Search: “framework”

Quality-constrained Entropy Maximization Policy Optimization for LLM Diversity

arXiv:2602.15894v2 Announce Type: replace Abstract: In many large language model (LLM) alignment applications, users expect not only high-quality outputs but also substantial diversity. However, exist

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Research

Quantum Machine Learning-based 6G edge Network: Enabling Adaptive Communication and Model Aggregation

DGX agent

arXiv:2605.27417v1 Announce Type: cross Abstract: With the advent of sixth-generation (6G) mobile communication technology, vehicle-to-everything (V2X) communication faces unprecedented challenges in

researcharxiv-cs-ai

28 May 2026

RE-TRIANGLE: Does TRIANGLE Enable Multimodal Alignment Beyond Cosine Similarity in Retrieval?

arXiv:2605.27436v1 Announce Type: cross Abstract: Multimodal alignment is critical for bridging the semantic gap in information retrieval. However, traditional pairwise strategies introduce a geometri

Real Time NILM Based Power Monitoring of Identical Induction Motors Representing Cutting Machines in Textile Industry

arXiv:2601.01616v2 Announce Type: replace Abstract: The textile industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in i

researcharxiv-cs-lg

Realizing Robotic Swimming with Unified Fluid-Robot Multiphysics

arXiv:2506.05012v2 Announce Type: replace Abstract: Matching the swimming efficiency and agility of fish has remained an elusive goal in underwater robotics. Such locomotion capabilities rely on compl

researcharxiv-cs-ro

Relational Semantic Reasoning on 3D Scene Graphs for Open World Interactive Object Search

arXiv:2603.05642v2 Announce Type: replace-cross Abstract: Open-world interactive object search in household environments requires understanding semantic relationships between objects and their surroun

RelaxFlow: Text-Driven Amodal 3D Generation

arXiv:2603.05425v2 Announce Type: replace-cross Abstract: Image-to-3D generation faces inherent semantic ambiguity under occlusion, where partial observation alone is often insufficient to determine o

Residualized Temporal Sparse Autoencoders for Interpreting Diffusion Models

arXiv:2605.27813v1 Announce Type: cross Abstract: Text-to-image diffusion models generate images through an iterative denoising process, so internal neural layers produce trajectories of activations r

Restoring the Sweet Spot: Pass-Rate Weighted Self-Distillation for LLM Reasoning

arXiv:2605.27765v1 Announce Type: cross Abstract: Self-Distillation Policy Optimization (SDPO) provides dense token-level credit assignment for reinforcement learning with large language models by lev

Agents

Rethinking Memory as Continuously Evolving Connectivity

arXiv:2605.28773v1 Announce Type: cross Abstract: Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which i

agentsarxiv-cs-ai

Applications

Rethinking Video-Language Model from the Language Input Perspective

arXiv:2605.27920v1 Announce Type: new Abstract: Driven by the wave of large language models, Video-Language Models (VLMs) have become a significant yet challenging technology to bridge the gap between

applicationsarxiv-cs-cv

Rethinking Visual Neglect: Steering via Context-Preference for MLLM Hallucination Mitigation

arXiv:2605.27993v1 Announce Type: new Abstract: Object hallucination remains a primary obstacle to the reliable deployment of Multimodal Large Language Models (MLLMs). Current inference-time mitigatio

researcharxiv-cs-cl

REVEAL: Reference-Grounded Reasoning for Multimodal Manipulation Detection

arXiv:2605.28459v1 Announce Type: new Abstract: Multimodal manipulation detection aims to simultaneously identify forged image--text pairs and localize tampered regions, yet existing methods typically

researcharxiv-cs-cv

Tutorials

Revealing Algorithmic Deductive Circuits for Logical Reasoning

arXiv:2605.27824v1 Announce Type: new Abstract: Recent studies have shown that Large Language Models (LLMs) can achieve strong reasoning performance by incorporating functional symbolic representation

tutorialsarxiv-cs-ai

Local Ai

Reverse Probing: Supervised Token-level Uncertainty Quantification for Large Language Models in Clinical Text

arXiv:2605.28740v1 Announce Type: cross Abstract: As large language models are increasingly deployed for clinical text, ensuring they can reliably signal their own uncertainty becomes critical. Most e

local-aiarxiv-cs-ai

Revisiting Graph Autoencoders as Implicit Contrastive Learners

arXiv:2410.10241v2 Announce Type: replace-cross Abstract: Graph autoencoders (GAEs) and graph contrastive learning (GCL) are two major paradigms for self-supervised representation learning on graphs,

RL Squeezes, SFT Expands: A Comparative Study of Reasoning LLMs

arXiv:2509.21128v2 Announce Type: replace Abstract: Large language models (LLMs) are typically trained by reinforcement learning (RL) with verifiable rewards (RLVR) and supervised fine-tuning (SFT) on

RMPL: Relation-aware Multi-task Progressive Learning with Stage-wise Training for Multimedia Event Extraction

model-releasesarxiv-cs-cl

arXiv:2602.13748v2 Announce Type: replace Abstract: Multimedia Event Extraction (MEE) aims to identify events and their arguments from documents that contain both text and images. It requires groundin

Local Ai

Robust Contrastive Graph Clustering with Adaptive Local-Global Integration

arXiv:2605.28209v1 Announce Type: new Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contr

local-aiarxiv-cs-lg

Agents

Roles with Rails: Contract-Preserving Role Evolution in Multi-Agent Structured Reasoning

arXiv:2605.28433v1 Announce Type: new Abstract: Role-based LLM multi-agent systems need adaptive role pools, yet adapting such systems is not merely a matter of prompt optimization: roles often carry

agentsarxiv-cs-cl

ROOM: A Physics-Based Continuum Robot Simulator for Photorealistic Medical Datasets Generation

arXiv:2509.13177v2 Announce Type: replace Abstract: Continuum robots are advancing bronchoscopy procedures by accessing complex lung airways and enabling targeted interventions. However, their develop

safetyarxiv-cs-ro

ROSD: Reflective On-Policy Self-Distillation for Language Model Reasoning across Domains

arXiv:2605.28014v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves the reasoning performance of large language models (LLMs) by providing dense token-level supervision for on-

safetyarxiv-cs-cl

Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Models

arXiv:2605.28306v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models have emerged as a dominant paradigm for efficient LLM scaling, yet adapting them to non-English downstream tasks remai

S-Cheetah: A Novel Quadrupedal Robot with a 3-DOF Active Spine Learning Agile Locomotion

arXiv:2605.27909v1 Announce Type: new Abstract: The biological spine of quadrupeds enables sagittal flexion/extension, lateral bending, and axial rotation, playing a crucial role in highly agile and d

researcharxiv-cs-ro

Safe In-Context Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2509.25582v3 Announce Type: replace Abstract: In-context reinforcement learning (ICRL) is an emerging RL paradigm where an agent, after pretraining, can adapt to out-of-distribution test tasks w

Safety-Critical Adaptive Impedance Control via Nonsmooth Control Barrier Functions under State and Input Constraints

arXiv:2605.28367v1 Announce Type: new Abstract: Safe physical interaction is critical for deploying robotic manipulators in human-robot interaction and contact-rich tasks, where uncertainty, external

safetyarxiv-cs-ro

Tutorials

SAFEVPR: Patch-Based Conformal Verification for Safe Cross-Condition Sequence Visual Place Recognition

arXiv:2605.28048v1 Announce Type: new Abstract: Sequence-based visual place recognition (VPR) for SLAM and robot relocalization must decide whether the retrieved top-1 candidate is safe to accept. Con

tutorialsarxiv-cs-ro

SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving

arXiv:2605.28583v1 Announce Type: cross Abstract: Ensuring both safety and efficiency in decision-making for autonomous driving systems remains a fundamental challenge. Traditional Deep Reinforcement

Self-Prophetic Decoding to Unlock Visual Search in LVLMs

arXiv:2605.28741v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) are rapidly evolving toward true multimodal reasoning, with visual search representing a concrete instantiation of

researcharxiv-cs-cv

Self-Supervised Online Robot-Agnostic Traversability Estimation for Open-World Environments

model-releasesarxiv-cs-cv

arXiv:2605.28442v1 Announce Type: cross Abstract: Self-supervised online traversability estimation enables robots to continuously learn from unlabeled open-world experiences and adapt their navigation

SEMAGIC: Learning Semantically Consistent Deformable 3D Representations from In-the-Wild Images

arXiv:2605.27938v1 Announce Type: new Abstract: Learning deformable 3D object models from single-view in-the-wild images has enabled impressive 3D shape reconstruction without supervision. However, it

safetyarxiv-cs-cv

Semantic-Aware Interpretable Multimodal Music Auto-Tagging

arXiv:2505.17233v3 Announce Type: replace Abstract: Music auto-tagging is essential for organizing and discovering music in extensive digital libraries. While foundation models achieve exceptional per

researcharxiv-cs-lg

Semantic Optimal Transport for Sparse Autoencoder Feature Matching and Circuit Compression

arXiv:2605.28567v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have become a central tool for interpreting language models. However, two key SAE analyses that remain difficult to scale a

Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity

arXiv:2605.27526v1 Announce Type: cross Abstract: We develop semiparametrically efficient inference for kernel measures of noise heterogeneity in additive noise models. In many applications, the regre

safetyarxiv-cs-lg

Simulation-Informed Diffusion for Decentralized Multi-robot Motion Planning

arXiv:2605.27697v1 Announce Type: cross Abstract: Decentralized multi-robot motion planning requires each robot to generate collision-free trajectories from local observations, without global sensing

Agents

Skill0.5: Joint Skill Internalization and Utilization for Out-of-Distribution Generalization in Agentic Reinforcement Learning

arXiv:2605.28424v1 Announce Type: new Abstract: Equipping large language models with explicit skills has emerged as a promising paradigm for enabling autonomous agents to solve complex tasks. Agent sk

agentsarxiv-cs-cl

SKILLC: Learning Autonomous Skill Internalization in LLM Agents via Contrastive Credit Assignment

arXiv:2605.27899v1 Announce Type: new Abstract: Structured skill prompts improve exploration in long-horizon agentic reinforcement learning (RL). Skill-augmented RL methods retain external skills at i

SkillGrad: Optimizing Agent Skills Like Gradient Descent

arXiv:2605.27760v1 Announce Type: new Abstract: Agent skills provide a lightweight way to adapt LLM agents to specialized domains by storing reusable procedural knowledge in structured files. However,

Applications

SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter

arXiv:2605.28084v1 Announce Type: cross Abstract: Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tas

applicationsarxiv-cs-ai

Soft-SVeRL: Self-Verified Reinforcement Learning with Soft Rewards

arXiv:2605.28561v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has improved language models in domains such as mathematics and code, where correctness can be che

safetyarxiv-cs-cl

SPAR: Support-Preserving Action Rectification

arXiv:2605.27877v1 Announce Type: cross Abstract: Offline policy improvement faces an inherent conflict between maximizing value and fitting the data distribution. While in-sample weighted regression

SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection

arXiv:2605.28030v1 Announce Type: cross Abstract: Fine-tuning large language models often undermines their safety alignment, a problem further amplified by harmful fine-tuning attacks in which adversa

Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks

model-releasesarxiv-cs-lg

arXiv:2605.27756v1 Announce Type: cross Abstract: High-performance computing enables simulation of high-dimensional physical systems, but downstream analyses such as inverse problems and control remai

Applications

Stage-wise Distortion-Perception Traversal in Zero-shot Inverse Problems with Diffusion Models

arXiv:2605.28711v1 Announce Type: new Abstract: The distortion-perception (D-P) tradeoff is a fundamental phenomenon of Bayesian inverse problems, which characterizes the inherent tension between dist

applicationsarxiv-cs-lg

STFlow: Data-Coupled Flow Matching for Geometric Trajectory Simulation

arXiv:2505.18647v3 Announce Type: replace-cross Abstract: Simulating trajectories of dynamical systems is a fundamental problem in a wide range of fields such as molecular dynamics, biochemistry, and

Stochastic Gradient Descent with Momentum is Algorithmically Stable

arXiv:2605.28517v1 Announce Type: cross Abstract: Stochastic gradient descent with momentum (SGDM) is one of the most widely used optimization algorithms in machine learning. While optimization proper

StoryLens: Preference-Aligned Story Rewriting via Context-Aware Narrative Enrichment

arXiv:2605.28073v1 Announce Type: cross Abstract: Story rewriting aims to adapt existing narratives to diverse reader preferences while preserving plot consistency and narrative coherence. Unlike conv

StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation

arXiv:2605.27393v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent dialogue, but prior works lack situational grounding, dynamic strategy control, and evaluation aligne