🚨 Excited to share VisionCoach, an RL framework for reinforcing grounded video reasoning via visual-perception prompting and self-distillation! 🧠 Video reasoning models often miss where to look or rely on language priors. Instead of only supervising final answers, we encourage the model to learn to attend to the right visual evidence. ⚽️ VisionCoach uses RL to reward correct visual attention, with dynamic visual prompting as a training-time coach for better spatio-temporal grounding, while keeping inference simple and tool-free via self-distillation. ⭐️ Achieves state-of-the-art zero-shot performance across video reasoning, video understanding, and temporal grounding benchmarks (V-STAR, VideoMME, World-Sense, VideoMMMU, PerceptionTest, and Charades-STA). 👇🧵

I'll be at #CVPR2026, feel free to ping if you want to meet up! Will be giving 4 different keynotes at these exciting @CVPR workshops and looking forward to engaging discussions on diverse topics 🙂 (also happy to discuss hiring at all levels: PhD, postdoc, faculty) ps. also meet several of our awesome students/postdocs who will be attending

🚨 Excited to introduce PhyMotion🤸: Structured 3D Motion Reward for Physics-Grounded Human Video Generation! ❌ Existing 2D video rewards misleadingly assign high scores to videos with floating feet, self-penetrating limbs, and physics-violating motions. ✅ PhyMotion lifts generated videos into 3D, grounds them in a physics simulator, and scores motion along kinematic / contact / dynamic feasibility. ➡️ RL post-training with PhyMotion improves 1.3B model to match 14B models performance in human prefence. 🧵(1/n)👇

🚨 Excited to share EgoMemReason, a benchmark for multi-level memory-driven reasoning (entity, event, and behavior memory) over week-long egocentric videos (average 25.9 hours of temporal backtracking)! 📉 Current long video approaches can retrieve isolated event, but struggle with long-horizon memory that requires retrieve and understand across multiple events and long time: tracking evolving entities across days, linking temporally distant events, and abstracting recurring behavior patterns from long observations. 🎥 EgoMemReason evaluates these challenges through 500 human-verified questions spanning entity, event, and behavior memory, requiring aggregation over an average of 5.1 evidence segments and 25.9 hours of temporal backtracking. ⭐️ Across 17 models/frameworks, even the best model achieves only 39.6% accuracy, revealing that long-horizon multimodal memory remains far from solved.

Looking forward to giving a keynote at the Midwest Machine Learning Symposium (MMLS) 2026 (being held at Purdue University this year) & meeting folks from all the strong universities in the midwest, with their inspiring, long tradition of these exciting symposiums! 🙂 👇👇

🎉 Excited to share EPiC is accepted to #ICML2026! We show that learning precise camera control for video diffusion doesn't need expensive 3D supervision or large-scale data. No camera or point cloud processing — just mask source videos based on visibility to construct precise training anchor videos, and learn a SoTA camera controller with only 30M params, trained >100× faster on >100× less data than prior work, while generalizing across both I2V and V2V camera control tasks.

🚨Cog-DRIFT: Breaking the Exploration Barrier in RLVR RLVR has pushed LLM reasoning forward BUT hits a ceiling: if a model can't solve a problem (rollouts never succeed), it gets 0 learning signal 👉Hard problems stay unsolved, and training stalls. We introduce✨Cog-DRIFT✨to reformulate hard problems into "cognitively" easier, structured variants (MCQ and cloze), then curriculum-train models from easy → hard to unlock new learning signals. Key takeaways: 1⃣ Breaks the ceiling on "unsolvable" hard problems from 0% → 10.11% (Qwen) & 0% → 8.64% (Llama) in absolute gains 2⃣ Consistent gains across 6 benchmarks & 2 models → 4.72% (Qwen), 3.23% (Llama) over strong baselines 3⃣ Reformulations span discriminative → generative formats, enabling effective knowledge transfer back to hard open-ended reasoning 4⃣ Adaptive curriculum matters: training progresses from easier variants to harder ones, leading to continued improvement and improved sample efficiency 5⃣ Also boosts test-time performance (pass@k), showing acquisition of new reasoning patterns from hard problems that were beyond a model's accessibility w/o Cog-DRIFT 🧵👇

Huge congratulations @jmin__cho! Very well deserved 🎉🥳

🔥 Check out my close coworker @shoubin621’s interesting new egocentric benchmark! I believe this could be a groundbreaking step toward AR glasses applications. 🔎 I've always wondered about the exact name or brand of a product I encounter in the real world. Ego2Web is a benchmark for real-world grounded web agents that must understand egocentric video and use that context to perform actions on the web. Both interesting and highly practical! ⚡️

✨ Excited to share V-Co, a systematic look and recipe for visual co-denoising in pixel-space diffusion. Instead of loosely injecting pretrained features, we study how pixels and semantics should be jointly denoised and properly aligned. - Dual-stream design for clean interaction - Structural masking for stronger CFG - Hybrid loss for richer supervision - Simple scaling for stable training A simple yet principled recipe, delivering strong and consistent gains. Details👇

🚀 Excited to share V-Co, a diffusion model that jointly denoises pixels and pretrained semantic features (e.g., DINO). We find a simple but effective recipe: 1️⃣ architecture matters a lot --> fully dual-stream JiT 2️⃣ CFG needs a better unconditional branch --> semantic-to-pixel masking for CFG 3️⃣ the best semantic supervision is hybrid --> perceptual-drifting hybrid loss 4️⃣ calibration is essential --> RMS-based feature rescaling We conducted a systematic study on V-Co, which is highly competitive at a comparable scale, and outperforms JiT-G/16 (~2B, FID 1.82) with fewer training epochs. 🧵 👇

🚨Excited to share our work on VisionCoach! -Video reasoning isn’t failing because models can’t reason —it’s failing because they don’t see correctly. -Instead of adding more tools at inference, we teach models how to look during training. ✨VisionCoach = visual prompting (train-time) RL with self-distillation -> grounded reasoning with tool-free inference

Check out our new work ⚽️VisionCoach, an RL self-distillation framework for complex video reasoning. We combine reinforcement learning with dynamic visual prompting, where a visual prompt selector adaptively augments hard training examples based on reward signals. Visual grounding is key to accurate video reasoning. Instead of adding complexity at inference, we use visual prompting during training to guide models toward better spatio-temporal attention—then distill this capability into a simple, single-path model.