PaperBench: OpenAI Benchmark for Evaluating AI Agents on Research Replication
OpenAI introduces PaperBench, a benchmark designed to evaluate AI agents' ability to replicate state-of-the-art AI research papers end-to-end. The benchmark targets a high-complexity capability: reproducing experimental results from frontier AI research, which requires code generation, experimental design, and scientific reasoning. This positions PaperBench as a tool for tracking progress toward autonomous AI research agents.
Related guides (4)
Related events (8)
Introducing HealthBench
OpenAI has released HealthBench, a new evaluation benchmark designed to assess AI model performance and safety in healthcare settings. The benchmark was developed with input from over 250 physicians and targets realistic clinical scenarios. It aims to establish a shared standard for measuring how well AI models handle health-related tasks.
Benchmark Agent: Autonomous system for end-to-end benchmark construction
Researchers introduce Benchmark Agent, a fully autonomous agentic system that orchestrates the complete benchmark construction pipeline — from query analysis and subtask design to data annotation and quality control. The system was used to produce 15 benchmarks spanning text understanding, multimodal understanding, and domain-specific reasoning, with evaluation via human judges, LLM-as-a-judge, and consistency checks. The work addresses two persistent problems in the field: the labor intensity of benchmark creation and rapid performance saturation after release. Code and a demo will be publicly released.
OpenAI introduces LifeSciBench, a life sciences AI evaluation benchmark
OpenAI has released LifeSciBench, a benchmark designed to evaluate AI systems on real-world life science research tasks and decisions. The benchmark is described as expert-authored and expert-reviewed, targeting domain-specific evaluation in biology and related fields. This addresses a gap in specialized scientific benchmarking for AI systems.
Automated Benchmark Auditing for AI Agents and Large Language Models (ABA)
The paper introduces Auto Benchmark Audit (ABA), an agentic framework that systematically audits AI benchmark tasks for issues such as ambiguous specifications, environment conflicts, and incorrect ground truths. Applied to 168 benchmarks across nine domains including NeurIPS publications, ABA identifies critical issues in over 25.7% of evaluated tasks. The authors demonstrate that filtering out flawed tasks materially shifts model rankings and improves average performance on SWE-bench Verified and Terminal-Bench 2 by 9.9% and 9.6% respectively, indicating that current benchmark scores are significantly distorted by task quality problems. The agentic tool and annotations are released publicly.
AARRI-Bench evaluates frontier LLMs and agents on granular research-intern-level tasks
Researchers introduce AARR (Act As a Real Researcher), a new benchmark series targeting whether AI agents can emulate the professionalism, thoroughness, and nuanced judgment of human researchers in granular research scenarios—not just macro-level task execution. The first benchmark, AARRI-Bench, tests frontier models and agentic harnesses, finding that even the best configuration (Mini-SWE-Agent with Claude Opus 4.7) achieves only 68.3% success, frequently missing subtle but critical details obvious to human researchers. The work argues that closing the gap requires deeper modeling of research behavior rather than more complex scaffolding.
New paper: AI agents that matter
A paper from the AI Snake Oil / Normal Tech group critiques current AI agent benchmarking and evaluation practices. The work argues that existing agent benchmarks are poorly designed for assessing real-world utility, and calls for rethinking how agent performance is measured. The commentary targets the gap between benchmark scores and practical deployment value.
AssetOpsBench: Bridging the Gap Between AI Agent Benchmarks and Industrial Reality
IBM Research introduces AssetOpsBench, a benchmark designed to evaluate AI agents on industrial asset operations tasks, hosted on Hugging Face. The benchmark targets the gap between existing general-purpose agent benchmarks and real-world industrial deployment scenarios. It provides a playground environment for testing agent capabilities in enterprise/industrial contexts.
Can AI automate computational reproducibility?
This commentary introduces a new benchmark aimed at measuring AI's ability to automate computational reproducibility in scientific research. The piece examines whether AI systems can reliably re-execute and validate scientific computations, a key bottleneck in research integrity. It frames reproducibility automation as a concrete, measurable capability for evaluating AI's impact on science.