What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents
arXiv:2607.08032v1 Announce Type: new Abstract: Large language models, and the agents built on them, spend an ever-growing share of their compute and memory on remembering: caching attention keys and values, carrying long prompts, maintaining recurrent state, and storing what happened in previous turns and sessions. Because none of this memory is free, four largely separate research communities have each learned to compact it. They evict or quantize the KV cache, prune or distill prompts, bound architectural state, and consolidate agent memory. We argue that these are instances of one problem:
The proliferation of increasingly complex and memory-intensive LLMs and AI agents necessitates novel solutions for efficient memory management to overcome computational and financial constraints.
Efficient memory compaction is critical for scaling LLM and agent capabilities, directly impacting their performance, cost, and ultimately, the viability of deploying sophisticated AI systems.
The explicit recognition and unification of diverse memory compaction techniques across different AI communities signal a concerted effort towards architectural and algorithmic optimization rather than just brute-force scaling.
- · AI model developers
- · Cloud computing providers (through efficiency gains)
- · AI agent developers
- · Hardware manufacturers (for specialized memory solutions)
- · Inefficient AI architectures
- · Organizations with unlimited compute budgets (as efficiency becomes a differenti
Improved memory efficiency leads to more powerful and cost-effective LLMs and AI agents.
This efficiency could enable new applications and agentic capabilities that were previously economically or computationally unfeasible.
Reduced computational overhead might lower the barrier to entry for developing and deploying advanced AI, expanding its reach and impact across various sectors.
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