Search: “inference”

Dynamic Mixture of Latent Memories for Self-Evolving Agents

arXiv:2605.21951v1 Announce Type: new Abstract: Achieving self-evolution in intelligent agents requires the continual accumulation of new knowledge across changing task sequences without forgetting pr

Local Ai

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F-TIS: Harnessing Diverse Models in Collaborative GRPO

DGX agent

arXiv:2605.22537v1 Announce Type: new Abstract: Reinforcement learning methods such as GRPO have seen great popularity in LLM post-training. In GRPO, models produce completions to a set of prompts, wh

local-aiarxiv-cs-lg

23 May 2026

Factored Diffusion Policies:Compositionally Generalized Robot Control with a Single Score Network

arXiv:2605.22596v1 Announce Type: new Abstract: Robotic tasks are typically specified by a tuple of factors, such as the object to be grasped, the obstacles to be avoided, the color of the target, and

From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching

arXiv:2605.22340v1 Announce Type: new Abstract: Single-cell RNA sequencing (scRNA-seq) provides high-dimensional profiles of cellular states, enabling data-driven modeling of cellular dynamics over ti

Heterogeneous Agent Collaborative Reinforcement Learning

arXiv:2603.02604v2 Announce Type: replace Abstract: We introduce Heterogeneous Agent Collaborative Reinforcement Learning (HACRL), a new Reinforcement Learning from Verifiable Reward (RLVR) problem th

Tutorials

Learning Causal Orderings for In-Context Tabular Prediction

arXiv:2605.22335v1 Announce Type: new Abstract: In-context learning for tabular data sets strong predictive standards in observational settings; it however primarily relies on correlational structure,

tutorialsarxiv-cs-lg

LEMUR: Learned Multi-Vector Retrieval

arXiv:2601.21853v2 Announce Type: replace-cross Abstract: Multi-vector representations generated by late interaction models, such as ColBERT, enable superior retrieval quality compared to single-vecto

Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts

arXiv:2506.21035v5 Announce Type: replace Abstract: Continual learning (CL) with large pre-trained models aims to incrementally acquire knowledge without catastrophic forgetting. Existing LoRA-based M

MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data

arXiv:2605.22775v1 Announce Type: new Abstract: Real-time cognitive load assessment from eye-tracking signals could potentially enable adaptive human-centered-AI such as safety-critical applications s

Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization

model-releasesarxiv-cs-lg

arXiv:2605.21751v1 Announce Type: new Abstract: Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coeffi

Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction

arXiv:2605.22338v1 Announce Type: new Abstract: Reconstructing continuous physical fields from sparse measurements is a central inverse problem, but data-driven generative models can produce states th

applicationsarxiv-cs-lg

Physics Priors Offer Useful Accuracy-Carbon Trade-Offs in Spatio-Temporal Forecasting

arXiv:2509.24517v2 Announce Type: replace Abstract: Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics). This sole focus o

Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices

arXiv:2605.22097v1 Announce Type: cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challen

Riemannian geometry meets fMRI: the advantages of modeling correlation manifolds and eigenvector subspaces

arXiv:2605.22334v1 Announce Type: new Abstract: Correlation matrices are fundamental summaries of functional brain networks, yet standard analyses often treat entries independently, ignoring the curve

applicationsarxiv-cs-lg

SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection

arXiv:2605.22331v1 Announce Type: new Abstract: Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by syste

Soft Bayesian Context Tree Models for Real-Valued Time Series

arXiv:2601.11079v2 Announce Type: replace Abstract: This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers

Symbolic Density Estimation for Discrete Distributions

model-releasesarxiv-cs-lg

arXiv:2605.21813v1 Announce Type: new Abstract: Discrete probability laws underpin statistical modeling, yet the catalog of interpretable distributions has expanded only gradually through centuries of

Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging

arXiv:2603.21717v4 Announce Type: replace Abstract: Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating

Uncertainty quantification for Markov chain induced martingales with application to temporal difference learning

arXiv:2502.13822v3 Announce Type: replace-cross Abstract: We establish novel and general high-dimensional concentration inequalities and Berry-Esseen bounds for vector-valued martingales induced by Ma

Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation

arXiv:2605.22765v1 Announce Type: new Abstract: Discrete diffusion models are often trained through clean-data prediction, but the prediction can be used in different ways to define the reverse dynami

Hardware

WarmServe: Enabling One-for-Many GPU Prewarming for Multi-LLM Serving

arXiv:2512.09472v2 Announce Type: replace-cross Abstract: Deploying multiple models within shared GPU clusters is a key strategy to improve resource efficiency in large language model (LLM) serving. E

hardwarearxiv-cs-lg

What are the Right Symmetries for Formal Theorem Proving?

arXiv:2605.22257v1 Announce Type: new Abstract: Formal theorem provers based on large language models (LLMs) are highly sensitive to superficial variations in problem representation: semantically equi

A Robust Semantic Segmentation Pipeline for the CVPR 2026 8th UG2+ Challenge Track 2

arXiv:2605.22216v1 Announce Type: new Abstract: This report presents our solution for the WeatherProof Dataset Challenge, namely CVPR 2026 8th UG2+ Challenge Track 2: Semantic Segmentation in Adverse

Action with Visual Primitives

arXiv:2605.22183v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have emerged as a promising paradigm for generalist robotic manipulation. A common design in current architectures m

researcharxiv-cs-ro

BeLink: Biomedical Entity Linking Meets Generative Re-Ranking

arXiv:2605.22501v1 Announce Type: new Abstract: Despite recent progress, Biomedical Entity Linking (BEL) with large language models (LLMs) remains computationally inefficient and challenging to deploy

applicationsarxiv-cs-cl

Comparing LLM and Fine-Tuned Model Performance on NVDRS Circumstance Extraction with Varying Prompt Complexity

model-releasesarxiv-cs-cl

arXiv:2605.21845v1 Announce Type: new Abstract: Suicide is a leading cause of death in the United States, and understanding the circumstances that precede it requires extracting structured information

Do Vision Models Encode Object-Level Semantic Relatedness? A Cognitive Psychology-Inspired Benchmark

arXiv:1709.03806v2 Announce Type: replace Abstract: Modern vision models have achieved strong object-recognition performance, yet it remains unclear whether their representations encode object-level s

EntmaxKV: Support-Aware Decoding for Entmax Attention

arXiv:2605.21649v1 Announce Type: cross Abstract: Long-context decoding is increasingly limited by KV-cache memory traffic since each generated token attends over a cache whose size grows linearly wit

researcharxiv-cs-cl

Agents

EvoIR-Agent: Self-Evolving Image Restoration Agentic System via Experience-Driven Learning

arXiv:2605.22208v1 Announce Type: new Abstract: Multimodal Large Language Model (MLLM)-driven image restoration agent demonstrates effectiveness in degradation coupling scenarios by flexibly selecting

agentsarxiv-cs-cv

ForeSplat: Optimization-Aware Foresight for Feed-Forward 3D Gaussian Splatting

arXiv:2605.22020v1 Announce Type: new Abstract: Feed-forward 3D Gaussian Splatting (3DGS) models offer fast single-pass reconstruction,but scaling them to match per-scene optimization quality is funda

From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding

arXiv:2605.22413v1 Announce Type: new Abstract: Extracting structured information from visual documents (Visual Information Extraction, VIE) is a cornerstone of business automation. While recent Multi

Higher Order Reasoning for Collaborative Communicationless Mobile Robot Operations

arXiv:2605.21901v1 Announce Type: new Abstract: In communicationless environments, multi-robot systems must operate without the constant information exchange that many coordination strategies typicall

researcharxiv-cs-ro

Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild

model-releasesarxiv-cs-cl

arXiv:2605.22064v1 Announce Type: new Abstract: Hy-MT2 is a family of fast-thinking multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B,

InfVSR: Breaking Length Limits of Generic Video Super-Resolution

arXiv:2510.00948v2 Announce Type: replace Abstract: Real-world videos often extend over thousands of frames. Existing generative video super-resolution (VSR) approaches, however, face two persistent c

Interpreting and Enhancing Emotional Circuits in Large Vision-Language Models via Cross-Modal Information Flow

arXiv:2605.21980v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) represent a significant leap towards empathetic agents, demonstrating remarkable capabilities in emotion understand

LACO: Adaptive Latent Communication for Collaborative Driving

arXiv:2605.22504v1 Announce Type: cross Abstract: Collaborative driving aims to improve safety and efficiency by enabling connected vehicles to coordinate under partial observability. Recent approache

safetyarxiv-cs-cv

Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models

arXiv:2605.21573v1 Announce Type: new Abstract: We introduce Lens, a 3.8B-parameter T2I model that achieves performance competitive with, and in several cases surpassing, state-of-the-art models with

Agents

LIDSA: Cognitive Arbitration for Signal-Free Autonomous Intersection Management

arXiv:2605.12321v2 Announce Type: replace Abstract: Large language models (LLMs) show strong potential for Intelligent Transportation Systems (ITS), particularly in tasks requiring situational reasoni

agentsarxiv-cs-ai

Mapping Tomato Cropping Systems in California Using AlphaEarth Geospatial Embeddings and Deep Learning Analysis

arXiv:2605.21804v1 Announce Type: cross Abstract: Field-scale crop maps support supply-chain forecasting and policy, yet statewide crop identification still often depends on retrospective surveys or r

safetyarxiv-cs-cv

MLLMs Know When Before Speaking: Revealing and Recovering Temporal Grounding via Attention Cues

arXiv:2605.21954v1 Announce Type: new Abstract: Video temporal grounding (VTG), which localizes the start and end times of a queried event in an untrimmed video, is a key test of whether multimodal la

MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction

arXiv:2605.22121v1 Announce Type: new Abstract: Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquire

Probabilistic Attribution For Large Language Models

arXiv:2605.21726v1 Announce Type: new Abstract: The generative nature of Large Language Models (LLMs) is reflected in the conditional probabilities they compute to sample each response token given the

researcharxiv-cs-cl

Quantizing Whisper-small: How design choices affect ASR performance

arXiv:2511.08093v2 Announce Type: replace-cross Abstract: Large speech recognition models like Whisper-small achieve high accuracy but are difficult to deploy on edge devices due to their high computa

applicationsarxiv-cs-cl

Reflective Prompt Tuning through Language Model Function-Calling

model-releasesarxiv-cs-cl

arXiv:2605.21781v1 Announce Type: new Abstract: Large language models (LLMs) have become increasingly capable of following instructions and complex reasoning, making prompting a flexible interface for

Reinforcing VLAs in Task-Agnostic World Models

arXiv:2605.12334v2 Announce Type: replace Abstract: Post-training Vision-Language-Action (VLA) models via reinforcement learning (RL) in learned world models has emerged as an effective strategy to ad

safetyarxiv-cs-ai

SceneGraphGrounder: Zero-Shot 3D Visual Grounding via Structured Scene Graph Matching

arXiv:2605.21788v1 Announce Type: new Abstract: Zero-shot 3D visual grounding requires localizing objects in unstructured environments from free-form natural language. Recent vision-language model (VL

Search-E1: Self-Distillation Drives Self-Evolution in Search-Augmented Reasoning

arXiv:2605.22511v1 Announce Type: cross Abstract: Post-training has become the dominant recipe for turning a language model into a competent search-augmented reasoning agent. A line of recent work pus

safetyarxiv-cs-cl

SEGA: Spectral-Energy Guided Attention for Resolution Extrapolation in Diffusion Transformers

arXiv:2605.22668v1 Announce Type: new Abstract: Diffusion transformers (DiTs) have emerged as a dominant architecture for text-to-image generation, yet their performance drops when generating at resol