Information Retrieval · Cross-listed from cs.AI
Advancing Model Research in AgentX: Long-Horizon Autonomy for Industrial Recommender Systems
Shuang Yang, Zijie Zhuang, Changxin Lao et al. (+20 more)
Sustaining industrial recommendation research requires using the results of one experiment to decide what to investigate next. We present AgentX-Model, the next generation of AgentX's model research framework, which connects proposal development and model experimentation within sandboxes defined by business inputs and prediction tasks. AgentX-Model adopts a dual-
agent architecture comprising a Research
Agent and a Model
Agent. The Research
Agent develops independently reviewed proposals from papers and experimental findings, while the Model
Agent conducts multi-round investigations and returns code, measurements, and unresolved questions. Using the returned results, the Research
Agent selects a starting implementation and formulates the next research question, allowing subsequent experiments to build on earlier findings. We organize this continuing research around four actions: Reproduce, Follow-up, Composition, and Diagnose. The first three actions drive routine research, while Diagnose acquires the evidence needed to choose a repair, including for issues raised by business feedback and online evaluation, such as prediction bias measured by PCOC. Across the production evaluation, 560 of 636 completed model-changing experiments recorded AUC above their business baselines. As research continued, some experiments recorded AUC above every comparable ancestor in their lineages. The five latest online A/B evaluations across different business settings reported gains including 10-15% in acquisition efficiency, 15-20% in target-segment advertising spend, and 0.3-0.8% in watch time; the watch-time model used approximately 10% fewer FLOPs and parameters. A dependency-aware historical-replay
benchmark further evaluates research allocation, with initial results showing no consistent efficiency gain from more complex scheduling when
agents already analyze and select concrete candidates.
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All authors Shuang Yang, Zijie Zhuang, Changxin Lao, Pengbo Xu, Hanwen Xu, Yusheng Huang, Han Gao, Guanchen Wang, Tianbao Ma, Linxun Chen, Peilin Song, Xuming Wang, Chen Li, Fan Wu, Tao Wang, Zibo Zhao, Xiangyu Wu, An Liu, Fei Pan, Peng Jiang, Chen Yang, Zhaojie Liu, Wenwu Ou
Information Retrieval
From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation
Mengdan Zhu, Yufan Zhao, Yao Zhao et al. (+5 more)
Semantic IDs (SIDs) encode each catalog item as a short
token sequence, enabling generative recommenders to predict the next item autoregressively.
Reasoning-enhanced variants, an increasingly common extension, first generate a textual trace and then decode a next-item SID by beam search. Such recommenders are commonly trained with group-relative policy optimization under an exact-match SID reward, which is sparse in large catalogs. Two failure modes follow. When all rollouts in a group miss the target, the group yields zero advantage and no learning signal. Rollouts sharing the same SID reward receive identical advantages, however much their traces differ. In both cases the reward reflects only the decoded SID, never the
reasoning that produced it. This creates a credit-assignment gap. We address this gap with
retrieval-grounded query attribution. Each trace is structured into a history summary, a set of interest hypotheses, and a final SID. A frozen retriever executes every hypothesis as a catalog query, so that each hypothesis becomes independently verifiable rather than judged only through the final SID. A rollout is rewarded when any of its queries retrieves the target within the \mbox{top-$K$}, and per-query hit indicators localize that reward to individual hypotheses. Credit is thus assigned at the span level: only hypotheses that individually hit receive positive
retrieval advantage, while the
retrieval channel never updates the final SID span. Rollouts that share a SID reward can therefore receive different updates. Across experiments on three Amazon Reviews
datasets, this yields consistent improvements in SID recommendation. On
Video Games, an oracle analysis further reveals the potential of interest-conditioned SID decoding: selecting the target-relevant query among generated interests improves both recall and ranking.
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All authors Mengdan Zhu, Yufan Zhao, Yao Zhao, Sophie Di, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao
Information Retrieval
Learning Better Reasoning for Generative Recommendation with Semantic IDs
Mengdan Zhu, Yufan Zhao, Sophie Di et al. (+5 more)
Generative recommendation reformulates item
retrieval as sequence generation, allowing a unified model to directly generate the next item from a user's interaction history. Semantic IDs further make this paradigm effective and scalable by representing each item as discrete codes, enabling knowledge sharing among semantically related items. Recent studies introduce explicit
reasoning before Semantic-ID generation, helping models summarize user interests and infer possible preference transitions. However,
reasoning is not inherently beneficial: Inaccurate or uninformative
reasoning may mislead subsequent item generation and ultimately degrade recommendation performance. This raises a central challenge: how can a recommender select and learn effective
reasoning traces and progressively evolve toward better
reasoning from its own generations? In this work, we propose Evo-Rec, a three-stage framework for learning better
reasoning and further enhancing it through reinforcement learning. First, we align Semantic IDs with their textual and behavioral contexts, enabling the model to understand and generate item identifiers. Second, we sample multiple candidate
reasoning traces and retain those that improve the prediction of the ground-truth item, providing a stronger
reasoning initialization through supervised fine-tuning. Third, we further optimize the
reasoning policy through reinforcement learning with catalog-constrained item generation and ranking-aware recommendation feedback. Experiments on three Amazon Review
benchmarks show that Evo-Rec consistently outperforms discriminative, generative, and
reasoning-enhanced recommenders across all evaluation metrics. These results demonstrate the effectiveness of our framework in learning better
reasoning for SID-based generative recommendation.
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All authors Mengdan Zhu, Yufan Zhao, Sophie Di, Yao Zhao, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao
Information Retrieval · Cross-listed from cs.CL
An Empirical Study of VLM Pipelines for Long-Document QA
Kenan E. Ak, Jay Mohta, Gwang Gook Lee et al. (+2 more)
Vision-
Language Models (VLMs) are increasingly used for long-document processing, where the inputs combine text with charts, tables, figures, and complex layouts. Deploying them means choosing how to feed the document to the model, which retriever to use when only a subset of pages is sent, and whether to run the model agentically or as a static pipeline. We study these choices on two long-document QA
benchmarks with both frontier API and open-weight VLMs. First, on MMLongBench-Doc our six-tool
agent with page, table, figure, and search calls pays off only once the answering VLM is large enough: with Qwen3.5-4B and 9B it trails static page input, with Qwen3.5-27B it draws level, and with Sonnet 4.5 it leads. On LongDocURL it is level with or ahead of static input at every reader. Its lead over the strongest static pipeline is clearest with the frontier reader on MMLongBench-Doc and narrows to within noise on LongDocURL. Second,
retrieval modality matters more than the specific retriever: the strongest image retriever leads the strongest text pipeline, and on the text side a single off-the-shelf cross-encoder rerank essentially matches a much heavier multi-stage LLM pipeline. Top-k image
retrieval is also the most
token-efficient input at every reader we paired it with, at roughly a seventh to a quarter of the
tokens of sending every page. Third, cutting across all three choices, three of our strongest pipelines succeed on different questions, and an oracle that picks the best pipeline per question gains roughly thirteen points over the best single pipeline, though evidence-type routing recovers almost none of it.
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All authors Kenan E. Ak, Jay Mohta, Gwang Gook Lee, Yan Xu, Dimitrios Dimitriadis
Information Retrieval · Cross-listed from cs.CY
Fair Feed Ranking for Participatory Budgeting
Carina I. Hausladen
In large-scale participatory budgeting, citizens cannot inspect the full proposal pool, so the order in which proposals are shown becomes a form of agenda-setting power. We argue that fair exposure should therefore be treated as a democratic-design goal. We study Consul Democracy, a widely deployed open-source digital-democracy platform, and show that its proposal feeds are typically ordered by popularity, recency, or comment activity. Building on this diagnosis, we propose FairFeed, a feed-ranking design for PB that uses transparently declared preferences, boosts under-exposed proposals, and admits a rate-limited reject channel for crowd-sourced vetting. We evaluate the design in a simulation anchored in Munich's 2025 PB process and compare it with random, newest, and most-commented feeds. In this simulation, FairFeed broadens proposal discovery, distributes visibility more evenly across the eligible pool, increases cross-cutting support, and improves resistance to manipulation relative to comment-based ranking. We conclude by outlining the human-subjects evaluation needed to test whether onboarding can recover voter preferences accurately enough for deployment in practice.
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Information Retrieval
LSF-SR: Latent Semantic Fusion for Sequential Recommendation via Flow-based Conditional Variational Autoencoders
Shih-Hong Chen, Josh Jia-Ching Ying and Vincent S. Tseng
Sequential recommendation aims to predict users' future interests from their historical interactions. Although Large
Language Models (LLMs) capture rich item semantics, existing methods often struggle to align collaborative signals with textual semantic knowledge. As a result, the learned item representations fail to capture the complementary strengths of both signals, leading to suboptimal recommendation quality. To address this limitation, we propose Latent Semantic Fusion for Sequential Recommendation via Flow-based Conditional Variational Autoencoders (LSF-SR), a novel framework that uses a Conditional Variational Autoencoder (CVAE) with Normalizing Flows to fuse item ID
embeddings and LLM-generated semantic signals. At the core of LSF-SR is a conditional fusion module augmented with planar or radial flows. This module learns a flexible latent space that encourages items with similar semantic profiles to cluster together within the latent
manifold. Through extensive experiments on five public
benchmark datasets, we demonstrate that LSF-SR consistently outperforms state-of-the-art baselines, achieving gains of up to 12.98% and 14.13% in Recall@20 and NDCG@20, respectively.
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Information Retrieval
SEEK: Skill-Routed Evaluation with Evolvable Knowledge for Industrial Search
Zhongxin Huang, Songyang Li, Renzhe Zhou et al. (+5 more)
Search quality evaluation provides essential supervision and diagnostic signals for the development and iteration of industrial search systems. Although large
language models (LLMs) offer a scalable alternative to manual assessment, reliable automatic evaluation remains challenging: users experience search results at the page level, while the applicable evaluation criteria are multi-dimensional and continuously evolving. Packing all evaluation criteria into a unified
prompt introduces irrelevant context and potential criterion interference, whereas internalizing them through post-training tightly couples rule updates with costly model retraining cycles. To address these issues, we propose Skill-routed Evaluation with Evolvable Knowledge (SEEK). Specifically, SEEK externalizes specific search evaluation criteria into a skill bank, dynamically routes relevant skills for each query-result list pair, and employs a task-adapted listwise evaluator to produce page-level judgments and failure mode attribution. A two-stage training pipeline teaches the evaluator to align evaluation criteria with human preferences, while a replay-gated skill bank allows recurring evaluation knowledge gaps to be incorporated without model retraining. Experiments on industrial short-
video search show that SEEK improves listwise quality evaluation accuracy and achieves significant progress in attribution diagnosis. SEEK has been deployed at Kuaishou, a short-
video platform with over 400 million daily active users, significantly improving the scale and quality of online search evaluation.
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All authors Zhongxin Huang, Songyang Li, Renzhe Zhou, Feiran Zhu, Chenglei Dai, Zhen Xiao, Xuanping Li, Jingwei Zhuo
Information Retrieval · Cross-listed from cs.AI
C3M: Cross-Session Multimodal Memory Maintenance for Long-Horizon Tasks
Xueshu Chen, Yan Wang, Zihao Xue et al. (+5 more)
Long-horizon tasks require preserving and later recovering cross-session evidence under a bounded, query-blind
memory budget. Existing compression can discard fine-grained visual cues or conflate semantically similar but incompatible observations. We present C3M, a cross-session
multimodal memory organization that maintains a bounded active index over persistent source text-image evidence. Relation-aware updates consolidate safe redundancy while preserving complementary and incompatible records. At query time, budgeted routing selects useful index pages and expands their associated source evidence under a fixed reader budget. Together, these mechanisms establish a compact, provenance-preserving
multimodal memory organization for cross-session long-horizon tasks, retaining temporal distinctions and source links required for reliable downstream
reasoning. Code is available at https://github.com/HuzhouNLP/C3M.
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All authors Xueshu Chen, Yan Wang, Zihao Xue, Jiefu Li, Zhenfang Liu, Jayden Chen, Zhen Bi, Jungang Lou
Information Retrieval · Cross-listed from cs.CV
SALI: Shot-Aware Late Interaction for Cross-Shot Relation Matching in Text-to-Video Retrieval using Film-Grammar Knowledge
Toya Oyama, Rainer Lienhart and Shin'ichi Satoh
Text-to-
video retrieval usually represents a
video clip by a single
embedding. This
embedding often loses important relations between people. E.g., an interaction "Anna confronts Mark" is regularly filmed as alternating shot and reverse shot of both (Fig. 1a). No single shot or averaged
embedding over clip shots captures this relation. Thus, we propose SALI (Shot-Aware Late Interaction). It extracts the subject and object from a single-sentence query, and matches the query, its subject and object text
embeddings against each visual shot
embedding of a
video clip. The matching operator is greedy max or optimal transport. A film-grammar penalty in fine-tuning adds a small, consistent shift. Built on CLIP4Clip-meanP, SALI keeps overall recall on par on Condensed Movies and ActivityNet while raising R@1 on multi-shot relation queries by 3 and 12 points, the most among all compared methods, and improves such queries on MSR-VTT at a cost of 1.4 R@1 overall.
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Information Retrieval · Cross-listed from cs.SE
CodeGraph: Open-Taxonomy Knowledge Graph for Source Code with Wikidata Grounding
Federico Pennino, Andrea Gurioli, Stefano Zacchiroli et al. (+2 more)
Public software repositories, like GitHub and Software Heritage Archive, store billions of files, yet extracting their implicit engineering knowledge —i.e., the
algorithms they implement, the paradigms they follow, the patterns they instantiate, and the application domains they serve— remains challenging, as current tools are constrained to syntactic and
token-level analysis. We present a pipeline for building an open-taxonomy semantic annotation of source code using a code-specialised Large
Language Model. The extracted entities are grounded in Wikidata through a three-stage linking procedure: a deterministic SPARQL stage handles unambiguous entities, a Deep Research
Agent resolves the residual long tail, and a hierarchy-rollup stage imports the parent-of closure of each resolved Wikidata identifier. The resulting annotations are materialised as a source-code-specific open-taxonomy knowledge graph. We further introduce a calibrated quality-assurance protocol that quantifies annotation precision by combining a small human gold set with an LLM-as-a-judge filter. We applied our pipeline to the 167 million files of the Stack-Edu corpus, creating the first known large-scale open-taxonomy knowledge graph for source code. Our graph, named CodeGraph, contains approximately 158 million nodes, which include around 145 million files, about 63,000 extracted concept entities (such as
algorithms, paradigms, design patterns, and application domains), and roughly 19,800 grounded Wikidata entities. Furthermore, CodeGraph features approximately 1 billion typed edges that connect files to their respective concepts, link these concepts to their grounded Wikidata identifiers, and relate them to their parent categories, covering 14 programming
languages.
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All authors Federico Pennino, Andrea Gurioli, Stefano Zacchiroli, Maurizio Gabbrielli, Paolo Ferragina
Information Retrieval
A Systematic Multi-Domain Evaluation of Document Retrievers
Valentin Velev and Andreas Spitz
Document
retrieval is a crucial component of many modern AI systems, directly influencing their effectiveness, robustness, and
fairness in downstream tasks. While recent years have seen a growing number of retrievers, comparative studies in the literature are typically limited in scope or focused on singular
benchmarks, domains, or model families. This fragmentation makes it difficult to draw reliable conclusions about the relative strengths, weaknesses, and trade-offs of document retrievers. To address this gap, we conduct a large-scale empirical evaluation of document retrievers, covering three families (sparse, dense, and expansion-based) and evaluating 33 retrievers across seven IR
datasets, analyzing
retrieval quality, runtime, and failure points. Rather than tuning each model individually, we evaluate every retriever off the shelf, under the configuration reconstructable from its public documentation and a uniform compute budget. Our results show that NV-Embed-v2 achieves the strongest performance on four of the seven
datasets, albeit at the cost of substantial query latencies. Among sparse retrievers, we find that SPLADE-v3 rivals the top-performing approach despite much lower latency, and even achieves top scores on MS MARCO. On instruction-following
datasets, GritLM delivers the best performance. Finally, an analysis of the retrievers' failure points reveals contrasts between models and families that indicate potential for unrealized gains in
retrieval performance.
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Information Retrieval · Cross-listed from cs.LG
Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores
Sam Urmian, Qinyi Liu and Mohammad Khalil
We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-
privacy guarantee passes through selection and its audit trace by post-processing. End-to-end
privacy holds only when selector inputs are public or independent, previous private outputs, or separately
privacy-accounted; fixing raw state or candidate information instead yields only a conditional guarantee. Second, we derive a logged margin certificate: bounded score-induced objective movement below half the smallest greedy decision margin guarantees that the ordered slate is unchanged. Controlled fixed-margin tests show near-linear exponent scaling, with an empirical slope of $-0.220$ (95% CI $[-0.231,-0.210]$) against the independent-noise reference $-1/4$. Real-anchor experiments on OULAD, MovieLens-25M, and Amazon Musical Instruments show that greater anchor weight reduces score-noise-induced ranking churn. OULAD and EdNet certificate checks validate the implementation of the logged inequality, while closed-loop simulations show bounded target drift and setting-dependent downstream utility. The contribution is therefore a
privacy-scope contract and a certifiable score-to-slate stability mechanism, not a universal utility claim.
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Information Retrieval
Asymmetric Dynamic Routing: Balancing Reasoning Depth and Computational Efficiency in Hypergraph RAG
Qi Sun, Yijia Zhang, Caibo Li et al. (+2 more)
While graph-based and hypergraph-based
Retrieval-Augmented Generation (RAG) significantly mitigate
hallucinations in Large
Language Models (LLMs), existing structure-based RAG systems typically adopt static traversal strategies regardless of the query complexity. We identify this “static
retrieval fallacy” as a primary source of computational redundancy for simple queries and cognitive context gaps for complex
reasoning tasks. To balance
reasoning quality and inference efficiency, we propose Asymmetric Dynamic Routing (ADR), an intent-conditioned
retrieval framework operating over hierarchical knowledge graphs. ADR employs a lightweight structured classifier to dynamically dispatch queries among three asymmetric topological traversal operators: localized fact anchoring, bottom-up adjacency
diffusion, and top-down insight grounding, which collectively enable bidirectional information flow across hierarchical knowledge layers. Extensive empirical evaluations across five domain-specific corpora demonstrate that ADR maintains strong
reasoning performance while reducing
prompt token consumption by up to 48.7% and end-to-end query latency by 45.3%, yielding a favorable quality–efficiency trade-off for query-adaptive Hypergraph RAG.
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All authors Qi Sun, Yijia Zhang, Caibo Li, Qiang Li, Yu Guo
Information Retrieval · Cross-listed from cs.AI
ASIRF: An Agentic Framework for Context-Dependent Sensitive Information Redaction
Sudha Priyadarshini and Mohamed Chahine Ghanem
Sensitive information is defined by domain and intent, not a universal category, yet redaction systems such as
privacy filters and named-entity recognizers fix a taxonomy at training time, requiring retraining for each new domain. We introduce ASIRF (Agentic Sensitive Information Redaction Framework), which retrieves domain-specific definitions based on the input's domain from a flexible knowledge base at inference time, needing no retraining to adapt. Two architectures, a three-call multi-
agent pipeline and a single-
agent variant, are evaluated across ten small open-weight models and eight
datasets, including out-of-distribution fictional domains, against the OpenAI
Privacy Filter (OPF) as a trained-classifier baseline. With only a few dozen expert-authored definitions per domain and no training data, ASIRF's recall exceeds OPF's in 68 of 80 model-domain combinations (85 percent), by at least one of the two architectures, with shortfalls confined mostly to OPF's training-distribution domains.
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Information Retrieval
ScalarLens: Numerical Embeddings with Stable Coordinates and Contextual Responses for CTR Prediction
Heng Yao, Tianying Liu, Yulou Shu et al. (+6 more)
Numerical
embeddings for click-through rate (CTR) prediction are built on a convenient but restrictive premise: a scalar has one representation. This premise conflates where a value lies with what it means for the current sample. On the Criteo validation split, the same numerical interval carries residual click evidence with opposite signs across categorical and numerical contexts, even after additive main effects are removed. Production pipelines compound this mismatch because externally normalized features require transformations and statistics to remain synchronized between training and serving. We introduce ScalarLens, a numerical
embedding that preserves what a value is while adapting how it should be interpreted. A monotone local mesh constructs a stable coordinate from the focal scalar alone; bounded low-rank dynamics then produce a contextual response without moving that coordinate or replacing categorical
tokens and the CTR backbone. In a 1,539-run primary evaluation covering 19 representations, three
datasets, nine backbones, and three seeds, ScalarLens ranks first in 25 of 27 settings on original numerical scales and second in the remaining two. Matched ablations show that scale correction, additional local capacity, and generic conditioning do not reproduce the gain. A controlled study further recovers categorical, numerical, and mixed response mechanisms under context shift while the focal coordinate remains exactly invariant. A complete rerun under shared standardization retains significant advantages over DEER, DAES, and NaryDis, showing that the result is not explained by tolerance to raw scales alone. ScalarLens therefore recasts numerical
embedding as a measurement problem: coordinates belong to values, while predictive responses belong to values in context.
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All authors Heng Yao, Tianying Liu, Yulou Shu, Yong He, Chuan Yuan, Kaibin Qiu, Guowei Chen, Jiayu Zhao, Siyun Hou
Information Retrieval
X-Rec Technical Report
Chenglei Shen, Chenzhe Huang, Dong Jiang et al. (+15 more)
Recent advances in generative modeling have reshaped recommender systems by formulating recommendation as a next-item generation problem. Existing
retrieval approaches primarily follow two paradigms: user-to-item (U2I) methods represent user context using one or a few deterministic
embeddings, which limits the ability to capture diverse and multi-mode interests, while semantic-ID-based autoregressive (SID-AR) methods model more expressive distributions but suffer from quantization errors and the low throughput of sequential decoding. To address these limitations, we propose X-Rec to directly learn the recommendation distribution in the continuous item
embedding space through flow matching and generate
embedding triggers for approximate nearest neighbor
retrieval. X-Rec incorporates three key designs to make this formulation effective and efficient. First, we introduce anchor conditioning to decompose generation into coarse semantic-region selection and fine-grained refinement. Second, we adopt Riemannian flow matching to align generative trajectories with the hyperspherical geometry of item
embeddings. Third, we design a late-interaction
diffusion Transformer that restricts repeated velocity-field estimation to the final
Transformer layer. On a streaming
benchmark, X-Rec substantially outperforms U2I baselines, matches the
retrieval quality of SID-AR methods, and delivers 3.46x higher inference throughput than SID-AR. X-Rec has also been deployed as a new
retrieval source for a specific vertical content on TikTok, where two consecutive launches have yielded significant improvements in both vertical engagement (+4.1484%) and general engagement (+0.0111%).
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All authors Chenglei Shen, Chenzhe Huang, Dong Jiang, Hongjie Gao, Jue Zhang, Kun Xú, Lincan Cai, Nan Zhuang, Pan Zhang, Shi Chen, Shunchi Zhang, Xiaoyu Ye, Yang Jin, Yu Zhang, Zhenwei An, Zhongtao Jiang, Zhiwei Wang, Kun Xǔ
Information Retrieval · Cross-listed from cs.AI
Claim-Gated Source-Risk Auditing for Generative Search
Kainan Zhou, Chuhong Xu, Gangzhen Qian et al. (+1 more)
A generative search answer can cite a supported passage yet omit a source relationship that changes its interpretation. We specify a claim-gated audit of the query-source-answer tuple. An omission is resolved only when relationship evidence, answer adoption, materiality, and disclosure are all observed; incomplete evidence remains unresolved rather than being treated as independence. The specification separates this endpoint from citation support and review priority, and binds decisions to versioned evidence spans. A reference checker makes the record contract executable. On an exhaustive synthetic suite, it reproduces all 81 three-state predicate combinations and rejects 192 deliberately malformed records. Common-guard baselines and predicate ablations isolate endpoint logic from missing-evidence handling, while controlled transitions check support separation and evidence removal. These are finite contract-conformance results, not detector accuracy or evidence of improved user outcomes. We define the independent annotation, held-out evaluation, and paired utility tests still required to establish semantic validity and deployment benefit.
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All authors Kainan Zhou, Chuhong Xu, Gangzhen Qian, Zhaoyi Li
Information Retrieval
Seek: Self-Evaluative Exploration for Knowledge Retrieval
Amin Bigdeli, Radin Hamidi Rad, Negar Arabzadeh et al. (+4 more)
LLM-based retrievers and rerankers have advanced passage ranking, yet both paradigms interact with the corpus in a single pass and commit to the resulting candidate set, leaving relevant documents permanently unrecoverable once missed. We introduce Seek, Self-Evaluative Exploration for Knowledge
Retrieval, a training-free framework that addresses this limitation through iterative corpus interaction at test time. At each round, an LLM generates pseudo-passages conditioned on accumulated relevance feedback, a retriever surfaces fresh candidates, and a dedicated assessor assigns graded relevance judgments that guide subsequent rounds. On TREC Deep Learning, Seek matches trained rerankers in ranking quality while consistently improving Recall@100 over single-pass BM25. On the
reasoning-intensive BRIGHT
benchmark, Seek with Qwen2.5-7B achieves an 82% relative gain over BM25, surpassing all trained baselines, and Seek with GPT-4.1 reaches 37.4 average nDCG@10, exceeding the strongest baseline by 37%.
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All authors Amin Bigdeli, Radin Hamidi Rad, Negar Arabzadeh, Sajad Ebrahimi, Hai Son Le, Charles L. A. Clarke, Ebrahim Bagheri
Information Retrieval
Cross-Country Code-Mixing for Generative Recommendation
Yuan Gao, Hao Deng, Haibo Xing et al. (+5 more)
Cross-country recommendation on modern e-commerce platforms is typically deployed with disjoint user and item ID spaces across
markets, removing the shared anchors that conventional cross-domain methods rely on. Generative recommendation (GR) mitigates this by mapping items into a shared
token space and training a unified model, but existing approaches keep behavior sequences strictly country-specific, so knowledge transfer occurs only at the parameter level and remains absent at the data level. Inspired by code-switching corpora in multilingual natural
language processing, we propose CMRec, a cross-country GR framework that injects cross-country supervision at the data level via dual-constrained, context-aware code-mixing. CMRec first learns a shared semantic codebook from multi-modal content and behavioral co-occurrence across countries. It then uses this codebook to synthesize mixed-country sequences via
token-level substitutions that satisfy both static (content) and dynamic (e.g., price, audience, popularity) constraints. Finally, it introduces a context-aware loss that reweights mixed samples according to their plausibility in the current sequence. Experiments on two real-world multi-country
datasets and an online A/B test show that CMRec substantially improves recommendation quality in data-sparse countries while preserving performance in data-rich countries, achieving +1.77% advertising revenue and +2.64% orders on a large-scale e-commerce platform.
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All authors Yuan Gao, Hao Deng, Haibo Xing, Yi Xu, Lingyu Mu, Jinxin Hu, Yu Zhang, Xiaoyi Zeng