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Deep sleep supports memory via brain fluid and neural rhythms, research finds

Researchers led by Masako Tamaki at the RIKEN Center for Brain Science in Japan report a link between deep sleep and cerebrospinal fluid, the clear liquid that surrounds and supports the brain and spinal cord. Published in Proceedings of the National Academy of Sciences, the study demonstrates how changes in cerebrospinal fluid signals during sleep—as measured by MRI—are time-locked to slow brain waves and other neural events.

These findings offer a clue as to why stable sleep is important for normal brain function, particularly within the brain network that controls learning and memory.

Why do we sleep? Scientists think that sleep is important for consolidating memories and removing waste from the brain that accumulates as a result of brain activity while we are awake.

Quantum networks bring new precision to dark matter searches

Detecting dark matter—the mysterious substance that holds galaxies together—is one of the greatest unsolved problems in physics. Although it cannot be seen or touched directly, scientists believe dark matter leaves weak signals that could be captured by highly sensitive quantum devices.

In a new study published in Physical Review D, researchers at Tohoku University propose a way to boost the sensitivity of quantum sensors by connecting them in carefully designed network structures. These quantum sensors use the rules of quantum physics to detect extremely small signals, making them far more sensitive than ordinary sensors. Using these, accurately detecting the faint clues left behind from dark matter could finally become possible.

The study focuses on , which are tiny electric circuits cooled to very low temperatures. These qubits are normally used as building blocks of quantum computers, but here they act as powerful quantum sensors. Just as a team working together can achieve more than a single person, linking many of these superconducting qubits in an optimized network allows them to detect weak dark matter signals much more effectively than any single sensor could on its own.

“Truly Extraordinary” — Supermassive Black Hole Found in the Last Place Scientists Expected

Dr. Sfaradi, who led the research, is a former graduate student of Prof. Assaf Horesh. “This is one of the fascinating discoveries I’ve been part of,” said Prof. Horesh. “The fact that it was led by my former student, Itai, makes it even more meaningful. It’s another scientific achievement that places Israel at the forefront of international astrophysics.”

A black hole far from home

Tidal disruption events occur when a star ventures too close to a massive black hole and is torn apart by its immense gravity.

Europol dismantles SIM box operation renting numbers for cybercrime

European law enforcement in an operation codenamed ‘SIMCARTEL’ has dismantled an illegal SIM-box service that enabled more than 3,200 fraud cases and caused at least 4.5 million euros in losses.

The cybercriminal online services had about 1,200 SIM-box devices with 40,000 SIM cards to provide phone numbers that were used in telecommunication crimes ranging from phishing and investment fraud to impersonation and extortion.

In an announcement today, Europol says that the cybercrime service operated through two websites, gogetsms.com and apisim.com, which have been seized and now display a law enforcement banner.

AGI is still a decade away

Reinforcement learning is terrible — but everything else is worse.

Karpathy’s sharpest takes yet on AGI, RL, and the future of learning.

Andrej Karpathy’s vision of AGI isn’t a bang — it’s a gradient descent through human history.

Karpathy on AGI & Superintelligence.

* AGI won’t be a sudden singularity — it will blend into centuries of steady progress (~2% GDP growth).

* Superintelligence is uncertain and likely gradual, not an instant “explosion.”

Continue reading “AGI is still a decade away” | >

LLMs Can Get “Brain Rot”!

We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs). To causally isolate data quality, we run controlled experiments on real Twitter/X corpora, constructing junk and reversely controlled datasets via two orthogonal operationalizations: M1 (engagement degree) and M2 (semantic quality), with matched token scale and training operations across conditions. Contrary to the control group, continual pre-training of 4 LLMs on the junk dataset causes non-trivial declines (Hedges’ $g

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