| name | agentic-science-cognitive-accumulation |
| title | Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.10402 |
| keywords | ["agentic-science","long-horizon","context-management","knowledge-distillation","ML-engineering"] |
| description | Enables agents to maintain strategic coherence over extended experimental cycles through hierarchical cognitive caching that distills execution traces into stable knowledge, achieving 56.44% on MLE-Bench within 24-hour budgets. |
Overview
Implement a hierarchical cognitive caching system for autonomous agents conducting multi-day ML engineering experiments. Rather than maintaining static context windows, the system dynamically distills execution traces into reusable knowledge representations, allowing agents to decouple immediate execution from long-term experimental strategy.
When to Use
- For multi-day autonomous research or engineering projects requiring hundreds of experimental steps
- When agents need to explore high-dimensional problem spaces beyond human precedent
- For ML hyperparameter tuning, architecture search, or scientific discovery tasks
- When you need agents to learn from prior experimental failures and optimize future attempts
When NOT to Use
- For short-horizon tasks (single-session experiments under 1 hour)
- When all relevant context fits in static context windows
- For real-time systems where knowledge consolidation adds unacceptable latency
- For tasks with simple, deterministic experimental spaces
Key Technical Components
Hierarchical Cognitive Caching (HCC)
Implement multi-tier knowledge distillation that converts verbose execution traces into compressed representations.
class HierarchicalCache:
def __init__(self):
self.immediate_context = {}
self.session_knowledge = {}
self.cross_task_insights = {}
def consolidate_traces(self, execution_trace, level="session"):
"""Distill trace into stable knowledge at specified level"""
if level == "session":
pattern = self.extract_principles(execution_trace)
.session_knowledge.update(pattern)
level == :
insight = .abstract_to_meta_strategy(execution_trace)
.cross_task_insights.update(insight)
