HiMPO: Hindsight-Informed Memory Policy Optimization for Less-Entangled Credit in Long-Horizon Agents
arXiv:2606.16285v1 Announce Type: new Abstract: Long-horizon agents rely on memory mechanisms to compress interaction history, but optimizing memory writing faces a distinct credit assignment challenge: a memory update may be rewarded or penalized due to downstream tool failures, noisy observations, or reasoning errors rather than its own contribution. This causally entangled credit can lead agents to discard useful evidence or preserve irrelevant information. We propose HiMPO, a Hindsight-Informed Memory Policy Optimization framework for assigning less-entangled credit to memory-writing actio
The continuous drive for more autonomous and capable AI agents necessitates advancements in how these systems manage and learn from long-term dependencies and memory.
Improving credit assignment in memory-writing mechanisms is crucial for developing robust, less error-prone, and more efficient AI agents capable of complex, multi-step tasks.
This research introduces a novel framework that can lead to AI agents with more effective long-term memory, enhancing their ability to learn and adapt across extended interactions.
- · AI research institutions
- · Developers of autonomous agents
- · Software companies leveraging AI for complex workflows
- · High-performance computing providers
- · AI systems with poor memory management
- · Companies relying on less efficient agent designs
More capable AI agents will emerge, reducing the need for human intervention in certain long-horizon tasks.
This improved agency could accelerate the automation of knowledge work and complex operational processes.
The enhanced reliability of AI agents may lead to greater public trust and broader adoption across critical infrastructure and decision-making systems.
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Read at arXiv cs.CL