Finding Belief Geometries with Sparse Autoencoders explores A pipeline to discover and validate belief-like geometric structures within large language model representations.. Commercial viability score: 4/10 in LLM Interpretability.
Use This Via API or MCP
This route is the stable paper-level surface for citations, viability, references, and downstream handoffs. Use it as the proof layer behind Signal Canvas, workspace creation, and launch-pack generation.
Freshness
Canonical route: /paper/finding-belief-geometries-with-sparse-autoencoders
Proof data is outside the preferred freshness window.
Proof Quality
One canonical proof ledger now drives the badge, counts, indexing, and commercialization gating.
Commercialization rails stay hidden until proof clears: proof_status, references_count, source_count, coverage.
Search indexing stays off until proof clears: proof_status, references_count, source_count, coverage.
Agent Handoff
Canonical ID finding-belief-geometries-with-sparse-autoencoders | Route /paper/finding-belief-geometries-with-sparse-autoencoders
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/finding-belief-geometries-with-sparse-autoencodersMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.02685"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "Finding Belief Geometries with Sparse Autoencoders",
"normalized_query": "2604.02685",
"route": "/paper/finding-belief-geometries-with-sparse-autoencoders",
"paper_ref": "finding-belief-geometries-with-sparse-autoencoders",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
Preparing verified analysis
Dimensions overall score 4.0
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