Evidence Receipt. Related Resources.
Evidence Receipt. Related Resources.
Compared to this week’s papers
Verification pending
Use This Via API or MCP
Signal Canvas is the citation-first public layer for turning one paper into a structured commercialization narrative. Use it to hand off into REST, MCP, Build Loop, and launch-pack execution without losing source lineage.
Use This Via API or MCP
Route this paper proof surface into REST, MCP, or developer workflows while preserving the same evidence receipt and related-resource context.
Page Freshness
Canonical route: /signal-canvas/subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretability
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretability | Route /signal-canvas/subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretability
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretabilityMCP example
{
"tool": "search_signal_canvas",
"arguments": {
"mode": "paper",
"paper_ref": "subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretability",
"query_text": "Summarize Subspace-Aware Sparse Autoencoders for Effective Mechanistic Interpretability"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "Subspace-Aware Sparse Autoencoders for Effective Mechanistic Interpretability",
"normalized_query": "2606.06333",
"route": "/signal-canvas/subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretability",
"paper_ref": "subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretability",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Claims: 1
References: Pending verification
Proof: Verification pending
Freshness state: computing
Source paper: Subspace-Aware Sparse Autoencoders for Effective Mechanistic Interpretability
PDF: https://arxiv.org/pdf/2606.06333v1
Source count: 3
Coverage: 50%
Last proof check: 2026-06-06T03:18:09.727Z
Signal Canvas receipt window
/buildability/subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretability
Subject: Subspace-Aware Sparse Autoencoders for Effective Mechanistic Interpretability
Verdict
Ignore
Verdict is Ignore because current viability and proof state do not clear the buildability gate.
Preparing verified analysis
Dimensions overall score 0.0
No public code linked for this paper yet.
{"file name": "input.pdf", "number of pages": 36, "author": "Seyed Arshan Dalili; Mehrdad Mahdavi", "title": "Subspace-Aware Sparse Autoencoders for Effective Mechanistic Interpretability", "creation date": null
Implication not extracted yet.
partial
Related resources will appear here when this paper maps cleanly to topic, benchmark, or dataset surfaces.
Use an AI coding agent to implement this research.
Lightweight coding agent in your terminal.
Agentic coding tool for terminal workflows.
AI agent mindset installer and workflow scaffolder.
AI-first code editor built on VS Code.
Free, open-source editor by Microsoft.
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Time to first demo
Insufficient data
No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.
Structured compute envelope
Insufficient data
No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.
Receipt path
/buildability/subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretability
Paper ref
subspace-aware-sparse-autoencoders-for-effective-mechanistic-interpretability
arXiv id
2606.06333
Generated at
2026-06-06T03:18:09.727Z
Evidence freshness
fresh
Last verification
2026-06-06T03:18:09.727Z
Sources
3
References
0
Coverage
50%
Lineage hash
10c05950393aea77c825751291a5c337924d7688c6822d22fa66ad76f0a2769e
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.
Pending verification refs / 3 sources / Verification pending
repo_url
references