Newest with code
Every paper with a repository link. Each page shows two streams, newest first within each, counted separately: papers newer than the archive snapshot come from Syntology's graph Syntology; the rest are archive rows archive 2025-07-28. The two are never added together.
Newer than the archive snapshot Syntology
Cards 1–15 of 9,581 graph papers newer than 2025-07-28; this feed shows the newest 150, newest arXiv id first. Dates and the abstract sentence are from arXiv's metadata (CC0) for 9,280 of 9,581; for the other 301 the month is read from the id.
From the archive archive 2025-07-28
Cards 1–15 of 218,469 archive papers with a code link; this feed shows the newest 150, archive date first (newest archive date 2025-09-24). Within a month, dated rows come first, then the 49,179 undated rows placed by the month in their arXiv id. 4 archive rows carry a date after the snapshot and are placed by that date. 382 archive papers with neither a date nor an arXiv id cannot be placed and are not listed. 1 code-linked slug has no paper row in the archive and is not listed (so 218,469 listed + 382 unplaceable + 1 = 218,852 papers with code).
ProxyFusion: Face Feature Aggregation Through Sparse Experts
Face feature fusion is indispensable for robust face recognition, particularly in scenarios involving long-range, low-resolution media (unconstrained environments) where not all frames or features are equally…
NeuroXAI: Adaptive, robust, explainable surrogate framework for determination of channel importance in EEG application
Electroencephalogram (EEG)-based applications often require numerous channels to achieve high performance, which limits their widespread use.
DeepCS-TRD, a Deep Learning-based Cross-Section Tree Ring Detector
Here, we propose Deep CS-TRD, a new automatic algorithm for detecting tree rings in whole cross-sections.
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation
Spiking Neural Networks (SNNs) are highly energy-efficient during inference, making them particularly suitable for deployment on neuromorphic hardware.
Leveraging Context for Multimodal Fallacy Classification in Political Debates
In this paper, we present our submission to the MM-ArgFallacy2025 shared task, which aims to advance research in multimodal argument mining, focusing on logical fallacies in political debates.
One Step is Enough: Multi-Agent Reinforcement Learning based on One-Step Policy Optimization for Order Dispatch on Ride-Sharing Platforms
On-demand ride-sharing platforms face the fundamental challenge of dynamically bundling passengers with diverse origins and destinations and matching them with vehicles in real time, all under significant uncertainty.
Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation
Emotion Recognition in Conversation (ERC) is a practical and challenging task.
Real Time Captioning of Sign Language Gestures in Video Meetings
It has always been a rather tough task to communicate with someone possessing a hearing impairment.
Adaptive Multi-Agent Reasoning via Automated Workflow Generation
The rise of Large Reasoning Models (LRMs) promises a significant leap forward in language model capabilities, aiming to tackle increasingly sophisticated tasks with unprecedented efficiency and accuracy.
RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction
Existing studies on bundle construction have relied merely on user feedback via bipartite graphs or enhanced item representations using semantic information.
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended results.
APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
We propose the APTx Neuron, a novel, unified neural computation unit that integrates non-linear activation and linear transformation into a single trainable expression.
OntView: What you See is What you Meant
In the field of knowledge management and computer science, ontologies provide a structured framework for modeling domain-specific knowledge by defining concepts and their relationships.
Graph-Structured Data Analysis of Component Failure in Autonomous Cargo Ships Based on Feature Fusion
To address the challenges posed by cascading reactions caused by component failures in autonomous cargo ships (ACS) and the uncertainties in emergency decision-making, this paper proposes a novel hybrid feature fusion…
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
In recent years, models based on Graph Convolutional Networks (GCN) have made significant strides in the field of graph data analysis.
The feed is static: 10 pages of up to 15 cards per stream, rebuilt with the site. Older papers are reachable from task, dataset and method pages and from search. No repository stars are tracked and nothing here is ranked by popularity. Machine-readable twin: JSON.