Search: “reasoning”

arXiv:2606.29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine. Graph neural network

local-aiarxiv-cs-lg

GROW^2: Grounding Which and Where for Robot Tool Use

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2606.30632v1 Announce Type: cross Abstract: Can the robot use a plate to cut a cake if no knife is available? Tool use greatly expands robot capabilities, but to use tools creatively beyond thei

agentsarxiv-cs-ai

30 Jun 2026

Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence

arXiv:2507.00719v3 Announce Type: replace-cross Abstract: Typically, numerical simulations of Earth systems are coarse, and Earth observations are sparse and gappy. We apply four generative diffusion

researcharxiv-cs-lg

Hardware

HARD-KV: Head-Adaptive Regularization for Decoding-time KV Compression

arXiv:2606.28831v1 Announce Type: cross Abstract: Long-context LLM inference faces a fundamental conflict: head-adaptive compression algorithms (e.g., Top-p nucleus sampling) offer superior accuracy b

hardwarearxiv-cs-ai

HiRes: A Hierarchical Cascaded Method for Resistor Value Identification

arXiv:2606.30179v1 Announce Type: new Abstract: Accurate identification of resistor values from unconstrained images remains a challenging computer vision task due to variations in lighting, orientati

applicationsarxiv-cs-cv

How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generation

arXiv:2606.28358v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) aims to enhance the trustworthiness of Large Language Models (LLMs) by grounding their outputs in external docume

How Should World Models Be Evaluated for Embodied Decision-Making? A Decision-Making-Centric Position

arXiv:2606.15032v2 Announce Type: replace Abstract: World models have become a central abstraction in modern AI. The term now refers to several different objects: action-conditioned environment models

safetyarxiv-cs-lg

HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation

arXiv:2606.29744v1 Announce Type: new Abstract: Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of coronary

researcharxiv-cs-cv

Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning

arXiv:2606.28813v1 Announce Type: cross Abstract: Human videos are a scalable source of supervision for robot manipulation, as they are abundant and naturally capture rich object interactions. However

applicationsarxiv-cs-ai

Local Ai

Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model

arXiv:2606.28475v1 Announce Type: cross Abstract: Nowadays mobile robots have wide engineering applications. Simultaneous localization and mapping (SLAM) is an important task of these robots. The majo

local-aiarxiv-cs-ai

IMU-HOI: A Symbiotic Framework for Coherent Human-Object Interaction and Motion Capture via Contact-Conscious Inertial Fusion

arXiv:2606.28604v1 Announce Type: new Abstract: Capturing full-body human motion with object interactions is crucial for AR/VR and robotics applications, yet it remains challenging for conventional vi

researcharxiv-cs-cv

Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents

arXiv:2606.29459v1 Announce Type: cross Abstract: Inverse design of metal-organic frameworks (MOFs) requires searching a combinatorially vast space where property labels are expensive and most machine

agentsarxiv-cs-ai

Latent Bridges for Multi-Table Question Answering

arXiv:2606.28916v1 Announce Type: cross Abstract: We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering. Our method lifts relational data into an heterogeneous graph, e

researcharxiv-cs-ai

Little Brains, Big Feats: Exploring Compact Language Models

arXiv:2606.30062v1 Announce Type: cross Abstract: While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains;

Local Ai

LLM-Ideoplasticity: Measuring Ideological Plasticity in the Political Behavior of LLMs as a Context-Conditioned Distribution

arXiv:2606.28335v1 Announce Type: cross Abstract: We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution P(

local-aiarxiv-cs-ai

LLMs and their Limited Theory of Mind: Evaluating Mental State Annotations in Situated Dialogue

arXiv:2509.02292v2 Announce Type: replace Abstract: What if large language models could not only infer human mindsets but also expose every blind spot in team dialogue such as discrepancies in the tea

researcharxiv-cs-cl

MAM-AI: An On-Device Medical Retrieval-Augmented Generation System for Nurses and Midwives in Zanzibar

model-releasesarxiv-cs-cl

arXiv:2606.29580v1 Announce Type: new Abstract: Maternal and newborn mortality remain among the highest in sub-Saharan Africa, where midwifery care is often delivered by nurses who lack midwifery trai

Meta-learning as a principle for human-like visual representations

arXiv:2606.28399v1 Announce Type: new Abstract: The structure of human visual representations underpins our capacity for adaptive behaviour. While pretrained neural networks model human visual represe

safetyarxiv-cs-cv

meta-pipe: An LLM-agent pipeline for end-to-end automated systematic review and meta-analysis

arXiv:2606.28363v1 Announce Type: cross Abstract: Objective: To describe the architecture and design rationale of meta-pipe, an open-source large language model (LLM)-agent pipeline that integrates th

Modelling Human Values for Value-Aware Multi-Agent Systems

arXiv:2402.06359v2 Announce Type: replace Abstract: One of today's most pressing societal challenges is building AI systems whose behaviour, or the behaviour it enables within communities of interacti

safetyarxiv-cs-ai

NaLA: A 3D Native LLM Layout Agent for High-quality 3D Scene Generation

arXiv:2606.29395v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have emerged as promising layout agents for 3D scene generation. Existing layout agents still suffer from implaus

agentsarxiv-cs-cv

Not-quite-human tastes: the stylized omnivorousness of LLM survey surrogates

model-releasesarxiv-cs-cl

arXiv:2606.30085v1 Announce Type: new Abstract: Large-language models have proven to be remarkable if inconsistent parrots of public attitudes and opinions. The extent to which LLMs are able to produc

Occlusion-Robust Multi-Object Decoupling for Physics-Based Interaction

arXiv:2606.29303v1 Announce Type: new Abstract: We propose a mask-free method for lossless multi-object 3D reconstruction from sparse and occluded real-world views, enabling physically plausible inter

applicationsarxiv-cs-cv

On the Policy Gradient Foundations of Group Relative Policy Optimization: Credit Assignment, Gradient Sparsity, and Rank Collapse

model-releasesarxiv-cs-lg

arXiv:2606.29238v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) eliminates the learned critic in PPO by using the mean reward of grouped rollouts as a baseline. We provide a

OP3DSG: Open-Vocabulary Part-Aware 3D Scene Graph Generation for Real-World Environments

arXiv:2606.29786v1 Announce Type: new Abstract: 3D scene graphs (3DSGs) provide a compact and structured abstraction of 3D environments. Although advances in foundation models have enabled open-vocabu

Tutorials

Open-Vocabulary and Referring Segmentation for 3D Gaussians Using 2D Detectors

arXiv:2606.30638v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged at the forefront of 3D scene reconstruction. Extending 3DGS with language-driven, open-vocabulary understanding

tutorialsarxiv-cs-cv

Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints

arXiv:2606.28342v1 Announce Type: cross Abstract: Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does

researcharxiv-cs-ai

OSWorld2.0: Benchmarking Computer Use Agents on Long-Horizon Real-World Tasks

arXiv:2606.29537v1 Announce Type: new Abstract: Existing computer-use benchmarks fail to capture the realism, complexity, and long-horizon demands of real-world computer use, limiting their ability to

Personalizing MLLMs via Reinforced Multimodal Reference Game

arXiv:2606.28845v1 Announce Type: new Abstract: Personalizing Multimodal Large Language Models (MLLMs) aims to recognize users' unique concepts from visual data and provide personalized responses. Alt

researcharxiv-cs-cv

Pointer-CAD v2: Plan-Then-Construct CAD Generation with Dimension-Aware Parametric Precision

arXiv:2606.29301v1 Announce Type: new Abstract: Computer-aided design (CAD) plays a fundamental role in modern manufacturing by providing the high precision required for industrial production. Recent

SADL: What to Ignore? A Benchmark for Subject-Aware Distractor Localization

arXiv:2606.30393v1 Announce Type: new Abstract: Photographs frequently contain visual distractors besides foregrounds and backgrounds of the intended subject, competing for attention and weakening com

SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing

arXiv:2606.29887v1 Announce Type: new Abstract: In real-world applications, guardrails are often expected to identify unsafe user-model interactions according to application-specific safety policies,

SEATauBench: Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages

arXiv:2606.28715v1 Announce Type: cross Abstract: While AI development and evaluation for Southeast Asia (SEA) has grown rapidly, agent capabilities in regional languages are still poorly understood d

Self-Evolving Agentic Image Restoration via Deliberate Planning and Intuitive Execution

arXiv:2606.28971v1 Announce Type: new Abstract: Real-world image restoration (IR) remains challenging due to complex and coupled degradations. While recent agentic IR frameworks leverage Large Languag

agentsarxiv-cs-cv

Self-Evolving World Models for LLM Agent Planning

arXiv:2606.30639v1 Announce Type: new Abstract: World models offer a principled way to equip long-horizon LLM agents with foresight: predictions of action consequences before execution. However, unrel

agentsarxiv-cs-ai

Semantic-Driven Scale and Spatial Selection for Efficient Cross-Modal Alignment in Referring Remote Sensing Image Segmentation

arXiv:2606.30244v1 Announce Type: new Abstract: Referring Remote Sensing Image Segmentation (RRSIS) seeks to localize and segment the target object or region specified by a natural language expression

SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution

arXiv:2606.29713v1 Announce Type: cross Abstract: Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers

SFBench: The SciFy Scientific Feasibility Benchmark

arXiv:2606.29630v1 Announce Type: new Abstract: We present SFBench, a benchmark dataset for evaluating systems that assess the feasibility of scientific claims. SFBench includes 197 claims in material

SIMAX: A Scalable and Interpretable Framework for Multi-Fidelity and Annotated Clinician-Patient Dialogue Simulation

arXiv:2606.30491v1 Announce Type: cross Abstract: Background. The widespread deployment of ambient digital scribes is driving large-scale capture of clinician-patient dialogues. Human coding of clinic

applicationsarxiv-cs-ai

Situation Perception: A Necessary Primitive to Artificial Superintelligence

arXiv:2606.30481v1 Announce Type: cross Abstract: Current large language models are extraordinary statistical engines. They compress vast amounts of text into useful patterns and can explain science,

researcharxiv-cs-ai

SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference

arXiv:2602.20610v3 Announce Type: replace-cross Abstract: Specifications are vital for ensuring program correctness, yet writing them manually remains challenging and time-intensive. Recent large lang

safetyarxiv-cs-cl

StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley

arXiv:2507.07445v3 Announce Type: replace Abstract: Autonomous agents navigating human society must master both production activities and social interactions, yet existing benchmarks rarely evaluate t

SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios

arXiv:2511.17649v4 Announce Type: replace-cross Abstract: Tangible control interfaces (TCIs), such as appliance panels, remotes, elevators, and embedded GUIs, are a fundamental component of everyday h

Synthetic Interaction Data for Scalable Personalization in Large Language Models

arXiv:2602.12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization meth

agentsarxiv-cs-lg

The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models

arXiv:2606.29799v1 Announce Type: new Abstract: This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automatin

The Hidden Cost of Structured Generation in LLMs: Draft-Conditioned Constrained Decoding

arXiv:2603.03305v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to generate executable outputs, JSON objects, and API calls, where a single syntax error ca

The Joint Effect of Quantization and Sampling Temperature on LLM Safety Alignment: A Factorial Analysis

arXiv:2606.29581v1 Announce Type: cross Abstract: Modern LLM deployments routinely compress models and raise sampling temperature to reduce cost, latency, or repetition, yet safety evaluations usually

safetyarxiv-cs-ai

The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning

arXiv:2606.29526v1 Announce Type: new Abstract: Reinforcement learning (RL) has gained growing attention in large language model (LLM) post-training, yet RL training remains fragile and can suffer fro

safetyarxiv-cs-lg