The paper presents SearchLog, a web browser extension for collecting natural search logs during lab-based studies.

This paper evaluates the use of AI-generated summaries on search engine results pages (SERPs) in academic search for social science information.

The paper introduces WebKnoGraph, an open-source framework for systematically evaluating internal linking strategies on websites by modeling the site as a graph and assessing trade-offs between author…

This paper introduces IntentTune, a framework for inferring user intent from under-specified queries in e-commerce search using user-specific behavioral signals and population-level demand patterns.

The paper argues that current search agents often verify existing knowledge rather than genuinely searching, and introduces LiveBrowseComp, a new benchmark to measure true evidence-driven discovery.

The paper introduces a framework for measuring AI visibility using two complementary criteria: rank stability and structural sufficiency.

Xetrieval introduces an embedding-level framework to mechanistically explain dense retrieval decisions by decomposing high-dimensional embeddings into sparse, human-interpretable features.

This paper introduces a behavior-grounded Large Language Model (LLM) judge for evaluating search engine result pages, improving alignment with user preferences by up to 15% in a multilingual dataset.

SkillPager is a novel two-stage framework that efficiently selects minimal, execution-sufficient context from large procedural skill documents by leveraging typed semantic nodes, significantly reducin…

The paper proposes formulating RAG design as an architecture search problem and introduces RAISE, a comprehensive framework and benchmark for systematically optimizing RAG hyperparameters.

This paper systematically analyzes the resilience of LLM-enhanced search engines against black-hat SEO attacks, finding that while they block most traditional attacks, they remain vulnerable to sophis…

This paper studies the linear decodability of query-document relevance from residual-stream activations in instruction-tuned large language models (LLMs) and compares it with generated relevance judgm…

The paper presents CoRe, a query rewriter system that uses the deployed multimodal relevance model as its source for reward and closes the simulation-production gap, allowing for weekly redeployment.

This paper presents PermR, a lightweight algorithm for reranking search results in e-commerce platforms to maximize revenue while preserving relevance and other constraints.

The paper proposes a low-cost and interpretable fine-tuning extraction strategy for automatic term extraction, demonstrating consistent and balanced performance on the ATE Shared Task.

This paper proposes TSGR, a generative retrieval framework for industrial e-commerce search that incorporates value awareness into item representation and candidate ranking.

The paper introduces ChunkGroupSHAP, a listwise Shapley method that clusters semantantly related chunks into shared cross-document features for dense semantic ranking.

The paper introduces PlanRAG, a framework for Retrieval-Augmented Generation (RAG) that models exploratory reasoning problems as logical query trees, addressing representation and optimization gaps be…