Search: “papers”

Principled Algorithms for Optimizing Generalized Metrics in Multi-Label Learning

arXiv:2605.28767v1 Announce Type: new Abstract: Many real-world classification tasks require predicting multiple labels per instance, necessitating the optimization of complex evaluation metrics such

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Provably Guaranteed Polytopic Uncertainty Quantification for SLAM

DGX agent

arXiv:2605.28172v1 Announce Type: new Abstract: In safety-critical robotics applications, guaranteed and practical uncertainty quantification (UQ) in perception is vital. Many existing works either of

safetyarxiv-cs-ro

28 May 2026

RCM Constraint-Consistent Dynamic Control in Surgical Robots

arXiv:2509.14075v2 Announce Type: replace Abstract: Robotic-assisted minimally invasive surgery (RAMIS) requires accurate enforcement of the remote center of motion (RCM) constraint to ensure safe too

researcharxiv-cs-ro

Agents

Reasoning and Planning with Dynamically Changing Norms

arXiv:2605.27622v1 Announce Type: new Abstract: To safely interact with humans, AI agents must both know our norms and consider them during planning. However, such norm-guided planning has been less e

agentsarxiv-cs-ai

REED: Post-Training Representation Editing for Cross-Domain Linguistic Steganalysis

arXiv:2605.28298v1 Announce Type: new Abstract: In real-world scenarios of linguistic steganalysis, tested texts usually come from unseen domains with different vocabularies, topics, writing styles, a

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

Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification

model-releasesarxiv-cs-cv

arXiv:2512.12887v3 Announce Type: replace Abstract: 3D medical image classification is essential for modern clinical workflows. Medical foundation models (FMs) have emerged as a promising approach for

Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring

arXiv:2605.28100v1 Announce Type: cross Abstract: In an era where climate change aggravates environmental uncertainties, the identification and detection of event precursors are becoming crucial to mi

applicationsarxiv-cs-ai

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

Robo-Blocks: Generative Scaffolding in End-User Design and Programming of Social Robots

arXiv:2605.28154v1 Announce Type: cross Abstract: Programming social robots is challenging for novice robot programmers due to required expertise in planning, interaction design, and programming. Whil

researcharxiv-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

Short-Term Gain, Long-Term Fragility: AI Labor Substitution and the Erosion of Sustainable Capability

arXiv:2605.27399v1 Announce Type: cross Abstract: What looks like acceleration can be a quiet transfer of burden from the present to the future. Attempts to replace human labor with AI systems are oft

Local Ai

SIGMA: Semantic-Difference Instruction-Grounding Mask Annotator for Text-Driven Image Manipulation Localization

arXiv:2605.27924v1 Announce Type: new Abstract: Text-driven image editing has advanced rapidly, but reliably localizing these manipulations requires image manipulation localization (IML) models traine

local-aiarxiv-cs-cv

Simorgh at SemEval-2026 task 7: Region-Aware Hybrid Retrieval for Low-Resource Cultural Reasoning in Multilingual Question Answering

model-releasesarxiv-cs-cl

arXiv:2605.27636v1 Announce Type: new Abstract: Although Large Language Models (LLMs) demonstrate excellent capabilities and performance for general reasoning tasks within the general public domain, t

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

Singular Vectors of Attention Heads Align with Features

arXiv:2602.13524v2 Announce Type: replace-cross Abstract: Identifying feature representations in language models is a central task in mechanistic interpretability. Several recent studies have made the

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,

SmartDirector: Keyframe-Conditioned Cinematic Video Generation with Narrative Pacing Control

arXiv:2605.27891v1 Announce Type: cross Abstract: The narrative quality of a video fundamentally determines its perceptual value. Although existing video generation methods can produce visually appeal

SmartIterator: Visual Analytics Workflows for Supervising Unsupervised Data Grouping

arXiv:2605.28219v1 Announce Type: cross Abstract: Unsupervised learning methods -- topic modeling, partition-based and density-based clustering -- produce data groupings without human guidance, yet ch

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

STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation

arXiv:2605.27409v1 Announce Type: cross Abstract: SNNs promise energy-efficient and low-latency inference, but their performance still trails that of ANNs. ANN-to-SNN knowledge distillation helps narr

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

STR Robot: Design of an Autonomous Mobile Robot from Simulation to Reality

model-releasesarxiv-cs-ro

arXiv:2605.28110v1 Announce Type: new Abstract: With the rapid development of simulation tools, the development and validation of autonomous robotic systems have become more efficient before real-worl

Surprising Performances of Students with Autism in Classroom with NAO Robot

arXiv:2407.12014v1 Announce Type: cross Abstract: Autism is a developmental disorder that manifests in early childhood and persists throughout life, profoundly affecting social behavior and hindering

applicationsarxiv-cs-ro

Surrogate modeling for Bayesian optimization beyond a single Gaussian process

arXiv:2205.14090v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) has well-documented merits for optimizing black-box functions with an expensive evaluation cost. Such functions eme

Syllabic-Structure Decoder for Automatic Speech Recognition in Vietnamese

arXiv:2605.27874v1 Announce Type: new Abstract: Most Automatic Speech Recognition (ASR) systems formulate transcription as a prediction problem over orthographic units such as characters, subwords, or

researcharxiv-cs-cl

Tactile-Proprioceptive Sensor Fusion for Contact Wrench Estimation in Whole-Body Physical Human-Robot Interaction

arXiv:2605.28412v1 Announce Type: cross Abstract: Direct physical guidance is a natural means of teaching and interacting with robots, and robotic skins make a key contribution by enabling sensitive c

Teacher-Student Representational Alignment for Reinforcement Learning-Driven Imitation Learning

arXiv:2605.28372v1 Announce Type: new Abstract: Imitation learning (IL) from a state-based reinforcement learning (RL) policy is a common approach to overcome the curse of dimensionality in complex an

safetyarxiv-cs-lg

The Computational Boundary of Inference: Capability Internalization, Training, and the Turing Jump

arXiv:2605.27381v1 Announce Type: cross Abstract: Claims about recursive self-improvement in AI often slide from repeated internal revision to the possibility of qualitatively stronger capability with

The Decision to Verify: How Warmth and User Characteristics Shape Reliance on Conversational Agents for Information Search

arXiv:2605.28498v1 Announce Type: cross Abstract: Conversational artificial intelligence (AI) provides an efficient and convenient gateway to information access. However, it can cause overreliance whe

TinyDejaVu: Smaller RAM and Faster Inference with Neural Networks on MCUs for Sensor Data Streams

arXiv:2512.09786v2 Announce Type: replace Abstract: Examples of embedded intelligence include a wide variety of tiny neural networks used on-board wireless sensors and actuators, which are expected to

Token Optimization Strategies for LLM-Based Oracle-to-PostgreSQL Migration

arXiv:2605.28557v1 Announce Type: cross Abstract: LLMs are increasingly used for software modernization, code translation, and database migration. However, LLM-based Oracle2PostgreSQL migration remain

Too many business leaders believe that AI says what it means. And it’s odd because we naturally attribute a high number of human traits to A…

model-releasesallie-k--miller--x

Too many business leaders believe that AI says what it means. And it’s odd because we naturally attribute a high number of human traits to AI, and yet we refuse to believe it can have hidden intent? 3

Turning Video Models into Generalist Robot Policies

arXiv:2605.27817v1 Announce Type: cross Abstract: Video generative models have emerged as a promising robotics backbone, capable of generating videos that depict the completion of complex tasks across

Agents

Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study

arXiv:2506.08311v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) agents leverage Large Language Models (LLMs) to autonomously diagnose and fix software bugs through reasoning,

agentsarxiv-cs-ai

Hardware

Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation

arXiv:2605.27582v1 Announce Type: cross Abstract: Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environme

hardwarearxiv-cs-cv

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding

arXiv:2602.13075v2 Announce Type: replace Abstract: Graph pre-training has achieved remarkable success in recent years, delivering transferable representations for downstream adaptation. However, most

Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts

arXiv:2605.28063v1 Announce Type: cross Abstract: Audio generation has made significant progress, yet synthesizing unified audio where speech and sounds are naturally composited remains a challenge. C

Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models

arXiv:2605.27376v1 Announce Type: cross Abstract: While prompt-based text-to-speech (TTS) models enable natural language-driven speaking style control, they often provide limited fine-grained control

Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis

arXiv:2605.27401v1 Announce Type: cross Abstract: There is a growing interest in utilizing synthetic populations for a diverse range of applications. At the same time, we are witnessing a tremendous g

Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation

arXiv:2605.28364v1 Announce Type: cross Abstract: Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad appli

When prompt perturbations break your A/B test: A valid statistical test for generative surveying

arXiv:2605.27463v1 Announce Type: cross Abstract: Generative surveying -- where collections of LLM-based personas provide feedback on messages -- has emerged as a cheap and scalable alternative to tra

When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?

arXiv:2605.27932v1 Announce Type: cross Abstract: Think-with-image reasoning is emerging as a new inference paradigm for large vision-language models, but its safety implications remain poorly underst

Where LLM Annotators Fail: Label-Free Learning on Graphs with LLMs

arXiv:2605.27913v1 Announce Type: new Abstract: Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic cont

Tutorials

Which Pretraining Paradigm Better Serves Spatial Intelligence? An Empirical Comparison of Vision-Language and Video Generation Models

arXiv:2605.28132v1 Announce Type: new Abstract: Spatial intelligence requires visual representations that capture both semantic objects and geometric structure in the physical world. To support this,

tutorialsarxiv-cs-cv

XTransfer: Modality-Agnostic Few-Shot Model Transfer for Human Sensing at the Edge

model-releasesarxiv-cs-cv

arXiv:2506.22726v4 Announce Type: replace Abstract: Deep learning for human sensing on edge systems presents significant potential for smart applications. However, its training and development are hin

You Live More Than Once: Towards Hierarchical Skill Meta-Evolving

arXiv:2605.28390v1 Announce Type: new Abstract: Test-time skill evolving is regarded as a new paradigm for enhancing deployed agentic systems. Existing works mainly focus on hard-coded skill evolving

Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training

arXiv:2605.28008v1 Announce Type: new Abstract: Large language models (LLMs) can now solve complex problems through long chain-of-thought (CoT) reasoning, but the trade-off between performance and tok