Search: “framework”

Robust Basis Spline Decoupling for the Compression of Transformer Models

arXiv:2605.18794v1 Announce Type: cross Abstract: Decoupling is a powerful modeling paradigm for representing multivariate functions as compositions of linear transformations and univariate nonlinear

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Agents

Robust Checkpoint Selection for Multimodal LLMs via Agentic Evaluation and Stability-Aware Ranking

DGX agent

arXiv:2605.18852v1 Announce Type: cross Abstract: Checkpoint selection for multimodal large language models (MLLMs) presents significant challenges when performance differentials are marginal and eval

agentsarxiv-cs-ai

20 May 2026

Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation

arXiv:2605.19230v1 Announce Type: new Abstract: Age dependent performance disparities in medical image classification often arise because age acts as a confounder, linking imaging morphology with dise

RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations

arXiv:2605.19924v1 Announce Type: new Abstract: Human-in-the-loop reinforcement learning systems achieve near-perfect success on the workstation where they are trained, but collapse when the same robo

safetyarxiv-cs-ro

RoomPilot: Controllable Indoor Scene Synthesis via Multimodal Semantic Parsing

arXiv:2512.11234v2 Announce Type: replace Abstract: Generating controllable indoor scenes is fundamental to applications in game development, architectural visualization, and embodied AI. However, exi

Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference

arXiv:2605.19218v1 Announce Type: cross Abstract: Vision-Language Models suffer severe KV cache pressure at inference, as a single image often encodes into thousands of tokens. Most existing methods e

researcharxiv-cs-ai

RoVLA: Multi-Consistency Constraints for Robust Vision-Language-Action Models

arXiv:2605.19678v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown strong performance on embodied manipulation, yet they remain brittle under visual observation changes, pa

safetyarxiv-cs-ro

SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs

arXiv:2605.18864v1 Announce Type: cross Abstract: Recent studies observe that reinforcement learning with verifiable rewards (RLVR) reliably improves pass@1 on reasoning tasks, yet often fails to yiel

Scalable, Energy-Efficient Optical-Neural Architecture for Multiplexed Deepfake Video Detection

arXiv:2605.19360v1 Announce Type: new Abstract: The rapid proliferation of AI-generated visual media has created an urgent need for efficient, trustworthy deepfake detection systems. However, existing

applicationsarxiv-cs-cv

Scene-Action Prompt Fusion for Coherent Text-to-Video Storytelling

arXiv:2503.06310v4 Announce Type: replace Abstract: Generating coherent long-form video sequences from discrete text prompts remains challenging due to difficulties in maintaining temporal coherence,

safetyarxiv-cs-cv

SceneCode: Executable World Programs for Editable Indoor Scenes with Articulated Objects

arXiv:2605.19587v1 Announce Type: new Abstract: Indoor scene synthesis underpins embodied AI, robotic manipulation, and simulation-based policy evaluation, where a useful scene must specify not only w

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation

arXiv:2605.19340v1 Announce Type: new Abstract: Vision foundation models (VFMs) have achieved strong performance across various vision tasks. However, it still remains challenging to apply VFMs for cr

Self-Creative Text-to-Object Generation using Semantic-Aware Spatial Weighting

arXiv:2605.19554v1 Announce Type: new Abstract: Instilling creativity in text-to-image (T2I) generation presents a significant challenge, as it requires synthesized images to exhibit not only visual n

safetyarxiv-cs-cv

Self-Filtered Distillation with LLMs-generated Trust Indicators for Reliable Patent Classification

model-releasesarxiv-cs-cl

arXiv:2510.05431v4 Announce Type: replace Abstract: Organizing large-scale patent corpora according to classification schemes is a core information management task that determines the accuracy and eff

Set-Valued Policy Learning

arXiv:2605.19830v1 Announce Type: new Abstract: Conventional treatment policies map patient covariates to a single recommended intervention in order to maximize expected clinical outcomes. Although a

safetyarxiv-cs-lg

Skinned Motion Retargeting with Spatially Adaptive Interaction Guidance

arXiv:2605.19355v1 Announce Type: cross Abstract: Retargeting motion across characters with varying body shapes while preserving interaction semantics, such as self-contact and near-body proximity, re

researcharxiv-cs-ai

Local Ai

Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM

arXiv:2505.17726v3 Announce Type: replace-cross Abstract: Recently, multimodal large language models (MLLMs) have emerged as a key approach in achieving artificial general intelligence. In particular,

local-aiarxiv-cs-ai

Tutorials

Smartphone-based Circular Plot Sampling for Forest Inventory

arXiv:2605.19213v1 Announce Type: new Abstract: Circular sample plots are a cornerstone of forest inventory, yet accurate measurement of tree diameter at breast height (DBH) and spatial location withi

tutorialsarxiv-cs-cv

Smooth Piecewise Cutting for Neural Operator to Handle Discontinuities and Sharp Transitions

arXiv:2605.19823v1 Announce Type: cross Abstract: Neural operators have achieved strong performance in learning solution operators of partial differential equations (PDEs), but their inherently contin

Hardware

Soft Learning

arXiv:2605.18889v1 Announce Type: cross Abstract: Modern machine learning forces practitioners to choose between powerful but expensive deep networks and fast but limited classical algorithms. Here we

hardwarearxiv-cs-ai

Spatial-MLLM: Boosting MLLM Capabilities in Visual-based Spatial Intelligence

arXiv:2505.23747v2 Announce Type: replace-cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced performance on 2D visual tasks. However, improving

applicationsarxiv-cs-ai

Stitched Value Model for Diffusion Alignment

arXiv:2605.19804v1 Announce Type: cross Abstract: For practical use, diffusion- or flow-based generative models must be aligned with task-specific rewards, such as prompt fidelity or aesthetic prefere

Local Ai

STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning

arXiv:2605.18851v1 Announce Type: new Abstract: Recent advances in Reinforcement Learning (RL) have underscored its potential for incentivizing reasoning capabilities of Large Language Models (LLMs).

local-aiarxiv-cs-lg

Structural Energy Guidance for View-Consistent Text-to-3D Generation

arXiv:2605.19876v1 Announce Type: new Abstract: Text-to-3D generation based on diffusion models often suffers from the Janus problem, leading to inconsistent geometry across viewpoints. This work iden

safetyarxiv-cs-cv

Structured Style-Rewrite with Chain-of-Thought Planning for Low-Resource Character Dialogue

arXiv:2603.05933v2 Announce Type: replace Abstract: Applying Small Language Models (SLMs) to Chinese character-driven generation remains challenging due to data scarcity and the difficulty of disentan

researcharxiv-cs-cl

SVG360: Editable Multiview Vector Graphics from a Single SVG

arXiv:2511.16766v3 Announce Type: replace Abstract: Scalable Vector Graphics are a standard representation for editable visual design, yet they are usually authored as single view two dimensional illu

SWEET: Sparse World Modeling with Image Editing for Embodied Task Execution

arXiv:2605.19319v1 Announce Type: new Abstract: Visual prediction has emerged as a promising paradigm for embodied control, where future observations are generated and then translated into actions. Ho

SynGR: Unleashing the Potential of Cross-Modal Synergy for Generative Recommendation

arXiv:2605.18920v1 Announce Type: cross Abstract: Generative Recommendation (GR) has emerged as a promising paradigm by formulating item recommendation as a sequence-to-sequence generation task over i

Taming Real-World Space-Time Video Super-Resolution with One-Step Diffusion

arXiv:2601.20308v2 Announce Type: replace Abstract: Diffusion models have demonstrated exceptional success in video super-resolution (VSR), exhibiting powerful capabilities for generating fine-grained

applicationsarxiv-cs-cv

Taming the Thinker: Conditional Entropy Shaping for Adaptive LLM Reasoning

model-releasesarxiv-cs-cl

arXiv:2605.19358v1 Announce Type: new Abstract: Entropy-based deep reasoning has emerged as a promising direction for improving the reasoning capabilities of Large Language Models (LLMs), but existing

TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection

arXiv:2605.19738v1 Announce Type: cross Abstract: Graph Anomaly Detection (GAD) aims to identify atypical graph entities, such as nodes, edges, or substructures, that deviate significantly from the ma

applicationsarxiv-cs-ai

TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards

model-releasesarxiv-cs-cv

arXiv:2605.19320v1 Announce Type: new Abstract: Faithful text rendering remains a persistent weakness of large text-to-image generative models, as it requires both semantic instruction following and f

The World Won't Stay Still: Programmable Evolution for Agent Benchmarks

arXiv:2603.05910v2 Announce Type: replace Abstract: LLM-powered tool-calling agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Y

Theory-optimal Quantization Based on Flatness

arXiv:2605.18800v1 Announce Type: cross Abstract: Post-training quantization has emerged as a widely adopted technique for compressing and accelerating the inference of Large Language Models (LLMs). T

Throwing Vines at the Wall: Structure Learning via Random Search

arXiv:2510.20035v3 Announce Type: replace-cross Abstract: Vine copulas offer flexible multivariate dependence modeling and have become widely used in machine learning. Yet, structure learning remains

applicationsarxiv-cs-lg

TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization

model-releasesarxiv-cs-cv

arXiv:2605.20150v1 Announce Type: new Abstract: Training 3D Gaussian Splatting (3DGS) at billion-primitive scale is fundamentally memory-bound: each Gaussian primitive carries a large attribute vector

TORQ: Two-Level Orthogonal Rotation for MXFP4 Quantization

arXiv:2605.19561v1 Announce Type: cross Abstract: As Large Language Models (LLMs) advance toward practical deployment, the Microscaling FP4 (MXFP4) format has emerged as a cornerstone for next-generat

researcharxiv-cs-ai

Towards Consistent Detection of Cognitive Distortions: LLM-Based Annotation and Dataset-Agnostic Evaluation

model-releasesarxiv-cs-cl

arXiv:2511.01482v2 Announce Type: replace Abstract: Text-based automated Cognitive Distortion detection is a challenging task due to its subjective nature, with low agreement scores observed even amon

TravExplorer: Cross-Floor Embodied Exploration via Traversability-Aware 3-D Planning

model-releasesarxiv-cs-ro

arXiv:2605.19958v1 Announce Type: new Abstract: Zero-shot Object Navigation (ZSON) has shown promise for open-vocabulary target search in unseen environments, yet most existing systems remain tied to

Trust It or Not: Evidential Uncertainty for Feed-Forward 3D Reconstruction with Trust3R

arXiv:2605.19539v1 Announce Type: new Abstract: Geometric foundation models hold promise for unconstrained dense geometry prediction from uncalibrated images. However, in current feed-forward designs,

TSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents

arXiv:2602.11767v3 Announce Type: replace Abstract: Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative, multi-turn int

Tutorials

TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation

arXiv:2602.09023v4 Announce Type: replace Abstract: Despite strong generalization capabilities, Vision-Language-Action (VLA) models remain constrained by the high cost of expert demonstrations and lim

tutorialsarxiv-cs-ro

Understanding Dynamics of Adam in Zero-Sum Games: An ODE Approach

arXiv:2605.19392v1 Announce Type: new Abstract: The remarkable success of the Adam in training neural networks has naturally led to the widespread use of its descent-ascent counterpart, Adam-DA, for s

researcharxiv-cs-lg

UniRefiner: Teaching Pre-trained ViTs to Self-Dispose Dross via Contrastive Register

arXiv:2605.19622v1 Announce Type: new Abstract: Representation learning with Vision Transformers (ViTs) has advanced rapidly, yet the utility of large-scale models in spatially sensitive tasks is hind

VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals

arXiv:2605.18837v1 Announce Type: cross Abstract: Wearable devices enable continuous health monitoring from multimodal signals, but real-world deployment is hindered by limited labeled data and pervas

applicationsarxiv-cs-ai

Agents

VL-DPO: Vision-Language-Guided Finetuning for Preference-Aligned Autonomous Driving

arXiv:2605.20082v1 Announce Type: cross Abstract: The rapid growth of autonomous driving datasets has enabled the scaling of powerful motion forecasting models. While large-scale pretraining provides

agentsarxiv-cs-ai

Agents

What and When to Distill: Selective Hindsight Distillation for Multi-Turn Agents

arXiv:2605.19447v1 Announce Type: new Abstract: Reinforcement learning can train LLM agents from sparse task rewards, but long-horizon credit assignment remains challenging: a single success-or-failur

agentsarxiv-cs-ai

What Do Evolutionary Coding Agents Evolve?

arXiv:2605.20086v1 Announce Type: cross Abstract: Recent work pairs LLMs with evolutionary search to iteratively generate, modify, and select code using task-specific feedback. These systems have prod