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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Towards Data Science

Jun 2

From Regex to Vision Models: Which RAG Technique Fits Which Problem

Enterprise Document Intelligence [Vol. 1 #4] - A diagnostic across PDFs and questions, and a map of the techniques the rest of the series will cover The post From Regex to Vision Models: Which RAG Technique Fits Which Problem appeared first on Towards Data Science .

By angela shi

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Hugging Face Trending Papers

Jun 2

$A^2$: Smaller Self-Supervised ViTs Localize Better than Larger Ones

Robust visual classification often depends on localizing the main foreground objects in an image while ignoring contextual distractors. Surprisingly, we find that the attention maps of smaller self-supervised ViTs localize foreground objects better than those of larger ViTs.

rag benchmarks

arXiv AI

Jun 2

"I've Seen How This Goes": Characterizing Diversity via Progressive Conditional Surprise

arXiv:2606. 01811v1 Announce Type: cross Abstract: Measuring the diversity of creative outputs is central to evaluating post-training mode collapse, comparing decoding strategies, and quantifying creative behavior in both AI and human writing.

By Matthew Khoriaty, David Williams-King, Shi Feng

llms rag reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models

arXiv:2605. 04638v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate.

By Mingda Li, Rundong Lv, Xinyu Li, Weinan Zhang, Ting Liu

llms rag benchmarks

arXiv Machine Learning

Jun 2

Beyond the Simplex: Balanced Prototype Geometry for Scorer-Agnostic Open-Set Recognition

arXiv:2606. 01883v1 Announce Type: new Abstract: Open-set recognition (OSR) requires a classifier to reject inputs from unseen classes which is essential in safety-critical settings such as medical imaging.

By Mayank Sharma, Rohit Kumar Mourya

rag benchmarks safety

arXiv Machine Learning

Jun 2

Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents

arXiv:2605. 28850v2 Announce Type: replace Abstract: We study behavioral alignment and representation dynamics of large language model (LLM) agents in financial decision environments.

By Weicheng Xue

llms rag agents fine-tuning safety

arXiv Machine Learning

Jun 2

Massive Spikes in LLMs are Bias Vectors: Mechanistic Uncovering and Spike-Free Quantization

arXiv:2606. 02288v1 Announce Type: new Abstract: Massive activation spikes in Large Language Models (LLMs) severely degrade quantization by stretching dynamic ranges.

By Yung-Chin Chen, Chung Peng Lee, Ze-Wei Liou, Naveen Verma

llms rag efficiency benchmarks safety

arXiv Machine Learning

Jun 2

Beyond Sinusoids: A Morlet Wavelet Framework for Transformer Positional Encoding

arXiv:2606. 01258v1 Announce Type: new Abstract: Standard positional encodings for transformers - sinusoidal and rotary (RoPE) - treat every position as equally local: they encode where a token is, but not how far its positional influence should extend.

By Athanasios Zeris

llms rag

arXiv Machine Learning

Jun 2

CART: Context-Anchored Recurrent Transformer -- A Parameter-Efficient Architecture with Learned Stability

arXiv:2606. 01495v1 Announce Type: new Abstract: We present CART (Context-Anchored Recurrent Transformer), a parameter-efficient language model that reuses a single shared core block R times across depth.

By Chad A. Capps

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arXiv AI

Jun 2

Detect Before You Leap: Mirage Detection in Vision-Language Models

arXiv:2606. 00435v1 Announce Type: cross Abstract: Vision-language models (VLMs) can produce confident visual answers even when the required visual evidence is missing, blank, or unrelated to the question.

By Sayeed Shafayet Chowdhury, Md. Shaown Miah

llms rag nlp multimodal safety

arXiv Machine Learning

Jun 2

MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment

arXiv:2605. 29987v2 Announce Type: replace Abstract: Although multi-scales representation learning enables elastic-dimension embeddings, nested subspaces often suffer from dimensional redundancy and spectral collapse.

By Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu Pham

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arXiv Machine Learning

Jun 2

PaCX-MAE: Physiology-Augmented Chest X-Ray Masked Autoencoder

arXiv:2606. 01537v1 Announce Type: cross Abstract: Clinical diagnosis often requires combining imaging with physiological measurements, yet deployed models typically operate on unimodal data.

By Yancheng Liu, Kenichi Maeda, Manan Pancholy

rag computer-vision efficiency multimodal 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

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arXiv AI

Jun 2

Beyond String Matching: Semantic Evaluation of PDF Table Extraction

arXiv:2603. 18652v2 Announce Type: replace-cross Abstract: Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluation approaches rely on rule-based metrics that fail to capture semantic equivalence of table content.

By Pius Horn, Janis Keuper

llms rag benchmarks

arXiv AI

Jun 2

GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks

arXiv:2606. 01560v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges in assortative graphs and assortative edges in disassortative graphs.

By Canyixing Cui, Tao Wu, Xingping Xian, Xiao-Ke Xu, Mao Wang, Weina Niu

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arXiv AI

Jun 2

Language Model Networks: Supervision-Efficient Learning through Dense Communication

arXiv:2505. 12741v3 Announce Type: replace Abstract: Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time scaling to multi-agent collaboration.

By Shiguang Wu, Yaqing Wang, Quanming Yao

llms rag agents

arXiv AI

Jun 2

MLLM-Microscope: Unlocking Hidden Structure Within Multimodal Large Language Models

arXiv:2606. 00909v1 Announce Type: cross Abstract: This work presents MLLM-Microscope, a novel system designed for analyzing the hidden representations within Multimodal Large Language Models (MLLMs).

By Ravil Mussabayev, Rustam Mussabayev

llms rag multimodal benchmarks

arXiv AI

Jun 2

Self-Conditioned Positional HNSW for Overlap-Aware Retrieval in Chunked-Document RAG Systems: Method and Industrial Evidence-Quality Audit

arXiv:2606. 01542v1 Announce Type: cross Abstract: Chunked-document retrieval is a common component of retrieval-augmented generation (RAG) systems.

By Nataraj Agaram Sundar, Tejas Morabia

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arXiv AI

Jun 2

UR-JEPA: Uniform Rectifiability as a Regularizer for Joint-Embedding Predictive Architectures

arXiv:2606. 01443v1 Announce Type: cross Abstract: A central difficulty in training Joint-Embedding Predictive Architectures (JEPAs) is preventing representation collapse.

By Triet M. Le

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