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Yuhan Wu

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4

AAAI Conference 2026 Conference Paper

Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation

  • Hua Ye
  • Siyuan Chen
  • Ziqi Zhong
  • Canran Xiao
  • Haoliang Zhang
  • Yuhan Wu
  • Fei Shen

Large language models (LLMs) equipped with retrieval—the Retrieval-Augmented Generation (RAG) paradigm—should combine their parametric knowledge with external evidence, yet in practice they often hallucinate, over-trust noisy snippets, or ignore vital context. We introduce TCR (Transparent Conflict Resolution), a plug-and-play framework that makes this decision process observable and controllable. TCR (i) disentangles semantic match and factual consistency via dual contrastive encoders, (ii) estimates self-answerability to gauge confidence in internal memory, and (iii) feeds the three scalar signals to the generator through a lightweight soft-prompt with SNR-based weighting. Across seven benchmarks TCR improves conflict detection (+5–18 F₁), raises knowledge-gap recovery by +21.4 percentage points and cuts misleading-context overrides by –29.3 percentage points, while adding only 0.3% parameters. The signals align with human judgements and expose temporal decision patterns.

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AAAI Conference 2025 Conference Paper

Affirm: Interactive Mamba with Adaptive Fourier Filters for Long-term Time Series Forecasting

  • Yuhan Wu
  • Xiyu Meng
  • Huajin Hu
  • Junru Zhang
  • Yabo Dong
  • Dongming Lu

In long-term series forecasting (LTSF), it is imperative for models to adeptly discern and distill from historical time series data to forecast future states. Although Transformer-based models excel at capturing long-term dependencies in LTSF, their practical use is limited by issues like computational inefficiency, noise sensitivity, and overfitting on smaller datasets. Therefore, we introduce a novel time series lightweight interactive Mamba with an adaptive Fourier filter model (Affirm). Specifically, (i) we propose an adaptive Fourier filter block. This neural operator employs Fourier analysis to refine feature representation, reduces noise with learnable adaptive thresholds, and captures inter-frequency interactions using global and local semantic adaptive Fourier filters via element-wise multiplication. (ii) A dual interactive Mamba block is introduced to facilitate efficient intra-modal interactions at different granularities, capturing more detailed local features and broad global contextual information, providing a more comprehensive representation for LTSF. Extensive experiments on multiple benchmarks demonstrate that Affirm consistently outperforms existing SOTA methods, offering a superior balance of accuracy and efficiency, making it ideal for various challenging scenarios with noise levels and data sizes.

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NeurIPS Conference 2025 Conference Paper

DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response

  • Junjue Wang
  • Weihao Xuan
  • Heli Qi
  • Zhihao Liu
  • Kunyi Liu
  • Yuhan Wu
  • Hongruixuan Chen
  • Jian Song

Large vision-language models (VLMs) have made great achievements in Earth vision. However, complex disaster scenes with diverse disaster types, geographic regions, and satellite sensors have posed new challenges for VLM applications. To fill this gap, we curate the first remote sensing vision-language dataset (DisasterM3) for global-scale disaster assessment and response. DisasterM3 includes 26, 988 bi-temporal satellite images and 123k instruction pairs across 5 continents, with three characteristics: **1) Multi-hazard**: DisasterM3 involves 36 historical disaster events with significant impacts, which are categorized into 10 common natural and man-made disasters. **2) Multi-sensor**: Extreme weather during disasters often hinders optical sensor imaging, making it necessary to combine Synthetic Aperture Radar (SAR) imagery for post-disaster scenes. **3) Multi-task**: Based on real-world scenarios, DisasterM3 includes 9 disaster-related visual perception and reasoning tasks, harnessing the full potential of VLM's reasoning ability with progressing from disaster-bearing body recognition to structural damage assessment and object relational reasoning, culminating in the generation of long-form disaster reports. We extensively evaluated 14 generic and remote sensing VLMs on our benchmark, revealing that state-of-the-art models struggle with the disaster tasks, largely due to the lack of a disaster-specific corpus, cross-sensor gap, and damage object counting insensitivity. Focusing on these issues, we fine-tune four VLMs using our dataset and achieve stable improvements (up to 10. 4\%$\uparrow$QA, 2. 1$\uparrow$Report, 40. 8\%$\uparrow$Referring Seg. ) with robust cross-sensor and cross-disaster generalization capabilities. Project: https: //github. com/Junjue-Wang/DisasterM3.

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