Search: “retrieval”

Skill-Contracted Agents for Evidence-Aware Materials Literature Analysis

arXiv:2607.20431v1 Announce Type: new Abstract: Materials science literature analysis requires simultaneous attention to composition, processing, characterization, and property relationships, yet conv

agentsarxiv-cs-cl

24 Jul 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents

DGX agent

arXiv:2607.13157v1 Announce Type: new Abstract: Agent memory is a systems problem for long-horizon agents. Practical deployments require retention of task state across extended conversations, recovery

agentsarxiv-cs-ai

16 Jul 2026

CANDI: Contextual Alignment for Niche Domains Question Answering

arXiv:2607.11891v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) in specialized domains like medical diagnostics and financial advisory necessitates evaluating capabili

15 Jul 2026

Token Reduction Is Not Cost Reduction

arXiv:2607.12161v1 Announce Type: new Abstract: Context-reduction layers for API-based coding agents, including command-output compressors, retrieval rankers, and payload-optimizing proxies, are usual

15 Jul 2026

Transforming LLMs into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking

arXiv:2607.11933v1 Announce Type: new Abstract: Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-ti

15 Jul 2026

Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing

arXiv:2607.08497v1 Announce Type: cross Abstract: Recent unified multimodal models show a single architecture can jointly perform vision/language understanding and image generation/editing. However, t

10 Jul 2026

PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs

arXiv:2607.08269v1 Announce Type: new Abstract: Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML. We prese

10 Jul 2026

Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search

arXiv:2607.05970v1 Announce Type: cross Abstract: Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems. We study six

8 Jul 2026

Modality Relevance is not Modality Utility: Post-hoc Selective Modality Escalation for Cost-Aware Multimodal RAG

arXiv:2607.05438v1 Announce Type: cross Abstract: Multimodal retrieval-augmented generation (RAG) grounds a generator in evidence drawn from heterogeneous modalities -- text, tables, and images. The d

researcharxiv-cs-ai

8 Jul 2026

A Failure-Mode Benchmark for Polymorphic Sybil Poisoning in RAG

arXiv:2607.03739v1 Announce Type: cross Abstract: We release a benchmark and failure-mode-aware evaluation framework for grounded QA under coordinated retrieval poisoning. The framework partitions rea

Beyond Post-Quantization: Native Hash Learning with a Dedicated HASH Token

arXiv:2607.03328v1 Announce Type: new Abstract: Efficient large-scale image retrieval requires compact representations that preserve semantic similarity under fast Hamming-space search. Deep hashing i

researcharxiv-cs-cv

Distill Where the Student Goes: Teacher-Regularized RL for English-Evidence Cross-Lingual RAG

arXiv:2607.02966v1 Announce Type: new Abstract: Cross-lingual retrieval-augmented generation (RAG) is often deployed in an English-evidence regime, where users query in diverse languages but retrieved

Local Ai

Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecture

arXiv:2607.04391v1 Announce Type: new Abstract: Long-term memory remains a structural weakness of AI agents. The dominant approach, retrieval-augmented generation (RAG), relies on embedding-based simi

local-aiarxiv-cs-cl

Aligning Sentence Embeddings to Human Concepts via Sparse Autoencoders

arXiv:2607.00023v1 Announce Type: cross Abstract: Dense sentence embeddings are fundamental to modern Retrieval-Augmented Generation (RAG) systems but suffer from a lack of interpretability due to fea

safetyarxiv-cs-ai

2 Jul 2026

Local Ai

CORTEX: Token-Level Hallucination Detection in RAG via Comparative Internal Representations

arXiv:2606.31033v1 Announce Type: new Abstract: In this paper, we propose CORTEX, a token-level hallucination detection method for Retrieval-Augmented Generation (RAG). In long-form RAG outputs, hallu

local-aiarxiv-cs-cl

1 Jul 2026

Rethinking On-policy Optimization for Query Augmentation

arXiv:2510.17139v3 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have led to a surge of interest in query augmentation for information retrieval (IR). Two main appro

1 Jul 2026

Benchmarking LLM Agents on Meta-Analysis Articles from Nature Portfolio

arXiv:2606.17041v4 Announce Type: replace Abstract: Meta-analysis is a demanding form of evidence synthesis that combines literature retrieval, PI/ECO-guided study selection, and statistical aggregati

agentsarxiv-cs-cl

EVAF: A Test-Retest Protocol for Selective Parametric Consolidation

arXiv:2606.29916v1 Announce Type: cross Abstract: Long-running language agents need mechanisms for deciding which experiences should persist after the working context is gone. Retrieval systems can re

Regime-Aware Peer Specialization for Robust RAG under Heterogeneous Knowledge Conflicts

arXiv:2606.30518v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves language models by grounding generation in external context. However, it can be fragile when the retrieved

When Does Overlap Help? OSU-Mem and a Cell-Conditional Analysis of Trajectory Memory for LLM Agents

arXiv:2606.28376v1 Announce Type: cross Abstract: Long-horizon large language model (LLM) agents accumulate interaction trajectories that quickly exceed any practical prompt budget, and existing memor

Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors

arXiv:2510.00586v3 Announce Type: replace-cross Abstract: Existing data poisoning attacks on retrieval-augmented generation (RAG) systems scale poorly because they require costly optimization of poiso

researcharxiv-cs-cl

Memory Depth, Not Memory Access: Selective Parametric Consolidation for Long-Running Language Agents

arXiv:2606.26806v1 Announce Type: new Abstract: Long-running language agents need more than memory access. Retrieval systems can fetch past facts at query time, but they do not decide which experience

MIRROR: Novelty-Constrained Memory-Guided MCTS Red-Teaming for Agentic RAG

arXiv:2606.26793v1 Announce Type: cross Abstract: Multimodal agentic retrieval-augmented generation (RAG) systems expand the attack surface beyond prompt injection to include text poisoning, image inj

ProvenAI: Provenance-Native Traces of Evidence in Generated Answers

arXiv:2606.26449v1 Announce Type: cross Abstract: Retrieval-augmented systems routinely present citations alongside generated answers, yet a citation does not confirm that the corresponding source mea

Membox: Weaving Topic Continuity into Long-Range Memory for LLM Agents

arXiv:2601.03785v3 Announce Type: replace Abstract: Long-term human-agent dialogues are organized by topic continuity: adjacent turns often develop the same goal, plan, problem, or event, while relate

agentsarxiv-cs-cl

25 Jun 2026

Are We Ready For An Agent-Native Memory System?

arXiv:2606.24775v1 Announce Type: new Abstract: Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports

24 Jun 2026

Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite

arXiv:2606.11257v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) pipelines are compute-intensive, combining embedding, retrieval, reranking, and large language model (LLM) generati

11 Jun 2026

Findings of the MAGMaR 2026 Shared Task

arXiv:2606.12295v1 Announce Type: cross Abstract: This overview paper presents the results of the shared task for the second workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMa

researcharxiv-cs-cl

11 Jun 2026

One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA

arXiv:2606.10572v1 Announce Type: new Abstract: External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal

researcharxiv-cs-ai

10 Jun 2026

DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking

arXiv:2602.00238v2 Announce Type: replace-cross Abstract: Existing retrieval-augmented generation (RAG) systems often assume that each query has a single correct answer. This assumption overlooks open

safetyarxiv-cs-ai

9 Jun 2026

Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems

arXiv:2605.12213v2 Announce Type: replace Abstract: LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context. While RAG-based approaches are

9 Jun 2026

Local Ai

Queen-Bee Agents: A BeeSpec-Centered Architecture for Governed Enterprise MCP Orchestration

arXiv:2606.06545v1 Announce Type: cross Abstract: Enterprise agent systems increasingly need to connect large language models to private tools, internal knowledge, and Model Context Protocol (MCP) int

local-aiarxiv-cs-ai

8 Jun 2026

SEEK: Steering LLM Reasoning for RAG via Internal Reasoning Sketches

arXiv:2601.09402v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge into the generation process. Benefiti

researcharxiv-cs-cl

8 Jun 2026

Answer Presence Drives RAG Rewriting Gains

arXiv:2606.05633v1 Announce Type: new Abstract: Retrieval-augmented QA pipelines often route retrieved passages through an LLM rewriter before a smaller reader, lifting F1 by tens of points on multi-h

researcharxiv-cs-ai

6 Jun 2026

Applications

RAG Security and Privacy: Formalizing the Threat Model and Attack Surface

arXiv:2509.20324v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with ex

applicationsarxiv-cs-ai

6 Jun 2026

ExpWeaver: LLM Agents Learn from Experience via Latent RAG

arXiv:2606.01041v1 Announce Type: new Abstract: Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge.

2 Jun 2026

Applications

WISE: A Multimodal Search Engine for Visual Scenes, Audio, Objects, Faces, Speech, and Metadata

arXiv:2602.12819v2 Announce Type: replace-cross Abstract: In this paper, we present WISE, an open-source audiovisual search engine which integrates a range of multimodal retrieval capabilities into a

applicationsarxiv-cs-cv

2 Jun 2026

ElasticMem: Latent Memory as a Learnable Resource for LLM Agents

arXiv:2605.30690v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. Howeve

1 Jun 2026

Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages

arXiv:2605.30529v1 Announce Type: cross Abstract: Sentence-embedding models for semantic search are overwhelmingly developed and evaluated on English corpora. When applied to clinical retrieval in oth

1 Jun 2026

Skill is Not One-Size-Fits-All: Model-Aware Skill Alignment for LLM Agents

arXiv:2605.30723v1 Announce Type: new Abstract: LLM agents increasingly retrieve externally curated skills-procedural instructions retrieved at decision time-to improve performance on long-horizon int

1 Jun 2026

RAISE: RAG Design as an Architecture Search Problem

arXiv:2605.30029v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems expose numerous design choices spanning query rewriting, chunking, retrieval depth, reranking, and context

29 May 2026

Same Question, Different Source, Different Answer: Auditing Source-Dependence in Medical Multi-Source RAG

arXiv:2605.29084v1 Announce Type: cross Abstract: A retrieval-augmented generation (RAG) system deployed over a multi-author institutional corpus can give a different answer to the same question depen

29 May 2026

Applications

A Wolf in Sheep's Clothing: Targeted Routing Hijacking in Federated RAG

arXiv:2605.28112v1 Announce Type: cross Abstract: Federated Retrieval-Augmented Generation (FedRAG) is attractive for privacy-sensitive applications because raw data remain local. As a result, routing

applicationsarxiv-cs-cl

28 May 2026

Plan Before Search: Search Agents Need Plan

arXiv:2605.28354v1 Announce Type: new Abstract: Training large language models as retrieval-augmented reasoning agents typically combines reinforcement learning with an SFT cold start distilled from a

28 May 2026

CIRCLED: A Multi-turn CIR Dataset with Consistent Dialogues across Domains

model-releasesarxiv-cs-cv

arXiv:2605.26734v1 Announce Type: new Abstract: Existing Multi-Turn Composed Image Retrieval (MTCIR) datasets lack dialogue-history consistency and are restricted to the fashion domain. To address the

27 May 2026

FAB-Bench: A Framework for Adaptive RAG Benchmarking in Semiconductor Manufacturing

arXiv:2605.26476v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become critical for knowledge-intensive applications, yet evaluating its performance in vertical domains remain

27 May 2026

OMD-GraphRAG: Enhancing GraphRAG with Ontology-Guided Extraction, Multi-Dimensional Clustering and Dual-Channel Fusion

arXiv:2603.25152v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries, and domain-specific QA. While exis

27 May 2026

PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation

arXiv:2605.25572v1 Announce Type: cross Abstract: The growing complexity of quantum programming frameworks has exposed a critical limitation in existing large language model (LLM)-based code assistant