Matching users to interest categories at scale is central to personalized shopping, but the task is challenging in large e-commerce platforms, where label spaces continually evolve and user-interest signals are sparse and long-tailed. Autoregressive language models are appealing because their world knowledge and semantic priors over descriptors generalize across extreme label spaces and accommodate multiple valid label assignments. Yet under teacher-forced fine-tuning, inference-time predictions become part of the conditioning context: early errors steer later outputs toward co-occurring labels, over-generating near-correlates and missing unrelated true interests. We present MERIT, a framework for user-interest propensity modeling that mitigates this exposure bias through a self-correction objective. A permutation-invariant multi-target loss over shuffled mixtures of gold and mined hard-negative labels exposes the generator to erroneous prefixes while preserving the efficiency of teacher-forced training. This training objective concentrates supervision at classification positions, yielding propensity-aligned hidden states powering a lightweight scorer for bidirectional retrieval (interests for users and users for interests). On a proprietary e-commerce dataset with 250k+ interest categories, MERIT improves global recall by at least 11.9% and average Hit@k by 6.1%. In production A/B tests, it achieves +0.26% gain in user conversion.

Can an LLM read a 30+ page Standard Operating Procedure (SOP) and produce a team of agents that reliably execute it? Industrial SOPs bury workflow logic in prose, leave inter-step dependencies implicit, and scatter referenced entities across sections, forcing planners to parse, sequence, and tool-map within one context window. We argue that the missing primitive is procedural chunking: decomposing a document into self-contained executable units. Unlike RAG-style chunking, each chunk must be independently executable and chunks must jointly form a valid DAG encoding data and process dependencies, turning a prose SOP into a compact orchestrable workflow graph. We present ProChunker, a three-phase self-optimizing LLM pipeline (Boundary Detection, Metadata Extraction, Dependency Construction) with Stochastic Best-of-N prompt exploration guided by five custom metrics (Boundary Clarity, Entity Reference Coverage, Agent Self-Containedness Agreement, Step Coverage, Graph Validity); we then fine-tune Qwen3-1.7B on the pipeline’s own phase outputs, cutting latency by ∼27 min. Across three corpora (500 BioProBench protocols, six Industrial SOPs, 14 SOP-Bench documents), ProChunker improves the five-metric composite over the strongest baseline by +0.036 to +0.111 across corpora. Our study shows ProChunker increases agent construction success rate by +17% and resolves >90% of procedural ambiguity.

Automated construction of shopping cart frommedical prescriptions is a vital prerequisite forscaling up online pharmaceutical servicesin emerging markets due to the high prevalence of paper prescriptionsthat are challenging for customers to interpret.We present RxLens, a multi-step end-end Large Language Model (LLM)-based deployed solutionfor automated pharmacy cart construction comprisingmultiple steps: redaction of Personal Identifiable Information (PII),Optical Character Recognition (OCR), medication extraction, matching against the catalog, and bounding box detection for lineage. Our multi-step design leverages the synergy between retrieval and LLM-based generationto mitigate the vocabulary gaps in LLMs and fuzzy matching errors during retrieval.Empirical evaluation demonstrates that RxLens can yield up to 19% - 40% and 11% - 26% increase in Recall@3 relative to SOTA methods such as Medical Comprehend and vanilla retrieval augmentation of LLMs on handwritten and printed prescriptions respectively.We also explore LLM-based auto-evaluation as an alternative to costly manual annotations and observe a 76% - 100% match relative to human judgements on various tasks.

Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions to Structured Query Language (SQL) queries. With advancements in Large Language Models (LLM), in-context learning (ICL) has emerged as a popular choice for building Text-to-SQL systems. Real world, industry-scale databases, often comprise thousands of tables and hundreds of columns, and makes passing the entire schema as context to an LLM infeasibly expensive. This requisites access to the correct database and the set of tables. Recently Retrieval Augmented Generation (RAG) based methods have been proposed for retrieving relevant subset of databases and tables for a given query. However, we observe that the existing methods of synthetic query generation can generate predominantly simple queries which might not be sufficiently representative of complex, real world queries, thus, negatively affecting the quality of the generated SQL. To address this, we propose an innovative in-context reinforcement learning (ICRL) based framework which refines the question generation process by enhancing the model’s ability to produce intricate queries that practitioners may pose during inference. In contrast to the existing approaches, our framework ensures the generation of synthetic SQL queries which are diverse and complex. We demonstrate the effectiveness of our approach via multiple experiments comparing against the representative state-of-the-art models on public benchmark datasets and observe substantial improvements in performance and scalability. Our method achieves 15-20% higher recall in database/table retrieval task compared to the existing state-of-the-art models for schema identification and upto 2% higher execution accuracy for SQL generation.

Accurate mapping of queries to product categories is crucial for efficient retrieval and ranking of relevant products in e-commerce search. Conventionally, such query classification models rely on supervised learning using historical user interactions, but their effectiveness diminishes in cold-start scenarios, where new categories or products lack sufficient training data. This results in poor query-to-category mappings, negatively affecting retrieval and ranking. Synthetic query generation has emerged as a promising solution by augmenting training data; however, existing methods do not incorporate feedback from the query relevance model, limiting their ability to generate queries that enhance product retrieval. To address this, we propose an adversarial reinforcement learning framework that optimizes an LLM-based generator to expose weaknesses in query classification models. The generator produces synthetic queries to augment the classifier’s training set, ultimately improving its performance. Additionally, we introduce a structured reward signal to ensure stable training. Experiments on public datasets show an average PR-AUC improvement of +1.82% on benchmarks and +3.26% on a proprietary dataset, demonstrating the framework’s effectiveness in enhancing query classification and mitigating cold-start challenges.

Query Auto-Complete (QAC) is an essential search feature that suggests users with a list of potential search keyword completions as they type, enabling them to complete their queries faster. While the QAC systems in eCommerce stores generally use the Learning to Rank (LTR) approach optimized based on customer feedback, it struggles to provide diverse suggestions, leading to repetitive queries and limited navigational suggestions related to product categories, attributes, and brands. This paper proposes a novel DiAL framework that explicitly optimizes for diversity alongside customer feedback signals. It achieves this by leveraging a smooth approximation of the diversity-based metric (𝛼NDCG) as a listwise loss function and modifying it to balance relevance and diversity. The proposed approach yielded an improvement of 8.5% in mean reciprocal rank (MRR) and 22.8% in 𝛼NDCG compared to the pairwise ranking approach on an eCommerce dataset, while meeting the ultra-low latency constraints of QAC systems. In an online experiment, the diversity-aware listwise QAC model resulted in a 0.48% lift in revenue. Furthermore, we replicated the proposed approach on a publicly available search log, demonstrating improvements in both diversity and relevance of the suggested queries.