proof blockPage 576%
This equation defines the score or evaluation function that determines model quality.
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QuantSightBench: Evaluating LLM Quantitative Forecasting with Prediction Intervals explores QuantSightBench evaluates LLM quantitative forecasting with prediction intervals, revealing significant overconfidence and calibration issues across frontier models.. Commercial viability score: 7/10 in LLM Evaluation.
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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.
Page Freshness
Canonical route: /paper/quantsightbench-evaluating-llm-quantitative-forecasting-with-prediction-intervals
Page-specific freshness sourced from this paper's evidence receipt and score bundle.
Agent Handoff
Canonical ID quantsightbench-evaluating-llm-quantitative-forecasting-with-prediction-intervals | Route /paper/quantsightbench-evaluating-llm-quantitative-forecasting-with-prediction-intervals
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/quantsightbench-evaluating-llm-quantitative-forecasting-with-prediction-intervalsMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.15859"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "QuantSightBench: Evaluating LLM Quantitative Forecasting with Prediction Intervals",
"normalized_query": "2604.15859",
"route": "/paper/quantsightbench-evaluating-llm-quantitative-forecasting-with-prediction-intervals",
"paper_ref": "quantsightbench-evaluating-llm-quantitative-forecasting-with-prediction-intervals",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Paper proof page receipt window
/buildability/quantsightbench-evaluating-llm-quantitative-forecasting-with-prediction-intervals
Subject: QuantSightBench: Evaluating LLM Quantitative Forecasting with Prediction Intervals
Verdict
Build Now
Verdict is Build Now because viability and implementation proof cleared the Wave 1 scaffold thresholds.
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/quantsightbench-evaluating-llm-quantitative-forecasting-with-prediction-intervals
Paper ref
quantsightbench-evaluating-llm-quantitative-forecasting-with-prediction-intervals
arXiv id
2604.15859
Generated at
2026-04-20T20:23:38.814Z
Evidence freshness
fresh
Last verification
2026-04-20T20:23:38.814Z
Sources
4
References
0
Coverage
67%
Lineage hash
bd07e1df0b852eb352bacf28955d05127f069fcbe7f64fea592b7b7b6c3a96a3
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 / 4 sources / Verification pending
references
proof_status
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
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Preparing verified analysis
Dimensions overall score 7.0
No public claim map is available for this paper yet.
Visual citation anchors from the paper document graph.
This equation defines the score or evaluation function that determines model quality.
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. N ∑ i=1 1(li ≤yi ≤ui) Coverage = N
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. N ∑ i= (log ui −log li) + α(log yi −log ui)1(yi > ui) MLIS = α(log li −log yi)1(yi < li) + N
Page and bbox are available; crop image is pending.
No public competitor map is available for this paper yet.
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References are not available from the internal index yet.