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posted an update 2 days ago

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5589

🛰️ Introducing Awesome-Remote-Sensing-Agents: The Largest Curated Collection of Intelligent Remote Sensing Agents

We are excited to share our new repository Awesome-Remote-Sensing-Agents – a comprehensive, community-driven collection of 100+ papers at the intersection of remote sensing and intelligent agents (LLMs, VLM, multi‑agent systems, etc.).

🔗 GitHub Repository: https://github.com/PolyX-Research/Awesome-Remote-Sensing-Agents

Our repository organizes this rapidly growing field into a structured, easy‑to‑navigate resource for researchers, practitioners, and enthusiasts.

📚 What’s Inside?
We’ve carefully curated papers across 6 key application domains:
🌿 Ecological Monitoring – forest fires, biodiversity, climate science
🚨 Emergency Response – flood mapping, wildfire tracking, disaster geolocalization
⛏️ Geological Exploration – mineral mapping, lithological recognition, geologic reasoning
🌊 Marine Supervision – ocean science, autonomous surface vehicles
🌾 Precision Agriculture – crop disease detection, land use simulation
🏙️ Urban Governance – change detection, urban planning, embodied navigation

🤝 Join the Community!
We warmly welcome contributions to keep this list up‑to‑date:
📝 Add missing papers via Pull Request
🏷️ Propose new or refined categories
🔗 Report broken links or outdated entries
💬 Discuss via GitHub Issues or contact the authors

DedeProGames

posted an update 3 days ago

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5142

Introducing GRM2, a powerful 3 billion parameter model designed for long-term reasoning and high performance in complex tasks.

Even with only 3 billion parameters, it outperforms qwen3-32b in several benchmarks and complex reasoning tasks.

With just 3 billion parameters, it can also generate extensive and complex code with over 1000 lines, utilize tools comparable to larger models, and is perfect for agentic tasks.

GRM2 is licensed under Apache 2.0, making it ideal as a base for FineTune in other tasks.
You can see more here: OrionLLM/GRM2-3b

Shrijanagain

posted an update 3 days ago

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5398

We are thrilled to announce the launch of SKT-OMNI-CORPUS-146T-V1, a massive-scale, high-quality dataset designed to power the next generation of Foundation Models (LLMs) from scratch.
Developed at SKT AI LABS, this corpus is not just a collection of data; it’s a mission to decentralize high-grade AI training for regional languages and global knowledge.

💎 Key Highlights:

•• Massive Scale: Targeting a multi-terabyte architecture for 146T-level tokenization.

•• Pure Quality: Curated from 500+ Elite Sources

•• Structured for MoE: Perfectly sharded into 3.5GB standardized units (SKT-𝕻 series) for seamless distributed training.

🤝 Open for Collaboration!

We are looking for AI researchers, CUDA engineers, and data scientists to join us in this journey of building Project Surya and the ST-X Series models. Whether it's optimization, custom tokenization, or architecture design—let’s build the future together.

Explore the Dataset on Hugging Face:

🔗 Shrijanagain/SKT-OMNI-CORPUS-146T-V1

DSR -- 🔗 Shrijanagain/SKT-DSRx10000

#AI #MachineLearning #OpenSource #IndicAI #SKTAILABS #LLM #BigData #HuggingFace #InnovationIndia

branikita

posted an update about 18 hours ago

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2405

Progress update on our Robonine mobile robotic platform. We've wired up the full electronics stack - Raspberry Pi, motor controllers, and a camera module - and added Bluetooth joystick and keyboard control with WiFi-based management. A 7 kg counterweight keeps the platform stable. Powered by a powerbank and ready to roll.

1 reply

BibbyResearch

posted an update about 24 hours ago

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489

🍌 Paper Banana is now live! Create academic illustrations using natural language

We just launched Paper Banana — a tool that lets you generate clean academic illustrations simply by describing them in natural language.

🔗 Try it here: https://trybibby.com/paper-banana

Whether you need diagrams for papers, presentations, or teaching materials, Paper Banana helps you turn ideas into visuals in seconds.

We’d love your feedback:

What did you like?
What features should we add next?

Give it a spin and let us know what you think! 🚀

Dear Huggingface, show this post to all my fellow researchers!

MaziyarPanahi

posted an update 1 day ago

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601

We annotated 119K medical images with two frontier VLMs (Qwen 3.5, Kimi K2.5), cross-validated at 93% agreement, and produced 110K training records, all for under $500. Fine-tuning 3 small models (2-3B params) improved all benchmarks: best model reaches +15.0% average exact match.

Everything is open-sourced: datasets, adapters, and code.

https://huggingface.co/blog/OpenMed/synthvision

1 reply

kanaria007

posted an update 2 days ago

Post

151

✅ Article highlight: *Law as Goal Surfaces* (art-60-048, v0.1)

TL;DR:
Most “AI + law” discussions go wrong in one of two ways: either an LLM is asked to explain the law and everyone hopes it is right, or a rules engine gets bolted onto the side of the system.

This article sketches a different approach: treat *law itself as structure* inside SI-Core. Legal constraints sit alongside safety, fairness, and budget in the same GoalSurface / ETH machinery, while procedure — who may do what, when, with which approvals, exceptions, and appeals — becomes first-class runtime structure.

Read:
kanaria007/agi-structural-intelligence-protocols

Why it matters:
• moves law from “best-effort compliance” to structural constraints
• makes legal procedure explicit instead of hiding it in side channels
• supports both *ex-ante* prevention of illegal actions and *ex-post* auditability
• treats appeals, exceptions, and discretion as governed objects, not ad hoc overrides

What’s inside:
• *LegalSurface* as a GoalSurface specialization for regulation and policy
• hard rules in *ETH constraints* + soft legal/policy objectives for optimization
• roles, principals, jurisdictions, approvals, and source provenance
• procedural structure for conditions, exceptions, and appeals
• a mental model: *law = goal surfaces + hard ETH constraints + roles/principals*
• SI-Core as a kind of *procedural VM* for executing those bundles on real events

Key idea:
Law should not be an afterthought around intelligent systems. It should be part of the runtime structure that determines what is admissible, what needs review, and how decisions remain explainable.

Severian

posted an update about 11 hours ago

Post

446

I’ve been working on a new mathematical approach to real-time video compositing and background removal, and I wanted to share a live demo.

Traditionally, real-time keyers either use 3D color-space bounding boxes (which struggle with semi-transparent hair and motion blur) or heavy Machine Learning models (which require massive GPU compute and often suffer from temporal "jitter" on the edges).

I wanted to see if I could solve this using purely deterministic math so it could run client-side in a standard browser.

The engine uses a custom mathematical framework I call CMT SRL SEFA. Instead of looking at raw color values or guessing semantics like an AI, it treats the video feed as complex-encoded sequences. It uses harmonic frequencies to map phase geometry and applies a "Stability Cost Function" to find the global minimum stability. In short: it isolates the foreground from the background by measuring signal complexity and structural contradictions.

Give it a try using your own messy plates and such. As I am not a VFX artist, I am curious to hear thoughts and what should be improved upon and made better

https://severian-cmt-sefa-realtime-vfx-keyer.hf.space/

1 reply

karstenskyt

posted an update 1 day ago

Post

495

🚀 𝗟𝗮𝘂𝗻𝗰𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗜/𝗠𝗟 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱

Now that our Taipy architecture is humming along on Hugging Face Spaces, we just shipped the most complex feature of the (𝘙𝘪𝘨𝘩𝘵! 𝘓𝘶𝘹𝘶𝘳𝘺!) 𝘓𝘢𝘬𝘦𝘩𝘰𝘶𝘴𝘦 to date: the 𝗔𝗜/𝗠𝗟 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱.

Managing 16 different machine learning pipelines (from Expected Goals to Space Creation) across Databricks Serverless and HF Jobs is a logistical challenge. To solve this, we built a dynamic operations center (the 13th page in our app).

It features:

  • 𝗔𝗻 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗗𝗔𝗚: Powered by Cytoscape.js, it visually maps exactly how our models and data grids feed into each other.

  • 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴: Tracks run volumes and data freshness SLAs across the entire platform.

  • 𝗔 𝟯-𝘁𝗶𝗲𝗿 𝗵𝘆𝗯𝗿𝗶𝗱 𝗰𝗼𝘀𝘁 𝗲𝗻𝗴𝗶𝗻𝗲: Merges "cold" Databricks billing data with "warm/hot" live HF Jobs estimates to give a unified view of pipeline expenses.

Check out the live interactive graph here:
luxury-lakehouse/soccer-analytics-app

omarkamali

posted an update 1 day ago

Post

472

I just might have cracked tokenizer-free LLMs. No vocab, no softmax.

I'm training a 22M params LLM rn to test this "thing" and it's able to formulate coherent sentences 🤯

Bear in mind, this is a completely new, tokenizer-free LLM architecture with built-in language universality.

Check the explainer video to understand what's happening. Feedback welcome on this approach!

5 replies

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