arXiv:2607.02255v1 Announce Type: cross Abstract: Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls, and reflections to every prompt, which makes prior context easy to access but also turns it into a jumbled mixture in which the effect of any single memory component is hard to isolate. We introduce and instrument an alternative bounded contract: every decision is made from a fresh user message assembled by typed retrieval, with no raw cross-decision transcript appended. The prompt thu
Ongoing research into LLM agency is rapidly progressing, making memory management a critical bottleneck for deploying robust, long-horizon agents.
Improving LLM agent memory and decision-making directly impacts their reliability and capability for complex, multi-step tasks, accelerating their practical application in various domains.
The proposed 'bounded contract' memory system offers an alternative to undifferentiated context, potentially leading to more efficient, predictable, and scalable LLM agents.
- · AI developers
- · Automation software companies
- · Enterprises adopting AI agents
- · Legacy process automation
- · Undifferentiated SaaS providers
More capable and robust LLM agents will emerge for complex, multi-step tasks.
This improved agency will enable automation of previously human-intensive white-collar workflows, leading to significant productivity gains.
The collapse of certain workflow layers could disrupt existing business models and create new industries centered around agentic orchestration.
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Read at arXiv cs.CL