Search: “reasoning”

CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation

arXiv:2602.20409v2 Announce Type: replace Abstract: Recent vision-language models (VLMs) such as CLIP demonstrate impressive cross-modal reasoning, extending beyond images to 3D perception. Yet, these

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Safety

Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models

DGX agent

arXiv:2510.26782v3 Announce Type: replace-cross Abstract: A world model is an internal model that simulates how the world evolves. Given past observations and actions, it predicts the future physical

safetyarxiv-cs-ai

22 Apr 2026

Do Emotions Influence Moral Judgment in Large Language Models?

arXiv:2604.19125v1 Announce Type: new Abstract: Large language models have been extensively studied for emotion recognition and moral reasoning as distinct capabilities, yet the extent to which emotio

safetyarxiv-cs-cl

EssayCBM: Rubric-Aligned Concept Bottleneck Models for Transparent Essay Grading

arXiv:2512.20817v2 Announce Type: replace Abstract: Automated essay scoring (AES) has advanced significantly with neural language models, yet most systems remain opaque, offering little visibility int

Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents

arXiv:2604.19034v1 Announce Type: new Abstract: Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory constructio

agentsarxiv-cs-cv

GenerativeMPC: VLM-RAG-guided Whole-Body MPC with Virtual Impedance for Bimanual Mobile Manipulation

model-releasesarxiv-cs-ro

arXiv:2604.19522v1 Announce Type: new Abstract: Bimanual mobile manipulation requires a seamless integration between high-level semantic reasoning and safe, compliant physical interaction - a challeng

KG-ViP: Bridging Knowledge Grounding and Visual Perception in Multi-modal LLMs for Visual Question Answering

arXiv:2601.11632v2 Announce Type: replace Abstract: Multi-modal Large Language Models (MLLMs) for Visual Question Answering (VQA) often suffer from dual limitations: knowledge hallucination and insuff

researcharxiv-cs-cv

LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models

model-releasesarxiv-cs-ai

arXiv:2604.18803v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly deployed in settings where reliable visual grounding carries operational consequences, yet their behavi

MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments

arXiv:2511.04320v2 Announce Type: replace Abstract: Autonomous navigation in unknown environments requires multi-scale spatial understanding that captures geometric details, topological connectivity,

safetyarxiv-cs-ro

Local Ai

Memory Over Maps: 3D Object Localization Without Reconstruction

arXiv:2603.20530v2 Announce Type: replace-cross Abstract: Target localization is a prerequisite for embodied tasks such as navigation and manipulation. Conventional approaches rely on constructing exp

local-aiarxiv-cs-cv

Tutorials

Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties

arXiv:2604.19383v1 Announce Type: cross Abstract: Metal-organic frameworks (MOFs) are a major target of machine-learning-based property prediction, yet most models assume that a single framework repre

tutorialsarxiv-cs-ai

OMAC: A Holistic Optimization Framework for LLM-Based Multi-Agent Collaboration

arXiv:2505.11765v3 Announce Type: replace-cross Abstract: Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications. Recentl

agentsarxiv-cs-ai

Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery

arXiv:2604.19049v1 Announce Type: cross Abstract: LLM-assisted defect discovery has a precision crisis: plausible-but-wrong reports overwhelm maintainers and degrade credibility for real findings. We

agentsarxiv-cs-ai

Rethinking Information Synthesis in Multimodal Question Answering A Multi-Agent Perspective

arXiv:2505.20816v2 Announce Type: replace Abstract: Recent advances in multimodal question answering have primarily focused on combining heterogeneous modalities or fine-tuning multimodal large langua

agentsarxiv-cs-cl

RF-HiT: Rectified Flow Hierarchical Transformer for General Medical Image Segmentation

arXiv:2604.19570v1 Announce Type: new Abstract: Accurate medical image segmentation requires both long-range contextual reasoning and precise boundary delineation, a task where existing transformer- a

researcharxiv-cs-cv

Storage innovations to accelerate your AI workloads at Next ‘26

model-releasesgoogle-cloud-ai

At Google Cloud Next, we are announcing innovations across every layer of our storage stacks — performance, intelligence, and management — to ensure your data is as fast and as useful as the AI models

Subgraph Concept Networks: Concept Levels in Graph Classification

arXiv:2604.18868v1 Announce Type: new Abstract: The reasoning process of Graph Neural Networks is complex and considered opaque, limiting trust in their predictions. To alleviate this issue, prior wor

researcharxiv-cs-lg

This actually happened. Smart second-graders know better. WATF. 🤯

Gary Marcus expresses skepticism or concern about an artificial intelligence claim or incident that he finds implausible, suggesting that even young children would recognize the flaw in the reasoning

safetygary-marcus--x

Local Ai

What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search

arXiv:2604.19440v1 Announce Type: new Abstract: Recent work has demonstrated the promise of orchestrating large language models (LLMs) within evolutionary and agentic optimization systems. However, th

local-aiarxiv-cs-cl

When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification

model-releasesarxiv-cs-cl

arXiv:2602.11199v2 Announce Type: replace Abstract: Large language models (LLMs) often respond even when prompts omit critical details or include misleading information, leading to hallucinations or r

3 new ways Ads Advisor is making Google Ads safer and faster

Ads Advisor is an agentic conversational experience built with Gemini in Google Ads designed to help maximize performance based on business goals. The tool can help users get personalized answers, und

safetygoogle-ai

A Computational Method for Measuring 'Open Codes' in Qualitative Analysis

arXiv:2411.12142v4 Announce Type: replace Abstract: Qualitative analysis is critical to understanding human datasets in many social science disciplines. A central method in this process is inductive c

A Survey on the Security of Long-Term Memory in LLM Agents: Toward Mnemonic Sovereignty

arXiv:2604.16548v1 Announce Type: cross Abstract: Research on large language model (LLM) security is shifting from 'will the model leak training data' to a more consequential question: can an agent wi

agentsarxiv-cs-cl

Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL

model-releasesarxiv-cs-cl

arXiv:2604.17073v1 Announce Type: new Abstract: Reinforcement fine-tuning improves the reasoning ability of large language models, but it can also encourage them to answer unanswerable queries by gues

Tutorials

AI Data Transformation Guide for Data Engineers and Data Scientists

This guide from Databricks covers data transformation techniques and best practices essential for preparing data for AI/ML projects, addressing workflows that both data engineers and data scientists e

tutorialsdatabricks

Annotating Dimensions of Social Perception in Text: A Sentence-Level Dataset of Warmth and Competence

arXiv:2601.06316v2 Announce Type: replace Abstract: Warmth (W) (often further broken down intoTrust (T) and Sociability (S)) and Competence (C) are central dimensions along which people evaluate indiv

AutoVQA-G: Self-Improving Agentic Framework for Automated Visual Question Answering and Grounding Annotation

arXiv:2604.17488v1 Announce Type: new Abstract: Manual annotation of high-quality visual question answering with grounding (VQA-G) datasets, which pair visual questions with evidential grounding, is c

agentsarxiv-cs-cv

Beyond Black-Box Labels: Interpretable Criteria for Diagnosing SubjectiveNLP Tasks

arXiv:2604.17022v1 Announce Type: new Abstract: Subjective NLP datasets typically aggregate annotator judgments into a single gold label, making it difficult to diagnose whether disagreement reflects

Beyond Verifiable Rewards: Rubric-Based GRM for Reinforced Fine-Tuning SWE Agents

arXiv:2604.16335v1 Announce Type: new Abstract: Despite recent progress in Large Language Model (LLM) Agents for Software Engineering (SWE) tasks, end-to-end fine-tuning typically relies on verifiable

researcharxiv-cs-lg

Bias-constrained multimodal intelligence for equitable and reliable clinical AI

arXiv:2604.16884v1 Announce Type: new Abstract: The integration of medical imaging and clinical text has enabled the emergence of generalist artificial intelligence (AI) systems for healthcare. Howeve

safetyarxiv-cs-cv

Characterizing Model-Native Skills

arXiv:2604.17614v1 Announce Type: cross Abstract: Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on

safetyarxiv-cs-cl

CLAG: Adaptive Memory Organization via Agent-Driven Clustering for Small Language Model Agents

arXiv:2603.15421v2 Announce Type: replace Abstract: Large language model agents heavily rely on external memory to support knowledge reuse and complex reasoning tasks. Yet most memory systems store ex

agentsarxiv-cs-cl

Countdown-Code: A Testbed for Studying The Emergence and Generalization of Reward Hacking in RLVR

arXiv:2603.07084v2 Announce Type: replace-cross Abstract: Reward hacking is a form of misalignment in which models overoptimize proxy rewards without genuinely solving the underlying task. Precisely m

DeInfer: Efficient Parallel Inferencing for Decomposed Large Language Models

arXiv:2604.17709v1 Announce Type: new Abstract: Existing works on large language model (LLM) decomposition mainly focus on improving performance on downstream tasks, but they ignore the poor parallel

Dual Alignment Between Language Model Layers and Human Sentence Processing

arXiv:2604.18563v1 Announce Type: new Abstract: A recent study (Kuribayashi et al., 2025) has shown that human sentence processing behavior, typically measured on syntactically unchallenging construct

safetyarxiv-cs-cl

E2E-GMNER: End-to-End Generative Grounded Multimodal Named Entity Recognition

arXiv:2604.17319v1 Announce Type: cross Abstract: Grounded Multimodal Named Entity Recognition (GMNER) aims to jointly identify named entity mentions in text, predict their semantic types, and ground

E3VS-Bench: A Benchmark for Viewpoint-Dependent Active Perception in 3D Gaussian Splatting Scenes

arXiv:2604.17969v1 Announce Type: new Abstract: Visual search in 3D environments requires embodied agents to actively explore their surroundings and acquire task-relevant evidence. However, existing v

EasyVideoR1: Easier RL for Video Understanding

arXiv:2604.16893v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large languag

EgoSound: Benchmarking Sound Understanding in Egocentric Videos

arXiv:2602.14122v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have recently achieved remarkable progress in vision-language understanding. Yet, human perception is inher

Emergent Misalignment via In-Context Learning: Narrow in-context examples can produce broadly misaligned LLMs

model-releasesarxiv-cs-cl

arXiv:2510.11288v4 Announce Type: replace Abstract: Recent work has shown that narrow finetuning can produce broadly misaligned LLMs, a phenomenon termed emergent misalignment (EM). While concerning,

Evaluating Multimodal LLMs for Inpatient Diagnosis: Real-World Performance, Safety, and Cost Across Ten Frontier Models

model-releasesarxiv-cs-lg

arXiv:2604.16980v1 Announce Type: new Abstract: Background: Large language models (LLMs) are increasingly proposed for diagnostic support, but few evaluations use real-world multimodal inpatient data,

From log pi to pi: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight

model-releasesarxiv-cs-lg

arXiv:2603.14389v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has catalyzed a leap in Large Language Model (LLM) reasoning, yet its optimization dynamics re

From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents

arXiv:2603.01455v2 Announce Type: replace-cross Abstract: While multimodal large language models have demonstrated impressive short-term reasoning, they struggle with long-horizon video understanding

GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning

arXiv:2604.16871v1 Announce Type: cross Abstract: Neuro-symbolic Reinforcement Learning (NeSy-RL) combines symbolic reasoning with gradient-based optimization to achieve interpretable and generalizabl

agentsarxiv-cs-lg

Applications

HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification

arXiv:2604.17271v1 Announce Type: new Abstract: Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommenda

applicationsarxiv-cs-cl

Human Cognition in Machines: A Unified Perspective of World Models

arXiv:2604.16592v1 Announce Type: cross Abstract: This comprehensive report distinguishes prior works by the cognitive functions they innovate. Many works claim an almost 'human-like' cognitive capabi

agentsarxiv-cs-cv

Infrastructure-Centric World Models: Bridging Temporal Depth and Spatial Breadth for Roadside Perception

arXiv:2604.17651v1 Announce Type: new Abstract: World models, generative AI systems that simulate how environments evolve, are transforming autonomous driving, yet all existing approaches adopt an ego

safetyarxiv-cs-cv

Applications

Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes

arXiv:2604.18381v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) typically relies on large quantities of high-quality annotated data, or questions with well-defined ground tr

applicationsarxiv-cs-lg