PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR | Signal Canvas | ScienceToStartup

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PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR

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Canonical route: /signal-canvas/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

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Agent Handoff

PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR

Canonical ID papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr | Route /signal-canvas/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr",
    "query_text": "Summarize PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR",
  "normalized_query": "2601.18207",
  "route": "/signal-canvas/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr",
  "paper_ref": "papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Paper mode· single-doc scopescope: papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
we release a search corpus of 16 million biomedical paper abstracts
Implicationpartial
Explicitly stated in the abstract with specific numeric value
Verificationpartial
partial
Evidencepartial
construct a challenging factoid QA dataset called PaperSearchQA with 60k samples answerable from the corpus
Implicationpartial
Explicitly stated in the abstract with specific numeric value
Verificationpartial
partial
Evidencepartial
We train search agents in this environment to outperform non-RL retrieval baselines
Implicationpartial
Directly stated in abstract as a result of training
Verificationpartial
partial
Evidencepartial
our data creation methods are scalable and easily extendable to other scientific domains
Implicationpartial
Directly stated in abstract but without specific evidence of scalability
Verificationpartial
partial
Evidencepartial
Most RLVR search agents tackle general-domain QA, which limits their relevance to technical AI systems in science, engineering, and medicine
Implicationpartial
Explicitly stated as a limitation of existing methods
Verificationpartial
partial
Evidencepartial
this tests technical question-answering, it is directly relevant to real scientists
Implicationpartial
Directly stated in abstract as a motivation for the work
Verificationpartial
partial
Evidencepartial
Our corpus, datasets, and benchmarks are usable with the popular Search-R1 codebase for RLVR training
Implicationpartial
Explicitly stated with specific technical details
Verificationpartial
partial
Evidencepartial
we also perform further quantitative analysis and observe interesting agent behaviors like planning, reasoning, and self-verification
Implicationpartial
Directly stated in abstract but without detailed evidence in provided text
Verificationpartial
partial

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