A few of the ideas, topics, and commonplaces that have been gaining steam on arXiv during the last few months (yes, I do a lot of filtering to keep away most of the genAI stuff; explainer).
1. Extraterrestrial Intelligence: This is only a hypothesis, but I suspect the increase in SETI research has been driven by much-improved observational capabilities (most significantly, exoplanets) — but also through some of the cultural side effects of the AGI hype. Nonhuman intelligences have a cultural plausibility that they hadn't had in a long time.
Some recent articles:
- Hybrid Strategy for Coordinated Interstellar Signaling: Linking the Galactic Center and Extragalactic Bursts
- Dual-Backend Multibeam Position Switching Targeted SETI Observations toward Nearby Active Planet-Hosting Systems with FAST
- A Deep SETI Search for Technosignatures in the TRAPPIST-1 System with FAST
- Technosignature Searches of Interstellar Objects
- Detecting Extraterrestrial Civilizations That Employ an Earth-level Deep Space Network
2. Uhlmann's Theorem: An important theorem in quantum information theory. As skeptical as I am about quantum computers in the VC/popular culture sense (revolutionary sensors and devices, on the other hand, I'm very much looking forward to), it's clear that the search for feasible large-scale quantum computing has put quite a bit of resources into some useful and interesting basic research questions.
Some recent articles:
- Optimal symmetry operators
- Local transformations of bipartite entanglement are rigid
- Quantum algorithms for Uhlmann transformation
- Uhlmann's theorem for relative entropies
- Continuous majorization in quantum phase space for Wigner-positive states and proposals for Wigner-negative states
3. uMLIPs: Speaking of useful quantum theory and even more topically, of things where ML is really making a huge difference: universal machine learning interatomic potentials
are (I think) the state of the art in a series of computational problems in materials science. There's a balance here: you have to be skeptical of some nonsensical AI hype — these are specialized tools that facilitate some very technical problems, not sci-fi "who needs scientists anymore?" gadgets — but on the other hand, while not as immediately visible as things like text generation, materials science is, if you'll excuse the pun, what engineering is made of. Computational advances today make possible new materials later, and those new materials are often what make the previously impossible, possible.
Some recent articles:
- Massive Discovery of Low-Dimensional Materials from Universal Computational Strategy
- Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2
- Surface Stability Modeling with Universal Machine Learning Interatomic Potentials: A Comprehensive Cleavage Energy Benchmarking Study
- Universal Machine Learning Potentials under Pressure
- Accelerating Discovery of Ternary Chiral Materials via Large-Scale Random Crystal Structure Prediction