Evidence Receipt. Related Resources.
One-for-All Model Initialization with Frequency-Domain Knowledge
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Signal Canvas proof surface
Canonical route: /signal-canvas/one-for-all-model-initialization-with-frequency-domain-knowledge
- 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
One-for-All Model Initialization with Frequency-Domain Knowledge
Canonical ID one-for-all-model-initialization-with-frequency-domain-knowledge | Route /signal-canvas/one-for-all-model-initialization-with-frequency-domain-knowledge
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/one-for-all-model-initialization-with-frequency-domain-knowledgeMCP example
{
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"paper_ref": "one-for-all-model-initialization-with-frequency-domain-knowledge",
"query_text": "Summarize One-for-All Model Initialization with Frequency-Domain Knowledge"
}
}source_context
{
"surface": "signal_canvas",
"mode": "paper",
"query": "One-for-All Model Initialization with Frequency-Domain Knowledge",
"normalized_query": "2603.07523",
"route": "/signal-canvas/one-for-all-model-initialization-with-frequency-domain-knowledge",
"paper_ref": "one-for-all-model-initialization-with-frequency-domain-knowledge",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Preparing verified analysis
Dimensions overall score 8.0
GitHub Code Pulse
No public code linked for this paper yet.
Claim map
- Evidencepartial
we empirically demonstrate that a model's foundational, task-agnostic knowledge, its 'learngene', is encoded within the low-frequency components of its weights
ImplicationpartialDirectly stated in abstract as empirical finding, though specific evidence details not provided in given text
Verificationpartialpartial
- Evidencepartial
Extensive experiments demonstrate that FRONT achieves the state-of-the-art performance
ImplicationpartialExplicitly stated in abstract with mention of extensive experiments
Verificationpartialpartial
- Evidencepartial
accelerates convergence by up to 15 times in vision tasks
ImplicationpartialClear numeric claim directly stated in abstract
Verificationpartialpartial
- Evidencepartial
reduces training FLOPs by an average of 40.5% in language tasks
ImplicationpartialClear numeric claim directly stated in abstract
Verificationpartialpartial
- Evidencepartial
we propose FRONT (FRequency dOmain kNowledge Transfer), a novel framework that uses the Discrete Cosine Transform (DCT) to isolate the low-frequency 'learngene'
ImplicationpartialDirectly and explicitly stated in abstract as core method
Verificationpartialpartial
- Evidencepartial
This learngene can be seamlessly adapted to initialize models of arbitrary size via simple truncation or padding, a process that is entirely training-free
ImplicationpartialDirectly stated in abstract with clear description of process
Verificationpartialpartial
- Evidencepartial
recent approaches typically resort to either parameter selection, which fails to capture the interdependent structure of this knowledge
ImplicationpartialDirectly stated as limitation of previous approaches in abstract
Verificationpartialpartial
- Evidencepartial
or parameter prediction using generative models that depend on impractical access to large network collections
ImplicationpartialDirectly stated as limitation of alternative approaches in abstract
Verificationpartialpartial
