The paper introduces a contrastive curriculum for teaching large language models to use a zoom‑in tool without supervised fine‑tuning, leveraging an InfoNCE‑style intrinsic reward that progressively hardens negative examples. Experiments on V*, HRBench, and MME‑RealWorld demonstrate competitive performance and efficiency, with the method even surpassing baselines when replacing traditional SFT. A new synthetic dataset, Muffin&Chihuahua (M&C), is created to directly evaluate zoom‑in capability, revealing that recall best correlates with task success.
arXiv:2610.02117v1 Announce Type: cross Abstract: On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a froze...
arXiv:2609.37002v1 Announce Type: cross Abstract: High-resolution visual question answering often fails because a multimodal model does not acquire the small, spatially localized evidence needed to a...
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...
High-resolution visual question answering often fails because a multimodal model does not acquire the small, spatially localized evidence needed to answer a question. Sequential zooming can recover de...
arXiv:2605. 18740v4 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evidence in the full image.
arXiv:2505. 21457v2 Announce Type: replace-cross Abstract: Active vision, also known as active perception, refers to actively selecting where and how to look in order to gather task-relevant information.
EviRover is a perception agent that goes beyond a single glance by actively gathering information to resolve perceptual queries. The authors created two data generation pipelines, producing EviRover-SFT-5K and EviRover-RL-12K, and a human‑verified benchmark called EviLens with 688 instances across five perception categories. Trained with supervised fine‑tuning and agentic reinforcement learning, the 4B EviRover outperforms its backbone by an average of 30 points on EviLens and shows strong transfer to other benchmarks such as WebEyes and BrowseComp‑VL.
AdaGround is a training‑free framework for GUI grounding that addresses shortcomings of existing zooming methods. It introduces Token‑Normalized Adaptive Zooming to match magnification to target size, a Verifier‑in‑the‑Loop Refinement that uses a simple Yes/No click to accept or reject zooms and decide when to abstain, and Compositional Structure Grounding for spreadsheet cells. Experiments on Qwen3‑VL models show AdaGround outperforms state‑of‑the‑art training‑free zooming techniques and many trained GUI models, boosting accuracy by 7.4 to 20.6 points on OSWorld‑G and ScreenSpot‑Pro and maintaining gains on unseen UI‑Vision data.
arXiv:2608. 08907v1 Announce Type: cross Abstract: Thinking with images allows a multimodal model to compensate for limited perception by invoking visual tools through code.
arXiv:2608. 03979v1 Announce Type: cross Abstract: We introduce Video-DeepResearch (Video-DR), extending multimodal agents from static images to continuous video streams, a setting that demands dense spatiotemporal grounding coupled with open-web exploration.
The paper introduces MIME, a task‑agnostic visual in‑context learning (vICL) dataset that removes textual shortcuts by omitting explicit rules linking demonstration images to answers, forcing models to infer the rule visually. Fine‑tuning vision‑language models on MIME boosts vICL accuracy by 8.1–18.0 points across three VLMs while preserving single‑image performance and improving multi‑image and text‑only in‑context learning. The authors also propose DTAD, a technique that randomly drops demonstration labels during training to encourage visual inference, achieving comparable accuracy with more consistent attention to demonstration images and outperforming frontier models on the TrueMICL benchmark.
arXiv:2605.15195v2 Announce Type: replace Abstract: Recent feed-forward reconstruction models, such as VGGT, have proven competitive with traditional optimization-based reconstructors while also prov...