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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arXiv Machine Learning

Jun 4

Causal Multi-fidelity Surrogate Forward and Inverse Models for ICF Implosions

arXiv:2509. 05510v3 Announce Type: replace-cross Abstract: Continued progress in inertial confinement fusion (ICF) requires solving inverse problems relating experimental observations to simulation input parameters, followed by design optimization.

By Tyler E. Maltba, Ben S. Southworth, Jeffrey R. Haack, Marc L. Klasky

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arXiv AI

Jun 4

Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods

arXiv:2511. 03304v2 Announce Type: replace-cross Abstract: With the on-going integration of machine learning systems into the everyday social life of millions the notion of fairness becomes an ever increasing priority in their development.

By Felix St\"orck, Fabian Hinder, Barbara Hammer

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arXiv AI

Jun 4

EvoPrompt: Guided Prompt Evolution for Vision-Language Models Adaptation

arXiv:2603. 09493v2 Announce Type: replace-cross Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge.

By Enming Zhang, Jiayang Li, Yanlong Wang, Yanru Wu, Zhenyu Liu, Yang Li

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jun 4

LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation

arXiv:2605. 29280v2 Announce Type: replace-cross Abstract: Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from diminishing transfer ratio -- the fraction of FM improvement captured by the VM -- as a single scalar cannot convey the rich intermediate knowledge that larger FMs learn.

By Shali Jiang, Hua Zheng, Boyang Liu, Laming Chen, Kenny Lov, Chuanqi Xu, Lisang Ding, Qinghai Zhou, Can Cui, Xiaolong Liu, Xiaoyi Liu, Yasmine Badr, Xin Xu, Jiyan Yang, Ellie Dingqiao Wen, Gerard Jonathan Mugisha Akkerhuis, Chenxiao Guan, Rong Jin, Ruichao Qiu, Xian Chen, Shifu Xu, Zhehui Zhou, Ping Chen, Rui Yang, Haicheng Chen, Xiangge Meng, Song Zhou, Dharak Kharod, Shuyu Xu, Qiang Jin, Qiao Yang, Wankun Zhu, Qin Huang, Yuzhen Huang, Darren Liu, Parish Aggarwal, Hui Zhou, Erzhuo Wang, Shuo Chang, Xiaorui Gan, Wenlin Chen, Santanu Kolay, Huayu Li

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arXiv Machine Learning

Jun 4

Towards Pretraining Text Encoders for TabPFN

arXiv:2606. 04876v1 Announce Type: new Abstract: Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numerical and categorical data, but do not natively handle high-cardinality text features.

By Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker, Frank Hutter

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arXiv Machine Learning

Jun 4

Graph Set Transformer

arXiv:2606. 05116v1 Announce Type: new Abstract: We introduce the Graph Set Transformer (GST), a neural network architecture for learning on sets of graphs, designed for tasks in which per-element predictions depend on set-wide context as well as local structure.

By Jose E. Escrig Molina, Baoquan Chen, Daniel Probst

llms rag computer-vision benchmarks

arXiv Machine Learning

Jun 4

Test-Time Compute Scaling for ASR with Depth-Conditioned Looped Transformers

arXiv:2606. 04678v1 Announce Type: new Abstract: End-to-end ASR systems typically use fixed-depth acoustic encoders at inference, making it difficult to trade additional test-time computation for improved recognition without training a larger model.

By Yacouba Kaloga, Shashi Kumar, Shakeel A. Sheikh, Driss Khalil, Petr Motlicek, Ina Kodrasi

llms rag multimodal

arXiv Machine Learning

Jun 4

QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation

arXiv:2606. 04689v1 Announce Type: cross Abstract: Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is often limited by severe long-tail predicate imbalance.

By Prerana Ramkumar, Nouhaila Innan, Muhammad Shafique

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arXiv AI

Jun 4

Toward Pre-Deployment Assurance for Enterprise AI Agents: Ontology-Grounded Simulation and Trust Certification

arXiv:2606. 04037v1 Announce Type: new Abstract: Pre-deployment verification of enterprise artificial intelligence (AI) agents remains a critical gap between large language model (LLM) capability benchmarking and production deployment.

By Thanh Luong Tuan, Abhijit Sanyal

llms rag agents benchmarks safety

arXiv AI

Jun 4

FinTradeBench: A Financial Reasoning Benchmark for LLMs

arXiv:2603. 19225v3 Announce Type: replace-cross Abstract: Real-world financial decision-making is a challenging problem that requires reasoning over heterogeneous signals, including company fundamentals derived from regulatory filings and trading signals computed from price dynamics.

By Yogesh Agrawal, Aniruddha Dutta, Md Mahadi Hasan, Santu Karmaker, Aritra Dutta

llms rag benchmarks safety

arXiv AI

Jun 4

Curvature-aware dynamic precision approach for physics-informed neural networks

arXiv:2606. 04736v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have become a promising framework for simulating partial differential equations (PDEs) by embedding physical laws directly into neural network training.

By Yingjie Shao, Ioannis N. Athanasiadis, George van Voorn, Taniya Kapoor

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arXiv AI

Jun 4

Dual Advantage Fields

arXiv:2606. 04188v1 Announce Type: cross Abstract: Offline goal-conditioned reinforcement learning requires both long-horizon reachability estimates and local action comparisons.

By Alexey Zemtsov, Maxim Bobrin, Alexander Nikulin, Dmitry V. Dylov, Fakhri Karray, Vladislav Kurenkov, Martin Tak\'a\v{c}, Arip Asadulaev

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arXiv AI

Jun 4

Geometry-Aware Hallucination Detection in Large Language Models

arXiv:2601. 06196v3 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate factually incorrect or unsupported content, commonly referred to as hallucinations.

By Bodla Krishna Vamshi, Rohan Bhatnagar, Haizhao Yang

llms rag nlp fine-tuning benchmarks safety

arXiv AI

Jun 4

No More K-means: Single-Stage Sparse Coding for Efficient Multi-Vector Retrieval

arXiv:2605. 30120v3 Announce Type: replace-cross Abstract: Multi-vector retrieval (MVR) models, exemplified by ColBERT, have established new benchmarks in retrieval accuracy by preserving fine-grained token-level interactions.

By Lixuan Guo, Yifei Wang, Tiansheng Wen, Aosong Feng, Stefanie Jegelka, Chenyu You

rag efficiency benchmarks

arXiv AI

Jun 4

Adaptive Calibration for Fair and Performant Facial Recognition

arXiv:2606. 04469v1 Announce Type: cross Abstract: We introduce Adaptive Calibration (AC), a novel calibration strategy for facial recognition that maps cosine similarity between normalized embeddings to well-calibrated probabilities.

By Ryan Brown, Chris Russell

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arXiv AI

Jun 4

HEPA: A Self-Supervised Horizon-Conditioned Event Predictive Architecture for Time Series

arXiv:2605. 11130v4 Announce Type: replace-cross Abstract: Critical events in multivariate time series, from turbine failures to cardiac arrhythmias, demand accurate prediction, yet labeled data is scarce because such events are rare and costly to annotate.

By Jonas Petersen, Gian-Alessandro Lombardi, Riccardo Maggioni, Camilla Mazzoleni, Federico Martelli, Philipp Petersen

llms rag benchmarks

arXiv Machine Learning

Jun 4

HalfNet: Randomized Neural Networks with Learned Subspace Geometry

arXiv:2606. 04583v1 Announce Type: new Abstract: Many researchers investigated neural networks with some of their weights fixed to values randomly drawn from a given distribution, e.

By Ethem Alpaydin

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arXiv Machine Learning

Jun 4

Training-Free Lexical-Dense Fusion for Conversational-Memory Retrieval

arXiv:2606. 04194v1 Announce Type: new Abstract: Retrieving the few past turns that answer a new query across long multi-session histories is the retrieval bottleneck behind long-term conversational memory (LoCoMo, LongMemEval).

By Christian Lysenst{\o}en

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arXiv AI

Jun 4

Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics

arXiv:2602. 12643v2 Announce Type: replace-cross Abstract: We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-based approaches, without incurring planning overhead.

By Jashaswimalya Acharjee, Balaraman Ravindran

rag diffusion reinforcement-learning robotics

arXiv AI

Jun 4

BRAINCELL-AID: An Agentic AI Created Brain Cell Type Resource for Community Annotation

arXiv:2510. 17064v4 Announce Type: replace Abstract: Single-cell RNA sequencing has transformed our ability to identify diverse cell types and their transcriptomic signatures.

By Rongbin Li, Wenbo Chen, Zhao Li, Rodrigo Munoz-Castaneda, Jinbo Li, Neha S. Maurya, Arnav Solanki, Huan He, Hanwen Xing, Meaghan Ramlakhan, Zachary Wise, Nelson Johansen, Zhuhao Wu, Hua Xu, Michael Hawrylycz, W. Jim Zheng

llms rag agents safety

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