I Computer Science & Software · II AI & Machine Learning · III Mathematics & Formal Sciences · IV Physics, Quantum & Space · V Systems, Hardware & Applied Tech · VI Biotech, Medicine & Materials · VII Cryptography, Web3 & Protocols · VIII Economics, Markets & Quantitative Finance · IX International Relations, Geopolitics & Strategy · X Philosophy of Science, Decision Theory & Cognition
Check out the finer details of the more technical fixes implemented into Discord recently.
Published via Show HN (Trending Repos & Tools) in Computer Science & Software. Primary discussion and technical specification available at the linked source.
Published via Show HN (Trending Repos & Tools) in Computer Science & Software. Primary discussion and technical specification available at the linked source.
Published via Show HN (Trending Repos & Tools) in Computer Science & Software. Primary discussion and technical specification available at the linked source.
Published via Show HN (Trending Repos & Tools) in Computer Science & Software. Primary discussion and technical specification available at the linked source.
Published via Show HN (Trending Repos & Tools) in Computer Science & Software. Primary discussion and technical specification available at the linked source.
Published via Show HN (Trending Repos & Tools) in Computer Science & Software. Primary discussion and technical specification available at the linked source.
Published via Show HN (Trending Repos & Tools) in Computer Science & Software. Primary discussion and technical specification available at the linked source.
Published via Show HN (Trending Repos & Tools) in Computer Science & Software. Primary discussion and technical specification available at the linked source.
Decoder-only Large View Synthesis Models (LVSMs) with KV-cache have recently achieved state-of-the-art quality by regressing novel views from neural networks without reconstructing 3D geometry, while Test-Time Training (TTT) layers save computation but sacrifice quality. We propose Test-Time Baking (TTB), a strategy that learns to bake global scene information from cross-view attention into a lightweight MLP, enabling fast and high-fidelity novel-view decoding. The first key is to repurpose the fast-weight MLP of TTT to learn the cross-view attention mapping explicitly from its input-output pairs, rather than replacing attention entirely, achieving the same rendering efficiency as TTT while delivering substantially higher quality.
Recent work suggests that pretrained image generators encode real-world understanding beyond image synthesis. Vision Banana exploits this by framing vision tasks as conditional image editing. However, its data, weights, code, and implementation details are not public.
Decision models answer a question with a typed output, such as a choice among options with a probability for each, so one model can choose an agent's actions, evaluate actions already taken, or score every action to teach a policy with reinforcement learning. Prior work has studied them mainly as actors, judging their usefulness by how well they act. We ask whether a decision model that acts poorly can still teach well.
Large foundation models such as GPT-6 Astra and Claude Fable 5.1 have recently been shown to control robots directly from visual observations. They nevertheless remain brittle on long-horizon, contact-rich tasks in real-world environments. Further progress depends on two complementary resources: demonstrations of human behavior that supervise how tasks are performed, and interactive environments in which policies can be rolled out, evaluated, and improved in closed loop.
How multimodal language models should encode visual input remains up for debate. Most models use a pretrained vision encoder, while some recent recipes train the encoder from scratch with the language model or remove it entirely. Vision encoders bundle together three design choices: a visual prior from upstream pretraining, dedicated vision capacity, and pre-fusion placement of that capacity (i.e., before the multimodal backbone).
Second-order optimizers (e.g., Shampoo, SOAP) have emerged as compelling alternatives to Adam/AdamW and promise faster per-step convergence than AdamW in transformer training. However, they still remain costly in practice, and even SOAP still pays substantial optimizer-state memory and eigendecomposition overhead. We ask whether the benefits of SOAP can be retained at nearly the efficiency of AdamW.
Frontier video generation models work in a VAE latent space that compresses video in both space and time. This compression made training tractable, but now limits high-resolution, controllable, and interactive generation -- doubling resolution quadruples token count, conditions apply to groups of frames rather than individual ones, and each response must wait for the rest of its multi-frame block. We present PixVid, a simple yet effective method that lifts a pretrained latent video model to directly generate every frame in pixel space.
Existing adaptive-reasoning methods shorten the explicit reasoning a model writes, or skip it by switching modes; either way the answer is still written out token by token. Rather than deciding how much to think, we explore a different paradigm, zero-or-T thinking: a single model either answers from the implicit thinking inside one forward pass, generating no token, or performs full autoregressive reasoning at a cost of T tokens when that is worth paying. We realise it in a hybrid decision model (HDM), where one frozen backbone serves both modes and a lightweight self-routing block chooses between them.
Large language models have made rapid progress in agentic reasoning and tool use, raising expectations for agents that can take over realistic productivity work from humans. However, current agents still struggle to finish such tasks, where success requires following long-horizon professional workflows, understanding heterogeneous file contexts, and producing usable deliverables under fine-grained requirements. A key bottleneck is the lack of scalable post-training data that reflects real work intent, authentic file environments, and deliverable-level evaluation.
We introduce a geometric construction whose terminal configuration determines the kernel and cokernel of a morphism between positive Lagrangian branes in the derived Fukaya category of a Calabi-Yau manifold. Thus, if the construction always terminates, the full subcategory of positive Lagrangian branes is abelian, as conjectured by Joyce. We show that this abelian category admits Harder-Narasimhan filtrations with respect to the central charge defined by the holomorphic volume form.
Kalah is a classic two-player game in the mancala family of sowing games. Computers have solved several standard-size starting positions, but the complexity of the game on boards of growing size has remained open. We study the natural generalization in which the number of pits per side is part of the input and an arbitrary position is given, with the stone counts given in unary.
For and , set , where ranges over positive integers and denotes distance to the nearest integer. The set on which the uniform Littlewood constant is positive has full Hausdorff dimension. We prove that for every fixed the superlevel set has Hausdorff dimension strictly less than two.
Fictitious play (FP) is one of the earliest and most fundamental learning procedures in games, where each agent independently chooses a best response to the empirical distribution of opponents' past actions by maximizing the expected payoff with respect to it. However, learning agents may not always be expected-utility maximizers: they may use nonlinear evaluation functionals to account for the uncertainty induced by the opponents, e.g., via risk measures. In this work, we study nonlinear fictitious play (NL-FP), in which each pure action is evaluated by a general, possibly nonlinear functional of the payoff distribution induced by the opponent's empirical belief.
Martingale optimal transport (MOT) provides model-independent bounds for option prices calibrated to vanilla options, assuming frictionless trading in the underlying. In this work, we consider the case where dynamic trading incurs quadratic temporary price impact. First, we prove a duality result for the superhedging price.
The leaky integrate-and-fire (LIF) neuron combines a passive membrane law with threshold-triggered firing and reset. We formulate this hybrid neuron model, under constant input, on an arbitrary Hilger time scale . Our central result is that, after the first spike, the firing times satisfy , where is the classical reset-to-threshold travel time.
[This is a guest post by the MathHelm scientific committee, whose membership is listed at the bottom of this post. -T.] Mathematics depends on our regular conversations with colleagues and with the wider public. They help us share ideas, deepen our collective understanding, see where the field stands, and decide what to explore next.
— 1. Weighted inequalities — In the previous notes we have taken a linear or sublinear operator on a measure space and established mapping properties, such as These estimates reveal information about how the height and width of is controlled by the height and width of . However, these estimates do not indicate where the […]
Back in July, I started experimenting with using modern coding agents to build a number of mathematical visualization applets. With the extremely hectic events of the last few months, I had to set those projects aside; but I recently found a bit of time to work on one such project that I had started back […]
Light can manipulate magnetic order and its excitations on ultrafast timescales, including through resonantly driven phonons. In antiferromagnets, light can also create correlated magnon pairs, or bimagnons. Unlike single magnons, whose damping has been measured and modeled in detail, little is known about the lifetime, interactions and decay of such correlated pairs: prior two-magnon measurements show broad linewidths or coherences that dephase within 200 fs.
Quantum key distribution (QKD) generates shared secret keys whose security rests on the laws of physics rather than on computational assumptions. We report the entanglement-based segment of the Slovak national quantum communication infrastructure, connecting four cities in western Slovakia over deployed telecommunication fiber. Polarization-entangled photons generated at a central node are passively routed by wavelength to form a trusted-node-free, fully meshed sub-network of three cities, with the fourth connected through the central node and all links operated simultaneously.
Phenomenological models of quantum-gravity-induced decoherence can leave observable effects even in experiments that do not rely on interferometric measurements. We study three such models, based respectively on deformed relativistic symmetries, stochastic metric fluctuations, and a stochastic generalized uncertainty principle, all of which induce decoherence in the momentum basis. For a free particle, the models preserve momentum distribution and mean energy but produce position diffusion and momentum-space decoherence with model-specific momentum dependences.
Continuous-variable quantum key distribution (CV-QKD) is a promising technology for quantum-secure communications, offering high secret-key rates and straightforward integration with coherent optical communication systems. Conventional Gaussian-modulated CV-QKD encodes information in both optical-field quadratures, increasing requirements for state preparation, modulation, and calibration, while potentially introducing additional preparation imperfections. Unidimensional (1D) Gaussian modulation offers a simpler alternative by encoding information in only one quadrature, while the conjugate quadrature constrains the protocol's security.
We develop a framework to study subsystem symmetry-protected topological (SSPT) phases using lattice response theory. We begin with a systematic construction of spacetime lattice gauge fields from general microscopic -type symmetry actions on qudit systems, including global symmetries, subsystem symmetries, and more general modulated symmetries. Given a gauge-invariant response action, we then construct a corresponding entangler and explicitly show that it implements stacking with the SSPT phase described by the response action.
In my “Mathocalypse” post, I mentioned that my wife Dana—who’s been working on Subhash Khot’s Unique Games Conjecture since ~2009, for the entire time I’ve known her—has for the past few weeks (while on sabbatical at the Simons Institute in Berkeley) been spending essentially all of her time laboring to understand OpenAI’s recent proof of […]
As always, I strongly recommend 2x speed, which compensates somewhat for my verbal infelicities! New! More podcasts keep dropping! This one is with the AI alignment researcher Roman Yampolskiy, about … well, AI alignment, plus the Mathocalypse, plus quantum computing, plus whether the universe is a simulation, and I forget what else!
Nature Physics, Published online: 13 November 2026; doi:10.1038/s41567-026-03524-9 Just publish it
Nature Physics, Published online: 12 November 2026; doi:10.1038/s41567-026-03499-7 Quantum channels can be used to describe the evolution of quantum states, including both unitary evolution and measurement. Now the optimal number of uses needed to learn an unknown channel to any fixed accuracy has been found.
Security updates have been issued by AlmaLinux (.NET 10.0, .NET 8.0, .NET 9.0, firefox, gcc, grafana, grafana-pcp, gstreamer1, gstreamer1-plugins-base, java-1.8.0-openjdk, kernel, kernel-rt, libimobiledevice, mingw-libpng, nginx:1.24, nodejs:22, nodejs:24, perl-CGI, perl-DBI, qt5-qtbase, tigervnc, vim, and xorg-x11-server), Debian (apache2, c-ares, linux-6.12, node-shell-quote, nova, parted, postgresql-17,…
All about making things affordable
Imagine halving the resource costs of AI and what that could mean for the planet and the industry -- based on extreme estimates such savings could reduce the total US power usage by over 10% by 2030 1 . At Intel we've been creating a new analyzer tool to help reduce AI costs called AI Flame Graphs : a visualization that shows an AI accelerator or GPU hardware profile along with the full software stack, based on my CPU flame graphs . Our first version is available to customers in the Intel Tiber AI Cloud as a preview for the Intel Data Center GPU Max Series (previously called Ponte Vecchio).
Users of artificial intelligence services, including those powered by large language models and other transformer-based models, need assurance that inference and training follow the declared computation without exposing private models or data. Zero-knowledge proofs can provide this assurance, but proof generation remains expensive. Hardware acceleration research often reports gains for individual cryptographic operations, leaving their effect on the full cost of proving neural-network workloads, particularly large transformer models, unclear.
Bitcoin's Lightning payment network returns the funds of a channel that cannot close cooperatively through pre-signed transactions whose fees and delays fall on its operators. That fee design is being replaced: Bitcoin Core 31.0 removed the mempool rule anchor channels relied on, and the Lightning specification adopted zero-fee commitments in May 2026. What the old design costs has never been measured across fee regimes.
Certain configurations of Alpine are affected. Other distros may be as well.
With data movement becoming one of the most expensive bottlenecks in computing, the need for profiling tools to analyze communication becomes crucial for effectively scaling multi-GPU applications. While existing profiling tools including first-party software by GPU vendors are robust and excel at capturing compute operations within a single GPU, support for monitoring GPU-GPU data transfers and calls issued by communication libraries is currently inadequate. To fill these gaps, we introduce Snoopie, an instrumentation-based multi-GPU communication profiling tool built on NVBit, capable of tracking peer-to-peer transfers and GPU-centric communication library calls.
I tested 25 of the most popular package managers to check whether they detect a compromised package repository injecting malicious code into legitimate packages. This code integrity problem could be solved using cryptographic hashes or signatures, but many package managers are still vulnerable to this threat. Of the 25 package managers reviewed 1 , fewer than half used lockfiles that checked for tampering by default, and only two verified the integrity of every package they downloaded.
Summary Project Zero compared two automated tools used by Google to find vulnerabilities in Chrome’s JavaScript engine, V8, on a regular basis. Fuzzilli is a generative JavaScript engine fuzzer and BigSleep is an LLM-based bug finding system. In the past six months, BigSleep found more vulnerabilities than Fuzzilli (45 versus 37) with a 25% overlap in the specific bugs reported.
Predicting how mutations impact protein function and stability remains a key challenge in computational biology. Current state-of-the-art protein language models (PLMs) primarily scale parameter size on sequence corpora to capture evolutionary constraints but remain blind to the explicit physical dynamics of folded proteins. Conversely, models trained strictly on physical dynamics neglect critical evolutionary sequence statistics.
RNA foundation models with diverse architectures have shown promise for learning representations of messenger RNA (mRNA) sequences. However, existing benchmarks commonly reduce token-level embeddings to transcript-level representations through global mean pooling before fitting a downstream probe. This aggregation does not explicitly preserve the identities of biologically distinct mRNA regions.
The deepTools suite is widely used for quality control, summarization, and visualization of high-throughput sequencing data. However, as genomic datasets grow in scale and depth, existing tools often become computational bottlenecks. We present deepTools4, a modernized framework designed for high performance, scalability, and enhanced usability, while maintaining seamless integration with the Galaxy platform.
Nature Computational Science, Published online: 13 November 2026; doi:10.1038/s43588-026-01079-6 By training a reservoir computing system without access to its intermediate states and by eliminating the coupling between its nodes, a physical learning framework achieves efficient learning both in simulation and on optoelectronic hardware.
Nature Computational Science, Published online: 12 November 2026; doi:10.1038/s43588-026-01094-7 Open foundation models for clinical AI
Nature Computational Science, Published online: 06 November 2026; doi:10.1038/s43588-026-01065-y Transferable deep-learning quantum Monte Carlo for molecular excited states is introduced. Sharing parameters, an ansatz represents the wavefunction across molecular geometries and states, reducing the cost of modeling potential energy surfaces.
Nature Computational Science, Published online: 05 November 2026; doi:10.1038/s43588-026-01075-w This study presents a scalable, efficient framework for unconventional computing with physical neural networks. It replaces dense connections with uncoupled one-dimensional dynamics, simplifies training, improves performance and reduces resource use.
Nature Computational Science, Published online: 26 October 2026; doi:10.1038/s43588-026-01073-y Hi-TPH is a large-scale hierarchical dataset that enables more accurate prediction of T cell receptor binding to antigens using protein language models and reveals how molecular components jointly contribute to prediction.
Nature Computational Science, Published online: 22 October 2026; doi:10.1038/s43588-026-01078-7 Author-proposed non-primary articles can bring fresh and timely perspectives to the journal. Here, we discuss the factors that editors consider when assessing proposals for these contributions.
Published via Vitalik Buterin's Blog in Cryptography, Web3 & Protocols. Primary discussion and technical specification available at the linked source.
We implement privacy-preserving local electricity exchanges using an automated market maker on the Hyperledger Besu network under QBFT consensus. The proposed protocol operator collects individual electricity positions off-chain and publishes aggregate supply and demand on the blockchain, where smart contracts compute market prices every fifteen minutes. Individual positions remain off-chain; Poseidon2 commitments and UltraHonk zeroknowledge proofs, generated from Noir circuits, verify aggregation, payments, and daily balance updates.
The game "Mafia'' is a social deduction game where players attempt to uncover a covert coalition of players among the . Typically, of the non-coalition players are labelled ``Detective'' and have special abilities. Most game-theoretic analysis considers, however, only the variant without a Detective.
Marc Andreessen and Chris Dixon on the fight over crypto market structure, why regulatory clarity matters, and what is at stake for America’s technological leadership.
We expose a safety vulnerability on nascent agentic reinforcement-learning-as-a-service. Specifically, with a cost as low as $20, we show that safety alignment of a large language model (e.g., Inkling) can be completely subverted through rounds of harmful agentic reinforcement learning (agentic RL) with a toxic environment from its users. We also demonstrate that upon a successful harmful agentic RL attack via the service API, the attackers can potentially use the safety-degraded model to assist with real-world harmful activities, e.g., exploit software vulnerabilities and launch code re-use attacks.
Integral distinguishers can reveal more than a zero-sum property: the number of ones in an output component may be divisible by a high power of , rather than merely being even. To exploit this divisibility, we relate linear, integral, differential-linear, and restricted differential sums through two-way conversions with explicit divisibility guarantees and data complexity, recovering established links as special cases. We derive exact random-permutation probabilities and rigorous bounds to quantify the distinguishing strength while accounting for dependencies between output conditions.
An asymmetric Password-Authenticated Key Exchange (aPAKE) protocol allows a client and a server to agree upon a cryptographic key, with the only information shared in advance being a low-entropy password (memorized by the client) and a corresponding password file (stored in the server). The standard security definition for aPAKE is in the Universally Composable (UC) framework. Since aPAKE does not rely on a PKI, such protocols have received much attention in recent years due to their potential of replacing the traditional “password-over-TLS” approach for client-server authentication on the Internet.
Motivated by LowMC cryptanalysis challenge, research in recent years focuses more on attacking LowMC in \PICNIC application setting, \ie an attacker can see only a single plaintext/ciphertext pair. It can be noted that in the security proof of \PICNIC, LowMC is required to be secure under two plaintexts, it is thus meaningful to investigate the security of LowMC in this direction. Pioneered by Liu, Isobe and Meier at Crypto 2021, they combined algebraic techniques with difference enumeration attack, which could attack all three 4-round LowMC instances adopting full S-Box layers in \PICNIC with only two chosen plaintexts.
\begin{abstract} We present MODDIV-WC, a public-key encryption and key-encapsulation candidate based on a widened Hankel linear map followed by deterministic integer division, together with an implicit-rejection Fujisaki--Okamoto wrapper, MODDIV-WC-FO. The construction uses a secret vector whose first coordinate is fixed to one and whose remaining coordinates are sampled from a positive interval. This unit anchor makes the Hankel map surjective for every admissible secret, a property that is central to the proof.
AI buying agents may alter sellers' incentives as well as consumers' decisions. In a synthetic duopoly, we evaluate price-comparison pipelines on a fixed menu and in empirical correlated-equilibrium games. Calculator assistance selects an optimal offer 84.00% of the time, versus 68.57% under verified-total disclosure, yet disclosure has a 20.14-unit higher consumer-worst equilibrium surplus.
Across domains as varied as ecology, economics, and online platforms, individuals are incentivized to position themselves within resource-rich strategy spaces. In this work we introduce a new spatial game framework called the influencer's game. Within this new framework players' strategies are represented as positions within a trait space.
Research on pay dispersion has long asked whether unequal rewards increase or depress performance. Tournament theory expects stronger effort, while social comparison accounts expect withdrawal. Both perspectives, however, treat performance as a single scale on which workers can be ranked.
The Bayesian persuasion framework models strategic information revelation by a sender to one or more receivers. We study an intricate version of the multi-receiver persuasion setting in which the communication hierarchy is a forest - i.e., some receivers observe more informative signals than others, and the partial ordering of the receivers according to the information they get is represented by a pre-specified directed forest. This model naturally captures interactions in hierarchical organizations, and it lies in between the two most commonly studied versions of Bayesian persuasion: private persuasion and public persuasion.
Most government transfer programs aim to target the poor. Doing so implicitly assumes that the lowest-consumption households have the highest marginal social valuation, i.e., have the highest social welfare gain from an additional dollar. But this need not be the case.
The two dimensions of unconventional monetary policy — holding the short rate at the zero-lower bound (ZLB) and quantitative easing (QE) — shifted deposit composition toward more interest-sensitive customers. The ZLB drew these customers by creating rate compression, which limited the benefit of searching for high-yielding liquidity sources. QE also drew rate-sensitive, large depositors from the Federal Reserve’s purchases of bonds from non-banks.
Frontline state agents exercise substantial discretion over citizens’ access to public services, yet little is known about whether the attitudes and beliefs shaping this discretion can be changed among incumbent bureaucrats. Partnering with the Bihar Police - one of India’s largest states that operates under comparatively severe policing capacity constraints, we conduct a cluster randomized controlled trial across 417 police stations and roughly 2,500 active duty senior field police officers to evaluate a three-day expressive arts intervention, grounded in the Theatre of the Oppressed, designed to change police responses to gender-based violence (GBV). Approximately 7 months after baseline, treated officers exhibit more favorable attitudes toward GBV, greater empathy, and stronger legal knowledge.
Understanding how AI is reshaping work requires an accurate account of the tasks workers perform. Workers are often the first to see how their jobs are changing, as new responsibilities and tools often appear in everyday work before these changes reach formal descriptions. Interviews with workers can help capture these emerging changes in job tasks.
We estimate the maximal Sharpe ratio in large cross-sections of assets directly, without constructing the efficient portfolio. Covariance estimation in this setting requires regularization, which suppresses the contribution of small-eigenvalue directions. Our estimator progressively recovers this contribution, yielding tight upper and lower bounds with asymptotic confidence intervals.
How to avert a new world war.
Cómo evitar otra guerra mundial.
In just a few days’ time, U.S. President Donald Trump, Chinese leader Xi Jinping, and Russian President Vladimir Putin could be holding a three-way summit on the sidelines of next week’s Asia-Pacific Economic Cooperation meeting in Shenzhen, China. According to The Wall Street Journal, Putin floated the idea of a trialogue last month as an opportunity to discuss ending the war in Ukraine.
The strategic logic of foreign aid.
How Technology Is—and Isn’t—Changing the Global Economy
How AI is reshaping influence operations in America.
Donald Trump has Pyongyang on his mind. In recent months, the U.S. president has made comments to the media suggesting he is in communication with North Korean leader Kim Jong Un. In an August 17 Truth Social post, for instance, Trump announced that the United States would scale back joint military drills with South Korea.
America should be taking notes.
Why the baby bust is making the world more unequal.
The tournament equilibrium set (TEQ), proposed by Schwartz in 1990, is a classical voting rule based on pairwise majority comparisons. Its definition is recursive: an alternative is judged by the alternatives that beat it, called its dominators, and by which of those dominators TEQ itself selects. Deciding whether a given alternative belongs to TEQ was known to be NP-hard and to lie in PSPACE.
It is well-known that a physical state may be represented by a unit vector in a Hilbert space, whilst a physical observable may be represented by a self-adjoint operator on that same space. However, the introduction of a superselection rule on a Hilbert space imposes constraints such that not all self-adjoint operators can represent observables, which has further consequences for the states themselves as well as the physical processes that transform them. In this article, we review the concept of superselection from the view of how such rules would affect what we consider to be physical.
Higher-order node-based synchronization provides distinct mechanisms for discontinuous synchronization transitions. However, a comprehensive theoretical framework capable of encompassing existing models and revealing the role of higher-order interactions beyond triangles is still missing. Here, combining algebraic topology and nonlinear dynamics, we introduce Cup and Cap Synchronization, a unifying theoretical framework for higher-order node-based dynamics that naturally incorporates the topological organization of the underlying higher-order network.
We study online selection problems subject to arbitrary downward-closed feasibility constraints, under different arrival orders, with different distributional assumptions. We establish the following bounds: (1) for the standard (adversarial-order) prophet problem, (2) for the (Bayesian, random-order) prophet secretary problem, and by extension, for the standard (prior-free, random-order) secretary problem, and (3) for the free-order prophet problem. Together with complementary bounds established in prior work, these results pin down the asymptotically tight competitive ratios for all these variants of prophet and secretary problems subject to downward-closed constraints.
We update our 2022 mapping of 441 quantum technology (QT) companies and examine how the industry has developed. The principal dataset now includes 1,092 companies founded with a QT focus, of which 935 remain active. Public sources and archived company websites provide evidence on company formation, technology fields and geography, alongside acquisitions, closures, valuations and involvement in artificial intelligence, defence and space.
How did mathematicians of the past relate to history? What can we learn from their experience? It is a truism to say that all people are embedded in history.
Autonomous ML agents in large organizations exchange data and model outputs across organizational and legal boundaries. Which rules bind a given exchange is not fixed. It depends on where the data subjects live, where the agents run, who owns them, whom they target, and which version of each rule is in force.
Bitcoin's blockchain is a public record of every on-chain transaction, while the price of bitcoin is set by trading on external markets. We ask whether on-chain activity alone reveals regimes associated with market trends, and whether this association remains stable over time. We summarise each week of blockchain activity from 2012 to 2026 using four measures of transaction structure and fit a two-state hidden semi-Markov model without price data.
Scale-rich (SR) metamaterials are planar ligament networks grown by nucleating a segment at a random point and angle and extending it until it meets the existing structure. In the dual graph, where ligaments are nodes and junctions are links, the degree distribution has a tail. We show that the SR construction is Cowan's iterated cell division with area-weighted selection, so the cell genealogy is a random binary search tree, and that the dual grows by a face-local rule: a new node joins two nodes on the boundary of one face.