Papers nuevos sobre Búsqueda y recomendación
88 papers nuevos sobre búsqueda y recomendación en los últimos 7 días, dentro de IA y aprendizaje automático. Acá están los 50 que Pipette considera más valiosos, con el resultado principal en palabras de sus autores.
Lo mejor de la semana
Machine-Interpretable Information: Compiling Documents into Searchable and Readable Protocol States
We introduce Machine-Interpretable Information (MII), the first agent-to-agent (A2A) document-to-state protocol.
PreprintAfirmaciones fuertes, leer con cuidadoDice ser un gran avanceUniK: Universal Knowledge Perception for Digital and Physical AI
Across five digital AI domains (medical literature, open-domain QA, chemistry, legal video proceedings, and government open data) UniK combined with an open-source 70-billion-parameter model consistently matches or outperforms frontier proprietary LLMs that are orders of magnitude larger: 76% RAG accuracy on government data versus 47% for GPT-5; 77.9% on medical QA without fine-tuning; topping all open-source chemistry pipelines.
PreprintAfirmaciones fuertes, leer con cuidadoUso en el mundo realPRISM-RAG: Multimodal Hypergraph Retrieval-Augmented Generation for Tobacco Product and Legislative Policy Reasoning
PRISM-RAG retrieves passages from the correct jurisdiction in 93.9% of policy compliance queries, a 48.6 percentage point advantage over standard RAG (p<0.001), using zero LLM calls at index time and one at query time, and is competitive with or outperforms SOTA RAG frameworks across keyword, semantic, jurisdiction-, and compliance-accuracy metrics.
PreprintUso en el mundo realMeet, Compare, or Abstain: LatWeave for Deterministic Multi-Hop Question Answering on Knowledge Lattices
We present LatWeave, which organizes knowledge into a multidimensional knowledge lattice and compiles multi-hop QA into three deterministic operators -- meet (constraint intersection), compare (lattice-order comparison), and abstain (structural abstention); LLMs appear only on the construction side (one-shot extraction) and the query-planning side, while the answer-generation path is zero-LLM, zero-task-training, and auditable end to end -- so that question answering over Web-published knowledge becomes reproducible item by item.
PreprintWhen More Evidence Hurts: Publication-Bias Drift and Principled Stopping for Biomedical Causal Search
Against full-budget retrieval, DACG-agent reduces evidence drift from 15.7% to 6.4% and improves null-effect accuracy by 21 percentage points (40.0%61.4%) while using 67% fewer retrieval steps; overall accuracy rises from 61.4% to 69.3% (95% CI 61--77).
PreprintUso en el mundo realComputation Over Geometry: Meaning Identity Is Computed, Not Shipped in the Embeddings
We show that, for frozen off-the-shelf encoders and language models, it is not: identity is computed when both sentences share one forward pass, and is not a property of the embedding geometry those systems ship.
PreprintLarge Knowledge Model: From Papers to a Scientific Reasoning Landscape
We introduce the Large Knowledge Model (LKM), a scientific knowledge infrastructure that transforms the literature into a shared, computationally accessible reasoning resource.
PreprintAfirmaciones fuertes, leer con cuidadoBeyond Raw Engagement: A Counterfactual Observability Framework for Recommender Systems at Netflix
In this work, we present a general evaluation framework that enhances observability across multiple recommender systems at Netflix and demonstrate its effectiveness through several production deployments.
PreprintUso en el mundo realHybrid GPU-CPU Retrieval for Personalized Search at Ultra-Large Scale
We present a hybrid GPU-CPU co-serving system that resolves the paradox through orchestration rather than a new model class.
PreprintUso en el mundo realOneTrans-V2: Unifying Retrieval, Pre-rank, and Fine-rank with One Transformer in Industrial Recommender
Building on OneTrans' model-level unification, we present OneTrans-V2, one Transformer that unifies the entire cascade.
PreprintUso en el mundo realSeek: Self-Evaluative Exploration for Knowledge Retrieval
We introduce Seek, Self-Evaluative Exploration for Knowledge Retrieval, a training-free framework that addresses this limitation through iterative corpus interaction at test time.
Preprint con versión publicadaReducing Barriers to Academic Support: Evaluating a Course-Specific RAG System for Addressing Help-Seeking Disparities in Higher Education
The findings suggest that carefully designed course-specific AI systems may reduce barriers to academic support by occupying an intermediary space between independent study and formal support.
PreprintUso en el mundo realAn Interpretable Memory Decision Controller for LLM Agents Based on Three-Signal Complementarity: Decoupling Confidence and Consistency
Evaluations on mainstream large language models and multiple open-source datasets show that MDL reduces the hallucination rate under conflicting memories by about 56.04% in general scenarios and approaches zero hallucination in high-risk scenarios.
PreprintUso en el mundo realGraded-Relevance Composed Multimodal Retrieval for E-commerce Visual Search at Scale
We propose a methodology for training CIR retrievers on graded relevance, consisting of: (i) a VLM to curate training data, generating both queries (object detection + modifier synthesis) and 4-level relevance labels without manual annotation, (ii) an iterative relevance-feedback loop that expands the training set by mining hard negatives from the in-training retriever, and (iii) a hierarchy-aware angular objective to train the retriever directly on the graded labels rather than collapsing them to a binary split.
PreprintUso en el mundo realOvis-Embedding: Pushing the Frontiers of Universal Omni-Modal Embeddings
In this report, we introduce Ovis-Embedding, a state-of-the-art omni-modal embedding family built on native integration of text, image, video, and audio.
PreprintAfirmaciones fuertes, leer con cuidadoReFilter: Bridging Embeddings and LLM Filtering for Similar Mobile App Retrieval
To address this gap, we propose ReFilter, a hybrid framework that first Retrieves semantically related candidate apps using embeddings and then applies LLM-based contextual Filtering to identify true functionally similar apps with higher precision.
PreprintUso en el mundo realCross-Country Code-Mixing for Generative Recommendation
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.
PreprintUso en el mundo realExplainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery
We demonstrate that combining LLM-backed recommendations with these explanatory rationales significantly reduces the trust barrier for new content, yielding statistically significant improvements in both user exploration and overall engagement on the discovery surfaces.
PreprintUso en el mundo realRAG-NAROK: Retrieval-Aware Knowledge Corpus Poisoning in RAG with Source-specific Refutation
Our results demonstrate that RAG-NAROK significantly outperforms static baselines across diverse domains, revealing a fundamental tension between RAG transparency and AI security.
PreprintAfirmaciones fuertes, leer con cuidadoCRISS: A Retrieval-Augmented AI Chatbot for Assisting Cancer Registrars
Domain-specific RAG improved evidence grounding and response quality for cancer registry questions while enabling citation-supported assistance across complexity levels.
PreprintUso en el mundo realPredictable Failure in Multi-Hop Retrieval: Score-Distributional Confidence Scoring and Abstention
Multi-hop retrieval failures are not uniformly distributed across queries: they cluster in structurally predictable subpopulations.
PreprintAutoViewMem: Self-Configuring Orthogonal Views for Conversational Long-Term Memory
We present AutoViewMem, a data-driven framework that organizes long-term conversational memory into self-configuring, low-overlap semantic views before indexing.
PreprintConnected Content Retriever: Dense Graph Edge Features Powering Pre-Ranking at LinkedIn
At its core is a sorted-search GPU primitive that joins dense graph affinity features (viewer to author) with document level features stored on the GPU at runtime in 5-10 ms. The shift to GPU served scoring enabled a 50x scale up of the ranking model's parameters and delivered a +2.5% lift in content time spent on the LinkedIn Feed in online experiments, significantly higher than the typical gains observed in LinkedIn Feed experiments.
PreprintUso en el mundo realKnowledge-as-Skill: A Structural Design for Autonomous Knowledge-Base Use by LLM Agents
We propose Knowledge-as-Skill, an organization scheme that makes a knowledge base discoverable, navigable, and self-descriptive.
PreprintUso en el mundo realTEMPS: Temporal Sentence Embeddings for Temporal Information Retrieval
On three temporal benchmarks, TEMPS improves MRR for every semantic backbone tested and, on TS- Retriever, lifts R@1 from 19.92 to 25.39 over the prior temporal state of the art.
PreprintUso en el mundo realBeyond a Scalar: Distributional Serving Interfaces for Watch-Time Prediction
To address this limitation, we propose the Distributional Serving Interface (DSI), which has a distribution provider, a compact, low-dimensional summary, and lightweight readouts tailored to each task.
PreprintUso en el mundo realThe Visual Target Matters: Learning across the Visual Hierarchy for Brain-to-Image Retrieval
To this end, we introduce NeuroGlyph, which learns a trial-independent visual target from multiple depths of a frozen visual backbone.
PreprintLightweight Ranking Heads: Accelerating Multi-Task Experimentation in Production Recommender Systems
To address the critical challenge of slow experimentation velocity, we introduce the Lightweight Ranking Heads (Light Heads) framework.
PreprintUso en el mundo realOBLIQ-IR: Training a Dense Retriever for Oblique Queries
We address this with OBLIQ-IR, a single-vector dense retriever whose training mixture combines per-mechanism synthetic queries with a new form of cross-model supervision: kNN-graph distillation from a frozen authorship encoder, which transfers a style-versus-topic inductive bias into the student.
PreprintCódigo disponibleX-Rec Technical Report
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.
PreprintUso en el mundo realEfficient Iterative Retrieval with Heterogeneous Batching
To address these, we present Orthrus, a serving system that performs heterogeneous batching within a unified inference loop.
PreprintUso en el mundo realCódigo disponibleScoring With the Engine: Retrieval Exposure, Cross-Engine Divergence, and the Limits of Engine-Agnostic GEO Scores
A score computed without a live engine can estimate page quality or query-page fit, while end-to-end visibility additionally depends on engine-specific exposure and selection.
PreprintLearned Cross-Task Relationships in Multi-Task Models
We propose a framework that learns cross-task relationships in multi-task models by approximating the joint distribution of task labels through targeted pairwise relationships.
Preprint con versión publicadaUso en el mundo realA Systematic Multi-Domain Evaluation 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.
PreprintEvidenT: Building Trustworthy Enterprise Assistants through Evidence Groundedness and Traceability
On approximately 500 real enterprise queries, EvidenT improves gold-source hit rate by an average of 29% over prompting baselines, produces no citations to nonretrieved urls, and achieves near-saturated answer-to-source lexical coverage.
PreprintUso en el mundo realPotential for Enhanced Learning in Machine Learning Classes by Using Wiki LLM Indexing
The compiled wiki remained accurate and grounded (9.93; 100% grounded in cited sources), while retrieval scored lower and was markedly less grounded (8.14; 64%).
PreprintUso en el mundo realAdaMerge: Tuning-Free Patch Compression for Multi-Vector Visual Document Retrieval
On the long-document benchmark ViDoRe-V2 (4 datasets, two backbones), AdaMerge significantly outperforms tuned PtM across the operating range (p < 10^-4); on the short-document benchmark ViDoRe-V1 (10 datasets, two backbones), where all merging methods are already near-lossless, AdaMerge matches tuned PtM without any per-dataset tuning.
Preprint con versión publicadaAsymmetric Dynamic Routing: Balancing Reasoning Depth and Computational Efficiency in Hypergraph RAG
To balance reasoning quality and inference efficiency, we propose Asymmetric Dynamic Routing (ADR), an intent-conditioned retrieval framework operating over hierarchical knowledge graphs.
PreprintAnalyzing Public Discourse on Urbanism: Topic Clustering, Sentiment Analysis and Retrieval-Augmented Generation using YouTube Comments
We present a pipeline and conversational system that combines geographic entity resolution, topic modeling, sentiment analysis, and Retrieval-Augmented Generation (RAG) over 22,788 chunks of YouTube transcripts and comments spanning 309 North American cities.
PreprintUNIQUE: A Unified Retrieval and Ranking System for Large-Scale Feed Recommendation
To address them, we present UNIQUE, a unified retrieval and ranking recommendation framework with single-layer flat quantization.
PreprintUso en el mundo realFrom Prompt to Recommendation: A Fitted Stage Model of Brand Visibility in AI Search
We fit a chronological diagnostic model using prior-run history and contemporaneous retrieval indicators: On the latest 30% holdout, the full model achieves AUC 0.963 on GPT and 0.942 on Gemini, compared with 0.937/0.917 for prior history alone and 0.880/0.840 for live signals alone.
PreprintWhat Should We Ask Next? Retrieval-Aware Question Learning under Partial Evidence
We establish that candidate discriminativeness and perceived usefulness provide weak supervision for this objective, then introduce RAVEL, a retrieval-aware online reinforcement learning framework for interactive person re-identification.
PreprintBoundaryMORPH: Budgeted Reranking via Active Set Selection for Diffuse Retrieval
We demonstrate that BoundaryMORPH achieves state-of-the-art set retrieval quality across multiple models and datasets with open-ended queries ( nCG@100 over the strongest baseline).
PreprintGroundedGEO: Auditing the Evidence Gap in Generative Search Rankings
On the frozen listwise ranker Qwen2.5-7B, unsupported-rich variants show significant normalized rank gain over clean candidates (+0.065 to +0.092 across claim profiles, Holm-corrected), while supported and neutral controls do not; the effect is model-dependent (marginal on MiMo-v2.5, absent on GLM-5.3-Flash).
PreprintMuSeR: Scalable Long-sequence Recommendation with Multi-interest Modeling
Rather than proposing a new modeling primitive, our contribution is a system-level integration that makes long-term, multi-interest, and multimodal modeling jointly deployable in a real-time production pipeline, together with the engineering practices required to sustain it.
PreprintUso en el mundo realCalibrating Reproduced Claims in Recommender Systems
We introduce claim calibration as a way of stating the strongest claim supported by a follow-up study, together with the conditions under which it holds and the parts that remain untested.
PreprintAuditing Source Exposure in Baidu and Google AI Search
The results reveal substantial differences across platform-language settings in overview availability and visible source exposure.
PreprintDecoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores
The contribution is therefore a privacy-scope contract and a certifiable score-to-slate stability mechanism, not a universal utility claim.
Preprint con versión publicadaBridging Static and Agentic RAG for Taiwanese Historical Question Answering
We therefore introduce a post-hoc selector that compares the two responses and their cited evidence, significantly outperforming either individual pipeline and recovering 60.34% of the oracle headroom.
PreprintTest-Time Adaptation with Query-Dependent Residuals for Visual Document Retrieval
We introduce Q-REACT, a query-side test-time adaptation method that converts limited reranker feedback into reusable retrieval improvements.
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