Memory Control for Long-Horizon Agents
Free while signed in. Answers cite the passages they came from.
This paper introduces the Agent Cognitive Compressor (ACC), a bio-inspired mechanism that addresses degraded agent behavior in long multi-turn workflows caused by loss of constraint focus, error accumulation, and memory-induced drift. ACC replaces continuous transcript retention with a bounded internal state that updates incrementally during each interaction turn. - **The problem with unbounded context:** Traditional approaches using transcript replay or retrieval-based memory systems create unbounded context growth and introduce vulnerabilities to corrupted information, causing agent performance to degrade over extended interactions. - **Bio-inspired bounded memory:** Drawing from biological memory systems, ACC maintains a bounded internal state rather than continuously growing context, enabling stable performance without the computational costs of ever-expanding transcripts. - **Agent-judge evaluation framework:** The authors developed an agent-judge-driven evaluation framework to assess both task success and memory-related anomalies across extended workflows in IT operations, cybersecurity response, and healthcare contexts. - **Reduced cognitive drift:** ACC demonstrated substantially improved stability in multi-turn interactions, showing significantly reduced hallucination and cognitive drift compared to traditional transcript replay and retrieval-based systems. - **Practical foundation:** The research suggests that implementing cognitive compression principles provides a practical foundation for developing reliable long-horizon AI agent systems that maintain consistent behavior over extended deployments.
The problem with unbounded context: Traditional approaches using transcript replay or retrieval-based memory systems create unbounded context growth and introduce vulnerabilities to corrupted information, causing agent performance to degrade over extended interactions.
Bio-inspired bounded memory: Drawing from biological memory systems, ACC maintains a bounded internal state rather than continuously growing context, enabling stable performance without the computational costs of ever-expanding transcripts.
Agent-judge evaluation framework: The authors developed an agent-judge-driven evaluation framework to assess both task success and memory-related anomalies across extended workflows in IT operations, cybersecurity response, and healthcare contexts.
Reduced cognitive drift: ACC demonstrated substantially improved stability in multi-turn interactions, showing significantly reduced hallucination and cognitive drift compared to traditional transcript replay and retrieval-based systems.
Practical foundation: The research suggests that implementing cognitive compression principles provides a practical foundation for developing reliable long-horizon AI agent systems that maintain consistent behavior over extended deployments.
Abstract
AI agents are increasingly used in long, multi-turn workflows in both research and enterprise settings. As interactions grow, agent behavior often degrades due to loss of constraint focus, error accumulation, and memory-induced drift. This problem is especially visible in real-world deployments where context evolves, distractions are introduced, and decisions must remain consistent over time. A common practice is to equip agents with persistent memory through transcript replay or retrieval-based mechanisms. While convenient, these approaches introduce unbounded context growth and are vulnerable to noisy recall and memory poisoning, leading to unstable behavior and increased drift. In this work, we introduce the Agent Cognitive Compressor (ACC), a bio-inspired memory controller that replaces transcript replay with a bounded internal state updated online at each turn. ACC separates artifact recall from state commitment, enabling stable conditioning while preventing unverified content from becoming persistent memory. We evaluate ACC using an agent-judge-driven live evaluation framework that measures both task outcomes and memory-driven anomalies across extended interactions. Across scenarios spanning IT operations, cybersecurity response, and healthcare workflows, ACC consistently maintains bounded memory and exhibits more stable multi-turn behavior, with significantly lower hallucination and drift than transcript replay and retrieval-based agents. These results show that cognitive compression provides a practical and effective foundation for reliable memory control in long-horizon AI agents.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack