Papers with online adaptation
Adapting LLM Agents with Universal Communication Feedback (2025.findings-naacl)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated potential for LLM agents. |
| Approach: | They propose a universal buffer and iterative pipeline to store feedback and itersative pipelines to enable LLM agents to explore and update their policy in an environment. |
| Outcome: | The proposed approach outperforms supervised instruction fine-tuning baselines on four datasets. |
Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation (2026.acl-long)
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| Challenge: | Large Language Models face the "knowledge cutoff" problem because their parametric memory remains frozen after pretraining, preventing them from natively internalizing new information or tools on the fly. |
| Approach: | They propose a framework that supports modular skill transfer for efficient and effective knowledge adaptation by extracting a domain-agnostic **Skill Vector from a source domain. |
| Outcome: | Experiments on knowledge-incorporation QA (SQuAD, LooGLE) and agentic tool-use benchmarks show that the proposed framework outperforms state-of-the-art self-editing SFT by 9.9 points. |
Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams (2026.acl-long)
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Jiyeon Kim, Hyunji Lee, Dylan Zhou, Sue Hyun Park, Seunghyun Yoon, Trung Bui, Franck Dernoncourt, Sungmin Cha, Minjoon Seo
| Challenge: | Existing models and agentic memory systems fail to adapt robustly to OAKS, demonstrating delays in state-tracking and susceptibility to distraction within streaming environments. |
| Approach: | They propose a benchmark to evaluate models' ability to adapt to changing knowledge over streaming . they use two datasets to analyze how facts evolve over time . |
| Outcome: | The proposed benchmark evaluates models in an online adaptation setting over streaming, continually updating knowledge. |