Papers Retrieval
“Retrieval” 태그가 달린 논문 14,297편 · 필터 해제
From Roots to Rewards: Dynamic Tree Reasoning with RL
Modern language models address complex questions through chain-of-thought (CoT) reasoning (Wei et al., 2023) and retrieval augmentation (Lewis et al., 2021), yet struggle with error propagation and knowledge integration.…
Computational EfficiencyQuestion AnsweringRetrievalHapticCap: A Multimodal Dataset and Task for Understanding User Experience of Vibration Haptic Signals
Haptic signals, from smartphone vibrations to virtual reality touch feedback, can effectively convey information and enhance realism, but designing signals that resonate meaningfully with users is challenging. To facilit…
Contrastive LearningRetrievalA Survey of Context Engineering for Large Language Models
The performance of Large Language Models (LLMs) is fundamentally determined by the contextual information provided during inference. This survey introduces Context Engineering, a formal discipline that transcends simple …
RAGRetrievalRetrieval-augmented GenerationSurveyMCoT-RE: Multi-Faceted Chain-of-Thought and Re-Ranking for Training-Free Zero-Shot Composed Image Retrieval
Composed Image Retrieval (CIR) is the task of retrieving a target image from a gallery using a composed query consisting of a reference image and a modification text. Among various CIR approaches, training-free zero-shot…
Image RetrievalRe-RankingRetrievalDeveloping Visual Augmented Q&A System using Scalable Vision Embedding Retrieval & Late Interaction Re-ranker
Traditional information extraction systems face challenges with text only language models as it does not consider infographics (visual elements of information) such as tables, charts, images etc. often used to convey com…
RAGRetrievalLanguage-Guided Contrastive Audio-Visual Masked Autoencoder with Automatically Generated Audio-Visual-Text Triplets from Videos
In this paper, we propose Language-Guided Contrastive Audio-Visual Masked Autoencoders (LG-CAV-MAE) to improve audio-visual representation learning. LG-CAV-MAE integrates a pretrained text encoder into contrastive audio-…
Image CaptioningRepresentation LearningRetrievalContext-Aware Search and Retrieval Over Erasure Channels
This paper introduces and analyzes a search and retrieval model that adopts key semantic communication principles from retrieval-augmented generation. We specifically present an information-theoretic analysis of a remote…
DecoderRetrievalRetrieval-augmented GenerationSemantic CommunicationSeq vs Seq: An Open Suite of Paired Encoders and Decoders
The large language model (LLM) community focuses almost exclusively on decoder-only language models, since they are easier to use for text generation. However, a large subset of the community still uses encoder-only mode…
DecoderLarge Language ModelRetrievalText GenerationFrom Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation
The growing volume of unstructured data within organizations poses significant challenges for data analysis and process automation. Unstructured data, which lacks a predefined format, encompasses various forms such as em…
Information RetrievalRetrievalKodezi Chronos: A Debugging-First Language Model for Repository-Scale, Memory-Driven Code Understanding
Large Language Models (LLMs) have advanced code generation and software automation, but are fundamentally constrained by limited inference-time context and lack of explicit code structure reasoning. We introduce Kodezi C…
Code GenerationLanguage ModelingLanguage ModellingRetrievalRadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features
Medical image retrieval is a valuable field for supporting clinical decision-making, yet current methods primarily support 2D images and require fully annotated queries, limiting clinical flexibility. To address this, we…
Contrastive LearningImage RetrievalMedical Image RetrievalRetrieval+1CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs
Large language models (LLMs), including zero-shot and few-shot paradigms, have shown promising capabilities in clinical text generation. However, real-world applications face two key challenges: (1) patient data is highl…
ChunkingRAGRetrievalRetrieval-augmented Generation+1Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation
Large language models (LLMs) incorporated with Retrieval-Augmented Generation (RAG) have demonstrated powerful capabilities in generating counterspeech against misinformation. However, current studies rely on limited evi…
InformativenessMisinformationRAGRetrieval+1Temporal Information Retrieval via Time-Specifier Model Merging
The rapid expansion of digital information and knowledge across structured and unstructured sources has heightened the importance of Information Retrieval (IR). While dense retrieval methods have substantially improved s…
Information RetrievalmodelRetrievalOrchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning
Modern Artificial Intelligence (AI) increasingly relies on multi-agent architectures that blend visual and language understanding. Yet, a pressing challenge remains: How can we trust these agents especially in zero-shot …
BenchmarkingImage RetrievalOptical Character Recognition (OCR)RAG+3SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
Retrieval-augmented Generation (RAG) extends large language models (LLMs) with external knowledge but faces key challenges: restricted effective context length and redundancy in retrieved documents. Pure compression-base…
Evidence SelectionRAGRerankingRetrieval+4Differential Mamba
Sequence models like Transformers and RNNs often overallocate attention to irrelevant context, leading to noisy intermediate representations. This degrades LLM capabilities by promoting hallucinations, weakening long-ran…
Language ModelingLanguage ModellingMambaRetrievalSemantic Certainty Assessment in Vector Retrieval Systems: A Novel Framework for Embedding Quality Evaluation
Vector retrieval systems exhibit significant performance variance across queries due to heterogeneous embedding quality. We propose a lightweight framework for predicting retrieval performance at the query level by combi…
Data AugmentationQuantizationRetrievalAutomatic Synthesis of High-Quality Triplet Data for Composed Image Retrieval
As a challenging vision-language (VL) task, Composed Image Retrieval (CIR) aims to retrieve target images using multimodal (image+text) queries. Although many existing CIR methods have attained promising performance, the…
Image RetrievalLarge Language ModelRetrievalTripletAn analysis of vision-language models for fabric retrieval
Effective cross-modal retrieval is essential for applications like information retrieval and recommendation systems, particularly in specialized domains such as manufacturing, where product information often consists of …
AttributeCross-Modal RetrievalImage RetrievalInformation Retrieval+3