ARXIV:2601.18207 · SCIENTIFIC PAPER SEARCH · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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

arXiv

Develop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain Develop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine. Most RLVR search agents tackle general-domain QA, which limits their relevance to technical AI systems in science, engineering, and…

METHOD

Full abstract

Search agents are language models (LMs) that reason and search knowledge bases (or the web) to answer questions; recent methods supervise only the final answer accuracy using reinforcement learning with verifiable rewards (RLVR). Most RLVR search agents tackle general-domain QA, which limits their relevance to technical AI systems in science, engineering, and medicine. In this work we propose training agents to search and reason over scientific papers -- this tests technical question-answering, it is directly relevant to real scientists, and the capabilities will be crucial to future AI Scientist systems. Concretely, we release a search corpus of 16 million biomedical paper abstracts and construct a challenging factoid QA dataset called PaperSearchQA with 60k samples answerable from the corpus, along with benchmarks. We train search agents in this environment to outperform non-RL retrieval baselines; we also perform further quantitative analysis and observe interesting agent behaviors like planning, reasoning, and self-verification. Our corpus, datasets, and benchmarks are usable with the popular Search-R1 codebase for RLVR training and released on https://huggingface.co/collections/jmhb/papersearchqa. Finally, our data creation methods are scalable and easily extendable to other scientific domains.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Finally, our data creation methods are scalable and easily extendable to other scientific domains.

WHY NOW

Scientific Paper Search moved forward this cycle; last verified April 2026. Public score 8.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score8.0

PainDevelop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Develop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2601.18207 · SCIENTIFIC PAPER SEARCH · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

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

arXiv

Develop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

Blocked on Code›Score8.0Evidence unverified

Opportunity summary

Pain Develop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Finally, our data creation methods are scalable and easily extendable to other scientific domains.

WHY NOW

Scientific Paper Search moved forward this cycle; last verified April 2026. Public score 8.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score8.0

PainDevelop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Develop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2601.18207

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

Develop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Parse run linked

A document parse run is attached to this paper.

Proof status

unverified

0 refs; 0 sources; 33% coverage.

What was readable

linkedon file17 anchors8 extracted44 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

8.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Finally, our data creation methods are scalable and easily extendable to other scientific domains.

WHY NOW

Scientific Paper Search moved forward this cycle; last verified April 2026. Public score 8.0/10.

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

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linkedDocument parse run

Markets

Scientific Paper Search

Competitors

not indexed

Competitive landscape

Develop a search agent utilizing RLVR to enhance scientific paper QA in fields like biomedicine.

Segment

Scientific Paper Search

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2601.18207 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(44)

Reference metadata pending (a67c0fb651e7175be1dd457dea336fd4a12f6130)

Reference metadata pending (6ec784f37e0889390e77a3151c92104e7e4f5f4a)

Reference metadata pending (6f88719fa7c73ae4b66bdce13e6f19088f2b3c13)

Reference metadata pending (8dc4dc204b6ee3b78fd2c52122ee0dc90d2f0018)

Reference metadata pending (638d402d7c097391d154043554467d077f45f4b1)

Reference metadata pending (23f655a7a596ab5f431ab9e18d5c641de3f8afb9)

Reference metadata pending (2dcbeed697289dfd4f6ad3c75eeb873df4fee1a5)

Reference metadata pending (e42054d042e2e5b3efe6e96cd6dd1c76ab3ca358)

Reference metadata pending (8402e446158252992b6ddf1ff1b0658c39d7604e)

Reference metadata pending (5d266400187708cea1305fa34d2bc5e1a47b1a57)

Reference metadata pending (466c7118d3070a1f88a0cd45108a9640e3013eda)

Reference metadata pending (1fcaafeb72142fe3d1a5d698a072d69778d244b0)

Reference metadata pending (3d99a3805ee7c2edadbe44915ee603c5fbb1d48a)

Reference metadata pending (a99dee9602e21a71526b9681d8dba37c55b66941)

Reference metadata pending (afe5441c9185638d687c6f6843e3f907c36ac1f4)

Reference metadata pending (5b86fbaf12fd50710066e3995e33ece2185f8ada)

Reference metadata pending (8231379e152a6d7d6870502649883cfccde74eb1)

Reference metadata pending (f2d0f3d47ae850f49a58f4977393bd0025af4bec)

Reference metadata pending (fa5f9aa1cb6f97654ca8e6d279ceee1427a87e68)

Reference metadata pending (33161a5a9b5dcb635b5a97475e6a6209a69ada7d)

Showing 20 of 44 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkQuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning

8.0

Prior WorkLongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards

8.0

Prior WorkKARL: Knowledge Agents via Reinforcement Learning

8.0

Extension

Builds On ThisProtRLSearch: A Multi-Round Multimodal Protein Search Agent with Large Language Models Trained via Reinforcement Learning

6.0

Builds On ThisACL-Verbatim: hallucination-free question answering for research

7.0

Builds On ThisSUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions

7.0

Builds On ThisPlan Before Search: Search Agents Need Plan

7.0

Builds On ThisORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget

7.0

Builds On ThisAutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery

7.0

Builds On ThisSearch, Do not Guess: Teaching Small Language Models to Be Effective Search Agents

7.0

Commercially relevant

none indexed

Conflicting

none indexed

Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2601.18207
Route: /paper/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr
Active tab: read
Artifact: papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr/paper-pack
REST build passport API/api/v1/paper/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
  "contract_version": "paper-r2",
  "paper_id": "40873f46-0b11-48ac-ae16-fd49a1c766e8",
  "arxiv_id": "2601.18207",
  "canonical_route": "/paper/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr",
  "endpoints": {
    "paper_pack": "/api/v1/paper/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr/paper-pack",
    "build_passport": "/api/v1/paper/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

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

stale

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%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

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 /paper/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr

REST example

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

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2601.18207"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR",
  "normalized_query": "2601.18207",
  "route": "/paper/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
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

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

/buildability/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr

Watchwatch

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

Verdict

Watch

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

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr

Paper ref

papersearchqa-learning-to-search-and-reason-over-scientific-papers-with-rlvr

arXiv id

2601.18207

Freshness

Generated at

2026-03-19T21:31:49.672Z

Evidence freshness

stale

Last verification

2026-03-19T21:31:49.672Z

Sources

References

Coverage

33%

Hash state

Lineage hash

a13e5439af8c7098bc20aeab2515a7354969f19db29a4134efd32b3de216f3cb

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

Source proof

Visual citation anchors from the paper document graph.

17 anchors

proof blockPage 1482%

This equation captures one of the core mathematical components of the system. setups, r is binary (r ∈{0, 1}) to indicate pass/fail

Page and bbox are available; crop image is pending.

proof blockPage 1482%

This equation captures one of the core mathematical components of the system. J(θ) = Ex∼D, y∼πθ(·|x)[ r ]

Page and bbox are available; crop image is pending.

proof blockPage 1582%

This equation captures one of the core mathematical components of the system. J(θ) = Ex∼D, τ∼πθ(·|x)[ r ].

Page and bbox are available; crop image is pending.

JSON-LD twin

The application/ld+json payload rendered for agents.

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

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

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Claim map

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References(44)

Related Papers

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline