AutoLab benchmark evaluates frontier models on ultra long-horizon iterative research and engineering tasks
AutoLab is a new benchmark of 36 expert-curated tasks across system optimization, puzzle-solving, model development, and CUDA kernel optimization, designed to test agents on sustained closed-loop improvement under wall-clock budgets rather than single-turn or short-horizon settings. Evaluation of 17 frontier models finds that persistence in iterative benchmarking and feedback incorporation — not initial attempt quality — is the dominant success predictor. Claude Opus 4.6 stands out as the strongest performer, while most models including proprietary ones either terminate early or exhaust budgets with minimal progress. The benchmark, harness, and task artifacts are open-sourced.
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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.
Opus 4.6, Codex 5.3, and the post-benchmark era
A Interconnects commentary piece examining how to compare frontier AI models in 2026, using Anthropic's Opus 4.6 and OpenAI's Codex 5.3 as case studies. The piece appears to argue that traditional benchmarks are no longer sufficient for distinguishing model capabilities at the frontier. This reflects a broader industry shift toward more nuanced, task-specific evaluation methods.
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.
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.
MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
OpenAI introduces MLE-bench, a benchmark designed to measure AI agent performance on machine learning engineering tasks. The benchmark draws from Kaggle competitions to evaluate agents on realistic ML engineering workflows. Initial results show that current agents, including those powered by o1-preview, achieve competitive performance on a subset of tasks but fall well short of top human competitors. The benchmark is intended to track progress in agentic ML capabilities over time.
DeepSWE, ProgramBench, and ITBench-AA emerge as harder successors to SWE-bench for agent evaluation
Three new benchmarks — DeepSWE (by Datacurve), ProgramBench (Meta/Stanford/Harvard), and ITBench-AA (IBM/Artificial Analysis) — are positioned as more rigorous replacements for the SWE-bench family, which models have largely saturated. DeepSWE tests feature implementation using private codebases and human-written problems; ProgramBench evaluates agents' ability to recreate functional programs from scratch; ITBench-AA measures root-cause diagnosis in real-world IT incident scenarios. Current top performers include GPT-5.5 (70% on DeepSWE), Claude Opus 4.7 (46.7% on ITBench-AA), and Claude Opus 4.7 (3% on ProgramBench at the 95% pass threshold), illustrating that even frontier models have substantial headroom.
RealClawBench: Live benchmark framework built from real developer-agent sessions
RealClawBench is a new benchmark framework that converts real OpenClaw developer-agent sessions into reproducible, automatically scored evaluation tasks. It addresses realism gaps in existing agent benchmarks through reconstructed execution environments and deterministic verifiable scorers, releasing 281 executable tasks sampled to preserve the source session distribution. Evaluation of 14 contemporary models shows the best system solves only 65.8% of tasks, indicating substantial headroom on realistic developer-agent workloads.
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.