Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization

Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/frontier-eng-benchmarking-self-evolving-agents-on-real-world-engineering-tasks-with-generative-optimization

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-15
Score updated: 2026-04-15
Score fresh until: 2026-05-15
References: 0
Source count: 3
Coverage: 50%

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 frontier-eng-benchmarking-self-evolving-agents-on-real-world-engineering-tasks-with-generative-optimization | Route /signal-canvas/frontier-eng-benchmarking-self-evolving-agents-on-real-world-engineering-tasks-with-generative-optimization

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/frontier-eng-benchmarking-self-evolving-agents-on-real-world-engineering-tasks-with-generative-optimization

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "frontier-eng-benchmarking-self-evolving-agents-on-real-world-engineering-tasks-with-generative-optimization",
    "query_text": "Summarize Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization",
  "normalized_query": "2604.12290",
  "route": "/signal-canvas/frontier-eng-benchmarking-self-evolving-agents-on-real-world-engineering-tasks-with-generative-optimization",
  "paper_ref": "frontier-eng-benchmarking-self-evolving-agents-on-real-world-engineering-tasks-with-generative-optimization",
  "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: Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization

PDF: https://arxiv.org/pdf/2604.12290v1

Source count: 3

Coverage: 50%

Last proof check: 2026-04-15T16:59:53.827Z

Signal Canvas receipt window

Watch and verify: Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization

/buildability/frontier-eng-benchmarking-self-evolving-agents-on-real-world-engineering-tasks-with-generative-optimization

Watchwatch

Subject: Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization

Verdict

Watch

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

Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization

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