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 AI

Jun 2

Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models

arXiv:2606. 02374v1 Announce Type: new Abstract: Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale.

By Steffen Knoblauch, Hao Li, Gengchen Mai, Konstantin Klemmer, Song Gao, WenWen Li

rag multimodal

arXiv AI

Jun 2

Dual-Anchoring: Addressing State Drift in Vision-Language Navigation

arXiv:2604. 17473v3 Announce Type: replace-cross Abstract: Vision-Language Navigation(VLN) requires an agent to navigate through 3D environments by following natural language instructions.

By Kangyi Wu, Pengna Li, Kailin Lyu, Xi Lin, Lin Zhao, Qingrong He, Jinjun Wang, Jianyi Liu

llms rag agents multimodal

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 Machine Learning

Jun 2

Closing the Alignment-Maturity Gap in Federated Prototype Learning

arXiv:2606. 02172v1 Announce Type: new Abstract: Learning discriminative visual representations from distributed, heterogeneous data is a fundamental challenge in Federated Learning (FL).

By Mario Casado-Diez, Alejandro Dopico-Castro, Ver\'onica Bol\'on-Canedo, Bertha Guijarro-Berdi\~nas

rag benchmarks safety

arXiv AI

Jun 2

SortingHat: Redefining Operating Systems Education with a Tailored Digital Teaching Assistant

arXiv:2606. 00015v1 Announce Type: cross Abstract: Operating Systems (OS) courses are among the most challenging in computer science education due to the complexity of internal structures and the diversity of running environments.

By Yifan Zhang, Xinkui Zhao, Zuxin Wang, Zhengyi Zhou, Guanjie Chen, Shuiguang Deng, Jianwei Yin

llms rag agents reinforcement-learning

arXiv AI

Jun 2

DLLM-JEPA: Joint Embedding Predictive Architectures for Masked Diffusion Language Models

arXiv:2606. 00091v1 Announce Type: cross Abstract: Joint Embedding Predictive Architectures (JEPAs) have reshaped self-supervised representation learning in vision.

By Sangdae Nam

llms rag diffusion fine-tuning benchmarks

arXiv AI

Jun 2

Multimodal Music Recommendation System using LLMs

arXiv:2606. 00125v1 Announce Type: cross Abstract: Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content.

By Srikar Prabhas Kandagatla, Sreehitha R. Narayana, Chandana Magapu, Swetha Mohan, Shamanth Kuthpadi, Hongjie Chen, Ryan A. Rossi, Franck Dernoncourt, Nesreen Ahmed

llms rag fine-tuning multimodal benchmarks

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 AI

Jun 2

SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction

arXiv:2606. 00120v1 Announce Type: cross Abstract: This paper proposes SpikeWFM, a novel hybrid architecture that integrates spiking neural networks (SNNs) with conventional artificial neural network (ANN)-based transformers for wireless foundation models (WFMs).

By Liwen Jing, Yisha Lu, Tingting Yang, Li Sun, Yuxuan Shi, Yuwei Wang, Mengfan Zheng, Leiyang Xu

llms rag efficiency

arXiv AI

Jun 2

MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation

arXiv:2606. 00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge.

By Chuanjie Wu, Zhishang Xiang, Yunbo Tang, Zerui Chen, Qinggang Zhang, Jinsong Su

llms rag agents benchmarks safety

arXiv AI

Jun 2

Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation

arXiv:2604. 20861v3 Announce Type: replace-cross Abstract: Semantic IDs (SIDs) provide the discrete item vocabulary used by generative recommendation, but their quality depends on what item evidence is preserved before quantization.

By Yangchen Zeng, Jinze Wang

llms rag reinforcement-learning efficiency multimodal

arXiv AI

Jun 2

SilentDrift: Exploiting Action Chunking for Stealthy Backdoor Attacks on Vision-Language-Action Models

arXiv:2601. 14323v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are increasingly deployed in safety-critical robotic applications, yet their security vulnerabilities remain underexplored.

By Bingxin Xu, Yuzhang Shang, Binghui Wang, Emilio Ferrara

rag robotics multimodal 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 AI

Jun 2

DSL-LLaDA: Scaling Continuous Denoising to 8B Masked Diffusion LMs

arXiv:2606. 01024v1 Announce Type: cross Abstract: Discrete Masked diffusion language models generate text by iterative parallel decoding, but few-step decoding suffers from a tradeoff between length and quality: with a fixed step budget, standard methods can generate a short, high-quality output, or they can produce long but repetitive text.

By Longxuan Yu, Yunshu Wu, Yu Fu, Siheng Xiong, Rob Brekelmans, Hui Liu, Yue Dong, Greg Ver Steeg

llms rag diffusion nlp benchmarks

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

BRo-JEPA: Learning Modular Arithmetic in Latent Space

arXiv:2606. 01372v1 Announce Type: cross Abstract: Can neural networks learn abstract algebraic rules, or do they merely memorize training patterns?

By Divyansh Jha, Yuanfang Xie, Varan Mehra, Brennen Yu

rag diffusion

arXiv AI

Jun 2

Bridging Requirements and Architecture: Multi-Agent Orchestration with External Knowledge and Hierarchical Memory

arXiv:2606. 01385v1 Announce Type: cross Abstract: Software architecture design is a critical yet inherently complex and knowledge-intensive phase that requires balancing competing quality attributes and adapting to evolving requirements.

By Ruiyin Li, Yiran Zhang, Xiyu Zhou, Yangxiao Cai, Peng Liang, Weisong Sun, Jifeng Xuan, Zhi Jin, Yang Liu

llms rag agents

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

UR-JEPA: Uniform Rectifiability as a Regularizer for Joint-Embedding Predictive Architectures

arXiv:2606. 01443v1 Announce Type: cross Abstract: A central difficulty in training Joint-Embedding Predictive Architectures (JEPAs) is preventing representation collapse.

By Triet M. Le

rag

arXiv AI

Jun 2

Self-Conditioned Positional HNSW for Overlap-Aware Retrieval in Chunked-Document RAG Systems: Method and Industrial Evidence-Quality Audit

arXiv:2606. 01542v1 Announce Type: cross Abstract: Chunked-document retrieval is a common component of retrieval-augmented generation (RAG) systems.

By Nataraj Agaram Sundar, Tejas Morabia

rag computer-vision

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