@inproceedings{lu-etal-2026-llm-rnn,
title = "{LLM}-as-{RNN}: Feedback-Driven Memory Rewriting for Online Learning and Sequential Prediction",
author = "Lu, Yuxing and
Tamo, J. Ben and
Fang, Jinrui and
Zhao, Weichen and
Sun, Nan and
Zhao, Xukai and
Zhong, Yishan and
Shi, Wenqi and
Wang, Jinzhuo and
Wang, May Dongmei",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.222/",
pages = "4977--4997",
ISBN = "979-8-89176-439-2",
abstract = "Large language models (LLMs) are strong sequence predictors, but standard context accumulation offers no explicit mechanism for revising the memory carried between predictions. We propose LLM-as-RNN, an inference-time framework for online learning with frozen LLMs through a bounded natural-language memory state. At each timestep, the model predicts from the current observation and its memory, receives feedback, and rewrites the state to correct errors and retain task-relevant patterns without updating model parameters. Implemented as a structured system prompt, this state makes the adaptation process inspectable through human-readable memory traces. We evaluate LLM-as-RNN on sequential prediction benchmarks in healthcare, meteorology, and finance across Llama, Gemma, and GPT-oss model families. Comparisons with static-context and feedback-aligned baselines examine predictive performance, feedback sources, memory budgets, and inference cost. Our results demonstrate that feedback-driven memory rewriting supports online error correction and sequential prediction under a fixed memory budget, with performance varying across tasks and backbones."
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<abstract>Large language models (LLMs) are strong sequence predictors, but standard context accumulation offers no explicit mechanism for revising the memory carried between predictions. We propose LLM-as-RNN, an inference-time framework for online learning with frozen LLMs through a bounded natural-language memory state. At each timestep, the model predicts from the current observation and its memory, receives feedback, and rewrites the state to correct errors and retain task-relevant patterns without updating model parameters. Implemented as a structured system prompt, this state makes the adaptation process inspectable through human-readable memory traces. We evaluate LLM-as-RNN on sequential prediction benchmarks in healthcare, meteorology, and finance across Llama, Gemma, and GPT-oss model families. Comparisons with static-context and feedback-aligned baselines examine predictive performance, feedback sources, memory budgets, and inference cost. Our results demonstrate that feedback-driven memory rewriting supports online error correction and sequential prediction under a fixed memory budget, with performance varying across tasks and backbones.</abstract>
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%0 Conference Proceedings
%T LLM-as-RNN: Feedback-Driven Memory Rewriting for Online Learning and Sequential Prediction
%A Lu, Yuxing
%A Tamo, J. Ben
%A Fang, Jinrui
%A Zhao, Weichen
%A Sun, Nan
%A Zhao, Xukai
%A Zhong, Yishan
%A Shi, Wenqi
%A Wang, Jinzhuo
%A Wang, May Dongmei
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F lu-etal-2026-llm-rnn
%X Large language models (LLMs) are strong sequence predictors, but standard context accumulation offers no explicit mechanism for revising the memory carried between predictions. We propose LLM-as-RNN, an inference-time framework for online learning with frozen LLMs through a bounded natural-language memory state. At each timestep, the model predicts from the current observation and its memory, receives feedback, and rewrites the state to correct errors and retain task-relevant patterns without updating model parameters. Implemented as a structured system prompt, this state makes the adaptation process inspectable through human-readable memory traces. We evaluate LLM-as-RNN on sequential prediction benchmarks in healthcare, meteorology, and finance across Llama, Gemma, and GPT-oss model families. Comparisons with static-context and feedback-aligned baselines examine predictive performance, feedback sources, memory budgets, and inference cost. Our results demonstrate that feedback-driven memory rewriting supports online error correction and sequential prediction under a fixed memory budget, with performance varying across tasks and backbones.
%U https://aclanthology.org/2026.aacl-long.222/
%P 4977-4997
Markdown (Informal)
[LLM-as-RNN: Feedback-Driven Memory Rewriting for Online Learning and Sequential Prediction](https://aclanthology.org/2026.aacl-long.222/) (Lu et al., AACL-IJCNLP 2026)
ACL
- Yuxing Lu, J. Ben Tamo, Jinrui Fang, Weichen Zhao, Nan Sun, Xukai Zhao, Yishan Zhong, Wenqi Shi, Jinzhuo Wang, and May Dongmei Wang. 2026. LLM-as-RNN: Feedback-Driven Memory Rewriting for Online Learning and Sequential Prediction. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4977–4997, Hengqin, China. Association for Computational Linguistics.