Papers with a repository link where Syntology ran at least one harvested code sample. 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.
Cards 46–60 of 5,105 graph papers newer than 2025-07-28 with a Syntology-ran sample; this feed shows the newest 150, newest arXiv id first. Dates and the abstract sentence are from arXiv's metadata (CC0) for 5,105 of 5,105.
Explainable artificial intelligence (XAI) increasingly calls for actionable counterfactual recourse, yet current methodologies face challenges related to causal invalidity, excessive cognitive burden, and predictive…
Recent prominent post-training methods, such as Reinforcement Learning (RL) and On-Policy Self-Distillation (OPSD), have driven rapid progress in mathematical reasoning for large language models, yet their reliance on…
Friend recommendation is inherently graph-structured: the relevance of a potential connection depends on multi-hop social context rather than user attributes alone.
Language model pretraining has become almost synonymous with prohibitive cost, placing it out of reach for much of the academic and open-source communities.
Operator learning in scientific machine learning is concerned with approximation of maps between infinite-dimensional function spaces; such maps frequently arise as the solution operators of partial differential…
LLM agents are increasingly applied to anomaly detection and root-cause analysis in time-series observations collected from real-world systems; however, their performance on these tasks has not been systematically…
Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless communication.
Privacy-Enhancing Technologies (PETs) in computer vision often rely on noise or image perturbations to protect visual data while securely processing it, creating a trade-off between task performance and protection.
Korean constituency parsing raises a representational challenge because the terminal units of a phrase-structure tree do not straightforwardly correspond to simple surface words.
Direct Preference Optimization (DPO) is a widely used objective for aligning language models from preference data, with the coefficient $β$ commonly interpreted as controlling the KL constraint to a reference policy.
Pre-training under limited data requires a different view of scaling than web-scale language modeling.
The National Oceanic and Atmospheric Administration (NOAA) employs independent prediction systems for distinct forecast products.
Rerankers, reward models and multi-document QA scorers score candidate documents or responses in one LLM prompt, so each score depends on their order.
In recent years, transductive knowledge graph embedding models have been applied to tasks such as link prediction and query answering.
While AI-assisted text-based counseling is gaining attention, it remains empirically unclear which counselor behaviors are associated with higher dialogue quality.
Cards 46–60 of 31,700 archive papers with a code link, filtered to those where Syntology ran at least one sample (the archive chip labels the rows, the Syntology chip labels the filter; the count is the archive rows that pass it); this feed shows the newest 150, archive date first (newest archive date 2025-07-17). Within a month, dated rows come first, then the 819 undated rows placed by the month in their arXiv id. 1 archive paper with neither a date nor an arXiv id cannot be placed and is not listed.
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to execute sequential navigation actions in complex environments guided by natural language instructions.
Recent advances in optical flow estimation have prioritized accuracy at the cost of growing GPU memory consumption, particularly for high-resolution (FullHD) inputs.
The recent introduction of diffusion models in dataset distillation has shown promising potential in creating compact surrogate datasets for large, high-resolution target datasets, offering improved efficiency and…
We present Seg-R1, a preliminary exploration of using reinforcement learning (RL) to enhance the pixel-level understanding and reasoning capabilities of large multimodal models (LMMs).
Image restoration is a key task in low-level computer vision that aims to reconstruct high-quality images from degraded inputs.
In this paper, we present LLaVA-Scissor, a training-free token compression strategy designed for video multimodal large language models.
Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge.
Learning the hierarchical structure of data in vision-language models is a significant challenge.
Causal reasoning capability is critical in advancing large language models (LLMs) toward strong artificial intelligence.
The real world naturally has dimensions of time and space.
Fine-tuning vision language models (VLMs) has achieved remarkable performance across various downstream tasks; yet, it requires access to model gradients through backpropagation (BP), making them unsuitable for…
Large language models are trained on massive corpora of web data, which may include private data, copyrighted material, factually inaccurate data, or data that degrades model performance.
Pre-training state-of-the-art large language models (LLMs) requires vast amounts of clean and diverse text data.
Contrastive learning (CL) has become a cornerstone of self-supervised pretraining (SSP) in foundation models, however, extending CL to pixel-wise representation, crucial for medical vision, remains an open problem.
Survival prediction using whole slide images (WSIs) can be formulated as a multiple instance learning (MIL) problem.
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.