Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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Hugging Face Trending Papers

Jun 3

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information. Learning disentangled representations is a principled way to identify these underlying shared and unique factors that are hidden in observational data.

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Hugging Face Trending Papers

Jun 3

Neetyabhas: A Framework for Uncertainty-Aware Public Policy Optimization in Rational Agent-Based Models

Purpose The WHO's COVID-19 non-pharmaceutical interventions (e. g.

rag agents reinforcement-learning

Hugging Face Trending Papers

Jun 3

MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning

AI systems increasingly need to combine two demanding capabilities: navigating multi-session conversation history and performing deep reading comprehension within long documents. Yet no existing benchmark evaluates both simultaneously.

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arXiv AI

Jun 3

$\mathbb{R}^{2k}$ is Theoretically Large Enough for Embedding-based Top-$k$ Retrieval

arXiv:2601. 20844v3 Announce Type: replace-cross Abstract: This paper studies the Minimal Embeddable Dimension (MED): the least dimension in which there exists a configuration of $m$ object vectors so that every subset of size at most $k$ is exactly retrieved by score comparison.

By Zihao Wang, Hang Yin, Lihui Liu, Hanghang Tong, Yangqiu Song, Ginny Wong, Simon See

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arXiv AI

Jun 3

Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors

arXiv:2606. 02814v1 Announce Type: cross Abstract: Neural retrievers are trained to estimate query-document relevance from annotated query-document pairs.

By Francisco Valentini, Edgar Altszyler, Martin Fajcik

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arXiv AI

Jun 3

vLLM Semantic Router: Signal Driven Decision Routing for Mixture-of-Modality Models

arXiv:2603. 04444v3 Announce Type: replace-cross Abstract: As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of intelligent request routing -- selecting the right model for each query at inference time -- has become a critical systems challenge.

By Xunzhuo Liu (Steve), Huamin Chen (Steve), Samzong Lu (Steve), Yossi Ovadia (Steve), Guohong Wen (Steve), Hao Wu (Steve), Zhengda Tan (Steve), Jintao Zhang (Steve), Senan Zedan (Steve), Yehudit Kerido (Steve), Liav Weiss (Steve), Haichen Zhang (Steve), Bishen Yu (Steve), Asaad Balum (Steve), Noa Limoy (Steve), Abdallah Samara (Steve), Baofa Fan (Steve), Brent Salisbury (Steve), Ryan Cook (Steve), Zhijie Wang (Steve), Qiping Pan (Steve), Rehan Khan (Steve), Avishek Goswami (Steve), Houston H. Zhang (Steve), Shuyi Wang (Steve), Ziang Tang (Steve), Fang Han (Steve), Zohaib Hassan (Steve), Jianqiao Zheng (Steve), Avinash Changrani (Steve), Xue (Steve), Liu, Bowei He

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arXiv AI

Jun 3

IdiomX A Multilingual Benchmark for Idiom Understanding, Retrieval, and Interpretation

arXiv:2606. 02584v1 Announce Type: cross Abstract: Idiomatic expressions remain a persistent challenge for natural language processing because their meanings are often non-compositional, context-dependent, and difficult to align across languages.

By Ayman Ali Sharara

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arXiv Machine Learning

Jun 3

Spherical Flows for Sampling Categorical Data

arXiv:2605. 05629v3 Announce Type: replace-cross Abstract: We study the problem of learning generative models for discrete sequences in a continuous embedding space.

By Jannis Chemseddine, Gregor Kornhardt, Gabriele Steidl

rag

arXiv AI

Jun 3

Attention Calibration for Position-Fair Dense Information Retrieval

arXiv:2606. 02737v1 Announce Type: cross Abstract: Dense retrieval models exhibit positional bias: retrieval effectiveness degrades when relevant information appears later in a passage (Zeng et al.

By Andrianos Michail, Elias Schuhmacher, Juri Opitz, Simon Clematide, Rico Sennrich

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arXiv Machine Learning

Jun 3

Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification

arXiv:2606. 03292v1 Announce Type: cross Abstract: Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously learnt by the classification model.

By Pablo Garc\'ia-Santaclara, Bruno Fern\'andez-Castro, Rebeca Pilar D\'iaz-Redondo

rag benchmarks

arXiv Machine Learning

Jun 3

Link Prediction or Perdition: the Seeds of Instability in Knowledge Graph Embeddings

arXiv:2606. 03365v1 Announce Type: new Abstract: Embedding models (KGEMs) constitute the main link prediction approach to complete knowledge graphs.

By Guillaume M\'erou\'e, Fabien Gandon, Pierre Monnin

rag benchmarks

arXiv Machine Learning

Jun 3

Reconstructing Content with Collaborative Attention for Universal Multimodal Representation Learning

arXiv:2603. 01471v3 Announce Type: replace-cross Abstract: Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across diverse tasks such as retrieval and classification.

By Jiahan Chen, Da Li, Hengran Zhang, Yinqiong Cai, Lixin Su, Jiafeng Guo, Daiting Shi, Dawei Yin, Keping Bi

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arXiv Machine Learning

Jun 3

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit

arXiv:2501. 02173v2 Announce Type: replace-cross Abstract: The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance between computational efficiency and predictive accuracy.

By Huixue Zhou, Hengrui Gu, Xi Liu, Kaixiong Zhou, Mingfu Liang, Yongkang Xiao, Srinivas Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

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arXiv Machine Learning

Jun 3

A Single-Loop Bilevel Deep Learning Method for Optimal Control of Obstacle Problems

arXiv:2601. 04120v2 Announce Type: replace-cross Abstract: Optimal control of obstacle problems arises in a wide range of applications and is computationally challenging due to its nonsmoothness, nonlinearity, and bilevel structure.

By Yongcun Song, Shangzhi Zeng, Jin Zhang, Lvgang Zhang

rag benchmarks

arXiv Machine Learning

Jun 3

Visual Instruction Tuning Aligns Modalities through Abstraction

arXiv:2606. 03871v1 Announce Type: cross Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text.

By Luis Palacios, Lorenzo Basile, Diego Doimo, Alberto Cazzaniga

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 3

Explainable Forecasting of Scientific Breakthroughs from Concept Network Dynamics

arXiv:2606. 03864v1 Announce Type: cross Abstract: We introduce an explainable machine-learning approach that forecasts the structural precursors of scientific breakthroughs -- the emergence and intensification of links between research concepts -- by modelling how OpenAlex concept networks evolve over time.

By Thomas Maillart, Thibaut Chataing, Ntorina Antoni, David Dosu, Paul Bagourd, Julian Jang-Jaccard, Alain Mermoud

rag

arXiv Machine Learning

Jun 3

Multi-Segment Attention: Enabling Efficient KV-Cache Management for Faster Large Language Model Serving

arXiv:2606. 02964v1 Announce Type: cross Abstract: Large Language Model (LLM) inference relies on key-value (KV) caches to avoid redundant attention computation.

By Chunan Shi, Yilei Chen, Yilin Chen, Xupeng Miao, Bin Cui

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arXiv Machine Learning

Jun 3

Contrastive Neural Algorithmic Reasoning for Graph Coloring

arXiv:2606. 03923v1 Announce Type: new Abstract: Graph coloring seeks to assigns colors to a graph's nodes so that adjacent nodes receive different colors, using as few colors as possible.

By Thien Le, Tianyu Zhao, Melanie Weber

rag safety

arXiv Machine Learning

Jun 3

The Impact of Temporal Granularity on Socio-Demographic Inference from Household Load Profiles

arXiv:2606. 03358v1 Announce Type: new Abstract: Smart meter data can reveal sensitive socio-demographic characteristics of households, raising privacy concerns.

By Dejan Radovanovic, Maximilian Schirl, Andreas Unterweger, G\"unther Eibl

rag safety

arXiv Machine Learning

Jun 3

Learning Coherent Representations: A Topological Approach to Interpretability

arXiv:2606. 02841v1 Announce Type: new Abstract: Deep neural networks learn representations where individual features often lack interpretable meaning; a single neuron may activate for scattered, unrelated inputs.

By Sigurd Gaukstad, Melvin Vaupel, Valdemar Karg{\aa}rd Olsen, Erik Hermansen, Benjamin Dunn

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