Search: “reasoning”

Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators

arXiv:2503.06778v3 Announce Type: replace Abstract: Event annotation is important for identifying market changes, monitoring breaking news, and understanding sociological trends. Although expert annot

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Learning from Medical Entity Trees: An Entity-Centric Medical Data Engineering Framework for MLLMs

DGX agent

arXiv:2604.25296v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown transformative potential in medical applications, yet their performance is hindered by conventional

safetyarxiv-cs-cl

29 Apr 2026

Learning from Noisy Preferences: A Semi-Supervised Learning Approach to Direct Preference Optimization

arXiv:2604.24952v1 Announce Type: new Abstract: Human visual preferences are inherently multi-dimensional, encompassing aesthetics, detail fidelity, and semantic alignment. However, existing datasets

safetyarxiv-cs-cv

LegalMidm: Use-Case-Driven Legal Domain Specialization for Korean Large Language Model

arXiv:2604.25297v1 Announce Type: new Abstract: In recent years, the rapid proliferation of open-source large language models (LLMs) has spurred efforts to turn general-purpose models into domain spec

applicationsarxiv-cs-cl

MAIC-UI: Making Interactive Courseware with Generative UI

arXiv:2604.25806v1 Announce Type: new Abstract: Creating interactive STEM courseware traditionally requires HTML/CSS/JavaScript expertise, leaving barriers for educators. While generative AI can produ

safetyarxiv-cs-cl

MTPano: Multi-Task Panoramic Scene Understanding via Label-Free Integration of Dense Prediction Priors

arXiv:2602.05330v2 Announce Type: replace Abstract: Comprehensive panoramic scene understanding is critical for immersive applications, yet it remains challenging due to the scarcity of high-resolutio

researcharxiv-cs-cv

Hardware

Practical exposure correction via compensation

arXiv:2212.14245v2 Announce Type: replace Abstract: In computer vision, correcting the exposure level is a fundamental task for enhancing the visual quality of observations with inappropriate lightnes

hardwarearxiv-cs-cv

Principled Detection of Hallucinations in Large Language Models via Multiple Testing

arXiv:2508.18473v3 Announce Type: replace Abstract: While Large Language Models (LLMs) have emerged as powerful foundational models to solve a variety of tasks, they have also been shown to be prone t

QFlash: Bridging Quantization and Memory Efficiency in Vision Transformer Attention

arXiv:2604.25306v1 Announce Type: new Abstract: FlashAttention improves efficiency through tiling, but its online softmax still relies on floating-point arithmetic for numerical stability, making full

researcharxiv-cs-lg

Local Ai

RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy

arXiv:2604.05594v2 Announce Type: replace Abstract: Pixel-level annotation is costly in low-resource dermoscopy. We present RABC-Net, a reliability-aware annotation-free segmentation system that combi

local-aiarxiv-cs-cv

Relational In-Context Learning via Synthetic Pre-training with Structural Prior

arXiv:2603.03805v2 Announce Type: replace Abstract: Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstac

applicationsarxiv-cs-lg

Robust Graph Matching through Semantic Relationship Generation for SLAM

arXiv:2604.25404v1 Announce Type: new Abstract: Graph-based representations such as Scene Graphs enable localization in structured indoor environments by matching a locally observed graph, constructed

researcharxiv-cs-ro

SciDER: Scientific Data-centric End-to-end Researcher

arXiv:2603.01421v2 Announce Type: replace-cross Abstract: Automated scientific discovery with large language models is transforming the research lifecycle from ideation to experimentation, yet existin

agentsarxiv-cs-cl

The founder’s AI foundation: The top announcements for startups from Next ‘26

model-releasesgoogle-cloud-ai

The momentum is undeniable: the world’s fastest-growing AI startups are building with Google Cloud. Instead of stitching together fragmented point solutions, founders are building their businesses her

Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution

arXiv:2604.03472v2 Announce Type: replace Abstract: Co-evolutionary self-play, where one language model generates problems and another solves them, promises autonomous curriculum learning without huma

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We’re excited to introduce KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI, accepted at #I…

model-releasesdavid-ha--x

We’re excited to introduce KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI, accepted at #ICASSP2026! 🐢 Blog https://pub.sakana.ai/kame/ Paper https://

Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspective

arXiv:2603.14248v2 Announce Type: replace-cross Abstract: Large language model (LLM) web agents are increasingly used for web navigation but remain far from human reliability on realistic, long-horizo

A Systematic Approach for Large Language Models Debugging

arXiv:2604.23027v1 Announce Type: new Abstract: Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based re

Accelerating Eigenvalue Dataset Generation via Chebyshev Subspace Filter

arXiv:2510.23215v2 Announce Type: replace-cross Abstract: Eigenvalue problems are among the most important topics in many scientific disciplines. With the recent surge and development of machine learn

AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models

arXiv:2409.01579v2 Announce Type: replace-cross Abstract: Retrieved documents containing noise will hinder RAG from detecting answer clues and make the inference process slow and expensive. Therefore,

Agent-Aided Design for Dynamic CAD Models

arXiv:2604.15184v2 Announce Type: replace Abstract: In the past year, researchers have created agentic systems that can design real-world CAD-style objects in a training-free setting, a new variety of

Agentic Data Engineering with Genie Code and Lakeflow

This Databricks blog post discusses agentic data engineering capabilities enabled by Genie Code and Lakeflow, which are tools designed to automate and streamline data engineering workflows through AI-

agentsdatabricks

Agentic Witnessing: Pragmatic and Scalable TEE-Enabled Privacy-Preserving Auditing

arXiv:2604.24203v1 Announce Type: cross Abstract: Auditing the semantic properties of proprietary data creates a fundamental tension: verification requires transparent access, while proprietary rights

AI Identity: Standards, Gaps, and Research Directions for AI Agents

arXiv:2604.23280v1 Announce Type: new Abstract: AI agents are now running real transactions, workflows, and sub-agent chains across organizational boundaries without continuous human supervision. This

Always Tell Me The Odds: Fine-grained Conditional Probability Estimation

arXiv:2505.01595v2 Announce Type: replace-cross Abstract: We present a state-of-the-art model for fine-grained probability estimation of propositions conditioned on context. Recent advances in large l

An Information-Geometric Framework for Stability Analysis of Large Language Models under Entropic Stress

arXiv:2604.24076v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in high-stakes and operational settings, evaluation strategies based solely on aggregate accur

safetyarxiv-cs-ai

Automating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models

arXiv:2604.23430v1 Announce Type: cross Abstract: The relentless expansion of scientific literature presents significant challenges for navigation and knowledge discovery. Within Research Information

BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models

arXiv:2601.22925v2 Announce Type: replace-cross Abstract: Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ su

applicationsarxiv-cs-ai

Breaking Lock-In: Preserving Steerability under Low-Data VLA Post-Training

arXiv:2604.23121v1 Announce Type: cross Abstract: Have you ever post-trained a generalist vision-language-action (VLA) policy on a small demonstration dataset, only to find that it stops responding to

safetyarxiv-cs-cv

Case-Specific Rubrics for Clinical AI Evaluation: Methodology, Validation, and LLM-Clinician Agreement Across 823 Encounters

arXiv:2604.24710v1 Announce Type: new Abstract: Objective. Clinical AI documentation systems require evaluation methodologies that are clinically valid, economically viable, and sensitive to iterative

Contrastive Learning for Multimodal Human Activity Recognition with Limited Labeled Data

arXiv:2604.23281v1 Announce Type: cross Abstract: Human activity recognition serves as the foundation for various emerging applications. In recent years, researchers have used collaborative sensing of

applicationsarxiv-cs-cv

Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition

arXiv:2604.24170v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epist

Local Ai

DeepSignature: Digitally Signed, Content-Encoding Watermarks for Robust and Transparent Image Authentication

arXiv:2604.23016v1 Announce Type: cross Abstract: AI-powered generative models have significantly expanded the possibilities for editing, manipulating, and creating high-quality images. Particularly,

local-aiarxiv-cs-ai

DeepTaxon: An Interpretable Retrieval-Augmented Multimodal Framework for Unified Species Identification and Discovery

model-releasesarxiv-cs-cl

arXiv:2604.24029v1 Announce Type: cross Abstract: Identifying species in biology among tens of thousands of visually similar taxa while discovering unknown species in open-world environments remains a

Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing

arXiv:2604.24162v1 Announce Type: cross Abstract: Defending against backdoor attacks in large language models remains a critical practical challenge. Existing defenses mitigate these threats but typic

Local Ai

DYMAPIA: A Multi-Domain Framework for Detecting AI-based Video Manipulation

arXiv:2604.24426v1 Announce Type: new Abstract: AI-generated media are advancing rapidly, raising pressing concerns for content authenticity and digital trust. We introduce DYMAPIA, a multi-domain Dee

local-aiarxiv-cs-cv

Efficient Rationale-based Retrieval: On-policy Distillation from Generative Rerankers based on JEPA

arXiv:2604.23336v1 Announce Type: cross Abstract: Unlike traditional fact-based retrieval, rationale-based retrieval typically necessitates cross-encoding of query-document pairs using large language

safetyarxiv-cs-cl

Evaluating whether AI models would sabotage AI safety research

arXiv:2604.24618v1 Announce Type: new Abstract: We evaluate the propensity of frontier models to sabotage or refuse to assist with safety research when deployed as AI research agents within a frontier

Evaluation Framework for Highlight Explanations of Context Utilisation in Language Models

arXiv:2510.02629v3 Announce Type: replace Abstract: Context utilisation, the ability of Language Models (LMs) to incorporate relevant information from the provided context when generating responses, r

Fix Initial Codes and Iteratively Refine Textual Directions Toward Safe Multi-Turn Code Correction

arXiv:2604.23989v1 Announce Type: cross Abstract: Recent work on large language models (LLMs) has emphasized the importance of scaling inference compute. From this perspective, the state-of-the-art me

safetyarxiv-cs-ai

Tutorials

// From Skill Text to Skill Structure // One of the more practical skill papers I've seen this month. SKILL.md files entangle invocation int…

// From Skill Text to Skill Structure // One of the more practical skill papers I've seen this month. SKILL.md files entangle invocation interface, execution flow, and tool/resource side effects in on

tutorialsdair-ai--x

Gated Tree Cross-Attention for Checkpoint-Compatible Syntax Injection in Decoder-Only LLMs

arXiv:2602.15846v2 Announce Type: replace Abstract: Decoder-only large language models achieve strong broad performance but are brittle to minor grammatical perturbations, undermining reliability for

HAC: Parameter-Efficient Hyperbolic Adaptation of CLIP for Zero-Shot VQA

model-releasesarxiv-cs-cv

arXiv:2604.23665v1 Announce Type: new Abstract: Recent advances in representation learning have shown that hyperbolic geometry can offer a more expressive alternative to the Euclidean embeddings used

Implicit Humanization in Everyday LLM Moral Judgments

arXiv:2604.22764v1 Announce Type: cross Abstract: Recent adoption of conversational information systems has expanded the scope of user queries to include complex tasks such as personal advice-seeking.

INHerit-SG: Incremental Hierarchical Semantic Scene Graphs with RAG-Style Retrieval

model-releasesarxiv-cs-ro

arXiv:2602.12971v2 Announce Type: replace Abstract: Driven by recent advancements in foundation models, semantic scene graphs have emerged as a promising paradigm for high-level 3D environmental abstr

InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning

arXiv:2508.19679v2 Announce Type: replace Abstract: Recent advances in Vision-Language Models (VLMs) have enabled mobile agents to perceive and interact with real-world mobile environments based on hu

IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Models

arXiv:2604.24002v1 Announce Type: cross Abstract: Improving the effectiveness of human-robot interaction requires social robots to accurately infer human goals through robust intention understanding.

K-SENSE: A Knowledge-Guided Self-Augmented Encoder for Neuro-Semantic Evaluation of Mental Health Conditions on Social Media

arXiv:2604.23493v1 Announce Type: cross Abstract: Early detection of mental health conditions, particularly stress and depression, from social media text remains a challenging open problem in computat