News / #paper Tag Research papers 500 articles archived under #paper · RSS Sign in to follow arXiv — Machine Learning research 2h ago Representation as a Bottleneck for Mechanistic Interpretability: The Manifestation Unit Protocol arXiv:2607.00089v1 Announce Type: new Abstract: Mechanistic interpretability has produced a rich inventory of component-level analyses that characterise what neural-network components encode and how they interact. Their outputs, however, are not easily reusable: selectivity… 8 arXiv — Machine Learning research 2h ago SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modeling arXiv:2607.00095v1 Announce Type: new Abstract: Generative models have emerged as scalable surrogates for physical simulation, yet they offer no guarantee that their outputs respect the conservation laws, boundary conditions, and nonlinear invariants that govern the underlying… 15 arXiv — Machine Learning research 2h ago SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification arXiv:2607.00113v1 Announce Type: new Abstract: Background. Labeled data for security classification is scarce. Semi-supervised learning (SSL) propagates labels from a small labeled pool to larger unlabeled pools. Yet security applications often use SSL as a black box: default… 7 arXiv — Machine Learning research 2h ago A Filtered Mixture-of-Generators for Fully Synthetic Survival Training arXiv:2607.00127v1 Announce Type: new Abstract: Survival analysis models time-to-event data, but in clinical settings training data are costly and scarce: events accrue over years of follow-up, cohorts are small, and privacy regulations restrict sharing across institutions.… 26 arXiv — NLP / Computation & Language research 2h ago GRPO, Dr. GRPO, and DAPO Are Three Operations on One Number: The Group-Standard-Deviation Identity arXiv:2607.00152v1 Announce Type: cross Abstract: Three of the most popular methods for training language models to reason look like three different tricks. They are not. All three adjust a single number: standard deviation, reflecting how much a prompt's sampled answers… 38 arXiv — Machine Learning research 2h ago EVOTS: Evolutionary Transformer Search for Time Series Forecasting arXiv:2607.00154v1 Announce Type: new Abstract: Evolutionary neural architecture design for multivariate time-series forecasting remains underexplored, with most approaches relying on fixed Transformer architectures despite substantial variation across tasks and forecasting… 27 arXiv — Machine Learning research 2h ago FRAME: Learning the Adaptation Domain with a Mixture of Fractional-Fourier Experts arXiv:2607.00162v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) reparameterizes weight updates in a fixed basis: low-rank adapters operate in the spatial domain, while a recent line of spectral methods operates in a fixed Fourier domain. We argue that the… 36 arXiv — Machine Learning research 2h ago Verifiable Rewards for Calibrated Probabilistic Forecasting arXiv:2607.00164v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards can in principle train calibrated probabilistic forecasters, since a proper scoring rule such as the Brier score is computed from outcomes alone and is minimized in expectation by the… 37 arXiv — Machine Learning research 2h ago Scaling Up Thermodynamic AI Models arXiv:2607.00170v1 Announce Type: new Abstract: Thermodynamic computing devices based on the Ising model show great promise for low-power AI inference and edge computing, but scalable methods for training large models for such hardware remain limited. Prior theory shows that the… 28 arXiv — Machine Learning research 2h ago TallyTrain: Communication-Efficient Federated Distillation arXiv:2607.00173v1 Announce Type: new Abstract: Federated learning is bandwidth-bound on two orthogonal axes: model size, which limits how often parameter-averaging methods can afford to merge, and class count, which makes per-probe soft-label distillation prohibitive at large… 31 arXiv — Machine Learning research 2h ago Play Like Champions: Counterfactual Feedback Generation in Latent Space arXiv:2607.00190v1 Announce Type: new Abstract: Recent advances in reinforcement learning have produced superhuman agents across a wide range of competitive games. As a byproduct, researchers have begun studying how these agents play, extracting behavioral representations,… 37 arXiv — Machine Learning research 2h ago TRIE: An Evaluation Framework for Stochastic PDE Surrogates arXiv:2607.00196v1 Announce Type: new Abstract: Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally distributional rather than pointwise. For such… 37 arXiv — Machine Learning research 2h ago StateFlow: Dual-State Recurrent Modeling for Long-Horizon Time Series Forecasting arXiv:2607.00197v1 Announce Type: new Abstract: Long-horizon multivariate time series forecasting (LTSF) remains challenging due to non-stationarity, regime shifts, and error accumulation. The Variability-Aware Recursive Neural Network (VARNN) is designed to track such… 17 arXiv — Machine Learning research 2h ago Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts arXiv:2607.00249v1 Announce Type: new Abstract: New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a plausible solution if they are able to generalize to new layouts effectively. To… 28 arXiv — Machine Learning research 2h ago Distributionally Robust Linear Regression With Block Lewis Weights arXiv:2607.00252v1 Announce Type: new Abstract: We present an algorithm for the group distributionally robust (GDR) least squares problem. Given $m$ groups, a parameter vector in $\mathbb{R}^d$, and stacked design matrices and responses $\mathbf{A}$ and $\mathbf{b}$, our… 4 arXiv — Machine Learning research 2h ago Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression arXiv:2607.00257v1 Announce Type: new Abstract: Accurate prediction of complex dynamical systems from noisy measurements remains a significant challenge in scientific computing. Kernel ridge regression learning strategies are often effective when applied to clean data, but have… 17 arXiv — Machine Learning research 2h ago Validating Causal Abstraction Metrics on Simulated Complex Systems arXiv:2607.00267v1 Announce Type: new Abstract: A central goal of science is to produce valid explanations of complex systems: high-level causal accounts that faithfully reflect the behavior of lower-level mechanisms. Yet no consensus exists on how to measure whether a proposed… 33 arXiv — Machine Learning research 2h ago Entropy-Regularized Probabilistic Gates for Sparse Model Discovery in Scarce-Data Federated Learning arXiv:2607.00275v1 Announce Type: new Abstract: Federated Learning (FL) is a distributed machine learning (ML) paradigm with collaboration among multiple clients without sharing data. FL is challenging under data heterogeneity and partial client participation. Learning sparse… 14 arXiv — NLP / Computation & Language research 2h ago Testing Frontier Large Language Models' Physics Literacy in Parallel Physical Worlds arXiv:2607.00276v1 Announce Type: cross Abstract: Current large-language-model (LLM) physics benchmarks are usually scored by answer accuracy, which cannot distinguish genuine reasoning from recall of familiar problem patterns and reveals little about where a model's reasoning… 10 arXiv — Machine Learning research 2h ago Understanding Guest Preferences and Optimizing Two-sided Marketplaces: Airbnb as an Example arXiv:2607.00280v1 Announce Type: new Abstract: Airbnb is a community based on connection and belonging -- many hosts on Airbnb are everyday people who share their worlds to provide guests with the feeling of connection and being at home; Airbnb strives to connect people and… 14 arXiv — NLP / Computation & Language research 2h ago EPC: A Standardized Protocol for Measuring Evaluator Preference Dynamics in LLM Agent Systems arXiv:2607.00297v1 Announce Type: cross Abstract: When LLM agents use evaluator feedback to adapt their behavior in closed loops, evaluator biases propagate through the agent's strategy distribution -- a phenomenon known as evaluator preference coupling. Prior work has… 37 arXiv — Machine Learning research 2h ago Generative Modeling of Quantum Distribution with Functional Flow Matching arXiv:2607.00301v1 Announce Type: new Abstract: The emergence of powerful deep generative models based on diffusion and flow matching has enabled the learning and modeling of complex distributions. Learning quantum distributions, however, remains challenging due to the inherent… 23 arXiv — NLP / Computation & Language research 2h ago Mapping the Evaluation Frontier: An Empirical Survey of the Bias-Reliability Tradeoff Across Eleven Evaluator-Agent Conditions arXiv:2607.00304v1 Announce Type: cross Abstract: The bias-reliability tradeoff conjectures that LLM evaluation systems are constrained in (gamma, H, CV) space, where evaluator coupling (gamma), strategy diversity (H), and small-sample measurement reliability (CV(N)) cannot be… 7 arXiv — NLP / Computation & Language research 2h ago Watermarking for Proprietary Dataset Protection arXiv:2607.00325v1 Announce Type: cross Abstract: A growing body of literature suggests that training data membership inference problems are fundamentally hard tasks in modern language modeling settings. We argue that output watermarking techniques are the right gadget to make… 8 arXiv — Machine Learning research 2h ago K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks arXiv:2607.00329v1 Announce Type: new Abstract: Recursive Feature Machines (RFMs) are a class of kernel machines that utilize the Average Gradient Outer Product (AGOP) as a mechanism for feature learning. They have been shown to effectively replicate the learning dynamics and… 7 arXiv — Machine Learning research 2h ago PRISM: Prioritized Channel Importance with Semi-supervised Domain Adaptation for Cross-Subject EEG Emotion Recognition arXiv:2607.00358v1 Announce Type: new Abstract: Electroencephalogram (EEG) captures endogenous brain activity with high temporal fidelity and holds substantial promise for precise emotion decoding. However, channel redundancy and pronounced inter-subject variability remain key… 18 arXiv — Machine Learning research 2h ago SAOT: Self-Supervised Continual Graph Learning with Structure-Aware Optimal Transport arXiv:2607.00377v1 Announce Type: new Abstract: Self-supervised Continual Graph Learning (CGL) aims to successively learn from a graph sequence with different tasks without label supervision - a paradigm that has attracted widespread attention. Most existing self-supervised CGL… 31 arXiv — Machine Learning research 2h ago Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL arXiv:2607.00392v1 Announce Type: new Abstract: Unsupervised Reinforcement Learning (URL) aims to pre-train scalable, skill-conditioned policies without extrinsic rewards, serving as a foundation for downstream control tasks. Despite recent progress, we argue that current… 34 arXiv — Machine Learning research 2h ago Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins arXiv:2607.00431v1 Announce Type: new Abstract: Forecasting models for health-signal digital twins must preserve the oscillatory, frequency, phase, and state-transition dynamics of physiological signals, yet the pointwise metrics used to benchmark them cannot detect when these… 7 arXiv — Machine Learning research 2h ago Gauging, Measuring, and Controlling Critic Complexity in Actor-Critic Reinforcement Learning arXiv:2607.00452v1 Announce Type: new Abstract: Actor-critic methods depend on learned critics, but critic quality is often evaluated only indirectly through return, temporal-difference error, or value loss. Critic complexity is introduced as an additional diagnostic and… 28 arXiv — NLP / Computation & Language research 2h ago MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules arXiv:2607.00464v1 Announce Type: cross Abstract: Current molecular generation benchmarks emphasize task complexity, molecule novelty, and property alignment; they largely overlook a critical concern: the potential safety risks of AI-generated molecules. In practice, many… 22 arXiv — Machine Learning research 2h ago How Early Is Early Enough? Design-Dependent Observation-Window Sufficiency in Subscription Churn Prediction arXiv:2607.00473v1 Announce Type: new Abstract: How many days of early behavior suffice for subscription churn prediction? In the public KKBox dataset, the early indicator of churn is typically an indicator of someone's contract status; however, when looking in the heavily… 36 arXiv — Machine Learning research 2h ago Interpretable vs Learned Encoders for High-Cardinality Fraud Detection arXiv:2607.00477v1 Announce Type: new Abstract: A total of seven categorical encoding methods were tested on the IEEE-CIS fraud benchmark dataset (590,540 records, 3.5% positives, 8 high-cardinality columns). The encoders were evaluated using a stratified 5-fold cross-validation… 7 arXiv — Machine Learning research 2h ago Ghost in the Kernel: In-Context Learning with Efficient Transformers via Domain Generalization arXiv:2607.00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different tasks by leveraging a context-aware attention module for in-context learning. With richer context, transformers adapt more… 18 arXiv — Machine Learning research 2h ago PAPA: Online Personalized Active Preference Alignment arXiv:2607.00486v1 Announce Type: new Abstract: Diffusion models are highly effective at modeling complex data distributions, including images and text. However, in applications like personalized recommender systems, the objective often shifts to modeling specific regions of the… 11 arXiv — Machine Learning research 2h ago Prototype Language Models arXiv:2607.00510v1 Announce Type: new Abstract: Knowing which training examples drive outputs is fundamental to auditing, correcting, and understanding language models, yet for modern LLMs this remains expensive, approximate, and largely post-hoc. Standard language models… 22 arXiv — Machine Learning research 2h ago From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Research arXiv:2607.00512v1 Announce Type: new Abstract: Structural equation modelling (SEM) is widely used in survey-based business and information systems research to assess latent constructs and theory-driven structural relationships. However, SEM path significance is obtained within… 7 arXiv — Machine Learning research 2h ago Active-GRPO: Adaptive Imitation and Self-Improving Reasoning for Molecular Optimization arXiv:2607.00531v1 Announce Type: new Abstract: Scientific reasoning is an increasingly important capability of large language models, yet improving the robustness and efficiency of training such reasoning remains a key open challenge. We study this problem in instruction-based… 22 arXiv — Machine Learning research 2h ago Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition arXiv:2607.00535v1 Announce Type: new Abstract: Few-step flow-map generators, such as consistency models and MeanFlow, accelerate sampling by directly learning long-range transport maps between noise and data. However, these models are typically deterministic, which makes them… 18 arXiv — Machine Learning research 2h ago Group-Equivariant Poincar\'e Convolutional Networks arXiv:2607.00556v1 Announce Type: new Abstract: While recent advancements like the Poincar\'e ResNet have demonstrated the potential of learning visual representations directly in hyperbolic space, their optimisation remains hampered by the computationally intensive nature of… 29 arXiv — Machine Learning research 2h ago Decision-focused Sparse Tangent Portfolio Optimization arXiv:2607.00581v1 Announce Type: new Abstract: Sparse tangent portfolio optimization aims to learn an interpretable, low-cardinality portfolio in the tangency direction of the mean-variance frontier. However, the associated cardinality-constrained formulation is NP-hard, and… 32 arXiv — Machine Learning research 2h ago Measuring Dead Directions: Decomposing and Classifying Singular Structure off Canonical Alignment arXiv:2607.00603v1 Announce Type: new Abstract: We give a descent-free, alignment-free measurement of singular structure on trained networks. At a single frozen checkpoint the read recovers the order $k$ of each dead direction from the directional-Fisher rate, the master… 34 arXiv — Machine Learning research 2h ago Loss Smoothing for Stable Adaptation Under Distribution Shift arXiv:2607.00634v1 Announce Type: new Abstract: In settings such as fine-tuning and reinforcement learning, neural networks are often adapted under distribution shift. Standard adaptation methods typically optimize the target objective directly, inducing an abrupt change from… 38 arXiv — Machine Learning research 2h ago Multi-Label Node Classification with Label Influence Propagation arXiv:2607.00671v1 Announce Type: new Abstract: Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI networks with multiple functions and users in… 5 arXiv — Machine Learning research 2h ago Distributed Online Bandit Submodular Maximization with Bounded Sampling Violations arXiv:2607.00680v1 Announce Type: new Abstract: We study distributed online submodular maximization under partition matroid constraints, in which multiple agents select a limited number of actions from their own subsets sequentially to maximize the cumulative value of a sequence… 30 arXiv — Machine Learning research 2h ago AdaBoosting Text Prompts for Vision-Language Models arXiv:2607.00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts. Handcrafted templates and Large Language Model (LLM)-generated descriptions not only make predictions more… 25 arXiv — Machine Learning research 2h ago Generative Refinement for Low-Budget Black-Box Optimization arXiv:2607.00691v1 Announce Type: new Abstract: Black-box optimization is a fundamental science and engineering tool that makes it possible to optimize objectives without gradient information. Unfortunately, as it often requires many function evaluations, it can be challenging… 31 arXiv — Machine Learning research 2h ago Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning arXiv:2607.00720v1 Announce Type: new Abstract: Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge. In large-scale industrial… 13 arXiv — Machine Learning research 2h ago LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data arXiv:2607.00733v1 Announce Type: new Abstract: Mechanistic modeling via ordinary differential equations (ODEs) provides interpretable descriptions of complex dynamics and enables inference of underlying mechanisms, which is particularly valuable in clinical settings. However,… 36 arXiv — Machine Learning research 2h ago MosaicKV: Serving Long-Context LLM with Dynamic Two-D KV Cache Compression arXiv:2607.00760v1 Announce Type: new Abstract: Long-context LLM services now sustain prompts with hundreds of thousands to millions of tokens, making the key-value (KV) cache a first-order serving cost. Because the cache grows linearly with context length, it can exhaust GPU… 9 Page 1 of 10 · 500 articles Older →
