Search: “framework”

Conditional Score-Based Modeling of Effective Langevin Dynamics

arXiv:2604.23952v1 Announce Type: cross Abstract: Stochastic reduced-order models are widely used to represent the effective dynamics of complex systems, but estimating their drift and diffusion coeff

researcharxiv-cs-lg

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Context-Aware Hospitalization Forecasting Evaluations for Decision Support using LLMs

DGX agent

arXiv:2604.23949v1 Announce Type: new Abstract: Medical and public health experts must make real-time resource decisions, such as expanding hospital bed capacity, based on projected hospitalization tr

safetyarxiv-cs-ai

28 Apr 2026

Context-Integrated Adversarial Learning for Predictive Modelling of Stock Price Dynamics

arXiv:2604.22801v1 Announce Type: cross Abstract: It is a challenging task to forecast equity prices in fast moving financial markets as this becomes even more difficult when the predictive signal is

researcharxiv-cs-lg

Contextual Linear Activation Steering of Language Models

arXiv:2604.24693v1 Announce Type: new Abstract: Linear activation steering is a powerful approach for eliciting the capabilities of large language models and specializing their behavior using limited

Agents

ContextWeaver: Selective and Dependency-Structured Memory Construction for LLM Agents

arXiv:2604.23069v1 Announce Type: new Abstract: Large language model (LLM) agents often struggle in long-context interactions. As the agent accumulates more interaction history, context management app

agentsarxiv-cs-cl

Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data

arXiv:2604.23281v1 Announce Type: cross Abstract: Human activity recognition serves as the foundation for various emerging applications. In recent years, researchers have used collaborative sensing of

applicationsarxiv-cs-cv

Control Barrier Functions Solved with Hierarchical Quadratic Programming for Safe Physical Human-Robot Interaction

arXiv:2604.23039v1 Announce Type: new Abstract: Physical human-robot interaction offers the potential to leverage human intelligence and robot physical capabilities to enable a range of exciting appli

safetyarxiv-cs-ro

Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning

arXiv:2604.23179v1 Announce Type: cross Abstract: Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization a

Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation

arXiv:2604.24361v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorl

DARC-CLIP: Dynamic Adaptive Refinement with Cross-Attention for Meme Understanding

model-releasesarxiv-cs-cl

arXiv:2604.23214v1 Announce Type: new Abstract: Memes convey meaning through the interaction of visual and textual signals, often combining humor, irony, and offense in subtle ways. Detecting harmful

Decentralized Heterogeneous Multi-Robot Collaborative Exploration for Indoor and Outdoor 3D Environments

arXiv:2604.23693v1 Announce Type: new Abstract: Heterogeneous multi-robot systems feature significant adaptability for complex environments. However, effective collaboration that fully exploits the ro

researcharxiv-cs-ro

DeepImagine: Learning Biomedical Reasoning via Successive Counterfactual Imagining

arXiv:2604.23054v1 Announce Type: cross Abstract: Predicting the outcomes of prospective clinical trials remains a major challenge for large language models. Prior work has shown that both traditional

Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI

arXiv:2604.24492v1 Announce Type: cross Abstract: Designing deep networks that meet strict latency and accuracy constraints on edge accelerators increasingly relies on hardware-aware optimization, inc

local-aiarxiv-cs-ai

DepthKV: Layer-Dependent KV Cache Pruning for Long-Context LLM Inference

arXiv:2604.24647v1 Announce Type: cross Abstract: Long-context reasoning is a critical capability of large language models (LLMs), enabling applications such as long-document understanding, summarizat

researcharxiv-cs-ai

Designing Instance-Level Sampling Schedules via REINFORCE with James-Stein Shrinkage

arXiv:2511.22177v2 Announce Type: replace-cross Abstract: Most post-training methods for text-to-image samplers focus on model weights: either fine-tuning the backbone for alignment or distilling it f

safetyarxiv-cs-cv

DGHMesh: A Large-scale Dual-radar mmWave Dataset and Generalization-focused Benchmark for Human Mesh Reconstruction

arXiv:2604.22827v1 Announce Type: new Abstract: Millimeter-wave (mmWave) radar has shown great potential for contactless, privacy-preserving, and robust human sensing, yet existing mmWave-based human

Differentiable Faithfulness Alignment for Cross-Model Circuit Transfer

model-releasesarxiv-cs-cl

arXiv:2604.24302v1 Announce Type: new Abstract: Mechanistic interpretability has made it possible to localize circuits underlying specific behaviors in language models, but existing methods are expens

DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation

arXiv:2604.24719v1 Announce Type: new Abstract: Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural

researcharxiv-cs-cv

Discovering Agentic Safety Specifications from 1-Bit Danger Signals

arXiv:2604.23210v1 Announce Type: new Abstract: Can large language model agents discover hidden safety objectives through experience alone? We introduce EPO-Safe (Experiential Prompt Optimization for

Do Protective Perturbations Really Protect Portrait Privacy under Real-world Image Transformations?

arXiv:2604.23688v1 Announce Type: new Abstract: Proactive defense methods protect portrait images from unauthorized editing or talking face generation (TFG) by introducing pixel-level protective pertu

applicationsarxiv-cs-cv

Tutorials

Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation

arXiv:2506.21107v3 Announce Type: replace Abstract: Estimating single-cell responses across various perturbations facilitates the identification of key genes and enhances drug screening, significantly

tutorialsarxiv-cs-lg

Domain-Filtered Knowledge Graphs from Sparse Autoencoder Features

arXiv:2604.23829v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) extract millions of interpretable features from a language model, but flat feature inventories aren't very useful on their ow

local-aiarxiv-cs-ai

DreamAudio: Customized Text-to-Audio Generation with Diffusion Models

arXiv:2509.06027v3 Announce Type: replace-cross Abstract: With the development of large-scale diffusion-based and language-modeling-based generative models, impressive progress has been achieved in te

DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models

arXiv:2505.13975v4 Announce Type: replace Abstract: While Large Reasoning Models (LRMs) have demonstrated success in complex reasoning tasks through long chain-of-thought (CoT) reasoning, their infere

Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction

arXiv:2503.18836v2 Announce Type: replace-cross Abstract: Magnetic resonance imaging (MRI) is a vital diagnostic tool, but its inherently long acquisition times reduce clinical efficiency and patient

applicationsarxiv-cs-ai

DVPO: Distributional Value Modeling-based Policy Optimization for LLM Post-Training

arXiv:2512.03847v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has shown strong performance in LLM post-training, but real-world deployment often involves noisy or incomplete su

DynProto: Dynamic Prototype Evolution for Out-of-Distribution Detection

arXiv:2604.23729v1 Announce Type: new Abstract: Recent studies show that using potential out-of-distribution (OOD) labels from large corpora as auxiliary information can improve OOD detection in visio

Agents

each startup has a page where i can see all the data in one place from financials, analysis of the financials/kpis, asks, wins, challenges, …

A startup dashboard or management system that consolidates key business information in a single view, including financial data, KPI analysis, funding requests, business wins, and operational challenge

agentsyohei-nakajima--x

EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models

arXiv:2602.17419v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can enrich industrial anomaly detection with semantic descriptions and anomaly reasoning, but they still la

Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning

arXiv:2603.05212v2 Announce Type: replace-cross Abstract: Early warning of intraoperative adverse events plays a vital role in reducing surgical risk and improving patient safety. While deep learning

Edit Where You Mean: Region-Aware Adapter Injection for Mask-Free Local Image Editing

arXiv:2604.23763v1 Announce Type: new Abstract: Large diffusion transformers (DiTs) follow global editing instructions well but consistently leak local edits into unrelated regions, because joint-atte

researcharxiv-cs-cv

Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA

arXiv:2604.23336v1 Announce Type: cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language

safetyarxiv-cs-cl

EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving

model-releasesarxiv-cs-cl

arXiv:2604.22851v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) have advanced highlevel reasoning in autonomous driving, their ability to ground this reasoning in the underlying

EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models

arXiv:2502.04424v4 Announce Type: replace-cross Abstract: With the integration of multimodal large language models (MLLMs) into robotic systems and AI applications, embedding emotional intelligence (E

Emotion-Conditioned Short-Horizon Human Pose Forecasting with a Lightweight Predictive World Model

arXiv:2604.23532v1 Announce Type: cross Abstract: Short-term human pose prediction plays a crucial role in interactive systems, assistive robots, and emotion-aware human-computer interaction[1-3]. Whi

researcharxiv-cs-ai

EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs

arXiv:2604.23348v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and generation, and are increasingly used in

Enhancing molecular dynamics with equivariant machine-learned densities

arXiv:2604.24563v1 Announce Type: cross Abstract: Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces

researcharxiv-cs-lg

Agents

Escher-Loop: Mutual Evolution by Closed-Loop Self-Referential Optimization

arXiv:2604.23472v1 Announce Type: new Abstract: While recent autonomous agents demonstrate impressive capabilities, they predominantly rely on manually scripted workflows and handcrafted heuristics, i

agentsarxiv-cs-ai

ESPADA: Execution Speedup via Semantics Aware Demonstration Data Downsampling for Imitation Learning

arXiv:2512.07371v3 Announce Type: replace-cross Abstract: Behavior-cloning based visuomotor policies enable precise manipulation but often inherit the slow, cautious tempo of human demonstrations, lim

applicationsarxiv-cs-ai

Evaluating the Search Agent in a Parallel World

arXiv:2603.04751v2 Announce Type: replace Abstract: Integrating web search tools has significantly extended the capability of LLMs to address open-world, real-time, and long-tail problems. However, ev

Evaluating whether AI models would sabotage AI safety research

arXiv:2604.24618v1 Announce Type: new Abstract: We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier

Explaining Sources of Uncertainty in Automated Fact-Checking

arXiv:2505.17855v2 Announce Type: replace Abstract: Understanding sources of a model's uncertainty regarding its predictions is crucial for effective human-AI collaboration. Prior work proposes using

Explaining Temporal Graph Predictions With Shapley Values

arXiv:2604.24078v1 Announce Type: new Abstract: Temporal Graph Neural Networks (TGNNs) have become increasingly popular in recent years due to their superior predictive performance by combining both s

local-aiarxiv-cs-lg

Exploring Audio Hallucination in Egocentric Video Understanding

arXiv:2604.23860v1 Announce Type: cross Abstract: Egocentric videos provide a distinctive setting in which sound serves as crucial cues to understand user activities and surroundings, particularly whe

researcharxiv-cs-ai

Exploring Hierarchical Consistency and Unbiased Objectness for Open-Vocabulary Object Detection

arXiv:2604.23344v1 Announce Type: new Abstract: Conventional object detectors typically operate under a closed-set assumption, limiting recognition to a predefined set of base classes seen during trai

Exploring the Secondary Risks of Large Language Models

arXiv:2506.12382v5 Announce Type: replace-cross Abstract: Ensuring the safety and alignment of Large Language Models is a significant challenge with their growing integration into critical application

Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors

arXiv:2603.21768v3 Announce Type: replace-cross Abstract: Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar-only models frequently suffer from a lack of

Factual and Edit-Sensitive Graph-to-Sequence Generation via Graph-Aware Adaptive Noising

arXiv:2604.24104v1 Announce Type: new Abstract: Fine-tuned autoregressive models for graph-to-sequence generation (G2S) often struggle with factual grounding and edit sensitivity. To tackle these issu

local-aiarxiv-cs-cl