RIP Classic Reasoning Benchmarks. What’s Next?

2026-01-28 · Source: Epoch AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Research Methodology & Innovation · Depth: Advanced, medium

Summary

Classic AI reasoning benchmarks, characterized by text-only tasks, easy grading, short completion times, and human expert superiority, are becoming obsolete due to saturation, as seen with GPQA. Epoch AI's Gradient Updates proposes a new recipe for benchmarks by relaxing one of these traditional constraints. Future benchmarks should explore multimodal reasoning, such as the IKEA furniture assembly task where top models scored around 40%, or push longer time horizons, like sequential game runs or multi-week software engineering projects. Another direction involves accepting hard-to-grade outputs for real-world relevance, exemplified by CRUX evaluations and AI solutions graded in the 2025 International Math Olympiad. Finally, benchmarks should target well above human expert ability, focusing on open math problems like FrontierMath: Open Problems or optimization challenges such as PostTrainBench, where current AI scores are around 51.1%. These new approaches aim to diagnose AI failures in complex, real-world scenarios.

Key takeaway

For AI scientists and ML engineers developing advanced models, you should prioritize creating or utilizing next-generation reasoning benchmarks. Focus on multimodal tasks, extended time horizons, or problems requiring human-like judgment for grading. This shift will better diagnose current AI limitations and guide development towards more robust, real-world capable systems, moving beyond easily saturated classic benchmarks.

Key insights

Classic AI reasoning benchmarks are saturated; new benchmarks must relax constraints on modality, time, grading, or human superiority.

Principles

• Benchmarks must evolve beyond text-only tasks.
• Longer time horizons reveal deeper reasoning.
• Real-world relevance often means hard-to-grade outputs.

Method

The proposed method for creating new AI reasoning benchmarks involves relaxing one of four classic constraints: incorporate multimodal inputs, extend time horizons for tasks, accept complex or hard-to-grade outputs, or target problems exceeding human expert ability.

In practice

• Develop multimodal tasks like IKEA assembly.
• Design sequential game-play benchmarks.
• Collaborate with professions for human-graded evaluations.

Topics

• AI Benchmarking
• Multimodal AI
• Reasoning Tasks
• Long-Context AI
• Evaluation Metrics
• Open Problems

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• Scientists built the hardest AI test ever and the results are surprising — New benchmarks are essential to accurately measure AI capabilities beyond pattern recognition and identify gaps in expert-level understanding.
• Are AI benchmarks doomed? — AI benchmarks, despite rapid saturation, remain vital for evaluating capabilities, with future development using AI and human judgment.
• We’ve Been Optimizing for the Wrong Thing: What Stanford’s Latest AI Paper Reveals About True… — AI benchmarks reward pattern recognition, not true reasoning, leading to "application-specific failure" in real-world tasks.
• Should we benchmark conceptual capabilities using judgment prediction tasks? — Benchmarking subjective AI conceptual reasoning benefits from models predicting specific human judgments to isolate capability.
• The Next AI Breakthrough May Not Be a Bigger Model — Future AI breakthroughs may stem from extended inference-time computation rather than solely increasing model parameters.
• AI benchmarks are broken. Here’s what we need instead. — Current AI benchmarks misrepresent real-world performance by ignoring human teams and organizational context.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Epoch AI.