Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory
📰 ArXiv cs.AI
Learn how to optimize agent memory using a rate-distortion framework to preserve decision-relevant information, crucial for long-horizon language agents
Action Steps
- Apply the rate-distortion framework to agent memory design
- Configure the framework to prioritize decision-relevant information
- Test the framework using simulated environments
- Evaluate the framework's performance using decision-quality metrics
- Compare the results with existing memory mechanisms
Who Needs to Know This
AI researchers and engineers working on long-horizon language agents can benefit from this framework to improve agent decision-making under limited runtime memory
Key Insight
💡 Agent memory should prioritize preserving distinctions between histories that support good decisions, rather than faithfully describing the past
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🤖 Optimize agent memory with a rate-distortion framework to preserve decision-relevant info! 📊
Key Takeaways
Learn how to optimize agent memory using a rate-distortion framework to preserve decision-relevant information, crucial for long-horizon language agents
Full Article
Title: Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory
Abstract:
arXiv:2605.10870v1 Announce Type: new Abstract: Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, salience, or summary quality. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must remain separated under a fixed budget to support good decisions. We cast this as a decis
Abstract:
arXiv:2605.10870v1 Announce Type: new Abstract: Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, salience, or summary quality. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must remain separated under a fixed budget to support good decisions. We cast this as a decis
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