Newest with code · page 10
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 136–150 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 136–150 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).
HNOSeg-XS: Extremely Small Hartley Neural Operator for Efficient and Resolution-Robust 3D Image Segmentation
In medical image segmentation, convolutional neural networks (CNNs) and transformers are dominant.
Multi-Scale Network Dynamics and Systemic Risk: A Model Context Protocol Approach to Financial Markets
This paper introduces a novel framework for analyzing systemic risk in financial markets through multi-scale network dynamics using Model Context Protocol (MCP) for agent communication.
MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Vector Quantized Variational Autoencoders (VQ-VAEs) are fundamental models that compress continuous visual data into discrete tokens.
OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
Recent advances in multimodal large language models (MLLMs) have shown remarkable capabilities in integrating vision and language for complex reasoning.
Scaling RL to Long Videos
We introduce a full-stack framework that scales up reasoning in vision-language models (VLMs) to long videos, leveraging reinforcement learning.
SAMO: A Lightweight Sharpness-Aware Approach for Multi-Task Optimization with Joint Global-Local Perturbation
Multi-task learning (MTL) enables a joint model to capture commonalities across multiple tasks, reducing computation costs and improving data efficiency.
Identifying the Smallest Adversarial Load Perturbations that Render DC-OPF Infeasible
What is the globally smallest load perturbation that renders DC-OPF infeasible?
Rethinking Query-based Transformer for Continual Image Segmentation
Class-incremental/Continual image segmentation (CIS) aims to train an image segmenter in stages, where the set of available categories differs at each stage.
SCOOTER: A Human Evaluation Framework for Unrestricted Adversarial Examples
Unrestricted adversarial attacks aim to fool computer vision models without being constrained by $\ell_p$-norm bounds to remain imperceptible to humans, for example, by changing an object's color.
HiM2SAM: Enhancing SAM2 with Hierarchical Motion Estimation and Memory Optimization towards Long-term Tracking
This paper presents enhancements to the SAM2 framework for video object tracking task, addressing challenges such as occlusions, background clutter, and target reappearance.
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Graph Neural Networks (GNNs) are widely used in collaborative filtering to capture high-order user-item relationships.
Towards Interpretable Time Series Foundation Models
In this paper, we investigate the distillation of time series reasoning capabilities into small, instruction-tuned language models as a step toward building interpretable time series foundation models.
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs.
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation
Time, cost, and energy efficiency are critical considerations in Deep-Learning (DL), particularly when processing long texts.
Seg-Wild: Interactive Segmentation based on 3D Gaussian Splatting for Unconstrained Image Collections
Reconstructing and segmenting scenes from unconstrained photo collections obtained from the Internet is a novel but challenging task.
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.