Search: “inference”

Thought Graph Traversal for Test-time Scaling in Chest X-ray VLLMs

arXiv:2506.11989v3 Announce Type: replace Abstract: Test-time scaling offers a promising way to improve the reasoning performance of vision-language large models (VLLMs) without additional training. I

researcharxiv-cs-cv

4 May 2026

VGR: Visual Grounded Reasoning

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arXiv:2506.11991v3 Announce Type: replace-cross Abstract: In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which

safetyarxiv-cs-cl

4 May 2026

VLAs are Confined yet Capable of Generalizing to Novel Instructions

model-releasesarxiv-cs-ro

arXiv:2505.03500v5 Announce Type: replace Abstract: Vision-language-action models (VLAs) often achieve high performance on demonstrated tasks but struggle significantly when required to extrapolate, c

4 May 2026

FastSDCPU release v1.0.0-beta.301

local-air-stablediffusion

FastSDCPU is an optimized fork of Stable Diffusion designed to run efficiently on CPUs and devices without dedicated GPUs by leveraging Latent Consistency Models and Adversarial Diffusion Distillation

3 May 2026

v0.23.0

The search results show releases from v0.20.0 through v0.22.0, but do not contain specific information about v0.23.0. Based on the available release information from Ollama and the version timeline sh

local-aiollama-releases

3 May 2026

A Real-time Scale-robust Network for Glottis Segmentation in Nasal Transnasal Intubation

arXiv:2604.27383v1 Announce Type: cross Abstract: Nasotracheal intubation (NTI) is a critical clinical procedure for establishing and maintaining patient airway patency. Machine-assisted NTI has emerg

researcharxiv-cs-cv

ANCORA: Learning to Question via Manifold-Anchored Self-Play for Verifiable Reasoning

arXiv:2604.27644v1 Announce Type: cross Abstract: We propose a paradigm shift from learning to answer to learning to question: can a language model generate verifiable problems, solve them, and turn t

BoostLoRA: Growing Effective Rank by Boosting Adapters

arXiv:2604.27308v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) methods face a tradeoff between adapter size and expressivity: ultra-low-parameter adapters are confined to fix

Contextual Online Uncertainty-Aware Preference Learning for Human Feedback

arXiv:2504.19342v3 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human pr

Continuous-tone Simple Points: An ell_0-Norm of Cyclic Gradient for Topology-Preserving Data-Driven Image Segmentation

arXiv:2604.28159v1 Announce Type: new Abstract: Topological features play an essential role in ensuring geometric plausibility and structural consistency in image analysis tasks such as segmentation a

researcharxiv-cs-cv

Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning

model-releasesarxiv-cs-cv

arXiv:2604.27604v1 Announce Type: new Abstract: We introduce SPUR, a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answe

Differentiable latent structure discovery for interpretable forecasting in clinical time series

arXiv:2604.27967v1 Announce Type: new Abstract: Background: Timely, uncertainty-aware forecasting from irregular electronic health records (EHR) can support critical-care decisions, yet most approache

DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models

model-releasesarxiv-cs-cl

arXiv:2604.27929v1 Announce Type: new Abstract: With the widespread adoption of large language models (LLMs), understanding their personality representation mechanisms has become critical. As a novel

Echo-{alpha}: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretation

arXiv:2604.28011v1 Announce Type: new Abstract: Ultrasound interpretation requires both precise lesion localization and holistic clinical reasoning, yet existing methods typically excel at only one of

local-aiarxiv-cs-cv

Epistemic reflections on AI answering our questions: overwatch, erudite, logician, interlocutor

arXiv:2304.14352v2 Announce Type: replace-cross Abstract: Currently, there is a trend for the wider public to rely on LLMs for financial or legal consultation, medical and mental support (Chatterji et

applicationsarxiv-cs-ai

Exploring the Limits of Pruning: Task-Specific Neurons, Model Collapse, and Recovery in Task-Specific Large Language Models

model-releasesarxiv-cs-cl

arXiv:2604.27115v1 Announce Type: new Abstract: Neuron pruning is widely used to reduce the computational cost and parameter footprint of large language models, yet it remains unclear whether neurons

Exponential families from a single KL identity

arXiv:2604.28036v1 Announce Type: new Abstract: Exponential families encompass the distributions central to modern machine learning -- softmax, Gaussians, and Boltzmann distributions -- and underlie t

FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering

arXiv:2601.06992v2 Announce Type: replace-cross Abstract: Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics,

applicationsarxiv-cs-ai

Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs

arXiv:2506.07180v3 Announce Type: replace-cross Abstract: As video large language models (Video-LLMs) become increasingly integrated into real-world applications that demand grounded multimodal reason

Agents

From Context to Skills: Can Language Models Learn from Context Skillfully?

arXiv:2604.27660v1 Announce Type: new Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learn

agentsarxiv-cs-ai

From surveillance to signalling: escalation channels as environmental controls for agentic AI

arXiv:2510.05192v2 Announce Type: replace-cross Abstract: When AI agents operating with access to sensitive information encounter a conflict between completing an assigned task and following rules or

Generalizable Sparse-View 3D Reconstruction from Unconstrained Images

model-releasesarxiv-cs-cv

arXiv:2604.28193v1 Announce Type: new Abstract: Reconstructing 3D scenes from sparse, unposed images remains challenging under real-world conditions with varying illumination and transient occlusions.

Generate Your Talking Avatar from Video Reference

model-releasesarxiv-cs-cv

arXiv:2604.27918v1 Announce Type: new Abstract: Existing talking avatar methods typically adopt an image-to-video pipeline conditioned on a static reference image within the same scene as the target g

Gradient-Based Optimization on Godel Logic as Discrete Local Search

arXiv:2503.01817v2 Announce Type: replace Abstract: A fundamental challenge in neurosymbolic systems is applying continuous gradient-based optimization to discrete logical domains. While fuzzy relaxat

local-aiarxiv-cs-lg

Grounding Agent Memory in Contextual Intent

arXiv:2601.10702v2 Announce Type: replace-cross Abstract: Deploying large language models in long-horizon, goal-oriented interactions remains challenging because similar entities and facts recur under

Agents

Heterogeneous Scientific Foundation Model Collaboration

arXiv:2604.27351v1 Announce Type: new Abstract: Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally

agentsarxiv-cs-ai

How to Guide Your Flow: Few-Step Alignment via Flow Map Reward Guidance

arXiv:2604.27147v1 Announce Type: cross Abstract: In generative modeling, we often wish to produce samples that maximize a user-specified reward such as aesthetic quality or alignment with human prefe

In-Context Examples Suppress Scientific Knowledge Recall in LLMs

arXiv:2604.27540v1 Announce Type: new Abstract: Scientific reasoning rarely stops at what is directly observable; it often requires uncovering hidden structure from data. From estimating reaction cons

researcharxiv-cs-ai

Intern-Atlas: A Methodological Evolution Graph as Research Infrastructure for AI Scientists

arXiv:2604.28158v1 Announce Type: new Abstract: Existing research infrastructure is fundamentally document-centric, providing citation links between papers but lacking explicit representations of meth

Knowledge Graph Representations for LLM-Based Policy Compliance Reasoning

arXiv:2604.27713v1 Announce Type: new Abstract: The risks posed by AI features are increasing as they are rapidly integrated into software applications. In response, regulations and standards for safe

Learning Generalizable Multimodal Representations for Software Vulnerability Detection

arXiv:2604.25711v2 Announce Type: replace-cross Abstract: Source code and its accompanying comments are complementary yet naturally aligned modalities-code encodes structural logic while comments capt

Learning to Reason: Targeted Knowledge Discovery and Fuzzy Logic Update for Robust Image Recognition

arXiv:2604.27759v1 Announce Type: cross Abstract: Integrating domain knowledge into deep neural networks is a promising way to improve generalization. Existing methods either encode prior knowledge in

applicationsarxiv-cs-ai

Lightweight Distillation of SAM 3 and DINOv3 for Edge-Deployable Individual-Level Livestock Monitoring and Longitudinal Visual Analytics

arXiv:2604.27128v1 Announce Type: cross Abstract: Foundation-model pipelines for individual-level livestock monitoring -- combining open-vocabulary detection, promptable video segmentation, and self-s

Linear-Core Surrogates: Smooth Loss Functions with Linear Rates for Classification and Structured Prediction

arXiv:2604.27742v1 Announce Type: new Abstract: The choice of loss function in classification involves a fundamental trade-off: smooth losses (like Cross-Entropy) enable fast optimization rates but yi

LLMs Capture Emotion Labels, Not Emotion Uncertainty: Distributional Analysis and Calibration of Human--LLM Judgment Gaps

arXiv:2604.27345v1 Announce Type: new Abstract: Human annotators frequently disagree on emotion labels, yet most evaluations of Large Language Model (LLM) emotion annotation collapse these judgments i

researcharxiv-cs-cl

Agents

Machine Collective Intelligence for Explainable Scientific Discovery

arXiv:2604.27297v1 Announce Type: new Abstract: Deriving governing equations from empirical observations is a longstanding challenge in science. Although artificial intelligence (AI) has demonstrated

agentsarxiv-cs-ai

Mapping the Phase Diagram of the Vicsek Model with Machine Learning

model-releasesarxiv-cs-lg

arXiv:2604.28167v1 Announce Type: cross Abstract: In this study, we use machine learning to classify and interpolate the phase structure of the Vicsek flocking model across the three-dimensional param

METASYMBO: Multi-Agent Language-Guided Metamaterial Discovery via Symbolic Latent Evolution

arXiv:2604.27300v1 Announce Type: new Abstract: Metamaterial discovery seeks microstructured materials whose geometry induces targeted mechanical behavior. Existing inverse-design methods can efficien

MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction

model-releasesarxiv-cs-cl

arXiv:2604.27393v1 Announce Type: new Abstract: Recent progress in multimodal large language models (MLLMs) has brought AI capabilities from static offline data processing to real-time streaming inter

Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO

arXiv:2603.21016v2 Announce Type: replace-cross Abstract: Large language models (LLMs) used for multiple-choice and pairwise evaluation tasks often exhibit selection bias due to non-semantic factors l

MoCapAnything V2: End-to-End Motion Capture for Arbitrary Skeletons

arXiv:2604.28130v1 Announce Type: new Abstract: Recent methods for arbitrary-skeleton motion capture from monocular video follow a factorized pipeline, where a Video-to-Pose network predicts joint pos

local-aiarxiv-cs-cv

Tutorials

Neural Aided Kalman Filtering for UAV State Estimation in Degraded Sensing Environments

arXiv:2604.28107v1 Announce Type: new Abstract: Accurate state estimation of nonlinear dynamical systems is fundamental to modern aerospace operations across air, sea, and space domains. Online tracki

tutorialsarxiv-cs-lg

📢 Official Announcement: Qwen Partners with Fireworks AI to Accelerate Access to Qwen Family Models We are pleased to announce a strategic …

📢 Official Announcement: Qwen Partners with Fireworks AI to Accelerate Access to Qwen Family Models We are pleased to announce a strategic partnership between Qwen and Fireworks AI to deliver optimize

model-releasesqwen--x

Path-Lock Expert: Separating Reasoning Mode in Hybrid Thinking via Architecture-Level Separation

arXiv:2604.27201v1 Announce Type: cross Abstract: Hybrid-thinking language models expose explicit think and no-think modes, but current designs do not separate them cleanly. Even in no-think mode, mod

researcharxiv-cs-ai

PhyCo: Learning Controllable Physical Priors for Generative Motion

arXiv:2604.28169v1 Announce Type: cross Abstract: Modern video diffusion models excel at appearance synthesis but still struggle with physical consistency: objects drift, collisions lack realistic reb

Predicting Covariate-Driven Spatial Deformation for Nonstationary Gaussian Processes

arXiv:2604.27280v1 Announce Type: new Abstract: Nonstationary Gaussian processes (GPs) are essential for modeling complex, locally heterogeneous spatial data. A common modeling approach is the spatial

applicationsarxiv-cs-lg

Proactive Dialogue Model with Intent Prediction

arXiv:2604.27379v1 Announce Type: new Abstract: Dialogue models are inherently reactive, responding to the current user turn without anticipating upcoming intents, which leads to redundant interaction

researcharxiv-cs-cl

REBENCH: A Procedural, Fair-by-Construction Benchmark for LLMs on Stripped-Binary Types and Names (Extended Version)

model-releasesarxiv-cs-lg

arXiv:2604.27319v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable progress in recent years, driving their adoption across a wide range of domains, including compu