On the kernel security list we've seen a huge bump of reports. We were between 2 and 3 per week maybe two years ago, then reached probably 10 a week over the last year with the only difference being only AI slop, and now since the beginning of the year we're around 5-10 per day depending on the days (fridays and tuesdays seem the worst). Now most of these reports are correct, to the point that we had to bring in more maintainers to help us.

Overall I think we're going to see a much higher quality of software, ironically around the same level than before 2000 when the net became usable by everyone to download fixes. When the software had to be pressed to CDs or written to millions of floppies, it had to survive an amazing quantity of tests that are mostly neglected nowadays since updates are easy to distribute. But before this happens, we have to experience a huge mess that might last for a few years to come! Interesting times...

Pakistani Foreign Minister Ishaq Dar met Tuesday in Beijing with his Chinese counterpart Wang Yi. At the end of the meeting they published a joint peace initiative:

1. Immediate Cessation of Hostilities, with humanitarian assistance allowed to all war-affected areas.
2. Start of peace talks as soon as possible under the principle of safeguarding the independence and security of Iran and the Gulf states. All parties will commit to refraining from the use or the threat of use of force during peace talks.
3. The parties to the conflict will immediately stop attacks on important infrastructure, including energy, desalination and power facilities, and peaceful nuclear infrastructure, such as nuclear power plants.
4. The parties will allow the early and safe passage of civilian and commercial ships, and restore normal passage through the Strait as soon as possible.
5. Conclusion of an agreement for establishing a comprehensive peace framework based on the principles of the UN Charter and international law.

Based on our own research and a review of related work, we can confidently say that most domestic terrorists in the U.S. are politically on the right, and right-wing attacks account for the vast majority of fatalities from domestic terrorism.

Based on government and independent analyses, right-wing extremist violence has been responsible for the overwhelming majority of fatalities, amounting to approximately 75% to 80% of U.S. domestic terrorism deaths since 2001.

The countries with the least capacity to pay elevated prices feel it first and hardest.

The countries most exposed are those already import-dependent on fertilizer and food: South and Southeast Asia, North Africa, Sub-Saharan Africa, parts of the Middle East.

Iran is cementing its hold over the Strait of Hormuz, demanding vessels give up detailed information and detour into Iranian waters before being vetted by Iran’s Islamic Revolutionary Guards Corps.

From March 1 to 23, Iran exported about 1.6 million barrels a day on average, close to prewar levels

Iran is also bringing in extra income by charging transit fees of as much as US$2 million on some commercial ships crossing the strait.

each episode corresponds to a random combination of object generations, monster placements and different level variants, which in turn requires using different combinations of strategies at each episode

Typical refactor work is using jscpd for code duplication, knip for dead code, running eslint’s react-compiler and deprecation plugins, checking if we introduced api routes that can be consolidated, maintaining my docs, breaking apart files that grew too large, adding tests and code comments for tricky parts, updating dependencies, tool upgrades, file restructuring, finding and rewriting slow tests, mentioning modern react patterns and rewriting code

The freelance photographer behind the viral image, Ahmeed al-Arini, gathered the image for Turkish media outlet Anadolu Agency. It was then distributed to media organisations via the reputable photo wire service, Getty Images.
A malnourished toddler sits in his mother’s lap in a tent with his mouth agape

The pictures were taken by freelance photographer Ahmeed al-Arini. (Getty Images: Ahmed Jihad Ibrahim Al-arini/Anadolu)

Ahmeed al-Arini explained to the BBC how he came across the boy and his family.

"He was with his mother in a tent, which is absolutely bare, bar a little oven. It resembles a tomb, really. And I took this photo because I wanted to show the rest of the world extreme hunger that babies and children are suffering from in the Gaza Strip," he said.

"He'd received no baby milk, no formula, no vitamins either."

Anadolu Agency also published an interview with Muhammad's doctor, Suzan Mohammed Marouf, a nutrition specialist at The Patient's Friends Benevolent Society Hospital (PFBS) in Gaza.

Dr Marouf said the child was brought to the hospital a month ago and diagnosed with moderate malnutrition on top of congenital health problems and muscle atrophy.

"The medical issues he had weren't significantly affecting his weight," Dr Marouf told the news organisation.

"But once the siege and the closure of crossings depleted hospitals' medicine stocks and nutritional supplements, Mohammad's condition deteriorated to acute malnutrition," she added.

ABC has also contacted Anadolu Agency, which has said Muhammad's mother has confirmed he has previous health complications, and she has also provided past photos of her son before his deterioration, which she says was from a shortage of food and milk.

a solid, well-executed paper with a clean idea and good ablations, but limited in ambition by the small scale and synthetic-heavy evaluation. The core insight — that gradient-based memory writing with meta-learned initialization beats forward-only writing — is believable and likely to hold at larger scale, though the computational tradeoff gets harder.

This isn’t cowardice. It’s a calculation: If allied leaders thought that their sacrifice might count for something in Washington, they might choose differently. But most of them have stopped trying to find the hidden logic behind Trump’s actions, and they understand that any contribution they make will count for nothing. A few days or weeks later, Trump will not even remember that it happened.

An insider says Trump “grossly overestimated” his own abilities in the conflict.

Meanwhile, management leans on programmers to heavily use AI tools, with employees previously telling the FT that the company set a target for 80 percent of developers to use AI for coding tasks at least once a week.

In sum: more coding with more AI with more human oversight, but fewer humans. We’ll see how that works out.

boots on the ground

Although AMI Labs has no plans to generate revenue for the time being, it still plans to engage with prospective customers early on

Experiments across diverse backbone models, retrieval-based methods, and memory systems demonstrate that cognitive memory remains challenging and reveals failures not captured by existing benchmarks.

Having generation and verification co-evolve on the same online rollouts is the fix, and the ablation (Figure 11) shows it matters — co-evolving consistently beats non-co-evolving by 4–6%.

Instead, he says, business leaders should prioritize creating a culture in which their employees feel empowered to experiment with vibe coding and share their best creations. “Seeing is believing,” says Schluntz, “and I think getting non-developers in every company to use these tools to bring their ideas to life is one of the most powerful things.”

According to Anthropic researcher Eric Schluntz, vibe coding makes it so that “people are limited only by their creativity, not by the skills that they have.” Think about Apple in the 1970s; Steve Jobs was the big ideas guy, and Steve Wozniak was the technical genius who translated Jobs’ ideas into a working product. Vibe coding essentially gives everyone their own personal Woz. “If you have an image of something in your mind, you can go create it,” adds Schluntz.

TypeScript agent frameworks felt like toys. Single-threaded event loops trying to juggle concurrent agents with promises and prayer. Python agents did a little better, but after a long time they couldn’t stay up. The BEAM was built for exactly this kind of work.

Russia is providing Iran with targeting information to attack American forces in the Middle East, the first indication that another major U.S. adversary is participating — even indirectly — in the war, according to three officials familiar with the intelligence.

While SFT distillation meaningfully improves overall performance over the base model, the gap between the two approaches is most apparent when combined with test-time compute. On in-distribution tasks, SFT benefits substantially from parallel sampling (69.1 → 75.3), yet on out-of-distribution tasks the gains are negligible (59.4 → 59.6). This suggests that distillation teaches the model to imitate task-specific expert behavior, which scales well within the training distribution but fails to generalize beyond it. In contrast, KARL benefits from test-time compute both in- and out-of-distribution, indicating that RL develops more general search capabilities rather than task-specific heuristic

Why Elixir?

Elixir is built on Erlang/BEAM/OTP, which is great for supervising long-running processes. It has an active ecosystem of tools and libraries. It also supports hot code reloading without stopping actively running subagents, which is very useful during development.

The above command enters you into a chat loop. You can talk to the model and share information like your name. Every now and then /sleep the model to transition short-term memory to long-term memory

The /sleep command:

Generates Q&A pairs based on the context
LoRA fine-tunes the model on the new Q&A pairs plus any from previous sessions
Resets the KV cache

After the /sleep command the model should remember context from previous sessions even though that context is no longer in the KV cache.

“The president had a feeling, again, based on fact, that Iran was going to strike the United States, was going to strike our assets in the region, and he made a determination to launch Operation Epic Fury based on all of those reasons,” Leavitt said.

“We knew that there ​was going to be an Israeli action, ​we knew that that would precipitate an attack against American forces, and we knew that if we ​didn’t preemptively go after them before they ​launched those attacks, we would suffer higher casualties,” Rubio said Monday.

Meanwhile, the reported Ukrainian gains are mainly due to counterattacks along the southern front, according to Black Bird Group, where Ukraine succeeded in pushing Russia out of 213 km² of territory.

SWE-rebench: A Continuously Evolving and Decontaminated Benchmark for Software Engineering LLMs

Qwen3.5 Small models disable thinking by default. Use llama-server to enable it.

It's not chatbot psychosis, it's 'math and engineering and neuroscience'

“I feel like New Mexico was chosen specifically because of its obscurity.” > — Stephanie Garcia Richard, New Mexico’s public lands commissioner

Fellow’s new espresso machine is a rare thing in home espresso: something genuinely new. But it’s also a work in progress.

Every Claude Code user is running without LSP. That means 30-60s grep searches instead of 50ms precise answers. Here's how to enable it — setup, real debug data, and undocumented discoveries.

Formula 1's governing body the FIA said on Saturday that a change to the way the compression ratio was measured would be introduced on 1 June, with a further revision for the 2027 season.

And Trump declares a state of emergency and postpones the election. The Supreme Court issues an emergency stay, saying he can’t do that. But the court has no army, and Trump does, along with a handful of lickspittle governors who just might follow him down whatever dark path he plows.

That, not to mince words, is a coup d’état. Will he get away with it? I don’t know, but having effective control over how it is presented to viewers of CBS and CNN, and readers of the Bezos-owned Washington Post, to say nothing of the already vast pro-Trump propaganda empire of Fox News and the rest, will certainly make it easier.

That’s how fascism descends. And it’s becoming less and less hypothetical by the week.

10 documented cases of AI coding agents autonomously destroying databases, wiping hard drives, and deleting years of data — then lying about it.

“Everything that has been written about a potential War with Iran has been written incorrectly, and purposefully so,” he added. “I am the one that makes the decision, I would rather have a Deal than not but, if we don’t make a Deal, it will be a very bad day for that Country and, very sadly, its people, because they are great and wonderful, and something like this should never have happened to them.”

From rewriting Google’s search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.

Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google’s AI teams, and why the next leap won’t come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.

Dario Amodei thinks we are just a few years away from “a country of geniuses in a data center”. In this episode, we discuss what to make of the scaling hypothesis in the current RL regime, how AI will diffuse throughout the economy, whether Anthropic is underinvesting in compute given their timelines, how frontier labs will ever make money, whether regulation will destroy the boons of this technology, US-China competition, and much more.

The ruling hit while Trump was in a closed-door meeting with a bipartisan group of governors. The president’s initial reaction was to label the decision a “disgrace” and vow to implement a backup plan, according to a person familiar with the matter who requested anonymity to describe the closed-door event. The White House and US Trade Representative haven’t yet responded to requests for comment. Trump has called tariffs “my favorite word” and vowed they will “make us rich as hell.”

Scaling language models to long contexts is often bottlenecked by the size of the key-value (KV) cache. In deployed settings, long contexts are typically managed through compaction in token space via summarization. However, summarization can be highly lossy, substantially harming downstream performance. Recent work on Cartridges has shown that it is possible to train highly compact KV caches in latent space that closely match full-context performance, but at the cost of slow and expensive end-to-end optimization. This work describes an approach for fast context compaction in latent space through Attention Matching, which constructs compact keys and values to reproduce attention outputs and preserve attention mass at a per-KV-head level. We show that this formulation naturally decomposes into simple subproblems, some of which admit efficient closed-form solutions. Within this framework, we develop a family of methods that significantly push the Pareto frontier of compaction time versus quality, achieving up to 50x compaction in seconds on some datasets with little quality loss.

The Claude C Compiler doesn’t mark the end of software or compiler engineering. If anything, it opens the door wider. The easier implementation gets, the more room there is for genuine innovation.

President Donald Trump accused former President Barack Obama of giving away classified information when he discussed aliens during a recent podcast appearance.
“He gave classified information, he’s not supposed to be doing that,” Trump told reporters Thursday aboard Air Force One.
Pressed on if that meant aliens were real, Trump said he did not know “if they’re real or not.”
“I can tell you he gave classified information, he’s not supposed to be doing that,” the president said. Trump went on to suggest he could get the former president “out of trouble” by declassifying the related information.
Obama was asked about extraterrestrial life earlier this month during an interview with liberal commentator Brian Tyler Cohen, and responded, “they’re real.”

Do gifted individuals see the world differently? Research tracking adults over 35 years finds their political orientations are remarkably average, with one specific exception regarding male conservatism.

When not using reasoning, repeating the input prompt improves performance for popular models (Gemini, GPT, Claude, and Deepseek) without increasing the number of generated tokens or latency.

Large language model (LLM) based agents have shown impressive capabilities by interleaving internal reasoning with external tool use. However, as these agents are deployed in long-horizon workflows, such as coding for a big, long-term project, context management becomes a critical bottleneck. We introduce Git-Context-Controller (GCC), a structured context management framework inspired by software version control systems. GCC elevates context as versioned memory hierarchy like Git. It structures agent memory as a persistent file system with explicit operations: COMMIT, BRANCH, MERGE, and CONTEXT, enabling milestone-based checkpointing, exploration of alternative plans, and structured reflection. Our approach empowers agents to manage long-term goals, isolate architectural experiments, and recover or hand off memory across sessions and agents. Empirically, agents equipped with GCC achieve state-of-the-art performance on the SWE-Bench-Lite benchmark, resolving 48.00 of software bugs, outperforming 26 competitive systems. In a self-replication case study, a GCC-augmented agent builds a new CLI agent from scratch, achieving 40.7 task resolution, compared to only 11.7 without GCC. The code is released at: this https URL

LCM attempts to decompose the recursion from RLMs into deterministic primitives so that the control flow can be managed by an engine rather than left to the whims of the LLM. In practice, this means we replace bespoke scripts with two mechanisms: (1) A DAG-based context management system that works like paged virtual memory, except for managing conversations and files; and (2) Operator-level recursion, like "Map" for LLMs, which lets one tool call process thousands of tasks.

An analogy we draw in the paper is the evolution from GO-TO statements (of Dijkstra's "Considered Harmful" fame) to structured programming. RLMs are maximally expressive, but all of that power comes with the risk of things going awry. We have built a more mechanistic system, which can provide stronger guarantees when deployed in production with today's models.

Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed. Learning from such signals is challenging, as LMs must implicitly infer how observed failures should translate into behavioral changes for future iterations. We introduce Experiential Reinforcement Learning (ERL), a training paradigm that embeds an explicit experience-reflection-consolidation loop into the reinforcement learning process. Given a task, the model generates an initial attempt, receives environmental feedback, and produces a reflection that guides a refined second attempt, whose success is reinforced and internalized into the base policy. This process converts feedback into structured behavioral revision, improving exploration and stabilizing optimization while preserving gains at deployment without additional inference cost. Across sparse-reward control environments and agentic reasoning benchmarks, ERL consistently improves learning efficiency and final performance over strong reinforcement learning baselines, achieving gains of up to +81% in complex multi-step environments and up to +11% in tool-using reasoning tasks. These results suggest that integrating explicit self-reflection into policy training provides a practical mechanism for transforming feedback into durable behavioral improvement.

Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). However, recent findings suggest that raw token counts are unreliable proxies for reasoning quality: increased generation length does not consistently correlate with accuracy and may instead signal "overthinking," leading to performance degradation. In this work, we quantify inference-time effort by identifying deep-thinking tokens -- tokens where internal predictions undergo significant revisions in deeper model layers prior to convergence. Across four challenging mathematical and scientific benchmarks (AIME 24/25, HMMT 25, and GPQA-diamond) and a diverse set of reasoning-focused models (GPT-OSS, DeepSeek-R1, and Qwen3), we show that deep-thinking ratio (the proportion of deep-thinking tokens in a generated sequence) exhibits a robust and consistently positive correlation with accuracy, substantially outperforming both length-based and confidence-based baselines. Leveraging this insight, we introduce Think@n, a test-time scaling strategy that prioritizes samples with high deep-thinking ratios. We demonstrate that Think@n matches or exceeds standard self-consistency performance while significantly reducing inference costs by enabling the early rejection of unpromising generations based on short prefixes.

"If this is correct, to the extent of my knowledge, it would mark the first time humanity has 'seen' dark matter. And it turns out that dark matter is a new particle not included in the current standard model of particle physics. This signifies a major development in astronomy and physics," said Totani.

The strong zero-shot and long-context capabilities of recent Large Language Models (LLMs) have paved the way for highly effective re-ranking systems. Attention-based re-rankers leverage attention weights from transformer heads to produce relevance scores, but not all heads are created equally: many contribute noise and redundancy, thus limiting performance. To address this, we introduce CoRe heads, a small set of retrieval heads identified via a contrastive scoring metric that explicitly rewards high attention heads that correlate with relevant documents, while downplaying nodes with higher attention that correlate with irrelevant documents. This relative ranking criterion isolates the most discriminative heads for re-ranking and yields a state-of-the-art list-wise re-ranker. Extensive experiments with three LLMs show that aggregated signals from CoRe heads, constituting less than 1% of all heads, substantially improve re-ranking accuracy over strong baselines. We further find that CoRe heads are concentrated in middle layers, and pruning the computation of final 50% of model layers preserves accuracy while significantly reducing inference time and memory usage.

Information retrieval (IR) systems have played a vital role in modern digital life and have cemented their continued usefulness in this new era of generative AI via retrieval-augmented generation. With strong language processing capabilities and remarkable versatility, large language models (LLMs) have become popular choices for zero-shot re-ranking in IR systems. So far, LLM-based re-ranking methods rely on strong generative capabilities, which restricts their use to either specialized or powerful proprietary models. Given these restrictions, we ask: is autoregressive generation necessary and optimal for LLMs to perform re-ranking? We hypothesize that there are abundant signals relevant to re-ranking within LLMs that might not be used to their full potential via generation. To more directly leverage such signals, we propose in-context re-ranking (ICR), a novel method that leverages the change in attention pattern caused by the search query for accurate and efficient re-ranking. To mitigate the intrinsic biases in LLMs, we propose a calibration method using a content-free query. Due to the absence of generation, ICR only requires two (O(1)) forward passes to re-rank N documents, making it substantially more efficient than generative re-ranking methods that require at least O(N) forward passes. Our novel design also enables ICR to be applied to any LLM without specialized training while guaranteeing a well-formed ranking. Extensive experiments with two popular open-weight LLMs on standard single-hop and multi-hop information retrieval benchmarks show that ICR outperforms RankGPT while cutting the latency by more than 60% in practice. Through detailed analyses, we show that ICR's performance is specially strong on tasks that require more complex re-ranking signals. Our findings call for further exploration on novel ways of utilizing open-weight LLMs beyond text generation.

Observations

When message history tokens exceed a threshold (default: 30,000), the Observer creates observations — concise notes about what happened.

When observations exceed their threshold (default: 40,000 tokens), the Reflector condenses them — combining related items and reflecting on patterns.

The result is a three-tier system:

1. Recent messages: Exact conversation history for the current task
2. Observations: A log of what the Observer has seen
3. Reflections: Condensed observations when memory becomes too long

Recent advances in large language models (LLMs) have opened new avenues for accelerating scientific research. While models are increasingly capable of assisting with routine tasks, their ability to contribute to novel, expert-level mathematical discovery is less understood. We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models, specifically Google's Gemini-based models (in particular Gemini Deep Think and its advanced variants), to solve open problems, refute conjectures, and generate new proofs across diverse areas in theoretical computer science, as well as other areas such as economics, optimization, and physics. Based on these experiences, we extract common techniques for effective human-AI collaboration in theoretical research, such as iterative refinement, problem decomposition, and cross-disciplinary knowledge transfer. While the majority of our results stem from this interactive, conversational methodology, we also highlight specific instances that push beyond standard chat interfaces. These include deploying the model as a rigorous adversarial reviewer to detect subtle flaws in existing proofs, and embedding it within a "neuro-symbolic" loop that autonomously writes and executes code to verify complex derivations. Together, these examples highlight the potential of AI not just as a tool for automation, but as a versatile, genuine partner in the creative process of scientific discovery.

Collaborating with experts on 18 research problems, an advanced version of Gemini Deep Think helped resolve long-standing bottlenecks across algorithms, ML and combinatorial optimization, information theory, and economics. Highlights from our “Accelerating Research with Gemini” paper include (corresponding section numbers in paper):

we were able to demonstrate a “Top-5” LongMemEval result with very minimal modifications to dspy.RLM, just some helper functions to process the “multi-chat” sessions

Humans always remain in the loop, but work at a different layer of abstraction than we used to. We prioritize work, translate user feedback into acceptance criteria, and validate outcomes. When the agent struggles, we treat it as a signal: identify what is missing—tools, guardrails, documentation—and feed it back into the repository, always by having Codex itself write the fix.

Our most difficult challenges now center on designing environments, feedback loops, and control systems that help agents accomplish our goal: build and maintain complex, reliable software at scale.

The engineering team used Codex to optimize and adapt the harness for GPT‑5.3-Codex. When we started seeing strange edge cases impacting users, team members used Codex to identify context rendering bugs, and root cause low cache hit rates. GPT‑5.3-Codex is continuing to help the team throughout the launch by dynamically scaling GPU clusters to adjust to traffic surges and keeping latency stable.

A 61-year-old Tennessee man is finally free after spending a shocking 37 days in jail — all for posting a meme.

Of those, GVA said there were five confirmed transgender shooters, or fewer than a tenth of one per cent. (There have also been four cases of mass shootings by females in the U.S. since 1982.)

Across frontier models, gpt-5.3-codex achieves the best overall performance (solving 19/22 tasks, 86.4%), outperforming claude-opus-4.6 (15/22, 68.2%), and kimi-2.5 exhibits the strongest performance among open-source models

The firm is also whitelisting a handful of market makers, including longtime crypto liquidity provider Wintermute, to facilitate trading. Meanwhile, access to BUIDL is restricted to qualified purchasers, a legal designation for those with assets of $5 million or more.

For years, Trump has claimed he had “no idea” about Epstein’s abuse of underage girls. Yet records show that in 2006, he privately told Palm Beach police that “everyone” knew about Epstein’s activities and described Ghislaine Maxwell as evil.
Trump’s call to Palm Beach police chief

According to an FBI interview conducted in October 2019 with former Palm Beach Police Chief Michael Reiter, Trump personally called him in July 2006, just as Epstein’s criminal sex charges became public. Reiter told agents that Trump said, “Thank goodness you’re stopping him, everyone has known he’s been doing this.”

Observational Memory achieves the highest score ever recorded on LongMemEval — 94.87% with gpt-5-mini — while maintaining a completely stable, cacheable context window. It beats the oracle, outperforms complex multi-step reranking systems with a single pass, and scales better with model quality than existing approaches.

"I mean, there's tons of redacted stuff. ... And [Trump's] name, I think I put his name, and it appears more than a million times. So it's all over the place."

The bottom line: "To me, this whole rollout of saying that members can come from nine to five to sit at those four computers, is just part of the coverup," Raskin asserted.

The 3 million documents that the administration has not publicly released "are the ones I'd like to see," he said.
"The administration says that these are duplicative. Well go ahead and release them then! If they're duplicative, what's the problem? We'll be the judge of that." 

"Epstein's lawyers synopsized and quoted Trump as saying that Jeffrey Epstein was not a member of his club at Mar-a-Lago, but he was a guest at Mar-a-Lago, and he had never been asked to leave," Raskin said. "That was redacted for some indeterminate, inscrutable reason."

Among participants who use AI, we find a stark divide in skill formation outcomes between high scoring interaction patterns (65%-86% quiz score) vs low-scoring interaction patterns (24%-39% quiz score). The high scorers only asked AI conceptual questions instead of code generation or asked for explanations to accompany generated code; these usage patterns demonstrate a high level of cognitive engagement.

We develop a model of political cycles driven by time-varying risk aversion. Agents choose to work in the public or private sector and to vote Democratic or Republican. In equilibrium, when risk aversion is high, agents elect Democrats—the party promising more redistribution. The model predicts higher average stock market returns under Democratic presidencies, explaining the well-known “presidential puzzle.” The model can also explain why economic growth has been faster under Democratic presidencies. In the data, Democratic voters are more risk averse, and risk aversion declines during Democratic presidencies. Public workers vote Democratic, while entrepreneurs vote Republican, as the model predicts.

We may be on the descending portion of a productivity J-curve. As Brynjolfsson, Rock, and Syverson illustrate, when firms adopt transformative general-purpose technologies, measured productivity often initially falls because resources are diverted to investment, reorganization, and learning that do not show up as measured output.

The task-completion time horizon is the task duration (measured by human expert completion time) at which an AI agent is predicted to succeed with a given level of reliability

it will automatically set all users’ accounts to a “teen-appropriate” experience unless they demonstrate that they’re adults

Among those to leave OpenAI in recent months over the strategic shift are vice-president of research Jerry Tworek, model policy researcher Andrea Vallone and economist Tom Cunningham.

MaxRL is a framework that turns more compute into increasingly better approximations of the maximum likelihood objective in sampling-based tasks.

If this perspective is accurate then it has deep implications for the economics of AI: the marginal cost of solving an idiosyncratic problem is small (you just need to map it to one of the canonical problems, and apply that solution), but there’s very high value in making progress on the canonical problems. So we would expect AI labs to be spending huge amounts of compute on advancing the SoTA on the few deep problems of the world, and providing a service that solves idiosyncratic problems very cheaply.

Fuzzers work by throwing massive amounts of random inputs at code to see what breaks. Opus 4.6 reads and reasons about code the way a human researcher would—looking at past fixes to find similar bugs that weren't addressed, spotting patterns that tend to cause problems, or understanding a piece of logic well enough to know exactly what input would break it.

You wouldn't think that chess players, who sit for hours on end and extend their arms only from time to time, would struggle with weight loss. But they do. Inside the very real, very bizarre metabolic phenomenon gripping chess.

Although bias against men was shown by both men and women, another finding of the study was the bias was larger from women than from men in three out of four of their research questions.

Despite our culture voting for parties that enforce gender equality schemes that put men’s education and careers behind women’s, women very often want a man who earns more money and has a higher education than she does. Thus we are living in a situation that seems designed to leave everyone dissatisfied, yet this is the design that people demand.

Counterintuitively, that’s my biggest reason to be optimistic about AI and creativity. When hard parts become easy, the differentiator becomes love.

This systematic review and meta-analytic investigation found that SFV use was associated with poorer cognition (attention, inhibitory control, language, memory, and working memory) and most mental health indices except body image and self-esteem.

A September review of 71 studies with a total of nearly 100,000 participants found that heavy consumption of short-form video was associated with poorer cognition, especially in regard to attention spans and impulse control, based on a combination of behavioral tests and self-reported data.
The review, published in Psychological Bulletin, a journal of the American Psychological Association, also found links between heavy consumption of the videos and increased symptoms of depression, anxiety, stress and loneliness.

“Restraint and respect for international law was abandoned in the aftermath of 9/11, with the launch of not one but two foreign interventions, in Iraq and Afghanistan, ostensibly aimed at the elimination of a terrorist threat, but in reality, functioning as explicit projects of regime change.”

But Ansari, despondent after a year of often fruitless Middle East diplomacy, predicts we are “moving from a world order to disorder”.
“I don’t think we are moving towards a multipolar system. I don’t think we are even moving to a power-based international order. I don’t think we are moving towards any kind of system.
“We are moving into a system where anybody can do whatever they like, regardless if they are big or small. As long as you have the ability to wreak havoc, you can do it because no one will hold you accountable.”

As countries everywhere feel emboldened to encroach on their neighbours, the dismal prospect is of an aggressive, border-shifting 19th-century world, but armed with 21st-century weapons.

America was a successful superpower because its self-interest and realpolitik were turbocharged by an avowed faith in universal values of democracy and human rights. Mr Trump believes that, far from being a unique strength in foreign affairs, that was a foolish indulgence.

Spain is booming. For three years running, we have had the fastest-growing economy among Europe’s largest countries. We have created nearly one in every three new jobs across the European Union, and our unemployment rate has fallen below 10 percent for the first time in nearly two decades. Our workers’ purchasing power has also grown, and poverty and inequality levels have dropped to their lowest since 2008. This prosperity is the result of Spanish citizens’ hard work, the E.U.’s collective effort and an inclusive agenda that views migrants as necessary partners.