IOP Publishing: Exploring inclusion, innovation, and equitable resilience in physics. Really!

The British journal publisher IOP Publishing still proudly promotes DEI ideology on their website. Appalling!

"IOP Publishing (formerly Institute of Physics Publishing) is a society-owned scientific publisher that serves as the publishing arm of the Institute of Physics (IOP). As a major international academic publisher based in Bristol, United Kingdom, it operates as a scientific charity where 100% of its financial profits are redirected back into the Institute to support public good, physics research, and professional resources for physicists." (Google Search)

"Exploring inclusion, innovation, and equitable resilience [???] in physics

 

As we also look ahead to the future of physics and STEM as a whole, we're reminded that progress in these fields is driven by diverse identities and perspectives.

Inclusion fuels innovation, and the contributions of BIPOC individuals, the LGBTQ+ community, women, immigrants, researchers with disabilities and neurodivergences, and all scientists from every stage and walk of life continue to shape STEM's trajectory in meaningful ways. ..."

Physics for a more inclusive world: Standing together in exploration

JWST May Have Discovered a New Cosmic Object: The Black Hole Star with Matt O'Dowd

Very recommendable!

(237) JWST May Have Discovered a New Cosmic Object: The Black Hole Star - YouTube

Agentic Laboratories of the Future: Towards World Models for Scientific Discovery

This could be an interesting, new paper by Jure Leskovec, Eric Xing and their team! It appears to be a survey/overview paper.

From the abstract:

"Scientific discovery is fundamentally a problem-solving process involving distributed intelligence. Human intuition, computational reasoning, and experimental execution are distributed across people, instruments, and software systems, limiting the speed and scale of discovery. Although automation, high-throughput experimentation, foundation models, and cloud infrastructure have accelerated individual stages of the scientific workflow, they have not unified the discovery process.

We hypothesize that the next generation of laboratories will be agentic: environments in which scientists, AI systems, and robotic platforms operate as collaborative discovery partners, with humans contributing the parts of discovery that remain hardest to make explicit: asking the right questions and holding provisional mechanistic models of how a system works.

The key missing layer is an agentic harnessing layer that continuously integrates hypothesis, literature-derived evidence, experimental data, uncertainty, and experimental state into a shared “laboratory world model”—a dynamic representation of the scientific system and its evolving context.

By maintaining and updating this lab world model, the agentic harnessing layer enables coordinated decision-making, adaptive planning, and increasingly autonomous scientific workflows across humans and machines.

A central challenge is that much of the scientific research process remains inaccessible to machines, including tacit knowledge, human observations, adaptive decision-making, and evolving experimental context.

Advances in multimodal AI and immersive interfaces may help bridge this gap, allowing humans, agents, and robotic systems to collaborate seamlessly in scientific discovery rather than simply automating isolated tasks.

Agentic laboratories could provide a new architecture for science, integrating human, artificial, and physical intelligence into a unified discovery system."

Agentic Laboratories of the Future: Towards World Models for Scientific Discovery (preprint, open access)

We Thought All Black Holes Came From Stars. We May Have Been Wrong with Matt O'Dowd

Very recommendable! Mostly about gravitational wave astronomy.

(477) We Thought All Black Holes Came From Stars. We May Have Been Wrong. - YouTube

10 Indians Who Changed the World (But Almost Nobody Knows Their Names)

Very recommendable! I recognized three of them like Bose, Raman and a third one.

(1570) 10 Indians Who Changed the World (But Almost Nobody Knows Their Names) - YouTube

The Mind-Bending Physics of Origami

Very recommendable!

(440) The Mind-Bending Physics of Origami | NOVA | PBS - YouTube

Fields Medal win: Wang Hong, Deng Yu are first Chinese nationals to score top maths honour

Update of 7/29/2026: Yu Deng ’11 and Hong Wang PhD ’19 awarded Fields Medal "MIT-trained mathematicians earn the honor, one of the most prestigious in the field, for their significant achievements."

Congratulations! China, a science superpower! Competition is good, more competition is better! This could be a milestone!

"For the first time, China has produced Fields Medal winners educated on its own soil – and two at once."

"Yu Deng

For his work in partial differential equations, including the rigorous derivation of the Boltzmann equation from hard-sphere dynamics for rarefied gases, the derivation of wave kinetic equations from nonlinear dispersive systems, and probabilistic approaches to nonlinear Schrodinger dynamics."

Hong Wang

For her work in harmonic analysis and geometric measure theory, including applications of multiscale and decoupling techniques to the local smoothing conjecture for the planar wave equation, and major advances in Fourier restriction, Falconer distance sets, Furstenberg sets in the plane, and the Kakeya problem in three dimensions."

Developing | Fields Medal win: Wang Hong, Deng Yu are first Chinese nationals to score top maths honour | South China Morning Post (behind paywall)

Living Fully in the Math World Means Threading the Needle "Focus, commitment, and insight resulted in a “once in a century” proof. Hong Wang is now just the third woman to win a Fields Medal" (Caveat: I did not read the article)

Amid Life’s Chaos, a Meticulous Mathematician Finds Stability "Before he won a Fields Medal for his pioneering work balancing randomness and order, Yu Deng had to achieve equilibrium in his own life." (Caveat: I did not read the article)

IMU: Fields Medals 2026

International Mathematical Union: Fields Medal

Yu Deng & Hong Wang

Fields Medal

The Evolution of Human Skin Colour | BBC Earth Science

Very disappointing video by BBC! Nothing new! All well known for several decades at least!

(814) The Evolution of Human Skin Colour | BBC Earth Science - YouTube

Thousandfold Expansion Microscopy

Amazing stuff!

"... In a preprint posted last month to bioRxiv, Boyden and colleagues describe “thousandfold expansion microscopy” or 1000ExM, a method that expands tissues as much as 1000x in every direction—a one billion times increase in volume.

First, they improved upon the expanding gel used to spread things apart. They also figured out how to bind target molecules to this gel, allowing them to break apart proteins and other bonded entities while keeping their pieces relative in space. Lastly, they tinkered with their technique to allow them to repeat the expansion step over and over again. ..."

From the abstract:

"Biological macromolecules, such as proteins, are made of concatenated building blocks. We hypothesized that individual protein residues could be imaged by anchoring their side chains to a swellable polymer, cleaving backbone amide bonds, and expanding residues away from each other to a degree that enables them to be visualized separately.

We introduce thousandfold expansion microscopy (1000ExM), a four-network interpenetrating hydrogel architecture that enables successive expansion from ∼18-fold to >1000-fold (one billion-fold in volume). Protein and peptide structures are maintained across these expansion factors, as verified by analyses of proteins with known structures (nanobodies, GFP) and a well-studied peptide (mCLING). Computational analysis indicates that 1000ExM resolves adjacent amino acid residues, thereby achieving sub-nanometer precision on conventional light microscopes. We anticipate that 1000ExM will find wide utility in protein visualization and identification, potentially even in intact cells and tissues."

ScienceAdviser

Thousandfold Expansion Microscopy (preprint, open access)

A small peptide with one end tagged in purple and certain amino acids in green, showing the difference in resolution between 18x expansion (top; full field on left, zoom in orange on right) and 1000x expansion.

Fig. 1 Design of a four-network interpenetrating polymer network (IPN) architecture enabling ∼1000× linear expansion via recursive ionic-in-ionic casting.

Europe physicists plan to build the next large, 91-kilometer particle collider

Good news!

"Particle physicists in Europe intend to build a 91-kilometer-long circular collider—the largest accelerator ever—to smash electrons into positrons, officials at the European particle physics laboratory, CERN, announced today in an online press conference. The Future Circular Collider (FCC) would be completed by the mid-2040s, after CERN’s current atom smasher, the 27-kilometer-long Large Hadron Collider (LHC), winds down. It would cost 15 billion Swiss francs, or about $19 billion—and it might pave the way for a much more powerful, and expensive, successor. ...

The new machine, officially the FCC-ee, would actually be the first of two new accelerators. It would occupy a huge new tunnel at CERN and smash electrons into positrons at energies up to 0.365 tera-electron volts (TeV), generating, among other things, large numbers of Higgs bosons. The Higgs, discovered in 2012 by the LHC, anchors physicists’ explanation of how fundamental particles get their mass. Although the FCC-ee’s collision energy would be lower than the LHC’s 13.6 TeV, its electron-positron collisions would be cleaner than the LHC’s proton-proton collisions, enabling physicists to study the Higgs in unprecedented detail. ..."

It’s official: Europe physicists plan to build 91-kilometer particle collider | Science | AAAS

Identical errors in more than 200 research papers raise red flags

Possibly bad news! The spelling of Russian names or terms can be tricky.

How widespread is this issue? How extensively are reputable journals affected?

"all the papers shared some odd typos, spelling mistakes, and phrases, such as “Kolmogorovor information complexity,” which misspells the last name of mathematician Andrey Kolmogorov. ...

found about 200 more papers that shared multiple features with the original 12 he analyzed. That’s statistically improbable, unless they all have the same source ... they’re all variants of the same paper, churned out and sold by a paper mill—an organization that produces fabricated papers and sells them to scientists eager to boost their publication record. Further investigation is required to determine whether the “accidental watermarks,” ... represent intentional misconduct, as the errors could also arise through legitimate use of the same translation software or editorial service. ..."

ScienceAdviser

Andrey Kolmogorov (1903-1987) (Source)

On GIANTS: Generative Insight Anticipation from Scientific Literature

How will machine learning & AI revolutionize scientific research? Here is an example, the latest research paper by Chelsea Finn and Noah D. Goodman.

Caveat: I have not yet read this paper.

From the abstract:

"Scientific breakthroughs often emerge from synthesizing prior ideas into novel contributions.

While language models (LMs) show promise in scientific discovery, their ability to perform this targeted, literature-grounded synthesis remains underexplored.

We introduce insight anticipation, a generation task in which a model predicts a downstream paper's core insight from its foundational parent papers.

To evaluate this capability, we develop GiantsBench, a benchmark of 17k examples across eight scientific domains, where each example consists of a set of parent papers paired with the core insight of a downstream paper.

We evaluate models using an LM judge that scores similarity between generated and ground-truth insights, and show that these similarity scores correlate with expert human ratings.

Finally, we present GIANTS-4B, an LM trained via reinforcement learning (RL) to optimize insight anticipation using these similarity scores as a proxy reward. Despite its smaller open-source architecture, GIANTS-4B outperforms proprietary baselines and generalizes to unseen domains, achieving a 34% relative improvement in similarity score over gemini-3-pro.

Human evaluations further show that GIANTS-4B produces insights that are more conceptually clear than those of the base model.

In addition, SciJudge-30B, a third-party model trained to compare research abstracts by likely citation impact, predicts that insights generated by GIANTS-4B are more likely to lead to higher citations, preferring them over the base model in 68% of pairwise comparisons. We release our code, benchmark, and model to support future research in automated scientific discovery."

[2604.09793] GIANTS: Generative Insight Anticipation from Scientific Literature

The biggest science breakthroughs of 2025

Very recommendable!

ArXiv preprint server clamps down on AI slop

Good news! What about existing, previous AI slop on arXiv?

arXiv is perhaps the most important preprint server (next to OpenReview) when it comes to machine learning & AI related research papers.

"The popular arXiv preprint server, which holds nearly 3 million manuscripts, mainly in computing science, physics, and math, has put up a new hurdle for first-time submitters.

Until now, someone wanting to submit to arXiv for the first time only needed an email address affiliated with a reputable academic or research institution, such as a university.

But a rule instituted on 21 January now requires first-time posters to be endorsed by an established arXiv author in their own field. People who have previously posted in the same disciplinary section of arXiv do not need an endorsement.

The move is an attempt to clamp down on a rising tide of fraudulent submissions,  ... A large fraction ... are generated with artificial intelligence (AI). ..."

ArXiv preprint server clamps down on AI slop | Science | AAAS

China's Universities are Climbing to the top in Global Rankings. Here's Why with Palki Sharma

Recommendable! But the ranking is based on research publication and citations!

Evaluating AI’s ability to perform scientific research tasks | OpenAI

Good news! A new benchmark!

"... As accelerating scientific progress is one of the most promising opportunities for AI to benefit humanity, we’re improving our models on difficult math and science tasks and working on the tools that will help scientists get the most from them. ..."

From the abstract:

"We introduce FrontierScience, a benchmark evaluating AI capabilities for expert level scientific reasoning.

FrontierScience consists of two tracks:

(1) Olympiad, which contains international olympiad problems (at the level of IPhO, IChO, and IBO), and

(2) Research, which contains PhD-level, open-ended problems representative of sub-problems in scientific research.

In total, FrontierScience is composed of several hundred questions (160 in the open-sourced gold set) covering subfields across physics, chemistry, and biology, from quantum electrodynamics to synthetic organic chemistry.

Recent model progress has nearly saturated existing science benchmarks, which often rely on multiple-choice knowledge questions or already published information.

In contrast, all Olympiad problems are originally produced by international olympiad medalists and national team coaches to ensure standards of difficulty, originality, and factuality.

All Research problems are research sub-tasks written and verified by PhD scientists (doctoral candidates, postdoctoral researchers, or professors). For Research, we also introduce a granular rubric-based architecture to evaluate model capabilities throughout the process of solving a research task, as opposed to judging a standalone answer. In initial evaluations of several frontier models, GPT-5.2 is the top performing model on FrontierScience, scoring 77% on the Olympiad set and 25% on the Research set."

Evaluating AI’s ability to perform scientific research tasks | OpenAI "We introduce FrontierScience, a new benchmark that evaluates AI capabilities for expert-level scientific reasoning across physics, chemistry, and biology."

Frontierscience: Evaluating Ai’S Ability To Perform Expert-Level Scientific Tasks (This paper seems to be published only internally as of now)

Credits: Last Week in AI

Science - Advancing Understanding with Interpretable and symbolic Machine Learning using Kolmogorov-Arnold Networks

Amazing stuff!

"[machine learning models] ... But despite such successes, these data-driven approaches suffer a major drawback in that they are generally “black boxes” that offer no human-accessible understanding of how they make their predictions. This shortcoming also extends to the models’ inputs: It is often desirable to build known domain knowledge into these models, but the data-driven approach excludes that option. [Researchers] have now made a notable step toward addressing these challenges by developing a machine-learning method designed to discover simple, interpretable laws from data ... This method could potentially enable the automated discovery of the physical laws governing a wide range of systems. ...

KANs have since been used to parameterize multivariate functions via such a composition of sums and univariate functions and learn the univariate functions in this representation from a set of training data. Since the learned part of a KAN is a collection of univariate functions, one can potentially gain insight into the function represented by the KAN―and thus understand what the KAN has learned―by inspecting these univariate functions after training. This arguably makes KANs more interpretable than standard neural networks. ...

By necessity, these initial tests are toy experiments where the correct answer is already known. It will be exciting to see how KANs perform on problems of real scientific interest, where the correct physical laws are not yet known. Applied to such problems, this approach has the potential to significantly accelerate the scientific process. ..."

From the abstract:

"A major challenge of AI plus science lies in its inherent incompatibility: Today’s AI is primarily based on connectionism, while science depends on symbolism.
To bridge the two worlds, we propose a framework to seamlessly synergize Kolmogorov-Arnold networks (KANs) and science. The framework highlights KANs’ usage for three aspects of scientific discovery:
identifying relevant features,
revealing modular structures, and
discovering symbolic formulas.
The synergy is bidirectional: science to KAN (incorporating scientific knowledge into KANs), and KAN to science (extracting scientific insights from KANs).
We highlight major new functionalities in pykan:
(1) MultKAN, KANs with multiplication nodes,
(2) kanpiler, a KAN compiler that compiles symbolic formulas into KANs;
(3) tree converter, convert KANs (or any neural networks) into tree graphs.
Based on these tools, we demonstrate KANs’ capability to discover various types of physical laws, including conserved quantities, Lagrangians, symmetries, and constitutive laws."

Physics - Advancing Physical Understanding with Interpretable Machine Learning "A new artificial neural-network architecture opens a window into the workings of a tool previously regarded as a black box."

Kolmogorov-Arnold Networks Meet Science

KAN: Kolmogorov-Arnold Networks (I believe, this is the original work on KANs by the same first author Ziming Liu published in April 2024)

Notice MultKAN have additional multiplication nodes

q.e.d: and other AI Tools for Smarter research Manuscript Review

Good news! I will add it to my Christmas wish list! 😊

A solution for the chronic shortage of expert reviewers?

"... There are only so many experts available to properly evaluate a study and provide meaningful feedback, while others may have conflicts of interest or biases. ...

To build a powerful AI reviewer that provides users with constructive feedback. Together, he and a team of scientists from various backgrounds including AI, engineering, and biology created q.e.d. Its name is derived from quod erat demonstrandum, a Latin phrase meaning “which was to be demonstrated,” which is typically signed at the end of mathematical proofs and philosophical arguments. Though not a peer, q.e.d is an online tool that aims to help scientists improve their research before submission.

With q.e.d, users simply upload their manuscript or even an early draft to q.e.d’s website. Within 30 minutes, they receive a report that breaks the research down into ... a “claim tree.” The AI identifies the claims made within the work, examines the logical connections between them, pinpoints strengths and weaknesses, and suggests both experimental and textual edits. ...

Since its launch in October 2025, researchers from more than 1,000 institutions worldwide have tried q.e.d, and it has garnered much buzz amongst the scientific community. ...

 Refine, another AI peer-review agent. ..."

q.e.d: An AI Tool for Smarter Manuscript Review | The Scientist "A team of researchers developed q.e.d, an AI-powered system designed to deliver rigorous and constructive feedback on scientific manuscripts in minutes."

q.e.d. website "Used by scientists at 1,000+ institutions to evaluate the science they read and write."

The following map displays the number of institutions in each country where researchers use qed

Mathematicians Discover a Strange New Infinity

Very recommendable! What hotel room this time?

Consciousness as the foundation: New theory addresses nature of reality

Amazing stuff!

According to Google Scholar Maria Stromme has a total lifetime citation count of 30595 as of 12/1/2025. That is not bad!

"... Stromme, who normally conducts research in nanotechnology, here takes a major leap from the smallest scales to the very largest – and proposes an entirely new theory of the origin of the universe. The article presents a framework in which consciousness is not viewed as a byproduct of brain activity, but as a fundamental field underlying everything we experience – matter, space, time, and life itself. ..."

From the abstract:

"The nature of consciousness and its relationship to physical reality remain among the most profound scientific and philosophical challenges. This paper presents a novel framework that integrates consciousness with fundamental physics, proposing that consciousness is not an emergent property of neural processes but a foundational aspect of reality.

Building upon insights from quantum field theory and non-dual philosophy, a model based on the three principles of universal mind, universal consciousness, and universal thought is introduced.

These principles describe an underlying, formless intelligence (mind), the capacity for awareness (consciousness), and the dynamic mechanism through which experience and differentiation arise (thought).

Within this framework, the emergence of space–time and individual awareness is modeled mathematically by treating universal consciousness as a fundamental field.

Differentiation into individual experience occurs via mechanisms such as symmetry breaking, quantum fluctuations, and discrete state selection—paralleling established concepts in physics, including Bohm’s implicate order, Heisenberg’s potentia, and Wheeler’s participatory universe.

This model suggests that the apparent separateness of individual consciousness is an illusion, with all experience ultimately arising from a unified, formless substrate.

The framework aligns with emerging theories in quantum gravity, information theory, and cosmology that posit classical space–time as emergent from a deeper pre-spatiotemporal order.

It offers a non-reductionist alternative in neuroscience, suggesting that consciousness interacts with physical processes as a fundamental field. By drawing from insights from physics, metaphysics, and philosophy, this conceptual framework proposes new directions for interdisciplinary inquiry into the nature of consciousness and the origins of structure and experience."

Consciousness as the foundation: New theory addresses nature of reality (An interview with the researcher Maria Stromme)

Consciousness as the foundation – new theory of the nature of reality (original news release) "Consciousness is fundamental; only thereafter do time, space and matter arise. This is the starting point for a new theoretical model of the nature of reality, presented by Maria Stromme, Professor of Materials Science at Uppsala University, in the scientific journal AIP Advances. The article has been selected as the best paper of the issue and featured on the cover."

Universal consciousness as foundational field: A theoretical bridge between quantum physics and non-dual philosophy featured (open access)

Maria Stromme (the foundational beauty of consciousness?)

FIG. 2. Illustration of the proposed framework and its implications for sentient beings. The universal consciousness field (Φ) exists beyond space–time in an undifferentiated state. Through differentiation, it gives rise to localized excitations ⁠, which manifest as physical structures or individual consciousness. Following the Big Bang, Φ evolves, generating complex systems capable of awareness—sentient beings with individual consciousness localized in space–time. Once differentiated, personal thought shapes individual awareness and perception, producing evolving subjective interpretations of reality over time. This process creates the illusion of separateness, even though all individual consciousness remains intrinsically connected within the universal consciousness field.