Search: “inference”

BEM: Training-Free Background Embedding Memory for False-Positive Suppression in Real-Time Fixed-Background Camera

arXiv:2604.11714v1 Announce Type: new Abstract: Pretrained detectors perform well on benchmarks but often suffer performance degradation in real-world deployments due to distribution gaps between trai

applicationsarxiv-cs-cv

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Beyond LLMs, Sparse Distributed Memory, and Neuromorphics <A Hyper-Dimensional SRAM-CAM 'VaCoAl' for Ultra-High Speed, Ultra-Low Power, and Low Cost>

DGX agent

arXiv:2604.11665v1 Announce Type: cross Abstract: This paper reports an unexpected finding: in a deterministic hyperdimensional computing (HDC) architecture based on Galois-field algebra, a path-depen

researcharxiv-cs-ai

14 Apr 2026

Local Ai

Beyond Model Design: Data-Centric Training and Self-Ensemble for Gaussian Color Image Denoising

arXiv:2604.11468v1 Announce Type: new Abstract: This paper presents our solution to the NTIRE 2026 Image Denoising Challenge (Gaussian color image denoising at fixed noise level sigma = 50). Rather th

local-aiarxiv-cs-cv

Closed-Form Concept Erasure via Double Projections

arXiv:2604.10032v1 Announce Type: cross Abstract: While modern generative models such as diffusion-based architectures have enabled impressive creative capabilities, they also raise important safety a

Agents

Collaborative Multi-Agent Scripts Generation for Enhancing Imperfect-Information Reasoning in Murder Mystery Games

arXiv:2604.11741v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown impressive capabilities in perceptual tasks, yet they degrade in complex multi-hop reasoning under multiplayer

agentsarxiv-cs-ai

Contour Refinement using Discrete Diffusion in Low Data Regime

arXiv:2602.05880v2 Announce Type: replace Abstract: Boundary detection of irregular and translucent objects is an important problem with applications in medical imaging, environmental monitoring and m

safetyarxiv-cs-cv

CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models

arXiv:2502.11008v2 Announce Type: replace Abstract: Counterfactual reasoning is widely recognized as one of the most challenging and intricate aspects of causality in artificial intelligence. In this

Agents

CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance

arXiv:2508.16644v4 Announce Type: replace Abstract: Diffusion models excel at photorealistic synthesis but struggle with precise object counts, especially in high-density settings. We introduce COUNTL

agentsarxiv-cs-cv

Detection Is Cheap, Routing Is Learned: Why Refusal-Based Alignment Evaluation Fails

arXiv:2603.18280v2 Announce Type: replace-cross Abstract: Current alignment evaluation mostly measures whether models encode dangerous concepts and whether they refuse harmful requests. Both miss the

DIB-OD: Preserving the Invariant Core for Robust Heterogeneous Graph Adaptation via Decoupled Information Bottleneck and Online Distillation

arXiv:2604.10882v1 Announce Type: cross Abstract: Graph Neural Network pretraining is pivotal for leveraging unlabeled graph data. However, generalizing across heterogeneous domains remains a major ch

researcharxiv-cs-ai

Local Ai

Did they stop the free cloud tier?

This Reddit thread on r/ollama discusses community questions around whether Ollama discontinued or changed its free cloud tier. Ollama Cloud launched in preview in September 2025 with fixed-price subs

local-air-ollama

Disentangled Point Diffusion for Precise Object Placement

arXiv:2604.11793v1 Announce Type: new Abstract: Recent advances in robotic manipulation have highlighted the effectiveness of learning from demonstration. However, while end-to-end policies excel in e

applicationsarxiv-cs-ro

Doc-PP: Document Policy Preservation Benchmark for Large Vision-Language Models

arXiv:2601.03926v2 Announce Type: replace Abstract: The deployment of Large Vision-Language Models (LVLMs) for real-world document question answering is often constrained by dynamic, user-defined poli

Dual-R-DETR: Resolving Query Competition with Pairwise Routing in Transformer Decoders

arXiv:2512.13876v2 Announce Type: replace Abstract: Detection Transformers (DETR) formulate object detection as a set prediction problem and enable end-to-end training without post-processing. However

researcharxiv-cs-cv

EdgeCIM: A Hardware-Software Co-Design for CIM-Based Acceleration of Small Language Models

model-releasesarxiv-cs-ai

arXiv:2604.11512v1 Announce Type: cross Abstract: The growing demand for deploying Small Language Models (SLMs) on edge devices, including laptops, smartphones, and embedded platforms, has exposed fun

Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation

arXiv:2604.11775v1 Announce Type: cross Abstract: Perturbation-based explainability methods such as KernelSHAP provide model-agnostic attributions but are typically impractical for patch-based 3D medi

Agents

Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT Networks

arXiv:2604.10662v1 Announce Type: new Abstract: Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constrain

agentsarxiv-cs-lg

Evaluating Reliability Gaps in Large Language Model Safety via Repeated Prompt Sampling

model-releasesarxiv-cs-ai

arXiv:2604.09606v1 Announce Type: new Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety risk through breadth-oriented evaluation ac

Evolutionary Profiles for Protein Fitness Prediction

arXiv:2510.07286v3 Announce Type: replace-cross Abstract: Predicting the fitness impact of mutations is central to protein engineering but constrained by limited assays relative to the size of sequenc

researcharxiv-cs-ai

ExpertEdit: Learning Skill-Aware Motion Editing from Expert Videos

arXiv:2604.10466v1 Announce Type: new Abstract: Visual feedback is critical for motor skill acquisition in sports and rehabilitation, and psychological studies show that observing near-perfect version

researcharxiv-cs-cv

Explainability and Certification of AI-Generated Educational Assessments

arXiv:2604.09622v1 Announce Type: cross Abstract: The rapid adoption of generative artificial intelligence (AI) in educational assessment has created new opportunities for scalable item creation, pers

FlowBind: Efficient Any-to-Any Generation with Bidirectional Flows

arXiv:2512.15420v2 Announce Type: replace Abstract: Any-to-any generation seeks to translate between arbitrary subsets of modalities, enabling flexible cross-modal synthesis. Despite recent success, e

researcharxiv-cs-lg

FreeScale: Scaling 3D Scenes via Certainty-Aware Free-View Generation

arXiv:2604.10512v1 Announce Type: new Abstract: The development of generalizable Novel View Synthesis (NVS) models is critically limited by the scarcity of large-scale training data featuring diverse

applicationsarxiv-cs-cv

From Speech-to-Spatial: Grounding Utterances on A Live Shared View with Augmented Reality

arXiv:2602.03059v2 Announce Type: replace-cross Abstract: We introduce Speech-to-Spatial, a referent disambiguation framework that converts verbal remote-assistance instructions into spatially grounde

researcharxiv-cs-cl

GenProve: Learning to Generate Text with Fine-Grained Provenance

arXiv:2601.04932v2 Announce Type: replace Abstract: Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as

safetyarxiv-cs-cl

GPU Acceleration of Sparse Fully Homomorphic Encrypted DNNs

arXiv:2604.11659v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) has recently attracted significant attention as both a cryptographic primitive and a systems challenge. Given the l

hardwarearxiv-cs-lg

Graph Retention Networks for Dynamic Graphs

model-releasesarxiv-cs-lg

arXiv:2411.11259v3 Announce Type: replace Abstract: In this paper, we propose Graph Retention Networks (GRNs) as a unified architecture for deep learning on dynamic graphs. The GRN extends the concept

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs

model-releasesarxiv-cs-ai

arXiv:2511.11653v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasonin

GTASA: Ground Truth Annotations for Spatiotemporal Analysis, Evaluation and Training of Video Models

arXiv:2604.10385v1 Announce Type: new Abstract: Generating complex multi-actor scenario videos remains difficult even for state-of-the-art neural generators, while evaluating them is hard due to the l

safetyarxiv-cs-cv

Industry

Helical raises $10M to bridge the gap between foundation models and drug discovery decisions

Pharma artificial intelligence startup Helical Ltd. announced today that it has raised 10 million in new funding to expand its virtual AI lab platform, which turns biological foundation models into re

industrysiliconangle

Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria

model-releasesarxiv-cs-lg

arXiv:2604.10560v1 Announce Type: new Abstract: Profiled Sparse Networks (PSN) replace uniform connectivity with deterministic, heterogeneous fan-in profiles defined by continuous, nonlinear functions

Industry

How Guidesly built AI-generated trip reports for outdoor guides on AWS

In this post, we walk through how Guidesly built Jack AI on AWS using AWS Lambda, AWS Step Functions, Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (Amazon RDS), Amazon

industryaws-ml-blog

IA local con NVIDIA RTX PRO™ 4000 Blackwell 16GB GDDR7

This Reddit post from the r/ollama community discusses running local AI/LLM workloads using the NVIDIA RTX PRO 4000 Blackwell GPU via Ollama, a framework for running large language models locally. The

hardwarer-ollama

Inductive Reasoning for Temporal Knowledge Graphs with Emerging Entities

arXiv:2604.10164v1 Announce Type: new Abstract: Reasoning on Temporal Knowledge Graphs (TKGs) is essential for predicting future events and time-aware facts. While existing methods are effective at ca

researcharxiv-cs-ai

Infusing Theory of Mind into Socially Intelligent LLM Agents

arXiv:2509.22887v2 Announce Type: replace Abstract: Theory of Mind (ToM)-an understanding of the mental states of others-is a key aspect of human social intelligence, yet, chatbots and LLM-based socia

Interactive Learning for LLM Reasoning

arXiv:2509.26306v4 Announce Type: replace Abstract: Existing multi-agent learning approaches have developed interactive training environments to explicitly promote collaboration among multiple Large L

Introducing Kernels on the Hugging Face Hub ✨ What if shipping a GPU kernel was as easy as pushing a model? - Pre-compiled for your exact GP…

Introducing Kernels on the Hugging Face Hub ✨ What if shipping a GPU kernel was as easy as pushing a model? - Pre-compiled for your exact GPU, PyTorch & OS - Multiple kernel versions coexist in one pr

hardwareclem-delangue--x

Jailbreaking the Matrix: Nullspace Steering for Controlled Model Subversion

arXiv:2604.10326v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak attacks -- inputs designed to bypass safety mechanisms and elicit harmful responses -- despite ad

K-Way Energy Probes for Metacognition Reduce to Softmax in Discriminative Predictive Coding Networks

arXiv:2604.11011v1 Announce Type: cross Abstract: We present this as a negative result with an explanatory mechanism, not as a formal upper bound. Predictive coding networks (PCNs) admit a K-way energ

Latent Instruction Representation Alignment: defending against jailbreaks, backdoors and undesired knowledge in LLMs

arXiv:2604.10403v1 Announce Type: new Abstract: We address jailbreaks, backdoors, and unlearning for large language models (LLMs). Unlike prior work, which trains LLMs based on their actions when give

safetyarxiv-cs-lg

Learnable Motion-Focused Tokenization for Effective and Efficient Video Unsupervised Domain Adaptation

arXiv:2604.09955v1 Announce Type: new Abstract: Video Unsupervised Domain Adaptation (VUDA) poses a significant challenge in action recognition, requiring the adaptation of a model from a labeled sour

applicationsarxiv-cs-cv

Legal2LogicICL: Improving Generalization in Transforming Legal Cases to Logical Formulas via Diverse Few-Shot Learning

arXiv:2604.11699v1 Announce Type: cross Abstract: This work aims to improve the generalization of logic-based legal reasoning systems by integrating recent advances in NLP with legal-domain adaptive f

Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge

arXiv:2509.08193v2 Announce Type: replace-cross Abstract: We present FlexiFlow, a lifetime-aware design framework for item-level intelligence (ILI) where computation is integrated directly into dispos

hardwarearxiv-cs-ai

Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit

arXiv:2604.09998v1 Announce Type: cross Abstract: Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasi

Local Ai

MAFIG: Multi-agent Driven Formal Instruction Generation Framework

arXiv:2604.10989v1 Announce Type: new Abstract: Emergency situations in scheduling systems often trigger local functional failures that undermine system stability and even cause system collapse. Exist

local-aiarxiv-cs-ai

Masked Contrastive Pre-Training Improves Music Audio Key Detection

arXiv:2604.10021v1 Announce Type: cross Abstract: Self-supervised music foundation models underperform on key detection, which requires pitch-sensitive representations. In this work, we present the fi

researcharxiv-cs-lg

MCAT: Scaling Many-to-Many Speech-to-Text Translation with MLLMs to 70 Languages

arXiv:2512.01512v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved great success in Speech-to-Text Translation (S2TT) tasks. However, current research is constr

Multi-Model Synthetic Training for Mission-Critical Small Language Models

arXiv:2509.13047v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across many domains, yet their application to specialized fields remain