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arXiv:2604.25457v1 Announce Type: new Abstract: Despite recent advances, single-image super-resolution (SR) remains challenging, especially in real-world scenarios with complex degradations. Diffusion

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GraphPL: Leveraging GNN for Efficient and Robust Modalities Imputation in Patchwork Learning

DGX agent

arXiv:2604.25352v1 Announce Type: new Abstract: Current research on distributed multi-modal learning typically assumes that clients can access complete information across all modalities, which may not

model-releasesarxiv-cs-lg

29 Apr 2026

Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs

arXiv:2412.08835v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are almost universally built on a single primitive: the neighborhood. Regardless of architectural variations, message p

researcharxiv-cs-lg

HANDFUL: Sequential Grasp-Conditioned Dexterous Manipulation with Resource Awareness

model-releasesarxiv-cs-ro

arXiv:2604.25126v1 Announce Type: new Abstract: Dexterous robot hands offer rich opportunities for multifunctional manipulation, where a robot must execute multiple skills in sequence while maintainin

Hidden States as Early Signals: Step-level Trace Evaluation and Pruning for Efficient Test-Time Scaling

arXiv:2601.09093v2 Announce Type: replace Abstract: Large Language Models (LLMs) can enhance reasoning capabilities through test-time scaling by generating multiple traces. However, the combination of

Hierarchical Reinforcement Learning for the Dynamic VNE with Alternatives Problem

arXiv:2512.05207v2 Announce Type: replace-cross Abstract: Virtual Network Embedding (VNE) is a key enabler of network slicing, yet most formulations assume that each Virtual Network Request (VNR) has

How RL Unlocks the Aha Moment in Geometric Interleaved Reasoning

arXiv:2603.01070v2 Announce Type: replace Abstract: Solving complex geometric problems inherently requires interleaved reasoning: a tight alternation between constructing diagrams and performing logic

safetyarxiv-cs-cl

HuM-Eval: A Coarse-to-Fine Framework for Human-Centric Video Evaluation

arXiv:2604.25361v1 Announce Type: new Abstract: Video generation models have developed rapidly in recent years, where generating natural human motion plays a pivotal role. However, accurately evaluati

I built a TPU-native medical Q&A fine-tuning pipeline using Gemma 3 with Keras and JAX The project fine-tunes Gemma-3 on medical dialogue da…

model-releasesfrancois-chollet--x

I built a TPU-native medical Q&A fine-tuning pipeline using Gemma 3 with Keras and JAX The project fine-tunes Gemma-3 on medical dialogue data from ChatDoctor and evaluates it on MedMCQA, with a large

Image Classification via Random Dilated Convolution with Multi-Branch Feature Extraction and Context Excitation

arXiv:2604.25188v1 Announce Type: new Abstract: Image classification remains a fundamental yet challenging task in computer vision, particularly when fine-grained feature extraction and background noi

Interactive Episodic Memory with User Feedback

arXiv:2604.24893v1 Announce Type: new Abstract: In episodic memory with natural language queries (EM-NLQ), a user may ask a question (e.g., 'Where did I place the mug?') that requires searching a long

safetyarxiv-cs-cv

Interpretable Fuzzy Modeling Reveals Population-Level Representation Differences in P300 Brain Computer Interfaces Across Neurodivergent and Neurotypical Cohorts

arXiv:2604.24765v1 Announce Type: cross Abstract: P300-based brain-computer interfaces (BCIs) are widely used for communication, but population heterogeneity may alter the neural patterns available fo

jina-embeddings-v5-text: Task-Targeted Embedding Distillation

arXiv:2602.15547v2 Announce Type: replace Abstract: Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpos

Large Language Models Explore by Latent Distilling

arXiv:2604.24927v1 Announce Type: new Abstract: Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-le

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

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

Less Is More: Fast and Accurate Reasoning with Cross-Head Unified Sparse Attention

arXiv:2508.07101v2 Announce Type: replace Abstract: Large reasoning models achieve strong performance through test-time scaling, but this incurs substantial computational overhead due to long decoding

researcharxiv-cs-cl

Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting

arXiv:2604.24788v1 Announce Type: new Abstract: Natural gas is undoubtedly an essential component of the global energy system. Accurate short-term forecasting of natural gas price is challenging due t

LLM-ReSum: A Framework for LLM Reflective Summarization through Self-Evaluation

arXiv:2604.25665v1 Announce Type: new Abstract: Reliable evaluation of large language model (LLM)-generated summaries remains an open challenge, particularly across heterogeneous domains and document

LongSumEval: Question-Answering Based Evaluation and Feedback-Driven Refinement for Long Document Summarization

arXiv:2604.25130v1 Announce Type: new Abstract: Evaluating long document summaries remains the primary bottleneck in summarization research. Existing metrics correlate weakly with human judgments and

Marco-MoE: Open Multilingual Mixture-of-Expert Language Models with Efficient Upcycling

arXiv:2604.25578v1 Announce Type: new Abstract: We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only

researcharxiv-cs-cl

MGTEVAL: An Interactive Platform for Systemtic Evaluation of Machine-Generated Text Detectors

arXiv:2604.25152v1 Announce Type: cross Abstract: We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT detectio

researcharxiv-cs-cl

MICo-150K: A Comprehensive Dataset Advancing Multi-Image Composition

arXiv:2512.07348v2 Announce Type: replace Abstract: In controllable image generation, synthesizing coherent and consistent images from multiple reference inputs, i.e., Multi-Image Composition (MICo),

Minimax Generalized Cross-Entropy

arXiv:2603.19874v3 Announce Type: replace-cross Abstract: Loss functions play a central role in supervised classification. Cross-entropy (CE) is widely used, whereas the mean absolute error (MAE) loss

Mitigating Coordinate Prediction Bias from Positional Encoding Failures

arXiv:2510.22102v2 Announce Type: replace-cross Abstract: While Multimodal Large Language Models (MLLMs) excel at general vision-language tasks, precise coordinate prediction remains a significant cha

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

Local Ai

OmniVTG: A Large-Scale Dataset and Training Paradigm for Open-World Video Temporal Grounding

arXiv:2604.25276v1 Announce Type: new Abstract: Video Temporal Grounding (VTG), the task of localizing video segments from text queries, struggles in open-world settings due to limited dataset scale a

local-aiarxiv-cs-cv

One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement

arXiv:2604.25444v1 Announce Type: new Abstract: Large Language Models (LLMs) often fail to utilize their latent reasoning capabilities due to a distributional mismatch between ambiguous human inquirie

safetyarxiv-cs-cl

Policy Improvement Reinforcement Learning

arXiv:2604.00860v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a central post-training paradigm for improving the reasoning capabilities of large

safetyarxiv-cs-lg

Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost

arXiv:2604.25441v1 Announce Type: cross Abstract: Commercial TTS systems produce near-native Indic audio, but the best open-source bases (Chatterbox, Indic Parler-TTS, IndicF5) trail them on measured

Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models

arXiv:2604.25642v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual-textual understanding, yet their reliability is critically undermined b

Prior-Aligned Data Cleaning for Tabular Foundation Models

arXiv:2604.25154v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) achieve state-of-the-art zero-shot accuracy on small tabular datasets by meta-learning over synthetic data-generating p

QB-LIF: Learnable-Scale Quantized Burst Neurons for Efficient SNNs

arXiv:2604.25688v1 Announce Type: new Abstract: Binary spike coding enables sparse and event-driven computation in spiking neural networks (SNNs), yet its 1-bit-per-timestep representation fundamental

Agents

R^3-SQL: Ranking Reward and Resampling for Text-to-SQL

arXiv:2604.25325v1 Announce Type: cross Abstract: Modern Text-to-SQL systems generate multiple candidate SQL queries and rank them to judge a final prediction. However, existing methods face two limit

agentsarxiv-cs-cl

RCProb: Probabilistic Rule Extraction for Efficient Simplification of Tree Ensembles

arXiv:2604.25304v1 Announce Type: new Abstract: Tree ensembles are widely used in industrial machine learning due to their strong predictive performance and efficient training procedures. However, as

Agents

Recursive Multi-Agent Systems

arXiv:2604.25917v1 Announce Type: cross Abstract: Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states

agentsarxiv-cs-cl

Representation Paradigms in AI-based 3D Radiological Image Reconstruction: A Systematic Review

arXiv:2504.11349v3 Announce Type: replace Abstract: The demand for high-quality medical imaging in clinical practice and assisted diagnosis has made 3D image reconstruction in radiological imaging a k

Residual-loss Anomaly Analysis of Physics-Informed Neural Networks: An Inverse Method for Change-point Detection in Nonlinear Dynamical Systems with Regime Switching

arXiv:2604.25655v1 Announce Type: cross Abstract: Nonlinear dynamical systems with regime transitions are typically described by ordinary differential equations with jumping parameters parameters. Tra

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective

arXiv:2510.10150v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) serves as a cornerstone technique for enhancing the reasoning capabilities of Large Language M

safetyarxiv-cs-lg

Revisiting the Past: Data Unlearning with Model State History

arXiv:2506.20941v3 Announce Type: replace Abstract: Large language models are trained on massive corpora of web data, which may include private data, copyrighted material, factually inaccurate data, o

researcharxiv-cs-lg

SaliencyDecor: Enhancing Neural Network Interpretability through Feature Decorrelation

arXiv:2604.25315v1 Announce Type: new Abstract: Gradient-based saliency methods are widely used to interpret deep neural networks, yet they often produce noisy and unstable explanations that poorly al

Agents

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

SecureScan: An AI-Driven Multi-Layer Framework for Malware and Phishing Detection Using Logistic Regression and Threat Intelligence Integration

arXiv:2602.10750v2 Announce Type: replace-cross Abstract: The growing sophistication of modern malware and phishing campaigns has diminished the effectiveness of traditional signature-based intrusion

Self-DACE++: Robust Low-Light Enhancement via Efficient Adaptive Curve Estimation

arXiv:2604.25367v1 Announce Type: new Abstract: In this paper, we present Self-DACE++, an improved unsupervised and lightweight framework for Low-Light Image Enhancement (LLIE), building upon our prev

Local Ai

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-Unify Architecture

SenseNova U1 is a new series of native multimodal models that unifies multimodal understanding, reasoning, and generation within a monolithic architecture, marking a fundamental paradigm shift in mult

local-air-localllama

ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching

arXiv:2604.25065v1 Announce Type: new Abstract: Object recognition (OR) in humans relies heavily on shape cues and the ability to recognize objects across varying 3D viewpoints. Unlike humans, deep ne

Shearlet Neural Operators for Anisotropic-Shock-Dominated and Multi-scale parametric partial differential equations

arXiv:2604.25181v1 Announce Type: new Abstract: Neural operators have emerged as powerful data-driven surrogates for learning solution operators of parametric partial differential equations (PDEs). Ho

Soft-TransFormers for Continual Learning

arXiv:2411.16073v3 Announce Type: replace-cross Abstract: Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-Transformer (Soft-TF), a parameter-efficient framework fo

Splatent: Splatting Diffusion Latents for Novel View Synthesis

arXiv:2512.09923v2 Announce Type: replace Abstract: Radiance field representations have recently been explored in the latent space of VAEs that are commonly used by diffusion models. This direction of