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

Jul 15

Beyond Logit Adjustment: A Residual Decomposition Framework for Long-Tailed Reranking

arXiv:2604. 01506v2 Announce Type: replace Abstract: Long-tailed classification, where a small number of frequent classes dominate many rare ones, remains challenging because models systematically favor frequent classes at inference time.

By Zhanliang Wang, Hongzhuo Chen, Quan Minh Nguyen, Mian Umair Ahsan, Kai Wang

rag nlp multimodal benchmarks

arXiv Machine Learning

Jul 15

Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence

arXiv:2607. 12916v1 Announce Type: new Abstract: In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations.

By Blanca Cano-Camarero, \'Angela Fern\'andez-Pascual, Jos\'e R. Dorronsoro

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arXiv AI

Jul 15

Matilda: Engine-Agnostic Search with Human Policy Guidance

arXiv:2606. 25176v2 Announce Type: replace Abstract: Chess engines have evolved from search-based systems optimized solely for strength to neural policies capable of modeling human decisions across much of the rating spectrum.

By Jason Carlson

llms rag benchmarks

arXiv AI

Jul 15

Knowledge- and Gradient-Guided Reinforcement Learning for Parametrized Action Markov Decision Processes

arXiv:2607. 12924v1 Announce Type: new Abstract: In this paper, we study Reinforcement Learning in Parametrized Action Markov Decision Processes (PAMDP), where each decision consists of a symbolic action and numerical parameters.

By Jonas Ehrhardt, Ren\'e Heesch, Oliver Niggemann

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arXiv Machine Learning

Jul 15

SlimPer: Make Personalization Model Slim and Smart

arXiv:2607. 12281v1 Announce Type: cross Abstract: Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely on per-token autoregressive prediction, which justifies maintaining large intermediate tensors that scale with sequence length.

By Siqi Wang, Xianjie Chen, Shaofeng Deng, Albert Chen, Romil Shah, Jiawei Huang, Zhaoqin Wang, Zhang Zhang, Yiqun Liu, Meilei Jiang, Anish Dubey, Moyan Mei, Tongxin Wang, Nathan Berrebbi, Misael Manjarres, Armand Sauzay, Shardul Kothapalli, Aryaman Vinchhi, Kevin Johnstone, Juheon Lee, Gufan Yin, Ziheng Huang, Justin Lin, Mert Terzihan, Yilin Qi, Cynthia Yang, Colin Peppler, Qi Ding, Ruohan Sun, Ge Song, Litao Deng, Parichay Kapoor, Matt Ma, Huihui Cheng, Jiyuan Zhang, Yanli Zhao, Yiping Han, Fangqiu Han, Ning Yao, Arun Singh, Jordan Edwards, Zhengyu Su, Abhishek Kumar, Guangdeng Liao, Ankit Asthana

llms rag multimodal safety

arXiv AI

Jul 15

Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning

arXiv:2607. 12112v1 Announce Type: cross Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations.

By Jing Liu, Chenxuanyin Zou, Jiayang Ren, Gaoyun Fang, Chengfang Li, Yan Wang, Zhenchao Ma, Bo Hu

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arXiv AI

Jul 15

Mitigating The Effect of Class Imbalance in Data with Hierarchical and Dependable Structure

arXiv:2607. 11994v1 Announce Type: cross Abstract: Classifying cybersecurity vulnerabilities using the Common Weakness Enumeration (CWE) taxonomy is challenging due to extreme class imbalance and strong hierarchical dependencies among weakness categories.

By Bipin Chhetri, Deepika Giri, Avishek Kadel, Rabin Kumar Karki, Akbar Siami Namin

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arXiv AI

Jul 15

An Omnilingual-ASR-Based Speech-LLM System for the 2nd MLC-SLM Challenge

arXiv:2607. 12468v1 Announce Type: cross Abstract: We describe our submission to Task 1 of the 2nd MLCSLM Challenge: a cascaded diarization-then-recognition system that combines DiariZen-Large-s80 (WavLM-Large) segmentation, CAM++ embedding-based two-speaker clustering, and a LoRA-adapted omniASR LLM 7B v2 recognizer, with no oracle segmentation or speaker labels at test time.

By Shuming Fang, Shuifei Zeng

llms rag computer-vision fine-tuning multimodal

arXiv Machine Learning

Jul 15

NetForge RL: A Multi-Agent Simulation Environment for Cyber Defense with Durative Actions

arXiv:2604. 09523v2 Announce Type: replace Abstract: Training reinforcement-learning agents for cyber defense requires an environment that reflects the operational setting: noisy, partial observations, several defenders coordinating across a network, and an adaptive adversary realized through self-play.

By Igor Jankowski

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arXiv Machine Learning

Jul 15

GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization

arXiv:2607. 11941v1 Announce Type: cross Abstract: Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality.

By Md Imam Ahasan, Guangchao Yang, A F M Abdun Noor, Kah Ong Michael Goh, S. M. Hasan Mahmud, Md Mahfuzur Rahman

rag diffusion benchmarks

arXiv Machine Learning

Jul 15

Contrastive Joint-Embedding Prediction for Representation Learning in Structural MRI

arXiv:2607. 11962v1 Announce Type: cross Abstract: Self-supervised learning offers a compelling approach for medical imaging, where labeled data are scarce and acquisition costs are high.

By Fabian Mager, Lars Kai Hansen

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arXiv AI

Jul 15

A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism

arXiv:2607. 12640v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards, and Group Relative Policy Optimization (GRPO) in particular, is now run routinely on a supervised checkpoint in the hope of producing a stronger agent.

By Chengguang Gan, Zhixi Cai, Yunhao Liang, Hanjun Wei, Shiwen Ni, Qinghao Zhang

llms rag agents reinforcement-learning multimodal

arXiv Machine Learning

Jul 15

Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection

arXiv:2607. 12454v1 Announce Type: new Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-specific models that are costly to train and hard to scale.

By Martin Uray, Saverio Messineo, Roland Kwitt, Stefan Huber

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arXiv Machine Learning

Jul 15

ReDiTT: Retrieval Augmented Conditional Diffusion Transformers for Asynchronous Time Series

arXiv:2607. 12391v1 Announce Type: new Abstract: We present a diffusion based model for asynchronous time series prediction, where the goal is to predict the next inter event time and event type.

By Saiyue Lyu, Zhitian Zhang, Ruizhi Deng, Thibaut Durand

llms rag diffusion

arXiv AI

Jul 15

The Sound of Absence: Audio-Language Embedding Models Struggle with Negation

arXiv:2607. 12290v1 Announce Type: cross Abstract: Audio-language embedding models such as CLAP are widely evaluated on matching present sound events, but rarely on negation.

By Chun-Yi Kuan, Hung-yi Lee

llms rag multimodal safety

arXiv AI

Jul 15

Prime Fourier Embeddings: A Principled Basis for Modular Arithmetic

arXiv:2606. 23044v2 Announce Type: replace-cross Abstract: Numbers have algebraic structure that standard neural embeddings often fail to expose.

By Hyunsang Hwang, Suhyun Bae, Donghun Lee

rag benchmarks

arXiv AI

Jul 15

The One-Word Census: Answer-Choice Conformity Across 44 Language Models

arXiv:2607. 12796v1 Announce Type: cross Abstract: When a language model must pick one answer from a large space of equally valid options, which does it pick -- and how often is it the same answer every other model picks?

By Tapan Parikh

llms rag

arXiv AI

Jul 15

Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast

arXiv:2603. 04113v2 Announce Type: replace-cross Abstract: Demographic attributes can be predicted from medical images, raising concerns about bias in clinical AI systems.

By Mehmet Yigit Avci (and for the Alzheimer's Disease Neuroimaging Initiative), Akshit Achara (and for the Alzheimer's Disease Neuroimaging Initiative), Andrew King (and for the Alzheimer's Disease Neuroimaging Initiative), Jorge Cardoso (and for the Alzheimer's Disease Neuroimaging Initiative)

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arXiv AI

Jul 15

In-Context Reinforcement Learning under Non-Stationarity: A Survey

arXiv:2607. 11906v1 Announce Type: new Abstract: The development of decision-pretrained transformers, algorithm distillation, long-context meta-RL, and retrieval-augmented agents has renewed interest in in-context reinforcement learning (ICRL): the ability of a pretrained or fine-tuned decision model to infer latent task rules and improve future behavior from interaction context, without test-time parameter updates.

By A Run, Ziluo Ding

llms rag agents reinforcement-learning fine-tuning efficiency safety

arXiv AI

Jul 15

LakeQuest: A Three-Domain Benchmark for Grounded Question Answering across Data Lakes

arXiv:2607. 12310v1 Announce Type: cross Abstract: While modern question answering (QA) systems excel on clean, schema-aligned corpora, real-world knowledge is rarely so neatly packaged.

By Michael Solodko, Steven Gong, Guangwei Yu, Satya Krishna Gorti, Jesse C. Cresswell, Victor Zhong

rag agents nlp multimodal benchmarks

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