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 Machine Learning

Aug 4

Crushing the Evidence: A Dual-Penalty Evasion Framework for Fooling White-Box Explainable AI Auditors

arXiv:2608. 00566v1 Announce Type: new Abstract: Post-hoc model explainers such as LIME, SHAP, and Integrated Gradients are widely deployed to audit models in high-stakes sensitive domains, including finance, healthcare, and social welfare.

By Niraj Kumar, Harsh Kasyap

rag benchmarks safety

arXiv Machine Learning

Aug 4

Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget

arXiv:2608. 00916v1 Announce Type: cross Abstract: Modern reranking recipes---billion-scale cross-encoders, mixture-of-experts (MoE) backbones, and distillation against strong teachers---have outpaced the training infrastructure available to most academic groups.

By Zhichao Xu, Xueguang Ma, Shengyao Zhuang, Luyu Gao, Wenqian Ye, Yu Wang, Jamie Callan, Jimmy Lin

llms rag fine-tuning efficiency benchmarks data-engineering

arXiv Machine Learning

Aug 4

Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations

arXiv:2607. 24834v2 Announce Type: replace-cross Abstract: Objective.

By Marzieh Zare

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arXiv Machine Learning

Aug 4

H+ Embedding: Harmonizing Global and Token-Level Retrieval with Context-Dependent Phrases

arXiv:2608. 00065v1 Announce Type: cross Abstract: Terminology-intensive retrieval, especially in medical settings, depends on preserving multi-word entities, abbreviations, numerical constraints, and compositional concepts.

By Shusen Zhang, Junyi Hu, Ye Feng, Ziteng Wang, Zhaoyuan Pan, Guosheng Dong, Xiaojun Yuan, Jiangshou Hong, Xiangzhi Wang

rag nlp

arXiv Machine Learning

Aug 4

Verification Without Sufficiency: Per-Chunk Filtering Fails on Multi-Hop RAG, and Decomposition Repairs It

arXiv:2608. 00585v1 Announce Type: cross Abstract: Verification for retrieval-augmented generation usually scores each retrieved chunk and drops the ones that fail.

By Randhir Kumar

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arXiv Machine Learning

Aug 4

How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule

arXiv:2608. 01268v1 Announce Type: cross Abstract: Detecting that a stream of high-dimensional embeddings has changed is usually framed as a choice of statistic.

By Adel Kaleche

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arXiv Machine Learning

Aug 4

What EEG Foundation Models Encode: Dataset Identity and a Negative-Control Suite for Clinical Benchmarks

arXiv:2607. 24519v2 Announce Type: replace Abstract: Pretrained EEG foundation models are proposed for clinical decoding, but whether reported gains transfer across populations or survive negative controls is unclear.

By Marzieh Zare

rag fine-tuning benchmarks

arXiv Machine Learning

Aug 4

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning

arXiv:2608. 02348v1 Announce Type: cross Abstract: Malware clustering is a critical task in cybersecurity that helps discover threats and analyze evolving malware families.

By Martin Mocko, Daniela Chud\'a

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arXiv Machine Learning

Aug 4

MUSS: Multilevel Subset Selection for Relevance and Diversity

arXiv:2503. 11126v4 Announce Type: replace Abstract: The problem of relevant and diverse subset selection has a wide range of applications, including recommender systems and retrieval-augmented generation (RAG).

By Vu Nguyen, Andrey Kan

rag nlp

arXiv Machine Learning

Aug 4

Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models

arXiv:2506. 07121v2 Announce Type: replace Abstract: Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible deployment of artificial intelligence.

By Ren-Jian Wang, Ke Xue, Zeyu Qin, Ziniu Li, Sheng Tang, Hao-Tian Li, Shengcai Liu, Zhi Yu, Yuanpeng Tan, Chao Qian

llms rag safety

arXiv Machine Learning

Aug 4

TextNCA: Neural Cellular Automata for Language Modeling via Hierarchical Local Attention

arXiv:2608. 02050v1 Announce Type: cross Abstract: Can a strictly local, iterated, weight-shared computation primitive support language modelling, and which of those three properties actually drives the model's behaviour?

By Avni Mittal, Avinash Anand, Ashutosh Kumar, Dikshant Kukreja, Kritarth Prasad, Sushane Dulloo, Erik Cambria, Timothy Liu, Zhengkui Wang, Rajiv Ratn Shah

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arXiv Machine Learning

Aug 4

ChaosProbe: A Neurochaotic Lens on Frozen Transformer Input-Embedding Spaces

arXiv:2608. 01968v1 Announce Type: new Abstract: Transformer models are most often understood through what they do: their benchmark performance, generation quality, or behavior on downstream tasks.

By Kunal Kumar Pant, Nithin Nagaraj

llms rag benchmarks

arXiv Machine Learning

Aug 4

LAYA: Layer-wise Attention Aggregation for Interpretable Depth-Aware Neural Networks

arXiv:2511. 12723v2 Announce Type: replace Abstract: Deep neural networks typically rely on the representation produced by their final hidden layer to make predictions, implicitly assuming that this single vector fully captures the semantics encoded across all preceding transformations.

By Gennaro Vessio

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arXiv Machine Learning

Aug 4

Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval

arXiv:2608. 01481v1 Announce Type: new Abstract: Short segments of perceived speech can be retrieved from non-invasive magnetoencephalographic (MEG) recordings by deep networks trained with a CLIP-style objective against wav2vec 2.

By Ilia Semenkov, Daria Kleeva, Ivan Dakhtin, Zarina Maksudova, Alex Ossadtchi

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arXiv Machine Learning

Aug 4

RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding

arXiv:2608. 00147v1 Announce Type: cross Abstract: Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared embedding space, so concept-level structure and interpretability must be recovered post hoc, limiting model transparency and, hence, clinical utility.

By Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu, Miriam Kumpf, Lena Schmitzer, Lea Schumann, Jannik Kahmann, Friedrich Puttkammer, Johannes Moll, Jannik L\"ubberstedt, Zeineb Ben Chaaben, Anirudh Narayanan, Cosmin I. Bercea, Sebastian Ziegelmayer, Marcus R. Makowski, Daniel Rueckert, Lisa C. Adams, Keno K. Bressem

llms rag multimodal safety

arXiv Machine Learning

Aug 4

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection

arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.

By Patrick Helm, Jan-Niklas Doerr, Joren Gijsbrechts, Stefan Minner

rag reinforcement-learning benchmarks

arXiv Machine Learning

Aug 4

Nonparametric Distribution Regression Re-calibration

arXiv:2602. 13362v2 Announce Type: replace-cross Abstract: A key challenge in probabilistic regression is ensuring that predictive distributions accurately reflect true empirical uncertainty.

By \'Ad\'am Jung, Domokos M. Kelen, Andr\'as A. Bencz\'ur

rag benchmarks safety

arXiv Machine Learning

Aug 4

Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments

arXiv:2608. 00419v1 Announce Type: new Abstract: Large language models deployed in real-time, regulated settings face knowledge staleness, catastrophic forgetting, hallucination, and weak feedback loops.

By Muhammad Faizan Raza (Luna), Shuo (Luna), Yang, Satish Mahadevan Srinivasan, Joanna F. DeFranco

llms rag reinforcement-learning safety

arXiv Machine Learning

Aug 4

Visualising Information Flow in Word Embeddings with Diffusion Tensor Imaging

arXiv:2601. 05713v2 Announce Type: replace-cross Abstract: Understanding how large language models (LLMs) represent natural language is a central challenge in natural language processing (NLP) research.

By Thomas Fabian

llms rag diffusion nlp efficiency safety

arXiv Machine Learning

Aug 4

Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data

arXiv:2608. 01336v1 Announce Type: cross Abstract: Modern autonomous-driving fleets record far more video than human reviewers can inspect.

By Advait Pavuluri, Shamik Karkhanis, Uzma Mushtaque

rag agents fine-tuning benchmarks

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