arXiv AI
Jul 3
The Agentic Garden of Forking Paths
arXiv:2607. 01507v1 Announce Type: new Abstract: Empirical research rarely admits a unique analysis.
AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable
arXiv:2606. 11456v1 Announce Type: cross Abstract: The deployment of LLM-based agents in scientific analysis raises opposing concerns: that agents may reduce methodological diversity, or that they may amplify the analytic flexibility through which researchers reach motivated conclusions.
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arXiv AI
Jun 11
Preregistration for Experiments with AI Agents
arXiv:2606. 11217v1 Announce Type: cross Abstract: The proliferation of large language models (LLMs) and autonomous AI agents has given rise to a rapidly growing methodological paradigm: "in silico" behavioral experiments.
arXiv AI
Jul 10
3100 Opinions on Code Review in an AI World: Building Causal Theory from Practitioner Discourse
arXiv:2607. 07980v1 Announce Type: cross Abstract: Coding agents now author entire pull requests, and practitioners sharply disagree about what this does to code review: whether it becomes the bottleneck, whether human review is still necessary, and whether it quietly erodes the understanding that it once built.
arXiv AI
1d ago
Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems
arXiv:2608. 14667v1 Announce Type: new Abstract: Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous capabilities of "AI Scientists".
arXiv AI
Jun 8
Act As a Real Researcher: A Suite of Benchmarks Evaluating Frontier LLMs and Agentic Harnesses in Research Lifecycle
arXiv:2606. 07462v1 Announce Type: new Abstract: As foundation models advance and agent scaffolding becomes increasingly sophisticated, agents have demonstrated remarkable proficiency in complex, long-horizon coding tasks and even autonomous experiment execution.
arXiv AI
Aug 7
Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap
arXiv:2608. 05179v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly used across the scientific research lifecycle: ideation, literature search, experiment design and execution, analysis, manuscript drafting, and review.
Hugging Face Trending Papers
Jun 23
To Compare, or Not to Compare: On Methodological Practices in Evaluating Social Bias
As Large Language Models are increasingly deployed in critical applications, robustly evaluating their social biases is paramount. However, the current literature suffers from widespread methodological fragmentation, which yields contradictory conclusions.
arXiv AI
Jul 24
pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development
arXiv:2607. 21268v1 Announce Type: cross Abstract: In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-readable correctness signal exists.
arXiv AI
Jun 6
Geographic Bias and Diversity in AI Evaluation
arXiv:2606. 05187v1 Announce Type: cross Abstract: Among the many challenges hindering the responsible development and deployment of AI, arguably none has faced more intense scrutiny than bias in its various forms.
arXiv AI
Jun 16
AgentFairBench: Do LLM Agents Discriminate When They Act?
arXiv:2606. 16723v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly take actions (screening applicants, recommending credit, triaging patients), yet fairness for LLMs is still measured by grading answers.
arXiv AI
Jul 9
Participatory provenance as representational auditing for AI-mediated public consultation
arXiv:2604. 20711v2 Announce Type: replace Abstract: Artificial intelligence is increasingly deployed to synthesize large-scale public input in policy consultations and participatory processes.
arXiv AI
Jul 1
RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studies
arXiv:2603. 11001v3 Announce Type: replace-cross Abstract: Human uplift studies, or studies that measure the effects of AI access on human performance via randomized controlled trials (RCT) or similar methodologies, increasingly inform frontier AI governance and deployment decisions.