Artificial Intelligence · 01.09.2026, 05:17 UTC
Ideation Arena: Evaluating LLM Generated Research Ideas with Battle-style Human Expert Assessment
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | arXiv cs.AI ↗ |
| Veröffentlicht | 01.09.2026 UTC |
Sicherheitsmeldung mit Schweregrad noch nicht bewertet. Technische Details im Tab „Originaltext“; empfohlene Schritte in der Checkliste.
arXiv:2608.29696v1 Announce Type: new Abstract: Evaluating research ideas generated by LLMs is difficult because their scientific value cannot be fully determined by objective criteria, and no single reference answer specifies what counts as a good idea. To address this challenge, we introduce Ideation Arena, a battle style platform that evaluates research ideas through pairwise human assessment. Ideation Arena evaluates ideas generated by 14 frontier LLMs and 5 research agent architectures built on 2 base models. To ensure a common starting point, Ideation Arena builds shared literature contexts from papers familiar to the participating researchers and provides the same contexts to all LLMs and agents. We collect over 6,000 double blind pairwise comparisons from 105 active computer science researchers and construct an Elo rating leaderboard of proposal-stage expert preferences in computer science under a shared closed-context protocol. We validate the rankings through interrater agreement and robustness analyses, showing that the leaderboard remains stable under changes in annotator composition and domain coverage. Our results show substantial variation in agent effectiveness, with some frameworks improving ideation quality over their backbones and others offering little benefit or even underperforming their base models. We further construct Ideation Arena Eval, a benchmark for assessing whether automated evaluators align with human preferences in research ideation. Experiments with current LLM judges show that they still cannot reliably reproduce expert preferences, …
Maßnahmen
⬇ Als MarkdownVerwandte Beiträge
- info When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning
- info BLOOM-WILT: Logit Tilting for Behaviour Elicitation in Automated LLM Auditing
- info Cross-Regional Grapevine Cold Hardiness Prediction via Learned Multimodal Latent Representations
- info Token-Efficient Data Reasoning Agents via Adaptive Structuring of Unstructured Data