Streaming video understanding requires Vision Language Models (VLLMs) to process growing video streams and answer user questions under tight latency constraints. Existing methods improve efficiency with token pruning and memory-bank schemes, but mainly reduce visual tokens after the LLM encoding stage. Therefore, downstream token pruning alone cannot substantially reduce end-to-end latency, since the expensive frame encoding cost has already been incurred. In this work, we propose CoFiE, a Coarse-to-Fine Evidence selection framework, that decouples evidence selection into a coarse query-agnostic filtering stage before the vision encoder and a fine query-specific refinement stage during LLM prefill. We introduce two lightweight modules, namely the coarse Novelty-Guided Frame Filtering and the fine Query-Specific Evidence Refinement, that retain visually distinctive candidates and refine the most relevant frames, respectively. This design removes substantial redundancy before frame encoding while retaining query-specific refinement once semantic information becomes available. Experimental results show that CoFiE establishes a new state-of-the-art accuracy–efficiency trade-off with open-source VLLMs across multiple video understanding benchmarks, reaching 78.86% on StreamingBench and 68.72% on OvO-Bench, improving over prior best methods by up to 3.15%. Notably, even under aggressive evidence-frame filtering up to 80%, CoFiE outperforms open-source frontier multimodal models while improving end-to-end inference latency by up to 2.54×.

Misinformation regarding climate change is a key roadblock in addressing one of the most serious threats to humanity. This paper investigates factual accuracy in large language models (LLMs) regarding climate information. Using true/false labeled Q&A data for fine-tuning and evaluating LLMs on climate-related claims, we compare open-source models, assessing their ability to generate truthful responses to climate change questions. We investigate the detectability of models intentionally poisoned with false climate information, finding that such poisoning may not affect the accuracy of a model’s responses in other domains. Furthermore, we compare the effectiveness of unlearning algorithms, fine-tuning, and Retrieval-Augmented Generation (RAG) for factually grounding LLMs on climate change topics. Our evaluation reveals that unlearning algorithms can be effective for nuanced conceptual claims, despite previous findings suggesting their inefficacy in privacy contexts. These insights aim to guide the development of more factually reliable LLMs and highlight the need for additional work to secure LLMs against misinformation attacks.