Temperon: Full-Time SAM Quality at a Third Less Wall-Clock
arXiv:2609.17575v1 Announce Type: new
Abstract: Sharpness-aware minimization (SAM) doubles the cost of every training step, yet its benefit concentrates where training ends. We study where an expensive training mode should be spent and propose Temperon: a plain-SGD explorer for the first 43% of the...
Making AI-Assisted Claims Independently Challengeable: Publication Authority and a Protocol for Falsifiable Publication Records
arXiv:2609.17631v1 Announce Type: new
Abstract: AI-assisted claims can appear authoritative when evidence, analysis, human authorization, presentation, and correction history refer to different states. Provenance, attestation, and transparency expose history but alone do not specify the publication...
arXiv:2609.17633v1 Announce Type: new
Abstract: The Graph Coloring Problem (GCP) is NP-hard and DSATUR stands as one of the fastest heuristics for it despite producing colorings that typically use more colors than state-of-the-art coloring algorithms. We propose SSLD (Semidefinite Spectral Learning...
Causal neural set filtering for online multi-target tracking
arXiv:2609.16054v1 Announce Type: new
Abstract: Transformer-based multi-target tracking (MTT) jointly learns data association and state estimation, but MT3/Track-MT3-style trackers repeatedly re-encode measurement windows, incurring redundant computation. We propose Causal Neural Set Filtering (CNS...
Driver Behavior Estimation at Signalized Intersections Using a Physics-Constrained Decision-Conditioned Autoregressive Transformer
arXiv:2609.16058v1 Announce Type: new
Abstract: Red-light violations and harsh braking at signalized intersections are major contributors to traffic accidents. This paper analyzes and predicts human driver decision-making and longitudinal trajectory behavior during traffic light signal transitions....
Calibrate, Then Route: A Measured Study of Learned Request Routing for Disaggregated LLM Serving
arXiv:2609.16206v1 Announce Type: new
Abstract: Disaggregated LLM serving places compute heavy prefill and memory heavy decode on separate GPU pools. Systems such as DistServe, Splitwise, and Mooncake make this separation fast, but routing still determines which instances handle each request. We st...
The shape behind the Einstein problem just revealed strange new physics
A mathematical shape famous for covering a surface without ever repeating has revealed an unexpected ability to twist light into unusual chiral patterns. The discovery could lead to new ways of controlling light, polarization, and advanced optical devices.
A derivative-fidelity failure mode in physics-informed neural networks: strengthened benchmark evidence from function-value training
arXiv:2609.13171v1 Announce Type: new
Abstract: Physics-informed neural networks (PINNs) use automatic differentiation to impose differential-equation residuals, but good agreement in function values does not necessarily imply accurate derivatives. This paper formulates derivative fidelity as a fai...
Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation
arXiv:2609.13396v1 Announce Type: new
Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a high-quality solut...
AI leaders who skip this meeting keep losing budget in Q3
Nearly every organization running AI in production is overspending against forecast. The fix rarely means a smaller budget. It means a meeting most AI leaders keep skipping.
Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems
arXiv:2609.11934v1 Announce Type: new
Abstract: In networked dynamical systems, the parameter of primary mechanistic interest is signed interaction structure. Recovering this structure from perturbation time-series data is a fundamental identification problem, compounded by three coupled obstacles:...
Physics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators
arXiv:2609.11935v1 Announce Type: new
Abstract: Neural operators such as the Fourier Neural Operator (FNO) achieve remarkable accuracy in approximating solutions to partial differential equations (PDEs). However, providing rigorous uncertainty estimates remains an open challenge. We propose Physics...
AI uncovers hidden Ozempic side effects across 400,000 Reddit posts
AI analysis of 400,000 Reddit posts found that users of drugs such as Ozempic, Wegovy, Mounjaro, and Zepbound reported unexpected symptoms including menstrual changes, chills, hot flashes, and fatigue. Researchers cannot say the medications caused these problems, but the patterns may reveal overlook...
Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language
arXiv:2609.10629v1 Announce Type: new
Abstract: Quadratic Unconstrained Binary Optimization (QUBO) is a central formulation for combinatorial optimization and has gained increasing attention due to its compatibility with quantum, hybrid quantum-classical, and quantum-inspired solvers. However, tran...
Quantifying the Memorization-to-Generalization Transition: Scaling Laws and Phase Structure in Grokking
arXiv:2609.10657v1 Announce Type: new
Abstract: Neural networks trained past memorization frequently undergo a delayed transition to generalization, a phenomenon known as grokking. Despite theoretical progress on \emph{why} this transition occurs, the quantitative structure of \emph{when} it occurs...
Tiny nanolaser could cut computer energy use in half
Scientists have created an ultra-small nanolaser that could eventually allow microchips to transmit information with light instead of electricity, potentially making computers faster while cutting energy use roughly in half. Thousands of the lasers could fit on a single chip, opening possibilities f...
Scientists just made quantum computer operations 1,000 times faster
Researchers have found a way to perform certain quantum operations more than 1,000 times faster, cutting thousands of repeated control cycles down to just one. The advance could reduce errors and bring reliable, fault-tolerant quantum computers closer to reality.
Halo: Improving forecast accuracy through heteroscedastic estimation
arXiv:2609.10589v1 Announce Type: new
Abstract: Heteroscedastic forecasting, where a network estimates a scale parameter alongside a location parameter, is normally motivated by uncertainty quantification. This paper shows it also improves the point estimate, in contrast to reported negative result...
Zero-shot rib design: merging training-free generative prior with topology optimization
arXiv:2609.10643v1 Announce Type: new
Abstract: Natural load-bearing patterns such as leaf venation, trabecular bone, and spider webs achieve high stiffness per unit mass, yet classical topology optimizers rarely reach such geometries, and few let engineers express structural design intent through ...
Byzantine-Robust Federated Fire Detection with a Rotating Coordinator
arXiv:2609.10647v1 Announce Type: new
Abstract: We study the application of federated learning (FL) to indoor fire detection. Such fire-detection systems use edge cameras that record sensitive footage which cannot easily be collected at a central server. Existing federated solutions leave three pra...
Artificial Intelligence Algorithms for the Detection of Pathologies Related to Lung Cancer through Image Analysis using Convolutional Neural Networks and Data Augmentation: a systematic mapping of the literature
arXiv:2609.10652v1 Announce Type: new
Abstract: Lung cancer is one of the leading causes of death worldwide, and its early diagnosis is crucial to improving patients prognosis and quality of life. However, the process of interpreting medical images for the detection of lung cancer is complex and re...