Search: “model-releases”

arXiv:2411.10636v2 Announce Type: replace-cross Abstract: In this study, we investigate extrinsic gender bias in Bangla pretrained language models, a largely underexplored area in low-resource languag

arXiv:2602.15313v2 Announce Type: replace Abstract: AI Memory, specifically how models organizes and retrieves historical messages, becomes increasingly valuable to Large Language Models (LLMs), yet e

arXiv:2604.08575v1 Announce Type: cross Abstract: Molecular generative models must jointly ensure validity, diversity, and property control, yet existing approaches typically trade off among these obj

arXiv:2604.07956v2 Announce Type: replace Abstract: Industry classification schemes are integral parts of public and corporate databases as they classify businesses based on economic activity. Due to

arXiv:2604.08582v1 Announce Type: cross Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is critical for real-world monitoring scenarios such as industrial control and aerospace systems. M

arXiv:2604.09263v1 Announce Type: cross Abstract: We consider machine learning tasks with low-rank functional tree tensor networks (TTN) as the learning model. While in the case of least-squares regre

arXiv:2604.08923v1 Announce Type: new Abstract: Dimensional Aspect-Based Sentiment Analysis (DimABSA) extends traditional ABSA from categorical polarity labels to continuous valence-arousal (VA) regre

arXiv:2604.08562v1 Announce Type: cross Abstract: Ensuring that Text-to-Speech (TTS) systems deliver human-perceived quality at scale is a central challenge for modern speech technologies. Human subje

arXiv:2604.09258v1 Announce Type: new Abstract: Pretraining is the cornerstone of Large Language Models (LLMs), dominating the vast majority of computational budget and data to serve as the primary en

arXiv:2604.09021v1 Announce Type: cross Abstract: Auditory large language models (ALLMs) have demonstrated strong general capabilities in audio understanding and reasoning tasks. However, their reliab

arXiv:2604.09030v1 Announce Type: new Abstract: This paper presents NTIRE 2026, the 3rd Restore Any Image Model (RAIM) challenge on multi-exposure image fusion in dynamic scenes. We introduce a benchm

Ocr benchmark We’re open sourcing the first document OCR benchmark for the agentic era, ParseBench. Document parsing is the foundation of every AI agent that works with real-world files. ParseBench is

Ollama version 0.20.6 has been released, featuring improved tool calling support for Google's Gemma 4 model. The update focuses on enhancing the reliability and functionality of function/tool calling

This Reddit post from r/ollama discusses integrating Ollama-served Mistral with MemPalace — a free, locally-run AI memory system — via the Model Context Protocol (MCP). MemPalace runs entirely on a us

arXiv:2604.08641v1 Announce Type: cross Abstract: Interpretation is essential to deciphering the language of art: audiences communicate with artists by recovering meaning from visual artifacts. Howeve

arXiv:2509.25214v3 Announce Type: replace-cross Abstract: As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective c

arXiv:2604.09303v1 Announce Type: cross Abstract: This paper presents an online intention prediction framework for estimating the goal state of autonomous systems in real time, even when intention is

This Reddit post discusses how open-source AI models have advanced to rival GPT-4-level performance and can be run locally for free using Ollama — a tool that lets users download and manage large lang

arXiv:2603.11178v3 Announce Type: replace Abstract: Standard LLM distillation treats all training problems equally -- wasting compute on problems the student has already mastered or cannot yet solve.

arXiv:2604.08707v1 Announce Type: new Abstract: Monadic second order logic (MSO2) plays an important role in parameterized complexity due to the Courcelle's theorem. This theorem states that the probl

ParseBench is here!📊 We’ve just released ParseBench, an open benchmark + dataset for evaluating document parsing at scale. It includes: • 2,000+ human-reviewed enterprise documents • 167,000 evaluatio

arXiv:2604.09415v1 Announce Type: cross Abstract: We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike exi

arXiv:2604.08987v1 Announce Type: new Abstract: As Large Language Models (LLMs) advance toward embodied AI agents operating in physical environments, a fundamental question emerges: can models trained

arXiv:2604.08991v1 Announce Type: cross Abstract: Small object-centric spatial understanding in indoor videos remains a significant challenge for multimodal large language models (MLLMs), despite its

arXiv:2604.08881v1 Announce Type: new Abstract: In real-world deployments, Vision-Language Large Models (VLLMs) face critical challenges from multilingual and multimodal composite attacks: harmful ima

arXiv:2601.04884v2 Announce Type: replace Abstract: Executing a multi-agent plan can be challenging when an agent is delayed, because this typically creates conflicts with other agents. So, we need to

Google AI shared a post on X (formerly Twitter) directing followers to watch their content on YouTube, suggesting their video is also available on that platform for viewers who prefer that experience.

arXiv:2604.08598v1 Announce Type: cross Abstract: Text-based person search faces inherent limitations due to data scarcity, driven by stringent privacy constraints and the high cost of manual annotati

arXiv:2508.04853v2 Announce Type: replace-cross Abstract: Post-training quantization (PTQ) has become a crucial tool for reducing the memory and compute costs of modern deep neural networks, including

arXiv:2604.08277v2 Announce Type: replace-cross Abstract: We present a quantum-inspired ARIMA methodology that integrates quantum-assisted lag discovery with fixed-configuration variational quantum ci

arXiv:2604.08703v1 Announce Type: cross Abstract: QoS-QoE translation is a fundamental problem in multimedia systems because it characterizes how measurable system and network conditions affect user-p

arXiv:2604.08570v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for code generation, yet quantum code generation is still evaluated mostly within single frameworks

arXiv:2604.08976v1 Announce Type: new Abstract: We report that model quantisation restructures domain-level metacognitive efficiency in LLMs rather than degrading it uniformly. Evaluating Llama-3-8B-I

arXiv:2604.08810v1 Announce Type: new Abstract: Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is

arXiv:2511.19704v2 Announce Type: replace Abstract: Open-vocabulary semantic segmentation (OVSS) underpins many vision and robotics tasks that require generalizable semantic understanding. Existing ap

arXiv:2604.08739v1 Announce Type: cross Abstract: Ransomware poses a serious and fast-acting threat to critical systems, often encrypting files within seconds of execution. Research indicates that ran

ComfyUI is developing a realism character reference feature and is collecting user interest through a survey prior to its official launch. Users can sign up via the provided survey link to receive not

arXiv:2602.02188v2 Announce Type: replace Abstract: While large language models (LLMs) have shown strong performance in math and logic reasoning, their ability to handle combinatorial optimization (CO

arXiv:2602.11354v2 Announce Type: replace Abstract: The literature has witnessed an emerging interest in AI agents for automated assessment of scientific papers. Existing benchmarks focus primarily on

arXiv:2510.17640v3 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable performance on complex tasks through imitation learning in recent robotic man

arXiv:2604.08628v1 Announce Type: cross Abstract: Unauthorized disclosure of confidential documents demands robust, low-leakage classification. In real work environments, there is a lot of inflow and

arXiv:2509.20006v3 Announce Type: replace Abstract: With the large models easing the labor-intensive manipulation process, image manipulations in today's real scenarios often entail a complex manipula

arXiv:2604.08571v1 Announce Type: cross Abstract: While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their underlying reasoning processes remain highly ov

arXiv:2604.09452v1 Announce Type: cross Abstract: Safety guarantees are a prerequisite to the deployment of reinforcement learning (RL) agents in safety-critical tasks. Often, deployment environments

arXiv:2604.09285v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has catalyzed automation in customer service, yet benchmarking their performance remains challenging. Ex

Scaling model size is hitting diminishing returns. The real gains are in orchestration. Our Co-Founder & Co-CEO @yshoham makes the case in a rare long-form profile by @Calcalistech today. The man tryi

arXiv:2604.08988v1 Announce Type: new Abstract: Current LLM-based agents demonstrate strong performance in episodic task execution but remain constrained by static toolsets and episodic amnesia, faili