In the final post of the Adaptive RAG series, we explore how to treat selective retrieval as a core, learned skill, moving from passive observation to active, intelligent decision-making. blog.reachsumit.com/posts/20…

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

TGR-EvoDAG: A DAG-based Multi-Agent Code Evolution Framework for Generative Recommendation on 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

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

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

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/Person…

System One Models Are Not Better Rerankers @M_Dinzinger et al find that decision (System One) finetuning never beats plain reranker finetuning on the same Qwen backbones & calibration advantage depends on the question wording. 📝arxiv.org/abs/2610.32318 👨🏽‍💻github.com/KanishkaGD/decisi…

Agentic Memory Management Is the Next Frontier of Information Retrieval @QingyaoAi presents a perspective paper arguing that managing an AI agent's memory is a natural extension of IR. 📝 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

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

I published Vol. 179 of "Top Information Retrieval Papers of the Week" on Substack. 🔗 recsys.substack.com/p/fixed-…

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 & retrieval cost with zero invalid reuse. 📝amazon.science/publications/…

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

Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval @yingtaoluo et al. train a retrieval model to stop reasoning once extra reasoning stops helping, cutting generation cost and latency. 📝 arxiv.org/abs/2610.21030 👨🏽‍💻 github.com/yingtaoluo/Adapti…

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

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/RECSOUP.…

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

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

Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap Shopify 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…

ListQA: A Benchmark for Evaluating List-Formatted Factual Knowledge Retrieval in Large Language Models @mayank_k91 et al. present a benchmark where LLMs recall facts from Wikipedia tables and return them as structured lists. 📝 arxiv.org/abs/2610.19165 👨🏽‍💻 listqa.github.io

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