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 2

A Theoretical Framework for Self-Play Theorem Proving Algorithms

arXiv:2606. 01861v1 Announce Type: new Abstract: Self-play, a type of training algorithm that enables a model to self-improve, has recently shown promising empirical results in the context of formal theorem proving using Large Language Models (LLMs).

By Thomas Chen, Zhiyuan Li

llms rag agents diffusion reinforcement-learning

arXiv AI

Jun 2

MLLM-Microscope: Unlocking Hidden Structure Within Multimodal Large Language Models

arXiv:2606. 00909v1 Announce Type: cross Abstract: This work presents MLLM-Microscope, a novel system designed for analyzing the hidden representations within Multimodal Large Language Models (MLLMs).

By Ravil Mussabayev, Rustam Mussabayev

llms rag multimodal benchmarks

arXiv AI

Jun 2

Improving Visual Representation Alignment Generation with GRPO

arXiv:2606. 00583v1 Announce Type: cross Abstract: Recent diffusion transformers have demonstrated strong image synthesis capabilities but remain inefficient to train due to weak alignment between generative and discriminative representations.

By Shentong Mo, Sukmin Yun

llms rag diffusion safety

arXiv AI

Jun 2

LLMs Need Encoders for Semantic IDs Too

arXiv:2606. 00324v1 Announce Type: cross Abstract: Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw token embeddings alone cannot capture modality-specific structure.

By Xiangyi Chen, Zelun Wang, Xinyi Li, Yi-Ping Hsu, Jaewon Yang, Jiajing Xu

llms rag multimodal

arXiv Machine Learning

Jun 2

Beyond the Simplex: Balanced Prototype Geometry for Scorer-Agnostic Open-Set Recognition

arXiv:2606. 01883v1 Announce Type: new Abstract: Open-set recognition (OSR) requires a classifier to reject inputs from unseen classes which is essential in safety-critical settings such as medical imaging.

By Mayank Sharma, Rohit Kumar Mourya

rag benchmarks safety

arXiv AI

Jun 2

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

arXiv:2606. 01560v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges in assortative graphs and assortative edges in disassortative graphs.

By Canyixing Cui, Tao Wu, Xingping Xian, Xiao-Ke Xu, Mao Wang, Weina Niu

rag safety

arXiv AI

Jun 2

On the Theoretical Limitations of Embedding-based Link Prediction

arXiv:2506. 22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces.

By Samy Badreddine, Emile van Krieken, Luciano Serafini

rag

arXiv AI

Jun 2

RASER: Recoverability-Aware Selective Escalation Router for Multi-Hop Question Answering

arXiv:2606. 02488v1 Announce Type: new Abstract: Multi-hop question-answering systems often use expensive retrieval on every question.

By Yuyang Li, Zihe Yan, Tobias K\"afer

llms rag nlp benchmarks

arXiv AI

Jun 2

Emergent Ordinal Geometry in Transformers Trained on Local Comparisons

arXiv:2606. 01269v1 Announce Type: new Abstract: Transitive inference is the challenge of inferring that A < C from knowing only adjacent relations (A < B, B < C).

By Nishit Singh

llms rag

arXiv AI

Jun 2

Logit Distillation on Manifolds: Mapping by Learning

arXiv:2606. 00771v1 Announce Type: cross Abstract: A simple way to improve the performance of almost any machine learning model is not to train a single but several models with diverse algorithms which will make slightly distinct kinds of predictions and errors on the same data, and thus improve the average predictions and robustness.

By Yiru Yang, Junling Wang, Nishant Kumar Singh, Luohong Wu, Haoran Yan

rag fine-tuning efficiency benchmarks

arXiv AI

Jun 2

RA-LWLM: Retrieval-Augmented In-Context Localization with Wireless Foundation Models

arXiv:2606. 01899v1 Announce Type: cross Abstract: Wireless localization is a fundamental capability of sixth-generation (6G) networks.

By Guangjin Pan, Hui Chen, Hei Victor Cheng, Henk Wymeersch

llms rag

arXiv AI

Jun 2

Echo: A Joint-Embedding Predictive Architecture for Speaker Diarization and Speech Recognition in a Shared Latent Space

arXiv:2606. 01909v1 Announce Type: cross Abstract: We present Echo, a proof-of-concept audio system built around a single 25 M-parameter ViT encoder.

By Louis Mouchon

rag diffusion fine-tuning multimodal benchmarks

arXiv AI

Jun 2

Universal Quantum Transformer

arXiv:2606. 00045v1 Announce Type: new Abstract: Classical continuous-space neural networks fundamentally struggle to lock into exact mathematical symmetries, such as modular arithmetic and non-commutative algebra.

By Sungyong Chung, Alireza Talebpour

llms rag safety

arXiv AI

Jun 2

Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design

arXiv:2507. 15336v3 Announce Type: replace-cross Abstract: Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency.

By Jialiang Wang, Hanmo Liu, Shimin Di, Zhili Wang, Jiachuan Wang, Lei Chen, Xiaofang Zhou

rag

arXiv AI

Jun 2

RuleEdit: Failure-Guided Human-AI Model Editing with Prospective Impact Preview

arXiv:2606. 00011v1 Announce Type: cross Abstract: Despite the promise of AI to assist complex decisions, practitioners still lack ways to detect likely failures and inspect the consequences of model edits before committing them.

By Min Hun Lee, Justin Yu Feng Teo

rag

arXiv Machine Learning

Jun 2

Understanding LoRA as Knowledge Memory: An Empirical Analysis

arXiv:2603. 01097v3 Announce Type: replace Abstract: Continuous knowledge updating for pre-trained large language models (LLMs) is increasingly necessary yet remains challenging.

By Seungju Back, Dongwoo Lee, Naun Kang, Taehee Lee, S. K. Hong, Youngjune Gwon, Sungjin Ahn

llms rag fine-tuning

arXiv AI

Jun 2

V-LynX: Token Interface Alignment for Video+X LLMs

arXiv:2606. 00508v1 Announce Type: cross Abstract: This study introduces an intriguing phenomenon in Video LLMs: rather than merely translating frames into textual embeddings, Video LLMs establish a continuous manifold, token interface, allowing visual tokens to operate as standalone entities within the architecture.

By Jungin Park, Jiyoung Lee, Kwanghoon Sohn

llms rag multimodal benchmarks safety

arXiv AI

Jun 2

DiffCrossGait: Trajectory-Level Alignment for 2D-3D Cross-Modal Gait Recognition via Latent Diffusion

arXiv:2606. 00153v1 Announce Type: cross Abstract: Cross-modal 2D-3D gait recognition is impeded by inherent domain discrepancies between 2D silhouette and 3D LiDAR range-view representations.

By Zhiyang Lu, Ming Cheng

rag diffusion multimodal benchmarks safety

arXiv Machine Learning

Jun 2

Reconstructing Content via Collaborative Attention to Improve Multimodal Embedding Quality

arXiv:2603. 01471v2 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

llms rag multimodal

arXiv Machine Learning

Jun 2

Efficient Synthetic Network Generation via Latent Embedding Reconstruction

arXiv:2606. 00934v1 Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems.

By Feifan Jiang, Yinan Bu, Shihao Wu, Gongjun Xu, Ji Zhu

rag diffusion efficiency

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