Hugging Face Trending Papers
Question-Guided Evidence Acquisition for Multimodal Visual Question Answering
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arXiv AI
Aug 21
Question-Guided Evidence Acquisition for Multimodal Visual Question Answering
arXiv:2608. 19739v1 Announce Type: cross Abstract: Multimodal LLMs can see a document, but they often can't read it reliably.
arXiv AI
Jul 20
Ask Twice, Look Twice: Prompt Echoing Resolves the Question-First Paradox in Vision-Language Models
arXiv:2607. 15565v1 Announce Type: cross Abstract: Where should the question go in a vision-language model (VLM) prompt: before the image or after it?
arXiv AI
Oct 12
The Evidence Anchoring Boundary: Where Visual Interventions Matter and When They Help in Multimodal LLMs
The paper investigates how visual interventions—training‑free edits applied to multimodal large language models—affect hallucinations and prediction quality. By applying a visual‑residual edit across successive decoder layers and matching its RMS strength, the authors identify an Evidence Anchoring Boundary (EAB) where the intervention’s influence drops sharply; effects before the EAB are 13.78–26.10× larger than after it. They further compare counterfactual subtraction across different question types and introduce calibrated selective routing, which only alters predictions when calibration supports an intervention, improving attribute accuracy by 1.56–1.81 percentage points while avoiding the harms of universal subtraction.
arXiv Computer Vision
Oct 26
The Answer Knows Where to Look: Zooming before MLLMs Answer under Visual-Token Budgets
arXiv:2610.32664v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) read images through visual tokens, and under the small per-image budgets that efficient deployment requires, t...
arXiv AI
Jun 16
Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering
arXiv:2606. 16494v1 Announce Type: cross Abstract: Knowledge-based visual question answering (KB-VQA) lets vision-language systems answer questions that exceed their parametric knowledge by conditioning a reader on passages retrieved from a Wikipedia-scale knowledge base.
arXiv Computation and Language
3d ago
VLMs Read Aloud: The Gap Between Recognizing and Using Visual Text
The paper investigates the disparity between a vision‑language model’s ability to recognize text in compressed images and its ability to use that text for answering questions, a phenomenon termed the read‑use gap. Experiments show that while models can transcribe images accurately, they often fail to answer based on the transcription, and that recognition saturates at lower pixel resolutions than usage. The authors propose ALOUD, a method that allocates visual resolution to evidence detection and language tokens to reasoning, which doubles the accuracy of a frozen Qwen3‑VL‑8B on heavily compressed documents without retaining the original text.