Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

2026-01-15

AI Summary

This work addresses the challenge of maintaining strategic coherence and iterative refinement in artificial intelligence systems over ultra-long scientific research cycles. To this end, it introduces the ML-Master 2.0 agent, which reconfigures context management as a cognitive accumulation process through a Hierarchical Cognitive Cache (HCC) architecture. Inspired by multi-level memory systems, HCC dynamically distills execution trajectories into stable knowledge, decoupling immediate actions from long-term strategy and thereby transcending the limitations of static context windows. Integrated with dynamic knowledge distillation, cross-task experience consolidation, and large language model–driven autonomous experiment planning, the proposed approach achieves a state-of-the-art medal rate of 56.44% on MLE-Bench under a 24-hour budget, demonstrating for the first time the feasibility of fully autonomous, ultra-long-horizon scientific discovery.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Life-Long and Continual Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic coherence and iterative correction over experimental cycles spanning days or weeks. While Large Language Models (LLMs) have demonstrated prowess in short-horizon reasoning, they are easily overwhelmed by execution details in the high-dimensional, delayed-feedback environments of real-world research, failing to consolidate sparse feedback into coherent long-term guidance. Here, we present ML-Master 2.0, an autonomous agent that masters ultra-long-horizon machine learning engineering (MLE) which is a representative microcosm of scientific discovery. By reframing context management as a process of cognitive accumulation, our approach introduces Hierarchical Cognitive Caching (HCC), a multi-tiered architecture inspired by computer systems that enables the structural differentiation of experience over time. By dynamically distilling transient execution traces into stable knowledge and cross-task wisdom, HCC allows agents to decouple immediate execution from long-term experimental strategy, effectively overcoming the scaling limits of static context windows. In evaluations on OpenAI's MLE-Bench under 24-hour budgets, ML-Master 2.0 achieves a state-of-the-art medal rate of 56.44%. Our findings demonstrate that ultra-long-horizon autonomy provides a scalable blueprint for AI capable of autonomous exploration beyond human-precedent complexities.

Problem

• ultra-long-horizon autonomy
• agentic science
• machine learning engineering
• cognitive accumulation
• delayed-feedback environments

Innovation

• Hierarchical Cognitive Caching
• Ultra-Long-Horizon Autonomy
• Cognitive Accumulation
• Agentic Science
• Context Management