daVinci-Env: Open SWE Environment Synthesis at Scale

daVinci-Env: Open SWE Environment Synthesis at Scale | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/davinci-env-open-swe-environment-synthesis-at-scale

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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 davinci-env-open-swe-environment-synthesis-at-scale | Route /signal-canvas/davinci-env-open-swe-environment-synthesis-at-scale

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/davinci-env-open-swe-environment-synthesis-at-scale

MCP example

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    "mode": "paper",
    "paper_ref": "davinci-env-open-swe-environment-synthesis-at-scale",
    "query_text": "Summarize daVinci-Env: Open SWE Environment Synthesis at Scale"
  }
}

source_context

{
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  "mode": "paper",
  "query": "daVinci-Env: Open SWE Environment Synthesis at Scale",
  "normalized_query": "2603.13023",
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  "paper_ref": "davinci-env-open-swe-environment-synthesis-at-scale",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: daVinci-Env: Open SWE Environment Synthesis at Scale

PDF: https://arxiv.org/pdf/2603.13023v1

Source count: Pending verification

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

Signal Canvas receipt window

Watch and verify: daVinci-Env: Open SWE Environment Synthesis at Scale

/buildability/davinci-env-open-swe-environment-synthesis-at-scale

Watchwatch

Subject: daVinci-Env: Open SWE Environment Synthesis at Scale

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

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
We present OpenSWE, the largest fully transparent framework for SWE agent training in Python, comprising 45,320 executable Docker environments spanning over 12.8k repositories
Implicationpartial
Explicitly stated in the abstract with specific numeric values.
Verificationpartial
partial
Evidencepartial
With $891K spent on environment construction and an additional $576K on trajectory sampling and difficulty-aware curation, the entire project represents a total investment of approximately $1.47 million
Implicationpartial
Directly stated in the abstract with specific cost breakdown.
Verificationpartial
partial
Evidencepartial
OpenSWE-32B and OpenSWE-72B achieve 62.4% and 66.0% on SWE-bench Verified, establishing SOTA among Qwen2.5 series
Implicationpartial
Explicitly stated in the abstract with specific performance metrics.
Verificationpartial
partial
Evidencepartial
SWE-focused training yields substantial out-of-domain improvements, including up to 12 points on mathematical reasoning and 5 points on science benchmarks, without degrading factual recall
Implicationpartial
Directly stated in the abstract with specific performance improvements.
Verificationpartial
partial
Evidencepartial
Beyond scale, we propose a quality-centric filtering pipeline that characterizes the inherent difficulty of each environment, filtering out instances that are either unsolvable or insufficiently challenging
Implicationpartial
Strongly supported in both abstract and analysis, though specific filtering criteria are not detailed.
Verificationpartial
partial
Evidencepartial
OpenSWE is built through a multi-agent synthesis pipeline deployed across a 64-node distributed cluster
Implicationpartial
Explicitly stated in the abstract with specific technical details.
Verificationpartial
partial
Evidencepartial
The cost and complexity associated with maintaining such a large-scale environment are significant
Implicationpartial
Directly stated in the analysis section as a caveat, though not quantified.
Verificationpartial
partial
Evidencepartial
existing open-source datasets remain limited in scale and repository diversity, while industrial solutions are opaque with unreleased infrastructure
Implicationpartial
Directly stated in the abstract as motivation for the work.
Verificationpartial
partial

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daVinci-Env: Open SWE Environment Synthesis at Scale

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