Search: “rag”

FinExam-10K: When Retrieval Helps Financial Reasoning?

arXiv:2608.28155v1 Announce Type: new Abstract: Professional financial examinations require models to combine domain knowledge, calculation, and judgment, yet no benchmark covers the full CFA and FRM

31 Aug 2026

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

My conversation with @jerryjliu0. He's building @llama_index: document infrastructure for AI agents, the layer underneath everything models …

DGX agent

My conversation with @jerryjliu0. He's building @llama_index: document infrastructure for AI agents, the layer underneath everything models need to work. Did you know he's the guy whose tool half the

agentsjerry-liu--x

31 Aug 2026

Knowledge-Graph-Gated Defactualization for Style-Controllable and Fact-Preserving Generation in Agentic Conversational AI

arXiv:2608.20393v1 Announce Type: cross Abstract: Agentic large language models (LLMs) deployed in fact-sensitive applications such as customer support must simultaneously preserve factual correctness

24 Aug 2026

D2-ScaleAgent: Dual-Dimensional Scaling for Long Document Understanding

arXiv:2608.16417v1 Announce Type: new Abstract: Multi-modal retrieval-augmented generation (RAG) is a key technique for visually rich long document understanding. Existing multi-modal RAG methods are

agentsarxiv-cs-cl

18 Aug 2026

When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation

arXiv:2608.16515v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trus

safetyarxiv-cs-ai

18 Aug 2026

Adaptive Stopping for Multi-Turn LLM Reasoning

arXiv:2604.01413v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly rely on multi-turn reasoning and interaction, such as adaptive retrieval-augmented generation (RAG)

applicationsarxiv-cs-ai

17 Aug 2026

Self-Knowledge Retrieval Augmented Generation Framework for Patent Matching

arXiv:2608.11030v1 Announce Type: cross Abstract: Patent retrieval and matching based on large language models (LLMs) play a vital role in intellectual property protection. However, due to the complex

12 Aug 2026

Bounding Hallucinations: Merlin-Arthur Protocols for Mutual-Information Bounds in Language Models

arXiv:2512.11614v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on retrieved context to guide large language models (LLM), yet treats the retrieval as a heuristic

agentsarxiv-cs-ai

11 Aug 2026

KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs

arXiv:2608.09779v1 Announce Type: cross Abstract: Answering complex conditional questions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) remains a challenge, particularly

11 Aug 2026

Bridging the Question-Answer Gap in Retrieval-Augmented Generation: Hypothetical Prompt Embeddings

arXiv:2607.29402v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems synergize retrieval mechanisms with generative language models to enhance the accuracy and relevance of r

safetyarxiv-cs-cl

3 Aug 2026

Agent-UCT: Upper Confidence Bounds Applied to Trees for Agentic Workflow Optimization with Cost-Awareness

arXiv:2607.24162v1 Announce Type: new Abstract: Optimizing agentic workflows, such as retrieval-augmented generation (RAG) pipelines, requires navigating a combinatorial space of discrete component ch

agentsarxiv-cs-ai

28 Jul 2026

SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs

arXiv:2607.22571v1 Announce Type: new Abstract: Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) enables natural language interaction with structured enterprise knowledge, yet existing ag

28 Jul 2026

TokenMem: Faithful Knowledge Injection for Frozen LLMs

arXiv:2607.22625v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge, but suffers from knowledge conflicts: when retrieved

28 Jul 2026

Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation

arXiv:2601.11443v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for enhancing large language models' question-answering capabilities through

15 Jul 2026

Pinecone has introduced Nexus, a knowledge engine built specifically for AI agents. Instead of repeatedly searching documents with tradition…

Pinecone has introduced Nexus, a knowledge engine built specifically for AI agents. Instead of repeatedly searching documents with traditional RAG, Nexus compiles knowledge once from sources like data

agentspinecone--x

13 Jul 2026

ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering

arXiv:2606.29706v1 Announce Type: cross Abstract: Telecom question answering (QA) is a challenging setting for retrieval-augmented generation (RAG): evidence is fragmented across standards, papers, en

30 Jun 2026

Query-Aware Spreading Activation for Multi-Hop Retrieval over Knowledge Graphs

arXiv:2606.30133v1 Announce Type: cross Abstract: Retrieval-augmented generation built on knowledge graphs (Graph RAG) outperforms flat passage retrieval on multi-hop question answering by leveraging

researcharxiv-cs-ai

30 Jun 2026

Diagnosing and Mitigating Compounding Failures in Agentic Persuasion via Taxonomic Strategy Retrieval

arXiv:2606.24976v1 Announce Type: cross Abstract: Foundation-model agents in multi-step, open-ended environments frequently suffer from compounding errors, where early mistakes contaminate long-horizo

agentsarxiv-cs-cl

25 Jun 2026

Epistemic Bias Injection: Manipulating LLM Opinion via Selective Context Retrieval

arXiv:2512.00804v3 Announce Type: replace-cross Abstract: When answering user queries, LLMs often retrieve knowledge from external sources stored in retrieval-augmented generation (RAG) databases. The

25 Jun 2026

Learning to Erase Private Knowledge from Multi-Documents for Retrieval-Augmented Large Language Models

arXiv:2504.09910v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) is a promising technique for applying LLMs to proprietary domains. However, retrieved documents may contain sen

25 Jun 2026

Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents

arXiv:2606.09316v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative ev

agentsarxiv-cs-ai

9 Jun 2026

When Knowledge Is Not Free: Cost-Aware Evidence Selection in Retrieval-Augmented Generation

arXiv:2606.02245v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) typically assumes that external knowledge is free, but many high-quality sources are paywalled, licensed, restricte

agentsarxiv-cs-cl

2 Jun 2026

Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding

arXiv:2512.17220v2 Announce Type: replace Abstract: Humans understand long and complex texts by relying on a holistic semantic representation of the content. This global view helps organize prior know

29 May 2026

DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling

arXiv:2510.21712v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs) through the dyna

19 May 2026

A Picture is Worth a Thousand Words? An Empirical Study of Aggregation Strategies for Visual Financial Document Retrieval

arXiv:2605.14581v1 Announce Type: cross Abstract: Visual RAG has offered an alternative to traditional RAG. It treats documents as images and uses vision encoders to obtain vision patch tokens. Howeve

15 May 2026

Safety and accuracy follow different scaling laws in clinical large language models

arXiv:2605.04039v1 Announce Type: new Abstract: Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation

6 May 2026

Benchmarking Retrieval Strategies for Biomedical Retrieval-Augmented Generation: A Controlled Empirical Study

arXiv:2605.02520v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) offers a well-established path to grounding large language model (LLM) outputs in external knowledge, yet the quest

5 May 2026

Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation

arXiv:2605.01302v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) systems predominantly rely on semantic relevance as a proxy for utility. However, this assumption collapse

safetyarxiv-cs-cl

5 May 2026

Reducing Redundancy in Retrieval-Augmented Generation through Chunk Filtering

arXiv:2604.24334v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) chunking methods often create excessive redundancy, increasing storage costs and slowing retrieval. This s

28 Apr 2026

RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA

arXiv:2510.20505v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) remains brittle on multi-step questions and heterogeneous evidence sources, trading accuracy against late

safetyarxiv-cs-ai

24 Apr 2026

Self-Aware Vector Embeddings for Retrieval-Augmented Generation: A Neuroscience-Inspired Framework for Temporal, Confidence-Weighted, and Relational Knowledge

arXiv:2604.20598v1 Announce Type: cross Abstract: Modern retrieval-augmented generation (RAG) systems treat vector embeddings as static, context-free artifacts: an embedding has no notion of when it w

23 Apr 2026

GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoning

arXiv:2508.05498v2 Announce Type: replace Abstract: Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) techniques have exhibited remarkable performance across a wide ran

safetyarxiv-cs-ai

22 Apr 2026

CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain

arXiv:2603.05569v2 Announce Type: replace-cross Abstract: Extracting insights from Electronic Health Record (EHR) databases often requires SQL expertise, creating a barrier for clinical decision-makin

21 Apr 2026

RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extraction

arXiv:2504.07415v2 Announce Type: replace-cross Abstract: Automated radiology report generation (RRG) holds potential to reduce the workload of radiologists, and recent advances in multimodal large la

21 Apr 2026

Hierarchical Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text

arXiv:2604.14166v1 Announce Type: new Abstract: Mapping Cyber Threat Intelligence (CTI) text to MITRE ATT&CK technique IDs is a critical task for understanding adversary behaviors and automating threa

safetyarxiv-cs-cl

17 Apr 2026

Tutorials

Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning

arXiv:2604.11407v1 Announce Type: cross Abstract: We revisit retrieval-augmented generation (RAG) by embedding retrieval control directly into generation. Instead of treating retrieval as an external

tutorialsarxiv-cs-ai

14 Apr 2026

Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation

arXiv:2604.08046v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing access to external knowledge. However, current res

10 Apr 2026

Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval

model-releasesarxiv-cs-cv

arXiv:2512.08410v2 Announce Type: replace Abstract: Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose

10 Apr 2026

Local Ai

ARMED: Adaptive Retrieval-Augmented Medication Recommendation

arXiv:2610.16440v1 Announce Type: new Abstract: Medication recommendation requires jointly considering diagnoses, comorbidities, longitudinal patient history, and treatment interactions. Although rece

local-aiarxiv-cs-lg

14 Oct 2026

Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation

arXiv:2603.07233v2 Announce Type: replace Abstract: Predicting transcriptional responses to genetic perturbations is fundamental to functional genomics and therapeutic discovery. Recent deep learning

researcharxiv-cs-lg

9 Oct 2026

Local Ai

Built an SQLite memory engine for Ollama that does not eat all your VRAM

Wanted to share a local memory engine I built called Hillock. The main problem I had with standard local RAG was that running document parsing and vector search chewed up so much VRAM that my actual O

local-air-ollama

7 Oct 2026

OmniConfess: Eliciting Token Confessions to Mitigate Omni-Modal Hallucination

arXiv:2610.02999v1 Announce Type: new Abstract: Omni-modal large language models (OmniLLMs) unify text, images, audio, and video, yet hallucinate when generation relies on the wrong evidence. Existing

5 Oct 2026

Local Ai

Replacing vector databases with SQLite and SIMD hypervectors in under 1.2GB VRAM (Hillock)

Disclosure: I am the creator of this project. After days of lurking and building up enough karma, I can finally post here. Every time I tried running local RAG on my own machine, I hit the exact same

local-air-localllama

3 Oct 2026

Beyond the Context Window: An Adaptive Entropy-Based Routing Framework for Hybrid Retrieval and Long-Context Language Models

arXiv:2609.35831v1 Announce Type: new Abstract: Modern large language models now support context windows of more than one million tokens, which has raised the question of whether retrieval-augmented g

30 Sept 2026

Retrieve, Reproduce, Reveal: Dissecting Retrieval-Augmented Software Vulnerability Detection

arXiv:2609.37669v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large Language Model (LLM)-based software vulnerability detection by grounding pr

30 Sept 2026

if this takes off, then i was 4 years too early 😭 https://x.com/jerryjliu0/status/1590192512639332353 only OGs remember gpt tree index

if this takes off, then i was 4 years too early 😭 https://x.com/jerryjliu0/status/1590192512639332353 only OGs remember gpt tree index The entire RAG industry is about to get cooked. Researchers devel

agentsjerry-liu--x

28 Sept 2026

Where Does Retrieval-Based Open-Ended Evaluation Fail? Automatic Taxonomy Induction from Long-Form Medical Answer Factuality Verification

arXiv:2609.30467v1 Announce Type: new Abstract: Retrieval-based factuality evaluation, where LLM-generated claims are verified against evidence from authoritative medical corpora, has become the domin

28 Sept 2026

Great paper from Google and colleagues. Trains Text-to-SQL agents using multi-agent RL. (bookmark it) This work proposes DualSQL, which spli…