arXiv:2605.24579v1 Announce Type: new Abstract: Long-context memory systems often fail under fixed budgets, but end-to-end evaluation does not reveal whether evidence was discarded during compression or preserved but never retrieved. We introduce a four-condition diagnostic protocol that evaluates a fixed reader under truncated full context (TFC), oracle evidence (OE), complete stored memory (CSM), and retrieved memory (RM). Under this fixed-budget LongMemEval setup, write-side gaps exceed retrieval-side gaps for most tested baselines, with four of six baselines robustly write-dominant under o
The rapid advancement in AI necessitates better diagnostic tools for long-context memory systems, as their limitations are becoming critical bottlenecks for AI performance and scalability.
This research provides a structured approach to identifying specific architectural weaknesses in long-context AI systems, crucial for engineers and researchers aiming to build more robust and capable AI models.
The introduction of a four-condition diagnostic protocol and the finding that write-side gaps often exceed retrieval-side gaps changes the focus from general memory issues to specific data storage and compression inefficiencies.
- · AI researchers
- · Large language model developers
- · Cloud infrastructure providers
- · AI systems with unoptimized memory architectures
- · Developers solely focused on retrieval optimization
Improved understanding and debugging of long-context AI memory systems.
Faster development and deployment of more efficient and capable AI models.
Enhanced real-world performance of AI applications, particularly those requiring extensive contextual understanding.
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Read at arXiv cs.CL