News / #paper Tag Research papers 500 articles archived under #paper · RSS Sign in to follow r/LocalLLaMA community 21h ago Were designing a tiny autonomous research agent This base model is only 43m parameters trained on 3m arXiv abstracts. We plan to continue pre-training and post training. If you create fine-tuning datasets or if you know of any datasets that can help shape the behavior for our goal we appreciate all contributors. The goal is… 11 arXiv — Machine Learning research 2d ago SLM-Conditioned Hierarchical Relation Routing for Labeled Property Graph Learning arXiv:2608.26132v1 Announce Type: new Abstract: Labeled property graphs combine relational structure with heterogeneous textual and categorical properties attached to both nodes and relationships. Conventional graph neural networks typically represent these properties as static… 30 arXiv — Machine Learning research 2d ago NeuronFuzz: Safety Neuron Guided Fuzzing for LLM Safety Evaluation arXiv:2608.26222v1 Announce Type: new Abstract: Safety evaluation is critical for assessing whether aligned Large Language Models (LLMs) remain robust against jailbreak attacks. Existing automated testing methods, however, largely rely on response-level feedback: each candidate… 12 arXiv — Machine Learning research 2d ago Pruning Binarized Neural Networks: A Dedicated Framework and Globally Weighted Algorithms arXiv:2608.26233v1 Announce Type: new Abstract: Extreme compression of deep neural networks, up to full binarization, dramatically reduces memory footprint and arithmetic complexity, facilitating deployment on constrained edge hardware with field-programmable gate arrays (FPGAs)… 16 arXiv — Machine Learning research 2d ago Muon with Finite Newton-Schulz: The Smoothing Benefit in Nonsmooth Nonconvex Optimization arXiv:2608.26288v1 Announce Type: new Abstract: Muon has emerged as a strong optimizer for the matrix-valued parameters in large language model pretraining, approximately orthogonalizing its momentum with a few Newton-Schulz iterations. Existing theory either replaces this… 29 arXiv — Machine Learning research 2d ago Algebraic Multigrid Acceleration for Efficient Label Spreading arXiv:2608.26309v1 Announce Type: new Abstract: Modern machine learning models rely on large amounts of labeled data. However, manual annotation of large-scale datasets is expensive and time-consuming. Label spreading is a semi-supervised learning technique that addresses this… 34 arXiv — Machine Learning research 2d ago Privacy Without Regret: Differentially Private Inference-Time Alignment arXiv:2608.26324v1 Announce Type: new Abstract: Best-of-N (BoN) sampling is the simplest and most widely deployed inference-time alignment strategy, but it suffers from two distinct problems: reward hacking, in which the selected response exploits errors in the proxy reward… 14 arXiv — Machine Learning research 2d ago Beyond Capability Benchmarks: Learning Operational Fingerprints of LLM Cloud Services from Production Incident Metadata arXiv:2608.26332v1 Announce Type: new Abstract: Managed LLM services are now part of real production systems, but model selection and service planning still rely heavily on capability benchmarks that reveal little about operational behavior after deployment. We present… 26 arXiv — Machine Learning research 2d ago CG4AI: A Column Generation Framework for Training AI Models Under Constraints arXiv:2608.26375v1 Announce Type: new Abstract: Standard machine-learning training minimizes a loss function over a dataset, but does not guarantee that the resulting model will satisfy predefined rules or constraints on its outputs. In many real-world applications, ranging from… 30 arXiv — Machine Learning research 2d ago The Latent Diagnostic Taxonomy: A Framework for Constructing Classifiers and Diagnosing Their Decisions, Applied to Prompt Injection Detection arXiv:2608.26423v1 Announce Type: new Abstract: This paper proposes a framework for constructing a classifier as a safeguard layer, and for developing a complementary diagnostic that identifies which of the classifier's confident decisions can be trusted. This framework, the… 35 arXiv — Machine Learning research 2d ago FedCMAPSS: A Benchmark for Federated Learning in Remaining Useful Life Estimation arXiv:2608.26433v1 Announce Type: new Abstract: Data-driven prognostics and health management has emerged as a key enabler for Industry 4.0, yet the development of robust remaining useful life (RUL) estimation models is often limited by the scarcity of run-to-failure data. While… 32 arXiv — Machine Learning research 2d ago NeoTriFuse: Reliability-Aware Multimodal Fusion under Missingness Heterogeneity for Neonatal Mortality Risk Prediction arXiv:2608.26436v1 Announce Type: new Abstract: Neonatal mortality risk prediction from bedside monitoring data remains challenging due to extreme class imbalance, heterogeneous clinical risk factors, multi-scale temporal dynamics, and substantial missingness. We propose… 16 arXiv — Machine Learning research 2d ago Subgraph Filtering for Fair Graph Neural Networks arXiv:2608.26437v1 Announce Type: new Abstract: Graph neural networks (GNNs) can exhibit unfair behavior even when sensitive attributes are excluded from node features, because graph topology and message passing propagate group-correlated signals under sensitive homophily.… 18 arXiv — Machine Learning research 2d ago Toward Equitable Low-Carbon Mobility: Fairness-Aware Demand Prediction for Expanding Bike-Sharing Systems arXiv:2608.26451v1 Announce Type: new Abstract: Bike-sharing systems are an important component of low-carbon urban mobility, but continued expansion creates challenges in both cold-start prediction and equitable resource allocation. Newly deployed stations lack historical… 36 arXiv — Machine Learning research 2d ago Distributed Training using an Intelligent Network arXiv:2608.26453v1 Announce Type: new Abstract: Distributed training across a wide area network (WAN) is challenging, as continuous parameter exchange by islands of compute is constrained by limited bandwidth, high latency, and uneven topology. We propose making the network an… 21 arXiv — Machine Learning research 2d ago Diff Mining: Logit Differences Reveal Finetuning Objectives arXiv:2608.26462v1 Announce Type: new Abstract: Finetuning has become the gold standard for refining existing behaviors and inducing new ones in language models, yet it often remains unclear exactly which behaviors emerge during this process. As models grow ever more capable,… 25 arXiv — Machine Learning research 2d ago Active Curriculum Refinement for Reinforcement Learning arXiv:2608.26469v1 Announce Type: new Abstract: In many reinforcement learning (RL) domains, environments are connected by prerequisite relations, such as difficulty-increasing edits or parameter increments, which induce a directed acyclic curriculum graph (DAG). Although this… 9 arXiv — Machine Learning research 2d ago Shared Actors Need Not Share Critics: Effects of Value Mismatch in Parallel Reinforcement Learning arXiv:2608.26481v1 Announce Type: new Abstract: When a single policy is trained in parallel across multiple environments of the same task, such as procedurally generated levels, randomized dynamics, or curricula, implementations commonly use one critic across all sampled… 19 arXiv — Machine Learning research 2d ago Bayesian methods and Markov chain Monte Carlo algorithms for curve reconstruction and point cloud data analysis arXiv:2608.26490v1 Announce Type: new Abstract: Point-cloud data routinely captured by modern imaging and sensor technologies provide detailed geometric descriptions of objects and environments, but their analysis is hindered by large data volumes, localization noise, and… 11 arXiv — Machine Learning research 2d ago A Unified Framework for Fair and Personalized Decentralized Learning under Communication Constraints arXiv:2608.26493v1 Announce Type: new Abstract: Decentralized learning systems aim to collaboratively train models across multiple clients without relying on a central coordinator. While decentralization improves scalability, privacy, and robustness, it also exacerbates three… 26 arXiv — Machine Learning research 2d ago A Single Suffix to Break Them All: Basin-Aware Jailbreaks for Merged Model Families arXiv:2608.26506v1 Announce Type: new Abstract: Model merging enables combining multiple fine-tuned models without additional training, but its safety implications remain poorly understood. Prior work primarily attributes merging risks to unsafe constituent models, implicitly… 24 arXiv — Machine Learning research 2d ago Algorithmic Principles For Multiclass Learning Are Hard To Come By: Limits of Regularization and Proper Learning arXiv:2608.26516v1 Announce Type: new Abstract: Two of the most fundamental questions in statistical learning theory are the following: which prediction problems are learnable, and how should they be learned? For the former, elegant answers often take the form of combinatorial… 14 arXiv — Machine Learning research 2d ago High Probability Derivative Bounds for Random tanh Neural Networks on a Hypercube arXiv:2608.26526v1 Announce Type: new Abstract: We establish high-probability bounds for mixed input derivatives of wide random neural networks whose activation derivatives satisfy a factorial growth bound. Our main result specializes these estimates to $\tanh$ networks with… 8 arXiv — Machine Learning research 2d ago Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling arXiv:2608.26538v1 Announce Type: new Abstract: High-throughput drug screening relies on low-cost primary assays to prioritize compounds for more expensive dose-response profiling, where potency is ultimately quantified. Current screening strategies largely focus on identifying… 20 arXiv — Machine Learning research 2d ago Chart2SVG: Editable SVG Generation from Raster Chart Images arXiv:2608.26544v1 Announce Type: new Abstract: We present Chart2SVG, a multimodal large language model that converts static raster charts into structurally organized, semantically enriched SVGs that support programmatic editing. By incorporating chart-specific semantic tokens… 4 arXiv — Machine Learning research 2d ago Arrive and Survive: Scaling Safe Goal-Conditioned Policy Learning from One-Bit Failure Signals arXiv:2608.26571v1 Announce Type: new Abstract: Contrastive reinforcement learning (CRL) scales effectively in goal-conditioned tasks by casting policy learning into a self-supervised contrastive objective. However, in a failure-terminated Markov decision process, established… 10 arXiv — Machine Learning research 2d ago Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs arXiv:2608.26581v1 Announce Type: new Abstract: Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent hardware support for low-precision formats, ranging from MXFP8 to ultra-low-bit… 25 arXiv — Machine Learning research 2d ago J-Zero: Unified Challenger--Solver--Judge Co-Evolution from Zero Data arXiv:2608.26582v1 Announce Type: new Abstract: Self-evolving language models have recently emerged as a promising path toward superintelligence, with the advantage of reducing the cost of human supervision. While considerable progress has been made in verifiable domains,… 30 arXiv — Machine Learning research 2d ago GRAS: Guided Reduced-Variance Proposals and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion arXiv:2608.26585v1 Announce Type: new Abstract: Discrete diffusion models have become a strong, widely adopted class of generators for sequence data, and steering them toward a downstream reward at inference time, without any retraining, is increasingly important. Such… 24 arXiv — Machine Learning research 2d ago SimCast-S2S: An Efficient Generative Model for Subseasonal Precipitation Forecasting via Transfer Learning from Climate Simulations arXiv:2608.26594v1 Announce Type: new Abstract: Subseasonal-to-seasonal (S2S) precipitation forecasting has substantial financial and societal impact, yet remains challenging because of weak predictive signals, high associated uncertainty, and the computational cost of… 31 arXiv — Machine Learning research 2d ago Technical Comparative Benchmarking Study: Advanced AI Hybrid Methods for Renewable Energy Farm Optimization and Forecasting arXiv:2608.26613v1 Announce Type: new Abstract: This study provides a comprehensive benchmarking of conventional machine learning (ML), ensemble learning, deep neural networks, recurrent architectures, Transformers, graph based models, and hybrid ensemble deep learning… 9 arXiv — Machine Learning research 2d ago Robust Neural Stimulation Response Modeling Through Meta-Learning and Pretraining arXiv:2608.26649v1 Announce Type: new Abstract: Objective: Model-based closed-loop neural stimulation holds promise for therapeutic applications ranging from Parkinson's disease to sensory restoration, but deployment has been limited by two obstacles: 1) forecasting models for… 11 arXiv — Machine Learning research 2d ago When Privacy Hurts Mergeability: Geometry-Aware Model Merging under Differential Privacy arXiv:2608.26655v1 Announce Type: new Abstract: Model merging promises to construct a single multi-task model from independently fine-tuned task models without accessing the original task data. This makes it attractive when task data cannot be centralized, but released task… 18 arXiv — Machine Learning research 2d ago Simple Actors and Deep Critics for Scalable Reinforcement Learning arXiv:2608.26659v1 Announce Type: new Abstract: Recent progress in offline reinforcement learning (RL) has been driven by expressive generative actors such as diffusion and flow-matching policies, which capture multimodal behavior in offline datasets. However, these actors… 16 arXiv — Machine Learning research 2d ago Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs arXiv:2608.26729v1 Announce Type: new Abstract: In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering. Although knowledge graphs often contain rich numerical attributes, most embedding models neglect… 20 arXiv — Machine Learning research 2d ago Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning arXiv:2608.26732v1 Announce Type: new Abstract: Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks, yet it remains unclear why neighborhood aggregation reliably outperforms node-wise multilayer perceptrons (MLPs). Despite its empirical… 32 arXiv — Machine Learning research 2d ago Self-Augmented Diffusion Guidance for Physics-Informed Generation arXiv:2608.26748v1 Announce Type: new Abstract: Diffusion models can be used to generate spatiotemporal signals of physical phenomena, such as time-series images of fluid dynamics. However, a major limitation of standard diffusion models is that they do not incorporate… 31 arXiv — Machine Learning research 2d ago Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actions arXiv:2608.26755v1 Announce Type: new Abstract: Contextual bandits are a standard framework for sequential decision-making under uncertainty, with applications in clinical trials, dosage selection, recommendation systems, and autonomous systems. Safety is central in many of… 18 arXiv — Machine Learning research 2d ago Beyond Client Averaging: A Client-Independent Second-Order Stationary-Bias Component in Stochastic SCAFFOLD arXiv:2608.26765v1 Announce Type: new Abstract: Existing constant-step analysis of stochastic \Scaf{} identifies a leading $O(\gamma/N)$ stationary mean bias and shows that higher-order bias can persist as the client count increases, but does not identify the first… 17 arXiv — Machine Learning research 2d ago On the Indistinguishability of Human v/s AI Generated Text arXiv:2608.26797v1 Announce Type: new Abstract: The rapid improvement of LLMs has made distinguishing AI-generated text from human writing a pressing problem. This challenge is further amplified by paraphrasing tools designed to make machine-generated text appear more "human".… 29 arXiv — Machine Learning research 2d ago SAGE: Variate-Wise Semantic Augmentation for Vision-Language Time Series Forecasting arXiv:2608.26829v1 Announce Type: new Abstract: Time series forecasting models operate on raw numerical sequences, lacking the semantic knowledge that domain experts implicitly leverage, such as the physical meaning of each variable, its statistical behavior, and its temporal… 22 arXiv — Machine Learning research 2d ago Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic arXiv:2608.26860v1 Announce Type: new Abstract: Connected and automated vehicle (CAV) platooning offers a promising approach to improving road safety and traffic capacity. However, platoon control in real-world traffic is challenging due to uncertainty and heterogeneous driving… 15 arXiv — Machine Learning research 2d ago When Is the Sharp Covariance Envelope Tight? Feature-Only Geometry for Volume-Sampled Least Squares arXiv:2608.26877v1 Announce Type: new Abstract: Prior analyses by Derezinski and Warmuth established all-size sampling identities, selected-OLS unbiasedness, and inverse moments for ordinary volume sampling, while their exact arbitrary-fixed-response loss and… 19 arXiv — Machine Learning research 2d ago Mitigating Strong-Modality Collapse in Multimodal Learning via Inverted Asymmetric Fusion arXiv:2608.26879v1 Announce Type: new Abstract: Fusing multiple modalities is expected to improve model performance. However, on the MultiHuSE dataset, early, late, and symmetric attention fusion often fail to outperform the best unimodal baseline (text). Pathway isolation of a… 18 arXiv — Machine Learning research 2d ago A Layer Importance Metric for Quantization Accounting for the Speed-Quality Trade-off in Autoregressive Models arXiv:2608.26926v1 Announce Type: new Abstract: Small language models (sLLMs) are nowadays hosted on devices with limited memory and computational budget. In an autoregressive setup, inference is memory-bandwidth bound: uniform quantization is often detrimental to such models,… 11 arXiv — Machine Learning research 2d ago Scaling Model-Generated Distillation Data Can Make Latent Teacher Traits More Recoverable arXiv:2608.26958v1 Announce Type: new Abstract: Scaling model-generated data is usually viewed as improving distillation: more examples should increase coverage, reduce noise, and produce stronger students. We show a second effect: larger datasets can make subtle… 38 arXiv — Machine Learning research 2d ago Gromov-Monge Flow Matching for Equivariant Graph Generation arXiv:2608.26961v1 Announce Type: new Abstract: Graphs are invariant under node permutations, motivating the use of permutation-equivariant architectures in generative models. In flow matching, however, symmetry may also enter the source--target coupling: once graph pairs are… 30 arXiv — Machine Learning research 2d ago Packora: Systematic Design for Generative Molecular Crystal Structure Prediction arXiv:2608.26962v1 Announce Type: new Abstract: Molecular crystal structure prediction (CSP) is important in pharmaceuticals, agrochemicals, and organic electronics, where subtle differences in molecular conformation and packing can strongly affect material properties. We… 8 arXiv — Machine Learning research 2d ago Adversarial Training Without Input Gradients via Low-Rank Householder Expansions arXiv:2608.26963v1 Announce Type: new Abstract: This work concerns adversarial training against the small-norm adversarial examples that arise from the inherent input instability of a trained deep neural network. Examples in this class are small as measured in the relative… 22 arXiv — Machine Learning research 2d ago Graph-Based Pseudo-multimodal Contrastive Learning for 12-Lead ECG Representations arXiv:2608.26964v1 Announce Type: new Abstract: 12-lead electrocardiogram (ECG) is a standard, non-invasive examination widely used for diagnosing coronary artery disease, where clinical interpretation relies on comparing waveform patterns across multiple leads. However, most… 35 Page 1 of 10 · 500 articles Older →
