Bounded definition
A source-bounded mechanism record for polysemantic neurons 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-polysemantic-neurons
What remains a separate question
Polysemantic neurons 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.
Neural feature superposition
outbound connection · concept
Polysemantic neurons is positioned after Neural feature superposition in this bounded dependency sequence; the edge is navigational and does not assert equivalence or causation beyond the cited source scope.
Toy models of superposition
inbound connection · method
Toy models of superposition is positioned after Polysemantic neurons 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 polysemantic neurons as a distinct mechanism 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
- Polysemantic neurons 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.