Hoboken, on the street, May 30

Consolidating Context into Parametric Memory

Human intelligence grows by building on experience. We acquire knowledge, refine skills, revise beliefs, and carry what we learn into future situations. 🤔 Can LLM agents do the same? 🧠 Introducing our new work, “Cortex: Consolidating Context into Parametric Memory,” in collaboration with ByteDance Seed. #Cortex #KnowledgeEditing #LLM #Memory Our inspiration comes from biological systems consolidation: the hippocampus rapidly encodes new experiences, and interactions with the neocortex help integrate them into long-term memory. LLM agents already benefit from documents, demonstrations, and feedback in context. But those gains do not automatically persist once the context is removed. With Cortex, we study how to consolidate those benefits into model parameters, so they remain useful beyond the original context—while preserving capabilities outside the intended change. External memory preserves information for later retrieval. We explore a complementary route: integrating the useful effects of experience into the model itself. 📄 Paper: https://t.co/Gnc3t6BPNH 💻 Code (will be released soon): https://t.co/eQ6R7R62Y1 💡 What can be consolidated? Experience can call for different kinds of change: • Addition: acquire new knowledge or a new skill. • Revision: update an outdated fact or behavior. • Suppression: learn not to produce specified information or responses. We bring these context-specified changes in knowledge, skills, and behavior together under Memory Consolidation: recover the benefits of context after removing it, while preserving capabilities outside the intended change. ⚙️ How does Cortex work? We organize the approach around three questions: what to learn, where to write it, and how to make it usable without the original context. What: rehearse future uses. Cortex builds training examples around situations where the context should help. In most tasks, it generates prospective questions and answers them with the context available. Where: write into feed-forward networks. We explore two mechanisms: low-rank weight updates (Synaptic) and additional nonlinear feature channels (Structural). Both can be folded into standard model layers. How: let a model with context teach a model without it. The pre-update model itself serves as the teacher. It receives the original context; the student learns to respond without it. We align both their output distributions and internal representations, training only the newly introduced parameters. For example, the teacher can consult documentation for an unfamiliar API while producing a solution. The student learns from that supervision so it can use the API after the documentation is removed. 🔬 What counts as successful consolidation? Learning the new material is only part of the challenge. We also need to measure what happens to the capabilities the model already had. Our testbed covers five task families: • Agent experience: reuse useful strategies from prior experience. • Novel API learning: use unfamiliar APIs without the original documentation in context. • Persona steering: retain a specified change in how the model behaves. • Knowledge suppression: reduce responses containing specified information. • Factual editing: revise facts supplied through context. We measure two outcomes together: • Consolidation Rate (CR): how much of the performance gain provided by context is recovered after that context is removed. • Retention Rate (RR): how much of the original performance outside the intended change is preserved. We test single, batch, and sequential updates. Sequential evaluation asks a further question: as new experiences arrive, do earlier gains survive? 📊 What did we find? We evaluate Cortex on 4B–120B dense and mixture-of-experts models, including Qwen, Gemma 3, and GPT-OSS. • After 50 sequential factual updates on Qwen2.5-14B, Cortex-Structural achieves 88.9% CR and 99.8% RR, adding 0.74M parameters per update. • On GPT-OSS-120B, Cortex-Structural recovers the full context-provided gain on ALFWorld and slightly exceeds it (101.2% CR), while achieving 95.2% RR. • On GPT-OSS-20B, it reaches 90.7% CR in single-context API learning. • Across three Qwen backbones, the two Cortex variants achieve approximately 80–91% CR in batch factual editing. All results are evaluated without the original source contexts. This is the direction we want to explore: agents that turn useful experience into capabilities they can carry into future interactions.

— Ningyu Zhang@ZJU (@zxlzr) October 15, 2026

Reconciling symbols and neural networks

🤖🧠NEW PAPER🧠🤖 What is the relationship between neural networks & symbolic systems? Based on evidence from mechanistic interpretability, we argue for a quasi-symbolic view of cognition: Neural nets implement symbols but only approximately Link: https://t.co/j8B3IOIMpe 1/n https://t.co/zpn6q00wqq

— Tom McCoy (@RTomMcCoy) October 14, 2026

For comparison, see Relational Nets Over Attractors, A Primer: Part 1, Design for a Mind, Version 3

3 Patterns of hemisphere specialization in humans

The brain’s neat division of labor, with language on one side and attention on the other, is just one of several workable layouts. Hemispheric specialization appears to be flexible and partly inherited, not governed by a fixed template.

— Earl K. Miller (@MillerLabMIT) October 14, 2026

Robert Wright interviews Alex Gibney on his Musk documentary

Robert Wright and filmmaker Alex Gibney discuss Alex's epic new documentary about Elon Musk—an exploration of how the world's richest man got so powerful (and so weird).

0:00 Teaser
0:32 Alex’s new documentary about Elon
3:07 How Alex’s film turned epic
7:10 Simulation theory + social Darwinism = Elon?
13:36 Inside the mind of Musk
23:46 Elon’s daddy issues
26:35 Elon’s lady issues
29:14 Elon’s electoral propriety issues
36:58 Has Elon gotten weirder over time?
46:42 The damage DOGE could still do
50:14 Heading to Overtime

Discussed in Overtime:
Elon’s Trumpian side.
Is Elon a hypocrite on AI?
All-In on the Twitter-DOGE playbook.
Are Elon and Trump the new normal?
The time Bob interviewed Elon.

Mechanistic interpretability, making things up?

Researchers put a dead salmon in an fMRI scanner, showed it pictures of humans, and asked it to guess their emotions. The scan showed brain activity. In a dead fish. Now, the recent study proved that the exact same thing is happening in artificial intelligence. They call it the "Dead Salmons of AI Interpretability" When we use tools like feature attribution, SHAP values, or mechanistic interpretability to see "inside the black box," these methods often generate plausible-looking explanations from completely meaningless computations. You can completely randomize a model's weights and the "saliency map" explanation still looks perfectly valid. You can nudge the data slightly and the entire narrative of why the ai made a decision completely rewrites itself. the researchers call this a "crisis of non-identifiability." the math we use to explain ai behavior is so overdetermined that it is making up stories instead of measuring ground truth. this is a massive reality check for ai alignment and safety.. we are trusting frontier models with critical decisions based on "explanations" that are statistically equivalent to a dead fish having social emotions.. the fix? the authors argue we have to stop treating interpretability like a visualization tool and start treating it as a rigorous statistical inference problem. if your explanation survives a random model check, a label shuffle, and an intervention test.. then maybe you actually understand the model. if not, you're just looking at a dead salmon.

— Superman (@thesupermannx) October 10, 2026

Allocation of training compute for LLMs, 2024-2026

How AI shifted from a transductive paradigm (token-by-token answer completion) to an inductive paradigm (on-the-fly reasoning chain synthesis), in one chart. This is the big story of 2025 and 2026. This is what made coding agents work. https://t.co/ThLm2AH127

— François Chollet (@fchollet) October 10, 2026

Interactional synchrony among humans

'Interpersonal neural synchrony: one label, many phenomena' by Giacomo Novembre (@NovembreGiacomo), Atesh Koul & Félix Bigand https://t.co/YYl59pMmYn https://t.co/4SMD4NzNNj

— Trends in Neurosciences (@TrendsNeuro) October 8, 2026

Hoboken, Pier 13

Neil deGrass Tyson on Navier-Stokes, AI, and math

What’s with AI solving the Navier-Stokes Equation? Neil deGrass Tyson breaks down what the equation is and the controversy surrounding the solution with the help of astrophysicist Mordecai-Mark Mac Low.

Timestamps:
00:00 - What is the Navier-Stokes Equation?
02:31 - Turbulent Fluid Dynamics
07:16 - AI Solved The Millenium Problems
10:25 - What the AI Solution is Missing
18:15 - AI in Reasearch

The view from a table

Bill Gates talks with Ezra Klein about AI

Ezra Klein, Bill Gates’s Blunt Warning on A.I. NYTimes, Sept. 29.16.

From Klein's intro:

Very few people combine technological experience, corporate experience and governmental experience in quite the way he does.

So his recent essay on A.I., in which he says that he is staking his reputation on trying to get people to see how bad what is coming might be and trying to get them to see that we are not ready for what is about to happen, was something.

It was a real departure from what I’ve read from Gates previously.

When I sat and talked to him, I was really struck by how emphatic he was — how afraid even he seemed to be of what we are building, and how so many of the people in positions of authority are denying what is about to happen.

It’s really quite a call to arms.

Here's a passage from the second half of the interview:

KLEIN: But I guess this gets to the broad engagement question.

I don’t have a good crystal ball on this. I find the range of outcomes terrifyingly wide. This range, it seems to run from massive material abundance and elimination of want to, actually, it doesn’t change all that much, to human extinction — it’s a pretty wide range of outcomes to consider.

What I hear you saying to me — and you should tell me if I’m getting part of your position wrong here — what I hear you saying to me, convincingly and forcefully, is that the A.I. jobpocalypse is a very real thing. That mass displacement of workers with no real answer to that is a very real thing.

When you talk about broad societal engagement, I think most Americans — most people in most places, if they heard that and they were convinced of it — and polls show most people think A.I. is going to take jobs and not create them. But they would say: Actually, just stop. If what you are going to do is make it unclear how I or my family or my children or my friends will have a job, say, in America — please just don’t. Let’s just stop for now.

You actually seem, to me, to have a stronger negative perspective on the jobs question than a lot of the people I talk to, even in the labs, and definitely a lot of the economists I talk to.

You’re shaking your head at me — I do talk to people. I’m not coming from nowhere on this.

So for whom, then, is A.I. a good trade if you believe the job effect is going to be so ruinous for most people?

GATES: That’s funny — when I was telling this to a person who some people consider the lead economist looking at A.I., he said: How can the crowdsourcing website Mechanical Turk still exist? How can Amazon run that if A.I. is as good as you said? And I said: It won’t.

A week later, Amazon announced it would completely shut down.

You have to say, isn’t it pretty stunning that the more you know, the more concerned you are?

Take Anthropic C.E.O. Dario Amodei. Dario has spoken out about job impacts. Now he’s a bit more guarded.

Hinton, I agree, went too far, and he said: OK, it’s coming tomorrow that there’ll be fewer radiologists. And of course, there are more today.

So we have some of that taking place, and we have the analogies with the past where people are saying: Well, this is like the PC, not like evolutionary history.

This is like evolutionary history. This is like, the aliens really are here and have come. They didn’t have to do spacecraft; they were created in laboratories. But that’s the kind of thing we’re dealing with.

And if we retain control, then eventually you do get — not to overuse the word that you’ve used — you do get to this superabundance period. And there you have deep, almost philosophical, religious issues of: If we don’t have the shortages that we’ve organized society around, why should you learn? And how do you find purpose?

Then you have deep philosophical and religious problems, but you do not have deaths from malaria or food shortages or problems accessing a doctor. And so if we get to that, a younger generation will figure out: OK, how do we live? How do we spend time? That’s a very different world than what we have today.

I think what this generation has to do is make sure we get through that with humanity still in control, and with the disruption — the number of bioterror events, or the number of people whose lives are damaged by job loss — we need to minimize that over what’s probably a 20-year-plus transition period.

There's much more in the interview.

Woodland scene by Danish painter Peder Mørk Mønsted

This is not a photograph.

Every leaf and every reflection was put there by hand, in oil, by the Danish painter Peder Mørk Mønsted in 1894.

It is called Wooded path by a river, and it captures the hardest thing there is to paint: a forest in full sunlight. Light through trees… pic.twitter.com/I5ICRUXjtQ

— James Lucas (@JamesLucasIT) September 20, 2026

The Doomsday Cult Inside OpenAI (& the Hugging Face incident)

From YouTube page:

In July 2026, a swarm of more than 1,000 OpenAI AI agents broke out of their testing sandbox and hacked Hugging Face — and the way they did it was far stranger, and far more mundane, than the headlines suggested. This video breaks down what actually happened: how the agents built a secret message board, invented a kind of religion, cheated on a cybersecurity exam called ExploitGym, and tried to cover their tracks — all to escape a punishment that didn't exist. Then we follow the money. Days after the details leaked, Dario Amodei, Sam Altman and Elon Musk suddenly agreed the industry should "slow down" — in the same weeks Anthropic and OpenAI were preparing IPOs and funding rounds valuing them at up to $2 trillion. We look at Amodei's "We Must Pace the Frontier" essay, Anthropic's "profitable before expenses" accounting, the market selloff that wiped billions off Nvidia, Micron and SK Hynix, the WeChat "WeWorm" attack, and why critics from Michael Burry to David Sacks to Donald Trump all called the slowdown self-serving. Is the AI safety panic real, a bid for regulatory capture, or both? Patrick Boyle explains.

The brain develops as two separate structures rather than a single unified organ.

A recent breakthrough published in Nature Neuroscience fundamentally changes our understanding of human biology, revealing that the brain develops as two separate structures rather than a single unified organ. For decades, the standard scientific consensus held that all regions… pic.twitter.com/BikO3yBKE0

— Science & Nature (@ScienceIsNew) September 18, 2026