QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Stale23d agoVerification pending / evidence receipt incomplete

Export Brief Open in Build Loop Connect with Author

View PDF ↗

Viability

0.0/10

Compared to this week’s papers

Verification pending

Use This Via API or MCP

Use Signal Canvas as the narrative proof surface

Signal Canvas is the citation-first public layer for turning one paper into a structured commercialization narrative. Use it to hand off into REST, MCP, Build Loop, and launch-pack execution without losing source lineage.

Signal Canvas API Paper Proof Page Open Build Loop Launch Pack Example

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/qed-nano-teaching-a-tiny-model-to-prove-hard-theorems

stale

Proof freshness: fresh
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-07
Score updated: 2026-04-07
Score fresh until: 2026-05-07
References: 0
Source count: 0
Coverage: 0%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Canonical ID qed-nano-teaching-a-tiny-model-to-prove-hard-theorems | Route /signal-canvas/qed-nano-teaching-a-tiny-model-to-prove-hard-theorems

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/qed-nano-teaching-a-tiny-model-to-prove-hard-theorems

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "qed-nano-teaching-a-tiny-model-to-prove-hard-theorems",
    "query_text": "Summarize QED-Nano: Teaching a Tiny Model to Prove Hard Theorems"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "QED-Nano: Teaching a Tiny Model to Prove Hard Theorems",
  "normalized_query": "2604.04898",
  "route": "/signal-canvas/qed-nano-teaching-a-tiny-model-to-prove-hard-theorems",
  "paper_ref": "qed-nano-teaching-a-tiny-model-to-prove-hard-theorems",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 0

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

PDF: https://arxiv.org/pdf/2604.04898v1

Source count: Pending verification

Coverage: 0%

Last proof check: 2026-04-07T20:14:45.148Z

Signal Canvas receipt window

Watch and verify: QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

/buildability/qed-nano-teaching-a-tiny-model-to-prove-hard-theorems

Watchwatch

Subject: QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/qed-nano-teaching-a-tiny-model-to-prove-hard-theorems

Paper ref

qed-nano-teaching-a-tiny-model-to-prove-hard-theorems

arXiv id

2604.04898

Freshness

Generated at

2026-04-07T20:14:45.148Z

Evidence freshness

fresh

Last verification

2026-04-07T20:14:45.148Z

Sources

References

Coverage

Hash state

Lineage hash

c3f81842c518f45b8e2666ae87017c58f38a605c532b45b970fdc0ce84c1ebd4

Canonical opportunity-kernel lineage hash.

Signature state

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.

Blockers

Missing: paper_evidence_receipts.references_count
Missing: paper_evidence_receipts.coverage
Unknown: Canonical evidence receipt has not been materialized yet.

Verification pending / evidence receipt incomplete

paper_evidence_receipts.references_count

paper_evidence_receipts.coverage

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Paper proof page Proof divergence Divergence API Brier outcomes API

Paper Conversation

Citation-first answers with explicit evidence receipts, disagreement handling, commercialization framing, and next actions.

Paper Mode

QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Overall score: 7/10

Lineage: c3f81842c518…

Canonical Paper Receipt

Last verification: 2026-04-07T20:14:45.148Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 0%

Missingness

- paper_evidence_receipts.references_count
- paper_evidence_receipts.coverage

Unknowns

- Canonical evidence receipt has not been materialized yet.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

No public claim map is available for this paper yet.

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

Startup potential card

Share on X LinkedIn

Related Resources

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

CursorIDE

AI-first code editor built on VS Code.

VS CodeIDE

Free, open-source editor by Microsoft.

Recommended Stack

FastAPIBackend

PyTorchML Framework

TensorFlowML Framework

JAXML Framework

KerasML Framework

Startup Essentials

Antigravity

AI Agent IDE

Render

Deploy Backend

Railway

Full-Stack Deploy

Supabase

Backend & Auth

Vercel

Deploy Frontend

Firebase

Google Backend

Hugging Face Hub

ML Model Hub

Banana.dev

GPU Inference

MVP Investment

$10K - $14K

6-10 weeks

Engineering

$8,000

GPU Compute

$800

LLM API Credits

$500

SaaS Stack

$300

Domain & Legal

$100

6mo ROI

0.5-1x

3yr ROI

6-15x

GPU-heavy products have higher costs but premium pricing. Expect break-even by 12mo, then 40%+ margins at scale.

Talent Scout

Yuxiao Qu

CMU

Amrith Setlur

CMU

Jasper Dekoninck

ETH Zurich

Edward Beeching

Hugging Face

Find Similar Experts

LLM experts on LinkedIn & GitHub

QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Use Signal Canvas as the narrative proof surface

Signal Canvas proof surface

QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Evidence Receipt

Watch and verify: QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Compute envelope

Evidence ids

Freshness

Hash state

Signature state

Blockers

Canonical Paper Receipt

GitHub Code Pulse

Claim map

Keep exploring

Startup potential card

Related Resources

BUILDER'S SANDBOX

Build This Paper

Recommended Stack

Startup Essentials

MVP Investment

Talent Scout