arXiv:2603.19250v2 Announce Type: replace Abstract: Evaluating language models in streaming environments is critical, yet underexplored. Existing benchmarks either focus on single complex events or provide curated inputs for each query, and do not evaluate models under the conflicts that arise when multiple concurrent events are mixed within the same document stream. We introduce StreamBench, a benchmark built from major news stories in 2016 and 2025, comprising 605 events and 15,354 documents across three tasks: Topic Clustering, Temporal Question Answering, and Summarization. To diagnose how
The increasing deployment of LLMs in real-world applications highlights the urgent need to evaluate their performance in dynamic, streaming data environments, moving beyond static benchmark shortcomings.
Reliable evaluation of LLMs in continuous data streams is critical for ensuring their robustness, safety, and effectiveness in autonomous AI systems and for understanding their susceptibility to information conflicts.
The introduction of StreamBench provides a more realistic and complex evaluation framework for LLMs, moving beyond single-event or curated inputs to address concurrent information conflicts in real-time document streams.
- · AI researchers
- · LLM developers
- · Companies deploying AI agents
- · Cloud infrastructure providers
- · LLM models performing poorly in dynamic environments
- · AI evaluation methods relying solely on static benchmarks
Improved LLM evaluation leads to more robust and reliable AI models capable of handling complex, real-time information.
The demand for LLMs optimized for streaming data will drive new research and development in online learning and continuous adaptation.
Enhanced LLM performance in dynamic environments could accelerate the deployment of autonomous AI agents across various sectors, further impacting white-collar workflows.
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