This equation captures one of the core mathematical components of the system. model’s recommended decoding parameters (temperature = 0.6, top_p = 0.9, top_k = 40, repeti-
Page and bbox are available; crop image is pending.
Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis explores An AI pipeline that generates challenging math problems by identifying LLM weaknesses, improving benchmark accuracy and adaptability.. Commercial viability score: 7/10 in LLM Evaluation.
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.
Page Freshness
Canonical route: /paper/automatically-generating-hard-math-problems-from-hypothesis-driven-error-analysis
This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.
Agent Handoff
Canonical ID automatically-generating-hard-math-problems-from-hypothesis-driven-error-analysis | Route /paper/automatically-generating-hard-math-problems-from-hypothesis-driven-error-analysis
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/automatically-generating-hard-math-problems-from-hypothesis-driven-error-analysisMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.04386"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis",
"normalized_query": "2604.04386",
"route": "/paper/automatically-generating-hard-math-problems-from-hypothesis-driven-error-analysis",
"paper_ref": "automatically-generating-hard-math-problems-from-hypothesis-driven-error-analysis",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Paper proof page receipt window
/buildability/automatically-generating-hard-math-problems-from-hypothesis-driven-error-analysis
Subject: Automatically Generating Hard Math Problems from Hypothesis-Driven Error Analysis
Verdict
Watch
Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.
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Structured compute envelope
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Dimensions overall score 7.0
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This equation captures one of the core mathematical components of the system. model’s recommended decoding parameters (temperature = 0.6, top_p = 0.9, top_k = 40, repeti-
Page and bbox are available; crop image is pending.
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Receipt path
/buildability/automatically-generating-hard-math-problems-from-hypothesis-driven-error-analysis
Paper ref
automatically-generating-hard-math-problems-from-hypothesis-driven-error-analysis
arXiv id
2604.04386
Generated at
2026-04-07T20:12:52.192Z
Evidence freshness
fresh
Last verification
2026-04-07T20:12:52.192Z
Sources
0
References
0
Coverage
0%
Lineage hash
a6f330c2c68baf7006fd7278151bc748e475acaf7039f03525a233a9b8560621
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.
Verification pending / evidence receipt incomplete
paper_evidence_receipts.references_count
paper_evidence_receipts.coverage
This equation captures one of the core mathematical components of the system. ture = 1.0, top_p = 0.9, top_k = 50, repetition_penalty = 1.05) to encourage variety. For each hypoth-
Page and bbox are available; crop image is pending.
This equation captures one of the core mathematical components of the system. configuration as in Stage 1 (Llama-3.3-70B-Instruct; temperature = 0.6, top_p = 0.9, top_k = 40, rep-
Page and bbox are available; crop image is pending.
No public competitor map is available for this paper yet.