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AI Idea Bench 2025: AI Research Idea Generation Benchmark
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Large-scale Language Models (LLMs) have revolutionized human-AI interaction and achieved significant success in the generation of novel ideas. However, current assessments of idea generation overlook crucial factors such as knowledge leakage in LLMs, the absence of open-ended benchmarks with grounded truth, and the limited scope of feasibility analysis constrained by prompt design. These limitations hinder the potential of uncovering groundbreaking research ideas. In this paper, we present AI Idea Bench 2025, a framework designed to quantitatively evaluate and compare the ideas generated by LLMs within the domain of AI research from diverse perspectives. The framework comprises a comprehensive dataset of 3,495 AI papers and their associated inspired works, along with a robust evaluation methodology. This evaluation system gauges idea quality in two dimensions: alignment with the ground-truth content of the original papers and judgment based on general reference material. AI Idea Bench 2025's benchmarking system stands to be an invaluable resource for assessing and comparing idea-generation techniques, thereby facilitating the automation of scientific discovery.
Forward citations
Cited by 7 Pith papers
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ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes
Conference accept/reject outcomes yield 15 operational ideation patterns that, as an LLM skill suite, improve automated-judged research-proposal quality over no-skill and generic-skill baselines.
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AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents
AD-Bench evaluates LLM agents on real advertising analytics tasks using replayed expert tool-call trajectories, and finds even top models drop sharply on hard multi-step queries.
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Creativity in LLM-based Multi-Agent Systems: A Survey
A taxonomy-driven survey organizes the emerging field of creativity in LLM-based multi-agent systems across workflows, techniques, personas, datasets, and evaluation metrics.
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SciDER: Scientific Data-centric End-to-end Researcher
SciDER is a data-centric multi-agent system that automates ideation, raw-data analysis, experiment coding, and critique, with reported leading results on six scientific-agent benchmarks.
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SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents
SafeScientist adds prompt, discussion, tool-use, and output-review safety checks to an AI scientist, with a new domain benchmark, but its reported evaluation is internally inconsistent.
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InternAgent: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification
A closed-loop LLM-agent framework that auto-generates research ideas and code, reported to improve baseline performance on all 12 tasks it was tested on.
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AI for Auto-Research: Roadmap & User Guide
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.
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