AI CFD Scientist autonomously discovers a Spalart-Allmaras runtime correction reducing lower-wall Cf RMSE by 7.89% on the periodic hill at Reh=5600 while using a vision-language gate to detect 14 of 16 silent failures missed by solver checks.
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Knows uses a YAML sidecar specification to provide structured, agent-consumable representations of research papers, yielding large accuracy gains for small LLMs on comprehension tasks and rapid community adoption via a public hub.
ResearchArena shows that agent-generated papers fail top-tier acceptance standards primarily due to fabricated results, underpowered experiments, and plan-execution mismatches that vary sharply by agent.
Malicious actors could use AI agents to submit large numbers of fake papers, inflating the submission count and thereby raising the acceptance odds for a small set of chosen legitimate papers under stable conference acceptance rates.
LLMs fine-tuned on time-sliced paper data generate proposals with up to 10.6% higher Future Alignment Score against actual later publications, with human experts and real implementations confirming gains.
An agentic system produces traceable review packages and an un-finetuned 196B model using it covers more major issues than Gemini-3.1-Pro on 134 ICLR 2025 submissions while winning most blind comparisons to human committees.
GenoMAS deploys six specialized LLM agents with guided planning to preprocess transcriptomic data and identify genes, reaching 89.13% composite similarity and 60.48% F1 on the GenoTEX benchmark while outperforming prior methods.
AiraXiv is a proposed AI-driven platform for open preprints that supports human and AI authors with interactive UI and MCP-based interactions, validated by serving as the submission system for ICAIS 2025.
pAI/MSc is a customizable multi-agent system that reduces human steering by orders of magnitude when turning a hypothesis into a literature-grounded, mathematically established, experimentally supported manuscript draft in ML theory.
A survey organizing AI-powered research automation into five workflow stages, defining AutoResearch and Vibe Research, and proposing five evaluation dimensions while noting domain-conditioned limits on autonomy.
The paper delivers a stage-by-stage roadmap for AI in research, showing reliable assistance in retrieval and tool tasks but fragility in novelty and judgment, advocating human-governed collaboration.
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
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AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents
AI CFD Scientist autonomously discovers a Spalart-Allmaras runtime correction reducing lower-wall Cf RMSE by 7.89% on the periodic hill at Reh=5600 while using a vision-language gate to detect 14 of 16 silent failures missed by solver checks.
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Knows: Agent-Native Structured Research Representations
Knows uses a YAML sidecar specification to provide structured, agent-consumable representations of research papers, yielding large accuracy gains for small LLMs on comprehension tasks and rapid community adoption via a public hub.
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How Far Are We From True Auto-Research?
ResearchArena shows that agent-generated papers fail top-tier acceptance standards primarily due to fabricated results, underpowered experiments, and plan-execution mismatches that vary sharply by agent.
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Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents
Malicious actors could use AI agents to submit large numbers of fake papers, inflating the submission count and thereby raising the acceptance odds for a small set of chosen legitimate papers under stable conference acceptance rates.
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Learning to Predict Future-Aligned Research Proposals with Language Models
LLMs fine-tuned on time-sliced paper data generate proposals with up to 10.6% higher Future Alignment Score against actual later publications, with human experts and real implementations confirming gains.
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DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review
An agentic system produces traceable review packages and an un-finetuned 196B model using it covers more major issues than Gemini-3.1-Pro on 134 ICLR 2025 submissions while winning most blind comparisons to human committees.
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GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis
GenoMAS deploys six specialized LLM agents with guided planning to preprocess transcriptomic data and identify genes, reaching 89.13% composite similarity and 60.48% F1 on the GenoTEX benchmark while outperforming prior methods.
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AiraXiv: An AI-Driven Open-Access Platform for Human and AI Scientists
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pAI/MSc: ML Theory Research with Humans on the Loop
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AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery
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AI for Auto-Research: Roadmap & User Guide
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