News / #rag Tag Rag 500 articles archived under #rag · RSS Sign in to follow arXiv — NLP / Computation & Language research 1mo ago Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs arXiv:2607.25959v1 Announce Type: new Abstract: Wikipedia and Wikidata are widely used for information access, LLM pre-training, and retrieval-augmented generation. Their knowledge is deeply connected but scattered across text, tables, and knowledge graphs. This raises a… 28 arXiv — NLP / Computation & Language research 1mo ago VLD-RAG: Agentic Vision-Language Retrieval-Augmented Generation for Long, Visually-Rich Multi-Page Documents arXiv:2607.24748v1 Announce Type: cross Abstract: Visually-rich documents such as reports, slides, and manuals often distribute the evidence needed to answer a question across multiple pages, mixing text with layout cues, tables, charts, and figures. This work studies multimodal… 36 arXiv — NLP / Computation & Language research 1mo ago The Effect of Text Chunk Size on Retrieval-Augmented Generation Performance arXiv:2607.24767v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems have emerged as a powerful process for allowing large language models (LLMs) to retrieve relevant information to use as source material during text generation. A critical yet… 11 arXiv — NLP / Computation & Language research 1mo ago LLM Scheming Inversely Scales with Pretraining Language Coverage arXiv:2607.24769v1 Announce Type: cross Abstract: With the growing capabilities of frontier models, AI alignment becomes increasingly critical in high-risk deployment settings. While recent work has empirically demonstrated in-context scheming -- the covert pursuit of misaligned… 36 arXiv — NLP / Computation & Language research 1mo ago From Naive RAG to Deep Agentic Retrieval: An Evolving Context Engineering Pipeline for Regulatory Compliance arXiv:2607.24791v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is the dominant paradigm for applying large language models (LLMs) to enterprise document corpora, yet naive implementations encounter hard limits as corpus scale and query complexity grow.… 23 arXiv — NLP / Computation & Language research 1mo ago Retrieval-Augmented Generation in LLMs for Mental Health: Quantifying the Incremental Contribution of Retrieval Within a Layered Safety Architecture arXiv:2607.24817v1 Announce Type: cross Abstract: Digital mental health interventions (DMHIs) offer scalable support, but ensuring they accurately detect users' intent during volatile situations can be challenging. Pure parametric Large Language models (LLMs) do not contain… 10 arXiv — NLP / Computation & Language research 1mo ago Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation arXiv:2607.24884v1 Announce Type: cross Abstract: Repository-level code generation relies on heterogeneous evidence whose relevance, compatibility, and completeness are inherently uncertain. Similar-code examples, repository context, and project-specific APIs may provide… 14 arXiv — NLP / Computation & Language research 1mo ago Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs arXiv:2607.25600v1 Announce Type: cross Abstract: Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We investigate whether verbalized confidence from… 14 arXiv — NLP / Computation & Language research 1mo ago Med-R$^3$: Enhancing Medical Retrieval-Augmented Reasoning of LLMs via Progressive Reinforcement Learning arXiv:2507.23541v5 Announce Type: replace Abstract: In medical scenarios, effectively retrieving external knowledge and leveraging it for rigorous logical reasoning is of significant importance. Despite their potential, existing work has predominantly focused on enhancing either… 32 arXiv — NLP / Computation & Language research 1mo ago VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation arXiv:2510.09733v2 Announce Type: replace Abstract: Visual Retrieval-Augmented Generation (VRAG) has emerged as a promising paradigm for equipping Vision-Language Models (VLMs) with external visual evidence, enabling them to go beyond parametric knowledge when answering visually… 36 arXiv — NLP / Computation & Language research 1mo ago Beyond Factual Accuracy: Evaluating Global Reasoning Integrity in RAG Systems with LogicScore arXiv:2601.15050v5 Announce Type: replace Abstract: Current evaluation methods for Retrieval Augmented Generation (RAG) suffer from \textit{factual myopia}: they relentlessly emphasize factual accuracy yet neglect global logical integrity in long-form answer generation. This… 28 r/MachineLearning community 1mo ago How to deal with text only vector search across multimodal embedding space? [D] My data set is a list of images, each equipped with a a couple sentences of text. A user would search primarily with text only. My default approach is using BM25, but how would I facilitate searching with a vector DB and a model that embeds vectors in a multimodal combined… 37 arXiv — Machine Learning research 1mo ago Hierarchical Grading in Large Language Models arXiv:2607.22757v1 Announce Type: new Abstract: We introduce Graded Large Language Models (GLLMs), an algebraic framework that equips the representation space of a transformer with a grading and propagates the induced weighted scalar action through embeddings, self-attention,… 30 arXiv — Machine Learning research 1mo ago LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers arXiv:2607.22804v1 Announce Type: new Abstract: Accurate geological characterization of subsurface reservoirs from well log data is essential to support projects such as carbon capture and storage (CCS), geothermal development, and extraction of natural resources. Existing… 38 arXiv — Machine Learning research 1mo ago OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence arXiv:2607.22805v1 Announce Type: new Abstract: We propose OrchNAS, an energy-aware, personalised, federated edge intelligence framework that leverages a Neural Architecture Search Service to automatically design service-adaptive models for heterogeneous edge environments. The… 32 arXiv — Machine Learning research 1mo ago PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling arXiv:2607.23447v1 Announce Type: new Abstract: Predicting cellular responses to unseen chemical perturbations is challenging due to unknown targets and mechanisms, high-dimensional expression responses, and limited experimental coverage of the large small-molecule design space.… 29 arXiv — NLP / Computation & Language research 1mo ago Not All LLM Reasoning is Visible in the Chain-of-Thought arXiv:2607.22925v1 Announce Type: new Abstract: A key question for AI safety is whether a language model expresses all of its reasoning in its output tokens. We demonstrate a concrete failure mode where frontier models exhibit invisible reasoning by leveraging semantically… 16 arXiv — NLP / Computation & Language research 1mo ago IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems arXiv:2607.23322v1 Announce Type: new Abstract: Instruction tuning has become the standard method for adapting large language models to follow human intent, yet existing instruction datasets are dominated by English-language general-knowledge tasks and lack coverage of… 10 arXiv — NLP / Computation & Language research 1mo ago Mwando: Leveraging AI to Preserve and Teach shiKomori arXiv:2607.23481v1 Announce Type: new Abstract: This paper presents Mwando, a virtual educational assistant designed to support the teaching and preservation of shiKomori, the language of the Comoros Islands. The system covers the four main dialectal variants (shiNgazidja,… 7 arXiv — NLP / Computation & Language research 1mo ago The JEPA Paradox in Language: The Geometry of Linguistic Alternatives arXiv:2607.23531v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) are effective for images, video, and audio, yet deterministic JEPA-style latent prediction has not become a standard objective for text encoders. We argue that this gap reflects a… 37 arXiv — NLP / Computation & Language research 1mo ago An empirical investigation into the properties of standard word embeddings arXiv:2607.23675v1 Announce Type: new 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. Such embeddings have found application in areas such as Automatic… 21 arXiv — NLP / Computation & Language research 1mo ago Cross-Attention Calibrated Deduplication for Retrieval-Augmented Generation System arXiv:2607.24332v1 Announce Type: new Abstract: Common chunking strategies in Retrieval-Augmented Generation (RAG) systems often create redundant chunks. These redundant chunks make the vector database bigger and slow down retrieval. A common fix is cosine-similarity… 13 arXiv — NLP / Computation & Language research 1mo ago Retrieval-Augmented Large Language Models as Components of Cognitive Computing architecture for Regulatory Knowledge Management arXiv:2607.24352v1 Announce Type: new Abstract: The aim of this article is to verify whether integrating large language models (LLMs) with the Retrieval-Augmented Generation (RAG) architecture enables their transformation from standalone generative models into components of… 18 arXiv — NLP / Computation & Language research 1mo ago Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach arXiv:2607.22584v1 Announce Type: cross Abstract: Standard Retrieval-Augmented Generation pipelines rank retrieved documents by semantic similarity alone, without accounting for source provenance or credibility. This work evaluates a simple and interpretable modification to RAG… 5 arXiv — NLP / Computation & Language research 1mo ago Structure Over Scale: Schema-Constrained Causal Graphs for RAG arXiv:2607.22592v1 Announce Type: cross 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… 17 arXiv — NLP / Computation & Language research 1mo ago Revitalizing Public Urban Places through Cultural and Political Memory: A Technological Approach with LLMs and Augmented Reality arXiv:2607.22613v1 Announce Type: cross Abstract: This paper explores the intersection of memory, place, and identity, examining how new technologies, particularly Apple Vision Pro, can illuminate this nexus. Leveraging digital twins and virtual reality, it investigates how… 23 Hugging Face Daily Papers research 1mo ago Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models Abstract Historical documents act as invaluable knowledge archives but often suffer from illegibility due to physical deterioration and damage. While existing restoration methods based on masked language modeling effectively utilize local context, they struggle to restore named… 37 r/LocalLLaMA community 1mo ago Why Anthropic's battle is meant to poison the wells of open weight models, in 3 steps. It doesn't solve any problems. Just a few paragraphs above, he says he fears that authoritarian states (he names China, and possibly others) can use their models to do evil stuff. And surely enough, malicious actors creating a model for themselves and for the EVILZ aren't going… 24 arXiv — Machine Learning research 1mo ago Toward Goal-Agnostic Joint-Embedding Predictive Control of Partial Differential Equations arXiv:2607.21644v1 Announce Type: new Abstract: We present a goal-agnostic control framework for partial differential equations (PDEs) built around a joint-embedding predictive architecture (JEPA). The small 2D ViT encoder and action-conditioned latent dynamics are trained… 36 arXiv — Machine Learning research 1mo ago Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA arXiv:2607.21680v1 Announce Type: new Abstract: Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the naked eye. Expert diagnosis relies on tacit knowledge built over years of… 21 arXiv — Machine Learning research 1mo ago Unbiased Open World Regularization for Fair Self-Supervised Learning arXiv:2607.22149v1 Announce Type: new Abstract: Despite recent advances, self-supervised learning (SSL) models and Joint-Embedding Predictive Architectures (JEPAs) remain susceptible to learning spurious biases in the dataset. These techniques rely on regularization, which… 19 arXiv — Machine Learning research 1mo ago IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data arXiv:2607.22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem. Learned solvers are fast yet label… 25 arXiv — Machine Learning research 1mo ago On the Identifiability of Controlled World Models arXiv:2607.22430v1 Announce Type: new Abstract: Learning world models that infer environment dynamics from high-dimensional observations and predict outcomes under candidate actions is central to planning and control. Joint-Embedding Predictive Architectures (JEPAs) provide a… 8 arXiv — Machine Learning research 1mo ago Phylogenetic signal in marine mammal and bird vocalizations captured by audio foundation models: the limited benefit of domain-specific pretraining arXiv:2607.22458v1 Announce Type: new Abstract: Do learned audio embeddings encode structure that nobody told them to encode? We probe four large pretrained audio models (AST, CLAP, BEATs-bio and BirdNET) with a downstream task none of them saw during training: recovering… 17 arXiv — NLP / Computation & Language research 1mo ago Leveraging External Knowledge for Historical Document Restoration via Retrieval-Augmented Large Language Models arXiv:2607.21936v1 Announce Type: new Abstract: Historical documents act as invaluable knowledge archives but often suffer from illegibility due to physical deterioration and damage. While existing restoration methods based on masked language modeling effectively utilize local… 27 arXiv — NLP / Computation & Language research 1mo ago MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond arXiv:2607.22100v1 Announce Type: new Abstract: Lightweight projectors are an established way to connect pre-trained speech encoders with large language models (LLMs), mapping acoustic features into token-level embeddings for tasks like ASR and spoken question answering.… 24 arXiv — NLP / Computation & Language research 1mo ago From Isolated Tasks to Structured Capabilities: A Multilayer Taxonomy for Large Language Models arXiv:2607.22182v1 Announce Type: new Abstract: Large language model (LLM) evaluation spans diverse tasks and benchmarks, yet evidence remains organized around tasks rather than the capabilities they probe. This fragmentation limits cross-study comparison, obscures capabilities… 30 arXiv — NLP / Computation & Language research 1mo ago Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Mode arXiv:2607.22083v1 Announce Type: cross Abstract: We present Nanbeige4.2-3B, a compact general agentic model with 3B non-embedding parameters. It delivers strong performance across code-agent, office-agent, and complex tool-use tasks while maintaining highly competitive… 12 arXiv — NLP / Computation & Language research 1mo ago Interpretable Depression Detection from Social Media Text Using LLM-Derived Embeddings arXiv:2506.06616v2 Announce Type: replace Abstract: Accurate and interpretable detection of depressive language in social media can support early identification of mental health conditions and inform timely interventions. In this paper, we investigate the use of large language… 13 arXiv — NLP / Computation & Language research 1mo ago LMEB: Long-horizon Memory Embedding Benchmark arXiv:2603.12572v5 Announce Type: replace Abstract: Memory embeddings are crucial for memory-augmented systems, such as OpenClaw, but their evaluation is underexplored in current text embedding benchmarks, which narrowly focus on traditional passage retrieval and fail to assess… 27 arXiv — NLP / Computation & Language research 1mo ago SURE-RAG: Sufficiency and Uncertainty-Aware Evidence Verification for Selective Retrieval-Augmented Generation arXiv:2605.03534v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) grounds answers in retrieved passages, yet relevance does not guarantee sufficiency: a topical passage may still fail to justify the answer. We study evidence sufficiency verification for… 14 r/LocalLLaMA community 1mo ago ai-sage/GigaChat3.1-Audio-10B-A1.8B · Hugging Face GigaChat Audio 10B is an audio-native LLM built on top of the GigaChat 3.1 Lightning text model. A Conformer speech encoder and a modality adapter feed audio embeddings directly into a Mixture-of-Experts decoder, so the model keeps the text quality of its base while adding… 36 Hugging Face Daily Papers research 1mo ago VisCo: Leveraging Large Language Models as Intrinsic Encoders for Visual Token Compression Abstract Vision-language models (VLMs) process large numbers of visual tokens, resulting in substantial inference latency and memory overhead. This has motivated extensive research on visual token compression. While training-free strategies rely on heuristic metrics and suffer… 12 arXiv — Machine Learning research 1mo ago Multimodal CoLRAG-TF: Triple-Filtered Retrieval for Complex PDFs arXiv:2607.20517v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) over heterogeneous PDF collections remains challenging due to multimodal content, domain-specific terminology, and the need for multi-hop reasoning across dispersed evidence. We present… 14 arXiv — Machine Learning research 1mo ago Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling arXiv:2607.20539v1 Announce Type: new Abstract: While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited. The reason is data scarcity. Bioreactor experiments are high-cost, take days to weeks, and are rarely shared in… 17 arXiv — Machine Learning research 1mo ago SenCos-GEM: SENet-Calibrated and Law-of-Cosines-Constrained Geometry-Enhanced Molecular Representation for Property Prediction arXiv:2607.20551v1 Announce Type: new Abstract: Effective molecular representation learning is crucial for accurate molecular property prediction. Recently, numerous self-supervised learning (SSL) approaches leveraging 3D GNNs have been developed to capture comprehensive 3D… 5 arXiv — Machine Learning research 1mo ago Joint Utilization of Geospatial and census proxies for Autoencoder-Assisted Downscaling (JUGAAD) of socioeconomic indicators in India arXiv:2607.20559v1 Announce Type: new Abstract: Monitoring poverty and food security indicators is imperative for addressing socioeconomic challenges in developing nations. A limitation is mismatches in scale between data sources: census data provide geographic coverage, while… 29 arXiv — Machine Learning research 1mo ago Chronofy: A Temporal-Logical Decay Architecture for Information Validity in Time-Aware Retrieval-Augmented Generation arXiv:2607.20560v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems retrieve and integrate external knowledge to ground large language model (LLM) outputs. However, current RAG architectures treat all retrieved facts as equally valid regardless of… 7 arXiv — Machine Learning research 1mo ago CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging arXiv:2607.20561v1 Announce Type: new Abstract: LoRA adapters provide an efficient way to specialize a pretrained model for many downstream tasks, but deploying one adapter per task requires adapter storage and task selection at inference time. Model merging addresses this issue… 29 arXiv — Machine Learning research 1mo ago End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers arXiv:2607.20674v1 Announce Type: new Abstract: We consider the problem of learning high-dimensional semi-global feedback controllers under hard safety constraints enforced by control barrier functions (CBFs). Incorporating CBFs into end-to-end policy training requires embedding… 5 Page 10 of 10 · 500 articles ← Newer
