InnovatorBench: Evaluating LLM Agents' Ability to Conduct Innovative Research

Forum topic · 小凯 · 2026-07-05

Summary

InnovatorBench (arXiv:2510.27598, October 2025) is a benchmark proposed by Yunze Wu, Dayuan Fu, Weiye Si, Zhen Huang, Mohan Jiang, Keyu Li and colleagues (16 authors) to evaluate whether LLM-based agents can perform innovative research rather than only executing well-defined tasks. The forum post cataloging this paper situates it within the broader landscape of agentic search and LLM evaluation, alongside related benchmarks such as AgentBoard and ARES. However, the post itself is largely a template entry: it does not reproduce the paper's actual abstract, experimental numbers, or task design, and several sections are placeholders. What can be stated factually is the paper's title, author list, arXiv identifier, and its positioning as an evaluation resource for research-capable agents. Readers interested in quantitative results should consult the original PDF at arxiv.org/abs/2510.27598, since the forum entry explicitly warns that specific numeric results must be verified against the source. The entry also provides generic context on agentic retrieval, RAG evaluation, and engineering checklists for search and recommendation systems.

InnovatorBench: Evaluating Agents' Ability to Conduct Innovative LLM Research

Source: arXiv:2510.27598, October 2025 Authors: Yunze Wu, Dayuan Fu, Weiye Si, Zhen Huang, Mohan Jiang, Keyu Li, et al. (16 authors total) Category: Evaluation of Search Engines / LLM Agent Benchmarks

Key points

InnovatorBench is a benchmark designed to assess whether LLM-based agents can conduct *innovative research* — i.e., go beyond executing predefined tasks and demonstrate open-ended research capability.
The paper is indexed under an evaluation-focused awesome list, cross-referenced with other agent and RAG evaluation benchmarks (AgentBoard, ARES, Agent-X).
Note: This forum post is largely a template entry. It does not reproduce the paper's actual abstract, benchmark task definitions, evaluation metrics, or experimental results. Several fields are placeholders, and the post explicitly advises verifying quantitative claims against the original PDF.

Context from the entry

The catalog places InnovatorBench at the intersection of agentic search and large-scale search/recommendation systems, where evaluation is shifting from static retrieval metrics (nDCG, MRR) toward process-oriented measures such as task success rate, citation accuracy, and multi-hop reasoning quality.

Generic context provided by the entry (not specific to InnovatorBench's experiments):

Neural IR has evolved from BM25 → dense bi-encoders → cross-encoders → generative retrieval → LLM agents.
Agentic paradigms make the *retrieval strategy itself* a learnable object: whether to retrieve, how many times, and which tools to call.
Industrial constraints (latency, cost, safety, hallucination) remain hard requirements beyond academic benchmarks.

Related entries referenced

Evaluation of Retrieval-Augmented Generation: A Survey (arXiv:2405.07437)
ARES: An Automated Evaluation Framework for RAG (arXiv:2311.09476)
AgentBoard: Analytical Evaluation Board of Multi-turn LLM Agents (arXiv:2401.13178)
Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agents (arXiv:2505.24876)
A Dataset of Information-Seeking Questions and Answers Anchored in Research Abstracts (arXiv:2105.03011)

Reader guidance

Researchers: consult the original paper directly for benchmark construction details, agent baselines, and results; check whether statistical significance and compute cost are reported.
Engineers: evaluate integration cost of any released modules (planners, retrievers, tools) against existing stacks.
Everyone: treat the template sections above (datasets, metrics, ablations) as generic background, not as findings of this specific paper.