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Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework

model-releasesarxiv-cs-lg

arXiv:2510.24074v2 Announce Type: replace-cross Abstract: The Heston stochastic volatility model is a widely used tool in financial mathematics for pricing European options. However, its calibration r

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Model Releases

Deep Learning for Virtual Reality User Identification: A Benchmark

DGX agent

arXiv:2604.16341v1 Announce Type: cross Abstract: Virtual Reality (VR) applications require robust user identification systems to ensure secure access to equipment and protect worker identities. Motio

model-releasesarxiv-cs-cv

21 Apr 2026

Applications

🔒`deepagents deploy` now supports custom auth: turn a single deployment into a multi-tenant platform. per-user resource and thread isolatio…

applicationsharrison-chase--x

🔒`deepagents deploy` now supports custom auth: turn a single deployment into a multi-tenant platform. per-user resource and thread isolation, access control, and your own auth provider, all without sp

DeepDetect: Learning All-in-One Dense Keypoints

arXiv:2510.17422v4 Announce Type: replace Abstract: Keypoint detection is the foundation of many computer vision tasks, including image registration, structure-from-motion, 3D reconstruction, visual o

DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting

arXiv:2604.18261v1 Announce Type: cross Abstract: The multi-scale and non-linear nature of phase-field models of solidification requires fine spatial and temporal discretization, leading to long compu

DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models

arXiv:2511.15669v2 Announce Type: replace Abstract: Does Chain-of-Thought (CoT) reasoning genuinely improve Vision-Language-Action (VLA) models, or does it merely add overhead? Existing CoT-VLA system

Defragmenting Language Models: An Interpretability-based Approach for Vocabulary Expansion

arXiv:2604.16656v1 Announce Type: new Abstract: All languages are equal; when it comes to tokenization, some are more equal than others. Tokens are the hidden currency that dictate the cost and latenc

researcharxiv-cs-cl

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

researcharxiv-cs-cl

DEM Refinement and Validation on the Lunar Surface Using Shape-from-Shading with Chandrayaan-2 OHRC Imagery

model-releasesarxiv-cs-cv

arXiv:2604.17436v1 Announce Type: new Abstract: This study presents a Shape from Shading (SfS) framework to enhance sub-metre resolution lunar digital elevation models (DEMs) using imagery from the Or

Industry

Democrats in Virginia today are voting to strip democracy from the process, eliminating Republican voices from congress They are fascists an…

Democrats in Virginia today are voting to strip democracy from the process, eliminating Republican voices from congress They are fascists and unrepentant The actions of Texas or any other state are im

industryelon-musk--x

Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization

arXiv:2510.19544v2 Announce Type: replace-cross Abstract: Combinatorial optimization problems are central to both practical applications and the development of optimization methods. While classical an

Demystifying the unreasonable effectiveness of online alignment methods

arXiv:2604.17207v1 Announce Type: cross Abstract: Iterative alignment methods based on purely greedy updates are remarkably effective in practice, yet existing theoretical guarantees of (O(log T)) KL-

Local Ai

Denoise and Align: Diffusion-Driven Foreground Knowledge Prompting for Open-Vocabulary Temporal Action Detection

arXiv:2604.18313v1 Announce Type: new Abstract: Open-Vocabulary Temporal Action Detection (OV-TAD) aims to localize and classify action segments of unseen categories in untrimmed videos, where effecti

local-aiarxiv-cs-cv

Tutorials

Densemarks: Learning Canonical Embeddings for Human Heads Images via Point Tracks

arXiv:2511.02830v2 Announce Type: replace Abstract: We propose DenseMarks - a new learned representation for human heads, enabling high-quality dense correspondences of human head images. For a 2D ima

tutorialsarxiv-cs-cv

Depth Adaptive Efficient Visual Autoregressive Modeling

arXiv:2604.17286v1 Announce Type: new Abstract: Visual Autoregressive (VAR) modeling inefficiently applies a fixed computational depth to each position when generating high-resolution images. While ex

Depth Registers Unlock W4A4 on SwiGLU: A Reader/Generator Decomposition

arXiv:2604.18128v1 Announce Type: new Abstract: We study post-training W4A4 quantization in a controlled 300M-parameter SwiGLU decoder-only language model trained on 5B tokens of FineWeb-Edu, and ask

Agents

Designing Explainable Conversational Agentic Systems for Guarani Speakers

arXiv:2603.05743v3 Announce Type: replace Abstract: Although artificial intelligence (AI) and Human-Computer Interaction (HCI) systems are often presented as universal solutions, their design remains

agentsarxiv-cs-cl

Detecting Alarming Student Verbal Responses using Text and Audio Classifier

arXiv:2604.16717v1 Announce Type: new Abstract: This paper addresses a critical safety gap in the use Automated Verbal Response Scoring (AVRS). We present a novel hybrid framework for troubled student

Detecting LLM-Generated Spam Reviews by Integrating Language Model Embeddings and Graph Neural Network

arXiv:2510.01801v2 Announce Type: replace Abstract: The rise of large language models (LLMs) has enabled the generation of highly persuasive spam reviews that closely mimic human writing. These review

Applications

DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks

arXiv:2604.16484v1 Announce Type: new Abstract: Deploying generative World-Action Models for manipulation is severely bottlenecked by redundant pixel-level reconstruction, O(T) memory scaling, and seq

applicationsarxiv-cs-cv

DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning

arXiv:2512.12022v2 Announce Type: replace Abstract: Decentralized federated learning (DFL) has emerged as a promising paradigm that enables multiple clients to collaboratively train machine learning m

DGSSM: Diffusion guided state-space models for multimodal salient object detection

arXiv:2604.17585v1 Announce Type: new Abstract: Salient object detection (SOD) requires modeling both long-range contextual dependencies and fine-grained structural details, which remains challenging

Diagnosing LLM-based Rerankers in Cold-Start Recommender Systems: Coverage, Exposure and Practical Mitigations

arXiv:2604.16318v1 Announce Type: cross Abstract: Large language models (LLMs) and cross-encoder rerankers have gained attention for improving recommender systems, particularly in cold-start scenarios

DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs

arXiv:2601.03559v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias a

Local Ai

Different Paths to Harmful Compliance: Behavioral Side Effects and Mechanistic Divergence Across LLM Jailbreaks

arXiv:2604.18510v1 Announce Type: cross Abstract: Open-weight language models can be rendered unsafe through several distinct interventions, but the resulting models may differ substantially in capabi

local-aiarxiv-cs-cl

Differential Privacy in Two-Layer Networks: How DP-SGD Harms Fairness and Robustness

arXiv:2603.04881v2 Announce Type: replace Abstract: Differentially private learning is essential for training models on sensitive data, but empirical studies consistently show that it can degrade perf

DifFoundMAD: Foundation Models meet Differential Morphing Attack Detection

model-releasesarxiv-cs-cv

arXiv:2604.17961v1 Announce Type: new Abstract: In this work, we introduce DifFoundMAD, a parameter-efficient D-MAD framework that exploits the generalisation capabilities of vision foundation models

DiffuSAM: Diffusion Guided Zero-Shot Object Grounding for Remote Sensing Imagery

arXiv:2604.18201v1 Announce Type: new Abstract: Diffusion models have emerged as powerful tools for a wide range of vision tasks, including text-guided image generation and editing. In this work, we e

Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems

arXiv:2604.16670v1 Announce Type: new Abstract: We present a framework leveraging a novel variant of the model-based diffusion algorithm to minimize the time required for a redundant dual-arm robot co

researcharxiv-cs-ro

Digging for clues about the North Pole’s past

In the past, even with an icebreaker and during peak melt season, getting to the North Pole wasn’t a sure bet. It took favorable winds to crack the frozen ocean surface, and ships had to fight through

researchmit-tech-review

Dimensional Criticality at Grokking Across MLPs and Transformers

arXiv:2604.16431v1 Announce Type: new Abstract: Abrupt transitions between distinct dynamical regimes are a hallmark of complex systems. Grokking in deep neural networks provides a striking example --

DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching

arXiv:2602.05449v3 Announce Type: replace Abstract: While diffusion models have achieved great success in the field of video generation, this progress is accompanied by a rapidly escalating computatio

Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining

model-releasesarxiv-cs-cv

arXiv:2604.16391v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have shown great potential in building generalist robots, but still face a dilemma-misalignment of 2D image foreca

Tutorials

Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems

arXiv:2604.18277v1 Announce Type: new Abstract: Accurate dynamical modeling is essential for simulation and control of embodied systems, yet first-principles models of electromechanical systems often

tutorialsarxiv-cs-lg

Distributional Off-Policy Evaluation with Deep Quantile Process Regression

arXiv:2604.18143v1 Announce Type: cross Abstract: This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of t

Distributionally Robust Regret Optimal Control Under Moment-Based Ambiguity Sets

arXiv:2512.10906v2 Announce Type: replace-cross Abstract: We consider a class of finite-horizon, linear-quadratic stochastic control problems, where the probability distribution governing the noise pr

Diverse Dictionary Learning

arXiv:2604.17568v1 Announce Type: new Abstract: Given only observational data X = g(Z), where both the latent variables Z and the generating process g are unknown, recovering Z is ill-posed without ad

Agents

Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation

arXiv:2604.18005v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly used for open-ended idea generation, driven by the expectation that collective interaction will broaden the

agentsarxiv-cs-cl

DMax: Aggressive Parallel Decoding for dLLMs

arXiv:2604.08302v2 Announce Type: replace Abstract: We present DMax, a new paradigm for efficient diffusion language models (dLLMs). It mitigates error accumulation in parallel decoding, enabling aggr

Do LLM-derived graph priors improve multi-agent coordination?

model-releasesarxiv-cs-lg

arXiv:2604.17191v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) is crucial for AI systems that operate collaboratively in distributed and adversarial settings, particularly i

Do LLMs Encode Functional Importance of Reasoning Tokens?

arXiv:2601.03066v2 Announce Type: replace Abstract: Large language models solve complex tasks by generating long reasoning chains, achieving higher accuracy at the cost of increased computational cost

researcharxiv-cs-cl

Do LLMs Use Cultural Knowledge Without Being Told? A Multilingual Evaluation of Implicit Pragmatic Adaptation

arXiv:2604.17718v1 Announce Type: new Abstract: Many benchmarks show that large language models can answer direct questions about culture. We study a different question: do they also change how they s

Tools

do not miss. one of the INSANE gets courtesy of @osanseviero and the @GoogleDeepMind london avengers. if you always felt out of the loop on …

do not miss. one of the INSANE gets courtesy of @osanseviero and the @GoogleDeepMind london avengers. if you always felt out of the loop on the SOTA on Imagegen, today or otherwise, this is the best 4

toolsswyx--x

DocQAC: Adaptive Trie-Guided Decoding for Effective In-Document Query Auto-Completion

arXiv:2604.18257v1 Announce Type: cross Abstract: Query auto-completion (QAC) has been widely studied in the context of web search, yet remains underexplored for in-document search, which we term DocQ

Document-as-Image Representations Fall Short for Scientific Retrieval

arXiv:2604.18508v1 Announce Type: cross Abstract: Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying

Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style

arXiv:2603.11024v2 Announce Type: replace Abstract: VLMs have become increasingly proficient at a range of computer vision tasks, such as visual question answering and object detection. This includes

Agents

does anyone else 'speed-type' when talking to AI? ignor spellign mistakes skip commas use agro abbrev etc

This post discusses the informal communication style some users adopt when interacting with AI systems, characterized by typing shortcuts like abbreviations, omitted punctuation, intentional spelling

agentsyohei-nakajima--x

Does 'Do Differentiable Simulators Give Better Policy Gradients?'' Give Better Policy Gradients?

arXiv:2604.18161v1 Announce Type: new Abstract: In policy gradient reinforcement learning, access to a differentiable model enables 1st-order gradient estimation that accelerates learning compared to