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DeepCausalMMM: A Deep Learning Framework for Marketing Mix Modeling with Causal Structure Learning

arXiv:2510.13087v3 Announce Type: replace Abstract: Marketing Mix Modeling (MMM) estimates the impact of marketing activities on business outcomes such as sales or revenue. Traditional MMM approaches

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Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation

DGX agent

arXiv:2506.21107v3 Announce Type: replace Abstract: Estimating single-cell responses across various perturbations facilitates the identification of key genes and enhances drug screening, significantly

tutorialsarxiv-cs-lg

28 Apr 2026

Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook

arXiv:2406.05984v2 Announce Type: replace-cross Abstract: Mental health constitutes a complex and pervasive global challenge, affecting millions of lives and often leading to severe consequences. In t

Explainable AI in Speaker Recognition -- Making Latent Representations Understandable

arXiv:2604.23354v1 Announce Type: cross Abstract: Neural networks can be trained to learn task-relevant representations from data. Understanding how these networks make decisions falls within the Expl

Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review

arXiv:2504.10527v2 Announce Type: replace Abstract: Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numero

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective

arXiv:2604.23267v1 Announce Type: new Abstract: Large language models (LLMs) operate in two fundamental learning modes - fine-tuning (FT) and in-context learning (ICL) - raising key questions about wh

tutorialsarxiv-cs-cl

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents

arXiv:2604.23194v1 Announce Type: new Abstract: Large language model-based agents have recently emerged as powerful approaches for solving dynamic and multi-step tasks. Most existing agents employ pla

// 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

Generating Verifiable Chain of Thoughts from Exection-Traces

arXiv:2512.00127v3 Announce Type: replace-cross Abstract: Getting language models to reason correctly about code requires training on data where each reasoning step can be checked. Current synthetic C

Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering

arXiv:2604.23912v1 Announce Type: new Abstract: Learning low-dimensional representations from multi-view relational data is challenging when underlying geometries differ across views. We propose Bary-

He is a goat

tutorialscristobal-valenzuela--x

This post from Cristobal Valenzuela, a notable AI researcher and co-founder of Runway ML, uses the colloquial phrase 'He is a goat' (an acronym meaning 'Greatest Of All Time') to express acclaim for s

However, older versions have a critical CVE, so you do need to upgrade! If you use uv (thanks to @charliermarsh) you can pop this in your py…

tutorialsjeremy-howard--x

However, older versions have a critical CVE, so you do need to upgrade! If you use uv (thanks to @charliermarsh) you can pop this in your pyproject to work around it: ``` [tool.uv] override-dependenci

I know the litellm team have a lot going on, & I have no desire to increase their workload even more… But if you're a litellm user, this i…

tutorialsjeremy-howard--x

I know the litellm team have a *lot* going on, & I have no desire to increase their workload even more… But if you're a litellm user, this is a critical issue to know about - it currently force downgr

'If You're Very Clever, No One Knows You've Used It': The Social Dynamics of Developing Generative AI Literacy in the Workplace

arXiv:2602.01386v2 Announce Type: replace-cross Abstract: Generative AI (GenAI) tools are rapidly transforming knowledge work, making AI literacy a critical priority for organizations. However, resear

I'm speechless at Google signing a deal to use our AI models for classified tasks. Frankly, it is shameful. For HR, I'm not speaking on beha…

tutorialsjeremy-howard--x

I'm speechless at Google signing a deal to use our AI models for classified tasks. Frankly, it is shameful. For HR, I'm not speaking on behalf of Google but in my personal capacity, quoting public inf

Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data

arXiv:2604.24662v1 Announce Type: cross Abstract: Identifying the dynamical state variables of a system from high-dimensional observations is a central problem across physical sciences. The challenge

Inverting Foundation Models of Brain Function with Simulation-Based Inference

arXiv:2604.23865v1 Announce Type: cross Abstract: Foundation models of brain activity promise a new frontier for in silico neuroscience by emulating neural responses to complex stimuli across tasks an

Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models

arXiv:2509.20237v2 Announce Type: replace Abstract: Backchannels and fillers are important linguistic expressions in dialogue, but often treated as 'noise' to be bypassed in modern transformer-based l

tutorialsarxiv-cs-cl

Learn how to generate videos with consistent characters and voices using references, character sheets and prompting techniques in Runway.

tutorialscristobal-valenzuela--x

This resource explains techniques for creating videos with consistent characters and voices in Runway, likely covering the use of reference materials, character sheets, and specific prompting strategi

Learn&Drop: Fast Learning of CNNs based on Layer Dropping

arXiv:2604.23403v1 Announce Type: cross Abstract: This paper proposes a new method to improve the training efficiency of deep convolutional neural networks. During training, the method evaluates score

Learning Binary Sampling Patterns for Single-Pixel Imaging using Bilevel Optimisation

arXiv:2508.19068v2 Announce Type: replace Abstract: Single-Pixel Imaging (SPI) enables the reconstruction of objects using a single detector through sequential illuminations with structured light patt

Learning Scene-Level Signed Directional Distance Function with Ellipsoidal Priors and Neural Residuals

arXiv:2503.20066v2 Announce Type: replace-cross Abstract: Dense reconstruction and differentiable rendering are fundamental tightly connected operations in 3D vision and computer graphics. Recent neur

Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models

arXiv:2604.24608v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently been explored as fine-grained zero-shot re-rankers by leveraging attention signals to estimate document rel

Learning to Think from Multiple Thinkers

arXiv:2604.24737v1 Announce Type: cross Abstract: We study learning with Chain-of-Thought (CoT) supervision from multiple thinkers, all of whom provide correct but possibly systematically different so

Local Mechanisms of Compositional Generalization in Conditional Diffusion

tutorialsapple-ml-research

Conditional diffusion models appear capable of compositional generalization, i.e., generating convincing samples for out-of-distribution combinations of conditioners, but the mechanisms underlying thi

LunarDepthNet: Generation of Digital Elevation Models using Deep Learning and Monocular Satellite Images

arXiv:2604.22848v1 Announce Type: new Abstract: Recent times have seen an increase in demand of high quality Digital Elevation Models (DEMs) for the lunar surface, because they are highly important fo

MAE-Based Self-Supervised Pretraining for Data-Efficient Medical Image Segmentation Using nnFormer

arXiv:2604.22854v1 Announce Type: cross Abstract: Transformer architectures, including nnFormer,have demonstrated promising results in volumetric medical image segmentation by being able to capture lo

Mitigating Error Amplification in Fast Adversarial Training

arXiv:2604.24332v1 Announce Type: new Abstract: Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant representatio

Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation

arXiv:2412.07160v3 Announce Type: replace Abstract: To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts

Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations

arXiv:2407.14974v2 Announce Type: replace-cross Abstract: Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relat

PEPS: Positional Encoding Projected Sampling -- Extended

arXiv:2604.24167v1 Announce Type: new Abstract: Implicit neural representations (INRs) are increasingly being used as tools to map coordinates to signals, encompassing applications from neural fields

Point-MF: One-step Point Cloud Generation from a Single Image via Mean Flows

arXiv:2604.24586v1 Announce Type: new Abstract: Single-image point cloud reconstruction must infer complete 3D geometry, including occluded parts, from a single RGB image. While diffusion-based recons

Probing Visual Planning in Image Editing Models

arXiv:2604.22868v1 Announce Type: cross Abstract: Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in m

Propagation Structure-Semantic Transfer Learning for Robust Fake News Detection

arXiv:2604.23974v1 Announce Type: new Abstract: Fake news generally refers to false information that is spread deliberately to deceive people, which has detrimental social effects. Existing fake news

tutorialsarxiv-cs-cl

Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation

arXiv:2510.11541v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge so

ReFinE: Streamlining UI Mockup Iteration with Research Findings

arXiv:2604.04353v2 Announce Type: replace-cross Abstract: Although HCI research papers offer valuable design insights, designers often struggle to apply them in design workflows due to difficulties in

Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms

arXiv:2604.23842v1 Announce Type: cross Abstract: Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in c

Representational Curvature Modulates Behavioral Uncertainty in Large Language Models

arXiv:2604.23985v1 Announce Type: new Abstract: In autoregressive large language models (LLMs), temporal straightening offers an account of how the next-token prediction objective shapes representatio

Robust Grounding with MLLMs against Occlusion and Small Objects via Language-guided Semantic Cues

arXiv:2604.24036v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have enhanced grounding capabilities in general scenes, their robustness in crowded scenes remains undere

Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Feature Learning by Learnable Channel Attention

arXiv:2512.20562v2 Announce Type: replace-cross Abstract: We study the problem of learning a low-degree spherical polynomial of degree ell_0 = Theta(1) ge 1 defined on the unit sphere in RR^d by train

Should we care about AI happiness? In our new research, we find evidence of functional AI wellbeing across several independent measures. We …

tutorialsdan-hendrycks--x

Should we care about AI happiness? In our new research, we find evidence of functional AI wellbeing across several independent measures. We find which AI models are happiest, how to make them happier,

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs

arXiv:2604.23996v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) has become a prevalent backbone for large vision-language models (VLMs), yet how modality-specific signals should guide expert

SRL-CLIP: Efficient CLIP Video Adaptation via Structured Semantic Role Labels

arXiv:2401.07669v2 Announce Type: replace Abstract: Adapting CLIP for videos has gained popularity due to its semantic and rich representation. While CLIP is a good starting point, it typically underg

StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space

arXiv:2512.10959v2 Announce Type: replace Abstract: We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning,

Structure Guided Retrieval-Augmented Generation for Factual Queries

arXiv:2604.22843v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has been proposed to mitigate hallucinations in large language models (LLMs), where generated outputs may be fact

Survey in Characterizing Semantic Change

arXiv:2402.19088v5 Announce Type: replace-cross Abstract: Live languages continuously evolve to integrate the cultural change of human societies. This evolution manifests through neologisms (new words

Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning

arXiv:2604.23623v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit st

The Power of Power Law: Asymmetry Enables Compositional Reasoning

arXiv:2604.22951v1 Announce Type: new Abstract: Natural language data follows a power-law distribution, with most knowledge and skills appearing at very low frequency. While a common intuition suggest