Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han, Bin Hu, Bin Jing, Binbin Hu, Bing Li, Cai Chen, Caizhi Tang, Changxin Tian, Chao Huang, …
https://t.co/l8T1aJFQ9k [𝚌𝚜.𝙲𝙻 𝚌𝚜.𝙰𝙸]
A Practical Evaluation Method for Long-Form Simultaneous Speech-to-Speech Translation
Yulin Xue, Siqi Ouyang, Lei Li
https://t.co/z7fDa1SFHc [𝚌𝚜.𝙲𝙻]
💬Accepted to IWSLT 2026 Scientific Track
Think @ultralytics YOLO is just code? Meet the YAML that defines it all. 💥 🚀
We published the model YAML configuration guide, a great resource for both researchers and developers to understand the layer workflows behind YOLO models.
More info 👇
#Research#DeepLearning #MachineLearning
164/365 of GPU Programming
Some additional interesting resources/papers/blog posts in this area
- https://t.co/gnjYKPwUph: collaboration between Cursor and Nvidia using Cursor's multi-agent harness to optimize CUDA kernels benchmarked against SOL-ExecBench
- https://t.co/4E4HAGtJSR: paper out of ByteDance seed using an agentic RL system for CUDA kernel optimization with data synthesis, verification + profiling, and long context training
- https://t.co/KO1de2WZvn: recent lit survey of the current LLM kernel generation space
Lots of interest in this space but seems like there are no consensus approaches that have emerged yet, so plenty of whitespace left
The entire vector database industry just got destroyed by A free tool from 1974.
For the last two years, every company building AI has obsessed over "RAG" (Retrieval-Augmented Generation).
They spent millions on complex vector databases, semantic search, and embedding models.
The promise? It’s the only way to give AI long-term memory.
But researchers published a paper proving that the most sophisticated AI agents actually do better with grep.
Yes. The basic search command invented 52 years ago.
Researchers tested modern AI agent harnesses, including Claude Code and Gemini CLI, on complex retrieval tasks.
They compared expensive vector retrieval against simple, old-school grep searches.
The results are humiliating for the AI infrastructure industry.
Across the board, grep didn't just compete with vector databases.
It beat them. It yielded consistently higher accuracy.
Why?
Vector search tries to be "smart." It finds documents that are semantically similar.
But when an autonomous AI agent is searching for exact code variables, specific log errors, or explicit names, "similar" isn't good enough.
"Similar" introduces noise. "Similar" distracts the agent.
grep is dumb, literal, and perfectly precise. It finds exactly what the agent asks for.
And as it turns out, when you combine a hyper-intelligent reasoning model with a perfectly literal search tool, the results are explosive.
The AI already has the semantic intelligence. It doesn't need the database to think for it.
It just needs the database to fetch.
When researchers intentionally added distracting, irrelevant text to the context, vector search collapsed. It grabbed the wrong information.
grep ignored the noise and pulled the exact string.