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ARXIV:2604.09537 · EVIDENCE VERIFICATION · SUBMITTED 13 APR · 20:33 UTC · FRESHNESS STALE
ARXIV:2604.09537EVIDENCE VERIFICATIONSUBMITTED 13 APR · 20:33 UTCFRESHNESS STALESoroosh Tayebi Arasteh · Mehdi Joodaki · Mahshad Lotfinia · Sven Nebelung · Daniel Truhn · arXiv
A framework for evidence-grounded reasoning that trains models to explicitly depend on provided evidence for decision-making, improving accuracy in domains like radiology.
Opportunity summary
Pain A framework for evidence-grounded reasoning that trains models to explicitly depend on provided evidence for decision-making, improving accuracy in domains like radiology.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
A framework for evidence-grounded reasoning that trains models to explicitly depend on provided evidence for decision-making, improving accuracy in domains like radiology. In practice, this often fails because supervision is weak, evidence is only…
Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports the target claim. In practice, this often fails because supervision…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports…
Evidence Verification moved forward this cycle; last verified April 2026. Public score 3.0/10.
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Score3.0Analysis summary
A framework for evidence-grounded reasoning that trains models to explicitly depend on provided evidence for decision-making, improving accuracy in domains like radiology.
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Paper Pack
10.48550/arXiv.2604.09537A framework for evidence-grounded reasoning that trains models to explicitly depend on provided evidence for decision-making, improving accuracy in domains like radiology.
Abstract
Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports the target claim. In practice, this often fails because supervision is weak, evidence is only loosely tied to the claim, and evaluation does not test evidence dependence directly. We introduce case-grounded evidence verification, a general framework in which a model receives a local case context, external evidence, and a structured claim, and must decide whether the evidence supports the claim for that case. Our key contribution is a supervision construction procedure that generates explicit support examples together with semantically controlled non-support examples, including counterfactual wrong-state and topic-related negatives, without manual evidence annotation. We instantiate the framework in radiology and train a standard verifier on the resulting support task. The learned verifier substantially outperforms both case-only and evidence-only baselines, remains strong under correct evidence, and collapses when evidence is removed or swapped, indicating genuine evidence dependence. This behavior transfers across unseen evidence articles and an external case distribution, though performance degrades under evidence-source shift and remains sensitive to backbone choice. Overall, the results suggest that a major bottleneck in evidence grounding is not only model capacity, but the lack of supervision that encodes the causal role of evidence.
Source availability
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Extraction status
Parse run linkedA document parse run is attached to this paper.
Proof status
unverified0 refs; 4 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 3.0
PROBLEM
A framework for evidence-grounded reasoning that trains models to explicitly depend on provided evidence for decision-making, improving accuracy in domains like radiology. In practice, this often fails because supervision is weak, evidence is only loosely tied to the claim, and...
METHOD
Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports the target claim. In practice, this often fails because supervision is weak, evidence is only loosely tied...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports the target claim.
WHY NOW
Evidence Verification moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework for evidence-grounded reasoning that trains models to explicitly depend on provided evidence for decision-making, improving accuracy in domains like radiology. In practice, this often fails because supervision is weak, evidence is only loosely tied to the claim, and evaluation does not test evidence dependence directly.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports the target claim. In practice, this often fails because supervision is weak, evidence is only loosely tied to the claim, and evaluation does not test evidence dependence directly.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Evidence-grounded reasoning requires more than attaching retrieved text to a prediction: a model should make decisions that depend on whether the provided evidence supports the target claim.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Evidence Verification moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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A framework for evidence-grounded reasoning that trains models to explicitly depend on provided evidence for decision-making, improving accuracy in domains like radiology.
Segment
Evidence Verification
Adoption evidence
No public code link in the paper record yet
Commercial read
3.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Foundation
Commercially relevant
Conflicting
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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.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 4 sources / 67% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
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, 4 sources, 67% evidence coverage.
Gaps
Next test
Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
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
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
No public artifacts yet.
DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
No verified OpportunityKernel changes since the last view.
COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.