Large Language Models (LLMs) encode parametric knowledge but can exhibit format bias when resolving knowledge conflicts. When evidence is rendered as text, tables, or knowledge graphs, LLMs can favor certain formats despite unchanged semantic content. Existing work mainly diagnoses this bias, while existing inference-time mitigation remains tied to a single rendered prompt. We propose FaDe, a cross-format knowledge distillation framework that treats evidence format as a controllable nuisance variable and marginalizes teacher predictions over controlled format assignments. FaDe constructs a format-marginalized soft target from these renderings, then trains the student with an asymmetric two-path objective that combines heterogeneous-format debiasing with all-text anchoring. Experiments on ConflictBank and two QA-derived controlled conflict sets with four model backbones show FaDe reduces format bias while improving dual-source coverage and cross-format stability under single-pass inference. Our code is available at https://github.com/Kaia91/FaDe.

Manual slide creation is labor-intensive and requires expert prior knowledge. Existing natural language-based LLM generation methods struggle to capture the visual and structural nuances of slide designs. To address this, we formalize the Reference Image to Slide Generation task and propose Slide2Code, the first benchmark with difficulty-tiered samples based on a novel Slide Complexity Metric. We introduce SlideCoder, a layout-aware, retrieval-augmented framework for generating editable slides from reference images. SlideCoder integrates a Color Gradient-based Segmentation algorithm and a Hierarchical Retrieval-Augmented Generation method to decompose complex tasks and enhance code generation. We also release SlideMaster, a 7B open-source model fine-tuned with improved reverse-engineered data. Experiments show that SlideCoder outperforms state-of-the-art baselines by up to 40.5 points, demonstrating strong performance across layout fidelity, execution accuracy, and visual consistency. Our code is available at https://github.com/vinsontang1/SlideCoder.