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 16

Dual-Granularity Orthogonal Disentanglement for Generalizable Audio Deepfake Detection

arXiv:2606. 16532v1 Announce Type: cross Abstract: Audio deepfake detectors often fail to generalize across speakers, as they learn speaker-identity features rather than synthesis artifacts, known as implicit identity leakage.

By Zhuodong Liu, Hugen Lv, Xiangyu Li, Chunhong Yuan

rag safety

arXiv AI

Jun 16

Akasha 2: Hamiltonian State Space Duality and Visual-Language Joint Embedding Predictive Architectur

arXiv:2601. 06212v2 Announce Type: replace-cross Abstract: We present Akasha 2, a state-of-the-art multimodal architecture that integrates Hamiltonian State Space Duality (H-SSD) with Visual-Language Joint Embedding Predictive Architecture (VL-JEPA).

By Yani Meziani

llms rag diffusion multimodal benchmarks

arXiv AI

Jun 16

Temporally Consistent and Controllable Video Generation of 2D Cine CMR via Latent Space Motion Modeling

arXiv:2606. 14759v1 Announce Type: cross Abstract: Cine cardiac magnetic resonance is the gold standard for assessing cardiac function, but the scarcity of public datasets limits the development of advanced data-driven models.

By Yiheng Cao (SyCoIA - IMT Mines Al\`es), Gustavo Andrade-Miranda (SyCoIA - IMT Mines Al\`es), Jiatian Zhang, Guillaume Sall\'e, Xin Gao

rag diffusion fine-tuning safety

arXiv Machine Learning

Jun 16

All Eyes on the Workflow: Automated and Efficient Event Discovery from Video Streams

arXiv:2604. 22476v2 Announce Type: replace-cross Abstract: Disciplines such as business process management and process mining aid organizations by discovering insights about processes on the basis of recorded event data.

By Marco Pegoraro, Jonas Seng, Dustin Heller, Wil M. P. van der Aalst, Kristian Kersting

rag computer-vision

arXiv AI

Jun 16

CRC-Screen: Certified DNA-Synthesis Hazard Screening Under Taxonomic Shift

arXiv:2605. 00074v2 Announce Type: replace-cross Abstract: DNA-synthesis providers screen incoming orders by searching the requested sequence against curated hazard lists.

By Najmul Hasan

llms rag

arXiv Machine Learning

Jun 16

Formalizing and Mitigating Structural Distortion in LLM Attention for Zero-Shot Graph Reasoning

arXiv:2606. 15633v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs).

By Donald Loveland, Puja Trivedi, Ari Weinstein, Edward W Huang, Danai Koutra

llms rag benchmarks

arXiv AI

Jun 16

FOUNDv2: Learning Unified User Quantized Tokenizers for User Representation

arXiv:2508. 00956v3 Announce Type: replace-cross Abstract: User representation learning serves as a fundamental pillar for personalized services on large-scale web platforms.

By Chuan He, Yang Chen, Bin Dou, Wuliang Huang, Baokun Wang, Yongchao Liu, Xing Fu, Yu Cheng, Chuntao Hong, Weiqiang Wang, Zhongle Xie, Jiajun Zheng, Xin-Wei Yao

rag nlp efficiency benchmarks safety

arXiv AI

Jun 16

Binary Tracking for Spatial QA and Navigation with Open Vision-Language Models

arXiv:2606. 16902v1 Announce Type: cross Abstract: This work addresses spatial question answering for service robots traversing long egocentric routes.

By Dongbin Na, Chanwoo Kim, Soonbin Rho, Giyun Choi, Gangbok Lee, Dooyoung Hong

llms rag agents nlp robotics multimodal benchmarks

arXiv AI

Jun 16

A Formal Framework for Declarative Agentic AI in Business Process Analysis

arXiv:2606. 15291v1 Announce Type: new Abstract: Agentic AI opens new opportunities for automating Business Process (BP), enabling autonomous decision-making and dynamic adaptation.

By Mohammad Azarijafari, Luisa Mich, Michele Missikoff

rag agents

arXiv AI

Jun 16

DeepRoot: A KG-Coordinated Multi-Agent System for Therapeutic Reasoning over Historical Medical Texts

arXiv:2606. 15931v1 Announce Type: cross Abstract: Historical medical archives and traditional medicines hold immense potential for drug discovery and remain a primary source for current drug development.

By Zijian Carl Ma, Sean J. Wang, Sijbren Kramer, Li Erran Li

llms rag agents

arXiv AI

Jun 16

MedAI: Evaluating TxAgent's Therapeutic Agentic Reasoning in the NeurIPS CURE-Bench Competition

arXiv:2512. 11682v2 Announce Type: replace Abstract: Therapeutic decision-making in clinical medicine constitutes a high-stakes domain in which AI guidance interacts with complex interactions among patient characteristics, disease processes, and pharmacological agents.

By Tim Cofala, Christian Kalfar, Jingge Xiao, Johanna Schrader, Michelle Tang, Wolfgang Nejdl

llms rag agents fine-tuning benchmarks safety

arXiv AI

Jun 16

Overcoming the Impedance Mismatch: A Theoretical Roadmap for Fusing Foundation Models and Knowledge Graphs

arXiv:2606. 15656v1 Announce Type: new Abstract: Modern artificial intelligence remains fundamentally divided between the continuous, probabilistic spaces of Foundation Models and the discrete, deterministic structures of Knowledge Graphs.

By Sahil Rajesh Dhayalkar

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arXiv AI

Jun 16

Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens

arXiv:2606. 16847v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality.

By Yizhen Yao, Qinglin Zhu, Runcong Zhao, Xiangxiang Dai, Yanzheng Xiang, Yulan He, Lin Gui

llms rag diffusion efficiency benchmarks

arXiv AI

Jun 16

StyleShield: Exposing the Fragility of AIGC Detectors through Continuous Controllable Style Transfer

arXiv:2605. 00924v2 Announce Type: replace-cross Abstract: AI-generated content (AIGC) detectors are increasingly deployed in high-stakes settings such as academic integrity screening, yet their reliability rests on a fundamental paradox: as language models are trained on human-written corpora, the statistical boundary between AI and human writing will inevitably dissolve as models improve.

By Guantian Zheng

llms rag diffusion benchmarks

arXiv AI

Jun 16

CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings

arXiv:2506. 22427v2 Announce Type: replace-cross Abstract: We propose CLoVE (Clustering of Loss Vector Embeddings), a novel algorithm for Clustered Federated Learning (CFL).

By Randeep Bhatia, Nikos Papadis, Murali Kodialam, TV Lakshman, Sayak Chakrabarty

rag benchmarks

arXiv AI

Jun 16

Rel-Zero: Harnessing Patch-Pair Invariance for Robust Zero-Watermarking Against AI Editing

arXiv:2603. 17531v2 Announce Type: replace-cross Abstract: Recent advancements in diffusion-based image editing pose a significant threat to the authenticity of digital visual content.

By Pengzhen Chen, Yanwei Liu, Xiaoyan Gu, Xiaojun Chen, Wu Liu, Weiping Wang

rag diffusion

arXiv AI

Jun 16

NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models

arXiv:2606. 15646v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for legal applications.

By Deepa Tilwani, Yash Saxena, Ankur Padia, Srinivasan Parthasarathy, Manas Gaur

llms rag nlp fine-tuning

arXiv AI

Jun 16

Momentum-Guided Semantic Forecasting (MoFore) for Self-Supervised Video Representation Learning

arXiv:2606. 14765v1 Announce Type: cross Abstract: Self-supervised video representation learning has recently advanced through contrastive learning, masked reconstruction, and predictive representation learning.

By Qinwu Xu

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arXiv AI

Jun 16

Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening

arXiv:2606. 14788v1 Announce Type: cross Abstract: Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging remains challenging due to the difficulty of integrating heterogeneous data.

By Qingfeng Zhang, Yuanxiong Guo, Yanmin Gong

llms rag multimodal

arXiv AI

Jun 16

When Do We Need LLMs? A Diagnostic for Language-Driven Bandits

arXiv:2604. 05859v2 Announce Type: replace Abstract: We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.

By Uljad Berdica, Fernando Acero, Anton Ipsen, Parisa Zehtabi, Michael Cashmore, Manuela Veloso

llms rag reinforcement-learning

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