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ARXIV:2604.27977 · AI AGENTS · SUBMITTED 01 MAY · 15:04 UTC · FRESHNESS STALE
ARXIV:2604.27977AI AGENTSSUBMITTED 01 MAY · 15:04 UTCFRESHNESS STALEHanane Nour Moussa · Yifei Li · Zhuoyang Li · Yankai Yang · Cheng Tang · Tianshu Zhang · +4 at arXiv
D3-Gym provides verifiable environments and a dataset for training AI agents on real-world scientific discovery tasks, significantly improving model performance.
Opportunity summary
Pain D3-Gym provides verifiable environments and a dataset for training AI agents on real-world scientific discovery tasks, significantly improving model performance.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
D3-Gym provides verifiable environments and a dataset for training AI agents on real-world scientific discovery tasks, significantly improving model performance. D3-Gym comprises (1) 565 tasks sourced from 239 real scientific repositories across four disciplines…
Despite recent progress in language models and agents for scientific data-driven discovery, further advancing their capabilities is held back by the absence of verifiable environments representing real-world scientific tasks.To fill this gap, we introduce…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Rigorous evaluation of the quality of the verification signal in D3-Gym confirms that our evaluation scripts achieve 87.5% agreement with human-annotated gold standards and…
AI Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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D3-Gym provides verifiable environments and a dataset for training AI agents on real-world scientific discovery tasks, significantly improving model performance.
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Paper Pack
10.48550/arXiv.2604.27977D3-Gym provides verifiable environments and a dataset for training AI agents on real-world scientific discovery tasks, significantly improving model performance.
Abstract
Despite recent progress in language models and agents for scientific data-driven discovery, further advancing their capabilities is held back by the absence of verifiable environments representing real-world scientific tasks.To fill this gap, we introduce D3-Gym, the first automatically constructed dataset with verifiable environments for scientific Data-Driven Discovery. D3-Gym comprises (1) 565 tasks sourced from 239 real scientific repositories across four disciplines where (2) each task is equipped with a natural language instruction, an executable environment with pre-installed dependencies, input dataset and artifact previews, a reference code solution, and an automatically synthesized evaluation script. Rigorous evaluation of the quality of the verification signal in D3-Gym confirms that our evaluation scripts achieve 87.5% agreement with human-annotated gold standards and strong alignment in domain-specific evaluation logic, showing their scientific soundness. Further, training on trajectories sampled from D3-Gym yields consistent and substantial gains across Qwen3 models of varying sizes on ScienceAgentBench, boosting Qwen3-32B by 7.8 absolute points and substantially shrinking the gap with strong proprietary models. All D3-Gym artifacts (environments, creation workflow, trajectories, and models) can be found at https://github.com/OSU-NLP-Group/D3-Gym.
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Proof status
unverified0 refs; 4 sources; 67% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Dimensions overall score 7.0
PROBLEM
D3-Gym provides verifiable environments and a dataset for training AI agents on real-world scientific discovery tasks, significantly improving model performance. D3-Gym comprises (1) 565 tasks sourced from 239 real scientific repositories across four disciplines where (2) each t...
METHOD
Despite recent progress in language models and agents for scientific data-driven discovery, further advancing their capabilities is held back by the absence of verifiable environments representing real-world scientific tasks.To fill this gap, we introduce D3-Gym, the first autom...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Rigorous evaluation of the quality of the verification signal in D3-Gym confirms that our evaluation scripts achieve 87.5% agreement with human-annotated gold standards and strong alignment in domain-spec...
WHY NOW
AI Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
{"file name": "input.pdf", "number of pages": 25, "author": "Hanane Nour Moussa; Yifei Li; Zhuoyang Li; Yankai Yang; Cheng Tang; Tianshu Zhang; Nesreen K. Ahmed; Ali Payani; Ziru Chen; Huan Sun"
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Concepts
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Materials
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Competitors
D3-Gym provides verifiable environments and a dataset for training AI agents on real-world scientific discovery tasks, significantly improving model performance.
Segment
AI Agents
Adoption evidence
Public code linked for build inspection
Commercial read
7.0/10 public viability
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2/3 checks · 67%
Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
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reason
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proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
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0 refs / 4 sources / 67% coverage
stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 4 sources, 67% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
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Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
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Regulatory load
missing
Current read
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Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
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Gaps
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Prototype owner missing.
Build Passport does not name an implementer.
People
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
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Gaps
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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