News / #rag Tag Rag 500 articles archived under #rag · RSS Sign in to follow arXiv — NLP / Computation & Language research 18d ago Position Encoding in Transformers: From Absolute and Relative Methods to Rotary Position Embeddings and Long-Context Scaling arXiv:2608.10021v1 Announce Type: new Abstract: Self-attention models content-dependent interactions between tokens but does not by itself encode token order. Position encoding addresses this limitation by introducing absolute coordinates, relative distances, or… 11 arXiv — NLP / Computation & Language research 18d ago Similarity Gates Approve Reversals: A Validity Audit of Embedding-Cosine Thresholds in Agent Systems arXiv:2608.10216v1 Announce Type: new Abstract: Agent frameworks ship quality gates that compare text blocks by embedding-cosine similarity and decide at a fixed cutoff. Deduplication filters, semantic caches, drift guards, and answer grader gates deploy to answer the question:… 32 arXiv — NLP / Computation & Language research 18d ago Off-Axis, On Purpose: Where a Transformer Computes Concepts and Why it Does So arXiv:2608.10251v1 Announce Type: new Abstract: A transformer's answer lives on one axis: the direction its unembedding reads. Its intermediate states largely do not, and that off-axis position is usually treated as an obstacle to interpretation. We show it is functional. A… 38 arXiv — NLP / Computation & Language research 18d ago Leveraging Human Reading Behavior for Keyphrase Extraction: A Webcam-based Eye-tracking Corpus arXiv:2608.10688v1 Announce Type: new Abstract: Purpose: Keyphrases are statistically and semantically important textual units that can also attract readers' attention during comprehension. However, existing keyphrase extraction (KPE) studies mainly focus on improving textual… 34 arXiv — NLP / Computation & Language research 18d ago SPIEval: Evaluating Large Language Models as Mobile Assistants over Scattered Personal Information arXiv:2608.10692v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as mobile assistants, where a key challenge is leveraging personal information scattered across multiple applications (apps) to complete user instructions. However, due to the… 21 arXiv — NLP / Computation & Language research 18d ago Certify or Refuse: A Cross-Model Map for Selective Risk Control with Coverage Floors under Covariate Shift arXiv:2608.10893v1 Announce Type: new Abstract: Certified selective predictors attain whatever coverage they attain; operators impose an automation floor: answer at least a $\beta$-fraction of shifted target traffic with at most an $\alpha$-fraction of answers wrong. Under… 4 arXiv — NLP / Computation & Language research 18d ago Attention-Path Fragility as an Uncertainty Signal in Large Language Models arXiv:2608.11138v1 Announce Type: new Abstract: We propose that a model's uncertainty about a token is reflected not only in the breadth of its output distribution but also in whether a confident prediction is \emph{fragile} under perturbation of its attention pathways. We… 27 Hugging Face Daily Papers research 18d ago Beyond Sequence Order: Syntax-Informed Positional Embeddings for Transformers Abstract SiPE integrates a lightweight syntactic prior from dependency parses into positional embeddings across transformer architectures, improving syntactic generalization and language understanding without altering self-attention or increasing inference cost. Generated by… 15 r/MachineLearning community 19d ago When associative memory fails, are the synapses degraded? I measured them directly — they aren't. [R] Capacity in associative memory is usually treated as a storage problem — patterns superpose in a shared weight matrix until interference makes them unrecoverable. I built a sparse binary assembly network where the weights can be read exactly at any point (4-bit counters whose… 30 arXiv — Machine Learning research 19d ago Finite Constant Frontiers and Auditable Regret Certificates for Average-Reward Reinforcement Learning arXiv:2608.07725v1 Announce Type: new Abstract: Average-reward reinforcement-learning regret is known up to logarithmic factors, but the numerical content of published guarantees is difficult to compare because probability mode, structural parameter, logarithmic normalization,… 24 arXiv — Machine Learning research 19d ago LLM-Based Embeddings for Program Analysis and Optimization arXiv:2608.07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs. We present the first application of program embeddings from LLMCompiler---an LLM… 31 arXiv — Machine Learning research 19d ago Quality-Diversity Stress Tests for Process Reward Models:What Archive Coverage Can and Cannot Certify arXiv:2608.08008v1 Announce Type: new Abstract: Process reward models (PRMs) score intermediate reasoning steps and are widely used for search, ranking, and training, but optimization can exploit these learned proxies by increasing reward while turning correct reasoning into… 15 arXiv — Machine Learning research 19d ago Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk arXiv:2608.08126v1 Announce Type: new Abstract: Credit scoring increasingly relies on models whose decision logic cannot be read off their parameters, in tension with supervisory expectations that adverse decisions be explainable. A common proposal closes that gap with a… 12 arXiv — Machine Learning research 19d ago SAGE: SLO-Aware Adaptive Retrieval for Production RAG Systems arXiv:2608.08237v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems in production operate under strict service level objectives (SLOs) on tail latency and infrastructure cost. However, standard retrieval pipelines rely on fixed retrieval budgets that… 12 arXiv — Machine Learning research 19d ago Out-of-Distribution Federated Distillation with Domain-Aware Proxy arXiv:2608.08525v1 Announce Type: new Abstract: Federated Learning is a distributed machine learning paradigm that trains a global model by aggregating local clients without sharing private data of each client. Federated Distillation (FD) builds upon this paradigm by leveraging… 29 arXiv — Machine Learning research 19d ago Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking arXiv:2608.08646v1 Announce Type: new Abstract: Trajectory-User Linking (TUL) aims to identify the owner of an anonymous trajectory from a set of candidate users, providing a basis for user mobility analysis and personalized location-aware services. Existing methods often learn… 6 arXiv — NLP / Computation & Language research 19d ago DocAtlas: Long-Document Understanding as Mutable-State Interaction arXiv:2608.07527v1 Announce Type: new Abstract: Long-document understanding requires models to find and combine evidence across many pages, layouts, tables, figures, and charts. Existing retrieval-augmented systems usually select evidence from a static index before generation,… 29 arXiv — NLP / Computation & Language research 19d ago Embedding Initialization for Unseen Low-resource Languages in Multilingual NMT: A Case Study on Limbum-English Translation arXiv:2608.07629v1 Announce Type: new Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon. When fine-tuning these models for an unseen language,… 18 arXiv — NLP / Computation & Language research 19d ago Prompt Embedding Probes (PEP): Hallucination Detection in LLMs from Hidden States arXiv:2608.08024v1 Announce Type: new Abstract: Large language models (LLMs) can generate fluent and useful responses but remain prone to hallucinations. We introduce Prompt Embedding Probes (PEP), a white-box method for answer-level hallucination detection from the hidden… 33 arXiv — NLP / Computation & Language research 19d ago Explicit Boundary Markers for Subword Vocabularies arXiv:2608.08847v1 Announce Type: new Abstract: Subword tokenizers represent many common words twice in space-using writing systems, once with a leading space and once without. The two entries have separate embeddings in models, so occurrences of one word are divided across rows… 37 arXiv — NLP / Computation & Language research 19d ago Verifiably grounded machine interpretation of lunar geology arXiv:2608.09276v1 Announce Type: new Abstract: Planetary geology relies on historical, interpretive reasoning to reconstruct past events from diverse observations. Here, we present a step toward an automated "machine intelligence geologist" by embedding this distinct… 33 arXiv — NLP / Computation & Language research 19d ago Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law arXiv:2608.09393v1 Announce Type: new Abstract: We identify and quantify temporal misgrounding: the systematic retrieval and citation of the currently in-force version of a legal article when the applicable version is an earlier or future one. Standard legal RAG treats the… 17 arXiv — NLP / Computation & Language research 19d ago Pragmatic Attack Surface: Vulnerabilities of Implicit Context in Large Language Models arXiv:2608.09551v1 Announce Type: new Abstract: In the era of large language models (LLMs), attackers often manipulate natural language to elicit unsafe or harmful outputs, creating a new natural language attack surface unique to LLM-based systems, where attacks directly exploit… 26 arXiv — NLP / Computation & Language research 19d ago PragMatch: Separating Pragmatic Incongruity from Cross-Modal Mismatch in Large Vision-Language Models arXiv:2608.09772v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have demonstrated strong performance on multimodal benchmarks, yet it remains unclear whether they genuinely reason about relationships between images and text or rely on superficial… 8 arXiv — NLP / Computation & Language research 19d ago KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs arXiv:2608.09779v1 Announce Type: new Abstract: Answering complex conditional questions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) remains a challenge, particularly in domain-specific contexts where general-purpose LLMs and RAG tend to… 9 Hugging Face Daily Papers research 19d ago RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States Abstract RoMeRL reduces trajectory-indexed memory utilities to fixed-dimensional per-task states to concentrate feedback, avoid reward contamination, and improve self-evolving LLM agent performance. Generated by thinkingmachines/Inkling-Small Learning-based memory systems for… 36 Hugging Face Daily Papers research 19d ago SPOT: Sparse Probing and Outcome Calibration for On-Policy Distillation Abstract SPOT improves on-policy distillation by selectively probing uncertain positions and calibrating targets to downstream outcomes, boosting reasoning quality and coverage. Generated by thinkingmachines/Inkling-Small On-policy distillation (OPD) provides dense teacher… 19 r/MachineLearning community 20d ago Comparing embedding models with synthetic query probing [R] Say you want to swap out your embedding models, for instance from ADA to Titan. Are these embedding models comparable? How do similarity score ranges compare? Where to put a threshold for minimum match when doing retrieval? Or more from a research point of view how can we relate… 11 arXiv — Machine Learning research 20d ago Risk-Aware Decision Policies for Agents Under Noisy Perception arXiv:2608.06420v1 Announce Type: new Abstract: Perception in biological systems is inherently noisy, requiring organisms to make decisions under uncertainty where misclassification can be costly or fatal. We present an Artificial Life predator-prey model of foraging under noisy… 23 arXiv — Machine Learning research 20d ago SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction arXiv:2608.06441v1 Announce Type: new Abstract: Full-graph GNN training delivers high accuracy but scales poorly on multi-server clusters due to heavy, irregular inter-node embedding exchanges. We present SNI-GNN, a SmartNIC-assisted full-graph training system that reduces… 21 arXiv — Machine Learning research 20d ago Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions arXiv:2608.06545v1 Announce Type: new Abstract: Distributionally robust Markov decision processes provide a principled framework for sequential decision making under model uncertainty. We study how many samples are necessary and sufficient to learn an $\varepsilon$-optimal… 12 arXiv — Machine Learning research 20d ago CrystalGRPO: Target-Aligned and Coverage-Preserving Reinforcement Learning for Flow-Based Crystal Structure Prediction arXiv:2608.06582v1 Announce Type: new Abstract: Flow-based generative models can efficiently produce candidate structures for crystal structure prediction (CSP), but their pretrained objectives do not directly optimize downstream target recovery. Reinforcement-learning… 33 arXiv — Machine Learning research 20d ago Sub-Quadratic Bisimulation Metrics via Approximate Nearest Neighbors: Coverage-Augmented Guarantees and Computable Two-Sided Certificates arXiv:2608.06762v1 Announce Type: new Abstract: Bisimulation metrics quantify behavioral similarity in Markov decision processes, but their Wasserstein fixed-point operator updates every state pair and incurs quadratic pairwise work. We give a certificate-carrying sub-quadratic… 18 arXiv — Machine Learning research 20d ago Understanding Differentiable Embeddings Through Differential and Integral Geometry arXiv:2608.06809v1 Announce Type: new Abstract: How can an analyst decide whether a nonlinear dimensionality reduction embedding can be trusted? Existing diagnostics provide only partial answers: projection glyphs characterize local sensitivity, map-continuity scores measure… 34 arXiv — Machine Learning research 20d ago MiCoPro: End-to-End Mixed Precision HW/SW Co-design with HW-aware Proxy Model arXiv:2608.06916v1 Announce Type: new Abstract: Quantized Neural Networks~(QNN) with low-bitwidth data have proven promising in efficient storage and computation on edge devices. To mitigate accuracy degradation while maximizing speedup, layer-wise mixed-precision… 31 arXiv — Machine Learning research 20d ago Every Cache Entry Earns Its Place: Global Allocation of Resolution and Coverage for KV Cache Compression arXiv:2608.07001v1 Announce Type: new Abstract: As large language models (LLMs) process increasingly long contexts, KV cache storage and repeated access have become a major bottleneck. Existing KV cache compression methods rely on predefined, fixed compression rules and are… 30 arXiv — Machine Learning research 20d ago Hyperbolic Graph Embedders for Link Prediction and Topology Reconstruction arXiv:2608.07029v1 Announce Type: new Abstract: Hyperbolic embeddings provide compact geometric representations of complex networks in hyperbolic spaces, but systematic comparisons of methods developed in machine learning, network science, and algorithmics remain rare. We… 18 arXiv — Machine Learning research 20d ago Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning arXiv:2608.07157v1 Announce Type: new Abstract: Sub-model federated learning lets resource-constrained clients train width-reduced versions of a global model, but existing methods allocate capacity by device resources alone. A natural next step, allocating capacity by each… 19 arXiv — Machine Learning research 20d ago TaskSense: Focusing on What Matters in World Models arXiv:2608.06544v1 Announce Type: cross Abstract: World models for visual control typically learn compact latent states by reconstructing observations, implicitly encouraging representations to preserve information across the entire visual input. However, task-relevant content… 9 arXiv — NLP / Computation & Language research 20d ago Separating Decision-Rule Misalignment from Readout-Coverage Limitations in Speech Language Models arXiv:2608.06409v1 Announce Type: new Abstract: Speech language models are increasingly evaluated on paralinguistic tasks by the accuracy of prompted answers, but answer accuracy combines failures at different stages of the audio-to-answer computation. We introduce a… 4 arXiv — NLP / Computation & Language research 20d ago Lost in Interpolation: Why Predictive Feedback Fails in Diffusion Language Models arXiv:2608.06529v1 Announce Type: new Abstract: Soft-masking accelerates the convergence of Masked Diffusion Language Models (MDLMs). Existing formulations build this blend with linear interpolation (LERP) in the raw embedding space, which implicitly treats that space as… 31 arXiv — NLP / Computation & Language research 20d ago TA-RAG: Tone Awareness as a Design Imperative for Retrieval-Augmented Generation arXiv:2608.06672v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a robust architecture for grounding large language models (LLMs) in trusted knowledge. However, standard RAG systems exhibit a structural limitation: retrieved documents carry their… 34 arXiv — NLP / Computation & Language research 20d ago Calibrating WEAT Against Anisotropy: ZCA Whitening as a Geometric Pre-Processing Step for Embedding Association Tests arXiv:2608.06908v1 Announce Type: new Abstract: We propose Zero-phase Component Analysis (ZCA) whitening as a geometric pre-processing step for the Word Embedding Association Test (WEAT). WEAT is a bias measurement method widely used in both computational social science and AI… 27 arXiv — NLP / Computation & Language research 20d ago Does More Retrieved Evidence Help Visual Retrieval-Augmented Generation with Diffusion Language Models? arXiv:2608.07006v1 Announce Type: new Abstract: Visual retrieval-augmented generation (RAG) commonly expands the retrieved evidence set to improve answer-page coverage, implicitly assuming that all available evidence should be passed to the generator. We show that this… 35 arXiv — NLP / Computation & Language research 20d ago HNR-DAC: Hard-Negative Reranking and Distribution-Aligned Classification for Scientific Claim Verification arXiv:2608.07204v1 Announce Type: new Abstract: Scientific claim verification over a cited paper requires predicting the claim--paper relation and identifying the paragraphs that justify that prediction. This setting poses two linked challenges: within-paper distractors often… 10 arXiv — NLP / Computation & Language research 20d ago Measuring Concept Content in Text from LLM Activations: ESG Evidence from Concept Vectors and Linear Probes arXiv:2608.07208v1 Announce Type: new Abstract: Existing measures of how much a text is about a concept read the surface of the text: dictionary word shares, topic proportions, embedding similarities. They score the words a text uses, not the judgment a reader forms about it.… 11 arXiv — NLP / Computation & Language research 20d ago LitTraceQA: A Benchmark for Multi-Stage Grounding and Verification in Scientific Question Answering arXiv:2608.07370v1 Announce Type: new Abstract: Scientific literature is increasingly used as a knowledge source for language models, retrieval-augmented generation systems, and research assistants, but answering research questions from papers requires more than fluent… 15 arXiv — NLP / Computation & Language research 20d ago CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG arXiv:2608.07458v1 Announce Type: new Abstract: Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved contexts for higher efficiency, while significant information redundancy and noise… 35 arXiv — NLP / Computation & Language research 20d ago DocMemo: Dynamic Evidence Discovery via Probabilistic Memory-Guided Retrieval for Multi-Modal Document Understanding arXiv:2608.07067v1 Announce Type: cross Abstract: Long-document understanding requires locating sparse and heterogeneous evidence across hundreds of pages, yet existing systems remain limited by static retrieval and fragile cross-round memory. Mainstream single-round methods… 34 arXiv — NLP / Computation & Language research 20d ago Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction arXiv:2411.00028v3 Announce Type: replace Abstract: Socioeconomic prediction aims to leverage various urban data to predict the socioeconomic indicators of regions such as population and commercial activity level, which plays an important role in understanding urban regions and… 8 Page 6 of 10 · 500 articles ← Newer Older →
