Rishita Agarwal, Himanshu Singhal, Peter Baile Chen, Manan Roy Choudhury, Dan Roth, Vivek Gupta. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Real-world open-domain questions can be complex, especially when answering them requires integrating information from multiple sources.Effectively identifying the necessary information involves aligning it with the available data and its organization.However, existing RAG solutions address the alignment problem in a limited manner.Using off-the-shelf LLMs for question decomposition lacks awareness of the available data and its structure, often resulting in suboptimal retrieval performance.Alternatively, iteratively generating follow-up queries and interacting with the data collection, as explored in agentic RAG approaches, shows potential but is often inefficient since each successive query depends on previous results rather than being guided by the overall organization of the available data.To address the alignment problem, we introduce an LLM-based retrieval method -ARM, designed to better align questions with the organization of the data collection.Instead of solely matching query utterance, ARM explores relationships among data objects, enabling a retrieve-all-atonce solution for complex queries.Experimental results demonstrate that ARM significantly outperforms existing RAG methods on various complex open-domain QA tasks across multiple modalities, achieving superior retrieval performance and downstream accuracy while significantly lowering monetary costs. 1

Query scheduling is a critical task that directly impacts query performance in database management systems (DBMS). Deeply integrated schedulers, which require changes to DBMS internals, are usually customized for a specific engine and can take months to implement. In contrast, non-intrusive schedulers make coarse-grained decisions, such as controlling query admission and re-ordering query execution, without requiring modifications to DBMS internals. They require much less engineering effort and can be applied across a wide range of DBMS engines, offering immediate benefits to end users. However, most existing non-intrusive scheduling systems rely on simplified cost models and heuristics that cannot accurately model query interactions under concurrency and different system states, possibly leading to suboptimal scheduling decisions. This work introduces IconqSched , a new, principled non-intrusive scheduler that optimizes the execution order and timing of queries to enhance total end-to-end runtime as experienced by the user — query queuing time plus system runtime. Unlike previous approaches, IconqSched features a novel predictor, Iconq , which treats the DBMS as a black box and accurately estimates the system runtime of concurrently executed queries under different system states. Using these predictions, IconqSched is able to capture system runtime variations across different query mixes and system loads. It then employs a greedy scheduling algorithm to effectively determine which queries to submit and when to submit them. We compare IconqSched to other schedulers in terms of end-to-end runtime using realistic workload traces. On Postgres, IconqSched reduces end-to-end runtime by up to 16.5% on average and 33.6% in the tail. Similarly, on Redshift, it reduces end-to-end runtime by up to 14.4% on average and 22.9% in the tail.

Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems.Previous methods assume the answer to such a question can be found either in a single table or multiple tables identified through question decomposition or rewriting.However, neither of these approaches is sufficient, as many questions require retrieving multiple tables and joining them through a join plan that cannot be discerned from the user query itself.If the join plan is not considered in the retrieval stage, the subsequent steps of reasoning and answering based on those retrieved tables are likely to be incorrect.To address this problem, we introduce a method that uncovers useful join relations for any query and database during table retrieval.We use a novel re-ranking method formulated as a mixed-integer program that considers not only table-query relevance but also table-table relevance that requires inferring join relationships.Our method outperforms the state-of-the-art approaches for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up to 5.4% in accuracy.

Causal inference can quantify cause-effect relationships in domains as varied as medicine, economics and public policy. Production computer systems exhibit a similar level of complexity and a recurring need to diagnose problems quickly. However, systems are only observed imperfectly, often via long, messy, semi-structured logs. In this work, we want to accelerate large systems debugging by applying causal inference over logs, enabling engineers to diagnose problems and assess interventions in a principled manner. Our framework achieves this through two human-in-the-loop modules: (1) The Candidate Cause Ranker , through which one can determine the causes of a variable without running a full causal discovery algorithm; and (2) the Interactive Causal Graph Refiner , which helps engineers compute an unbiased estimation of their effect of interest without extensive manual causal graph verification. Both modules are powered by the insight that only part of the causal graph of the system is needed to correctly quantify a given effect of interest. We also provide a data preparation pipeline, the Log Converter , which transforms raw, messy, real-world logs into an appropriate tabular input for causal inference, using methods drawn from data transformation, cleaning, and extraction. We evaluate LOGos, a prototype implementation, on both real-world and synthetic logs and find that: (1) The Candidate Cause Ranker achieved an average precision 1.08×-18× higher than the baselines, in interactive time; (2) The Interactive Causal Graph Refiner required a number of causal judgments 1.61 × - 16.83× lower than the baselines; and (3) The latency of Log Converter scaled linearly with three measures of the complexity of a log: length, distinct templates, and fraction of tokens that are variables.