SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents

2026-05-18 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

SPIKE is an adaptive dual controller framework designed for cost-efficient, long-horizon agents in open-world games, addressing challenges like token and latency budgets. It features a Strategic Controller for low-frequency global planning, failure analysis, and recovery, and a Reactive Controller for fast local execution within a strict token budget. An Event Trigger dynamically decides when to switch between reactive and strategic reasoning based on visual changes, task progress, repeated actions, and failure signals. The framework also incorporates Hierarchical Memory, separating short-term experience in a State-Action Memory Bank (SA-MB) from structured evidence in a State Action Knowledge Graph (SA-KG). On the Lite-100 split of StarDojo, SPIKE achieved a 5.0 percentage point (38.5% relative) improvement in success rate and a 9.3 point (75.6% relative) improvement in Budgeted SR over baselines, while reducing token consumption by 54.9% and latency by 40.8%.

Key takeaway

For AI Engineers developing long-horizon agents in resource-constrained environments, SPIKE's dual-controller and adaptive reasoning approach offers a blueprint for improved performance and cost efficiency. You should consider implementing event-triggered strategic reasoning and hierarchical memory structures to reduce token consumption and latency while enhancing task success and failure recovery in complex, open-world simulations.

Key insights

Adaptive dual control with hierarchical memory significantly improves long-horizon game agent efficiency and success.

Principles

• Reuse strategic reasoning across stable segments.
• Reinvoke strategic reasoning at event boundaries.
• Separate short-term and structured long-term memory.

Method

SPIKE uses a Strategic Controller for global planning and a Reactive Controller for local execution, with an Event Trigger to adaptively switch control and Hierarchical Memory for context retrieval.

In practice

• Implement event-driven control for efficiency.
• Design distinct strategic and reactive components.
• Utilize hierarchical memory for context management.

Topics

• SPIKE Framework
• Dual Controller Architecture
• Long-Horizon Game Agents
• Cost-Efficient AI
• Hierarchical Memory

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

Related on AIssential

• SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents — SPIKE uses a dual-controller, event-triggered architecture with hierarchical memory for efficient long-horizon game agents.
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• Hierarchical Planning for Long Context Agents — Hip If uses hierarchical planning and information folding to manage long-context agents via on-policy reinforcement learning.
• Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP — Programmatic state abstraction and clean task decomposition are more effective than deep per-agent reasoning in adversarial POMDPs.
• Organize then Retrieve: Hierarchical Memory Navigation for Efficient Agents — HORMA uses a hierarchical, file-system-like memory to enable efficient, detailed context retrieval for LLM agents in long-horizon tasks.
• LongMINT: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems — Current memory-augmented agents struggle with interference and multi-target reasoning in long, dynamic contexts.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.