Sumit

recsys.substack.comSearchJev: A Small Calibrated Model for the Routine Decisions in Search Agents, Training-Free Semantic IDs for Generative Recommendation, and More!Vol.177 for Oct 05 - Oct 11, 2026

I published Vol. 177 of "Top Information Retrieval Papers of the Week" on Substack. 🔗 recsys.substack.com/p/searchjev-...

doi.orgEffBench: A Holistic Efficiency Benchmark for CTR Models | ACM Transactions on Information SystemsClick-Through Rate (CTR) prediction is a critical task in recommender systems and online advertising, so CTR models design has been widely researched in both academia and industry. However, since the model parameters increase larger and their computations ...

EffBench: A Holistic Efficiency Benchmark for CTR Models Introduces a benchmark that compares CTR models on efficiency as well as accuracy, with latency and throughput metrics and a profiling tool that finds bottlenecks. 📝 doi.org/10.1145/3857... 👨🏽‍💻 github.com/Yeedzhi/EffB...

doi.orgER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Source | ACM Transactions on Knowledge Discovery from DataRetrieval-Augmented Generation (RAG) enhances the reliability of large language models (LLMs) by integrating external evidence for question-answering tasks. However, when supporting evidence is scattered across heterogeneous data sources (e.g., web pages, ...

ER-RAG: Enhance RAG with ER-Based Unified Modeling of Heterogeneous Data Source Introduces a RAG framework that retrieves from web pages, databases and knowledge graphs through one shared API, with a trained module that picks which sources to query. 📝 doi.org/10.1145/3860...

github.comGitHub - EthanNing/SearchHorizonBench: A benchmark for search value in AI agents. Paired execution, gain prediction, and cost-aware decisions across 45,632 executions.A benchmark for search value in AI agents. Paired execution, gain prediction, and cost-aware decisions across 45,632 executions. - EthanNing/SearchHorizonBench

SearchHorizonBench: A Benchmark for Search Value in AI Agents Introduces a benchmark for judging when an extra web search is worth its cost for an AI agent, using paired runs that either answer directly or search further from the same point. 📝 arxiv.org/abs/2610.34714 👨🏽‍💻 github.com/EthanNing/Se...

arxiv.orgMuSID-KD: Multigranularity Knowledge Distillation for Generative Recommendation with Semantic IDsGenerative recommendation with semantic IDs (SIDs) represents each item as a sequence of discrete tokens and autoregressively predicts the next item's SID. Despite strong recommendation performance, r...

MuSID-KD: Multigranularity Knowledge Distillation for Generative Recommendation with Semantic IDs Improves distillation of semantic ID recommenders by aligning candidate group distributions at each ID level. 📝 arxiv.org/abs/2610.35218 👨🏽‍💻 anonymous.4open.science/r/m7k2-revie...

arxiv.orgCan System-1-Style Decision Models Replace Dedicated Rerankers in RAG? A Systems EvaluationRetrieval-augmented generation (RAG) systems often use a dedicated reranker to choose which retrieved candidates reach the language model. Reranking improves relevance but adds latency, so teams look ...

Can System-1-Style Decision Models Replace Dedicated Rerankers in RAG? A Systems Evaluation Tests decision models as reranker replacements. Truncation makes some competitive, but a tuned dedicated reranker is still faster and uses far less GPU memory. 📝 arxiv.org/abs/2610.34816

arxiv.orgMemory Tax: Dissecting the Net Value of Agent MemoryMemory is increasingly used in long-horizon agents, yet the net value of building memory over direct retrieval from raw history remains largely unclear. Through a large-scale evaluation across multipl...

Memory Tax: Dissecting the Net Value of Agent Memory Finds that many agent memory systems score below naive RAG over raw history. Splits memory's net value into benefit and tax. 📝 arxiv.org/abs/2610.34073 👨🏽‍💻 hexuan21.github.io/MemoryTax 🤗 huggingface.co/datasets/hex...

arxiv.orgPinGER: Token-Efficient Generative Retrieval with Semantic IDsGenerative retrieval replaces nearest-neighbor search with token-by-token decoding of an item's semantic ID (SID). But existing SID-based methods primarily use L levels of tokens sequentially to repre...

PinGER: Token-Efficient Generative Retrieval with Semantic IDs Pinterest presents its generative retriever that replaces nearest-neighbor search. An asymmetric attention mask trains with semantic ID supervision but serves only one token per item. 📝 arxiv.org/abs/2610.33435

arxiv.orgRethinking Retrieval in the Long-Context Era: Training-Free Semantic Trees for Adaptive Context ConstructionLong-context language models can increasingly process entire documents, making full-context prompting a practical alternative to passage retrieval. However, greater capacity determines how much inform...

Rethinking Retrieval in the Long-Context Era: Training-Free Semantic Trees for Adaptive Context Construction Builds a training-free semantic tree of a long document and gives the LLM only relevant sections. 📝 arxiv.org/abs/2610.34146 👨🏽‍💻 anonymous.4open.science/r/treefuse-E...

arxiv.orgLoopGR: Parameter-Efficient Scaling for Generative Recommendation via Recurrent DepthGenerative recommendation formulates item recommendation as autoregressive generation rather than a discriminative scoring problem. Although empirical studies exhibit favorable scaling behavior in rec...

LoopGR: Parameter-Efficient Scaling for Generative Recommendation via Recurrent Depth Reuses a shared decoder and a light context updater in recurrent loops to add depth without adding parameters, then distills it into a cheaper low-loop student. 📝 arxiv.org/abs/2610.34157

arxiv.orgTheoretically Sound Item Similarity Measures for Recommender SystemsQuantifying the similarity between items is fundamental to neighborhood-based collaborative filtering, yet many commonly used similarity measures are heuristics that lack theoretical justification. Th...

Theoretically Sound Item Similarity Measures for Recommender Systems Derives item similarity measures from Bernoulli and multinomial Naive Bayes, and shows cosine similarity solves a specific optimization problem. 📝 arxiv.org/abs/2610.34402 👨🏽‍💻 github.com/noah-daniels...

arxiv.orgKnowing When the Evidence Is Enough: Requirement-Directed Search with Horn-Closure CertificatesAfter each Web search, a search-augmented model must decide whether its evidence suffices: answer, search again, or abstain. Prevailing systems leave this to holistic model judgement, which can accept...

Knowing When the Evidence Is Enough: Requirement-Directed Search with Horn-Closure Certificates Decides whether to answer, search again, or abstain using a per-query requirement hypergraph and Horn closure instead of having a model judge the evidence. 📝 arxiv.org/abs/2610.34684

arxiv.orgSLIDE: Search-to-List Item Distillation for Single-Rollout Generative RetrievalGenerative retrieval (GR) typically relies on item-level beam search to retrieve candidates, leaving cross-item dependencies unmodeled and limiting direct optimization of list-level objectives. A natu...

SLIDE: Search-to-List Item Distillation for Single-Rollout Generative Retrieval Distills a teacher model's offline beam-search results into list-wise targets, so a smaller generative retrieval model can produce a full ranked list in one greedy pass. 📝 arxiv.org/abs/2610.34764

arxiv.orgOcten-WebGR: Scaling Generative Retrieval to 200 Million Web PagesNavigational search aims to find a single target page among hundreds of millions of web pages. Click tables work well for queries seen before but cannot generalise to new formulations, while embedding...

Octen-WebGR: Scaling Generative Retrieval to 200 Million Web Pages Introduces a generative retriever that indexes 201 million web pages with two-token identifiers for navigational search. 📝 arxiv.org/abs/2610.35256 👨🏽‍💻 github.com/Octen-Team 🤗 huggingface.co/Octen

arxiv.orgUnified Heterogeneous Feature Interaction via Effective-Rank Scaling for Recommender SystemsLarge-scale industrial ranking models must jointly capture heterogeneous feature interactions, long-term user behaviors, and multi-scenario dependencies under stringent online serving constraints. Exi...

Unified Heterogeneous Feature Interaction via Effective-Rank Scaling for Recommender Systems Introduces a ranking model that combines behavior sequence modeling and feature interaction in a single backbone for multi-scenario recommendation. 📝 arxiv.org/abs/2610.35318

arxiv.orgTowards a Unified Framework of Optimal Input-Side Dimensional Configuration for Deep CTR PredictionDeep click-through rate (CTR) prediction has achieved substantial success through increasingly expressive feature interaction architectures. However, predictive performance also depends on how input-s...

Towards a Unified Framework of Optimal Input-Side Dimensional Configuration for Deep CTR Prediction Introduces a framework that jointly selects samples, features, and embedding units for CTR prediction, using optimal transport to align the levels. 📝 arxiv.org/abs/2610.35695

Do Similarity Measures Measure Similarity? How User Activity Creates Phantom Correlation in Recommender Systems Shows that differences in user activity alone make items look similar, even when users share no taste. 📝 arxiv.org/abs/2610.35765 👨🏽‍💻 github.com/Mfavier91/Ph...

arxiv.orgACARE: Agent-Context-Aware Reranker for Iterative SearchIn agentic search, a document can match an agent-generated sub-query without helping answer the user's original question. Conventional rerankers score documents against the sub-query alone, overlookin...

ACARe: Agent-Context-Aware Reranker for Iterative Search Introduces a compact reranker for search agents that scores documents using the original question and reasoning trace, not just the agent's sub-query, improving retrieval and cutting searches. 📝 arxiv.org/abs/2610.35840

arxiv.orgRetriever-Free Retrieval: Navigating Documents with Decision ModelsWhile language models have become dramatically more capable, retrieval still largely relies on lexical matching or similarity in fixed embedding spaces. This creates a capability asymmetry: what the r...

Retriever-Free Retrieval: Navigating Documents with Decision Models Retrieves by walking down a corpus hierarchy, with a decision model scoring all candidates at each level in a single pass, without generating tokens. 📝 arxiv.org/abs/2610.35868

arxiv.orgBeyond Post-Hoc Rewards: Privileged Reasoning Exploration for Multimodal EmbeddingsAdvances in Multimodal Large Language Models (MLLMs) have driven substantial progress in multimodal embeddings. Recent methods exploit generative capabilities by augmenting embedded inputs with self-g...

Beyond Post-Hoc Rewards: Privileged Reasoning Exploration for Multimodal Embeddings Uses training-time positive and negative candidates to guide reasoning exploration for multimodal embeddings, with input-only inference. 📝 arxiv.org/abs/2610.32474

arxiv.orgTGR-EvoDAG: A DAG-based Multi-Agent Code Evolution Framework for Generative Recommendation on TencentLarge Language Model (LLM) agents have enabled automated code evolution for algorithm discovery. However, current recursive self-improvement (RSI) research for sequential recommendation faces a dilemm...

TGR-EvoDAG: A DAG-based Multi-Agent Code Evolution Framework for Generative Recommendation on Tencent Tencent introduces a multi-agent framework that evolves generative recommendation code as a DAG of branches, with GPU cost planning for each pipeline stage. 📝 arxiv.org/abs/2610.32326

arxiv.orgMICE to Meet You: From Re-ranking Only to End-to-End RetrievalLate-interaction models offer state-of-the-art efficiency-effectiveness trade-offs for neural retrieval by enabling document representations to be pre-computed and efficiently indexed. Yet, their rela...

MICE to Meet You: From Re-ranking Only to End-to-End Retrieval Introduces MICE-P, which indexes MICE document representations with PLAID to build an end-to-end retrieval pipeline with cross-encoder-like effectiveness. 📝 arxiv.org/abs/2610.31932 👨🏽‍💻 github.com/xpmir/mice

arxiv.orgFrom Item Scores to Semantic Tokens: Test-Time Collaborative Adaptation for Generative RecommendationGenerative recommenders using semantic identifiers predict items through successive token decisions, but beam search may irreversibly prune promising item paths. This limitation is particularly conseq...

From Item Scores to Semantic Tokens: Test-Time Collaborative Adaptation for Generative Recommendation Introduces a training-free way to use discriminative item scores to guide beam search in a frozen generative recommender before prefixes get pruned. 📝 arxiv.org/abs/2610.32098

arxiv.orgPersonalQuery-Benchmark:Evaluating Retrieval Robustness to Personalized User Expression DifferencesExisting personalized information retrieval methods mainly model differences in user needs through user preferences and query intent, while paying little attention to the personalized expression diffe...

PersonalQuery-Benchmark: Evaluating Retrieval Robustness to Personalized User Expression Differences Introduces a benchmark where personalized query syntax and writing errors shift rankings for a fixed information need. 📝 arxiv.org/abs/2610.32164 👨🏽‍💻 github.com/cpxjazz123/P...

arxiv.orgSystem One models Are Not Better RerankersSystem One models, or decision models, answer a question about an input with a calibrated probability instead of generated text. Within weeks of the first release, in September 2026, they were used as...

System One Models Are Not Better Rerankers Finds that decision (System One) fine-tuning never beats plain reranker fine-tuning on the same Qwen backbones, and its calibration advantage depends on the question wording. 📝 arxiv.org/abs/2610.32318 👨🏽‍💻 github.com/KanishkaGD/d...

arxiv.orgAgentic Memory Management Is the Next Frontier of Information RetrievalInformation retrieval has long been focusing on connecting people with information they need to complete their tasks. Recently, the meaning of this has changed dramatically as more and more AI agents ...

Agentic Memory Management Is the Next Frontier of Information Retrieval Presents a perspective paper arguing that managing an AI agent's memory is a natural extension of IR. Also shares a memory framework, a comparison with classic IR, and a research agenda. 📝 arxiv.org/abs/2610.32464

Knowledge Distillation across Item and Code Spaces for Parallel Generative Recommendation Proposes two objectives that distill a sequential recommender's item scores into a parallel semantic ID model, matching item distributions or code marginals. 📝 arxiv.org/abs/2610.32943

arxiv.orgKnowledge Distillation across Item and Code Spaces for Parallel Generative RecommendationGenerative recommendation with semantic IDs represents each item as a sequence of discrete codes derived from its semantic representation and predicts these codes to generate recommendations. Existing...

arxiv.orgSeparating Thinking from Spelling for Efficient Generative RecommendationGenerative recommenders retrieve items by writing their semantic IDs (SIDs) code by code under trie-constrained beam search, an approach now deployed on large web platforms. Standard autoregressive in...

Separating Thinking from Spelling for Efficient Generative Recommendation Presents a GR system where the backbone reads the user history once into a compact concept and a light decoder runs the entire semantic ID beam search, which cuts serving cost. 📝 arxiv.org/abs/2610.32980

amazon.scienceSafe and sufficient caching: Per-request validated evidence reuse for production retrieval-augmented generationProduction Retrieval-Augmented Generation (RAG) systems serve repeated queries over evolving document corpora under user-specific access constraints. Standard pipelines perform full retrieval and gene...

Safe and Sufficient Caching: Per-Request Validated Evidence Reuse for Production Retrieval-Augmented Generation Amazon caches evidence chunks and checks access and source freshness before reuse, cutting latency and retrieval cost with zero invalid reuse. 📝 www.amazon.science/publications...

arxiv.orgTransX Technical Report: Scaling Sequence Transduction for RecommendationWe present TransX, a sequence-transduction framework and co-designed infrastructure for scaling industrial recommendation with explicit control over model-compute allocation and execution amortization...

TransX Technical Report: Scaling Sequence Transduction for Recommendation LinkedIn presents a recommender that separates history encoding, behavior memory, and candidate scoring so most work runs offline, letting models scale without raising serving cost. 📝 arxiv.org/abs/2610.20913

arxiv.orgLearning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text RetrievalReasoning-conditioned embeddings can improve retrieval by progressively refining semantic constraints. However, additional reasoning may introduce over-generalized or semantically noisy abstractions t...

Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval Trains a retrieval model to reason step by step and stop once extra reasoning stops helping, cutting generation cost and latency. 📝 arxiv.org/abs/2610.21030 👨🏽‍💻 github.com/yingtaoluo/A...

arxiv.orgBeyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAGMulti-hop retrieval-augmented generation (RAG) requires an agent to retrieve evidence for a sequence of dependent sub-questions. When a wrong answer lacks a required passage, the passage may be inacce...

Beyond Retrieval Failure: Understanding Why LLMs Miss Evidence in Multi-Hop RAG Finds that missing evidence in multi-hop RAG often stems from search drift, not retriever limits. 📝 arxiv.org/abs/2610.21184

arxiv.orgRecSoup: Efficient Model Merging Approach for Controllable Multi-Objective RecommendationRecommender systems often need to balance multiple conflicting objectives. Controllable Multi-Objective Recommendation (CMOR) enables dynamic adjustment of objective trade-offs at inference time witho...

RecSoup: Efficient Model Merging Approach for Controllable Multi-Objective Recommendation Merges objective-specific recommenders after pruning conflicting parameters, so trade-offs can be tuned without retraining. 📝 arxiv.org/abs/2610.21220 👨🏽‍💻 github.com/35798642/REC...

arxiv.orgAligning LLMs with Sparse Retrieval via Adaptive Sparse SupervisionWhile large language models (LLMs) offer strong semantic representations, their application to first-stage retrieval incurs substantial latency and storage costs. Neural sparse retrievers such as SPLA...

Aligning LLMs with Sparse Retrieval via Adaptive Sparse Supervision Aligns an LLM with SPLADE's vocabulary space using input-adaptive truncation, keeping inverted-index retrieval with much lower storage and latency than prior LLM sparse retrievers. 📝 arxiv.org/abs/2610.21877

arxiv.orgCASI: Credit Assignment via Set Intervention for Agentic RetrievalLarge language models increasingly generate multiple retrieval actions jointly, while downstream systems often provide only one reward for the resulting set. Broadcasting this reward provides a valid ...

CASI: Credit Assignment via Set Intervention for Agentic Retrieval Xiaohongshu proposes assigning credit to jointly generated retrieval actions under one shared reward by scoring subsets of cached retrievals with Shapley values. 📝 arxiv.org/abs/2610.20293

arxiv.orgRevisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the GapGenerative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers have improved. However, GR is not a uniformly stron...

Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap Finds that generative retrieval's item-value lead in e-commerce comes largely from popularity. 📝 arxiv.org/abs/2610.19065 👨🏽‍💻 anonymous.4open.science/r/gr-dr-rele...

arxiv.orgListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language ModelsMany real-world questions demand not a single fact but an organized collection of them, yet existing factual knowledge benchmarks almost exclusively target single answer retrieval. We introduce ListQA...

ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models Introduces a benchmark where LLMs recall several facts from Wikipedia tables and return them as structured lists. 📝 arxiv.org/abs/2610.19165 👨🏽‍💻 listqa.github.io

arxiv.orgRCER: Reconstructing Cross-Evidence Relations for Iterative Retrieval-Augmented GenerationIterative retrieval-augmented generation (RAG) accumulates knowledge from retrieved evidence to answer complex questions, but existing methods struggle to reconstruct entity correspondences and infere...

RCER: Reconstructing Cross-Evidence Relations for Iterative Retrieval-Augmented Generation Introduces an iterative RAG method that reconstructs relations across retrieved passages to guide retrieval, select complementary passages, and compose answers. 📝 arxiv.org/abs/2610.19398

arxiv.orgRetrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine TranslationRetrieving external knowledge can improve large language model (LLM) translation by providing supplementary information such as idiom interpretations and terminology. However, existing retrieval-augme...

Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation Uses RL to teach an LLM when to retrieve for translation, and trains the retriever on translation quality, not similarity. 📝 arxiv.org/abs/2610.19528

arxiv.orgCascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document SearchRetrieving information from visually rich documents requires matching queries against pages that contain complex evidence in tables, figures, and layout-dependent structures. Current vision-language r...

Cascaded Retrieval with Cross-Modal Query Refinement for Efficient Visual Document Search Introduces a cascaded framework that cuts latency in visual document retrieval by reranking a small candidate pool and refining the query with offline summaries. 📝 arxiv.org/abs/2610.20367

arxiv.orgConfiguring RAG Systems: What Breaks, Where Originates, and How to FixRetrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for improving Large Language Model (LLM) by intertwining it with other components, such as a vector/graph database. Therefore, t...

Configuring RAG Systems: What Breaks, Where Originates, and How to Fix Presents an empirical study of configuration issues in open-source RAG systems, categorizing symptoms, root causes, and fix patterns across the pipeline stages. 📝 arxiv.org/abs/2610.18567 👨🏽‍💻 github.com/ideas-labo/r...

arxiv.orgMany Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM RecommendersA recommendation request on Reddit draws several replies, each naming different titles. When an LLM recommender is trained on such threads with reinforcement learning (RL), which replies should a gene...

Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders Introduces a system that rewards recommender lists against all responders' titles at once, making GRPO training cheaper. 📝 arxiv.org/abs/2610.17771 👨🏽‍💻 lixin.ai/ReMaRk

arxiv.orgDRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ HomepageWe present DRanker (Disney+ Ranker), a transformer-based multitask ranking model serving personalized recommendations on the Disney+ homepage. We describe the encoder-only architecture over enriched e...

DRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ Homepage Disney describes a transformer ranker for the Disney+ homepage with a lightweight causal debiasing tower for popularity bias. 📝 arxiv.org/abs/2610.15696

arxiv.orgPersonalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token PredictionLarge-scale personalization is shifting from embedding-table retrieval to LLM-based recommenders that generate the Semantic ID (SID) of the item to recommend, where each SID is a short sequence of dis...

Personalized LLM Recommendation with Semantic IDs: Autoregressive Beam Search vs. Multi-Token Prediction at Industry Scale Netflix compares beam search & multi-token prediction for generating Semantic IDs in LLM recommenders. Beam search wins on ranking. 📝 arxiv.org/abs/2610.15864

arxiv.orgUniSGR-V2: Stepwise Target-Side Feature Anticipation and Crossing for Generative RecommendationSemantic-ID-based generative recommendation unifies retrieval and ranking through autoregressive decoding but differs structurally from discriminative models, which can directly access rich target-ite...

UniSGR-V2: Stepwise Target-Side Feature Anticipation and Crossing for Generative Recommendation Introduces a system that predicts continuous target-item features at each semantic-ID decoding step and uses them for both code generation and ranking. 📝 arxiv.org/abs/2610.16132

arxiv.orgLabel-Centric Knowledge Retrieval for Zero-Shot Text ClassificationFlagship large language models (LLMs) offer rich general knowledge and strong reasoning capabilities, but their high inference cost and latency limit their use for large-scale data labeling. We study ...

Label-Centric Knowledge Retrieval for Zero-Shot Text Classification Presents a system where a large LLM builds label definitions and boundary rules offline, and a small LLM classifies zero-shot by retrieving that knowledge instead of labeled examples. 📝 arxiv.org/abs/2610.16182

arxiv.orgFrom a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance PredictionRelevance prediction between user queries and items is a fundamental component of e-commerce search systems. Traditionally, high-quality relevance labels are obtained through human judgment, which is ...

From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance Prediction eBay distills a GPT-4.1 relevance judge into smaller multimodal Gemma-3 models for e-commerce search, keeping near-teacher quality at much lower cost and latency. 📝 arxiv.org/abs/2610.16569

arxiv.orgKNN Implementation Details Can Dramatically Change Performance: An Example from Cover TreesNearest neighbor search algorithms are widely used and have a long history, but we find that key implementation details are seldom reviewed in articles about them. These details can materially impact ...

KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees Shows how query mode, base parameter, and micro-optimizations affect cover tree speed. 📝 arxiv.org/abs/2610.16940 👨🏽‍💻 github.com/CrowdStrike/...

arxiv.orgWeGR: Unified Generative Retrieval for Industrial Search Systems in WeChatGenerative retrieval (GR) offers a promising paradigm for industrial search by unifying candidate generation within an autoregressive framework. However, its effectiveness is limited by two key challe...

WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat Presents generative retrieval for WeChat search, with a tokenizer that adds collaborative signals without codebook collapse and an RL method that rewards semantic ID prefixes. 📝 arxiv.org/abs/2610.16979