RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning

RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/ruleplanner-all-in-one-reinforcement-learner-for-unifying-design-rules-in-3d-floorplanning

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 ruleplanner-all-in-one-reinforcement-learner-for-unifying-design-rules-in-3d-floorplanning | Route /signal-canvas/ruleplanner-all-in-one-reinforcement-learner-for-unifying-design-rules-in-3d-floorplanning

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/ruleplanner-all-in-one-reinforcement-learner-for-unifying-design-rules-in-3d-floorplanning

MCP example

{
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    "mode": "paper",
    "paper_ref": "ruleplanner-all-in-one-reinforcement-learner-for-unifying-design-rules-in-3d-floorplanning",
    "query_text": "Summarize RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning"
  }
}

source_context

{
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  "mode": "paper",
  "query": "RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning",
  "normalized_query": "2601.22476",
  "route": "/signal-canvas/ruleplanner-all-in-one-reinforcement-learner-for-unifying-design-rules-in-3d-floorplanning",
  "paper_ref": "ruleplanner-all-in-one-reinforcement-learner-for-unifying-design-rules-in-3d-floorplanning",
  "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: RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning

PDF: https://arxiv.org/pdf/2601.22476v1

Source count: Pending verification

Coverage: 17%

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

Signal Canvas receipt window

Watch and verify: RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning

/buildability/ruleplanner-all-in-one-reinforcement-learner-for-unifying-design-rules-in-3d-floorplanning

Watchwatch

Subject: RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning

Verdict

Watch

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

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
we propose an all-in-one deep reinforcement learning-based approach to tackle these challenges
Implicationpartial
Explicitly stated in both abstract and analysis as the core contribution of the paper
Verificationpartial
partial
Evidencepartial
design novel representations for real-world IC design rules that have not been addressed by previous approaches
Implicationpartial
Directly stated in abstract as a key component and highlighted as novel
Verificationpartial
partial
Evidencepartial
transferability is well demonstrated on unseen circuits
Implicationpartial
Explicitly stated in abstract with supporting experimental results mentioned in analysis
Verificationpartial
partial
Evidencepartial
Current methods are only capable of handling specific and limited design rules
Implicationpartial
Direct comparison made in abstract with implication of superiority through problem statement
Verificationpartial
partial
Evidencepartial
Our framework is extensible to accommodate new design rules
Implicationpartial
Explicitly stated in abstract as a feature of the framework
Verificationpartial
partial
Evidencepartial
Adoption may be limited by the proprietary nature of IC design rules, potential resistance to change from traditional methodologies, and initial integration complexities within existing EDA workflows
Implicationpartial
Directly stated in analysis caveats section, though not in main paper text
Verificationpartial
partial
Evidencepartial
This leads to labor-intensive and time-consuming post-processing for expert engineers
Implicationpartial
Directly stated in abstract as motivation and benefit of the approach
Verificationpartial
partial
Evidencepartial
Experiments on public benchmarks demonstrate the effectiveness and validity of our approach
Implicationpartial
Explicitly stated in abstract with reference to experimental validation
Verificationpartial
partial

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.

RulePlanner: All-in-One Reinforcement Learner for Unifying Design Rules in 3D Floorplanning

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