Evidence Receipt. Related Resources.
OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data
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Page Freshness
Signal Canvas proof surface
Canonical route: /signal-canvas/openseeker-democratizing-frontier-search-agents-by-fully-open-sourcing-training-data
- Proof freshness
- stale
- Proof status
- partial
- Display score
- 9/10
- Last proof check
- 2026-03-19
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 0
- Source count
- 0
- 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
OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data
Canonical ID openseeker-democratizing-frontier-search-agents-by-fully-open-sourcing-training-data | Route /signal-canvas/openseeker-democratizing-frontier-search-agents-by-fully-open-sourcing-training-data
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/openseeker-democratizing-frontier-search-agents-by-fully-open-sourcing-training-dataMCP example
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"paper_ref": "openseeker-democratizing-frontier-search-agents-by-fully-open-sourcing-training-data",
"query_text": "Summarize OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data"
}
}source_context
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"query": "OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data",
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"paper_ref": "openseeker-democratizing-frontier-search-agents-by-fully-open-sourcing-training-data",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Preparing verified analysis
Dimensions overall score 9.0
GitHub Code Pulse
Claim map
- Evidencepartial
We fully open-source the complete training dataset and the model weights to democratize frontier search agent research
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
To bridge this gap, we introduce OpenSeeker, the first fully open-source search agent (i.e., model and data) that achieves frontier-level performance
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
Experimental results demonstrate that OpenSeeker... achieves state-of-the-art performance across multiple benchmarks including BrowseComp, BrowseComp-ZH, xbench-DeepSearch, and WideSearch.
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
OpenSeeker significantly outperforms the second-best fully open-source agent DeepDive (e.g., 29.5% v.s. 15.3% on BrowseComp)
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
even surpasses industrial competitors such as Tongyi DeepResearch... on BrowseComp-ZH (48.4% v.s. 46.7%)
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
OpenSeeker, trained (a single training run) on only 11.7k synthesized samples, achieves state-of-the-art performance
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
Fact-grounded scalable controllable QA synthesis, which reverse-engineers the web graph via topological expansion and entity obfuscation
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
the model's performance heavily depends on the quality of web data, and potential biases in dataset creation may impact results
ImplicationmissingImplication not extracted yet.
Verificationpartialpartial
- Evidencepartial
we introduce OpenSeeker, the first fully open-source search agent (i.e., model and data) that achieves frontier-level performance
ImplicationpartialExplicitly stated in the abstract as a primary contribution.
Verificationpartialpartial
- Evidencepartial
Fact-grounded scalable controllable QA synthesis, which reverse-engineers the web graph via topological expansion and entity obfuscation to generate complex, multi-hop reasoning tasks with controllable coverage and complexity.
ImplicationpartialDescribed as a core technical innovation in the abstract.
Verificationpartialpartial
- Evidencepartial
Denoised trajectory synthesis, which employs a retrospective summarization mechanism to denoise the trajectory, therefore promoting the teacher LLMs to generate high-quality actions.
ImplicationpartialDescribed as a core technical innovation in the abstract.
Verificationpartialpartial
- Evidencepartial
Experimental results demonstrate that OpenSeeker, trained (a single training run) on only 11.7k synthesized samples, achieves state-of-the-art performance across multiple benchmarks including BrowseComp, BrowseComp-ZH, xbench-DeepSearch, and WideSearch.
ImplicationpartialStated in the abstract with specific benchmark names.
Verificationpartialpartial
