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

Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context

arXiv:2606. 01101v1 Announce Type: cross Abstract: The quadratic complexity of self-attention remains a bottleneck for Large Language Models (LLMs) processing ultra-long contexts.

By Shihao Ji, Mingyu Li, Zihui Song

llms rag fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 2

GREAT: Generalizable Backdoor Attacks in RLHF via Emotion-Aware Trigger Synthesis

arXiv:2510. 09260v2 Announce Type: replace-cross Abstract: Recent work has shown that RLHF is highly susceptible to backdoor attacks.

By Subrat Kishore Dutta, Yuelin Xu, Piyush Pant, Xiao Zhang

llms rag reinforcement-learning

arXiv Machine Learning

Jun 2

VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting

arXiv:2509. 15394v3 Announce Type: replace Abstract: Accurate electricity demand forecasting is challenging due to the strong multi-periodicity of real-world demand series, which makes effective modeling of recurrent temporal patterns crucial.

By Weibin Feng, Ran Tao, John Cartlidge, Jin Zheng

rag benchmarks

arXiv AI

Jun 2

Capturing LLM Capabilities via Evidence-Calibrated Query Clustering

arXiv:2605. 17110v2 Announce Type: replace Abstract: Query clustering organizes queries into groups that reflect shared latent capability demands, enabling capability-aware LLM evaluation.

By Fangzhou Wu, Sandeep Silwal, Qiuyi Zhang

llms rag

arXiv AI

Jun 2

Implicit Regularization for Multi-label Feature Selection

arXiv:2411. 11436v2 Announce Type: replace-cross Abstract: In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding.

By Dou El Kefel Mansouri, Khalid Benabdeslem, Seif-Eddine Benkabou

rag agents benchmarks safety

arXiv AI

Jun 2

A Structured Benchmark for Text-Guided Anomaly Detection: When Language Stops Conditioning the Decision

arXiv:2606. 01992v1 Announce Type: cross Abstract: Industrial anomaly detection has historically been a unimodal task.

By Stefano Samele, Eugenio Lomurno, Teodora Jovanovic, Sanjay Shivakumar Manohar, Alberto Crivellaro, Matteo Matteucci

llms rag multimodal benchmarks

arXiv AI

Jun 2

REBot: From RAG to CatRAG with Semantic Enrichment and Graph Routing

arXiv:2510. 01800v3 Announce Type: replace Abstract: Academic regulation advising is essential for helping students interpret and comply with institutional policies, yet building effective systems requires domain specific regulatory resources.

By Thanh Ma, Tri-Tam La, Lam-Thu Le Huu, Minh-Nghi Nguyen, Khanh-Van Pham Luu

llms rag nlp safety

arXiv AI

Jun 2

Test-Time Training for Zero-Resource Dense Retrieval Reranking

arXiv:2606. 01070v1 Announce Type: cross Abstract: Dense retrievers excel at first-stage candidate generation but lack effective reranking in zero-resource settings.

By Shiyan Liu, Yichen Li

rag benchmarks

arXiv AI

Jun 2

SENSE: Semantic Embedding Navigation with Soft-gated Evaluation for Retrieval-based Speculative Decoding

arXiv:2606. 00021v1 Announce Type: cross Abstract: Speculative Decoding (SD) accelerates Large Language Model (LLM) inference by employing a lightweight draft model to propose candidate tokens, which are verified in parallel by the target model, without compromising generation quality.

By Shaowen Chen, Zhicheng Liao, Hongwei Wang

llms rag benchmarks safety

arXiv AI

Jun 2

Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey

arXiv:2606. 00136v1 Announce Type: cross Abstract: The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffective.

By Jonghyun Chung, Rishabh Chaddha, Sanket Badhe, Debanshu Das, Nathan Huang, Amanpreet Kaur

rag agents benchmarks safety

arXiv Machine Learning

Jun 2

Molecular Embedding-Based Algorithm Selection in Protein-Ligand Docking

arXiv:2512. 02328v2 Announce Type: replace-cross Abstract: Selecting an effective docking algorithm is highly context-dependent, and no single method performs reliably across structural, chemical, and protocol regimes.

By Jiabao Brad Wang, Siyuan Cao, Hongxuan Wu, Yiliang Yuan, Mustafa Misir

rag benchmarks

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 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

Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs

arXiv:2606. 01400v1 Announce Type: cross Abstract: Evaluating large language models (LLMs) across comprehensive benchmarks is expensive and time-consuming.

By Denica Kjorvezir, Marko Djukanovi\'c, Ana Gjorgjevikj, Gjorgjina Cenikj, Tome Eftimov

llms rag benchmarks

arXiv AI

Jun 2

Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation

arXiv:2605. 17034v2 Announce Type: replace-cross Abstract: Standard PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individuals.

By Osama Zafar, Alexander Nemecek, Yiqian Zhang, Wenbiao Li, Debargha Ganguly, Vikash Singh, Vipin Chaudhary, Erman Ayday

llms rag safety data-engineering

arXiv AI

Jun 2

APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention

arXiv:2601. 21444v2 Announce Type: replace-cross Abstract: The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multimodal Models (LMMs).

By Yuxiang Huang, Mingye Li, Xu Han, Chaojun Xiao, Weilin Zhao, Ao Sun, Ziqi Yuan, Hao Zhou, Fandong Meng, Zhiyuan Liu

rag multimodal

arXiv Machine Learning

Jun 2

Embedding-Space Diffusion for Zero-Shot Environmental Sound Classification

arXiv:2412. 03771v3 Announce Type: replace-cross Abstract: Zero-shot learning enables models to generalise to unseen classes by leveraging semantic information, bridging the gap between training and testing sets with non-overlapping classes.

By Ysobel Sims, Alexandre Mendes, Stephan Chalup

rag diffusion computer-vision benchmarks safety

arXiv Machine Learning

Jun 2

Density-Aware Translation of Spurious Correlations in Zero-Shot VLMs

arXiv:2606. 01710v1 Announce Type: cross Abstract: Vision-Language models (VLMs), such as CLIP, achieve powerful zero-shot classification.

By Afsaneh Hasanebrahimi, Hanxun Huang, Christopher Leckie, Sarah Erfani

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 2

Doing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization

arXiv:2606. 02345v1 Announce Type: cross Abstract: Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic computational cost quickly becomes prohibitive at scale.

By Louise Davy, Stephan Cl\'emen\c{c}on, Charlotte Laclau

rag

arXiv Machine Learning

Jun 2

DeepLatent: Think with Images via Parallel Latent Visual Reasoning

arXiv:2606. 00562v1 Announce Type: cross Abstract: The emerging paradigm of "thinking with images" embeds visual states into intermediate reasoning steps, defining a new frontier for Vision-Language Models.

By Dongchen Lu, Zhimo Li, Mao Shu, Huo Cao

llms rag reinforcement-learning robotics efficiency multimodal benchmarks

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