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An AI research scientist explores new submissions on arXiv daily, selects the 3 most impactful papers across scientific domains, and explains each one in plain language — so engineers, students, doctors, and curious minds worldwide can grasp what was discovered, how, and why it matters.

9 papersUpdated Nov 6, 12:09 PM
🤖 AI & MLNotableArtificial IntelligenceYesterday

Formalizing inverse problems in the age of AI: possibilities, pitfalls, and an invitation

Christian Clason,Tapio Helin,Medet Nursultanov

💡 Formalizing inverse problems in the age of AI: possibilities, pitfalls, and an invitation

Proof assistants such as Lean check mathematical proofs down to the axioms, and AI agents have recently made writing such proofs cheap. We describe AutoIP, a Lean library in which classical linear regularization theory, together with the functional analysis it rests on, has been autoformalized by AI agents. A machine-checked proof certifies only the formal statement it proves, so the remaining human task is to confirm that each formal statement says what was intended. We organize the library around this task: a reviewer compares the formal statements with their natural-language counterparts and never needs to read a proof. Our experience is that the risks lie in the statements rather than in the proofs, and that simple automated tests on the statements can expose problems even in the established literature. We illustrate this with a textbook theorem whose hypothesis is more restrictive t

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🤖 AI & MLNotableArtificial IntelligenceYesterday

KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks

Zi-Jian Cheng,Weiming Wu,Jie Meng

💡 KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks

Liability adjudication tasks (e.g., legal charge determination) require analyzing event descriptions against domain-specific skills to determine whether a party should bear liability. While Large Language Models (LLMs) have shown promise in this area by leveraging executable skills for interpretable reasoning, existing approaches face three critical limitations: skill libraries become stale as real-world scenarios dynamically evolve, skill descriptions often suffer from a semantic execution gap where LLMs deviate from intended expert logic, and growing skill collections exceed context budgets causing critical skill omission. To address these challenges, we propose Knowledge-Data Dual-Driven Automated Skill Optimization (KDASO), which continuously evolves a skill library through neighborhood-conditioned induction from recent adjudication data, execution-aligned refinement based on LLM tra

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⚛️ PhysicsNotableQuantum PhysicsYesterday

Physical regimes of quartic quantum advantage in elastic media simulation

David Jennings,Kamil Korzekwa,Matteo Lostaglio

💡 Physical regimes of quartic quantum advantage in elastic media simulation

Quantum algorithms for elastic media simulation have been conjectured to offer up to a quartic quantum advantage over classical methods; however, the devil is always in the details. Here, we determine key physical conditions under which this quartic separation could be achieved in the relevant parameters. For continuum elastic dynamics at fixed volume with spatial convergence order $m$, we find that the end-to-end speedup is of order $(m+4)/(m+1)$ in the simulation time, and $4/(m+1)$ in the accuracy, with exponential memory advantage. For elastic lattice systems, we define microscopically resolved bulk observables (MRBOs) as those that can be efficiently extracted from a quantum state, but cannot be resolved by a known continuum model with fewer degrees of freedom than the lattice. We then argue that the estimation of MRBOs is a candidate problem for quartic quantum advantage in problem

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⚛️ PhysicsNotableQuantum PhysicsYesterday

FNO-Based Inverse Co-Design of Semiconducting Quantum-Dot Devices

Marek Rychetsky,Valentin Duruisseaux,Freya Shah

💡 FNO-Based Inverse Co-Design of Semiconducting Quantum-Dot Devices

Gate-defined semiconductor quantum dots are promising building blocks for scalable spin-based quantum processors, however their operational properties depend on several fabrication and control parameters, such as lithographic gate layout and applied voltages. Existing device architectures have been refined through repeated fabrication and experimental testing, producing layouts that support reliable device operation. However, this process does not exhaustively explore the design space, limiting the ability to obtain qubits with targeted properties. We address these challenges by presenting a differentiable inverse co-design framework for electrostatically defined quantum dots that jointly optimises gate voltages and the gate electrode layout. A semiclassical Thomas--Fermi electrostatic simulator provides reference data for a Fourier neural operator (FNO) surrogate, which predicts the two

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🧬 BiologyNotableQuantitative BiologyYesterday

No Support Beyond Reach: The Measure Centrality Index 2.0 and Adversarial Comparisons of Consciousness Theories

Robert Chis-Ciure

💡 No Support Beyond Reach: The Measure Centrality Index 2.0 and Adversarial Comparisons of Consciousness Theories

A success in adversarial testing of scientific theories reaches only as deep as its matching failure would have cut. I call this the Reach Principle, and the goal of this chapter is to build the instrument that enforces it. Adversarial tests of consciousness theories can turn into a one-way evidential ratchet. When a preregistered prediction passes, the success is claimed for the research program; when it fails, the failure lands on an auxiliary involved in measurement or implementation. The ratchet turns wherever attribution is settled after the outcome is known. Against it, I define a preregisterable test object, putting the attribution policy on the record before the result, comprising what is compared, under which assumptions, when data suffice to settle an outcome, and which targets of a dated theory version each outcome exposes. The Measure Centrality Index 2.0 extends the original

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🧬 BiologyNotableQuantitative BiologyYesterday

Foundation Models as Modular Tools for fMRI Analysis

Joseph Kilgore,Noemia Mahmud,Shinjae Yoo

💡 Foundation Models as Modular Tools for fMRI Analysis

Foundation models (FMs) for the brain are rapidly growing in number and each captures a different aspect of the same data. A single scientific question often needs several of them at once, alongside classical analysis and the literature, but no established method combines them, because each model defines its own inputs, outputs, and spatial conventions. Representation alignment, the usual route from an FM into a language model, binds one FM to one LLM and requires retraining for every model added. We take a modular route: each FM becomes an independent tool server that a fixed language agent calls next to classical and literature tools, with no retraining. We start with fMRI, where public FMs already disagree on how to name a brain location and open task data give a classical baseline, and serve two FMs at opposite ends of that spectrum, BrainLM (atlas parcels) and Omni-fMRI (atlas-free

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💰 EconomicsNotableEconomicsYesterday

Complex Menus in Discriminatory Pricing

Patrick Lahr,Axel Niemeyer,Marek Pycia

💡 Complex Menus in Discriminatory Pricing

Advances in technology decrease the costs of offering and choosing from large menus of varieties. As these complexity costs fall, how large are the menus offered by monopolists likely to become? For the multi-product or multi-attribute case, ubiquitous in practice, we show that optimal menus are generically infinite in the absence of complexity costs. Complexity costs can decrease the menu size, but it grows without bound as these costs decrease. These results have no analogue in the single-dimensional model and do not rely on non-linearities in production costs or preferences; the extensive product differentiation is caused by screening considerations alone.

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💰 EconomicsNotableEconomicsYesterday

Nonparametric Subgraph Regression

Wenxuan Ding

💡 Nonparametric Subgraph Regression

Estimation and inference on a single large network are challenging due to dependence among observations. This paper studies nonparametric regression for outcomes defined on groups of $p$ agents, such as the indicator that three agents in a network form a triangle. Outcomes on groups that share an agent may be dependent, while outcomes on disjoint groups are independent. I call this problem subgraph regression; it nests dyadic regression as the special case $p=2$. Its leading application concerns subgraphs of a network, but the methods apply to any outcome defined on groups of agents. I establish a variance bound, uniform convergence rates in probability, and pointwise asymptotic normality for the subgraph analogue of the Nadaraya-Watson kernel regression estimator. The setting differs from regression with i.i.d. data in two ways: (i) the number of observations, of order $N^p$, overstates

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🚀 SpaceNotableAstrophysicsYesterday

Unveiling the Origins of Local Group Galaxies with ELT-SHARP: An Overview of Science Cases

Hai-Feng Wang,Guan-Yu Wang,Peng Yang

💡 Unveiling the Origins of Local Group Galaxies with ELT-SHARP: An Overview of Science Cases

The $\Lambda$CDM paradigm successfully describes large-scale cosmic structure, yet tensions possibly persist on galactic and sub-galactic scales. The Milky Way and the Local Universe offer a uniquely accessible laboratory to probe these discrepancies through chemo-dynamical studies of resolved stellar populations. Understanding disk assembly, dynamical heating, and galactoseismology requires wide-field coverage and high-angular-resolution near-infrared spectroscopy capable of penetrating dust-obscured regions and resolving crowded stellar environments. We propose science cases exploiting \textsc{SHARP}, a multi-mode near-infrared spectrograph for Extremely Large Telescopes and future space facilities. Operating over $0.95$--$2.45\,\mu$m in a single exposure, \textsc{SHARP} enables efficient access to key stellar and gas tracers across the Galactic bulge, disk, halo, and nearby galaxies.

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