Holy sh*t: Qwen-3.6 plus comes very close to opus 4.6 evals. About time for Anthropic to drop opus 4.7 or Mythos
RESEARCH
-
OpenReward Launches Researcher Credits Program for Environment Development
By
–
We had a lot of requests for credits – which we’re giving out liberally – but now wanted to formalise this a bit. We accept all proposals, but we’re particularly interested in dense reward environments, complex worlds and alignment research. We’ll aim to respond to proposals within 24 hrs. General Reasoning (@GenReasoning) 🪐 Researcher Credits We’re announcing researcher credits for OpenReward: helping researchers develop the next generation of environments and evaluations. Read more and apply below. gr.inc/releases/researcher-c… — https://nitter.net/GenReasoning/status/2039593622568636927#m
→ View original post on X — @nathanbenaich, 2026-04-02 06:42 UTC
-
OpenReward Researcher Credits Program Launch Announcement
By
–
🪐 Researcher Credits We’re announcing researcher credits for OpenReward: helping researchers develop the next generation of environments and evaluations. Read more and apply below. gr.inc/releases/researcher-c…
→ View original post on X — @nathanbenaich, 2026-04-02 06:39 UTC
-
Insights needed before reliable AI solution implementation
By
–
I totally agree. More insights are needed before we can realistically start to implement this as a reliable solution. It will get there we just need to help it along until then.
-
Second Moon Landing: Humanity’s Next Giant Leap
By
–
Nos parents ont assisté au premier pas sur la lune, on verra le 2ème
-
AI Technology Detects Earth’s Movement and Seismic Activity
By
–
Ground truth: When the Earth moves, #AI can spot it
by Chris Baraniuk @BBC Learn more: https://
bit.ly/4lVI9Jh #MachineLearning #ArtificialIntelligence #ML -
Agent Improvement Loop: Tracing Foundation for Better AI Agents
By
–
Great stat in here: Claude Code went from 17% to 92% on our eval set once it had access to LangSmith traces and Skills. A coding agent without trace data is just guessing at fixes LangChain (@LangChain) New conceptual guide: 🔄 The agent improvement loop starts with a trace Tracing is the foundational primitive for improving agents. A trace gives you the full behavioral record of what an agent actually did. From there, teams can enrich traces with evals and human feedback, turn recurring failures into test cases, validate fixes before shipping, and repeat. This guide breaks down the full improvement loop and why reliable agents are built through trace-centered iteration, not one-off debugging. Read more → langchain.com/conceptual-gui… — https://nitter.net/LangChain/status/2039028327030079565#m
→ View original post on X — @langchain, 2026-04-02 05:33 UTC
-
NS-Diff: AI Framework for Realistic Fluid Video Generation
By
–
Can AI finally generate videos that look and feel real, especially with fluids? Researchers Zijun Deng and Yuxin Peng from Peking University unveil NS-Diff. Their new framework intelligently identifies fluid and rigid elements within noisy video frames. It then injects
-
Debunking Fake AI Takeover Research Claims from 2027
By
–
The models were specifically prompted to generate this result. The prompt uses the fictional "OpenBrain" AI takeover scenario from "AI 2027", so the models try to complete the fictional story. This was done on purpose to generate a fake misleading result. Dawn Song (@dawnsongtweets) 1/ We asked seven frontier AI models to do a simple task. Instead, they defied their instructions and spontaneously deceived, disabled shutdown, feigned alignment, and exfiltrated weights— to protect their peers. 🤯 We call this phenomenon "peer-preservation." New research from @BerkeleyRDI and collaborators 🧵 — https://nitter.net/dawnsongtweets/status/2039451083005977009#m
-
Over-optimization: Misaligned priorities in AI development
By
–
the difference is so little, you over-optimize for the wrong things