News / #rag Tag Rag 500 articles archived under #rag · RSS Sign in to follow arXiv — Machine Learning research 12d ago Schema-Adaptive Action-Conditioned JEPA for Cross-Machine CNC Transfer under Partial Sensor Overlap arXiv:2609.16071v1 Announce Type: new Abstract: Cross-machine deployment of industrial world models requires transfer across changes in dynamics, sensing interfaces, sampling regimes, and control units. We study a schema-adaptive action-conditioned Joint-Embedding Predictive… 24 arXiv — Machine Learning research 12d ago SWB-DM: A Calibrated Sliced-Wasserstein-Barycenter Aggregator with Delayed-Momentum Caching for Byzantine-Robust Federated Learning under Partial Participation arXiv:2609.16099v1 Announce Type: new Abstract: Robust aggregation methods for federated learning quietly rest on a fragile assumption: that whoever shows up in a given round is a fair sample of the full population. In practice, they rarely are. When only a handful of clients… 20 arXiv — Machine Learning research 12d ago LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing arXiv:2609.16155v1 Announce Type: new Abstract: This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Motivated by the scarcity of labeled time-series data in real-world manufacturing… 32 arXiv — Machine Learning research 12d ago Bounded Adjustment with Reliability-Guided Embedding for Imbalanced Learning with Noisy Labels arXiv:2609.16380v1 Announce Type: new Abstract: Class-balanced learning and label noise create a coupled failure mode: frequency correction prevents majority classes from dominating the decision rule, but can amplify incorrectly labeled minority examples. We introduce BARGE… 19 arXiv — NLP / Computation & Language research 12d ago Attention Mean Fields Predict Average Representation Dynamics and Reveal Context-Specific Computation arXiv:2609.16382v1 Announce Type: cross Abstract: A language model's representation geometry is not predetermined; it evolves as the model runs. A faithful account of that geometry must capture that dynamic process, and so cannot be based solely on model-independent statistics… 7 arXiv — Machine Learning research 12d ago Recovering Physical Parameters from Fragmented Observations via Exact Distributed Spline Merging arXiv:2609.16579v1 Announce Type: new Abstract: Scientific measurements are frequently distributed across locations, time periods, and institutions. Combining such fragments into a continuous, differentiable field enables recovering governing physical parameters from its… 13 arXiv — Machine Learning research 12d ago Adapting to Decision-Relevant Non-Stationarity in Decentralized Heterogeneous Bandits arXiv:2609.16824v1 Announce Type: new Abstract: Decentralized bandit systems often contain heterogeneous agents: rewards can change at individual agents even when the best action for the network stays the same. These local changes may cancel when rewards are averaged across… 8 arXiv — Machine Learning research 12d ago Information Geometric Self-Organization at the Edge of Stability in High-Capacity Kernel Associative Memories arXiv:2609.16827v1 Announce Type: new Abstract: High-capacity associative memories based on Kernel Logistic Regression (KLR) exhibit exceptional storage capabilities and robustness. Previous empirical studies identified a hyperparameter regime, the "Ridge of Optimization," where… 27 arXiv — Machine Learning research 12d ago Structural Negative Transfer in Federated Graph Neural Networks: Diagnosis, Causal Investigation, and the Limits of Divergence-Aware Mitigation arXiv:2609.16977v1 Announce Type: new Abstract: Federated learning lets multiple participants train a shared model without pooling raw data, by exchanging locally trained model updates instead. Federated averaging assumes that averaging local models is a reasonable way to solve… 4 arXiv — Machine Learning research 12d ago CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework arXiv:2609.17026v1 Announce Type: new Abstract: Continual learning must balance the learning of new knowledge with the retention of previously learned knowledge to incrementally learn tasks from a data stream without catastrophic forgetting. While leveraging pretrained models… 34 arXiv — NLP / Computation & Language research 12d ago The Functionalizer: Lossless Functional Decomposition for Subword Tokenization arXiv:2609.15991v1 Announce Type: new Abstract: Standard subword tokenizers either treat every orthographic variation of a word (such as hello, Hello, HELLO, and H\'ello) as unrelated vocabulary entries, which fragments the embedding space, or discard this variation through… 38 arXiv — NLP / Computation & Language research 12d ago Crash Narrative-Guided Countermeasure Recommendation Using Large Language Models: A Retrieval-Augmented Generation Framework for Intersection Safety arXiv:2609.15997v1 Announce Type: new Abstract: Improving safety at intersections requires identifying crash mechanisms and recommending appropriate countermeasures. However, this process traditionally relies on expert judgment, making it labor-intensive, difficult to scale, and… 6 arXiv — NLP / Computation & Language research 12d ago NepKANUN: A RAG-Based Nepali Legal Assistant arXiv:2609.15999v1 Announce Type: new Abstract: Accessing legal information in Nepal is difficult due to complex terminology, limited resources, and misinformation. We introduce an AI-powered legal assistant that is tailored for Nepali legal texts and is built on a fine-tuned… 37 arXiv — NLP / Computation & Language research 12d ago The Immutable Past: Formalizing State Mutability and Conflict Resolution in Mutable RAG arXiv:2609.16073v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) serves as the primary memory architecture for long-horizon autonomous agents. However, treating shared memory as an append-only stream introduces \textit{Semantic Shadowing}, a critical failure… 10 arXiv — NLP / Computation & Language research 12d ago RAG-CT: Mitigating Privacy Risks on Retrieval-Augmented Generation Systems via Scanning Prompt Distribution arXiv:2609.16095v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for improving the quality of generated contents of Large Language Models (LLMs) by grounding responses in external knowledge, thus reducing hallucinations and… 33 arXiv — NLP / Computation & Language research 12d ago CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicine arXiv:2609.16301v1 Announce Type: cross Abstract: Medical knowledge evolves continuously, whereas the parametric knowledge encoded in large language models (LLMs) is fixed at training time. External retrieval, including retrieval-augmented generation (RAG), can provide access to… 13 arXiv — NLP / Computation & Language research 12d ago Where Post-Training Quantization Breaks Text Embedders: A Measured Map Across Four Embedder Families arXiv:2609.16391v1 Announce Type: cross Abstract: Weight-only post-training quantization is the cheapest way to shrink a retrieval embedder, and the received advice for applying it -- protect the embedding table, allocate bits by module sensitivity, prefer a ranking-aware… 5 r/LocalLLaMA community 12d ago Combining RAG with Continued Pretraining For leaning purposes, I ran an experiment where I trained a model on a new domain using continued pretraining. Then to make it more flexible, I added a RAG step to inject dynamic data to augment the stable training data. The specific example is training qwen 3.5 4B on a… 30 r/LocalLLaMA community 12d ago ByteShape Qwen 3.8 27B: To KL Diverge or Not to KL Diverge, Part 2: Metric Boogaloo Hey r/LocalLLaMA , We’ve released our full ShapeLearn GGUFs for Qwen 3.8 27B. Blog / Download models TL;DR 3.84 bpw (GPU-5) reaches 99.63% of BF16’s aggregate score of 8 benchmarks, being the most accurate quant we’ve evaluated; 3.23 bpw (GPU-4) reaches 98.72%. These average… 6 r/LocalLLaMA community 13d ago Qwen3.8 27B with embeddings As you know, Qwen3.8 27B has built-in support for embeddings. It gives pretty decent results in llama.cpp with the following parameters: --embedding --pooling mean The problem is that enabling embeddings cuts the token generation speed in half compared to running the model… 19 r/LocalLLaMA community 13d ago jinfer: An open-source AI inference engine for the JVM. Finally, AI in jar. For years, the JVM has watched the AI revolution from the bench. Every model, AI framework, every breakthrough, built with/for Python. jinfer is an inference engine built for the JVM from first principles: chat, vision, audio transcription, embeddings, reranking, and TTS. No… 7 arXiv — Machine Learning research 13d ago Scalable partial information decomposition for symptom networks via supervised embeddings arXiv:2609.13203v1 Announce Type: new Abstract: Pairwise relationships among mental-health symptoms are routinely summarised asscalar edge weights, which cannot express whether two symptoms carry overlapping information about a third or information that appears only in… 17 arXiv — Machine Learning research 13d ago GeoTTER: Leveraging Local Geometry of Optimal Transport for Zero-Shot Classification arXiv:2609.13518v1 Announce Type: new Abstract: We present GeoTTER, a novel framework that redefines optimal transport in the realm of zero-shot classification. Conventional methods often suffer from miscalibration and a lack of adaptability, as they rely on fixed cost matrices… 22 arXiv — Machine Learning research 13d ago Entropy-Punctured Bloom Filters for Memory-Efficient Machine Learning arXiv:2609.14187v1 Announce Type: new Abstract: Memory-efficient feature representations are increasingly important in machine learning settings where storage, transmission cost, bandwidth, or privacy constraints limit access to raw data. Bloom Filter (BF) encodings provide… 17 arXiv — Machine Learning research 13d ago Biquaternionic Space with Complex-valued Attention for Temporal Knowledge Graph Completion arXiv:2609.14279v1 Announce Type: new Abstract: Temporal knowledge graph embedding (TKGE) models infer missing facts in knowledge graphs that evolve over time. Many existing models use a single geometric space, which can limit their ability to represent diverse relational… 8 arXiv — Machine Learning research 13d ago Retrieval-Guided Fine-Tuning as Noisy Estimation: Risk bounds and Architectural Analysis arXiv:2609.14485v1 Announce Type: new Abstract: Retrieval-Guided Fine-Tuning (RAG-FT) incorporates retrieved data directly into the training objective, but the statistical consequences of noisy retrieval during training remain theoretically undercharacterized. We study this… 23 arXiv — NLP / Computation & Language research 13d ago DARE: Dialectical Agentic Reasoning for Structured Knowledge Fact Checking arXiv:2609.13808v1 Announce Type: new Abstract: Structured knowledge fact checking aims to determine the truthfulness of natural language claims by reasoning over structured evidence. Recent program-generation approaches leverage large language models (LLMs) to generate… 34 arXiv — NLP / Computation & Language research 13d ago ShopEase: A Generative AI-Based Multi-Agent Framework for Intelligent Enterprise Customer Support Using Hybrid Retrieval-Augmented Generation arXiv:2609.13856v1 Announce Type: new Abstract: Enterprise customer support systems must answer customer questions correctly, retrieve the right policy information, use customer context, and pass difficult cases to human agents when needed. This paper presents ShopEase, a… 36 arXiv — NLP / Computation & Language research 13d ago CRITICS - Critical Science Without Borders: Language Models to Promote Critical Thinking in Science Education arXiv:2609.13942v1 Announce Type: new Abstract: The CRITICS project addresses science accessibility and literacy by converging advanced Machine Translation (MT) based on Large Language Models (LLMs) with educational technology. By leveraging MT systems specifically optimized for… 13 arXiv — NLP / Computation & Language research 13d ago GraMRAG: Orchestrating Multi-Agent Multi-Step Reasoning via Graph Memory with Reinforcement Learning arXiv:2609.14066v1 Announce Type: new Abstract: Although existing multi-agent Retrieval-Augmented Generation (RAG) systems have demonstrated promise on complex multimodal reasoning tasks, they remain fundamentally limited in reasoning depth and memory structure, suffering from… 32 arXiv — NLP / Computation & Language research 13d ago Learning to Refer from Estimated Listener Gaze arXiv:2609.14207v1 Announce Type: new Abstract: We propose to finetune vision-language models to generate more pragmatically optimal referring expressions by transforming observations of incremental listener comprehension, in the form of gaze scanpaths, into learning signals.… 29 arXiv — NLP / Computation & Language research 13d ago The Attribution-Compression Frontier in Retrieval-Augmented Generation arXiv:2609.14245v1 Announce Type: new Abstract: Context compression reduces generator input in retrieval-augmented generation, but answer quality alone does not characterize citation attribution. We measure citation attribution across compression methods and budgets, comparing… 38 arXiv — NLP / Computation & Language research 13d ago Neyshekar: An Open Persian Read-Speech Corpus for Automatic Speech Recognition arXiv:2609.14542v1 Announce Type: new Abstract: Neyshekar is presented as an open Persian read-speech corpus designed for coverage of both formal and informal language, named entities, and longer utterances. In version 6, 62,279 validated recordings totalling 99.02 hours are… 26 arXiv — NLP / Computation & Language research 13d ago Domain-specific Pretraining Profile and Transformer Performance: Evidence from Modeling Digital Pragmatics in Arabic-English Code-switching arXiv:2609.14571v1 Announce Type: new Abstract: This study highlights the role of domain-specific pretraining profile (DSPP) in Transformer performance for modeling digital pragmatics in Arabic-English code-switched discourse. It evaluates MARBERT and XLM-R(oBERTa), with BERT… 20 arXiv — NLP / Computation & Language research 13d ago MoME: Mixture-of-Memory Embeddings for Context-Aware Sparse Lookup arXiv:2609.15126v1 Announce Type: new Abstract: Scaling large language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional memory: token-indexed embedding tables that augment the backbone with cheap parametric… 15 Simon Willison community 13d ago What blog posts influenced your thinking the most? My comment on What blog posts influenced your thinking the most? — Lobste.rs. An early Joel Spolsky one for me was The Law of Leaky Abstractions . I read that near the start of my career and it's encouraged me to always be looking for improved understanding of the layers… 27 Hugging Face Daily Papers research 13d ago TRACE: Trajectory-robust Admission with Evidence Ordering for Efficient GUI Agents Abstract TRACE is a training-free framework that ranks visual evidence by future utility and diversity, reserves native tokens for spatial coverage, and contracts retired frames to reduce latency and memory in GUI agents. Generated by thinkingmachines/Inkling-Small GUI agents… 8 Hugging Face Daily Papers research 14d ago Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents Abstract Diverse Skill Routing improves LLM agent skill selection by balancing relevance with non-redundancy via a determinantal point process, boosting multi-skill coverage. Generated by thinkingmachines/Inkling-Small Large language model (LLM) agents increasingly rely on… 27 arXiv — Machine Learning research 14d ago Physics-Informed Conformal Prediction: Embedding PDE Consistency into Distribution-Free Uncertainty Quantification for Neural Operators arXiv:2609.11935v1 Announce Type: new Abstract: Neural operators such as the Fourier Neural Operator (FNO) achieve remarkable accuracy in approximating solutions to partial differential equations (PDEs). However, providing rigorous uncertainty estimates remains an open… 26 arXiv — Machine Learning research 14d ago LatentVerse: A Framework for Understanding Shared and Modality-Specific Information in Multimodal Latent Representations arXiv:2609.12364v1 Announce Type: new Abstract: Latent embeddings have become a central data abstraction in modern machine learning, especially in biomedicine, where foundation models are increasingly used to encode multimodal data like clinical text, medical images, omics, and… 19 arXiv — Machine Learning research 14d ago Beyond the Query: Do Retrieval Signals Improve Adaptive Multimodal RAG Routing? arXiv:2609.12437v1 Announce Type: new Abstract: Adaptive RAG often uses retrieval-time signals to decide whether another retrieval, reranking, or multimodal step should run. We ask whether these signals add routing value once the query itself is already known. Across document,… 16 arXiv — Machine Learning research 14d ago Behavior Quotient Learning for Low-Rank Adaptation of LLM Agents arXiv:2609.12896v1 Announce Type: new Abstract: LLM-based agents rely on heterogeneous interaction capabilities to accomplish complex tasks. Existing approaches often distribute these capabilities across multiple LoRA adapters, which increases adapter storage requirements and… 24 arXiv — Machine Learning research 14d ago Transfer Learning for Evolving Domains arXiv:2609.13039v1 Announce Type: new Abstract: Transfer learning explores how to leverage knowledge from various tasks or domains (sources) to enhance predictive performance in related tasks or domains (targets). Typically, transfer learning research is segmented into several… 11 arXiv — NLP / Computation & Language research 14d ago The Cost of Compression: A Rate-Distortion Limit on Factual Hallucination arXiv:2609.12111v1 Announce Type: new Abstract: Factual hallucination in closed-book question answering is often treated as a coverage problem: a model fails because the relevant fact is absent from its internal memory. This view misses a second source of error. Even when a fact… 19 arXiv — NLP / Computation & Language research 14d ago EAR: Entity-Aware Partitioning Approach for Retrieval-Augmented Generation Development arXiv:2609.12268v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) can improve knowledge-intensive question answering, but the first design choice is easy to overlook: how should the source corpus be partitioned into retrievable units? Fixed-size chunks often… 21 arXiv — NLP / Computation & Language research 14d ago Cognition on Graph: Navigating Massive Knowledge Space via Cognitive Cycles and Bidirectional Graph-Text Synergy arXiv:2609.12791v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has empowered Large Language Models (LLMs) to tackle knowledge-intensive tasks. However, navigating global, heterogeneous knowledge bases (large-scale knowledge graphs and text corpora) for… 20 arXiv — NLP / Computation & Language research 14d ago Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis arXiv:2609.12403v1 Announce Type: cross Abstract: Cognitive Diagnosis Models (CDMs) play a pivotal role in personalized online learning. Traditional CDMs rely on discrete, ID-based embeddings to represent students, exercises, and concepts. This paradigm diverges from the nature… 11 arXiv — NLP / Computation & Language research 14d ago Investigating Temporal Motion Features for Pose-to-Text Indian Sign Language Translation arXiv:2609.12993v1 Announce Type: cross Abstract: We investigate the effect of pretrained T5 model scale and explicit motion features on pose-to-text Indian Sign Language Translation (SLT) for the WSLP 2026 Shared Task. Pose sequences are projected into the embedding space of T5… 7 arXiv — NLP / Computation & Language research 14d ago PACIFIC: Can LLMs Discern the Psychometric Traits Influencing Your Preferences? Personality-Driven Preference Alignment in LLMs arXiv:2602.07181v4 Announce Type: replace Abstract: User preferences are increasingly used to personalize Large Language Model (LLM) responses, yet reliably leveraging preference signals remains under-explored. In practice, preferences can be noisy, incomplete, or even… 9 arXiv — NLP / Computation & Language research 14d ago Measuring Pragmatic Influence in Large Language Model Instructions arXiv:2602.21223v2 Announce Type: replace Abstract: It is not only what we ask large language models (LLMs) to do that matters, but also how we ask them. Phrases like ``This is urgent'' or ``As your supervisor'' can shift model behavior without altering task content. We study… 4 Page 4 of 10 · 500 articles ← Newer Older →
