry within mechanistic interpretability.
What the cited work establishes
The work develops toy models in which neural networks represent more features than available dimensions under specified sparsity conditions.
Limited to Definitions, toy models, geometry, sparsity, and feature-interference experiments. in “Toy Models of Superposition”; this candidate records the concept boundary and does not pool results from uncited systems or studies.
Claims: urn:maha:claim:mechanistic-interpretability-superposition-geometry
What remains a separate question
Superposition geometry does not by itself establish system-level performance, safety, manufacturability, scalability, economic advantage, clinical benefit, or deployment readiness.
A toy-model mechanism does not establish that every feature in a production model has the same geometry or semantics.
Connected domain graph
Typed dependencies preserve publication state.
Only independently canonical records receive public links and relation statements. Draft graph topology remains private.
Toy models of superposition
outbound connection · method
Superposition geometry is positioned after Toy models of superposition in this bounded dependency sequence; the edge is navigational and does not assert equivalence or causation beyond the cited source scope.
Representation probing boundary
inbound connection · comparison
Representation probing boundary is positioned after Superposition geometry in this bounded dependency sequence; the edge is navigational and does not assert equivalence or causation beyond the cited source scope.
Claim ledger
Every proposition keeps its own evidence state.
The cited source supports treating superposition geometry as a distinct measurement within the stated mechanistic interpretability scope.
- Scope
- Limited to Definitions, toy models, geometry, sparsity, and feature-interference experiments. in “Toy Models of Superposition”; this candidate records the concept boundary and does not pool results from uncited systems or studies.
- Boundary
- Superposition geometry does not by itself establish system-level performance, safety, manufacturability, scalability, economic advantage, clinical benefit, or deployment readiness.
- Uncertainty
- No cross-source quantitative interval is asserted. Definitions, operating conditions, samples, instruments, and outcome measures must be checked against the exact cited locator during review.
- Replication
- Independent replication and cross-platform transfer have not been compiled for this candidate; the evidence maturity refers only to the bounded source contract.
Primary sources
Citation, locator, rights, and boundary travel together.
Source 1 · Transformer Circuits Thread
Toy Models of Superposition
Nelson Elhage, Tristan Hume, Catherine Olsson, et al.
- Exact locator
- Definitions, toy models, geometry, sparsity, and feature-interference experiments.
- Establishes
- The work develops toy models in which neural networks represent more features than available dimensions under specified sparsity conditions.
- Boundary
- A toy-model mechanism does not establish that every feature in a production model has the same geometry or semantics.
- Rights basis
- citation with paraphrase · The candidate uses original boundary language and a short paraphrase linked to the cited source. No source passage, figure, or table is reproduced.