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In flow matching, a coupling determines how noise and data samples are paired during training. The choice of coupling is important because it influences the geometry of trajectories at inference time. The simplest choice is the independent coupling, where noise and data points are paired arbitrarily. This can lead... show more

Alec Helbling

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65,798 görüntüleme • 5 ay önce •via X (Twitter)

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Flow matching produces smooth, deterministic trajectories. In contrast, the sampling process of a diffusion model is chaotic, resembling the random motion of gas particles.

Flow matching produces smooth, deterministic trajectories. In contrast, the sampling process of a diffusion model is chaotic, resembling the random motion of gas particles.

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$Lorenz attractor is the set of limit trajectories for a simplified mathematical model for atmospheric convection. It has a fractal geometry.$

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Flow-based generative models trained with flow matching tend to learn curved trajectories, which are challenging to approximate in a few steps. Rectified flows aim to learn straight trajectories, which are easier to simulate with less computation.

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LLMs are like Schrödinger’s cat: many possible trajectories, but you only see one outcome per run. To really understand models, and debug where they go wrong, you can find the "forking tokens" that lead to different trajectories. Our new research does this 100x more efficiently!

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This animation shows how a uniform grid of points is deformed by a flow matching model. The visualization highlights how the learned flow warps the underlying space—stretching, compressing, and bending it—to transform one probability distribution into another.

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We’re proud to share that Tamarind Bio has been selected to build, host, and operate the inference infrastructure layer for TuneLab2.0, the next evolution of the platform. Eli Lilly and Company TuneLab is a first-of-its-kind, collaborative AI/ML drug discovery platform, bringing models trained on over $1B worth of Lilly proprietary data to the biotech ecosystem. Tamarind will power TuneLab’s scalable drug discovery workflows and model inference.

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A curious property of diffusion models: when a sample is mostly noise, the model’s best guess is close to the mean. So early on samples can be pulled toward the center of the dataset, even when no data exists there, before rebounding toward the data manifold.

A curious property of diffusion models: when a sample is mostly noise, the model’s best guess is close to the mean. So early on samples can be pulled toward the center of the dataset, even when no data exists there, before rebounding toward the data manifold.

Alec Helbling

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OpenAI's Deep Research is getting a run for its money. Deep Lake was just released, and it's a different take on an AI system that can do deep research on your own data. You can use Deep Lake to build AI search with reasoning on your private and public data. (Look at the attached videos to get an idea of how it works.) If you want to research proprietary and sensitive data, Deep Research won't help you because it's limited to public data. Deep Lake, however, will allow you to use your private data. On top of that, Deep Lake supports multi-modal retrieval from the ground up. It uses vision language models for data ingestion and retrieval so that you can connect any data (PDFs, images, videos, structured data, etc.) You can even use mixed-data queries! Deep Lake can search your data from S3, Dropbox, and GCP. It learns from your queries over time, making the results as relevant to your work as possible!

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🚀New paper out - We present Video-MSG (Multimodal Sketch Guidance), a novel planning-based training-free guidance method for T2V models, improving control of spatial layout and object trajectories. 🔧 Key idea: • Generate a Video Sketch — a spatio-temporal plan with background, foreground, and motion in the pixel space. • Encode this structure directly into the latent space of the diffusion model during generation, which does not require fine-tuning or additional memory during inference. 🧵

🚀New paper out - We present Video-MSG (Multimodal Sketch Guidance), a novel planning-based training-free guidance method for T2V models, improving control of spatial layout and object trajectories. 🔧 Key idea: • Generate a Video Sketch — a spatio-temporal plan with background, foreground, and motion in the pixel space. • Encode this structure directly into the latent space of the diffusion model during generation, which does not require fine-tuning or additional memory during inference. 🧵

Jialu Li

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BIG NEWS. The Blockworks website has evolved: yesterday we were the home of news, today we are the home of onchain data. Head over to the site to see for yourself, but here's a little snippet of what you can expect: 1. Sector leaderboards (chains, DEXes, borrow lend, DATs, etc...) 2. Comprehensive data dashboards protocols 3. The ability to compare pricing and onchain data easily (coming soon) We're doing this because the industry still has a gigantic data problem. As investors get more sophisticated and fundamentals driven, basic high level facts are no longer sufficient. Investors need to be able to trust the data they are seeing and go much deeper than the surface level info that's available today. Additionally, because many data providers allow companies to essentially self report, you can't trust what you are seeing. This site is our contribution to fixing that problem and to ensuring clear, accurate data for investors. Blockworks is fully dedicated to becoming the most comprehensive data company in crypto in 2026. This is the first of many, many announcements like this this year, stay tuned.

BIG NEWS. The Blockworks website has evolved: yesterday we were the home of news, today we are the home of onchain data. Head over to the site to see for yourself, but here's a little snippet of what you can expect: 1. Sector leaderboards (chains, DEXes, borrow lend, DATs, etc...) 2. Comprehensive data dashboards protocols 3. The ability to compare pricing and onchain data easily (coming soon) We're doing this because the industry still has a gigantic data problem. As investors get more sophisticated and fundamentals driven, basic high level facts are no longer sufficient. Investors need to be able to trust the data they are seeing and go much deeper than the surface level info that's available today. Additionally, because many data providers allow companies to essentially self report, you can't trust what you are seeing. This site is our contribution to fixing that problem and to ensuring clear, accurate data for investors. Blockworks is fully dedicated to becoming the most comprehensive data company in crypto in 2026. This is the first of many, many announcements like this this year, stay tuned.

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