Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development
Autonomous agents are increasingly capable of improving models, systems, and other technical artifacts through long-horizon experimentation. To understand the current state of this capability, however, evaluation must go beyond final scores, which neither reveal where progress is gained or lost nor indicate whether accumulated experience improves later decisions. We therefore present a systematic evaluation of seven frontier models on 36 long-horizon tasks based on a new framework that uses rule
Record details
Published: 13 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy
Retrieved: 14 August 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Enhancing Virtual Agents through SLMs and Edge-Computing: An Exploratory Evaluation of Think and Memory Processes
arXiv cs.AI · 13 August 2026
Deliberate Practice: Learning Robot Skills under a Budget
arXiv cs.AI · 13 August 2026
ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models
arXiv cs.AI · 13 August 2026
Heterogeneity-Aware Belief Synchronization for Semantic Communication in AI-Native 6G Networks
arXiv · 13 August 2026
UniTexture: Cross-Task Universal Adversarial Textures for Vision-Language-Action Models
arXiv · 13 August 2026
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
arXiv · 13 August 2026
How to cite this record
ethics.ai (13 August 2026), “Beyond Final Scores: A Systematic Evaluation of Agents for Long-Horizon AI Research and Development,” evidence record 19442, https://ethics.ai/record/19442 (originally published by arXiv cs.AI).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.