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 Computer Vision

Aug 21

G3Ego: Gaze-Guided Graphs for Egocentric Action Understanding

arXiv:2608. 20157v1 Announce Type: new Abstract: Egocentric action understanding is often addressed using large video models pretrained on extensive exocentric datasets.

By Marko Haralovi\'c, Akash Ramakrishnan, Estefania Talavera Martinez

rag multimodal

arXiv Machine Learning

Aug 21

Which Eviction Policy Should an LLM Cache Use? A Systematic Study Across Workloads, Capacities, and Encoders

arXiv:2608. 20280v1 Announce Type: cross Abstract: Semantic caches reuse an LLM response when the incoming query embedding lies near a cached query, but proposed eviction policies have rarely been compared under one protocol.

By Yash Kulkarni, Shubham Harkare, Arvind Suresh Yogesh Babu

llms rag

arXiv AI

Aug 21

The First Drop of Ink: Nonlinear Impact of Distracting Information in Long-Context Reasoning

arXiv:2605. 10828v2 Announce Type: replace Abstract: As large language models are increasingly deployed in retrieval-augmented generation and agentic systems that accumulate extensive context, understanding how distracting information affects long-context performance becomes critical.

By Muhan Gao, Zih-Ching Chen, Kuan-Hao Huang

llms rag agents

arXiv Computer Vision

Aug 21

WithEveryone: Unified Planning and Identity Grounding for Group Image Generation

arXiv:2608. 20336v1 Announce Type: new Abstract: Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people.

By Hengyuan Xu, Qixun Wang, Yiji Cheng, Miles Yang, Zhao Zhong, Wei Cheng, Xingjun Ma, Yu-gang Jiang

llms rag diffusion benchmarks

arXiv Machine Learning

Aug 21

ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning

arXiv:2506. 07459v4 Announce Type: replace Abstract: Protein generative models have shown remarkable promise in protein design, yet their success rates remain constrained by reliance on curated sequence-structure datasets and by misalignment between supervised objectives and real design goals.

By Ziwen Wang, Jiajun Fan, Ruihan Guo, Thao Nguyen, Heng Ji, Ge Liu

rag reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 21

Automatic bioinformatic software named entity recognition from literature

arXiv:2608. 19201v1 Announce Type: cross Abstract: Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale.

By Hao Xuan, Rithvij Pasupuleti, Ben Liu, Haishuo Sun, Jun Zhang, Zijun Yao, Cuncong Zhong

llms rag nlp efficiency benchmarks

arXiv Computation and Language

Aug 21

Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment

arXiv:2608. 19825v1 Announce Type: cross Abstract: Medical image captioning is a technique that accelerates early-stage diagnostic workflows and enhances the interpretability of medical diagnostic AI systems.

By Yunseo Lee, Hyun Jun Kim, Heeseung Shin, Changwon Lim

llms rag multimodal safety data-engineering

arXiv Machine Learning

Aug 21

Far from the Crowd: Scalable Self-Supervised Learning via Geographic Isolation

arXiv:2608. 19766v1 Announce Type: cross Abstract: Self-supervised pretraining on remote sensing imagery typically treats all samples as equally informative, despite large variability in geographic and visual structure.

By Daniele Rege Cambrin, Francesco Rossi, Mattia Varile

rag benchmarks

arXiv AI

Aug 21

CADRE: Stable, Parameter Efficient Adaptation of Medical Vision Language Models with Bounded Forgetting and Prior Drift

arXiv:2606. 23487v2 Announce Type: replace Abstract: Medical vision-language models (VLMs) such as BiomedCLIP generalize broadly, but adapting them to a clinical service is as much a safety problem as an accuracy one.

By Rishabh Jha, Amrita Singh, Prashanna Chudal

llms rag fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Aug 21

PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening

arXiv:2608. 19906v1 Announce Type: new Abstract: Accurately ranking active ligands for a target protein pocket from massive chemical libraries remains a central challenge in virtual screening.

By Jia-Qi Lin, Yinghua Yao, Chang-Dong Wang, Yew-Soon Ong, Yuangang Pan

rag diffusion benchmarks

arXiv Computation and Language

Aug 21

Doc-V*:Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA

arXiv:2604. 13731v2 Announce Type: replace Abstract: Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents.

By Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang, Yuyi Zhang, Wenyu Ruan, Xiaojin Zhang, Zhongyu Wei, Zhenbo Luo, Jian Luan, Wei Chen, Xiang Bai

rag agents computer-vision nlp benchmarks

arXiv AI

Aug 21

SABET-QA: Temporal Knowledge Graph Question Answering

arXiv:2608. 20083v1 Announce Type: cross Abstract: Question Answering over Temporal Knowledge Graphs (TKGQA) requires reasoning over time-sensitive facts, yet existing embedding-based methods struggle with multi-step queries due to single-pass reasoning pipelines.

By Brahim Touayouch, Mirette Moawad, Dmitry Akulov

rag nlp

arXiv AI

Aug 21

GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance

arXiv:2508. 12682v2 Announce Type: replace Abstract: The global shift towards renewable energy presents unprecedented challenges for the electricity industry, making regulatory reasoning and compliance increasingly vital.

By Jinquan Shi, Yingying Cheng, Fan Zhang, Miao Jiang, Jun Lin, Yanbai Shen

llms rag benchmarks

arXiv Machine Learning

Aug 21

LODESTAR: Robust Entropy-Based Answer Selection in Retrieval-Augmented Generation for Question Answering -- Directing Frozen-LLM Entropy with a Reinforcement-Learned Prompt Polarizer under Misleading Passages

arXiv:2608. 11922v2 Announce Type: replace-cross Abstract: Predictive-distribution entropy is a strong answer-selection rule in retrieval-augmented generation (RAG) for question answering: across five QA benchmarks, selecting the answer a frozen respondent LLM produces with the lowest answer-token entropy lifts mean $F_1$ from 0.

By Hung-Chun Hsu, Po-Jen Ko, Che-Cheng Wu, Li-Yang Chang, Chuan-Ju Wang

llms rag nlp reinforcement-learning benchmarks

arXiv AI

Aug 21

Electronic Navigational Chart Change Classification

arXiv:2608. 20218v1 Announce Type: new Abstract: Electronic Navigational Charts (ENCs) are geospatial vector datasets used in maritime navigation systems that represent hydrographic and navigational information such as depths, navigational aids, traffic schemes, and hazards.

By Jacob Arndt, Abhishek Potnis, Alexandre Sorokine

rag safety

arXiv Computer Vision

Aug 21

DIFFCZSL: Compositional Zero-Shot Learning Regularized by Diffusion Representations

arXiv:2608. 19871v1 Announce Type: new Abstract: Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object compositions by leveraging knowledge of primitive concepts learned from seen compositions.

By Hangyu Tian, Zhenqi He, Yanghao Wang, Long Chen

llms rag diffusion multimodal benchmarks safety

arXiv AI

Aug 21

Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM-Simulated Expert Personas and Human Clinical Experts

arXiv:2508. 19008v3 Announce Type: replace Abstract: Building on a human-led thematic analysis of clinical life-story interviews (> 150,000 words) with inpatients with Borderline Personality Disorder, this study examines the capacity of large language models (OpenAI's GPT, Google's Gemini, and Anthropic's Claude) to support qualitative clinical analysis.

By Marcin Moskalewicz, Anna Sterna, Karolina Dro\.zd\.z, Kacper Dudzic, Marek Pokropski, Paula Flores

llms rag safety

arXiv Computation and Language

Aug 21

Linguistic Holonomy and Statistical Watermarks: Inner Geometry of Meaning-Preserving Transformations

arXiv:2608. 19369v1 Announce Type: new Abstract: Statistical watermarks for language models live in the freedom of the signifier: they choose among tokens that are nearly equivalent in meaning, and they are therefore eroded by exactly those transformations which move the form of a text while leaving its content in place.

By Daniele Corradetti

llms rag

arXiv AI

Aug 21

Fairness-Aware Network Embeddings: Methods, Applications, and Challenges

arXiv:2608. 19381v1 Announce Type: cross Abstract: Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization.

By Ella Has, Harshith Kumar Yadav, Gaurav Dixit, Mykola Pechenizkiy, Akrati Saxena

rag safety

arXiv AI

Aug 21

Can Agent Memory Systems Track Evolving State?

arXiv:2608. 19652v1 Announce Type: new Abstract: As LLM-based agents are deployed for longer and higher-stakes tasks, their memory systems continue to have crucial gaps.

By Xinyi Fan, Miri Liu, Ruozhen Yang, Siru Ouyang, Jiawei Han

llms rag agents benchmarks

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