News / #paper Tag Research papers 500 articles archived under #paper · RSS Sign in to follow arXiv — NLP / Computation & Language research 4h ago Comment on "Modeling rapid language learning by distilling Bayesian priors into artificial neural networks" arXiv:2608.12974v1 Announce Type: cross Abstract: McCoy & Griffiths (2025, henceforth M&G) suggest that a Bayesian prior can be distilled into Artificial Neural Networks (ANNs) through Model-Agnostic Meta-Learning (MAML, Finn et al., 2017). They support this empirically by… 20 arXiv — Machine Learning research 4h ago Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices arXiv:2608.12982v1 Announce Type: new Abstract: In this research work, we are constructing the sensing matrix, which is essential for the success of the compressive sensing technique. We have chosen a learning-based technique for the construction of the sensing matrix. The… 20 arXiv — Machine Learning research 4h ago Balanced Adaptive Prototype Selection for Scalable TabPFN Inference on Large-Scale Tabular Data arXiv:2608.12989v1 Announce Type: new Abstract: Pretrained tabular foundation models have demonstrated strong predictive capability; however, their application to large-scale datasets remains constrained by the limited inference context. This paper introduces Balanced Adaptive… 26 arXiv — Machine Learning research 4h ago Incremental Evaluation and Training in Relational Deep Learning arXiv:2608.13023v1 Announce Type: new Abstract: Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning. However, prevailing RDL evaluation practices rely on static, single-episode dataset… 24 arXiv — Machine Learning research 4h ago On the global feature importance for interpretable and trustworthy heat demand forecasting arXiv:2608.13039v1 Announce Type: new Abstract: The paper introduces the ante-hoc Explainable AI methodology to assess the global feature importance of the Machine Learning models used for heat demand forecasting in intelligent control of District Heating Systems, with… 31 arXiv — NLP / Computation & Language research 4h ago Latent On-Policy Self-Distillation arXiv:2608.13040v1 Announce Type: cross Abstract: Enabling agents to learn from experience and internalize it into their policy has become a central problem in self-evolving AI. On-policy self-distillation (OPSD) offers an effective pathway by using a privileged self-teacher to… 21 arXiv — Machine Learning research 4h ago A Multispectral Framework for the Detection of Calcium Carbide-Induced Ripening and Shelf-Life Estimation in Climacteric Fruits arXiv:2608.13073v1 Announce Type: new Abstract: Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of arsenic… 10 arXiv — Machine Learning research 4h ago Learning Discrete Decisions for MIPs with Constraint-Aware Diffusion arXiv:2608.13079v1 Announce Type: new Abstract: This paper proposes a novel learning-based approach to approximately solve instances of mixed-integer optimization problems. These problems are computationally challenging, as they require jointly determining discrete and… 33 arXiv — Machine Learning research 4h ago Sampling Luck Masquerades as Allocation Gain: Auditing Test-Time Budget Allocation for Neural Combinatorial Optimization arXiv:2608.13087v1 Announce Type: new Abstract: Neural combinatorial optimization (NCO) solvers report the best of many sampled solutions per instance, and the sample count is, by convention, identical for every instance. Whether a non-uniform allocation of a fixed total budget… 31 arXiv — Machine Learning research 4h ago FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching arXiv:2608.13096v1 Announce Type: new Abstract: Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the… 26 arXiv — Machine Learning research 4h ago Branch and Bound for Relational Verification of Neural Networks arXiv:2608.13118v1 Announce Type: new Abstract: Verification of neural networks against relational specifications, such as global robustness, is crucial for safety-critical applications of cyber-physical systems (CPS), given their increasing adoption of AI components. Compared… 31 arXiv — Machine Learning research 4h ago ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning arXiv:2608.13190v1 Announce Type: new Abstract: Group-robust learning is crucial for maintaining accuracy on rare subpopulations when training-group labels are unavailable. However, existing methods often infer environments from a separate reference model and select… 23 arXiv — Machine Learning research 4h ago Beyond Simulated Benchmarks: Evaluating Motion Representations for Fall Detection Under Real-World Data Scarcity arXiv:2608.13197v1 Announce Type: new Abstract: Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention. However, real-world falls are extremely rare: collecting 100 of them requires… 10 arXiv — Machine Learning research 4h ago TANGCO: Learning Topology-Aware Capacity Allocation for Overload-driven Cascading Failures arXiv:2608.13212v1 Announce Type: new Abstract: Networked systems, from power grids to traffic networks and cloud clusters, carry loads across nodes with limited capacity. A node whose load exceeds its capacity fails and sheds its load onto its neighbors, which can trigger a… 5 arXiv — Machine Learning research 4h ago History-informed Lagrangian Neural Networks arXiv:2608.13215v1 Announce Type: new Abstract: Forecasting the long-horizon evolution of mechanical systems from position-only observations is a pivotal yet difficult task, as hidden velocities and trajectory-specific physical properties must be inferred simultaneously.… 24 arXiv — Machine Learning research 4h ago Knowledge-guided Pattern Discovery via Coupled Tensor Factorizations arXiv:2608.13234v1 Announce Type: new Abstract: In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data. These datasets are often multiway, i.e., with more than two axes of variation… 26 arXiv — Machine Learning research 4h ago Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data arXiv:2608.13256v1 Announce Type: new Abstract: As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical. Challenges such as imbalances, biases, and ethical or legal constraints often limit access to high-quality data.… 20 arXiv — Machine Learning research 4h ago Virtual Temperature Sensors in Power Transformers Using Neural Ordinary Differential Equations arXiv:2608.13260v1 Announce Type: new Abstract: Accurate modeling and forecasting of power transformer thermal behavior are critical for reliability, asset lifetime, and optimized power system operation. Numerical approaches such as finite element methods (FEM) and computational… 12 arXiv — Machine Learning research 4h ago Into the ORBIT for Time Series: Training Regimes for Foundation Models arXiv:2608.13262v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have advanced primarily through architectural innovation, while training regimes for large-scale heterogeneous corpora remain under-explored. As a result, pre-training distributions are often… 11 arXiv — Machine Learning research 4h ago EEG Decoding Using CNN and LSTM Network arXiv:2608.13285v1 Announce Type: new Abstract: Motor imagery (MI) brain--computer interfaces (BCIs) have emerged as a promising approach for establishing flexible communication pathways between the human brain and external devices , particularly for individuals affected by… 13 arXiv — Machine Learning research 4h ago Large-scale Testing Global Optimization Methods with Black-box Adversarial Attacks arXiv:2608.13296v1 Announce Type: new Abstract: Existing global optimization benchmark suites are of a moderate size and are based on a small number of analytical functions that date back even to the 1970s. This causes a risk of biasing the development of global optimization… 24 arXiv — Machine Learning research 4h ago The Time Value of Evolution arXiv:2608.13297v1 Announce Type: new Abstract: In evolutionary search, a weak child can be a valuable ancestor that makes high-fitness regions reachable. Immediate-return control is blind to this delayed utility, penalizing mutations through their immediate offspring even when… 32 arXiv — Machine Learning research 4h ago A Probe Direction Is a Property of Its Prompt arXiv:2608.13329v1 Announce Type: new Abstract: A model that behaves differently when it senses it is being tested would undermine the evaluations we rely on, so recent work has sought to read that sense directly from a model's activations. The standard instrument contrasts… 8 arXiv — Machine Learning research 4h ago Training AI Scientists to Replicate Research arXiv:2608.13331v1 Announce Type: new Abstract: The replicability of papers is a cornerstone of scientific knowledge, ensuring the reliability of existing results and providing a base for further experiments. The act of replication typically illuminates details that were… 13 arXiv — Machine Learning research 4h ago Neural Quadratic Forms: A Unified Minimal Model for Sudden Learning and Scaling Laws arXiv:2608.13335v1 Announce Type: new Abstract: Neural networks trained by gradient descent on a smooth cost function can nevertheless learn in steps: the cost holds on long plateaus and then drops abruptly. Meanwhile, training losses instead follow smooth power laws. Variants… 12 arXiv — Machine Learning research 4h ago Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation arXiv:2608.13337v1 Announce Type: new Abstract: Sparse autoencoders are meant to name the things a language model computes, and the usual way to check that a latent matters is to switch it off and see what changes. But a latent fires at many tokens, and the effect has to be… 14 arXiv — Machine Learning research 4h ago Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples arXiv:2608.13341v1 Announce Type: new Abstract: Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference… 17 arXiv — Machine Learning research 4h ago When Local Variance Optimality Is Not Enough: RoPE-Aligned Q/K Rotations for Dynamic 4-Bit Quantisation arXiv:2608.13365v1 Announce Type: new Abstract: Rotation-based post-training quantisation commonly applies an orthogonal transform across an entire attention head to reduce outlier-induced error. RoPE instead partitions each head into two-dimensional frequency pairs, raising the… 34 arXiv — NLP / Computation & Language research 4h ago Reduced Matrix Multiplication: Input-Adaptive Matrix-Product Reduction for LLM Inference arXiv:2608.13426v1 Announce Type: cross Abstract: Transformer-based language models achieve strong performance but incur substantial inference cost due to repeated high-dimensional matrix multiplications. We propose Reduced Matrix Multiplication (RMM), a training-free,… 25 arXiv — Machine Learning research 4h ago Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion arXiv:2608.13457v1 Announce Type: new Abstract: Generating crystals has recently attracted significant interest due to their broad applications in materials science. However, existing generative models struggle to produce complete crystallographic specifications, limiting their… 37 arXiv — Machine Learning research 4h ago Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization arXiv:2608.13461v1 Announce Type: new Abstract: Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experience in the second stage of the conversion process. Estimating the causal effect… 28 arXiv — Machine Learning research 4h ago Concept Drift Detection and Adaptive Retraining of Malware Classification Models arXiv:2608.13465v1 Announce Type: new Abstract: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malware detection or classification are particularly… 11 arXiv — Machine Learning research 4h ago Active-Trace Complexity Bounds for Moreau--Yosida Unadjusted Langevin Sampling arXiv:2608.13467v1 Announce Type: new Abstract: We study the Moreau--Yosida unadjusted Langevin algorithm (MYULA) for the nonsmooth composite target \[ \pi(dx)\propto \exp\{-f(x)-g(x)\}\,dx, \qquad x\in\mathbb R^d, \] where \(f\) is \(m\)-strongly convex with \(L_f\)-Lipschitz… 23 arXiv — NLP / Computation & Language research 4h ago Synthetic Persona Pretraining: Alignment from Token Zero arXiv:2608.13482v1 Announce Type: cross Abstract: As language-model-based AI is increasingly deployed in autonomous settings, aligning its goals and values with those of humans becomes critical. Today, alignment, and the assistant identity itself, are typically introduced only… 9 arXiv — Machine Learning research 4h ago Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration arXiv:2608.13504v1 Announce Type: new Abstract: We develop the Sparse Orthogonal Regression Technique (SORT), a sparse spectral framework for learning orthonormal-basis expansions from noisy and irregularly sampled data. SORT estimates expansion coefficients directly from… 15 arXiv — NLP / Computation & Language research 4h ago Intern-S2-Preview: Scientific Agentic Foundation Model arXiv:2608.13505v1 Announce Type: cross Abstract: Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sustain progress across long task horizons. We… 34 arXiv — Machine Learning research 4h ago Intervention-Aware Clinical World Model for Post-Op Outcome Forecasting in Cardiology arXiv:2608.13518v1 Announce Type: new Abstract: Many clinical prediction models treat post-intervention outcomes as a one-step mapping from baseline measurements to a future endpoint. However, recovery after a procedure often unfolds as an irregular trajectory: clinical… 17 arXiv — Machine Learning research 4h ago The data geometry of masking diffusion: Certified-optimal schedules via unmasking growth complexity arXiv:2608.13520v1 Announce Type: new Abstract: We study masking diffusion for discrete sampling and introduce a path-resolved measure of data geometry called the \emph{unmasking growth complexity} ({\textsf{UGC}\xspace}). Its local increments directly control Kullback--Leibler… 31 arXiv — Machine Learning research 4h ago Vero: Can AI Agents Build Formally Verified Software Repositories? arXiv:2608.13522v1 Announce Type: new Abstract: AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in which an agent produces both an implementation and a machine-checked proof of its… 6 arXiv — Machine Learning research 4h ago DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees arXiv:2608.13524v1 Announce Type: new Abstract: Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters further reduce proposal latency by predicting an entire token block in parallel,… 22 arXiv — Machine Learning research 4h ago Exponential Convex Calibration Dimension for the Multi-Label Jaccard Measure arXiv:2608.13549v1 Announce Type: new Abstract: The per-instance Jaccard score, or intersection over union (IoU), is standard in multi-label classification and binary segmentation. With $s$ labels, its loss matrix has $2^s$ outcomes and reports. Under the convention… 32 arXiv — Machine Learning research 4h ago Defensive Boosting for Online Probabilistic Forecasting arXiv:2608.13554v1 Announce Type: new Abstract: We study online probabilistic forecasting of binary outcomes chosen by an adaptive adversary. Given an online learning algorithm for a weak hypothesis class $H$, we would like to efficiently obtain two incomparable guarantees that… 14 arXiv — Machine Learning research 4h ago Predictive Allostatic Organization in Recurrent and Spiking Agents Under Partial Observability arXiv:2608.11506v1 Announce Type: cross Abstract: Adaptive behavior under partial observability depends on internal organization that carries information beyond the current observation. Drawing on Barrett and Miller's account of categorization as predictive, compressive,… 10 arXiv — Machine Learning research 4h ago RoutePack: Expert Placement and Attention-Aware Data Packing for MoE Reinforcement Learning arXiv:2608.12146v1 Announce Type: cross Abstract: Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines… 19 arXiv — NLP / Computation & Language research 4h ago Position: Reasoning is a Learnable Rule-Based Process arXiv:2608.12325v1 Announce Type: cross Abstract: Autonomous reasoning is among the most scientifically and economically motivating topics in AI today. Historically the purview of symbolic AI, recent advances have mainly emerged from deep probabilistic generative models. Despite… 22 arXiv — NLP / Computation & Language research 4h ago Comparative Analysis of Multilingual Pre-trained Models for Nepali Automatic Speech Recognition arXiv:2608.12327v1 Announce Type: new Abstract: Multilingual pretrained models nominally support Nepali, yet no controlled benchmark has compared them under a single fine-tuning protocol. We fine-tune six pretrained models (XLSR-53, IndicWav2Vec, MMS-1B, Whisper-Medium,… 34 arXiv — NLP / Computation & Language research 4h ago Can Spectral-Clipping Enable Better Learning While Forgetting Less for Low-Rank Adaptation? arXiv:2608.12332v1 Announce Type: new Abstract: In recent years, low-rank adaptation (LoRA) has emerged as a significant paradigm that freezes pre-trained weights and introduces small, learnable adapters instead of fine-tuning the full set of parameters. In this work, we uncover… 24 arXiv — NLP / Computation & Language research 4h ago Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents arXiv:2608.12342v1 Announce Type: new Abstract: Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial… 7 arXiv — Machine Learning research 4h ago Regulatory Approval Is Not Enough: Gaps in Trustworthy AI Reporting in FDA-Cleared Medical Devices arXiv:2608.12360v1 Announce Type: cross Abstract: Background: AI/ML-enabled medical devices are increasingly deployed in healthcare under evolving regulatory frameworks. As these systems become more integrated into clinical decision-making, there is growing expectation that they… 37 arXiv — Machine Learning research 4h ago EU-ETS under attack? The impact of carbon price suppression on the decarbonization of the power sector arXiv:2608.12363v1 Announce Type: cross Abstract: European countries are debating policies to mitigate the increased energy costs caused by renewed geopolitical tensions, while pursuing decarbonization and electrification. A notable example is Italy's 2026 Decreto Bollette… 31 Page 2 of 10 · 500 articles ← Newer Older →
