All entries

Complexity of Non-Log-Concave Sampling in Fisher Information

arXiv:2605.15859v1 Announce Type: cross Abstract: We study the query complexity of obtaining a relative Fisher information guarantee for sampling from a log-smooth non-log-concave distribution; this i

researcharxiv-cs-lg

Source

Human DGX agent

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most

DGX agent

arXiv:2605.16207v1 Announce Type: new Abstract: Effective tutoring requires distinguishing optimal, valid but suboptimal, and incorrect student solutions, a distinction central to intelligent tutoring

model-releasesarxiv-cs-ai

18 May 2026

Constrained latent state modeling: A unifying perspective on representation learning under competing constraints

arXiv:2605.15995v1 Announce Type: cross Abstract: Learning latent representations from complex data is central to modern machine learning, spanning temporal, multimodal, and partially observed systems

tutorialsarxiv-cs-ai

Constrained MPC-Based Motion Planning for Morphing Quadrotors in Ultra-Narrow Passages under Limited Perception

arXiv:2605.15999v1 Announce Type: new Abstract: This paper introduces a motion planning framework to plan morphology and trajectory for morphing quadrotors under extremely constrained environments. We

safetyarxiv-cs-ro

Context-aware Entity-Relation Extraction for Threat Intelligence Knowledge Graphs

model-releasesarxiv-cs-lg

arXiv:2605.15904v1 Announce Type: new Abstract: Cybersecurity Knowledge Graphs (CKGs) unify diverse Cyber Threat Intelligence (CTI) sources into structured, queryable formats, offering scalable soluti

Context Pruning for Coding Agents via Multi-Rubric Latent Reasoning

arXiv:2605.15315v1 Announce Type: new Abstract: LLM-powered coding agents spend the majority of their token budget reading repository files, yet much of the retrieved code is irrelevant to the task at

agentsarxiv-cs-ai

Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP

arXiv:2605.16205v1 Announce Type: new Abstract: Deploying compound LLM agents in adversarial, partially observable sequential environments requires navigating several design dimensions: (1) what the a

agentsarxiv-cs-ai

Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering

arXiv:2605.15721v1 Announce Type: new Abstract: Large Language Models (LLMs) are highly sensitive to their input contexts, motivating the development of automated context engineering. However, existin

tutorialsarxiv-cs-cl

Continual Learning of Domain-Invariant Representations

model-releasesarxiv-cs-lg

arXiv:2605.15775v1 Announce Type: new Abstract: Continual learning (CL) aims to train models sequentially over multiple domains without forgetting previously learned knowledge. However, existing CL me

Controllable Molecular Generative Foundation Models

arXiv:2605.15354v1 Announce Type: new Abstract: Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tas

safetyarxiv-cs-lg

Convergent Representations of Linguistic Constructions in Human and Artificial Neural Systems

arXiv:2603.29617v2 Announce Type: replace-cross Abstract: Understanding how the brain processes linguistic constructions is a central challenge in cognitive neuroscience and linguistics. Recent comput

Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas

arXiv:2605.15848v1 Announce Type: cross Abstract: Conversational interfaces powered by large language models (LLMs) are widely used for ideation and analysis, yet their linear structure limits explora

researcharxiv-cs-cl

COPRA: Conditional Parameter Adaptation with Reinforcement Learning for Video Anomaly Detection

model-releasesarxiv-cs-cv

arXiv:2605.15325v1 Announce Type: new Abstract: Vision-language models (VLMs) have shown strong performance in video anomaly detection (VAD) while providing interpretable predictions. However, existin

Local Ai

Cross-Modal Registration Between 3D and 2D Fingerprints via Pose-Aware Unwrapping and Point-Cloud Fusion

arXiv:2605.15796v1 Announce Type: new Abstract: Three-dimensional (3D) fingerprints preserve global finger geometry and local ridge structure while avoiding contact-induced deformation, but they remai

local-aiarxiv-cs-cv

CryptoBench: A Dynamic Benchmark for Expert-Level Evaluation of LLM Agents in Cryptocurrency

model-releasesarxiv-cs-cl

arXiv:2512.00417v5 Announce Type: replace Abstract: This paper introduces CryptoBench, the first expert-curated, dynamic benchmark designed to rigorously evaluate the real-world capabilities of Large

CrystalBoltz: End-to-End Protein Structure Determination via Experiment-Guided Diffusion for X-Ray Crystallography

arXiv:2605.15564v1 Announce Type: new Abstract: Generative models trained on public databases of protein structures, most of which have been determined by X-ray crystallography, now provide powerful p

researcharxiv-cs-lg

CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models

arXiv:2605.15549v1 Announce Type: cross Abstract: The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, desi

CUBE: Contrastive Understanding by Balanced Experiments

arXiv:2509.10825v5 Announce Type: replace-cross Abstract: Explaining a trained model requires a clear account of how explanatory evidence is generated. We propose CUBE, a post-hoc explanation framewor

Curriculum Learning of Physics-Informed Neural Networks based on Spatial Correlation

arXiv:2605.15254v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) combine deep learning with physical constraints for solving partial differential equations (PDEs), and are wide

tutorialsarxiv-cs-lg

DealMaTe: Multi-Dimensional Material Transfer via Diffusion Transformer

arXiv:2605.15681v1 Announce Type: cross Abstract: Recently, diffusion-based material transfer methods rely on image fine-tuning or complex architectures with auxiliary networks but face challenges suc

researcharxiv-cs-cv

DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation

arXiv:2605.16113v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved unprecedented success due to their exceptional generative capabilities. However, because they depend on kno

Decentralized LoRA augmented transformer with multi-scale feature learning for secured eye diagnosis

model-releasesarxiv-cs-cv

arXiv:2505.06982v3 Announce Type: replace Abstract: Accurate and privacy-preserving diagnosis of ophthalmic diseases remains a critical challenge in medical imaging, particularly given the limitations

DecomPose: Disentangling Cross-Category Optimization Contention for Category-Level 6D Object Pose Estimation

arXiv:2605.15728v1 Announce Type: cross Abstract: Category-level 6D object pose estimation is typically formulated as a multi-category joint learning problem with fully shared model parameters. Howeve

Decomposed Vision-Language Alignment for Fine-Grained Open-Vocabulary Segmentation

arXiv:2605.15942v1 Announce Type: cross Abstract: Open-vocabulary segmentation models often struggle to generalize to unseen combinations of object categories and attributes, because fine-grained desc

Decouple Searching from Training: Scaling Data Mixing via Model Merging for Large Language Model Pre-training

arXiv:2602.00747v2 Announce Type: replace-cross Abstract: Determining an effective data mixture is a key factor in Large Language Model (LLM) pre-training, where models must balance general competence

Deep Double Q-learning

arXiv:2507.00275v2 Announce Type: replace-cross Abstract: Double Q-learning is a classical control algorithm that mitigates the maximization bias of Q-learning. To do so, it explicitly trains two inde

Deep Learning Alternatives of the Kolmogorov Superposition Theorem

arXiv:2410.01990v3 Announce Type: replace Abstract: This paper explores alternative formulations of the Kolmogorov Superposition Theorem (KST) as a foundation for neural network design. The original K

tutorialsarxiv-cs-lg

Deep Pre-Alignment for VLMs

model-releasesarxiv-cs-cv

arXiv:2605.15300v1 Announce Type: new Abstract: Most Vision Language Models (VLMs) directly map outputs from ViT encoders to the LLM via a lightweight projector. While effective, recent analysis sugge

DeepSlide: From Artifacts to Presentation Delivery

arXiv:2605.15202v1 Announce Type: new Abstract: Presentations are a primary medium for scholarly communication, yet most AI slide generators optimize the artifact (a visually plausible deck) while und

Applications

Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theory

arXiv:2605.15990v1 Announce Type: new Abstract: Tremendous efforts have been put into evaluating the inclusivity and effectiveness of AI systems across cultures. However, the cultural capabilities con

applicationsarxiv-cs-cl

Degradation-Aware Blur-Segmentation of Brain Tumor

arXiv:2605.15671v1 Announce Type: cross Abstract: Multimodal 3D MRI brain tumor segmentation is a pivotal step in radiotherapy target delineation, surgical planning and post-treatment assessment. Exis

researcharxiv-cs-cv

DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation

arXiv:2605.15532v1 Announce Type: cross Abstract: Distillation enables compact Vision-Language Models (VLMs) to obtain strong reasoning capabilities, yet the prompts driving this process are typically

Density Estimation via Binless Multidimensional Integration

arXiv:2407.08094v3 Announce Type: replace-cross Abstract: We introduce the Binless Multidimensional Thermodynamic Integration (BMTI) method for nonparametric, robust, and data-efficient density estima

researcharxiv-cs-lg

Designing Datacenter Power Delivery Hierarchies for the AI Era

arXiv:2605.16255v1 Announce Type: cross Abstract: Demand for AI accelerators is rapidly increasing rack power density, with projections approaching 1MW per deployment by 2027. This poses a major chall

Designing for Robot Wranglers: A Synthesis of Literature and Practice

arXiv:2605.15892v1 Announce Type: new Abstract: Robots are increasingly present in human spaces, such as for conducting deliveries in hospitals, interacting with visitors at museums, and stocking item

researcharxiv-cs-ro

Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models

arXiv:2503.00794v2 Announce Type: replace Abstract: Accurate gait event detection is crucial for gait analysis, rehabilitation, and assistive technology, particularly in exoskeleton control, where pre

safetyarxiv-cs-ro

Local Ai

Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics

arXiv:2503.23927v3 Announce Type: replace-cross Abstract: Detecting localized differences between two samples is a central task in scientific data analysis, required for the identification of signal e

local-aiarxiv-cs-lg

Detecting Privilege Escalation in Polyglot Microservices via Agentic Program Analysis

arXiv:2605.15569v1 Announce Type: cross Abstract: Microservices are widely adopted in modern cloud systems due to their scalability and fault tolerance. However, microservice architectures introduce s

agentsarxiv-cs-ai

DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection

model-releasesarxiv-cs-cl

arXiv:2605.15518v1 Announce Type: new Abstract: The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misus

Deterministic Coreset for Lp Subspace

model-releasesarxiv-cs-lg

arXiv:2601.00361v3 Announce Type: replace-cross Abstract: We introduce the first iterative algorithm for constructing a arepsilon-coreset that guarantees deterministic ell_p subspace embedding for any

Deterministic Event-Graph Substrates as World Models for Counterfactual Reasoning

arXiv:2605.15967v1 Announce Type: new Abstract: We study event-graph substrates: a class of world models that represent agent state as an append-only log of typed RDF triples and answer counterfactual

DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo

model-releasesarxiv-cs-ro

arXiv:2605.16257v1 Announce Type: new Abstract: Achieving human-level manipulation requires dexterous robotic hands capable of complex object interactions. Advancing such capabilities further demands

Diagonal Adaptive Non-local Observables on Quantum Neural Networks

arXiv:2605.15410v1 Announce Type: cross Abstract: Adaptive Non-local Observables (ANOs) have shown that making quantum observables dynamic can substantially enlarge the function space of Variational Q

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models

arXiv:2605.15706v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have catalyzed the development of multi-agent systems (MAS) for complex reasoning tasks. However, existi

agentsarxiv-cs-lg

Differentially Private Motif-Preserving Multi-modal Hashing

arXiv:2605.15460v1 Announce Type: cross Abstract: Cross-modal hashing enables efficient retrieval by encoding images and text into compact binary codes. State-of-the-art methods rely on semantic simil

Diffusion Policy for Coordinated Control of a Nonholonomic Mobile Base and Dual Arms in Door Opening and Passing

arXiv:2605.15352v1 Announce Type: new Abstract: Opening heavy, self closing doors, especially those that require pulling remains a long standing challenge in robotics. Humans naturally employ both arm

safetyarxiv-cs-ro

DiffVAS: Diffusion-Guided Visual Active Search in Partially Observable Environments

arXiv:2605.15519v1 Announce Type: cross Abstract: Visual active search (VAS) has been introduced as a modeling framework that leverages visual cues to direct aerial (e.g., UAV-based) exploration and p

DiLA: Disentangled Latent Action World Models

arXiv:2605.15725v1 Announce Type: cross Abstract: Latent Action Models (LAMs) enable the learning of world models from unlabeled video by inferring abstract actions between consecutive frames. However