Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning Humans often use visual aids, for example diagrams or sketches, when solving complex problems. Training multimodal models to do the same, known as Visual Chain of Thought (Visual CoT), is challenging ...

Zebra-CoT: A Dataset for Interleaved Vision Language Reasoning
arxiv: arxiv.org/abs/2507.16746
data: huggingface.co/datasets/mul...

ZEBRA-CoT

Dataset for vision-language reasoning where the model *generates images during the CoT*. Example: for geometry problems, it's helpful to draw lines in image space.

182K CoT labels: math, visual search, robot planning, and more.

Only downside: cc-by-nc license :(

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art...

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning
arxiv: arxiv.org/abs/2507.14137
code: github.com/valeoai/Franca

Cool technique: RASA, Removal of Absolute Spatial Attributes. They decode grid coords and find the plane in feature space that encodes position. They basically subtract this off, baking it into the last linear layer to leave the forward pass unchanged.

Beats or is competitive to SigLIP/2, DinoV2 on linear eval, OOD detection, linear segmentation.

Franca

Fully open vision encoder. Masks image, encodes patches, then trains student to match teacher's clusters. Key advance: Matryoshka clustering. Each slice of the embedding gets its own projection head and clustering objective. Fewer features == fewer clusters to match.

VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, is a critical challenge for autonomous driving systems, as crashes involving VRUs often result in severe or fatal...

VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding
arxiv: arxiv.org/abs/2507.098...
project: vru-accident.github.io

VRU-Accident

New benchmark of 1K videos, 1K captions, and 6K MCQs from accidents involving VRUs. Example: "why did the accident happen?" "(B): pedestrian moves or stays on the road."

Current VLMs get ~50-65% accuracy, much worse than humans (95%).

BlindSight: Harnessing Sparsity for Efficient VLMs Large vision-language models (VLMs) enable the joint processing of text and images. However, the inclusion of vision data significantly expands the prompt length. Along with the quadratic complexity o...

BlindSight: Harnessing Sparsity for Efficient VLMs
arxiv: arxiv.org/abs/2507.090...

Side note: I've always liked Pali/Gemma's Prefix-LM masking. Why have causal attention for image tokens?

BlindSight

AMD paper: they find attention heads often have stereotyped sparsity patterns (e.g. only attending within an image, not across). They generate sparse attention variants for each prompt. Theoretically saves ~35% FLOPs for 1-2% worse on benches.

Scaling RL to Long Videos We introduce a full-stack framework that scales up reasoning in vision-language models (VLMs) to long videos, leveraging reinforcement learning. We address the unique challenges of long video reasonin...

Scaling RL to Long Videos
arxiv: arxiv.org/abs/2507.07966
code: github.com/NVlabs/Long-RL

Long-RL

Nvidia paper scaling RL to long videos. First trains with SFT on a synthetic long CoT dataset, then does GRPO with up to 512 video frames. Uses cached image embeddings + sequence parallelism, speeding up rollouts >2X.

Bonus: code is already up!

Skywork-R1V3 Technical Report We introduce Skywork-R1V3, an advanced, open-source vision-language model (VLM) that pioneers a new approach to visual reasoning. Its key innovation lies in effectively transferring reasoning skills f...

Skywork-R1V3 Technical Report
arxiv: arxiv.org/abs/2507.06167
code: github.com/SkyworkAI/Sk...

They identify entropy of "wait" or "alternatively" to be strongly correlated with MMMU. Neat!

Fine-tuning the connector at the end gives a point or two of MMMU. I wonder how much of this is benchmaxxing--I haven't seen an additional SFT stage after RL before.

They construct their warm-start SFT data with synthetic traces from Skywork-R1V2.

GRPO is pretty standard, interesting that they just did math instead of math, grounding, other possible RLVR tasks. Qwen-2.5-Instruct 32B to judges the accuracy of the answer in addition to rule-based verification.

Skywork-R1V3: new reasoning VLM with 76% MMMU.

InternViT-6B stitched with QwQ-32B. SFT warmup, GRPO on math, then a small SFT fine-tune at the end.

Good benches, actual ablations, and interesting discussion.

Details: 🧵

High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning State-of-the-art large multi-modal models (LMMs) face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the ...

High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning
arxiv: arxiv.org/abs/2507.05920
code: github.com/EvolvingLMMs...

Training: use verl with vLLM for rollouts. Limit image resolution to 1280 visual tokens. Train on 32 H100s.

Results: +18 points better on V* compared to Qwen2.5-VL, and +5 points better than GRPO alone.

RL: GRPO. Reward: only correct answer, not valid grounding coordinates. Seems weird to not add that though.

Data: training subset of MME-RealWorld. Evaluate on V*.

Uses Qwen2.5-VL as a base model. The NaViT encoder makes it easy to have many images of different shapes.

They use a SFT warm-start, as the VLMs struggled to output good grounding coordinates. They constructed two-turn samples for this.

MGPO: multi-turn grounding-based policy optimization.

I've been waiting for a paper like this! Trains the LLM to iteratively crop regions of interest to answer a question, and the only reward is the final answer.

Details in thread 👇

DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction Autonomous driving has seen significant progress, driven by extensive real-world data. However, in long-tail scenarios, accurately predicting the safety of the ego vehicle's future motion remains a ma...

DriveMRP: Enhancing Vision-Language Models with Synthetic Motion Data for Motion Risk Prediction
arxiv: arxiv.org/abs/2507.02948
code: github.com/hzy138/Drive...

Using automatically generated risk category labels and the front-facing view, they have GPT4o caption the scenarios. The metrics are based on caption similarity + classification metrics on riskiness-type.

DriveMRP: interesting method to get a VLM to understand BEV maps + driving scenarios

They synthesize high-risk scenes derived from NuPlan. They render it as both a bird's eye view image and a front camera view.

👇

SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions Accurate lane topology is essential for autonomous driving, yet traditional methods struggle to model the complex, non-linear structures-such as loops and bidirectional lanes-prevalent in real-world r...

SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions
arxiv: arxiv.org/abs/2507.048...

SeqGrowGraph

Instead of segment + postprocess, generate lane graphs autoregressively. Node == vertex in BEV space, edge == control point for Bezier curves. At each step, a vertex is added and the adjacency matrix adds one row + column.

They formulate this process as next token prediction. Neat!

GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning We present GLM-4.1V-Thinking, a vision-language model (VLM) designed to advance general-purpose multimodal reasoning. In this report, we share our key findings in the development of the reasoning-cent...

GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
arxiv: arxiv.org/abs/2507.01006
code: github.com/THUDM/GLM-4....

Excitingly, in one of their few shown results, multi-domain RL shows positive cross-task-transfer. Training on GUI agent data improves STEM answers, OCR, and grounding.