arXiv:2603. 27476v2 Announce Type: replace Abstract: AI-powered people search platforms are increasingly used in recruiting, sales prospecting, and professional networking, yet no widely accepted benchmark exists for evaluating their performance.
arXiv:2603.27476v3 Announce Type: replace Abstract: AI-powered people search platforms are increasingly deployed for recruiting, sales prospecting, and professional networking, yet no standardized be...
arXiv:2606. 12871v1 Announce Type: new Abstract: Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information into comprehensive responses.
Clarify-Then-Search is a benchmark that tests whether large language models can ask clarification questions to improve the usefulness of deep search results. It uses 518 real-world query pairs from Baidu, where each intent query is paired with an underspecified version. The evaluation involves a clarifier asking up to three questions, a user answerer providing only explicit information, and a rewriter generating a new query that is then searched; performance is measured by a weighted nugget-recall score.
Q2D-Web is a new large‑scale benchmark for agentic Retrieval‑Augmented Generation (RAG) systems, featuring a 190 million‑document web corpus and 70 k machine‑reformulated search queries in ten languages. It supplies three sets of relevance judgments—agent citations, production rankings, and a combined set enriched with LLM‑based labels—to evaluate first‑stage retrievers. Experiments on 13 retrievers show consistent ranking across judgment sets but significant variation across domains, languages, and query types, and demonstrate that a carefully sampled sub‑corpus can approximate full‑corpus evaluation with minimal loss in Recall@1000.
arXiv:2607.10198v2 Announce Type: replace Abstract: Search APIs expose ranked snippets, URLs, and metadata on which agents decide whether to answer, search again, or fetch pages. We evaluate these in...
arXiv:2606.00660v2 Announce Type: replace Abstract: Agentic search requires language model agents to explore many sources and answer complex information-seeking questions. Scaling test-time compute i...
SEEK (Skill‑Routed Evaluation with Evolvable Knowledge) is a framework that externalizes search evaluation criteria into a skill bank, dynamically routes relevant skills for each query‑result pair, and uses a task‑adapted listwise evaluator to generate page‑level judgments and failure‑mode attribution. It employs a two‑stage training pipeline to align evaluation with human preferences and a replay‑gated skill bank to incorporate new evaluation knowledge without retraining the model. Experiments on industrial short‑video search demonstrate that SEEK improves listwise quality evaluation accuracy and significantly advances attribution diagnosis, leading to its deployment at Kuaishou with over 400 million daily active users.
Benchmark Radar is a living database and search engine that aggregates AI benchmark papers, datasets, code, and score histories. It automatically discovers new benchmark resources from 37 sources, maintains a catalog of 1,283 records with 12,916 numeric observations, and provides tools such as a web dashboard, CLI, and downloadable evidence for researchers. The system also offers visualizations like a Pareto frontier and trend views to help users assess benchmark saturation and adoption.
arXiv:2607. 28229v1 Announce Type: cross Abstract: The web is increasingly accessed by AI agents rather than humans.
The study examines how conversational LLM agents—specifically ChatGPT, Claude, Grok, and DeepSeek—use Web search, combining real user interactions with controlled API experiments. It finds that agents differ in when they decide to search, how they craft queries, and which domains they favor, and that more frequent searching does not always improve answer quality. While most responses are grounded in search results, some claims are unsupported, raising attribution concerns.
arXiv:2608. 06202v1 Announce Type: cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness.
SEEK (Skill‑Routed Evaluation with Evolvable Knowledge) is a framework that externalizes search evaluation criteria into a skill bank, dynamically routes relevant skills for each query‑result pair, and uses a task‑adapted listwise evaluator to generate page‑level judgments and failure mode attribution. It employs a two‑stage training pipeline to align the evaluator with human preferences and a replay‑gated skill bank to incorporate new evaluation knowledge without retraining the model. Experiments on Kuaishou’s short‑video search demonstrate that SEEK improves listwise quality evaluation accuracy and enhances attribution diagnosis, leading to better online search evaluation at scale.