A year since the launch of the TDS Author Payment Programhttps://towardsdatascience.com/announcing-the-towards-data-science-author-payment-program/, we couldn’t...

We quantify, uniformly over time and with high probability, the discrepancy between the predictions of a two-layer neural network trained by stochastic gradient...

Federated Learning (FL) faces major challenges in real-world deployments due to statistical heterogeneity across clients and system heterogeneity arising from r...

Embedding models are crucial to modern NLP. However, the creation of the most effective models relies on carefully constructed supervised finetuning data. For h...

Training tool-use agents typically relies on outcome-based filtering: Supervised Fine-Tuning (SFT) on successful trajectories and Reinforcement Learning (RL) on...

We introduce Legal RAG Bench, a benchmark and evaluation methodology for assessing the end-to-end performance of legal RAG systems. As a benchmark, Legal RAG Be...

Large language models (LLMs) have become an essential tool for natural language processing and artificial intelligence in general. Current open-source models ar...

While dense biomedical embeddings achieve strong performance, their black-box nature limits their utility in clinical decision-making. Recent question-based int...

Tool-using LLM agents face a reliability-cost tradeoff: routing every decision through the LLM improves correctness but incurs high latency and inference cost, ...

Speculative Decoding (SD) has emerged as a premier technique for accelerating Large Language Model (LLM) inference by decoupling token generation into rapid dra...

Determining a winner among a set of items using active pairwise comparisons under a limited budget is a challenging problem in preference-based learning. The go...

Autonomous Networks – The Next Frontier in Telecom Autonomous networks – intelligent, self‑managing telecommunications operations – are shifting from a future...