Information Retrieval · Cross-listed from cs.CL
Fine-Grained Emotion Classification from Mobile App Reviews: An Empirical Study with Large Language Models
Quim Motger, Carlota Catot and Marc Oriol
Context: Fine-grained emotion classification of mobile app reviews enables requirements engineering activities that go beyond polarity-based opinion mining, including emotionally informed issue prioritisation and feature-oriented feedback analysis. However, automatic fine-grained emotion extraction from app reviews remains understudied. Objectives: Building on a previously published annotation framework and human-labelled ground truth adapted from Plutchik's taxonomy, this paper investigates how large
language models can be leveraged for automatic multi-label emotion classification under severe class imbalance. Methods: We compare encoder-only fine-tuning under multi-label and binary-ensemble formulations, decoder-only zero- and few-shot prompting across open-source and proprietary models, and a catalogue of imbalance mitigation strategies (loss reweighting, resampling, generative data augmentation), with the synthetic-review generator and prompting strategy selected via an intrinsic augmentation-utility ranking. Results: Fine-tuned encoders trail the best decoder-only few-shot prompting (macro-F1 0.642) by a wide margin at baseline (multi-label: 0.387; binary ensemble: 0.450); pairing the best multi-label encoder with generative data augmentation and positive-weighted loss closes most of this gap (+0.204) at up to three orders of magnitude lower inference latency than the decoders, with the largest gains on the rarest emotions, from undetected to gains of up to +0.501 F1. Conclusion: Large
language models make fine-grained, multi-label emotion classification of app reviews feasible for requirements engineering pipelines, with modest macro-F1, and the best formulation and mitigation strategy are backbone- and formulation-dependent. We release the experimental pipeline, synthetic corpora, and fine-tuned checkpoints for replication and reuse.
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Information Retrieval
When History Misleads: Asymmetric Margin Supervision for Instruction-Guided LLM Generative Recommendation
Ming Yin, Yuhan Yang, Chen Chen et al. (+10 more)
In instruction-guided generative recommendation, LLM-based recommenders need to balance two goals: responding to the user's current request and aligning with the preferences in their interaction history. When the two conflict, history events can override the request. We show that turning the effect of individual history events into supervision faces two obstacles. First, the events that most influence a recommendation are not necessarily the ones that support the target item. Second, removing a misleading event can raise the target's score but a competing item's score even more, so a higher target score alone does not guarantee a better ranking. We propose Asymmetric Intervention-Guided Margin Supervision (AIMS), which converts the effect of removing individual history events into ranking supervision. For training requests already ranked correctly, a frozen reference model identifies request-specific deletions that improve both the target's score and its margin over a competitor near the recommendation cutoff. These margins serve as training targets, while the complete history is retained as input. Training combines cross-
entropy with an asymmetric auxiliary loss that penalizes margin shortfalls and routes its gradient only through the competitor score. Inference is unchanged, requiring no history editing or deletion search. Across six LLM backbones on an industrial
dataset and two public
benchmarks, AIMS improves Recall and NDCG over strong baselines. Ablations support request-specific margins and asymmetric supervision, and the selected deletions preferentially remove constraint-violating history.
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All authors Ming Yin, Yuhan Yang, Chen Chen, Xinyu Lin, Wentao Shi, Fangcong Yin, Chaofei Yang, Chao Yang, Jiyan Yang, Hui Zhang, Ning Jiang, Yiran Chen, Qifan Wang
Information Retrieval
Adaptive Sparsity Optimization with Learnable Soft Top-K and Per-Term Thresholding for Efficient Retrieval
Wentai Xie, Parker Carlson, Shanxiu He et al. (+1 more)
Recent work on neural sparse
retrieval has demonstrated strong relevance by leveraging Large
Language Models (LLMs) for semantic term expansion. However, learned models paired with previous sparsification techniques still yield overly long document and query vectors partly due to a large LLM vocabulary, imposing a serious challenge to
retrieval time and space efficiency. This paper proposes a scheme for optimizing model sparsity through a synergy of adaptive strategies, including learnable soft top-K, per-term thresholding, and FLOPs regularization to increase the sparsity of query and document vectors. Experimental results with Lion-SP model on the MS MARCO and BEIR
datasets demonstrate that the proposed scheme can outperform the baselines by significantly reducing the average query and document lengths. Our scheme can achieve much shorter
retrieval latency and lower storage cost while maintaining highly competitive relevance.
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All authors Wentai Xie, Parker Carlson, Shanxiu He, Tao Yang
Information Retrieval · Cross-listed from cs.AI
On-Premises Multi-Course RAG Tutoring for Business Education: Hardware-Software Trade-offs in a Campus AI Tutor
Sidney Shapiro and Joshua Lindemann
Campus AI tutors based on
retrieval-augmented generation (RAG) must ground answers in assigned course materials while keeping textbooks and student dialogue on institutional infrastructure. We present CourseChat, an on-premises, multi-course RAG tutor for undergraduate business education, deployed behind a campus web gateway and intended for use embedded in Moodle. Six isolated course offerings, each keyed by its own course reference number (CRN), share twin-edge AI hosts running a FastAPI service, a local vector database, and a local large
language model (LLM) served by Ollama. We report two generation-model bake-off rounds, a separate fixed-evidence source-fidelity comparison, and conversation and quiz audits. Several larger models failed the classroom speed gate, but a 12B model and a 7B alternative passed. A separate mixture-of-experts candidate improved some corrections while introducing new factual and continuity errors. We therefore retain the 8B production model pending a demonstrated overall improvement, rather than claiming that 8B is universally optimal. Software changes improved follow-up topic resolution while preserving course scope; 435 prebuilt questions across 65 modules decouple practice from live generation. The results support treating model choice, evidence selection, serving compatibility, and product design as a joint engineering decision. They do not establish learning gains: faculty ratings, peak-load capacity, and complete public-gateway acceptance remain separate evaluation needs.
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Information Retrieval · Cross-listed from cs.DB
SOLO: Certified-Recall Metric Similarity Search with Scan-Only Sampled Inverted Lists
Édgar Chávez
In every fast nearest-neighbor index, recall is measured, never predicted: each operating point is tuned by serving it against ground truth. SOLO is an index whose recall is computed from the index itself, before any query is served. SOLO is an inverted file whose vocabulary is a random sample of the database. Each object's $k_b$ nearest sample points are stored once, ranked; at serve time an object is posted under the first $b \le k_b$ of them, and a query scans the lists of its $k_s$ nearest sample points exhaustively, with the true distance. Recall depends on the product $b \cdot k_s$ (an equal-work law), so the search-side $k_s$ compensates for a small $b$ with no rebuild. There is no beam, vote or
pruning bound, so a true neighbor is missed only if it shares no sample point with the query – a membership event decided by stored integers, not by a search. Recall is therefore a count: one ground-truth pass over a sample of the operator's queries certifies every $(b, k_s)$ at once, with nothing served. The certificate matches served recall to four decimals from $10^6$ to $10^9$ objects, including 768-dimensional text
embeddings under inner product with shifted queries. No graph index has an analogous object. The rest is the same rule applied recursively: a list that outgrows a bound is sampled and split like the database, and so is the vocabulary itself, which is what drives resident
memory down. Deep-100M is served at recall 0.9977 from 1 GB of enforced resident
memory, Deep-1B at 0.9925 from 96 MB. Inserts are one search, deletes are exact, and throughput reaches $1.8\times$ a tuned HNSW at $10^8$. Every number is reported against HNSW, DiskANN, GRAFT, NAPP, misi, SPANN, ScaNN and RaBitQ on the same hardware and ground truth.
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Information Retrieval · Cross-listed from cs.AI
ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research
Sohyeon Kim, Yoonho Lee, Bo Liu et al. (+11 more)
What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-growing archive of research, a new problem needs. To study this skill, we draw on researchers who know firsthand which earlier work advanced their completed projects, with papers serving as pointers to the ideas within. Using our automated pipeline that makes author annotation scalable, we build ScholarCatalyst by having 184 lead authors of 207 recent computer science papers label which candidates did or could have advanced their project, each with a detailed rationale. We introduce a
retrieval task with author-provided judgments: given an initial research question, retrieve these papers from only the literature available when the project began. Agentic search does no better than
embedding retrieval (0.42 vs. 0.48 Recall@20) despite calling that same retriever as a tool. Even an
agent built on Claude Fable 5.1, which may have seen the completed papers during training, reaches only 0.51 R@20. These results highlight the need for new training recipes that equip models with expert intuition for searching broad corpora. We envision ScholarCatalyst as a step toward scientific
agents that can take a half-formed idea and point to the prior research it needs.
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All authors Sohyeon Kim, Yoonho Lee, Bo Liu, Dayoon Ko, Rulin Shao, Seungone Kim, Graham Neubig, Pang Wei Koh, Aakanksha Chowdhery, Akari Asai, Omar Khattab, Yejin Choi, Gunhee Kim, Chelsea Finn
Information Retrieval
Optimizing Effective Training Time for Large-Scale Recommendation Systems
Mingming Ding, Ruilin Chen, Yuzhen Huang et al. (+29 more)
Lifecycle overhead silently consumes accelerator capacity across large-scale recommendation training fleets. Our largest recommendation workloads process tens of billions train- ing examples per day on thousands of GPUs. Before this work, only 50-60% of their end-to-end wall time advanced training on new data. We present a fleet-scale study of this lifecycle overhead and a set of optimizations spanning the full training stack. We use Effective Training Time (ETT%) as an operational framework to instrument lost time, localize it to independently owned infrastructure components, and expose work repeated across job restarts. This analysis guides optimizations like communication elimination and pipeline overlap during trainer initialization; dynamic-shape handling, autotuning
pruning, and reusable Py- Torch 2 compilation caches; asynchronous checkpointing; stan- dalone model publishing; and reductions in recovery cost. We evaluate the optimizations on representative models and measure their impacts in our training fleet. ETT% improves on every
benchmark, by 15.5% on average, and reaches 85% on our largest workload. Fleet-wide ETT% rose from about 80% to above 90% after deployment.
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All authors Mingming Ding, Ruilin Chen, Yuzhen Huang, Hang Qi, Menglu Yu, San Tan, Damian Reeves, Boris Sarana, Kevin Tang, Satendra Gera, Gagan Jain, Sahil Shah, Vishwa Karia, Fuzail Khan, Yashasvi Makin, Edward Z. Yang, Oguz Ulgen, Jia Chen Ren, Laith Sakka, Mayank Garg, Meet Vadakkanchery, Aici Lin, Wei Sun, Mengjiao Zhou, Shuai Yang, Junqing Zhou, Max Leung, Apoorv Purwar, Musharaf Sultan, John Bocharov, Zhenyu Tang, Vivek Trehan
Information Retrieval · Cross-listed from cs.CL
A Matryoshka Hierarchical RAG for Efficient Multi-Hop Question Answering
Gianluca Bonifazi, Christopher Buratti, Michele Marchetti et al. (+5 more)
Retrieval-Augmented Generation (RAG) systems for multi-hop Question Answering (QA) must balance
retrieval quality with computational cost. This cost is incurred during indexing time, through the use of expensive Knowledge Graphs (KGs) or Large
Language Models (LLMs) to generate summaries, or during querying, through iterative LLM-driven
retrieval. To reduce it while maintaining
retrieval quality, we present MatRAG, a hierarchical framework that combines RAG systems with Matryoshka Representation Learning (MRL). MatRAG addresses both kinds of cost by aligning the semantic hierarchy of a clustering structure with the nested structure of MRL. Specifically, it organizes the corpus of documents into a Directed Acyclic Graph (DAG) of clusters with progressively coarser granularity. Each level is indexed by a lower Matryoshka dimension. MatRAG pairs an iterative, top-down traversal of the DAG with an entity-driven mechanism that controls the hop budget and re-ranks candidates. We evaluated MatRAG on three standard multi-hop QA
benchmarks against seven representative baselines. MatRAG outperforms its strongest competitors in terms of
retrieval quality; furthermore, it reduces indexing costs by avoiding KG construction and LLM-based summarization, and lowers query-time costs through dimension-aware similarity.
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All authors Gianluca Bonifazi, Christopher Buratti, Michele Marchetti, Federica Parlapiano, Giulia Quaglieri, Davide Traini, Domenico Ursino, Luca Virgili
Information Retrieval
AgentWebRec: Compact Evidence Fusion over the Agent Web for Personalized Recommendation
Haoran Qiang, Guannan Liu, Liang Zhang et al. (+1 more)
LLM-based personal
agents are emerging as persistent carriers of user semantics and intermediaries between users and recommendation platforms, maintaining richer user knowledge locally. As
agents interact with one another, the conventional User–Platform relation evolves into a User–
Agent Web–Platform information pathway, enabling distributed user-side information to complement item-side information. This new pathway, however, defies conventional recommendation: evidence is scattered across mutually opaque
agents and reachable only through bounded queries, only a small portion of it is relevant to the current recommendation decision, and the responses returned by different
agents are semantically heterogeneous. We therefore recast recommendation over the
agent web as a task-time evidence acquisition and fusion problem under a finite evidence budget by deciding what to ask and what to keep, rather than learning from aggregated data. We propose AgentWebRec, a user-
agent-oriented framework that progressively acquires and fuses distributed evidence for each user-item decision while keeping underlying
agent memories local. It grounds each decision in platform-provided item semantics and task-relevant evidence from the target user
agent's private
memory, and conditionally queries neighboring user
agents for complementary preference patterns when local evidence is insufficient. Experiments on four InstructRec
datasets show that AgentWebRec consistently outperforms baseline recommenders, and ablations verify that the evidence layers contribute complementary gains.
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All authors Haoran Qiang, Guannan Liu, Liang Zhang, Junjie Wu
Information Retrieval
From Rules to Neural Graphs: Scalable Structured Prediction for Patent Prior Art Search
Nikolai Zenovkin and Sebastian Björkqvist
Patent search requires processing documents routinely exceeding tens of thousands of
tokens. Most neural
retrieval approaches operate on truncated inputs, limiting their effectiveness. Graph-based
retrieval addresses this by representing each patent as a structured invention graph, but constructing these graphs relies on brittle rule-based parsers. We present the neural parser, which adapts biaffine
attention from dependency parsing to predict invention graphs directly from patent text. Our local biaffine
attention restricts pairwise scoring to a sliding window, reducing complexity from $O(n^2)$ to $O(n \cdot w)$. Since local and global scoring share the same weights, the model trains on short sequences and deploys on documents exceeding 40,000
tokens without retraining. Distilled from 1 million rule-parsed documents, it surpasses its teacher at 3$\times$ lower inference cost: neural graphs improve citation recall by 0.5% on short queries and 1.1% on full documents in a downstream Graph
Transformer retrieval system.
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Information Retrieval · Cross-listed from cs.AI
Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)
Aleksei Medvedev, Alejandro Ariza-Casabona, Steven Derby et al. (+3 more)
Existing work on Semantic IDs (SIDs) for generative recommendation treats SID construction as a representation learning problem: encode items into a quantised latent space and read off codes. We argue this view is incidental. SID construction is, at heart, a recursive clustering problem, and once stated this way the natural object to cluster is a graph whose nodes carry semantic content and whose edges carry collaborative signal; SID assignment becomes a hierarchical graph partition. This reframing yields a unified framework, Graph-Informed Semantic IDs (GrIS), that subsumes prior approaches rather than displacing them. RQ-VAE and RQ-KMeans are recovered as the special case where the graph is empty, exposing content-only quantisation as one corner of a larger design space along two so-far-collapsed axes: graph construction and recursive partition
algorithm. We explore two contrasting instantiations: RecDMoN, which performs hierarchical assignment via differentiable graph pooling, and RQ-GAE, which extends RQ-VAE with graph-aware item representations and a graph reconstruction objective. On multiple real-world
datasets, GrIS consistently improves over CF-aware SOTA, with gains of up to +52% Hit@10. Because graph construction and partition are explicit, separately configurable components, improvements on either axis can be combined and evaluated systematically.
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All authors Aleksei Medvedev, Alejandro Ariza-Casabona, Steven Derby, Gonzalo Fiz Pontiveros, Xinyang Shao, Florian Spiess
Information Retrieval
Learning to structure data from user-generated thematic corpora
Elishay Avram, Oren Glickman and Elad Yom-Tov
Thematic corpora, such as social media communities, contain unstructured text describing data that could be made structured. These include, for example, personal attributes, behaviors, and experiences mentioned in social media data. Extracting structured data is challenging as relevant attributes are often implicit, domain-dependent, and unknown in advance. We propose a fully automated, iterative framework for discovering and extracting domain-specific attribute schemas without a predefined ontology. Using large
language models (LLMs), the framework induces candidate attributes, sequentially consolidates semantically overlapping attributes, and assigns a structural type. These enable creating an ontology and populating it with values from the corpus. The framework also enables the use of smaller LLMs for value extraction with estimable accuracy loss compared to large LLMs. We evaluate the framework on 5 health-related Reddit communities. Discovered attributes achieved 61% agreement with human-identified attributes, close to the 62% agreement between independent annotators. In most cases, the
algorithm converges to a stable attribute set in fewer than 10 iterations. Structural type assignment achieves 82% accuracy, and value extraction reaches an F1 score of 0.8 compared to human annotations. Across four LLM families, smaller instruction-tuned models show statistically significant improvements in extraction performance with model scale when evaluated against a high-capacity reference LLM, supporting informed accuracy-cost trade-offs. These results show that attributes comparable to those identified by humans can be discovered automatically, enabling the creation of high-quality structured
datasets economically and at scale. By removing the need for predefined ontologies, iterative model-driven schema induction offers a practical and scalable foundation for mining thematic corpora.
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Information Retrieval · Cross-listed from cs.LG
Not All Is Lost: Repairing Lossy User Preference States of Personalization Encoders
Parthiv Chatterjee, Dhiraj Golhar, Ummesalma Diwan et al. (+3 more)
Personalization encoders compress evolving interaction histories into preference states used to rank items or condition text generation. A task head operating only on this state can miss useful evidence that remains in the frozen encoder's cached representations for individual timesteps. We study this recoverability gap and propose REPAIR, which compares cached representations with the current preference state in a compact learned coordinate space. It resolves corrective evidence over extended history, recent interactions, and localized bursts. It then selects which patterns at which timesteps contribute and adds their aggregate correction to the state before the task head. Encoder-host repair reuses representations from the existing forward computation without re-encoding the history. Across MovieLens, PENS, MIND, and Amazon Reviews 2023, training only REPAIR improves MRR and nDCG@10 for all twelve representative recommendation hosts while both encoder and task head remain frozen. Head-only finetuning of the same hosts yields smaller gains. For example, Mamba4Rec on MovieLens gains 3.96 MRR points, compared with 0.19 from head-only finetuning. Rank and temporal diagnostics support a compact, host-dependent corrective structure. In personalized generation, IMPerSumm improves the two reported weighted PerSEval variants, which assess responsiveness to user preference, by up to 25.23%. These results support post-compression state correction and distinguish the availability of preference evidence from its downstream use.
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All authors Parthiv Chatterjee, Dhiraj Golhar, Ummesalma Diwan, Sourish Dasgupta, Manjunath Joshi, Tanmoy Chakraborty
Information Retrieval
Do Multilingual Encoders Produce Language-Consistent Semantic IDs?
Abhinav Bohra and Anuj Bohra
Semantic IDs (SIDs) compress item
embeddings into discrete code sequences used in generative
retrieval. We ask whether a multilingual encoder is sufficient for different-
language renderings of the same product to receive
language-consistent SIDs. Using Amazon ESCI listings rendered in English, Spanish, and Japanese, we test whether translations remain close to their English source, whether residual quantization is unusually sensitive to translation-induced movement, and whether multilingual or
language-balanced quantizer fitting improves SID agreement. Multilingual E5 places translations measurably apart: under an English-heavy fit, a Japanese translation preserves the first SID code of its English counterpart in only 7.7% of cases, compared with 89.0% for an English rewording. Distance-matched product-directed controls produce nearly the same full-SID mismatch as translation, providing no evidence that the quantizer selectively amplifies
language directions. Balancing the fitting mixture makes codebook use more uniform but further reduces cross-lingual prefix agreement: Spanish first-code consistency falls from 28.3% to 6.6%, while an English-only fit preserves it for 67.6% of Spanish translations. These results show that multilingual exposure and balanced codebook use alone do not guarantee
language-consistent SIDs.
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Information Retrieval · Cross-listed from cs.CL
Madeleine: Learning Involuntary Recall for Conversational Memory from Simulated Lives
Zhiyun Shi
A long-term conversational assistant must recall the right
memory at the right moment, yet the
memory that matters most is often not similar to what the user says now. Current systems recover such associations by letting an LLM reason at write or read time, at a cost of hundreds to over a thousand LLM calls per
memory bank and up to several thousand context
tokens per query. We argue that association is a learnable relevance: the pointwise mutual information of
memories under how human lives unfold. We introduce Madeleine, which learns amortized association: offline, an LLM life simulator writes simulated lives, whose cue-trigger pairs teach a query encoder a residual association on top of frozen similarity; online, it calls no LLM and plugs into any vector
memory by replacing only the query encoder. On LoCoMo-Plus under the official protocol, Madeleine (I) reaches 66.6 when plugged into HyperMem, the highest among all systems evaluated under this protocol; (II) used alone, reaches the score of HyperMem as released (52.4 vs. 52.9) with zero LLM calls and about 1/21 of its answer context; and (III) lifts T-Mem by 26.2 points, significantly outperforms the same untrained backbone inside both systems, and leaves ordinary QA intact on the 4B backbone.
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Information Retrieval · Cross-listed from cs.CL
JoinGR: Learning to Traverse Join Graphs for Table Retrieval
Sandipan De, Abhijit Chakraborty, Sambaran Bandyopadhyay et al. (+1 more)
Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases. Dense table retrievers rank schema elements independently, but this ignores a key source of evidence: some required tables are not mentioned in the question and become identifiable only through their join relationships to already relevant tables. We introduce JOINGR, a join-aware table
retrieval method that treats the database join graph as the
retrieval space. Columns are represented as graph nodes, while intra-table and foreign-key relationships are represented as typed edges. Given a question, JOINGR selects semantically similar anchor tables, traverses join edges with a query-conditioned scorer, and aggregates the resulting edge deposits into table scores. The scorer is a lightweight MLP on top of frozen query, node, and edge
embeddings, trained with a pairwise margin loss over gold tables. On BIRD and Spider
datasets, JOINGR is competitive with the strongest
retrieval baselines. On BEAVER, a challenging enterprise
benchmark with multi-hop table requirements, JOINGR substantially improves recall over dense
retrieval and re-ranking baselines. Cross-domain experiments show that the learned scorer transfers across
benchmarks, indicating that the method captures reusable joingraph traversal behavior.
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All authors Sandipan De, Abhijit Chakraborty, Sambaran Bandyopadhyay, Vivek Gupta
Information Retrieval · Cross-listed from cs.DC
The Other Half of Workflow Portability: Evidence-Backed HPC Site Profiles with Agentic Discovery
Md Saiful Islam and Douglas Thain
Moving a workflow developed and tested at one HPC site to another rarely succeeds without some amount of trial and error. Package managers rebuild software environments, containers ship whole filesystems, and workflow specifications such as backpacks package a workflow with its software, data, and resource requirements. These approaches address one half of workflow portability: what a workflow needs. But none describes how a given HPC site must be used, and that missing half is why even a portable workflow requires manual adjustment at each new site. That gap includes the site's resource shape, storage configuration, network permissions, and operating policies. This information may be explicit in the batch system, hidden in the prose of documentation, or buried deep within a router's configuration, making it difficult for an automated deployment tool to turn site knowledge into useful deployment decisions. We propose the HPC site profile, a structured, evidence-backed document that makes this knowledge actionable. We automatically construct it in three steps that mirror where the information lives: measuring the login node, extracting typed fields from documentation with a bounded
language-model
agent, and submitting pilot jobs for eligible unresolved fields. Every field is verified against its evidence or discarded, so a rule, not the model, decides what enters the profile. The profile then preflights a workflow into an execution plan or an early, explainable failure. We build profiles at Purdue Anvil, TACC Stampede3, and Notre Dame CRC and present a case study of preflighting a real workflow.
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Information Retrieval
RPTune: Learned Context Curation for LLM Catalog Search
Chuxuan Hu, Hejie Cui, Norman Huang et al. (+3 more)
For small merchant businesses (SMBs) whose catalogs fit within a long-context LLM, full-catalog prompting offers a compelling alternative to multi-stage
retrieval designed primarily for large marketplaces with millions of items. However, fitting the full catalog into the context window does not ensure that the model can use it effectively, since LLMs do not exploit long contexts uniformly. We therefore study in-context catalog search through two complementary questions: (1) how to curate and present catalogs to the LLM, and (2) how to adapt the LLM for product selection on curated contexts. We propose RPTune, an end-to-end framework that couples learned catalog curation with LLM post-training using automatically generated, catalog-grounded supervision. An encoder-reorganizer curator orders and prunes products guided by downstream LLM feedback, while the resulting curated catalogs in turn improve the effectiveness of LLM post-training with a context-relative reward. We evaluate RPTune on 7 real merchants spanning distinct retail verticals, using 100 complex conversational queries per merchant. RPTune consistently improves search accuracy across both proprietary and open-weight LLMs, with context curation yielding gains of up to 31.4 percentage points and post-training adding a further 10.3 points on average.
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All authors Chuxuan Hu, Hejie Cui, Norman Huang, Shubham Kumar Bharti, Wang-Chiew Tan, Sercan Ö. Ar{\i}k
Information Retrieval
CANOPY: Adaptive-Granularity Evidence Compression for Multimodal RAG
Hyojeong Yun, Jueun Kim and Wook-Shin Han
Multimodal RAG retrieves text, tables, images, and
videos, but choosing a
retrieval granularity does not determine how much context to retain within each item. Coarse units include irrelevant content, while uniformly fine selection can remove context needed to interpret the evidence. Existing compressors address this trade-off with modality-specific mechanisms, leaving open a shared procedure for adapting the retained extent region by region across heterogeneous items. We introduce CANOPY (Canonical Projection over Hierarchy), a framework for adaptive-granularity post-
retrieval evidence compression. CANOPY represents retrieved items as hierarchies and uses a node encoder fine-tuned on gold evidence to score regions against the query. Parent-relative refinement compares these scores to select multiple regions at different granularities without LLM calls for node-level
pruning. Because compression cannot recover evidence that was never retrieved, a critic requests targeted follow-up
retrieval when it judges the accumulated evidence insufficient; newly retrieved items are compressed before being added. Across five QA
benchmarks over a 33M-item heterogeneous corpus, CANOPY achieves higher average answer accuracy than the evaluated
retrieval baselines. Ablations indicate that additional
retrieval drives the main accuracy gains on multi-hop QA. In the unrouted Qwen3-VL-8B-Instruct setting, compression reduces reader-input evidence
tokens by 14.2-27.7% relative to the same iterative pipeline without compression, with comparable answer accuracy.
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