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

Aug 6

MOON3.0: Reasoning-aware Multimodal Representation Learning for E-commerce Product Understanding

arXiv:2604. 00513v3 Announce Type: replace-cross Abstract: With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention.

By Junxian Wu, Chenghan Fu, Zhanheng Nie, Daoze Zhang, Bowen Wan, Wanxian Guan, Chuan Yu, Jian Xu, Bo Zheng

llms rag reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 6

Predicting Brain Morphometry with MT-GNN: Mesh Evolution in Continuous Time with Graph-Based Metric Tensor Embeddings

arXiv:2608. 05132v1 Announce Type: cross Abstract: Predicting how a subcortical structure's shape will evolve from a few prior scans could support prognosis and clinical-trial enrichment.

By Hao Ding, Daniel Semchin, Paul M. Thompson, Boris Gutman

llms rag

arXiv Machine Learning

Aug 6

Model Inversion meets Cryptographic Fuzzy Extractors

arXiv:2510. 25687v4 Announce Type: replace-cross Abstract: Model inversion attacks pose an open challenge to privacy-sensitive applications that use machine learning (ML) models.

By Mallika Prabhakar, Louise Xu, Prateek Saxena

rag safety

arXiv AI

Aug 6

From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking

arXiv:2608. 05030v1 Announce Type: new Abstract: Football score forecasting combines a strong statistical core with a difficult contextual edge.

By Shaopeng Liang

llms rag benchmarks

arXiv AI

Aug 6

Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Framework

arXiv:2608. 04366v1 Announce Type: cross Abstract: While retrieval-augmented generation systems partially address the hallucination issues in large language models, it also introduces new vulnerabilities to knowledge corruption attacks.

By Zhaoqi Wang, Daqing He, Zijian Zhang, Ye Liu, Jiamou Liu, Zhirui Zeng, Zhan Qin, Zhen Li, Xin Li, Hongwei Yao, Jincheng An, Yong Liu, Yi Li, Qi Sun, Xiulei Liu, Liehuang Zhu

llms rag agents safety

arXiv Machine Learning

Aug 6

A Mechanistic Analysis of Transformers for Dynamical Systems

arXiv:2512. 21113v2 Announce Type: replace Abstract: Transformers are increasingly adopted for modeling and forecasting time-series, yet their internal mechanisms remain poorly understood from a dynamical systems perspective.

By Gregory Duth\'e, Nikolaos Evangelou, Wei Liu, Ioannis G. Kevrekidis, Eleni Chatzi

llms rag

arXiv Machine Learning

Aug 6

Above-ground Biomass Estimation with Geospatial Foundation Models

arXiv:2608. 04792v1 Announce Type: new Abstract: Accurate estimation of Above-Ground Biomass (AGB) from satellite imagery is essential for the large-scale monitoring of carbon stocks, yet it remains a challenging regression task at global scale.

By Ghjulia Sialellia, Linus Scheibenreif, Jan Dirk Wegner, Konrad Schindler

rag computer-vision benchmarks

arXiv Machine Learning

Aug 6

Prototype-based Self-Supervised Multimodal Learning for PPG and Accelerometry Signals

arXiv:2510. 09764v2 Announce Type: replace Abstract: Modeling multi-modal time-series data is critical for capturing system-level dynamics, particularly in biosignals where modalities such as ECG, PPG, EDA, and accelerometry provide complementary perspectives on interconnected physiological processes.

By Wanting Mao, Maxwell A Xu, Harish Haresamudram, Mithun Saha, Santosh Kumar, James Matthew Rehg

rag multimodal

arXiv AI

Aug 6

A/B Agent: A Self-Evolving Agent for Strategy Iteration in Industrial A/B Testing

arXiv:2608. 04625v1 Announce Type: new Abstract: Industrial recommendation strategy iteration heavily relies on large-scale A/B experimentation.

By Zhuohang Jiang, Yuxin Chen, Yongsen Pan, Zheng Hu, Wenqi Fan, Qing Li, Hongyang Wang, Jun Wang, Wenwu Ou

rag agents safety

arXiv Machine Learning

Aug 6

DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery

arXiv:2608. 05120v1 Announce Type: new Abstract: Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes.

By Roberto Aliaga Medina, Paulina Quintanilla, Antonio del Rio Chanona

llms rag benchmarks

arXiv Machine Learning

Aug 6

BIM-Native Tokenization for Constraint-Aware Room Layout Synthesis

arXiv:2512. 04832v3 Announce Type: replace-cross Abstract: We present a BIM-native tokenization for room-level layout synthesis in Building Information Modeling (BIM) scenes.

By Manuel Ladron de Guevara, Jinmo Rhee, Ardavan Bidgoli, Vaidas Razgaitis, Michael Bergin

llms rag nlp benchmarks

arXiv AI

Aug 6

Interpreting GFlowNets for Drug Discovery: What probes can and cannot show

arXiv:2511. 19264v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) construct molecules through sequential decisions, but their internal policies remain opaque, limiting adoption in drug discovery, where chemists need interpretable rationales for proposed structures.

By Amirtha Varshini A S, Duminda S. Ranasinghe, Hok Hei Tam

rag efficiency safety

arXiv Machine Learning

Aug 6

Kathleen Writes: Autoregressive Generation and Data Scaling Without Attention

arXiv:2608. 04678v1 Announce Type: cross Abstract: Papers 1-2 of the Kathleen series showed that a byte-level, attention-free architecture built from a wavetable encoder and multi-scale reverberant state can match strong baselines on classification at ~450-700K parameters, without pretraining.

By George Fountzoulas

llms rag nlp benchmarks

arXiv Machine Learning

Aug 6

DeepInvert: Semi-Supervised Embedding Inversion Against Obfuscated Language Models

arXiv:2608. 04477v1 Announce Type: cross Abstract: Cloud-based language model services routinely process prompts containing sensitive information.

By Zhicong Huang, Cheng Hong, Tao Wei

llms rag

arXiv Machine Learning

Aug 6

Helping Music Co-Creation Agents 'Listen' Well: Hierarchical Self-Supervised World Models for Understanding and Generation

arXiv:2608. 04378v1 Announce Type: cross Abstract: Collaborative music agents need internal representations rich enough to support both understanding and generation, yet flexible enough for a workflow where the human retains agency.

By Scott H. Hawley

llms rag agents

arXiv AI

Aug 6

HALT: Verification-Aware Stopping for Retrieval-Augmented Search Agents

arXiv:2608. 02009v2 Announce Type: replace Abstract: Retrieval-augmented search agents answer multi-hop questions by repeatedly issuing search queries and accumulating evidence.

By Daeyoung Roh, Donghee Han

rag agents benchmarks safety

arXiv AI

Aug 6

When Outputs Disperse, Does Epistemic Revision Follow? A Black-Box Diagnostic for Machine Collectives

arXiv:2608. 03722v2 Announce Type: replace Abstract: Collective intelligence research treats disagreement as evidence of epistemic diversity: if agents express different views, the group should retain capacity to revise.

By Molood Arman

llms rag agents

arXiv AI

Aug 6

Adversarially Robust Abductive Fusion of Pre-trained Transformer-based Perception Models

arXiv:2608. 04190v1 Announce Type: new Abstract: Deploying pre-trained perception models in novel environments degrades their accuracy under distributional shift, and assembling them alone does not recover it: combiners such as majority voting trade recall for precision and are brittle to coordinated failures.

By Mario Leiva, Yue Ma, Qinru Qiu, Gerardo Simari, Paulo Shakarian

llms rag computer-vision benchmarks

arXiv AI

Aug 6

NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning

arXiv:2608. 04381v1 Announce Type: cross Abstract: Self-supervised learning on graphs is largely shaped by contrastive methods that depend on carefully designed augmentations, and by generative methods that reconstruct node attributes in the input space.

By Tinghe Zhang, Jian Xu, Jiaheng Chen, Jiaxing Li, Yucheng Xiao, Qiang Wang

rag diffusion fine-tuning benchmarks

arXiv Machine Learning

Aug 6

Right Reset: Chunking by Prefix Removal

arXiv:2608. 04330v1 Announce Type: cross Abstract: Removing the left context from a causal language model reveals a useful kind of boundary: an edge where the model processes the same right-hand tokens with little change.

By Mike Vegeto

llms rag computer-vision