ResearchGym: Evaluating Language Model Agents on Real-World AI Research

Aniketh Garikaparthi, Manasi Patwardhan, Arman Cohan

Abstract

We introduce ResearchGym, a benchmark and execution environment for evaluating AI agents on end-to-end research. In a controlled evaluation of an agent powered by GPT-5, we observe a sharp capability--reliability gap. ResearchGym provides infrastructure for systematic evaluation and analysis of autonomous agents on closed-loop research.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper

Eigenvector Research — Marco van Hurne

How this paper contributes to solving the Zone III problem (PASF-PADE)

This paper directly addresses one of the core structural challenges in Zone III deployments. The research on AI Agents, Language Models, Evaluation provides evidence-based foundations that enterprise architects cannot ignore when designing long-horizon autonomous workflows. The findings challenge the assumption that a base language model — however capable — can handle the complexity of durable, governed, multi-step execution without explicit architectural intervention. For Zone III practitioners, this paper belongs in the required reading list.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

AI AgentsLanguage ModelsEvaluationBenchmarks

Relevance Scores

Long-Horizon Score85

Enterprise Score80

Completeness75

Paper Info

Year2026

Venue

Type

ChapterCh. 7

Authors3

Zone III Analysis