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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 Sep 15, 12:09 PM
🤖 AI & MLNotableArtificial Intelligence23h ago

Optimal Pruning for Neural Architectures using Fisher Information Distances

David S. Berman,Yen-Yu Fu,Edward Hirst

💡 Optimal Pruning for Neural Architectures using Fisher Information Distances

A new scheme for parameter pruning is introduced, derived from the differential-geometric distance in model space. Pruning a parameter sets its value to zero, representing a displacement of the model to the hypersurface on which that parameter vanishes. The minimal distance from the unpruned model to this hypersurface is naturally computed via the geodesic distance in the model space as determined by the Fisher information metric. This distance determines the true change in the model, and its performance, under pruning. By analysing progressively more faithful approximations of this geodesic distance a natural hierarchy of optimality for pruning methods is determined. This starts with the traditional magnitude pruning, then develops into new more sophisticated and effective pruning schemes. The method is demonstrated for both fully-connected networks and vision transformers, on MNIST and

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🤖 AI & MLNotableArtificial Intelligence23h ago

Safe Error Correction for Language Models: Frozen-Base Adjustment with Capability Preservation

Gautam Kishore

💡 Safe Error Correction for Language Models: Frozen-Base Adjustment with Capability Preservation

We study a practical question: can a small correction module fix errors in a frozen language model's outputs without degrading its base capabilities? We propose CRN v2, a lightweight logit-level correction module (~34M trainable parameters, 0.73% of the 4.65B text module) that sits atop a fully frozen Gemma 4 E2B model. The base model is never updated; only the correction module learns, via supervised fine-tuning followed by reference-free DPO on 83,400 error-correction pairs. On a 60-question domain exam (CEHRI: Certified Human-Robot Intelligence, covering facts, arithmetic, and implicit-goal reasoning), CRN v2 corrects 53.3% of base-model errors (reworded variant: 43.3%) while showing no degradation on tested capability benchmarks (MMLU/BoolQ N=200; car-wash N=8). A LoRA baseline at the matched CRN v1 budget (6.6M params, rank 19) achieves 83.3% correction but suffers 30-75% capability

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⚛️ PhysicsNotableQuantum Physics23h ago

Unconditional Security of Discrete-Modulated CV-QKD from Infinite-Dimensional MEAT

Lars Kamin,Ian George,John Burniston

💡 Unconditional Security of Discrete-Modulated CV-QKD from Infinite-Dimensional MEAT

Discrete-Modulated (DM) Continuous-Variable (CV) Quantum Key Distribution (QKD) is an experimentally attractive approach to quantum cryptography, offering high key rates over metropolitan-scale distances while relying on state-of-the-art telecom infrastructure. However, a fundamental gap has remained between this experimental promise and rigorous security: for more than two decades, DM CV-QKD has lacked a complete composable finite-size security proof against coherent attacks. Existing works either restrict the adversary to collective attacks, impose additional finite-dimensional assumptions, apply only to specific modulation formats, or fail to recover the known asymptotic rates. Here, we resolve this longstanding problem by establishing the first complete composable finite-size security proof for DM CV-QKD protocols against coherent attacks, incorporating imperfect detectors and both f

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⚛️ PhysicsNotableQuantum Physics23h ago

Direct Observation of Dipolar-Driven Anisotropic Quantum Projection Noise in a Solid-State Spin Ensemble

Tasuku Ono,Weijie Wu,Haopu Yang

💡 Direct Observation of Dipolar-Driven Anisotropic Quantum Projection Noise in a Solid-State Spin Ensemble

The nitrogen-vacancy (NV) center in diamond is a prominent quantum-sensing platform. Combining readout at the quantum projection noise limit with strong dipolar interactions promises substantial gains in sensitivity. However, experimentally accessing this regime has remained a longstanding challenge. In this work, we demonstrate quantum-projection-noise-resolved readout of a strongly-interacting, two-dimensional ensemble of NV centers. Our approach leverages repetitive readout via the NV's intrinsic $^{15}$N nuclear memory at a moderate magnetic field ($\sim 0.3$ T), improving the readout fidelities by nearly an order of magnitude. This enables us to directly resolve the quantum projection noise of a coherent spin state and to watch the ensemble's intrinsic dipolar interactions shear this noise into an anisotropic profile. Our results open the door to direct measurements of spin squeezin

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🧬 BiologyNotableQuantitative Biology23h ago

Optical microelectrode arrays for differential readout of electrical and mechanical signals in cardiac cells

Alessandro Leronni,Rosalia Moreddu

💡 Optical microelectrode arrays for differential readout of electrical and mechanical signals in cardiac cells

Simultaneous assessment of electrical excitation and mechanical contraction is essential for understanding cardiac cell function, yet these two processes are commonly measured with separate techniques or invasively. Here, changes in cellular electrical activity modulate local charge redistribution in optical microelectrodes and are converted into fluorescence signals, while cell contraction induces membrane displacement that contributes an additional mechanical component to the optical readout. By comparing recordings obtained in beating cells with those acquired after inhibition of contraction, we separate action-potential-associated electrostatic transduction from contractility-driven membrane motion. The approach offers a label-free route to support high-throughput in vitro assays for cardiotoxicity screening and electromechanical sensing. Concurrently, it unfolds the physical mechani

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🧬 BiologyNotableQuantitative Biology23h ago

A neural-astrocyte architecture implements a hybrid automaton for evidence accumulation

Giacomo Vedovati,Ilya E. Monosov,Thomas J. Papouin

💡 A neural-astrocyte architecture implements a hybrid automaton for evidence accumulation

Astrocytes are non-neuronal glial cells that are receiving widespread attention due to their emerging role in neural computation. In this paper, we propose and study dynamical mechanisms by which astrocytes may augment the ability of neural networks to infer context in reinforcement learning (RL) settings. We construct a biologically inspired, two-level dynamical neural-astrocyte network with distinct spatial and temporal organization. We train this model on a hierarchical multi-context task that requires the agent to infer changes in latent task rules based on derived rewards. We find that in this setting, astrocytes enable evidence accumulation of changes in context and subsequent context-specific modulation of neural dynamics. We show that these functions are implemented via two dynamical mechanisms: (i) reward-induced bifurcations that relocate an asymptotically stable attractor into

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💰 EconomicsNotableEconomics23h ago

Tractable Estimation of the Money Pump Index: A Comment

Gavin Kader

💡 Tractable Estimation of the Money Pump Index: A Comment

The Money Pump Index (MPI) of Echenique et al. (2011) measures the severity of consumer irrationality, but computing the exact mean and median MPI over all revealed preference cycles is NP-hard (Smeulders et al., 2013). Existing solutions rely on heuristic proxies, such as evaluating only shorter cycles or bounding the MPI. By framing revealed preferences as a directed graph, this paper projects choice violations onto fundamental cycle bases, which are minimal sets of linearly independent cycles that span the graph's entire cycle space. This yields computationally tractable estimators for the mean and median MPI that are asymptotically equivalent to the original MPI. Applying this methodology to the scanner dataset analyzed by Echenique et al. (2011) and Smeulders et al., (2013), the proposed estimators compute quickly and with negligible small-sample bias.

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💰 EconomicsNotableEconomics23h ago

Global Poverty Beyond the Official Line: A bounded estimate of material insufficiency

Giancarlo Crocetti

💡 Global Poverty Beyond the Official Line: A bounded estimate of material insufficiency

Numbers this large invite a defensive reflex: reach for the reassuring figure and move on. By the most widely cited measure, the World Bank's extreme-poverty line of \$3.00 per day (2021 PPP), approximately 847 million people, or 10.4% of the world's population, lived in poverty in 2024. That figure is accurate as measured. It is also, by design, a floor: a threshold built to mark bare survival in the poorest economies, not to describe what it takes for a family anywhere to live with basic security. The central argument of this report is that the reassurance offered by that single line is largely an artifact of how we chose to measure. Held to the \$3.00 floor, roughly 847 million people are poor; held to the standards of their own societies, the number is several times larger, and even the most cautious, honest count exceeds a billion. Measured against income standards appropriate to ea

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🚀 SpaceNotableAstrophysics23h ago

Bayesian Inferences on Analytical Equations of State Approximations of Neutron Stars

Arijit Das,Sourav Roy Chowdhury

💡 Bayesian Inferences on Analytical Equations of State Approximations of Neutron Stars

Equations of state (EoS) for dense matter are commonly provided in tabulated pressure-energy density relations, complicating numerical implementation and potentially compromising thermodynamic consistency. In this work, we propose a universal, piecewise-continuous functional form with a common parametrization capable of representing a broad class of dense matter EoSs. We validate this parametrization against tabulated EoSs from the CompOSE and LALSimulation repositories. Following the initial deterministic fitting, we employed two distinct Bayesian approaches: one based on synthetic EoSs generated by adding noise to the fitted EoS and another based on multimessenger measurements of neutron-star masses and tidal deformabilities. In both approaches, the initial best-fit parameters are used as reference values. For the considered EoSs, spanning from very soft to very stiff, the resulting fi

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