Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, raw retrieved content often suffers from low signal-to-noise ratio, where answer-supportive information is diluted by irrelevant material, and error accumulation, where incomplete or misleading content degrades multi-hop reasoning. To address these challenges, we propose EviNoteRAG, a framework that enhances RAG through quality-augmented evidence notes. The model is trained to generate Supportive-Evidence Notes (SENs), concise abstractions that preserve only answer-critical information and explicitly highlight key and uncertain content to improve reasoning accuracy. To further ensure note quality, we introduce an entailment-based Evidence Quality Reward (EQR), which incentivizes SENs to be logically sufficient for deriving the correct answer. Experiments on both in-domain and out-of-domain benchmarks demonstrate that EviNoteRAG achieves state-of-the-art performance, with relative F1 gains of 20% on HotpotQA (+0.093), 40% on Bamboogle (+0.151), and 91% on 2Wiki (+0.256), while also improving training stability and efficiency.

Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world. However, known as visual illusions, human’s perception of reality isn’t always faithful to the physical world. This raises a key question: do VLMs have the similar kind of illusions as humans do, or do they faithfully learn to represent reality? To investigate this question, we build a dataset containing five types of visual illusions and formulate four tasks to examine visual illusions in state-of-the-art VLMs. Our findings have shown that although the overall alignment is low, larger models are closer to human perception and more susceptible to visual illusions. Our dataset and initial findings will promote a better understanding of visual illusions in humans and machines and provide a stepping stone for future computational models that can better align humans and machines in perceiving and communicating about the shared visual world. The code and data are available at github.com/vl-illusion/dataset.

For sentence-level extractive summarization, there is a disproportionate ratio of selected and unselected sentences, leading to flatting the summary features when maximizing the accuracy. The imbalanced classification of summarization is inherent, which can’t be addressed by common algorithms easily. In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework. Specifically, we first calculate and amplify the semantic difference between each sentence and all other sentences, and then apply the residual unit as the second item of the differential amplifier to deepen the architecture. Finally, to compensate for the imbalance, the corresponding objective loss of minority class is boosted by a weighted cross-entropy. In contrast to previous approaches, this model pays more attention to the pivotal information of one sentence, instead of all the informative context modeling by recurrent or Transformer architecture. We demonstrate experimentally on two benchmark datasets that our summarizer performs competitively against state-of-the-art methods. Our source code will be available on Github.