🧠 VLM Reasoning¶

🧠 NeurIPS2026 · 2 paper notes

Can Linguistic Reasoning Vectors Enhance Multimodal Reasoning Ability?: LIFT extracts the hidden-state difference at identical answer tokens with and without a reasoning trace from a base large language model, then injects it into the corresponding vision-language model's language layers without updating the backbone, raising six-benchmark average accuracy from 66.6/67.8 to 68.3/69.0 with static intervention and 69.1/69.4 with vector adaptation.
OneCanvas: 3D Scene Understanding via Panoramic Reprojection: OneCanvas lifts multi-view image patch features into 3D and places them as separate continuous tokens in a shared panoramic coordinate system, using native RoPE for angles and frame order and an additive embedding for metric position, then combines synthetic spatial pretraining with real-scene QA adaptation to score 65.3, 71.3, and 72.1 on SQA3D, VSI-Bench, and SPBench, respectively.