LANTERN: Layered Archival and Temporal Episodic Retrieval Network for Long-Context LLM Conversations
arXiv:2606.05182v1 Announce Type: new Abstract: Large language models discard critical details when conversation history is compacted to fit within finite context windows. We present LANTERN (Layered Archival aNd Temporal Episodic Retrieval Network), a lightweight memory layer that proactively archives every conversation turn and restores relevant details after compaction via hybrid retrieval -- requiring zero LLM calls and adding fewer than 25ms of latency per turn. On 94 real multi-turn conversations (1,894 ground-truth facts, human-validated at kappa=0.81), LANTERN-Rerank recovers 78.3% of
The proliferation of long-context LLMs highlights an urgent need for efficient memory management techniques to maintain conversational coherence and depth.
This development allows LLMs to sustain longer, more complex conversations without losing critical information, which is vital for advanced AI applications and agentic systems.
LLMs can now theoretically overcome context window limitations more effectively and without significant performance overhead, improving their utility in extended interactions.
- · LLM developers
- · AI agent platforms
- · Enterprise AI solutions
- · Users of conversational AI
- · Solutions based on frequent context window compaction
- · LLM architectures without robust memory management
LLMs become more capable of engaging in extended, context-aware dialogues for complex tasks.
This capability accelerates the development and deployment of more sophisticated AI agents that require long-term memory.
Improved LLM memory could lead to widespread adoption of AI agents in roles currently requiring human expertise, impacting white-collar work.
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Read at arXiv cs.CL