Customer-obsessed science

Large language models (LLMs) are increasingly deployed in decision-making tasks, where not only accuracy but also reliable confidence estimates are essential. Well-calibrated confidence enables downstream systems to decide when to trust a model and when to defer to fallback mechanisms. In this work, we conduct a systematic study of calibration in two widely used fine-tuning paradigms: supervised fine-tuning

Pretrained models are routinely extended with new vocabulary entries, for example new items in recommender systems and new tokens in large language models (LLMs). We show that jointly training old and new embeddings has a hidden failure mode: old-entry quality degrades while new entries are still learning. On sequential recommendation, old items overfit while new items improve, forcing premature early stopping

Pretrained time-series foundation models can condition on future-known covariates, but event covariates are often limited to binary indicators or sparse categorical labels. This is problematic for forecast-based anomaly detection: in financial transaction series, recurring holidays, promotions, and settlement cycles induce predictable shifts that event-unaware systems may flag as false positives. We propose

Many applications of LLM-based text regression require predicting a full conditional distribution rather than a single point value. We study distributional regression under empirical-quantile supervision, where each input is paired with multiple observed quantile outcomes, and the target distribution is represented by a dense grid of quantiles. We address two key limitations of current approaches: the lack

Multi-hop reasoning remains a fundamental challenge for Retrieval-Augmented Generation (RAG) systems. Recent approaches-from adaptive retrieval to agentic pipelines-struggle to maintain coherent intermediate reasoning states as chains grow longer. We introduce State-Aware RAG, a framework that addresses this limitation through an explicit working memory that serves as a dynamic cognitive workspace for reasoning