Opportunity summary
Score0.0Public score shown from the verified overall while the stale axis breakdown refreshesPaincustomer pain not on file
Evidence0 refs | 3 sources | 50% coverage
Blockerno shell-level blocker reported
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ARXIV:2605.25183 · UNCATEGORIZED · SUBMITTED 27 MAY · 01:09 UTC · FRESHNESS STALE
ARXIV:2605.25183UNCATEGORIZEDSUBMITTED 27 MAY · 01:09 UTCFRESHNESS STALEJake Stephen · Niraj K. Jha · arXiv
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Our results demonstrate that deep, mechanistic neuroscience understanding can be induced in the model without reliance on large, heterogeneous web-scale…
Opportunity summary
Pain customer pain not on file
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Knowledge graph (KG) is an abstraction that can be extracted from text corpora and used for in-depth reasoning.
Knowledge graph (KG) is an abstraction that can be extracted from text corpora and used for in-depth reasoning. Prior work has leveraged KGs to fine-tune language models (LMs), enabling domain-specific superintelligence.
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Our results demonstrate that deep, mechanistic neuroscience understanding can be induced in the model without reliance on large, heterogeneous web-scale corpora. Code availability is…
Uncategorized moved forward this cycle; last verified May 2026. Public score 0.0/10. Production flags indicate code availability.
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Score0.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Our results demonstrate that deep, mechanistic neuroscience understanding can be induced in the model without reliance on large, heterogeneous web-scale…
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10.48550/arXiv.2605.25183Abstract
Knowledge graph (KG) is an abstraction that can be extracted from text corpora and used for in-depth reasoning. Prior work has leveraged KGs to fine-tune language models (LMs), enabling domain-specific superintelligence. In this work, we explore whether KG-driven in-depth reasoning capabilities can emerge in neuroscience using only information contained within a single authoritative textbook. The central hypothesis is that structured knowledge, when distilled into a high-quality KG and converted into KG-grounded question-answer (QA) supervision, is sufficient to produce expert-level reasoning through a fine-tuned LM that surpasses large language models (LLMs) in accuracy, while employing orders of magnitude fewer parameters. We construct a textbook-derived KG via a dual-LLM validation pipeline, expand it with a masked LM trained on the KG topology, generate multi-hop QA items, which include QA pairs and reasoning traces, to fine-tune an LM exclusively on KG-derived supervision, and apply reinforcement learning using path-derived KG signals as implicit reward models. Our results demonstrate that deep, mechanistic neuroscience understanding can be induced in the model without reliance on large, heterogeneous web-scale corpora. The KG-based synthetic neuroscience curriculum that readers can quiz themselves on, and the fine-tuned LM, are available at the following GitHub location: https://kg-bottom-up-superintelligence.github.io/neuro-bench.
Source availability
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Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Commercial
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Preparing verified analysis
Dimensions overall score 0.0
PROBLEM
Knowledge graph (KG) is an abstraction that can be extracted from text corpora and used for in-depth reasoning.
METHOD
Knowledge graph (KG) is an abstraction that can be extracted from text corpora and used for in-depth reasoning. Prior work has leveraged KGs to fine-tune language models (LMs), enabling domain-specific superintelligence.
RESULT
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Our results demonstrate that deep, mechanistic neuroscience understanding can be induced in the model without reliance on large, heterogeneous web-scale corpora. Code availability is flagged in the produc...
WHY NOW
Uncategorized moved forward this cycle; last verified May 2026. Public score 0.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 27, "author": "Jake Stephen; Niraj K. Jha", "title": "Knowledge Graph-Driven Expert-Level Reasoning for Neuroscience", "creation date": null, "modification date": null
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partial
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Methods
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Uncategorized
Adoption evidence
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Commercial read
0.0/10 public viability
Direct
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Unknown
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CITED BY
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 3 sources, 50% evidence coverage.
Gaps
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Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
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Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
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Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
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Gaps
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Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Gaps
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No CRM or outreach source attached.
People
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Gaps
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Regulatory need unclassified.
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People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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