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

Jul 28

Cheap Probes Predict Expensive Training in 3D-CT Vision--Language Models

arXiv:2607. 22771v1 Announce Type: cross Abstract: Picking the frozen image encoder for a 3D~CT vision--language model (VLM), together with the token-compression scheme on top of it, is a search over many candidates.

By Renjie Liang

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jul 28

Evaluating RAG for French immigration law: a benchmark and baseline study

arXiv:2607. 24449v1 Announce Type: cross Abstract: International recruitment in France requires navigating a layered legal framework absent from existing legal AI benchmarks.

By Annia Abtout, Julien Delaunay, Monika Ewa Rakoczy

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arXiv AI

Jul 28

AutoCluster, AutoTopicModeling, AutoTrendAnalysis: A Complete AutoML Pipeline for Predicting Emerging Trends

arXiv:2607. 22641v1 Announce Type: cross Abstract: Predicting emerging trends is vital for businesses, researchers, and policymakers; yet traditional approaches often lack scalability and adaptability.

By Ahmed Abolfadl, Marwa Mahmoud Abla, Mervat Abu-Elkheir, Maggie Mashaly

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arXiv AI

Jul 28

SpecBox: Speculative Sandbox Scheduling for Efficient LLM Agent Serving

arXiv:2607. 23933v1 Announce Type: cross Abstract: As LLM agents increasingly rely on the Model Context Protocol (MCP) to invoke isolated external sandboxes, disaggregated sandbox deployment introduces a fundamental tension between resource utilization and interactive tail latency.

By Yihui Zhang (Beihang University), Tianyu Wo (Beihang University), Jinghao Wang (Beihang University), Xiaoyang Sun (University of Leeds), Menghao Zhang (Beihang University), Cangzhou Yuan (Beihang University), Li Li (Beihang University), Chunming Hu (Beihang University), Albert Y. Zomaya (The University of Sydney), Renyu Yang (Beihang University)

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arXiv AI

Jul 28

TriShieldRAG: A Three-Ring Defense-in-Depth Framework Against Knowledge Corruption in Retrieval-Augmented Generation

arXiv:2607. 23838v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) lets a large language model answer questions using documents retrieved from an external knowledge base at query time.

By Susil Kumar Mohanty, Rohit Patel, Kosuru Yuvaraj, Jeenal Chaudhary, Disha Singhania

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arXiv AI

Jul 28

Lexical discovery in unknown environments orchestrated by Large Language Models

arXiv:2607. 22591v1 Announce Type: new Abstract: Populations of autonomous agents deployed in unknown environments (e.

By Rafael Sendra-Arranz, I\~naki Dellibarda Varela, Eduardo Rocon, \'Alvaro Guti\'errez, Manuel Cebrian

llms rag agents

arXiv AI

Jul 28

LLM-Assisted Ontology Engineering and Construction of a French Legal Knowledge Graph

arXiv:2607. 24551v1 Announce Type: new Abstract: Maintenance regulations are complex legal texts that are difficult to exploit when addressing a specific case and challenging to integrate into operational systems.

By G{\'e}nesis Montenegro (WIMMICS), Mokhtar Boumedyen Billami (WIMMICS), Catherine Faron (WIMMICS), Fabien Gandon (WIMMICS), Pierre Monnin (WIMMICS)

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arXiv Machine Learning

Jul 28

SPRKD: Effective Knowledge Distillation for Deep Neural Networks via Saddle Region Approximation

arXiv:2607. 23346v1 Announce Type: new Abstract: Modern deep neural networks are potent catalysts for scientific and industrial impact, yet excessive parameter counts impede deployment in low-compute settings such as hospital equipment and energy infrastructure.

By Aditya Dewan, Arjun Yogeswaran, Benjamin Fedoruk

rag fine-tuning efficiency multimodal benchmarks

arXiv AI

Jul 28

An empirical investigation into the properties of standard word embeddings

arXiv:2607. 23675v1 Announce Type: cross Abstract: The embedding of word sequences into continuous vector spaces has been one of the most important developments in Natural Language Processing in the recent past.

By Salomon Kabongo

rag nlp multimodal

arXiv Machine Learning

Jul 28

LanteRn: Latent Visual Structured Reasoning

arXiv:2603. 25629v2 Announce Type: replace-cross Abstract: While language reasoning models excel in many tasks, visual reasoning remains challenging for current large multimodal models (LMMs).

By Andr\'e G. Viveiros, Nuno Gon\c{c}alves, Matthias Lindemann, Andr\'e Martins

llms rag diffusion reinforcement-learning fine-tuning multimodal benchmarks

arXiv AI

Jul 28

SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs

arXiv:2607. 22571v1 Announce Type: new Abstract: Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) enables natural language interaction with structured enterprise knowledge, yet existing agentic approaches that perform well on public benchmarks often fail to generalize to real-world enterprise Knowledge Graphs (KGs), which are dense, schema-driven, and operationally constrained.

By Prateek Chaturvedi, Yuqicheng Zhu, Hongkuan Zhou, Dongzhuoran Zhou, Yunjie He, Steffen Staab, Fei Du, Jie Tang, Evgeny Kharlamov

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arXiv AI

Jul 28

Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach

arXiv:2607. 22584v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation pipelines rank retrieved documents by semantic similarity alone, without accounting for source provenance or credibility.

By Yuktha Tata Koganti, Hugo Garrido-Lestache Belinchon

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arXiv Machine Learning

Jul 28

Does Faithfulness-Guided Alignment Hurt Accuracy? Unlocking Accurate and Faithful Post-Retrieval Reasoning

arXiv:2602. 01348v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) can achieve strong answer accuracy on multi-hop questions, but outcome-level rewards often leave reasoning traces weakly grounded and difficult to audit.

By Yu Liu, Wenxiao Zhang, Diandian Guo, Cong Cao, Fangfang Yuan, Qiang Sun, Yanbing Liu, Jin B. Hong, Zhiyuan Ma

rag nlp reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 28

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

arXiv:2607. 24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity.

By Zeyu Zhang, Xue Li, Iacer Calixto, Paul Groth, Sebastian Schelter

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jul 28

SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads

arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).

By Congfei Zhang, Jingxiao Ma, Xiaodong Liu, Hsiang-wei Chao, Siman Wang, Ge Liu, Shantanu Aggarwal, Vincent Zhang, Meghana Missula, Rachel Liao, Zichu Li, Xiao Bai, Yunzhi Zhou, Yajun Wang, Zhe Liu, Jinchao Li, Yu Zhang

llms rag

arXiv Machine Learning

Jul 28

DRP-FLR: Data-Driven Assessment of Demand Response Potential for Flexible Load Regulation in Smart Grids

arXiv:2607. 22590v1 Announce Type: cross Abstract: The rapid growth of AI workloads and renewable energy resources exacerbates supply-demand imbalance in power systems, making traditional load regulation designed for efficient allocation inadequate and motivating demand response (DR) mechanisms to enable load controllability in smart grids.

By Yunhao Yao, Siyu Jing, Yang Yang, Qiang Xu, Changqi Weng, Xiang-Yang Li

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arXiv Machine Learning

Jul 28

ADVERSARIAL: And-Inverter Graph-Assisted Hardware Trojan Detection At Scale

arXiv:2607. 23882v1 Announce Type: new Abstract: Modern System-on-Chip (SoCs) often contain hundreds of millions to tens of billions of gates, making existing Hardware Trojan (HT) detection methods impractical due to their immense scale.

By Yaroslav Popryho, Debjit Pal, Inna Partin-Vaisband

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arXiv Machine Learning

Jul 28

Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement

arXiv:2607. 22770v1 Announce Type: new Abstract: Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex overlapping symptoms among diseases.

By Siyuan Du, Mengxi Chen, Xinyang Jiang, Zilong Wang, Jiangchao Yao, Dongsheng Li, Ya Zhang, Lili Qiu, Yanfeng Wang

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arXiv Machine Learning

Jul 28

Repeated-Token Counting Reveals a Dissociation Between Representations and Outputs

arXiv:2605. 09239v2 Announce Type: replace-cross Abstract: Large language models fail at counting how many times a word repeats in a list, even though they perform well on far harder reasoning tasks.

By Sohan Venkatesh

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arXiv AI

Jul 28

Structure Over Scale: Schema-Constrained Causal Graphs for RAG

arXiv:2607. 22592v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (GraphRAG) grounds answers in structured knowledge, but current systems extract entities and relationships exhaustively, producing graphs whose size and construction cost scale with corpus length rather than with the reasoning a query requires.

By Marc Saouda (Boston Consulting Group), Rajprakash Bale (Boston Consulting Group), Eren Aldis (Boston Consulting Group), Cloves Almeida (Boston Consulting Group)

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