Newest with code · page 2
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 16–30 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 16–30 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).
From Roots to Rewards: Dynamic Tree Reasoning with RL
Modern language models address complex questions through chain-of-thought (CoT) reasoning (Wei et al., 2023) and retrieval augmentation (Lewis et al., 2021), yet struggle with error propagation and knowledge integration.
Making Language Model a Hierarchical Classifier and Generator
Decoder-only language models, such as GPT and LLaMA, generally decode on the last layer.
Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural Networks
Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system.
FLEXITOKENS: Flexible Tokenization for Evolving Language Models
Language models (LMs) are challenging to adapt to new data distributions by simple finetuning.
A Fuzzy Approach to Project Success: Measuring What Matters
This paper introduces a novel approach to project success evaluation by integrating fuzzy logic into an existing construct.
Best Practices for Large-Scale, Pixel-Wise Crop Mapping and Transfer Learning Workflows
Crop mapping involves identifying and classifying crop types using spatial data, primarily derived from remote sensing imagery.
Assay2Mol: large language model-based drug design using BioAssay context
Scientific databases aggregate vast amounts of quantitative data alongside descriptive text.
PhysX: Physical-Grounded 3D Asset Generation
3D modeling is moving from virtual to physical.
SpatialTrackerV2: 3D Point Tracking Made Easy
We present SpatialTrackerV2, a feed-forward 3D point tracking method for monocular videos.
Mitigating Object Hallucinations via Sentence-Level Early Intervention
Multimodal large language models (MLLMs) have revolutionized cross-modal understanding but continue to struggle with hallucinations - fabricated content contradicting visual inputs.
Describe Anything Model for Visual Question Answering on Text-rich Images
Recent progress has been made in region-aware vision-language modeling, particularly with the emergence of the Describe Anything Model (DAM).
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
The landscape of video recognition has evolved significantly, shifting from traditional Convolutional Neural Networks (CNNs) to Transformer-based architectures for improved accuracy.
Developing Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker
Traditional information extraction systems face challenges with text only language models as it does not consider infographics (visual elements of information) such as tables, charts, images etc.
Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation
Calisthenics skill classification is the computer vision task of inferring the skill performed by an athlete from images, enabling automatic performance assessment and personalized analytics.
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting.
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.