aiqbench analysis
MemAgent addresses the critical bottleneck of long-context processing by introducing an agent workflow that segments text and updates memory via a novel overwrite strategy. By extending DAPO for independent-context multi-conversation generation, it achieves near-linear scalability, maintaining under 5% performance loss when extrapolating from 32K to 3.5M tokens. This research offers a significant architectural advance for handling infinite-length documents without the typical degradation seen in current efficient attention methods.
MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
arXiv:2507.02259v2 Announce Type: replace-cross Abstract: Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation remains the ultimate challenge in long-text processing. We directly optimize for long-text tasks in an end-to-end fashion and introduce a novel agent workflow, MemAgent, which reads text in segments and updates the memory using an overwrite strategy. We extend the DAPO algorithm to facilitate training via independent-context multi-conversation generation. MemAgent has demonstrated superb long-context capabilities, being able to extrapolate from an 8K context trained on 32K text to a 3.5M QA task with performance loss < 5% and achieves 95%+ in 512K RULER test.
Abstract:Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation remains the ultimate challenge in long-text processing. We directly optimize for long-text tasks in an end-to-end fashion and introduce a novel agent workflow, MemAgent, which reads text in segments and updates the memory using an overwrite strategy. We extend the DAPO algorithm to facilitate training via independent-context multi-conversation generation. MemAgent has demonstrated superb long-context capabilities, being able to extrapolate from an 8K context trained on 32K text to a 3.5M QA task with performance loss < 5% and achieves 95%+ in 512K RULER test.
Accepted to ICLR 2026 as an Oral presentation. OpenReview: this https URL Project page: this https URL
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2507.02259 [cs.CL]
(or arXiv:2507.02259v2 [cs.CL] for this version)
Full text under the publisher's open licence · 141 words
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