Buildability / Receipt
This public receipt window renders only fields present in the canonical receipt object, deterministic fixture receipt, or canonical evidence receipt. Missing compute, demo, hash, signature, approval, telemetry, and adoption fields stay explicit.
Public buildability page receipt window
/buildability/a-geometric-view-for-understanding-concept-learning-and-neuron-interpretation-in-sparse-autoencoders
Subject: A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Data
{"file name": "input.pdf", "number of pages": 22, "author": "Chenhao Zhang; Chris Lin; Su-In Lee"
Truth Boundary
Buildability surfaces only report computed viability and proof receipts. They do not claim live production usage, pilot outcomes, founder sign-off, public Brier calibration, judge divergence, or external adoption unless explicitly sourced.
Compute
{"file name": "input.pdf", "number of pages": 22, "author": "Chenhao Zhang; Chris Lin; Su-In Lee", "title": "A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders", "creation date": null, "modification date": null, "kids": []}
Inference
{"file name": "input.pdf", "number of pages": 22, "author": "Chenhao Zhang; Chris Lin; Su-In Lee", "title": "A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders", "creation date": null, "modification date": null, "kids": []}
Hardware
{"file name": "input.pdf", "number of pages": 22, "author": "Chenhao Zhang; Chris Lin; Su-In Lee", "title": "A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders", "creation date": null, "modification date": null, "kids": []}
Receipt path
/buildability/a-geometric-view-for-understanding-concept-learning-and-neuron-interpretation-in-sparse-autoencoders
Paper ref
a-geometric-view-for-understanding-concept-learning-and-neuron-interpretation-in-sparse-autoencoders
arXiv id
2606.07007
Generated at
2026-06-08T17:16:26.737Z
Evidence freshness
fresh
Last verification
2026-06-08T17:16:26.737Z
Sources
3
References
0
Coverage
50%
Lineage hash
952a50bf51f1ce2e543002679afceee0ac87b23176a2dcb938618a1a09939425
Canonical opportunity-kernel lineage hash.
External signature
unsigned_external
No founder, registry, pilot, or production-adoption signature is attached to this receipt.
Verification
not_verified
Verification is blocked until an external signature is provided.
Canonical opportunity-kernel evidence is available for this receipt window.
repo_url
references