Retrieval-augmented search agents answer multi-hop questions by repeatedly issuing search queries and accumulating evidence. This creates a stopping problem: after the necessary evidence has appeared, further retrieval often adds cost, latency, and distracting context rather than useful information. We frame stopping as evidence coverage rather than generator confidence, and introduce HALT, a lightweight verification-aware policy that leaves the search agent unchanged. Given expected hop claims, HALT stops only when cumulative evidence supports each required claim. Across three multi-hop QA benchmarks, HALT reduces redundant search while largely preserving exact match, with stronger savings under gold claims and more conservative behavior under generated claims. Baseline comparisons and ablations show that this behavior is driven by claim–evidence alignment rather than generic sufficiency, fixed stop positions, or lexical overlap. Open-corpus pilots further suggest that HALT abstains when coverage cannot be reliably verified. Overall, evidence coverage provides a practical runtime control signal for improving retrieval-augmented agents without retraining or modifying the host agent.

Large Language Models (LLMs) have shown remarkable potential in recommendation systems but suffer from prohibitive inference latency. Existing distillation approaches typically target Small Language Models (SLMs) or Conventional Recommendation Models (CRMs), face a critical trade-off between computational cost and semantic reasoning capacity. To bridge this accuracy-efficiency gap, we introduce Reasoning-to-Encoder Distillation (R2END), a framework that establishes a text encoder as the optimal student architecture for scalable recommendation. Unlike methods that mimic token generation, R2END compresses the teacher’s reasoning into a dense vector space via a semantic alignment objective, effectively capturing user-item dynamics. Extensive experiments on four datasets demonstrate that R2END not only outperforms state-of-the-art baselines but also achieves drastically reduced latency, offering a sweet spot for recommendation.

Eliciting high-quality reasoning trajectories to supervise Large Language Models (LLMs) in sequential recommendation is notoriously difficult. In massive item pools, standard data curation methods such as autonomous exploration or rejection sampling suffer from severe sparsity. LLMs’ context-utilization bias exacerbate this challenge by overusing noisy interaction histories, yielding diffuse and weakly grounded reasoning paths that frequently fail to retrieve the target item. To overcome this data curation bottleneck, we propose GEARec (Grounded Reasoning Curation for Anchor-based Recommendation), a framework based on Dual-Perspective Anchor Extraction and Hybrid Reasoning Generation. By combining the semantic strengths of LLMs with collaborative filtering (CF) signals, GEARec identifies a high-relevance anchor item from the user’s history to construct highly focused, reachability-aware reasoning trajectories for supervised fine-tuning. Experiments on multiple datasets demonstrate that GEARec effectively curates superior supervision signals, reducing context overuse and consistently outperforming strong baselines in recommendation accuracy.

Documents that users query mix tables with prose, but the QA systems built for them do not: retrieve-and-read pipelines flatten tables, while text-to-SQL pipelines ignore passages. Corporate filings, scientific reports, and Wikipedia articles all sit on this fault line, and a single user question routinely crosses it. We present ATR (AgenticTableRAG), an agentic framework for hybrid table-text question answering. ATR decomposes each question into ordered sub-queries and dispatches each to one of four execution primitives: text reading, cell retrieval, SQL execution, or a hybrid of cell grounding and SQL. When the retrieved evidence does not support the predicted answer, the router is re-invoked with the failed-route history and dispatches a different primitive. Value grounding composed with constrained SQL execution rules out column and value hallucinations by construction. The routing policy is distilled into a lightweight student model that transfers across benchmarks without additional inference-time LLM calls. A single ATR configuration leads on token F1 across three benchmarks without per-dataset tuning.

Recent studies have explored integrating large langucage models (LLMs) into recommendation systems but face several challenges, including training-induced bias and bottlenecks from serialized architecture.To effectively address these issues, we propose a Query-to-Recommendation, a parallel recommendation framework that decouples LLMs from candidate pre-selection and instead enables direct retrieval over the entire item pool. Our framework connects LLMs and recommendation models in a parallel manner, allowing each component to independently utilize its strengths without interfering with the other. In this framework, LLMs are utilized to generate feature-enriched item descriptions and personalized user queries, allowing for capturing diverse preferences and enabling rich semantic matching in a zero-shot manner. To effectively combine the complementary strengths of LLM and collaborative signals, we introduce an adaptive reranking strategy. Extensive experiments demonstrate an improvement in performance up to 57%, while also improving the novelty and diversity of recommendations.

Recent advancements in large language models (LLMs) have boosted research on generating SQL queries from domain-specific questions, particularly in the medical domain. A key challenge is detecting and filtering unanswerable questions. Existing methods often relying on model uncertainty, but these require extra resources and lack interpretability. We propose a lightweight model that predicts relevant database schemas to detect unanswerable questions, enhancing interpretability and addressing the data imbalance in binary classification tasks. Furthermore, we found that LLM-generated schema descriptions can significantly enhance the prediction accuracy. Our method provides a resource-efficient solution for unanswerable question detection in domain-specific question answering systems.

Recently, deep learning-based language models have significantly enhanced text-to-SQL tasks, with promising applications in retrieving patient records within the medical domain. One notable challenge in such applications is discerning unanswerable queries. Through fine-tuning model, we demonstrate the feasibility of converting medical record inquiries into SQL queries. Additionally, we introduce an entropy-based method to identify and filter out unanswerable results. We further enhance result quality by filtering low-confidence SQL through log probability-based distribution, while grammatical and schema errors are mitigated by executing queries on the actual database.We experimentally verified that our method can filter unanswerable questions, which can be widely utilized even when the parameters of the model are not accessible, and that it can be effectively utilized in practice.