arXiv AI
Jul 17
Mixtures of SubExperts for Large Language Continual Learning
arXiv:2511. 06237v2 Announce Type: replace-cross Abstract: Enabling lifelong learning in LLMs demands resolving the stability-plasticity dilemma (i.
Inference efficiency
Quantization, distillation, pruning and serving work aimed at the same accuracy for less memory, latency and money.
arXiv Machine Learning
Jul 17
Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.
arXiv Machine Learning
Jul 17
ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM
arXiv:2607. 14622v1 Announce Type: cross Abstract: Low-bit GEMM is increasingly central to efficient ML inference, yet very-low-bit execution remains a poor fit for conventional CPUs.
arXiv AI
Jul 17
VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents
arXiv:2606. 19729v2 Announce Type: replace-cross Abstract: Planning under uncertainty is an essential capability for autonomous robots.
arXiv Machine Learning
Jul 17
MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion
arXiv:2607. 14249v1 Announce Type: new Abstract: Mobile usage traces are critical for tasks such as user behavior prediction and app recommendation, yet their use is constrained by privacy restrictions and costly large-scale data collection.
arXiv AI
Jul 17
Native Extrapolation Awareness in Flow-Based Conditional Generation
arXiv:2602. 13061v2 Announce Type: replace-cross Abstract: The ability of Flow Matching (FM) to model complex conditional distributions has established it as the state-of-the-art for prediction tasks (e.
arXiv AI
Jul 17
Multibit neural inference in a N-ary crossbar architecture
arXiv:2604. 26979v2 Announce Type: replace-cross Abstract: In-memory computing (IMC) is a paradigm that enables neural network inference by computing analog matrix-vector multiplications (MVM) directly in memory crossbar arrays, with the potential for energy efficiency gains over conventional von Neumann architectures.
arXiv Machine Learning
Jul 17
TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation
arXiv:2607. 14640v1 Announce Type: new Abstract: Battery health estimation is fundamental for battery management in battery-powered systems, where inaccurate health states may affect control, maintenance, and service life.
arXiv AI
Jul 17
Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network
arXiv:2406. 04737v2 Announce Type: cross Abstract: The cellular network of magnetic Induction (MI) communication holds promise in long-distance underground environments.
arXiv AI
Jul 17
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
arXiv:2607. 15257v1 Announce Type: new Abstract: Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents.
arXiv Machine Learning
Jul 17
Photonic convolutional neural network with pre-trained in situ training
arXiv:2604. 02429v2 Announce Type: replace-cross Abstract: Convolutional neural networks (CNNs) have transformed image processing, but the energy consumption and inference latency of electronic based implementations remain fundamental bottlenecks.
arXiv AI
Jul 17
Grow-Prune-Freeze Networks: Adaptive & Continual Learning Technique for Olfactory Navigation
arXiv:2605. 25170v2 Announce Type: replace-cross Abstract: Training data for olfaction is scattered through disparate, non-standardized datasets that limit the ability to build representative world models.
arXiv AI
Jul 17
Seeing the End at Step Zero: Accelerating Diffusion MLLMs via MLP Sparsity-Aware Truncation
arXiv:2607. 14557v1 Announce Type: new Abstract: Diffusion Multimodal Large Language Models (DMLLMs) are highly effective for multimodal reasoning, yet their inference efficiency is significantly hindered by fixed-length generation constraints.
arXiv Machine Learning
Jul 17
Quantize with Confidence? An Empirical Study of Quantization for Code Generation
arXiv:2607. 14181v1 Announce Type: cross Abstract: The growing adoption of local inference frameworks such as Ollama has made it increasingly common for developers to run large code models on laptops and other resource-constrained hardware.
arXiv Machine Learning
Jul 17
PolyQ: Codesigning End-to-End Quantization Framework for Scalable Edge CPU LLM Inference
arXiv:2607. 14618v1 Announce Type: new Abstract: CPUs are the most universal target for on-device LLM inference, but existing low-bit quantization methods offer either coarse operating points or fine-grained mixed precision that is difficult to execute efficiently on CPUs.
arXiv Machine Learning
Jul 17
Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network
arXiv:2607. 14293v1 Announce Type: cross Abstract: Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications.
arXiv Machine Learning
Jul 17
CASP: Learning-Augmented Offline Approximation with Verifiable Certificates and Bounded-Loss PAC Guarantees
arXiv:2607. 14545v1 Announce Type: new Abstract: Machine-learned predictions can speed up offline NP-hard optimization, but asking a predictor what to do amounts to asking it to solve the problem, and committing an unchecked prediction forfeits every worst-case guarantee.
Hugging Face Trending Papers
Jul 16
ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM
Low-bit GEMM is increasingly central to efficient ML inference, yet very-low-bit execution remains a poor fit for conventional CPUs. Practical deployment spans fragmented regimes-from 1/2/4-bit weights to varying activation precision-whose feasibility, reuse opportunity, and support cost differ under fixed SIMD and register-file budgets, making lightweight CPU support selection a first-class design problem.
arXiv Machine Learning
Jul 16
Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer
arXiv:2505. 14303v3 Announce Type: replace-cross Abstract: Using Resistive Random Access Memory (RRAM) crossbars in Computing-in-Memory (CIM) architectures offers a promising solution to overcome the von Neumann bottleneck.
arXiv Machine Learning
Jul 16
Precomputing the Future-Offset Average in TriAttention
arXiv:2607. 13051v1 Announce Type: cross Abstract: TriAttention is a recent method for shrinking the KV cache of long-reasoning LLMs: it scores each cached key by how much attention it is likely to receive and evicts the lowest-scoring ones.