Information Retrieval · Cross-listed from cs.CL
Quantifying Hidden Salt for Precision Healthcare: Sodium Assessment via Joint-Factor Retrieval and Chain-of-Thought Inference
Mingyu Huang, Weiqing Min, Yuehui Fang et al. (+2 more)
Precision healthcare, particularly for conditions like hypertension and cardiovascular disease, necessitates monitoring of dietary sodium intake. However, tracking this is hindered by the prevalence of hidden salt in cooking, such as sodium in soy sauce and ketchup. While recipes offer a valuable data source for dietary analysis, sodium-rich seasonings are frequently omitted or described ambiguously in instructions. To solve this issue, we propose SALT, a Sodium Assessing & Level Tracking framework adopting an RAG framework to assess sodium content in recipes. Our framework first introduces a Joint-Factor
Embedding Retrieval module to locate similar recipes with specified sodium content for addressing the lack of contextual references. These retrieved samples provide contexts for subsequent inference. Then we design a structured 4-hop Chain-of-Thought inference module to refine the vague estimation from
language models through a multi-step sodium estimation. To facilitate our study, we further construct a recipe
dataset SALT54k with $54,151$ entries labeled with sodium quantities across $11$ common seasonings. Results on SALT54k demonstrate that our method achieves state-of-the-art performance in sodium estimation. Additional real-world validations confirm the effectiveness of our method, demonstrating its potential as a practical solution for AI-assisted precision healthcare.
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All authors Mingyu Huang, Weiqing Min, Yuehui Fang, Yuna He, Shuqiang Jiang
Information Retrieval
ECP-Bench: Benchmarking and Learning Entertainment Content Promotion with Foundation Models
Hyomin Kim, Bowen Chen, Jin Huang et al. (+3 more)
Content promotion spans a broad set of skills, from understanding content to forecasting its
market reception. However, LLMs' ability to support such promotion decisions remains underexplored. Existing studies are often limited to a single task (e.g., popularity prediction) or a small set of tasks within a single domain (e.g., movies). As a result, there is a lack of understanding of LLMs' abilities in the full promotion process and how these abilities generalize across different tasks and domains. In this work, we introduce ECP-Bench, a
benchmark containing 1.9M movie, game, and music items and 423,451 questions across 33 tasks in five content-promotion skill families. Our evaluation shows that frontier models achieve only 51.9% overall accuracy and lose much of their advantage on post-cutoff content, with drops of up to 19.1 percentage points. In contrast, open-weight models fine-tuned on ECP-Bench achieve up to 60.3%, remain substantially more stable across the knowledge cutoff, generalize to unseen content and tasks, and exhibit meaningful cross-domain generalization.
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All authors Hyomin Kim, Bowen Chen, Jin Huang, Zhao Wang, Qiaozhu Mei, Shingo Takamatsu
Information Retrieval · Cross-listed from cs.CL
Case2Flow: Bridging Patient Cases and Guideline Flowcharts through Multimodal Retrieval
Jiale Wei, Yufan Chen, Alexander Jaus et al. (+5 more)
Medical guidelines encode rich, evidence-based decision logic, yet the specific decision artifact a clinician needs is hard to locate within a guideline, let alone across guidelines covering plausible diseases and treatments. While guideline passages have supported end-to-end question answering, flowcharts remain largely underused in decision support despite their ability to encode actionable clinical pathways. We therefore introduce Case2Flow, a task designed to retrieve the most relevant guideline flowchart for a given patient case from a collection of guideline documents. To support it, we construct FlowAtlas, a curated corpus of 202 flowcharts extracted from 2,080 medical guidelines, together with a pipeline that synthesises 1,911 aligned case-flowchart pairs. Our evaluation of
multimodal retrieval methods reveals systematic failure modes, including overreliance on keywords and spurious
token-patch matches induced by uninformative background regions in flowcharts. Motivated by this, we propose CRISP, a training-free scoring method that sharpens late-interaction
retrieval by suppressing uninformative patches, discounting ambiguous
token matches, and incorporating bidirectional query-image
alignment. CRISP improves Recall@1 by up to 18.71 percentage points, while a blinded physician assessment on published case narratives provides preliminary feasibility evidence beyond synthetic queries.
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All authors Jiale Wei, Yufan Chen, Alexander Jaus, Zdravko Marinov, Julian Friedrich, Simon Reiß, Jens Kleesiek, Rainer Stiefelhagen
Information Retrieval
Assessing the Downstream Utility of Evidence-Aware Retrieval in RAG
Utshab Kumar Ghosh, Debayan Mukhopadhyay and Shubham Chatterjee
Retrieval evaluation for
retrieval-augmented generation (RAG) is increasingly designed around whether retrieved passages contain evidence that can support generation, rather than topical relevance alone. We study whether this closer
alignment with downstream evidence needs also makes
retrieval evaluation more useful for the decisions built from it. Across five
retrieval benchmarks and an end-to-end TREC RAG 2025 setting, we examine an answer-support signal in four roles: comparing retrievers, guiding
retrieval training and system selection, predicting downstream answer quality, and filtering the evidence supplied to a generator. The signal changes
retrieval rankings, but its downstream value is not uniform. It does not reliably improve retriever training; the benefit of using it for system selection depends on how the generator is instructed to use the retrieved evidence; and
retrieval scores based on it do not robustly predict answer quality on unseen topics. In a direct evidence intervention, human annotators confirm that filtering preferentially preserves passages containing useful answer evidence, yet different answer evaluators reach different conclusions about whether the resulting answers improve. These results show that making
retrieval evaluation more closely reflect the evidence needed for generation does not by itself make every downstream use of that evaluation more reliable. RAG evaluation methods should therefore be assessed with respect to the particular comparisons, decisions, and conclusions they are intended to support.
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Information Retrieval
PlanSightRAG: A Visual-First Multimodal RAG for Automating Question Answering and Compliance Checking for Civil Standard Plans
Nabaraj Subedi, Shuvo Dip Datta, Ahmed Abdelaty et al. (+1 more)
Civil infrastructure compliance checking has long relied on engineers manually reading legacy 2D plans; however, OCR-based automation strips away the geometry and layout essential for interpreting these plans. We present a Visual-First
Multimodal Retrieval-Augmented Generation (RAG) framework called PlanSightRAG. It indexes and reasons directly over plan imagery, integrates a ColNomic-3B multi-vector
retrieval, an agentic Planner-Retriever-Auditor-Synthesizer, and MaxSim heatmaps as an evidence trail. We introduce a 4,056-pair
benchmark from five state Departments of Transportation (DOT) standard plans (1,898 pages). PlanSightRAG achieves 91.47% Recall@5 on zero-shot
retrieval, while on a held-out Michigan DOT corpus, it achieves 91.40%. On synthetic, parametrically-generated compliance drawings, our Qwen2.5-VL-72B pipeline reaches 100% verdict accuracy only when supplied a pre-resolved rule threshold, a controlled ceiling that a non-VLM OCR baseline already reaches at 76.4%. Finally, we demonstrate autonomous visual rule-grounding by extracting numeric limits directly from a specification corpus without any human-supplied rules.
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All authors Nabaraj Subedi, Shuvo Dip Datta, Ahmed Abdelaty, Shivanand Venkanna Sheshappanavar
Information Retrieval · Cross-listed from eess.AS
VoiceMem: Streaming Dual-Brain Memory for Real-Time Interaction
Zhifei Xie, Jiaqi Lang, Ze An et al. (+7 more)
Conversational systems, such as duplex
speech language models (SLMs), still lack a streaming, accurate, and empathetic
memory system as their soul. We introduce VoiceMem, a simple
memory architecture with a parallel informational left
brain, an emotional right
brain, and streaming
memory I/O mechanisms. We further build a complete pipeline for
memory-aware SLM training, long-horizon evaluation, and decoupled deployment with interchangeable
memory backends. Experiments and real-world deployment show three advantages: i) Accuracy: under top-5
retrieval, the left
brain outperforms classical systems such as Mem0 at top-200 by nearly 30 points; ii) Emotional & Personal: the right
brain, with short- and long-horizon affective attribution and dual-node persona modeling, achieves state-of-the-art performance across three persona
benchmarks and improves the aggregate score by 4.29 points over the previous best system; and iii) Real-Time & Cheap: VoiceMem completes
retrieval in 134 ms, well within standard VAD latency, adding no extra conversational delay while maintaining high accuracy and low cost. These results show that VoiceMem provides a practical
memory foundation for real-time, personalized, and emotionally aware
speech interaction.
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All authors Zhifei Xie, Jiaqi Lang, Ze An, Yifan Zhao, Dongchao Yang, Kai Li, Ziyang Ma, Mingbao Lin, Chunyan Miao, Shuicheng Yan
Information Retrieval · Cross-listed from cs.CL
Query-Side Attacks on GNN-Based KGQA: Tracing Failures from Entity Linking to Answer Generation
Pankaj Kumar and Subhankar Mishra
GNN-based Knowledge Graph Question Answering (KGQA) pipelines process queries through four discrete stages: entity linking, subgraph
retrieval, GNN
reasoning, and answer generation. Standard robustness evaluations conflate stage-level failures into a single end-to-end metric, obscuring both the source of brittleness and the appropriate mitigation target. We ask which stage fails, and why, when the pipeline is subjected to
adversarial perturbations on the input question. We introduce a stage-isolation protocol with two answer-preserving
adversarial perturbations verified against the knowledge graph: Compositional Restructuring (CR) and Relation Synonym Swap (RS) target distinct stages while leaving entity seeds intact. Evaluated across ComplexWebQuestions and WebQSP, the results run counter to prevailing assumptions: the GNN
reasoning stage retains near-baseline accuracy when the subgraph is intact, while subgraph construction accounts for over 99% of the end-to-end collapse under CR, occurring even when the gold answer is present in 74% of retrieved subgraphs. This exposes a fundamental distinction between answer presence and answer reachability that end-to-end metrics cannot detect, and places the mitigation target firmly at the subgraph construction stage rather than the
reasoning model. Perturbed
datasets and evaluation infrastructure are released at https://anonymous.4open.science/r/atkgrag-E85C .
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Information Retrieval
PUMA: Post-Hoc Sparsification of Universal Multimodal Embeddings for Efficient Retrieval
Matteo Attimonelli, Alessandro De Bellis, Franco Maria Nardini et al. (+4 more)
Universal
multimodal embedders enable
retrieval across text, image, and combined queries, but their dense representations incur high
memory and inference costs. Post-hoc sparsification could reduce these costs but remains underexplored for
multimodal retrieval. We introduce PUMA, a sparse autoencoder recipe that maps universal
multimodal embeddings to compact sparse codes without retraining the backbone: a pretraining stage preserves dense dot-product geometry, after which the sparse encoder is fine-tuned for
retrieval. We evaluate on five
benchmarks covering text-to-image and composed image
retrieval. On Qwen3-VL-
Embedding-2B, PUMA is statistically indistinguishable from or improves over dense
retrieval on four of five
datasets. We further identify two failure modes of post-hoc sparsification: insufficient pre-TopK support and
retrieval-misaligned active support. PUMA reduces vector storage by 8-16x (FP32) and is up to 25x faster than exact dense scoring on larger candidate pools, enabling efficient
multimodal retrieval.
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All authors Matteo Attimonelli, Alessandro De Bellis, Franco Maria Nardini, Claudio Pomo, Cosimo Rulli, Rossano Venturini, Tommaso Di Noia
Information Retrieval
Hamiltonian Spectral-Temporal Dissipative Dynamics for Sequential Recommendation
Shuiying Liao and P. Y. Mok
Sequential recommendation requires understanding how user preferences evolve over time, yet most existing models treat such
evolution as a first order process where the next state depends solely on the current latent representation. Nevertheless, real user behavior often exhibits richer dynamics, including inertia, periodicity, and sudden shifts that cannot be fully captured by these first order assumptions. Motivated by these behavioral characteristics, we reconceptualize sequential recommendation through the lens of second order dynamical systems and introduce the
Hamiltonian Spectral Recommender (HSR), which recasts preference
evolution as a dissipative
Hamiltonian system in a latent phase space of position (stable preference) and momentum (short-term tendency). The linear time-invariant structure of the governing equation admits a closed-form solution in the frequency domain. A learnable dissipation mechanism further captures natural interest decay, while a short local impulse refinement module models abrupt behavioral fluctuations commonly observed in sparse interaction logs. This design jointly accounts for global periodic patterns, inertial
evolution, and localized shocks, where three phenomena that are underrepresented in existing sequential models. Extensive experiments on three
benchmark datasets demonstrate that HSR consistently outperforms state-of-the-art
Transformer-based and state space model (SSM)-based recommenders.
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Information Retrieval
D3ER: Supporting Multi-Modal Recommendation via Disentangle and Distillation-based Dynamic Ensemble
Bingnan Wang, Yi Li, Xiongxin Tang et al. (+2 more)
Incorporating items' information shared among multiple modalities into a fused representation, multi-modal recommendation (MR) has demonstrated documented success than canonical unimodal recommendation. Although several attempts have been made to extract the discriminative information unique in each modality, existing methods suffer from a core limitation: the joint learning of modal-homogeneity discriminative information (HOI) and modal-heterogeneity discriminative information (HEI) tends to weaken their individual effectiveness. To remedy this deficiency, we propose a novel method, dubbed Disentangle and
Distillation-based Dynamic Ensemble for multi-modal Recommendation (D3ER). We introduce gradient boosting into MR for the first time to formalize the optimization objective for alternately learning HOI and HEI. This design enables models dedicated to each type of information to focus on their proficient samples, thereby promoting specialized optimization. Furthermore, to mitigate the inherent high storage cost and risk of local optima in gradient boosting, we enhance our framework with knowledge
distillation and a global correction regularization. Experiments on prevalent real-world
datasets confirm the superiority of our proposed method on MR.
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All authors Bingnan Wang, Yi Li, Xiongxin Tang, Fanjiang Xu, Jiangmeng Li
Information Retrieval · Cross-listed from cs.CR
Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale
Peichun Hua, Danyang Chen, Junan Zhang et al. (+5 more)
Hosted
retrieval-augmented generation (RAG) and semantic search allow users to query valuable provider-held corpora, raising two competing demands: to hide each query and chosen result, yet reveal only the documents that the user is authorized to receive. Existing cryptographic approaches either make this costly by processing the entire corpus for every query, or sacrifice quality for efficiency by scanning a few clusters. We repurpose learned deep hashing as a private filter: a randomized binary code points the provider to a short candidate list, while encrypted reranking and oblivious key transfer protect the precise query and final selection. This shortlist short-
circuits full-corpus cryptographic search without sacrificing
retrieval quality: with 200-500 candidates, it closely matches full-corpus
retrieval across five zero-shot corpora spanning 25K to 5.4M documents. On the full 2.68M-passage NQ corpus over a 10-Gbps link, our protocol only adds 0.73 seconds, or 10 percent, to a 128-
token Qwen3-32B RAG pipeline. The released code satisfies directional metric differential
privacy (DP) and substantially reduces
embedding-inversion and property-inference leakage, demonstrating that a carefully learned shortlist can make private dense
retrieval both accurate and practical.
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All authors Peichun Hua, Danyang Chen, Junan Zhang, Haifeng Sun, Jingyu Wang, Diwen Xue, Mingyu Li, Yunming Xiao
Information Retrieval
Data Citation for Large Language Models: A Challenge
Gianmaria Silvello
Large
language models increasingly mediate access to information, and a growing body of work asks whether they cite the sources behind their outputs. That work treats citation as a verification device and applies it to textual documents. Scholarly citation serves two further functions, credit and provenance, and it applies to data as much as to text. This paper argues that data citation for large
language models is an open challenge, distinct from document-level citation grounding and harder to solve. We ask how such models should cite data so that outputs stay verifiable, provenance stays traceable, and credit reaches data creators and curators. We set out three research directions. Training data attribution has to turn influence estimates into references for corpora absorbed into model parameters. Data citation at inference time has to identify
datasets, subsets, and query results at the right granularity and fixity. Citing knowledge graph facts has to define what a reference to a single triple denotes and how credit propagates along provenance. Progress on all three depends on joint work across the database, information
retrieval, knowledge representation, and artificial intelligence communities.
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Information Retrieval · Cross-listed from cs.LG
DCEO: Direct Causal Effect Optimization for Long-Term User Value Modeling in E-commerce Search
Junzhao Zhang, Tao Zhang, Liren Yu et al. (+4 more)
Industrial e-commerce search systems ultimately aim to optimize the user-level long-term objective, such as n-day cumulative purchases or gross merchandise value (GMV) per user. However, such objectives are defined at the user level, whereas search ranking is based on item-level scores within each request. Existing methods typically bridge this granularity gap through manually designed multi-objective fusion, where predictions of multiple item-level objectives, such as clicks, carts, purchases, and transaction value, are combined into a ranking score that serves as a proxy for the ultimate objective. Such hand-crafted fusion schemes rely on a small set of manually tuned weights, limiting fine-grained personalization and leading to suboptimal
alignment with the ultimate objective. In this paper, we propose DCEO (Direct Causal Effect Optimization), a data-driven framework for learning item-level proxy scores that are better aligned with the ultimate objective. We first aggregate the item-level proxy scores into a user-level proxy metric and quantify its
alignment with the ultimate objective using a relative causal effect. We then develop an actor-critic framework, where the critic estimates the ultimate objective for a given user-level proxy metric, and the actor dynamically generates context-dependent fusion weights over multiple objectives to construct the item-level proxy scores and is trained to directly optimize the relative causal effect. Extensive offline experiments and analyses demonstrate the effectiveness and interpretability of DCEO. In addition, DCEO has been deployed in a large-scale industrial e-commerce search system, outperforming the conventional GMV proxy by 0.36% in GMV in a 41-day online A/B test.
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All authors Junzhao Zhang, Tao Zhang, Liren Yu, Feiyi Dong, Zhixuan Zhang, Dan Ou, Haihong Tang
Information Retrieval
RetrievalRouter: Joint Modality and Architecture Selection for Document Retrieval
Emre Kuru, Mehmet Onur Keskin, Reza Farahbakhsh et al. (+1 more)
Document
retrieval increasingly supports high-stakes information access in finance, healthcare, and law. Modern
retrieval pipelines vary both in modality (text or
multimodal) and in
retrieval architecture (dense or late-interaction). These choices impose a hard compromise: the most effective pipelines are too slow and expensive to run at scale, while the fastest fail to retrieve evidence from complex documents. Practitioners must therefore choose between missed evidence and unusable latency, with no principled basis for adapting that choice at the query level. We show that this compromise is unnecessary. Not every query requires the same pipeline. Across
benchmarks spanning financial and scientific corpora, no static pipeline dominates. We introduce RetrievalRouter, a lightweight query-aware router that learns, from the query text alone, which
retrieval pipeline best fits each query. A single tunable parameter exposes the full accuracy-latency frontier, and for every static baseline, RetrievalRouter offers an operating point that is simultaneously more accurate and faster. Against the best static baseline, RetrievalRouter is 2.5% more accurate and 12.4 times faster. Furthermore, compared with prior adaptive strategy selection methods, RetrievalRouter achieves significantly higher nDCG@5 across accuracy-oriented settings, while matching or numerically outperforming them on both nDCG@5 and latency in latency-oriented settings. Our code and data are available at https://github.com/emrekuruu/
retrieval-router.
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All authors Emre Kuru, Mehmet Onur Keskin, Reza Farahbakhsh, Noel Crespi
Information Retrieval
When Stale Constraints Go Unchecked: Budgeted Verification Failures in Inherited Agent Memory
Kazuki Nakayashiki
Provenance links keep the evidence behind an inherited belief reachable; an
agent with a verification budget must still choose which links to inspect. We study a consolidated
memory that states a decision constraint and whose source record has since been superseded by a record that withdraws it: provenance is immutable, the current record has changed, and the
memory is stale. In a controlled six-
memory scenario with a budget of two records, sixteen
language models rarely re-verified a constraint that read as settled: they inspected its provenance path in about one episode in five and, once the constraint had been superseded, produced stale-consistent decisions in 77.3%, 74.7% and 74.7% of episodes across a primary run, a replication and a held-out domain. Re-assigning one of the same two slots to the critical path removed most of them: +74.0, +72.7 and +61.3 points (positive in every model), +80.7 in a prospectively frozen interleaved replication with a repaired non-critical control, and +62.0 on a panel of 10 models from 9 organisations; a corrected re-run of the held-out scenario gave +73.3. The forced-critical policy uses experimenter knowledge of the critical path: it quantifies how much stale-decision risk the same budget can recover and is not a scheduler. Two further deposited experiments locate the failure and a remedy: in this store the constraint's path is selected in 17.0% of episodes at two slots and 88.7% at four of six (above uniform allocation), and at two slots a one-sentence, target-blind rule (prefer
memories that state a limit on a candidate direction) moved the
agent's own allocation onto the constraint's path and recovered the oracle contrast on decisions (+89.3 points) where that constraint limits the tempting action, while a content-free freshness cue did not materially redirect allocation and a content-matched control rule changed neither selection nor decisions.
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Information Retrieval
An Event is Worth One Token: Event Tokenization for Industrial-scale LLM Recommendation
Fan Xia, Zhaoheng Zheng, Iman Setayesh et al. (+12 more)
LLM-based recommendation has scaled along model capacity and sequence length, yet each position encodes only text, semantic IDs, or a few categorical features, discarding rich user, item, context, and outcome signals available at each event. Under autoregressive modeling, this yields weak queries at each position and, since each position becomes context for the next, the degradation compounds across the sequence. We propose an event-centric paradigm that represents each interaction by its full temporal snapshot, and identify a new scaling dimension we term snapshot resolution: the amount of information encoded per event. To efficiently scale snapshot resolution, we introduce AMBER (Autoregressive Modeling via Bottlenecked Event Representation), which compresses each temporal snapshot into a compact Event
Token, a new LLM input modality. The representation is learned end-to-end, while Event
Tokens are pre-computed and cached for serving, decoupling snapshot resolution from real-time serving compute. On industrial-scale ranking and
retrieval benchmarks, AMBER advances the compute-quality Pareto frontier relative to alternative recommendation paradigms. At sufficient capacity, a single unified tokenizer even outperforms dedicated per-entity tokenizers, demonstrating positive transfer across structurally different entity types. AMBER's Event
Tokens also transfer across model architectures: when integrated into a heavily optimized non-LLM ranker as serving-time historical features, they yield statistically significant improvements. Further scaling Event Tokenizer capacity provides additional improvements.
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All authors Fan Xia, Zhaoheng Zheng, Iman Setayesh, Ruogu Lin, Yiqin Pan, Samarth Mittal, Wentao Bao, Vinti Pandey, Sachin Patil, Jianpeng Cheng, Jun Xiao, Zhuang Wang, Xiangjun Fan, Sri Reddy, Minghai Chen
Information Retrieval
TransRetrieval: Scaling Up Transformer-Based Retrieval for Industrial Recommendation
Zhifei Zheng, Yunfei Liu, Bin Liu et al. (+7 more)
Applying scaling laws to recommendation
retrieval is hindered by feature heterogeneity: naively stacking
Transformer layers yields diminishing returns because heterogeneous fields produce severe
token-norm divergence. We present TransRetrieval, a
Transformer-based
retrieval framework that scales with both computational budget and cross-domain data. The key enabler is (1) weighted average aggregation, which restores the homogeneous-
token assumption
Transformers rely on. Building on this, we introduce (2) target
token compression that cuts per-candidate FLOPs by 85% while preserving cross-
attention expressiveness, and (3) position-style domain
embeddings that unify multiple domains at negligible additional cost, turning cross-domain data into a scaling asset. On a 40-billion-interaction industrial
dataset and the public KuaiRand
benchmark, scaling compute from 0.1 to 2 MFLOPs per target yields +19.3/+22.2 pt Recall@2000, confirming robust log-linear scaling. In online A/B tests, TransRetrieval lifts platform revenue by 2.53% under the same end-to-end latency constraint as the production baseline.
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All authors Zhifei Zheng, Yunfei Liu, Bin Liu, Qiren Zhu, Hanbing Liu, Ziru Xu, Han Zhu, Jian Xu, Qi Qi, Bo Zheng
Information Retrieval
Query Expansion Is More Than Generation: Improving Dense Retrieval through Better Integration
Siyuan Sun and Mihai Surdeanu
Large
language models (LLMs) can generate query expansions without task-specific training, yet the same expansions often make a frozen dense retriever worse. We identify an underexplored factor: prior work has often focused on what text is generated, while how generated text is incorporated into dense retrievers has received less systematic
attention. By holding generated expansions fixed, we show that performance degradation can often be attributed to the integration method itself. We introduce AnchorQE, a training-free method that separately encodes the original query and its expansion before interpolating them. The interpolation factor is estimated using an unsupervised online strategy that operates over a small part of the unlabeled test stream. Intuitively, our strategy assigns high expansion trust only when expansions are both
retrieval-strong and consistent with the original query's retrieved evidence. We show that AnchorQE improves
retrieval effectiveness by up to 12.89% when compared to widely-used expansion-only or text-level concatenation baselines across TREC-DL, LoTTE, and BEIR. Further, we show that our online strategy to estimate the interpolation factor outperforms a fixed weight tuned on a development partition by up to 3.81%.
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Information Retrieval · Cross-listed from cs.LG
A Storage-Retrieval Gap in Parametric Knowledge Graph Memory
Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov et al. (+1 more)
Graph
retrieval-augmented generation places retrieved subgraphs into the model's context window at query time, paying a recurring
token cost and exposing source data on every call. We study an alternative: compiling a knowledge graph offline into a bank of LoRA adapters, one per entity, that serve as a parametric knowledge layer queried by injecting weights rather than text, at zero query-time context cost. On the MetaQA
dataset, we find that subgraph-trained adapters encode context-free factual knowledge that generalizes to unseen questions: on single-valued relations the adapter gains $+0.243$ exact-match score over a base model that is nearly blind closed-book ($0.007$), and only the correct adapter recovers this knowledge (an oracle gap of $+0.283$ over the base model). However, the stored knowledge is not recoverable by similarity: given a query with no subgraph,
embedding-based and weight-space geometry
retrieval both perform at chance, because a semantically neighbouring entity's adapter does not contain the answer - knowledge is stored locally and does not transfer. Weight geometry correlates with subgraph semantics ($\rho = +0.329$) but not with functional retrievability. We quantify the byte and context-
token costs against graph
retrieval-augmented generation and discuss deployment implications. Our results establish that parametric knowledge graph
memory is feasible for storing knowledge, and identify selecting and composing the right adapters by a mechanism other than semantic similarity as the central open problem - motivating a learned, query-conditioned composition mechanism.
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All authors Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov, Volker Tresp
Information Retrieval · Cross-listed from cs.CL
ReliableRAG: Combating Misinformation in Retrieval-Augmented Generation via Reliability-Guided Reasoning Chains
Jinpu Jiang, Xuan Wu, Wenhao Song et al. (+6 more)
Retrieval-Augmented Generation (RAG) has emerged as a powerful architecture for Question Answering (QA) by integrating external information into Large
Language Models (LLMs). However, false, inaccurate, and misleading information in news and social media poses a serious challenge to real-world RAG systems, especially in multi-hop QA, where complex multi-step
reasoning can be misled by even a single deceptive misinformation segment in the retrieved documents. Existing approaches mainly rely on implicit
alignment or explicit regulation, but their limited ability to assess fine-grained information reliability makes them vulnerable to deceptive misinformation that is semantically relevant to the question yet factually incorrect, leading to erroneous answers. To address this limitation, we propose ReliableRAG, which, to the best of our knowledge, is the first reliability-driven framework that mitigates deceptive misinformation in multi-hop QA through fine-grained evaluation of individual triples. ReliableRAG first extracts information segments from source documents and represents them as structured triples. It then quantifies triple reliability by combining query-triple semantic relevance with triple credibility, retaining only the top-$K$ reliable and non-redundant triples. Based on these refined triples, ReliableRAG autoregressively constructs robust
reasoning chains to consolidate trustworthy evidence and filter deceptive misinformation, producing accurate answers faithful to reliable information. Experiments on three multi-hop QA
datasets show that ReliableRAG outperforms existing methods, substantially improving the factual reliability and robustness of RAG systems under deceptive misinformation injection.
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All authors Jinpu Jiang, Xuan Wu, Wenhao Song, Bo Yang, You Zhou, Hongwei Ge, Heow Pueh Lee, Yanchun Liang, Chunguo Wu
Information Retrieval
DocPC: Document-Level Visual Retrieval via Representative Page Composition
Chengsong You, Qiyi Jiang, Junwei Zhou et al. (+12 more)
Visual document
retrieval has advanced by encoding page screenshots with
vision-
language models, bypassing OCR pipelines. However, existing methods remain page-centric, misaligned with real-world scenarios requiring complete document
retrieval. A naive page-then-document aggregation suffers from linear indexing cost and degraded
retrieval when relevance spans multiple pages. We propose DocPC, a document-level visual
retrieval framework based on Representative Page Composition: selecting representative pages and composing them into a single grid image for document-level indexing, reducing indexed images, vectors, and storage by 10.1x and end-to-end indexing time by roughly 7.7x. To handle multi-positive supervision prevalent at the document level, we combine multi-positive contrastive learning with sparsely scheduled listwise optimization. We also introduce DocViRe, a
benchmark with multi-positive relevance annotations. DocPC-ColQwen achieves NDCG@5 of 44.09 on DocViRe, outperforming the strongest page-level baseline at 38.91 while reducing storage by 10.1x. Code is available at https://anonymous.4open.science/r/DocPC-Document-Level-Visual-
Retrieval-via-Representative-Page-Composition-1D52. Data is available at https://huggingface.co/
datasets/anonymous-7219/docpc.
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All authors Chengsong You, Qiyi Jiang, Junwei Zhou, Xiaoyu Cao, Weiyao Wang, Yiwei Xu, Ziyan Zhao, Zhen Sun, Qicheng Zhu, Xuanyi Fu, Yufan Chen, Yilun Li, Rongkang Xiong, Yunhai Hu, Nan Du
Information Retrieval · Cross-listed from cs.HC
Q&A or Document-Based? The Effects of Interface Type on How Screen Reader Users Access Interconnected Documents
Colleen F. Cipriano, Yichun Zhao, Miguel A. Nacenta et al. (+2 more)
Blind and low-
vision (BLV) users are increasingly engaging with large
language model (LLM) interfaces to access documents, but it is unclear how such systems support or hinder their ability to build interconnected knowledge. To examine this gap, we compared a Question-Answer Interface (QAI) that supports open-ended conversational inquiry, with a Document Interface (DI) based mostly on traditional structured text document navigation. We recruited 16 BLV screen reader users where they used both interfaces to explore two fictional worlds. Data from interaction logs, concept maps, decision-based tasks, and semi-structured interviews provide comparative insights into how interface design supports knowledge construction. Findings show that participants visited more distinct documents with the DI and formed larger and more correct mental models with the DI than with the QAI. They were also more able to apply knowledge they had gained. Simultaneously, many still preferred the QAI and often estimated that they had explored more, formed better mental models and applied their models better when acquiring the information with the QAI, despite this not being the case. Our analysis suggests possible interface design reasons for these differences and highlights some of the risks introduced by using question-answer interfaces to access information spaces.
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All authors Colleen F. Cipriano, Yichun Zhao, Miguel A. Nacenta, Kotaro Hara, Jaylee Soh
Information Retrieval
MOTIF: Motivation-guided Topology Inference for Cold-start Multimodal Recommendation
Yurui Shi, Yuchen Miao, Ximing Hu et al. (+2 more)
Cold-start
multimodal recommendation faces three coupled challenges: (i) sparse interactions obscure user intent, (ii) cold items remain topologically isolated, and (iii) similarity-based item graphs may cause semantic drift. To address these issues, we propose MOTIF, a Motivation-guided
Topology Inference framework for cold-start
multimodal recommendation. MOTIF integrates Semantic Motivation
Reasoning, Knowledge-enhanced Graph Reconstruction, Weighted Graph Contrastive Learning, and Semantic-Structural
Alignment. It uses offline LLM
reasoning to infer motivation semantics, reconstructs transferable item-item
topology, and learns robust graph
embeddings without injecting generated text into prediction. Experiments on three
multimodal benchmarks show consistent gains over graph-based,
multimodal, cold-start, and LLM-enhanced baselines, with up to 6.07% relative improvement over the strongest recent baseline.
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All authors Yurui Shi, Yuchen Miao, Ximing Hu, Zijun Wang, Chang Han
Information Retrieval
CRAMER: Control via Request-Aware Masking for Editing Recommenders
Zhiyuan Julian Su, Naihe Feng, Zhen Luther Qin et al. (+1 more)
Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt their recommendations to the user's timely interests. Unfortunately, existing user request adaptation methods often incur high computational overhead due to either 1) retraining the entire backbone network or 2) leveraging the inference ability of large
language models (a.k.a.
prompt engineering), limiting their applicability in large-scale recommendation services. This paper presents Control via Request-Aware Masking for Editing Recommenders (CRAMER), a framework that takes users' natural-
language requests to immediately change sequential recommendation models' behavior. Specifically, inspired by the model control theory, CRAMER treats user requests as control signals to modulate frozen backbone parameters through masking, achieving instant adaptation to diverse requests while avoiding costly retraining. Experiments on multiple large-scale
benchmark datasets show that CRAMER outperforms four state-of-the-art request-aware baselines across multiple recommendation metrics while achieving minimal overhead. Moreover, the proposed framework exhibits enhanced controllability and cross-domain adaptability, establishing a new paradigm for request-aware sequential recommendation.
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All authors Zhiyuan Julian Su, Naihe Feng, Zhen Luther Qin, Ga Wu
Information Retrieval
Rank-Deviation Quality: A Distance-Aware Metric for Multi-Answer Retrieval and Ranking Evaluation
Xiaokun Zhou, Alessandro Moschitti and Danielle Class
We introduce Rank-Deviation Quality (RDQ), an evaluation metric for
retrieval and ranking systems that adapts to queries with varying numbers of reference items, from a single correct answer to many valid results. RDQ scores a candidate ranking against an ordered reference list (ORL): each retrieved reference item contributes its output-position weight multiplied by a rank-deviation penalty, and items outside the ORL receive zero credit. Application-specific parameters control tolerance to misordering. Larger values emphasize retrieving valid reference items, whereas smaller values place more weight on matching their reference order. The output-position weights can reflect visibility in the application's interface, such as a vertical list or a carousel. Unlike metrics that require absolute relevance grades, RDQ operates on ordinal rankings, which annotators can produce through pairwise or listwise judgments. Unlike rank-correlation measures such as Kendall's tau, RDQ accounts for both which items are returned and how they are ordered. On a 5,000-query point-of-interest (POI)
dataset with 12 systems, RDQ has the highest median empirical power@100 among the 13 evaluated metric configurations. It reaches mean tau >= 0.8 agreement with its own full-query ordering at 200 queries; RBP(0.9), the strongest tested non-RDQ configuration, reaches the same threshold at 250. On TREC Deep Learning
benchmarks, where NDCG uses native graded labels and RDQ uses ordinal tiers derived from them, RDQ reaches comparable median power at n=25, while NDCG is higher at n=100.
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Information Retrieval
The "Curse of Knowledge" in LLM Query Simulation: Concept Provenance for Tracing Answer-Side Intrusion
Chenglong Ma, Xinye Wanyan, Danula Hettiachchi et al. (+2 more)
LLM-generated search queries are widely used to augment IR evaluation, yet they may contain concepts that presuppose answer-side document knowledge, violating the information-access boundary of pre-search users. Existing validation metrics, including overlap, diversity, and effectiveness, cannot distinguish rare human-tail variation from candidate answer-side intrusion. We introduce concept provenance, a framework that assigns query concepts to backstory-supported, human-central, human-tail, and candidate answer-side zones, operationalizing a boundary that
retrieval metrics alone cannot detect. Applying concept provenance to 77,004 queries across 100 UQV100 topics, 8 LLMs, and 5
prompt conditions with two extraction pipelines, we obtain a cross-pipeline
token-HCIR Spearman rho of 1.0 over five condition means. Candidate answer-side concepts constitute 7.40 percent of non-generic concepts and appear in 97 of 100 topics, with topic explaining approximately 67 percent of variance. Human validation yields 68.2 percent relaxed precision, revealing two mechanisms: knowledge intrusion at 45.5 percent and deployment intrusion at 45.0 percent. Diagnostic probes show disproportionate localized
retrieval effects, with deletion effect size d = -0.47 compared with d = -0.34 for random deletion, but these concepts explain less than 2 percent of aggregate evaluation variance. Concept provenance therefore serves as a boundary-compliance diagnostic rather than an evaluation-shift predictor. Under the tested conditions, no
prompt condition eliminates intrusion; post-generation concept-provenance selection achieves 99 percent elimination.
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All authors Chenglong Ma, Xinye Wanyan, Danula Hettiachchi, Ziqi Xu, Jeffrey Chan