A Shared Learning Rate Is Not a Neutral Control in Selective On-Policy Distillation
arXiv:2609.22109v1 Announce Type: new
Abstract: Selective on-policy distillation trains a student only at the token positions a selector scores highest, and the literature compares selectors under a single shared learning rate--a control chosen to be neutral. We show it is not. Under LoRA on GSM8K ...
LoRA Enhanced Contrastive Learning with SAS Vision Transformers
arXiv:2609.21061v1 Announce Type: new
Abstract: Automatic target recognition (ATR) with synthetic aperture sonar (SAS) supports advanced naval capabilities, but deep learning is constrained by scarce target imagery, background clutter, and human-in-the-loop assessment. We adapt DINOv3 Vision Transf...
arXiv:2609.20883v1 Announce Type: new
Abstract: Despite the widespread use and success of generative AI techniques today, theoretical guarantees on learning a distribution supported in $d$ dimensions from $n$ samples degrade as $O(n^{-1/\Theta(d)})$, though shown to be minimax optimal. We hypothesi...
Bio-MF: Low-Latency and High-Fidelity EEG-to-fNIRS Cross-Modal Generation for Hybrid Motor-Imagery Brain--Computer Interfaces
arXiv:2609.20904v1 Announce Type: new
Abstract: Hybrid motor-imagery brain-computer interfaces (MI-BCIs) combining EEG and fNIRS can outperform EEG-only systems by exploiting complementary electrophysiological and hemodynamic information. To obtain such hybrid information when paired EEG-fNIRS acqu...
Regularized Emphatic Temporal-Difference Learning: Stability under Constant Stepsizes
arXiv:2609.19170v1 Announce Type: new
Abstract: Emphatic temporal-difference learning (ETD) stabilizes the expected off-policy TD update and changes its projection geometry, but neither property determines constant-stepsize sampled dynamics. We construct an ergodic two-state counterexample in which...
What Do Current Systematic Generalization Tasks Miss? A Reasoning-Centered Analysis
arXiv:2609.19212v1 Announce Type: new
Abstract: Systematic generalization, the ability to solve novel problems by recombining known atomic elements, is central to human intelligence but difficult to study rigorously under controlled settings. Existing studies therefore rely on simplifications such ...
Generative Query Suggestion via Intent Coverage and Query-Level Credit Assignment
arXiv:2609.19209v1 Announce Type: new
Abstract: Generative query suggestion aims to enhance user engagement by anticipating user intents and recommending relevant follow-up queries. A central challenge is to generate slates whose individual queries are useful while the slate covers distinct intents...
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...
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...
Where Grokking Happens: Distributed Utility and Fourier Recoding Without a Module Switch
arXiv:2609.17571v1 Announce Type: new
Abstract: Where in a Transformer is the change from memorization to generalization functionally expressed? We introduce Transition Games--behavior-aligned exact activation games with paired non-generalizing controls--and find distributed utility gain with a pro...
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...
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...
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....
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...
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...
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...
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...
In this article, you will learn how to fine-tune an agentic AI system holistically, covering all four critical dials: training data, parameter-efficient fine-tuning, runtime hyperparameters,...
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...
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...