Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents
arXiv:2606.08151v1 Announce Type: new Abstract: Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action time. We present CICL, a decision-aware context layer that turns instance evidence into a context graph, routes deterministic, Opus-assisted, Qwen, Codex/GPT-5.5, and Qwen-QLoRA judgments through a shared eight-field schema, scores units by action shift, outcome uplift, necessity, and negative-transfer risk, and packs high-utility evidence as typed memory cards for a budgeted agent. The design sepa
The rapid development and deployment of LLM agents are exposing critical bottlenecks in their ability to effectively use context, driving immediate research into solutions.
Improving context selection and compression directly enhances the efficacy and reliability of AI agents, making them more capable of complex tasks and reducing computational waste.
The proposed CICL layer fundamentally alters how LLM agents process and utilize information, moving towards more intelligent, decision-aware memory management rather than brute-force context stuffing.
- · LLM developers
- · AI agent platforms
- · Enterprises adopting AI agents
- · GPU manufacturers (indirectly, due to more efficient use)
- · Legacy AI context management approaches
LLM agents become significantly more reliable and capable for a wider range of high-stakes applications.
The cost of deploying complex AI agents decreases as their efficiency in context handling improves, leading to broader adoption across industries.
More robust and less 'hallucination-prone' AI agents could accelerate shifts in white-collar work, automating tasks previously deemed too complex dueed to contextual fragility.
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Read at arXiv cs.AI