arxivAug 3

Freeze, Then Select: Structured Field Adapters and Stability-Validated Weak Selection for PDE Discovery from Sparse Observations

arXiv:2607.29665v1 Announce Type: new Abstract: PDE discovery from sparse observations requires reconstructing a continuous field and selecting the correct differential terms. Our analysis of optimization paths in coupled neural PDE discovery reveals three behaviors: the exact support can persist to

#pde-discovery #neural-networks #machine-learning Read on arxiv →

arxivAug 3bullish

Application of machine learning to monster level prediction in tabletop RPG game design

arXiv:2607.09196v2 Announce Type: replace Abstract: Designing balanced adversaries is a central but labor-intensive task in tabletop role-playing game (TTRPG) development. In systems such as Pathfinder, each monster is described by many numerical attributes that jointly determine its power, summariz

#machine-learning #gaming #regression Read on arxiv →

arxivAug 3

A Hamiltonian driven Geometric Construction of Neural Networks via the Lognormal family, Application to Financial Fraud Detection and to Network Security

arXiv:2509.25778v3 Announce Type: replace Abstract: We presents a method for constructing neural networks intrinsically on statistical manifolds via the lognormal distribution. We demonstrate this approach by formulating a neural network architecture directly on statistical manifold. The constructio

#neural-networks #statistical-manifolds #machine-learning Read on arxiv →

arxivJul 31

Epistemic diversity across language models mitigates knowledge collapse

arXiv:2512.15011v3 Announce Type: replace Abstract: Artificial intelligence (AI) increasingly generates the very content used to train future AI systems. This feedback loop can degrade model quality, reduce informational diversity, and ultimately drive knowledge collapse, i.e. a degradation to a nar

#diversity #ecosystem #machine-learning Read on arxiv →

arxivJul 31

Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator wins

arXiv:2607.09306v3 Announce Type: replace Abstract: Behavioural auditing asks whether a language model behaves as it claims, but detection scores are reported without separating two targets: whether a reply was produced under a behaviour-inducing condition (exposure) and whether the behaviour surfac

AU1 model #language-models #evaluation #machine-learning Read on arxiv →

arxivJul 31

Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories

arXiv:2607.28567v1 Announce Type: cross Abstract: Longitudinal causal studies often record histories as irregular functional fragments: laboratory values, physiologic signals, sensor streams, and image-derived summaries measured at unequal and informative times. Standard doubly robust estimators usu

#machine-learning #causal-studies #functional-representation Read on arxiv →

arxivJul 31bullish

Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting

arXiv:2607.26643v1 Announce Type: new Abstract: Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications. A promising solution is to treat skills as trainable states and optimize them in the same way a

#machine-learning #optimization #overfitting Read on arxiv →

arxivJul 31

Dense Supervision, Sparse Updates: On the Sparsity and Geometry of On-Policy Distillation

arXiv:2606.13657v3 Announce Type: replace Abstract: On-policy distillation (OPD) has recently become a prominent post-training recipe by combining two desirable ingredients: on-policy student-generated trajectories and dense token-level teacher supervision. Yet how this hybrid training regime shapes

#on-policy #distillation #machine-learning Read on arxiv →

arxivJul 31bullish

Cybersecurity Detection Classification with Reasoning-enabled Language Models

arXiv:2607.28460v1 Announce Type: new Abstract: A major issue in Security Operations Centers (SOCs) is alert fatigue, as the number of detections reported is more than staff can triage in a given day. Prior work prompts or fine-tunes large language models (LLMs) to emit a triage label directly, but

CHDIFI3 models #security #machine-learning #triage Read on arxiv →

arxivJul 31bullish

The Role of Causality in Algorithmic Recourse

arXiv:2607.28497v1 Announce Type: new Abstract: Algorithmic recourse aims to provide individuals with actionable changes to improve their predicted outcomes in high-stakes classification settings, such as loan and mortgage applications. However, most existing approaches focus only on flipping a mode

#machine-learning #causality #game-theory Read on arxiv →

arxivJul 31bullish

Theatre Chapbooks At Scale: A Statistical Comparative Analysis of Typography

arXiv:2607.27266v1 Announce Type: cross Abstract: We propose a statistical methodology that quantifies the similarity of typefaces between printed historical books. This provides a tool that accelerates philological analysis. Using character prototypes derived from clustering and aligning automatica

#computer-vision #machine-learning #digital-bibliography Read on arxiv →

arxivJul 31bullish

Adaptively Robust LLM Monitoring via Activation Watermarking

arXiv:2603.23171v3 Announce Type: replace-cross Abstract: Providers monitor deployed large language models (LLMs) to detect misuse that they cannot prevent. LLM monitoring is deterministic and often openly available, so $\emph{adaptive}$ attackers with a local copy can search offline for prompts tha

AC1 model #security #cryptography #machine-learning Read on arxiv →

arxivJul 31

Towards joint scaling laws with optimal batch size schedules

arXiv:2607.27731v1 Announce Type: new Abstract: Modern deep learning typically keeps the batch size static throughout training, thus overlooking the joint effect of learning rate and batch size on the training dynamics. In this paper, we study the deep learning dynamics through the lens of convex op

#optimization #deep-learning #large-language-models Read on arxiv →

arxivJul 31bullish

DAS-PMVC: A Framework for Partial Multi-View Clustering via Dual Alignment and Structure Enhancement

arXiv:2607.27761v1 Announce Type: new Abstract: In recent years, multi-view clustering has attracted widespread research interest. However, due to limitations in data collection devices, data across different views often suffer from misalignment, leading to the partial view alignment problem (PVAP).

#clustering #multi-view #alignment Read on arxiv →

arxivJul 31bullish

Beyond the Best Teacher: Expanding and Compressing the Reasoning Solution Manifold

arXiv:2607.27770v1 Announce Type: new Abstract: A single reinforcement-learning run can produce a strong reasoner yet an incomplete teacher: it often amplifies only a subset of the valid solution modes. We argue that reinforcement learning (RL)-trained policies should therefore be viewed as local pr

QW1 model #reinforcement-learning #policy-distillation #machine-learning Read on arxiv →

arxivJul 31

Windowed thinning and query complexity for the bouncy particle and Zigzag samplers

arXiv:2607.28413v1 Announce Type: cross Abstract: Let $\mu(d x)\propto e^{-U(x)} d x$ on $\R^d$, where $U$ is $m$-strongly convex and $L$-smooth, and denote by $\kappa=L/m$ the condition number. We consider windowed thinning, an exact simulation method for the bouncy particle sampler and the coordin

BOCO2 models #simulation #sampling #numerical-analysis Read on arxiv →

arxivJul 30bullish

Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation

arXiv:2607.25060v1 Announce Type: cross Abstract: Monte Carlo simulation of calorimeter showers is a principal bottleneck for the High-Luminosity LHC, and diffusion models have emerged as fast, high-fidelity surrogates. Their denoising objective is purely statistical, however: a model can minimize i

LA1 model #machine-learning #diffusion-models #physics-informed Read on arxiv →

arxivJul 30bullish

DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution

arXiv:2607.26722v1 Announce Type: cross Abstract: Harness plays a critical role in large language model agent performance, and building a high-performing harness requires substantial expert effort. Therefore, recent research has increasingly explored harness self-evolution, which iteratively propose

#machine-learning #research #evolution Read on arxiv →

arxivJul 30bullish

Amortized Moment Matching for Visual Generation

arXiv:2607.26860v1 Announce Type: new Abstract: We propose amortized moment matching, utilizing neural networks to learn data moments as distributional training signals. By casting diffusion denoisers through polynomial projections, we establish a general framework for moment amortization, revealing

#machine-learning #generative-models #neural-networks Read on arxiv →

arxivJul 29

Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures

arXiv:2406.13128v2 Announce Type: replace-cross Abstract: Due to the intricate structure of vascular trees, minor segmentation errors can significantly alter connectivity patterns and increase variability in extracted morphological properties. Global metrics such as the Dice coefficient, precision,

#segmentation #computer-vision #machine-learning Read on arxiv →

arxivJul 29bullish

Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling

arXiv:2510.14717v2 Announce Type: replace Abstract: Increasing the batch size during training -- a ''batch ramp'' -- is a promising strategy to accelerate large language model pretraining. While for SGD, doubling the batch size can be equivalent to halving the learning rate, the optimal strategy for

SE1 model #optimization #pretraining #acceleration Read on arxiv →

arxivJul 27

A Linear Matching Bandit Approach to Online Multi-Human Multi-Robot Teaming

arXiv:2606.29221v2 Announce Type: replace Abstract: We address the problem of online multi-human multi-robot matching through the lens of a linear matching bandit framework, where a learner assigns robots with unknown features from a fixed pool to distinct sets of human agents over multiple rounds.

LI1 model #machine-learning #matching #optimization Read on arxiv →

arxivJul 27bullish

PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework

arXiv:2505.08784v3 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety. In this paper we introduce PCS-UQ, a framework based on the Predictability, Computability, and Stability (PCS) principle

#machine-learning #uncertainty-quantification #safety Read on arxiv →

arxivJul 27bullish

Spatially-Enhanced Temporal Fusion Transformer: Interpretable Multi-Output Prediction for Parametric Dynamical Systems with Time-Varying Inputs

arXiv:2505.00473v2 Announce Type: replace Abstract: We explore the promising performance of a transformer model in predicting outputs of parametric dynamical systems with external time-varying input signals. The outputs of such systems vary not only with physical parameters but also with external ti

TESP2 models #machine-learning #transformer #dynamical-systems Read on arxiv →

arxivJul 24

Are Diversity Metrics Measuring Diversity? A Capability-Controlled Audit of Majority-Vote Gain in LLM Ensembles

arXiv:2607.20768v1 Announce Type: cross Abstract: Majority voting over LLMs is widely assumed to benefit from diversity, and diversity measures are used to choose which models to combine. We ask whether five such measures track diversity or mainly re-express capability, auditing them as predictors o

#llms #diversity #machine-learning Read on arxiv →

arxivJul 24bullish

Adaptive Multi-Horizon Reinforcement Learning

arXiv:2607.20656v1 Announce Type: cross Abstract: Effective decision-making in complex and changing environments requires balancing short-term and long-term consequences. In reinforcement learning (RL), this trade-off is typically controlled through a fixed discount factor, which imposes a single ex

#reinforcement-learning #continual-learning #machine-learning Read on arxiv →

arxivJul 24bullish

CLOE: Christoffel Loss Autoencoder for Anomaly Detection

arXiv:2607.20530v1 Announce Type: cross Abstract: Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance. However, lightweight methods often struggle with high-dimensional data and typically require careful tuning of multiple hyperpar

CLAU2 models #anomaly-detection #dimensionality-reduction #machine-learning Read on arxiv →

arxivJul 24

Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds

arXiv:2607.06696v2 Announce Type: replace-cross Abstract: Weighted empirical measures on compact manifolds appear in importance sampling, particle approximations, posterior summaries, quadrature, and representation learning. Ordinary effective sample size and related weight summaries ignore the geom

#machine-learning #importance-sampling #representation-learning Read on arxiv →

arxivJul 24

Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channels

arXiv:2512.04966v2 Announce Type: replace-cross Abstract: Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especially in massive multiple-input multiple-output (MIMO) systems ope

#wireless-communication #channel-estimation #machine-learning Read on arxiv →

arxivJul 23bullish

Generative Augmented Inference of LLM-generated Data for Market Research: Theory and Empirical Evidence

arXiv:2604.14575v3 Announce Type: replace-cross Abstract: Marketing research often relies on parameters estimated from costly human-generated data, such as conjoint survey responses, purchase decisions, and field experiment outcomes. Recent advances in large language models (LLMs) and other AI syste

LA1 model #machine-learning #marketing #research Read on arxiv →

arxivJul 23bullish

Differentially Private Neural Network Training Under the Hidden State Assumption

arXiv:2407.08233v3 Announce Type: replace Abstract: Current differentially private learning paradigms face a severe utility bottleneck: DP-SGD degrades performance through noise accumulation over training steps, while aggregation-based approaches such as PATE suffer from data inefficiency due to dis

#machine-learning #privacy #differential-privacy Read on arxiv →

arxivJul 23

Spectral-transport stability and benign overfitting for minimum norm interpolation

arXiv:2604.08625v3 Announce Type: replace-cross Abstract: Benign overfitting describes the ability of minimum norm interpolating estimators to generalize despite fitting noisy data exactly. Existing characterizations depend on delicate spectral functionals of the population covariance operator, name

#machine-learning #research #statistics Read on arxiv →

arxivJul 23bullish

SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes

arXiv:2602.01051v5 Announce Type: replace Abstract: Repertoire-level analysis of T cell receptors offers a biologically grounded signal for disease detection and immune monitoring, yet practical deployment is impeded by label sparsity, cohort heterogeneity, and the computational burden of adapting l

#machine-learning #research #biological Read on arxiv →

arxivJul 23bullish

NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces

arXiv:2602.03901v5 Announce Type: replace Abstract: The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization. We develop NeuroPareto, a cohesive architecture that integrat

NEDEBA4 models · +1 #multi-objective-optimization #machine-learning #bayesian-methods Read on arxiv →

arxivJul 22bullish

Incomplete Observations Boost Evolutionary Performance in Ocean Modeling

arXiv:2607.19147v1 Announce Type: cross Abstract: Data-driven methods have revolutionized ocean modeling, yet current approaches rely heavily on complete reanalysis datasets, imposing computational constraints and limiting model performance to that of the training data. Here, we present a generative

HI1 model #machine-learning #ocean-modeling #earth-system Read on arxiv →

arxivJul 22

RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification

arXiv:2607.09816v2 Announce Type: replace Abstract: Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class samples are scarce yet critical for accurate classification. Existing oversampling methods generate syn

#class-imbalance #oversampling #fraud-detection Read on arxiv →

arxivJul 22bullish

SechKAN: Kolmogorov-Arnold Networks with Hyperbolic Secant Functions

arXiv:2607.18290v1 Announce Type: cross Abstract: In recent years, Kolmogorov-Arnold Networks (KANs) have attracted increasing attention due to their effectiveness in machine learning and scientific computing tasks, offering a new paradigm for neural network design. In this paper, we present SechKAN

SEMU2 models #machine-learning #neural-networks #scientific-computing Read on arxiv →

arxivJul 22

Mixing-Free and Signal-Optimal Learning of Gaussian Graphical Models from Glauber Dynamics

arXiv:2607.18559v1 Announce Type: cross Abstract: Gaussian graphical model selection is usually studied under independent sampling, but in many applications the data arise as a single trajectory of a dependent stochastic process. We study exact recovery of the graph from one trajectory of random-sca

#machine-learning #graphical-models #stochastic-process Read on arxiv →

arxivJul 22

Toward Learning POMDPs Beyond Full-Rank Actions and State Observability

arXiv:2601.18930v4 Announce Type: replace-cross Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms. We cast this problem as learning the parameters of a discrete Partially Observable Markov Decision Process (POMD

PR1 model #machine-learning #pomdp #autonomous-agents Read on arxiv →

arxivJul 22bullish

Physical Self-Supervised Learning: IMU Sensing without Manual Labels

arXiv:2607.18361v1 Announce Type: cross Abstract: Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogeneous devices, placements, and users. Existing unsupervised and self-sup

#machine-learning #self-supervised #sensor-fusion Read on arxiv →

arxivJul 21bullish

Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction

arXiv:2411.10921v2 Announce Type: replace Abstract: Accurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (secon

COSE2 models #machine-learning #computer-vision #renewable-energy Read on arxiv →

arxivJul 21bullish

Kernel Regression with Tensor Trains and Hadamard Overparameterization

arXiv:2607.17390v1 Announce Type: cross Abstract: Kernel regression with tensor trains and Hadamard overparameterization (KReTTaH) is introduced as a training-data-free, interpretable, and nonparametric framework for multi-way data imputation. The imputation problem is reformulated as regression in

KR1 model #imputation #machine-learning #tensor-train Read on arxiv →

arxivJul 21bullish

Trustworthy Protein-Ligand Binding Affinity Prediction via Reliability-Aware Multi-Engine Fusion

arXiv:2607.17601v1 Announce Type: cross Abstract: Accurate protein-ligand binding affinity prediction is central to computational drug discovery, yet modern docking engines frequently disagree without indicating which prediction to trust. Consensus scoring and ensemble methods improve mean accuracy

RE1 model #machine-learning #drug-discovery #protein-ligand-binding Read on arxiv →

arxivJul 20bullish

LLM-Guided Transportation Hub Capacity Planning with Textual Business Inputs

arXiv:2607.03651v2 Announce Type: replace Abstract: While traditional hub capacity planning models optimize effectively for quantitative inputs, they often fail to digest qualitative business context. We propose a novel framework where a large language model (LLM) agent iteratively proposes hub capa

LA1 model #optimization #machine-learning #operations-research Read on arxiv →

arxivJul 18bullish

GAttNHP: Group Attention Neural Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs

arXiv:2607.14733v1 Announce Type: new Abstract: Temporal Knowledge Graphs (TKGs) record how facts evolve over time, but forecasting future events on a TKG remains difficult for three reasons: (i) long-range temporal dependencies are hard to encode; (ii) events on different chains mutually excite or

GR1 model #machine-learning #temporal-knowledge-graphs #hawkes-process Read on arxiv →

arxivJul 18bullish

A Comparative Analysis of Machine Learning Models for Long and Short-Term Forecasting of the Egyptian Stock Market: A Focus on EGX30

arXiv:2607.14391v1 Announce Type: new Abstract: This study concentrates on predicting stock prices in the Egyptian market, focusing on the EGX30, an influential financial hub in the Middle East. While most research focuses on global stocks, there's a growing need to understand stock trends in develo

K-RAEX5 models · +2 #forecasting #stock-prices #machine-learning Read on arxiv →

arxivJul 18bullish

Counterfactual Optimal Action Trees (COAT): Interpretable Prescriptive Policies from Observational Data

arXiv:2607.14318v1 Announce Type: new Abstract: We introduce COAT (Counterfactual Optimal Action Tree), a framework for learning interpretable prescriptive policies from observational data. COAT combines counterfactual outcome estimation with large-scale mixed-integer optimization, using column gene

CO1 model #machine-learning #optimization #revenue Read on arxiv →

arxivJul 18

Depth-Dependent Hidden-State Collapse in Dynamical System Autoencoders for LiDAR Point-Cloud Classification

arXiv:2607.14463v1 Announce Type: new Abstract: We study Dynamical System Autoencoders (DSAE) for LiDAR point-cloud classification using spatial coordinates and Product Coefficient feature augmentations. The experiments compare separately trained DSAE architectures at encoder depths $K=1,\ldots,5$ a

DY1 model #machine-learning #computer-vision #classification Read on arxiv →

arxivJul 18

Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization

arXiv:2605.21751v2 Announce Type: replace Abstract: Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coefficient, index, and parameter in the concrete problem data. We study this via Text2Opt-Bench, a sc

#optimization #machine-learning #benchmark Read on arxiv →

arxivJul 18

PAC Learning in Turn-Based Stochastic Games with Reachability Objectives: A Decentralized Private Approach via Expected Conditional Distance

arXiv:2607.14877v1 Announce Type: new Abstract: Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional assumptions. This dif

#reinforcement-learning #game-theory #machine-learning Read on arxiv →

arxivJul 18

cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data

arXiv:2607.15018v1 Announce Type: cross Abstract: High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables. Existing methods either scale poorly, rely heavily on low-

#visualization #categorical-data #machine-learning Read on arxiv →

arxivJul 18

Integration Matters: Rollout-Based Training for Constrained Diffusion Models

arXiv:2607.14398v1 Announce Type: cross Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution. Existing constrained generation methods typically enforce constraints either through training-time op

#machine-learning #diffusion #generative-models Read on arxiv →

arxivJul 18bullish

A vision foundation model for single-cell biology via spatial gene cartography

arXiv:2607.14163v1 Announce Type: cross Abstract: Most single-cell foundation models are adapted from language models, representing each cell as a sequence of gene tokens. This discards the relationships among genes and often the magnitude of their expression. We present scVision, a vision foundatio

SC1 model #single-cell #computer-vision #machine-learning Read on arxiv →

arxivJul 16bullish

BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping

arXiv:2510.04876v3 Announce Type: replace-cross Abstract: Benthic habitat mapping is fundamental for understanding marine ecosystems, guiding conservation efforts, and supporting sustainable resource management. Yet, the scarcity of large, annotated datasets limits the development and benchmarking o

#machine-learning #dataset #computer-vision Read on arxiv →

arxivJul 16bullish

Topology-Agnostic Mesh Reconstruction of Deformable Objects from Sparse Touch

arXiv:2607.13479v1 Announce Type: cross Abstract: Estimating the full shape of a deformable object is especially challenging when vision is unavailable: in the dark, inside an opaque bag, behind the manipulating hand, or under heavy self-occlusion. Touch is the natural sensor in these settings, but

#robotics #machine-learning #reconstruction Read on arxiv →

arxivJul 16

Mechanistic Evidence for Preserved-but-Misaligned Representations in Non-IID FedAvg

arXiv:2512.23043v3 Announce Type: replace Abstract: Federated Averaging (FedAvg) often degrades under non-IID client data, but it remains unclear whether this degradation reflects the loss of client-learned representations or a failure to use representations that are still present. We study this que

FECNRE3 models #federated-learning #non-iid-data #machine-learning Read on arxiv →

arxivJul 16bullish

Quantum Topological Data Encoding

arXiv:2607.13847v1 Announce Type: cross Abstract: Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representations. Quantum machine learning offers the possibility of processing high

#quantum #topology #machine-learning Read on arxiv →

arxivJul 16bullish

NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache

arXiv:2505.18231v3 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference is typically memory-intensive, especially when processing large batch sizes and long sequences, due to the large size of key-value (KV) cache. Vector Quantization (VQ) is recently adopted to alleviate this

#machine-learning #vector-quantization #optimization Read on arxiv →

arxivJul 15bullish

Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems

arXiv:2607.11970v1 Announce Type: cross Abstract: We develop an enhanced in-context learning (ICL) framework to improve the performance of pilot-based beamforming in multi-user multiple-input single-output (MU-MISO) systems. The proposed scheme integrates the ICL-Transformer backbone with the pilot

ICTR2 models #machine-learning #beamforming #communication Read on arxiv →

arxivJul 14

Learnable Mixed Nash Equilibria are Collectively Rational

arXiv:2510.14907v2 Announce Type: replace-cross Abstract: We extend the study of learning in games to dynamics that exhibit non-asymptotic stability. We do so through the notion of uniform stability, which is concerned with equilibria of individually utility-seeking dynamics. Perhaps surprisingly, i

#game-theory #machine-learning #collective-rationality Read on arxiv →

arxivJul 14

The Spectral Structure of Latent Treatment Effects

arXiv:2607.10926v1 Announce Type: new Abstract: Identifying heterogeneous treatment effects under unobserved confounding is central in observational causal inference. In proxy models with a discrete latent confounder, prior Synthetic Potential Outcomes (SPO) [Mazaheri-Squires-Uhler '25] recover the

#causal-inference #machine-learning #observational-study Read on arxiv →

arxivJul 14bullish

Context by Distinct Information: An Auditable Dirichlet-Process Working Memory for Long, Redundant Context Streams

arXiv:2607.10441v1 Announce Type: cross Abstract: Context engineering decides what information a model carries forward, and current designs meter it in tokens: compressing the past into a bounded recurrent state, keeping a key-value entry for every token, or imposing a fixed budget through a window

#machine-learning #context-engineering #working-memory Read on arxiv →

arxivJul 13

Pitfalls and Remedies for Multi-Task Bayesian Optimization

arXiv:2607.09073v1 Announce Type: new Abstract: Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job. We revisit this default in a controlled setting and find that it misestimate

GA1 model #machine-learning #optimization #transfer-learning Read on arxiv →

arxivJul 13

iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis

arXiv:2607.08778v1 Announce Type: cross Abstract: Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide. Predicting AD conversion during the prodromal stage remains critical for disease understanding and patient care. As such, survival

IL1 model #machine-learning #artificial-intelligence #healthcare Read on arxiv →

arxivJul 11bullish

XALPHA: A Memory-Driven AI Quant Researcher for Hypothesis-to-Code Alpha Discovery

arXiv:2607.08332v1 Announce Type: new Abstract: Financial markets are noisy, non-stationary, and high-dimensional, making it difficult to discover predictive and robust trading signals. Alpha discovery has evolved from manual factor design to machine learning, evolutionary search, and recent LLM-bas

XA1 model #quantitative-research #trading #machine-learning Read on arxiv →

arxivJul 10

($\theta_l, \theta_u$)-Parametric Multi-Task Optimization: Joint Search in Solution and Infinite Task Spaces

arXiv:2503.08394v5 Announce Type: replace-cross Abstract: Multi-task optimization is typically characterized by a fixed and finite set of tasks. The present paper relaxes this condition by considering a non-fixed and potentially infinite set of optimization tasks defined in a parameterized, continuo

#optimization #machine-learning #evolutionary-computing Read on arxiv →

arxivJul 10

Persistent Multiscale Density-based Clustering

arXiv:2512.16558v3 Announce Type: replace Abstract: Clustering is a cornerstone of modern data analysis. Detecting clusters in exploratory data analyses (EDA) requires algorithms that make few assumptions about the data. Density-based clustering algorithms are particularly well-suited for EDA becaus

DBHDPL4 models · +1 #clustering #density-based #machine-learning Read on arxiv →

arxivJul 10bullish

TTHE: Test-Time Harness Evolution

arXiv:2607.08124v1 Announce Type: cross Abstract: The behavior of an LLM agent is determined not only by the underlying model, but also by its harness: the executable program that constructs context, invokes tools, verifies intermediate results, and recovers from failures. Existing approaches optimi

LL1 model #adaptation #machine-learning #software-engineering Read on arxiv →

arxivJul 10

Distributed Sketching on Data Partitions for OLS Regression

arXiv:2607.07888v1 Announce Type: new Abstract: This paper studies distributed sketching for ordinary least squares (OLS) regression, an approach that distributes small sketches of a large data set over multiple machines to separately construct OLS estimators and average them. Unlike prior studies t

#machine-learning #regression #distributed-computing Read on arxiv →

arxivJul 10bullish

Contrastive Order Learning: A General Framework for Ordinal Regression

arXiv:2607.08109v1 Announce Type: new Abstract: We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. While contrastive learning effectively leverages all samples in a batch, i

CO1 model #machine-learning #ordinal-regression #contrastive-learning Read on arxiv →

arxivJul 10

Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

arXiv:2603.16481v3 Announce Type: replace Abstract: Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control. In this domain, kernel methods such as Gaussian process regression ar

GA1 model #machine-learning #optimization #control Read on arxiv →

arxivJul 10bullish

Variational Phasor Circuits for Phase-Native Brain-Computer Interface Classification

arXiv:2603.18078v2 Announce Type: replace Abstract: We present the Variational Phasor Circuit (VPC), a deterministic classical learning architecture on the continuous $S^1$ unit-circle manifold. Inspired by variational quantum circuits, VPC replaces dense weight matrices with trainable phase shifts,

VA1 model #machine-learning #classification #neural-computation Read on arxiv →

arxivJul 10

The Regularization Parameter: Sparse Precision Matrix Estimation

arXiv:2607.07735v1 Announce Type: cross Abstract: Sparse precision matrix estimation provides an interpretable and computationally efficient framework for modeling conditional dependencies in high-dimensional, low-sample-size data. A recurring challenge is appropriately selecting the regularization

#machine-learning #estimation #optimization Read on arxiv →

arxivJul 10bullish

A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents

arXiv:2510.22052v2 Announce Type: replace Abstract: The field of artificial intelligence (AI) has taken a tight hold on broad aspects of society, industry, business, and governance in ways that dictate the prosperity and might of the world's economies. The AI market size is projected to grow from {\

OP1 model #artificial-intelligence #machine-learning #energy-efficiency Read on arxiv →

arxivJul 10

Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data

arXiv:2607.08103v1 Announce Type: new Abstract: Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption. In this paper, we reformulate rank estimation with noisy ordinal labels as a stochastic or

#machine-learning #research #label-noise Read on arxiv →

arxivJul 10

Spectral Stability of Pseudoinverse-Based Extreme Learning Machine

arXiv:2607.08581v1 Announce Type: new Abstract: Extreme Learning Machine (ELM) computes output weights analytically using the Moore-Penrose pseudoinverse. Although this leads to fast training, its numerical stability depends strongly on the conditioning of the hidden layer matrix. This paper studies

EX1 model #machine-learning #stability #pseudoinverse Read on arxiv →

arxivJul 10

Robustness Quantification for Discriminative Models: a New Robustness Metric and its Application to Dynamic Classifier Selection

arXiv:2603.23318v2 Announce Type: replace Abstract: Among the different possible strategies for evaluating the reliability of individual predictions of classifiers, robustness quantification stands out as a method that evaluates how much uncertainty a classifier could cope with before changing its p

#machine-learning #robustness #classification Read on arxiv →

arxivJul 10

From Performance to Viability: A Bootstrap Framework for Latent-Space Representation Learning in Adaptive Biological Systems

arXiv:2606.01374v2 Announce Type: replace Abstract: Observable performance is commonly used to characterize biological systems. In adaptive systems, however, similar performances may arise from distinct organizations, and configurations that appear comparable at a given time may follow different lon

#machine-learning #representation-learning #biological-systems Read on arxiv →

arxivJul 3

Scaling Laws for Grid-Based Approximate Nearest Neighbor Search in High Dimensions

arXiv:2607.01283v1 Announce Type: cross Abstract: Grid-based approaches to approximate nearest neighbor (ANN) search have been absent from modern scaling analyses. We present a systematic characterization of a multiprobe grid algorithm with respect to dataset size $N$ and dimensionality $d$. Our exp

#ann #scalability #machine-learning Read on arxiv →

arxivJul 3bullish

The risk of KV cache compression

arXiv:2607.01520v1 Announce Type: new Abstract: Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bottleneck is KV cache compression, which replaces the full cache with a compact summary. Despite its

#machine-learning #optimization #compression Read on arxiv →

arxivJul 3

Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games

arXiv:2607.01498v1 Announce Type: new Abstract: We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games. We make three contributions: First, we introduce methods of creating datasets of policies for a given game. Second, we

#game-theory #machine-learning #self-supervised-learning Read on arxiv →

arxivJul 3bullish

Efficient Temporal Point Processes via Monotone Alternating Splines

arXiv:2607.01752v1 Announce Type: new Abstract: Temporal point processes (TPPs) have widespread applications across various domains. Compared to modeling the conditional intensity of a TPP, modeling its cumulative conditional intensity function (CCIF) improves computational efficiency and eliminates

MOMO2 models #machine-learning #temporal-point-processes #neural-networks Read on arxiv →

arxivJul 3

Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score

arXiv:2607.01527v1 Announce Type: cross Abstract: Room embeddings derived from reverberant speech are often unreliable: speech content and recording degradation can alter the representation even when speaker, room, and source-receiver geometry remain unchanged, degrading downstream task performance.

#speech-processing #machine-learning #audio Read on arxiv →

arxivJul 3

An Additive MLP-GNN Framework for Characterizing Chemical and Structural Contributions to Aqueous Solubility

arXiv:2607.02212v1 Announce Type: cross Abstract: Aqueous solubility is a key property in early-stage drug discovery, but most predictive models merge physicochemical descriptors and molecular graph information into a single representation, obscuring whether a prediction is driven by global chemistr

MUGR2 models #machine-learning #drug-discovery #deep-learning Read on arxiv →

arxivJul 2bullish

Neural Certificate Pricing for Combinatorial Optimization Problems

arXiv:2607.01185v1 Announce Type: new Abstract: Combinatorial optimization (CO) problems are difficult because certifiable discrete structure induces exponential search. One needs to search over the set exponentially many candidates to certify optimality, however, the structural feasibility of a pat

NE1 model #optimization #machine-learning #research Read on arxiv →

arxivJul 1bullish

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders

arXiv:2606.15054v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) detect features via inner product, so a feature's activation scales with both its directional alignment and the input's norm. Features that fire on token norm therefore claim dictionary slots regardless of content alignme

#machine-learning #autoencoders #normalization Read on arxiv →

arxivJul 1

Conformalized Regression for Continuous Bounded Outcomes

arXiv:2507.14023v2 Announce Type: replace-cross Abstract: Regression problems with bounded continuous outcomes frequently arise in statistical and machine learning applications, such as the analysis of rates and proportions. A central challenge in this setting is predicting the response at a new cov

BELO2 models #regression #conformal-prediction #machine-learning Read on arxiv →

arxivJun 30

Learning the structure of open quantum systems

arXiv:2606.30358v1 Announce Type: cross Abstract: We design an algorithm for learning the coefficients of an $n$-qubit constant-local Lindbladian to $\varepsilon$ error with $O(g d^2 \log(n) / \varepsilon^2)$ total evolution time, where $g$ is the single-site energy and $d$ is the (approximate) degr

#quantum-physics #machine-learning #algorithms Read on arxiv →

arxivJun 30bullish

Randomized neural operator for parametric PDEs with fast training and conformal uncertainty quantification

arXiv:2606.29440v1 Announce Type: new Abstract: Repeatedly solving parametric PDEs is essential for uncertainty quantification, design optimization and inverse problems, but conventional neural operators require expensive non-convex training. We introduce PCA--RaNN, a randomized latent neural operat

PC1 model #machine-learning #numerical-analysis #optimization Read on arxiv →

arxivJun 29bullish

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605.03065v4 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning. This work introduces Off-policy Generative Policy Optimization (OGPO), a sample-efficient algori

#robotics #machine-learning #optimization Read on arxiv →

arxivJun 29

Categorical Optimization with Bayesian Anchored Latent Trust Regions for Structural Design under High-Dimensional Uncertainty

arXiv:2604.25241v2 Announce Type: replace Abstract: Categorical structural optimization under aleatoric uncertainty is challenging because each design variable must be selected from a finite catalog of admissible instances, while each candidate design may require expensive stochastic finite-element

CO1 model #optimization #machine-learning #uncertainty Read on arxiv →

arxivJun 27

Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation

arXiv:2606.00827v3 Announce Type: replace-cross Abstract: Strategic classification (SC) investigates scenarios where agents manipulate their features to obtain favorable decisions from predictive models. Existing fairness-aware SC approaches primarily focus on group fairness and typically assume tha

#machine-learning #fairness #strategic-classification Read on arxiv →

arxivJun 27

Unbiased Canonical Set-Valued Oracles Via Lattice Theory

arXiv:2606.26418v1 Announce Type: new Abstract: A non-agentic "oracle" AI that estimates probabilities of future events faces a self-reference problem: once its answer is learned and acted upon, it can change the very probability it was asked to report. One response, advocated for the Scientist AI p

#artificial-intelligence #machine-learning #self-reference Read on arxiv →

arxivJun 26

A Generalization Theory for JEPA-Based World Models

arXiv:2606.27014v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have recently emerged as a promising paradigm for world modeling by learning predictive dynamics in a latent space rather than generating future observations at the input level. Despite their empirical s

#machine-learning #world-modeling #generalization-theory Read on arxiv →

arxivJun 26

When are likely answers right? On Sequence Probability and Correctness in LLMs

arXiv:2606.27359v1 Announce Type: cross Abstract: Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, either locally at the token level or globally at the sequence level. Therefore, their success depends

#language-models #decoding-methods #machine-learning Read on arxiv →

arxivJun 26bullish

Transformer-Based Classification of Bacterial Raman Spectra with LOOCV

arXiv:2606.27096v1 Announce Type: new Abstract: Transformer-based models have recently attracted increasing attention for Raman spectral classification. In this study, a transformer-based approach was systematically evaluated using a nested leave-one-replicate-out cross-validation framework and comp

TR1 model #machine-learning #raman-spectra #classification Read on arxiv →

arxivJun 26

Hallucination in World Models is Predictable and Preventable

arXiv:2606.27326v1 Announce Type: new Abstract: Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics. We hypothesize that hallucination concentrates in

#world-models #machine-learning #computer-vision Read on arxiv →

arxivJun 25bullish

Blockwise Policy-Drift Gating for On-Policy Distillation

arXiv:2606.24084v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student policy using teacher signals computed on trajectories sampled by the student itself. Recent work shows that sampled-token OPD can be fragile on long-horizon reasoning tasks and that local teacher-support

#machine-learning #artificial-intelligence #computation Read on arxiv →

arxivJun 25bullish

On-Device Neural Architecture Search

arXiv:2606.24900v1 Announce Type: new Abstract: This paper proposes a new approach to near-sensor computing, in which a lightweight Neural Architecture Search (NAS) is performed directly on the deployment device to find the best tiny neural architecture for analyzing the real-time data acquired thro

NE1 model #near-sensor #neural-architecture-search #embedded-systems Read on arxiv →