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ARXIV:2604.01520 · AGENTS · SUBMITTED 03 APR · 20:50 UTC · FRESHNESS STALE
ARXIV:2604.01520AGENTSSUBMITTED 03 APR · 20:50 UTCFRESHNESS STALELei Wang · Yuanzi Li · Jinchao Wu · Heyang Gao · Xiaohe Bo · Xu Chen · +1 at arXiv
A platform that uses LLM agents to automate social science research by simulating human behavior and generating reports.
Opportunity summary
Pain A platform that uses LLM agents to automate social science research by simulating human behavior and generating reports.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A platform that uses LLM agents to automate social science research by simulating human behavior and generating reports. Here we present S-Researcher, an LLM-agent-based platform that assists researchers in conducting social science research more…
Traditional social science research often requires designing complex experiments across vast methodological spaces and depends on real human participants, making it labor-intensive, costly, and difficult to scale. Here we present S-Researcher, an LLM-agent-based platform…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Leveraging this system, S-Researcher supports researchers in designing social experiments, simulating human behavior with LLM agents, analyzing results, and generating reports, forming a complete…
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
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A platform that uses LLM agents to automate social science research by simulating human behavior and generating reports.
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Paper Pack
10.48550/arXiv.2604.01520A platform that uses LLM agents to automate social science research by simulating human behavior and generating reports.
Abstract
Traditional social science research often requires designing complex experiments across vast methodological spaces and depends on real human participants, making it labor-intensive, costly, and difficult to scale. Here we present S-Researcher, an LLM-agent-based platform that assists researchers in conducting social science research more efficiently and at greater scale by "siliconizing" both the research process and the participant pool. To build S-Researcher, we first develop YuLan-OneSim, a large-scale social simulation system designed around three core requirements: generality via auto-programming from natural language to executable scenarios, scalability via a distributed architecture supporting up to 100,000 concurrent agents, and reliability via feedback-driven LLM fine-tuning. Leveraging this system, S-Researcher supports researchers in designing social experiments, simulating human behavior with LLM agents, analyzing results, and generating reports, forming a complete human-AI collaborative research loop in which researchers retain oversight and intervention at every stage. We operationalize LLM simulation research paradigms into three canonical reasoning modes (induction, deduction, and abduction) and validate S-Researcher through systematic case studies: inductive reproduction of cultural dynamics consistent with Axelrod's theory, deductive testing of competing hypotheses on teacher attention validated against survey data, and abductive identification of a cooperation mechanism in public goods games confirmed by human experiments. S-Researcher establishes a new human--AI collaborative paradigm for social science, in which computational simulation augments human researchers to accelerate discovery across the full spectrum of social inquiry.
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Proof status
unverified0 refs; 0 sources; 33% 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 4.0
PROBLEM
A platform that uses LLM agents to automate social science research by simulating human behavior and generating reports. Here we present S-Researcher, an LLM-agent-based platform that assists researchers in conducting social science research more efficiently and at greater scale...
METHOD
Traditional social science research often requires designing complex experiments across vast methodological spaces and depends on real human participants, making it labor-intensive, costly, and difficult to scale. Here we present S-Researcher, an LLM-agent-based platform that as...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Leveraging this system, S-Researcher supports researchers in designing social experiments, simulating human behavior with LLM agents, analyzing results, and generating reports, forming a complete human-AI...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 4.0/10.
scalability via a distributed architecture supporting up to 100,000 concurrent agents
Directly stated in abstract with specific numeric value
partial
generality via auto-programming from natural language to executable scenarios
Explicitly stated in abstract as a core requirement
partial
inductive reproduction of cultural dynamics consistent with Axelrod's theory
Directly stated validation case study with specific theoretical reference
partial
deductive testing of competing hypotheses on teacher attention validated against survey data
Explicitly stated validation with external data source
partial
abductive identification of a cooperation mechanism in public goods games confirmed by human experiments
Directly stated validation with experimental confirmation
partial
forming a complete human-AI collaborative research loop in which researchers retain oversight and intervention at every stage
Explicitly stated as a key feature of the collaborative paradigm
partial
making it labor-intensive, costly, and difficult to scale
Directly stated problem description in abstract
partial
We operationalize LLM simulation research paradigms into three canonical reasoning modes (induction, deduction, and abduction)
Explicitly stated methodological framework
partial
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Concepts
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A platform that uses LLM agents to automate social science research by simulating human behavior and generating reports.
Segment
Agents
Adoption evidence
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Commercial read
4.0/10 public viability
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CITED BY
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Build Passport
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status
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reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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Evidence
0 references, 0 sources, 33% evidence coverage.
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Regulatory load
missing
Current read
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Build Passport ledger does not include regulatory flags.
Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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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.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
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