arXiv Machine Learning
Jun 2
Efficient Synthetic Network Generation via Latent Embedding Reconstruction
arXiv:2606. 00934v1 Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems.
Inference efficiency
Quantization, distillation, pruning and serving work aimed at the same accuracy for less memory, latency and money.
arXiv Machine Learning
Jun 2
Exploiting weight-space symmetries for approximating curvature
arXiv:2606. 00442v1 Announce Type: new Abstract: Many machine learning techniques rely on approximating a loss function's curvature, but this is notoriously hard to do at the scale of modern deep networks.
arXiv Machine Learning
Jun 2
Inner Product Aware Quantization: Provably Fast, Accurate, and Adaptive Algorithms
arXiv:2606. 00289v1 Announce Type: new Abstract: Quantization is a fundamental tool used to compress datasets, neural network weights, and memory usage in a range of computational tasks.
arXiv AI
Jun 2
Does Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction
arXiv:2606. 01850v1 Announce Type: new Abstract: Model compression techniques such as quantization and pruning are widely used to reduce the deployment cost of large language models (LLMs), with existing evaluations focusing almost exclusively on accuracy preservation.
arXiv Machine Learning
Jun 2
DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation
arXiv:2606. 00535v1 Announce Type: new Abstract: Speculative decoding (SD) has proven to be an effective technique for accelerating autoregressive generation in large language models (LLMs) however, its application to vision-language models (VLMs) remains relatively unexplored.
arXiv Machine Learning
Jun 2
CAREF: Calibration-Aware Regularization for Explanation Faithfulness Without Rationale Supervision
arXiv:2605. 27835v2 Announce Type: replace Abstract: We introduce CAREF, a parameter-efficient fine-tuning framework that jointly optimizes predictive accuracy and explanation faithfulness via calibration-aware regularization.
arXiv AI
Jun 2
PECKER: A Precisely Efficient Critical Knowledge Erasure Recipe For Machine Unlearning in Diffusion Models
arXiv:2604. 05634v2 Announce Type: replace Abstract: Machine unlearning (MU) has become a critical technique for GenAI models' safe and compliant operation.
arXiv Machine Learning
Jun 2
Why Are DMD Students Lazy? Understanding the Copying Behavior in Few-Step Distillation
arXiv:2606. 02237v1 Announce Type: new Abstract: Distribution Matching Distillation (DMD) compresses pretrained diffusion models into efficient few-step generators by aligning their noised distributions across all scales.
arXiv AI
Jun 2
T1: Tool-integrated Verification for Test-time Compute Scaling in Small Language Models
arXiv:2504. 04718v2 Announce Type: replace-cross Abstract: Recent studies have demonstrated that test-time compute scaling effectively improves the performance of small language models (sLMs).
arXiv Machine Learning
Jun 2
LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents
arXiv:2602. 01053v2 Announce Type: replace Abstract: Role specialization in multi-LLM agent systems is often realized via multi-LoRA, where agents share a pretrained backbone and differ only by lightweight adapters.
arXiv AI
Jun 2
ThinkSwitch: Context Distillation with LoRA and Weight Interpolation for Specific-Purpose Reasoning Tasks
arXiv:2606. 01080v1 Announce Type: cross Abstract: Large language models often improve on difficult tasks by spending inference-time compute on a reasoning trace before producing the final answer.
arXiv AI
Jun 2
Logit Distillation on Manifolds: Mapping by Learning
arXiv:2606. 00771v1 Announce Type: cross Abstract: A simple way to improve the performance of almost any machine learning model is not to train a single but several models with diverse algorithms which will make slightly distinct kinds of predictions and errors on the same data, and thus improve the average predictions and robustness.
arXiv AI
Jun 2
Property Prediction of Stacked Bilayer Materials: A Multimodal Learning Approach
arXiv:2606. 01012v1 Announce Type: new Abstract: AI for materials science is a critical topic within AI for science, aiming to accelerate materials discovery and produce accurate property predictions.
arXiv AI
Jun 2
Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning
arXiv:2508. 06588v3 Announce Type: replace-cross Abstract: Vector Quantization (VQ) has recently emerged as a promising approach for learning compressed and discrete representations for graph-structured data.
arXiv AI
Jun 2
Neural Network Compression by Approximate Differential Equivalence
arXiv:2606. 01402v1 Announce Type: cross Abstract: Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.
arXiv Machine Learning
Jun 2
Rethinking Amortized Neural Representations for High-Resolution Terrain Elevation Data
arXiv:2606. 00404v1 Announce Type: cross Abstract: Implicit neural representations (INRs) model a signal as a continuous coordinate-to-value function.
arXiv Machine Learning
Jun 2
UniPinRec: Unifying Generative Retrieval and Ranking at Pinterest Scale
arXiv:2606. 00422v1 Announce Type: cross Abstract: Modern recommendation systems predominantly train retrieval and ranking as separate models despite both increasingly relying on large transformers encoding the same user behavior data, duplicating parameters, compute, and serving cost.
arXiv Machine Learning
Jun 2
Massive Spikes in LLMs are Bias Vectors: Mechanistic Uncovering and Spike-Free Quantization
arXiv:2606. 02288v1 Announce Type: new Abstract: Massive activation spikes in Large Language Models (LLMs) severely degrade quantization by stretching dynamic ranges.
arXiv AI
Jun 2
IDLM: Inverse-distilled Diffusion Language Models
arXiv:2602. 19066v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have recently achieved strong results in text generation.
arXiv Machine Learning
Jun 2
LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
arXiv:2606. 01111v1 Announce Type: new Abstract: Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.