Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs explores HETA is a novel attribution framework for decoder-only LLMs that provides context-aware, causally faithful, and semantically grounded explanations of token contributions.. Commercial viability score: 8/10 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/hessian-enhanced-token-attribution-heta-interpreting-autoregressive-llms
Verification is still converging across references, source coverage, and proof checks.
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.
Search indexing stays off until proof clears: proof_status, references_count.
Agent Handoff
Canonical ID hessian-enhanced-token-attribution-heta-interpreting-autoregressive-llms | Route /paper/hessian-enhanced-token-attribution-heta-interpreting-autoregressive-llms
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/hessian-enhanced-token-attribution-heta-interpreting-autoregressive-llmsMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.13258"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs",
"normalized_query": "2604.13258",
"route": "/paper/hessian-enhanced-token-attribution-heta-interpreting-autoregressive-llms",
"paper_ref": "hessian-enhanced-token-attribution-heta-interpreting-autoregressive-llms",
"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.
Research neighborhood
Interactive graph renders after load.
Preparing verified analysis
Dimensions overall score 8.0
No public claim map is available for this paper yet.
No public competitor map is available for this paper yet.
Owned Distribution
Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.
References are not available from the internal index yet.