Search: “models”

arXiv:2605.05115v1 Announce Type: new Abstract: Neural representations carry rich geometric structure; but does that structure causally shape behavior? To address this question, we intervene along pat

arXiv:2605.04180v1 Announce Type: new Abstract: Large Language Models exhibit strong reasoning and semantic understanding capabilities but often hallucinate in domains that require expert knowledge, a

arXiv:2605.04772v1 Announce Type: new Abstract: Access to diverse, well-annotated medical images with interactive learning tools is fundamental for training practitioners in medicine and related field

arXiv:2602.02282v2 Announce Type: replace Abstract: Inferring spatial transcriptomics (ST) from histology enables scalable histogenomic profiling, yet current methods are largely restricted to single-

A LoRA adapter for FLUX.2 Klein that enables camera angle control across 72 unique positions using 8 azimuths, 9 elevations, and 3 distances . The model rotates objects through 360° azimuth and 60° el

arXiv:2511.09537v2 Announce Type: replace Abstract: We introduce negative space learning machine translation (NSL-MT), a training method for underresourced languages, that augments limited parallel da

one of the features i'm most excited about in our upcoming langgraph release is delta channels! the langgraph runtime lets you 'checkpoint' agent progress at every step (model call, tool call, hooks).

arXiv:2605.04364v1 Announce Type: new Abstract: We study online prediction for marginally stable, partially observed linear dynamical systems under nonstochastic disturbances. Our objective is to mini

arXiv:2605.04955v1 Announce Type: new Abstract: When a machine learning (ML) model forecasts an undesired event, one often seeks a decision to avoid it, known as the avoiding undesired future (AUF) pr

arXiv:2605.05084v1 Announce Type: new Abstract: Domain shift remains a key challenge in deploying machine learning models to the real world. Unsupervised domain adaptation (UDA) aims to address this b

arXiv:2605.03143v1 Announce Type: cross Abstract: Recent advances in large language models have led to the rise of software systems (i.e. agents) that execute with increasing autonomy on behalf of use

arXiv:2605.05120v1 Announce Type: new Abstract: An interpretable and scalable framework for decoding driving behaviors from multimodal physiological signals is proposed in this study. We utilize multi

arXiv:2510.25224v3 Announce Type: replace Abstract: While Large Language Models (LLMs) are increasingly used in agentic frameworks to assist individual users, there is a growing need for agents that c

arXiv:2505.18466v2 Announce Type: replace Abstract: Evaluations of Large Language Models (LLMs) often overlook intersectional and culturally specific biases, particularly in underrepresented multiling

arXiv:2605.04523v1 Announce Type: new Abstract: We present our winning system for Task~B (generation with reference passages) in SemEval-2026 Task~8: MTRAGEval. Our method is a heterogeneous ensemble

arXiv:2605.04920v1 Announce Type: cross Abstract: Compositional generalization refers to correctly interpret novel combinations of known primitives, which remains a major challenge. Existing approache

Research from Prof Julian Togelius found that despite AI's well-documented victories in chess, Go, and Atari games, humans still learn unfamiliar video games far faster than any AI model. #NYUTandonMa

arXiv:2605.04913v1 Announce Type: new Abstract: LLM post-training typically propagates task gradients through the full depth of the model. Although this end-to-end structure is simple and general, it

arXiv:2605.04531v1 Announce Type: new Abstract: Open-vocabulary object detection with vision-language models (VLMs) such as Grounding DINO suffers from performance degradation under test-time distribu

arXiv:2601.00264v2 Announce Type: replace Abstract: Multimodal learning has revolutionized general domain tasks, yet its application in scientific discovery is hindered by the profound semantic gap be

arXiv:2508.00049v2 Announce Type: cross Abstract: The formation of dark-matter halos from small cosmological perturbations generated in the early universe is a highly non-linear process typically mode

arXiv:2605.03202v1 Announce Type: new Abstract: Large language models offer a tempting solution to address the peer review crisis. This position paper argues that today's AI systems should not be used

arXiv:2605.05151v1 Announce Type: new Abstract: Transformer architectures have been widely adopted for time series forecasting, yet whether the representational mechanisms that make them powerful in N

Cognition AI announced that SWE 1.6 Fast, an upgraded version of their software engineering model, is now available for use in Devin's Terminal interface. This update likely provides faster performanc

arXiv:2605.04012v1 Announce Type: new Abstract: Language models excel at diagnostic assessments on currated medical case-studies and vignettes, performing on par with, or better than, clinical profess

Technical highlights: CogViT Vision Encoder - Built with dual-teacher distillation: SigLIP2 for semantics, DINOv3 for texture. A two-stage recipe, masked modeling, then contrastive pretraining, with Q

arXiv:2602.14872v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has been a main driver of recent breakthroughs in large reasoning models. Yet it remains a mys

arXiv:2512.20056v2 Announce Type: replace-cross Abstract: As Earth's climate changes, it is impacting disasters and extreme weather events across the planet. Record-breaking heat waves, drenching rain

arXiv:2605.04827v1 Announce Type: new Abstract: Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambigui

arXiv:2603.01097v2 Announce Type: replace Abstract: Continuous knowledge updating for pre-trained large language models (LLMs) is increasingly necessary yet remains challenging. Although inference-tim

arXiv:2508.08289v2 Announce Type: replace Abstract: Transformer architectures have revolutionized artificial intelligence (AI) through their attention mechanisms, yet the computational principles unde

arXiv:2505.11815v2 Announce Type: replace Abstract: Current vision-language models have been explored for multi-modal embedding tasks like information retrieval. However, they face significant challen

arXiv:2605.04635v1 Announce Type: new Abstract: Printed Circuit Board (PCB) defect inspection faces two compounding challenges: scarce and imbalanced defect samples that limit model training, and insu

arXiv:2605.04078v1 Announce Type: new Abstract: Reasoning distillation aims to transfer multi-step reasoning capabilities from large language models to smaller, more efficient ones. While recent metho

arXiv:2512.05226v2 Announce Type: replace Abstract: Domain shift remains a key challenge in deploying machine learning models to the real world. Unsupervised domain adaptation (UDA) aims to address th

arXiv:2507.20021v3 Announce Type: replace-cross Abstract: Recent ObjectNav systems credit large language models (LLMs) for sizable zero-shot gains, yet it remains unclear how much comes from language

arXiv:2605.04528v1 Announce Type: new Abstract: Mechanical equipment forms the critical backbone of modern industrial production, yet domain shift severely limits the generalization of deep learning b

arXiv:2605.01100v1 Announce Type: new Abstract: This work presents a knowledge-driven decision-support system that integrates structured defect knowledge with LLM-based reasoning to provide explainabl

arXiv:2401.12783v3 Announce Type: replace-cross Abstract: Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information, with broad

arXiv:2605.02249v1 Announce Type: new Abstract: We investigate the belief revision problem in epistemic planning, i.e., what will be the beliefs of all agents in a multi-agent system after an agent ga

arXiv:2605.03212v1 Announce Type: cross Abstract: Modeling latent clinical constructs from unconstrained clinical interactions is a unique challenge in affective computing. We present ADAPTS (Agentic

arXiv:2503.10070v2 Announce Type: replace-cross Abstract: Scaling Vision-Language-Action models for embodied manipulation demands large volumes of diverse manipulation data, yet the high cost of comme

arXiv:2605.03091v1 Announce Type: new Abstract: While pre-trained Transformer models achieve high accuracy on in-domain sentiment classification, they frequently experience severe performance degradat

llama.cpp b9041 is a release build of llama.cpp, an LLM inference library written in C/C++ . The build identifier indicates this is an intermediate development release from the ggml-org/llama.cpp proj

b9047 is a build release of llama.cpp, an open-source C/C++ library for running large language model inference on consumer hardware. The release includes pre-compiled binaries for multiple platforms i

B9048 is a build/release of llama.cpp, an open-source C/C++ implementation for running large language model inference. The release includes optimizations and improvements to the core llama.cpp softwar