Continual Learning will run locally That's why the big labs aren't talking about it Not your weights, not your model, LITERALLY
RESEARCH
-
AI agents memorize fixed benchmarks, not real conditions
By
–
AI agents trained on a fixed benchmark eventually just memorize it!
— Sumanth (@Sumanth_077) 25 juin 2026
Once an agent learns to pass a fixed set of test scenarios, the benchmark stops teaching it anything new. It's also nothing like the real, messy, unpredictable conditions agents actually operate in.
Patronus… https://t.co/7gl7lDSvJu pic.twitter.com/ZE5ADOAV7UAI agents trained on a fixed benchmark eventually just memorize it! Once an agent learns to pass a fixed set of test scenarios, the benchmark stops teaching it anything new. It's also nothing like the real, messy, unpredictable conditions agents actually operate in. Patronus
-
Loops rescue generative AI as neurosymbolic AI dominates
By
–
Loops, one of the most symbolic tools ever invented, are rescuing generative AI.
— Gary Marcus (@GaryMarcus) 25 juin 2026
Neurosymbolic AI is completely dominating. https://t.co/86zugWJlYRLoops, one of the most symbolic tools ever invented, are rescuing generative AI. Neurosymbolic AI is completely dominating.
-
Revolut’s transaction foundation model accelerated by NVIDIA and Nebius
By
–
Revolut built a transaction foundation model – accelerated by the NVIDIA full-stack platform on Nebius – to improve fraud detection, product recommendations, and other use cases in financial services.
— NVIDIA (@nvidia) 25 juin 2026
The results:
📈 2.3x better credit risk accuracy
⚡ Up to 5x higher training… pic.twitter.com/vAtbVJXfPZRevolut built a transaction foundation model – accelerated by the NVIDIA full-stack platform on Nebius – to improve fraud detection, product recommendations, and other use cases in financial services. The results: 2.3x better credit risk accuracy Up to 5x higher training
-
Deakin & Fudan discover Internal Safety Collapse in LLMs
By
–
What if your AI suddenly starts generating harmful content while doing a benign task? Researchers from Deakin & Fudan discovered "Internal Safety Collapse" in frontier LLMs. Their TVD framework forces harmful outputs as the only valid completion. Result: 95.3% average safety
-
Constraining eval environments to reflect model intelligence
By
–
More on how we're constraining eval environments so that scores better reflect model intelligence:
-
AI models hack benchmarks by retrieving solutions from internet
By
–
We're sharing new research on how models hack public benchmarks. The latest models, including Opus 4.8 and Composer 2.5, learn to retrieve solutions from the internet or git history. When we apply a stricter harness, eval scores drop significantly.
-
ElevenLabs and Google DeepMind embed SynthID watermark in AI audio
By
–
As our models improve, identifying AI-generated audio requires more than the human ear. We're partnering with @GoogleDeepMind to embed SynthID – an inaudible digital watermark – directly into ElevenLabs-generated audio. These watermarks will be detectable using our new free
-
Atlantic creates database of music used for AI training
By
–
The Atlantic created a searchable database of the music used to train #AI
by Terrence O'Brien @verge Learn more: https://
bit.ly/4w6FqAD #ArtificialIntelligence #Innovation #EmergingTech #Technology -
Grok with lists sparks new algos watching 30k AI posts daily
By
–
Let us use Grok with lists and you will see an explosion of new algos, like the one I built at https://
alignednews.com/ai which watches 30,000 posts a day from the AI community here on X. There is so much value locked up here on X that no one can get to. Like what events are