Next-Gen Sponsored Search: Crafting the Perfect Query with Inventory-Aware RAG (InvAwr-RAG) Based GenAI

Sponsored search plays a crucial role in e-commerce revenue generation, where advertisers strategically bid on keywords to capture the attention of users through relevant search queries. However, the process of identifying pertinent keywords for a given query presents significant challenges because of a vast and evolving keyword landscape, ambiguous intentions, and topic diversity. This paper highlights an opportunity for to earn a considerable amount of Ads revenue and user engagement where a significant proportion of queries fail to retrieve any sponsored ads. To utilize this opportunity, we introduce the Inventory-Aware RAG-based Generative AI model (InvAwr-RAG), which integrates advanced semantic retrieval and real-time inventory data. This model combines dynamically generated and historically successful queries to align with available inventory and ad campaigns while diversifying rewritten queries to enhance relevance and user engagement. Preliminary results show a significant 68% increase in fill rate and balanced relevance metrics, indicating a strong potential for increased ad revenue. The InvAwr-RAG model sets a new standard in dynamic query optimization, significantly improving ad relevancy, advertiser ROI, and user experience on Walmart's digital platform.

Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration

Deep search is brittle on underspecified user queries: missing constraints such as time, location, scope, or definitions can lead to retrieval drift and incomplete answers. We introduce Clarify-Then-Search, a benchmark for evaluating whether LLM-generated clarification questions improve downstream deep-search utility. Built on real-world query data from the Baidu search engine, the benchmark contains 518 curated instances, each with an intent query and a corresponding underspecified query. For each intent query, we run WebDancer once to archive evidence and construct a static golden reference as weighted, evidence-grounded nuggets with traceable source identifiers. At evaluation time, a Clarifier asks k in {1, 2, 3} questions; a closed-book User Answerer replies only with information explicitly stated in the intent query, otherwise returning unknown; and a closed-book Rewriter produces a rewritten query using only the underspecified query and the elicited question-answer pairs. WebDancer then executes on the rewritten query, and we score end-to-end utility using restore_score_100, a weighted nugget-recall score with partial credit against the static gold. Across all evaluated models, clarification improves over the no-interaction baseline at k=1, and larger budgets generally yield further gains. GPT-5.2 achieves the highest mean score at k=1, while ERNIE-4.5-Turbo-128K becomes the overall top-performing model at k=3. Diagnostics reveal a consistent failure mode: many systems over-ask region-only questions that are often unanswerable from the intent and thus elicit unknown. Clarify-Then-Search enables leakage-resistant and reproducible evaluation of clarify-then-search pipelines, with fine-grained analyses of question utility, answerability, and budget effects in deep search.

When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval

Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retrieval models have achieved notable progress, empirical studies show that BM25 continues to serve as a strong baseline in this domain. It motivates us to propose a self-evolving framework for rule-driven query rewriting that enhances BM25 without any parameter training. The framework equips an LLM-based agent with an automatic evaluation environment, enabling it to iteratively create rewriting rules, plan validation experiments over rule combinations, and eliminate ineffective rules based on historical feedbacks. We evaluate our method on the Chinese legal case retrieval benchmark LeCaRD-v2. Experimental results demonstrate that the proposed framework outperforms non-evolutionary baselines, including human-designed rules and greedy rule selection, particularly when powered by a highcapacity core LLM. We also conduct detailed analyses to investigate the mechanisms underlying self-evolution. Our findings reveal that LLM's capabilities to leverage previous experimental results and its intrinsic knowledge of rule elimination play critical roles in refining the rule set via self-evolution.

CoRe: A Continuously Reward-Finetuned LLM Query Rewriter for Multi-Stage Context-Aware Relevance in Web-Scale Video Search

LLM-based query rewriters in production face a tension: the training reward must reflect how the rewrite is consumed by the production ranker, yet the training procedure must be cheap enough to support continuous redeployment as data drifts. We present CoRe (Context Relevance), such a system, redeployed weekly for over five months in a major short-video search engine. Our reward uses the deployed multimodal relevance model as its source and a multiplicative ratio form mirroring the production fusion algebra, closing the simulation-production gap that offline reward proxies leave open. A semi-online Mixed Preference Optimization loop makes this reward affordable at multi-million-instance weekly scale: a DPO-style pairwise objective restricts the gradient pass to a small top-k/bottom-k subset of sampled trajectories, and a phase structure reduces trainer/inference-server parameter syncs from per-step to per-phase. An automated promotion gate over reward-like and stability metrics detected and recovered from a real reward-hacking incident in production. Rewriter output is consumed as parallel relevance signals at recall, rawrank, and finerank without displacing the original signals, bounding rewriter-failure blast radius. Online A/B from two sequential production launches, first deploying the rewriter at finerank, then extending consumption to recall and rawrank, delivers statistically significant reductions in change-query rate on rewrite-impacted queries, with all headline relevance and engagement metrics moving in the expected direction.

Sifei at SemEval-2026 Task 8: Hybrid Retrieval and Query Rewriting for Multi-Turn RAG

Multi-turn retrieval-augmented generation (RAG) is challenging due to evolving user intent, conversational noise, and strict context limits. We propose a training-free hybrid retrieval pipeline for SemEval-2026 Task 8 that combines dense and sparse retrieval with controlled query rewriting and cross-encoder reranking. On the official test set of Task A, our system achieves 0.5453 nDCG@5, ranking third among 38 teams and outperforming the strongest baseline score of 0.4795. For Task C, we reuse the documents retrieved for Task A and apply a lightweight generation pipeline guided by the official prompt, achieving 0.5312 as the harmonic mean of relevance and faithfulness and ranking 15th among 29 teams. All retrieval components are open-source, while query rewriting and answer generation rely on LLM APIs.

RCEM: Embedder Equipped with Query Rewriting Skill for Robust Conversational Search in Distributional Shift

Conversational search has become increasingly important in retrieval-augmented generation (RAG) systems, where users interact with AI assistants through multi-turn conversations containing context-dependent queries. We propose RCEM, a conversational dense retrieval model that distills the query reformulation capability of LLMs into the embedding model, enabling context-aware retrieval without explicit query rewriting during inference. Unlike prior conversational dense retrieval approaches that learn direct conversation-to-document matching, RCEM aligns conversational-query embeddings with rewritten-query embeddings, improving robustness under distributional shift. RCEM does not require conversational query-to-document relevance mappings for training, which are often expensive and difficult to obtain with high quality. Extensive experiments on QReCC, TopiOCQA, and TREC CAsT demonstrate that RCEM consistently outperforms strong conversational retrieval baselines, achieving particularly large gains under distributional shift, including up to 20% improvement in Recall@10. RCEM further extends the base embedding model with conversational query rewriting capability while preserving its original retrieval functionality, allowing both standalone and conversational queries to be encoded by a single model and searched against existing document indexes without rebuilding the retrieval database.