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 8

MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval

arXiv:2604. 09552v2 Announce Type: replace-cross Abstract: Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retrieval augmented generation (RAG) systems.

By Kiarash Naghavi Khanghah, Hoang Anh Nguyen, Anna C. Doris, Amir Mohammad Vahedi, Daniele Grandi, Faez Ahmed, Hongyi Xu

llms rag agents nlp multimodal benchmarks

arXiv AI

Jun 8

GP-Adapter: Gaussian Process CLIP-Adapter for Few-Shot Out-of-Distribution Detection

arXiv:2606. 07102v1 Announce Type: cross Abstract: We propose GP-Adapter, a training-free framework that augments CLIP (Contrastive Language-Image Pre-training) with Gaussian Process (GP) uncertainty modeling for few-shot classification and out-of-distribution (OOD) detection.

By Taisei Saito, Koretaka Ogata, Takafumi Hiroi

llms rag fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 8

Enhancing Conformal Prediction via Class Similarity

arXiv:2511. 19359v2 Announce Type: replace Abstract: Conformal Prediction (CP) has emerged as a powerful statistical framework for high-stakes classification applications.

By Ariel Fargion, Lahav Dabah, Tom Tirer

rag

arXiv AI

Jun 8

LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling

arXiv:2603. 19100v2 Announce Type: replace Abstract: Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building foundation models for EEG remains challenging due to differing electrode topologies and computational scalability, as Transformer architectures incur quadratic sequence complexity.

By Dana\'e Broustail, Anna Tegon, Thorir Mar Ingolfsson, Yawei Li, Luca Benini

llms rag

arXiv AI

Jun 8

Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection

arXiv:2606. 06748v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) reduces but does not eliminate hallucination in large language models.

By Jianru Shen

llms rag nlp safety

arXiv Machine Learning

Jun 8

Bootstrap Theory of Representational Emergence: Explanatory Insufficiency as a Driver of Representation Learning and World Models

arXiv:2606. 07303v1 Announce Type: new Abstract: Representation learning is central to modern machine learning, enabling transitions from handcrafted features to learned embeddings, latent spaces, foundation models, world models, and digital twins.

By Jacques Raynal, Pierre Slangen, Elsa Raynal, Jacques Margerit

rag

arXiv Machine Learning

Jun 8

CF-JEPA: Mask-free forward prediction with asymmetric encoder utilization for time-series representation learning

arXiv:2606. 07031v1 Announce Type: new Abstract: Self-supervised learning (SSL) for time-series representation learning is dominated by two paradigms: contrastive methods, which face challenges in constructing positive or negative pairs, and masking-based methods, which disrupt the temporal continuity of time-series signals.

By Jaehoon Lee, Sunghyun Sim

llms rag benchmarks

arXiv AI

Jun 8

Hierarchical Certified Semantic Commitment for Byzantine-Resilient LLM-Agent Collaboration

arXiv:2606. 07316v1 Announce Type: cross Abstract: Byzantine collaboration among large-language-model agents requires a finality-control primitive: given delivered stochastic, structured natural-language proposals, the protocol must decide whether the round supports a commit, what kind of commit, or a typed safe abort.

By Haoran Xu, Lei Zhang, Iadh Ounis, Xianbin Wang

llms rag agents benchmarks safety

arXiv AI

Jun 8

DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling

arXiv:2606. 07108v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) demonstrate remarkable performance improvements by iteratively reflecting, exploring, and executing complex tasks, yet suffer from inefficiencies due to redundant reasoning, known as "overthinking".

By Tengyao Tu, Yulin Li, Hui-Ling Zhen, Libo Qin, Zhoujun Wei, Jinghua Piao, Zhuotao Tian, Yong Li, Min Zhang

rag nlp benchmarks

arXiv AI

Jun 8

Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports

arXiv:2605. 17561v2 Announce Type: replace-cross Abstract: Issues faced when using software are reported in the form of bug reports.

By Mahmut Furkan Gon, Emre Dinc, Tevfik Emre Sungur, Eray Tuzun

llms rag agents benchmarks

arXiv AI

Jun 8

Declarative Skills for AI Agents in Knowledge-Grounded Tool-Use Workflows

arXiv:2606. 06923v1 Announce Type: new Abstract: We study orchestration mechanisms for tool-using AI agents in realistic customer-service workflows over an unstructured knowledge base.

By M. Danish Lim, I. Danial Bin Sharudin, Wen Han Chen, Cedric Lim, Laura Wynter

llms rag agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 8

SHAP-Guided Kernel Actor-Critic for Explainable Reinforcement Learning

arXiv:2512. 05291v3 Announce Type: replace Abstract: Actor-critic (AC) methods are a cornerstone of reinforcement learning (RL) but offer limited interpretability.

By Na Li, Hangguan Shan, Wei Ni, Wenjie Zhang, Xinyu Li

rag reinforcement-learning safety

arXiv Machine Learning

Jun 8

Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery

arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

rag multimodal benchmarks

arXiv AI

Jun 8

TokaMind: A Multi-Modal Transformer Foundation Model for Tokamak Plasma Dynamics

arXiv:2602. 15084v2 Announce Type: replace-cross Abstract: We present TokaMind, to our knowledge the first open-source foundation model for tokamak plasma dynamics, based on a Multi-Modal Transformer (MMT) and pretrained on heterogeneous diagnostics from the publicly available MAST dataset.

By Tobia Boschi, Andrea Loreti, Nicola C. Amorisco, Rodrigo H. Ordonez-Hurtado, C\'ecile Rousseau, George K. Holt, Eszter Sz\'ekely, Alexander Whittle, Samuel Jackson, Adriano Agnello, Stanislas Pamela, Alessandra Pascale, Robert Akers, Juan Bernabe Moreno, Vassil Alexandrov, Mykhaylo Zayats

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jun 8

RETROSPECT: RETROsynthesis via Sequential Prediction, and Chemically Transformed-ranking

arXiv:2606. 07181v1 Announce Type: cross Abstract: Single-step retrosynthesis needs both accurate first-ranked suggestions and candidate lists that are rich enough for downstream selection.

By Raja Sekhar Pappala, Shreyas Vinaya Sathyanarayana, Ronit Kumar Choudhary, Arjun Verma, Deepak Warrier

llms rag benchmarks

arXiv AI

Jun 8

NTILC: Neural Tool Invocation via Learned Compression

arXiv:2606. 06566v1 Announce Type: cross Abstract: Agentic tool-calling language models depend on large registries of callable APIs, functions, and local actions.

By Andrew Krikorian, Yayuan Li, Jason J. Corso

llms rag agents efficiency

arXiv Machine Learning

Jun 8

A Dynamic Self-Evolving Extraction System

arXiv:2603. 06915v2 Announce Type: replace-cross Abstract: The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation.

By Moin Amin-Naseri, Hannah Kim, Estevam Hruschka

llms rag nlp fine-tuning

arXiv AI

Jun 8

MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts

arXiv:2510. 05363v2 Announce Type: replace Abstract: Adapting Foundation Models to new domains with limited training data is challenging and computationally expensive.

By Abhinav Jain, Xinyu Yao, Thomas Reps, Christopher Jermaine

rag benchmarks

arXiv Machine Learning

Jun 8

Interpreting Learning Under Competing Models: Joint and Stepwise Approaches for Dynamic Cognitive Diagnosis

arXiv:2606. 06804v1 Announce Type: new Abstract: Digital learning environments record learners' responses to individual items, making it possible to study the development of specific skills rather than overall scores.

By Yawen Ma, Sahoko Ishida, Kate Cain, Gabriel Wallin

rag safety

arXiv AI

Jun 8

CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space

arXiv:2601. 05675v2 Announce Type: replace Abstract: Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI.

By Bingyi Liu, Jinbo He, Haiyong Shi, Enshu Wang, Weizhen Han, Jingxiang Hao, Peixi Wang, Zhuangzhuang Zhang

rag agents diffusion reinforcement-learning robotics benchmarks

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