Long-Horizon Scaling: How Model Capabilities Shape the Returns to Computation
cs.LG
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/Chihaya-Anon-chan/long-horizon-scaling
Sources
- Scaling Laws for Neural Language Models
- EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments
- You Don't Need to Run Every Eval
- Selecting Computations: Theory and Applications
- AutoLab: Can Frontier Models Solve Long-Horizon Auto Research and Engineering Tasks?
- Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
- NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
- SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
- APEX-Agents
- FrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Tasks
- MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
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