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Posted on Originally published at blogs.drix10.com

Computer Vision and AI Applications #241

#tech #ai #coding

🤖 AI Research - Writing and Collaboration

Writing and thinking hard about what words mean and evoke in readers is crucial for effective communication in the AI research community. Junior researchers can benefit from honing their writing skills and considering the impact of their words on readers.

Key Points:

  • Writing for Impact: Understanding the nuances of language and its impact on readers is essential for effective communication in AI research.

  • Collaborative Writing: Encouraging junior researchers to practice writing and thinking critically about their words can lead to improved collaboration and more effective communication.

  • Reader-Centric Approach: Focusing on the reader's perspective and understanding can help researchers craft more engaging and effective writing.

🔗 Resources:

🚀 ScienceBuddy - Interactive Research Workspace

ScienceBuddy is an interactive research workspace that improves through collaboration. It was initiated by @PhAILabs with @muchencq (Muchen AI) as the joint R&D partner. ScienceBuddy uses Recursive-in-Recursive Self-Improvement to improve through collaboration.

Key Points:

  • Interactive Research Workspace: ScienceBuddy is an interactive research workspace that improves through collaboration.

  • Recursive-in-Recursive Self-Improvement: ScienceBuddy uses a recursive approach to self-improve through collaboration.

  • Collaborative Research: ScienceBuddy enables collaborative research through its interactive workspace.

🔗 Resources:

📈 Meta's Segment Anything Model (SAM) 3.1

Meta's Segment Anything Model (SAM) 3.1 is now available on Meta Model API. It provides a fast and lightweight model for detection, segmentation, and tracking in a single call on inference tuned for SAM 3.1's architecture.

Key Points:

  • Segment Anything Model (SAM) 3.1: SAM 3.1 is a fast and lightweight model for detection, segmentation, and tracking.

  • Single Call Inference: SAM 3.1 provides a single call inference for detection, segmentation, and tracking.

  • Architecture Tuning: SAM 3.1's architecture is tuned for inference.

🔗 Resources:

🚀 Self-Evolving Agent Harnesses

NVIDIA's paper on self-evolving agent harnesses introduces SoL-Pi, which cuts token traffic by nearly half. SoL-Pi matches its baseline harness on GPT-5.6 Sol and Opus 5.

Key Points:

  • Self-Evolving Agent Harnesses: NVIDIA's paper introduces self-evolving agent harnesses.

  • SoL-Pi: SoL-Pi cuts token traffic by nearly half and matches its baseline harness on GPT-5.6 Sol and Opus 5.

  • Token Traffic Reduction: SoL-Pi reduces token traffic by nearly half.

🔗 Resources:

🤖 ABC - Open Teleop Dataset

ABC is a fully open teleop dataset that includes 3,500 hours, 130K+ episodes, 195 tasks, collected on an $8K bimanual setup. The full stack is open, including hardware, training code, sim, and eval.

Key Points:

  • ABC - Open Teleop Dataset: ABC is a fully open teleop dataset.

  • 3,500 Hours of Data: ABC includes 3,500 hours of data.

  • 130K+ Episodes: ABC includes 130K+ episodes.

  • 195 Tasks: ABC includes 195 tasks.

  • Full Stack Open: The full stack of ABC is open, including hardware, training code, sim, and eval.

🔗 Resources:

🤖 Booster MJLab

Booster MJLab is an interactive research workspace that includes a velocity tracking task, motion tracking, an AMP implementation, and more. The project page and demos are available at https://intelligentroboticslab.github.io/booster_mjlab/.

Key Points:

  • Booster MJLab: Booster MJLab is an interactive research workspace.

  • Velocity Tracking Task: Booster MJLab includes a velocity tracking task.

  • Motion Tracking: Booster MJLab includes motion tracking.

  • AMP Implementation: Booster MJLab includes an AMP implementation.

🔗 Resources:

🤖 SNAP3D

SNAP3D decomposes objects into semantic parts, generates complementary peg-and-socket connectors, optimizes parts and connectors for physical assembly, verifies that the assembled object is stable, and verifies that the assembled object is fabricable.

Key Points:

  • SNAP3D: SNAP3D decomposes objects into semantic parts.

  • Semantic Parts Decomposition: SNAP3D generates complementary peg-and-socket connectors.

  • Physical Assembly Optimization: SNAP3D optimizes parts and connectors for physical assembly.

  • Stability Verification: SNAP3D verifies that the assembled object is stable.

  • Fabricability Verification: SNAP3D verifies that the assembled object is fabricable.

🔗 Resources:

🚫 Quitting Google DeepMind

After 10 incredible years, I've decided to quit Google DeepMind. I'll be joining @sirbayes' team to continue the work on curriculum design, towards a general computational model of teaching.

Key Points:

  • Quitting Google DeepMind: I've decided to quit Google DeepMind after 10 incredible years.

  • Joining Sir Bayes' Team: I'll be joining @sirbayes' team to continue the work on curriculum design.

  • Curriculum Design: I'll be working on curriculum design towards a general computational model of teaching.

🔗 Resources:

🤖 Functionalization

Functionalization is a graph completion approach that adds missing structure and connectors to rectify motion and make models work. The SIGGRAPH Asia 2026 Paper, code, and data are available at https://mingrui-zhao.github.io/Functionalization/.

Key Points:

  • Functionalization: Functionalization is a graph completion approach.

  • Graph Completion: Functionalization adds missing structure and connectors to rectify motion.

  • Model Rectification: Functionalization makes models work by rectifying motion.

🔗 Resources:

🤖 Writing Fun Papers

You can slop-o-write a paper and submit it. But can you write one that's actually fun, cool, and a little nuts? We need more of them.

Key Points:

  • Writing Fun Papers: Writing fun papers is essential for the AI research community.

  • Fun Papers: Fun papers should be engaging, cool, and a little nuts.

  • Encouraging Fun Papers: We need more fun papers in the AI research community.

🔗 Resources:

Read More & Connect

Interactive version: blogs.drix10.com

Written by Drishtant Ghosh (Drix10), a technical founder and engineer working across AI systems, developer infrastructure, and cybersecurity.

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