{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/retrieval/papers/63","list_of":"/task/retrieval","task":"Retrieval","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":63,"pages_in_order":143,"rows_per_page":100,"rows":[6201,6300],"of":14297,"counts":{"archive_papers_tagged":14297,"with_a_code_link":5274,"where_syntology_ran_a_sample":1303,"not_listed_spam_title":0,"listed":14297,"listed_where_code_ran":1303,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1067,"every_run_a_failure_of_syntologys_instrument":236,"listed_with_a_run_with_no_instrument_failure":1067,"listed_every_run_a_failure_of_syntologys_instrument":236,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/retrieval","prev":"/task/retrieval/papers/62","next":"/task/retrieval/papers/64","papers":[{"url":null,"slug":"flipedrag-black-box-opinion-manipulation","title":"FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models","date":"2025-01-06","arxiv_id":"2501.02968","repositories_listed":0,"syntology":null},{"url":null,"slug":"gear-generation-augmented-retrieval","title":"GeAR: Generation Augmented Retrieval","date":"2025-01-06","arxiv_id":"2501.02772","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-based-retrieval-augmented-generation","title":"Graph-based Retrieval Augmented Generation for Dynamic Few-shot Text Classification","date":"2025-01-06","arxiv_id":"2501.02844","repositories_listed":0,"syntology":null},{"url":null,"slug":"political-events-using-rag-with-llms","title":"Political Events using RAG with LLMs","date":"2025-01-06","arxiv_id":"2502.15701","repositories_listed":0,"syntology":null},{"url":null,"slug":"quim-rag-advancing-retrieval-augmented","title":"QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance","date":"2025-01-06","arxiv_id":"2501.02702","repositories_listed":0,"syntology":null},{"url":null,"slug":"sustainable-digitalization-of-business-with","title":"Sustainable Digitalization of Business with Multi-Agent RAG and LLM","date":"2025-01-06","arxiv_id":"2502.15700","repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-based-rag-agent-recommendation-system-a","title":"Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test Data","date":"2025-01-06","arxiv_id":"2501.02727","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-impressions-of-music-be-extracted-from","title":"Can Impressions of Music be Extracted from Thumbnail Images?","date":"2025-01-05","arxiv_id":"2501.02511","repositories_listed":0,"syntology":null},{"url":null,"slug":"gentrec-the-first-test-collection-generated","title":"GenTREC: The First Test Collection Generated by Large Language Models for Evaluating Information Retrieval Systems","date":"2025-01-05","arxiv_id":"2501.02408","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-information-need-prediction-with","title":"Interactive Information Need Prediction with Intent and Context","date":"2025-01-05","arxiv_id":"2501.02635","repositories_listed":0,"syntology":null},{"url":null,"slug":"lwfnet-coherent-doppler-wind-lidar-based","title":"LWFNet: Coherent Doppler Wind Lidar-Based Network for Wind Field Retrieval","date":"2025-01-05","arxiv_id":"2501.02613","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-omni-rag-comprehensive-retrieval","title":"Towards Omni-RAG: Comprehensive Retrieval-Augmented Generation for Large Language Models in Medical Applications","date":"2025-01-05","arxiv_id":"2501.02460","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-graph-retrieval-augmented","title":"Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation","date":"2025-01-04","arxiv_id":"2501.02226","repositories_listed":0,"syntology":null},{"url":null,"slug":"phase-retrieval-by-quaternionic-reweighted","title":"Quaternionic Reweighted Amplitude Flow for Phase Retrieval in Image Reconstruction","date":"2025-01-04","arxiv_id":"2501.02180","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-efficiency-vs-accuracy-trade-off","title":"The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit","date":"2025-01-04","arxiv_id":"2501.02173","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-few-shot-prompting-for-machine","title":"Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models","date":"2025-01-03","arxiv_id":"2501.01679","repositories_listed":0,"syntology":null},{"url":null,"slug":"automating-legal-concept-interpretation-with","title":"Automating Legal Concept Interpretation with LLMs: Retrieval, Generation, and Evaluation","date":"2025-01-03","arxiv_id":"2501.01743","repositories_listed":0,"syntology":null},{"url":null,"slug":"carbonchat-large-language-model-based","title":"CarbonChat: Large Language Model-Based Corporate Carbon Emission Analysis and Climate Knowledge Q&A System","date":"2025-01-03","arxiv_id":"2501.02031","repositories_listed":0,"syntology":null},{"url":null,"slug":"icbir-sli-interpretable-content-based-image","title":"iCBIR-Sli: Interpretable Content-Based Image Retrieval with 2D Slice Embeddings","date":"2025-01-03","arxiv_id":"2501.01642","repositories_listed":0,"syntology":null},{"url":null,"slug":"personaai-leveraging-retrieval-augmented","title":"PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars","date":"2025-01-03","arxiv_id":"2503.15489","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-invariant-feature-learning-in-brain-mr","title":"Domain-invariant feature learning in brain MR imaging for content-based image retrieval","date":"2025-01-02","arxiv_id":"2501.01326","repositories_listed":0,"syntology":null},{"url":null,"slug":"rephotography-in-the-digital-era-mass","title":"Rephotography in the Digital Era: Mass Rephotography and re.photos, the Web Portal for Rephotography","date":"2025-01-02","arxiv_id":"2501.02017","repositories_listed":0,"syntology":null},{"url":null,"slug":"valuesrag-enhancing-cultural-alignment","title":"ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning","date":"2025-01-02","arxiv_id":"2501.01031","repositories_listed":0,"syntology":null},{"url":null,"slug":"bridging-modalities-improving-universal","title":"Bridging Modalities: Improving Universal Multimodal Retrieval by Multimodal Large Language Models","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ccin-compositional-conflict-identification","title":"CCIN: Compositional Conflict Identification and Neutralization for Composed Image Retrieval","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chat-based-person-retrieval-via-dialogue","title":"Chat-based Person Retrieval via Dialogue-Refined Cross-Modal Alignment","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cheb-gr-rethinking-k-nearest-neighbor-search","title":"Cheb-GR: Rethinking K-nearest Neighbor Search in Re-ranking for Person Re-identification","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-3d-representation-with-multi-view","title":"Cross-Modal 3D Representation with Multi-View Images and Point Clouds","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"decoupling-knowledge-and-reasoning-in","title":"Decoupling Knowledge and Reasoning in Transformers: A Modular Architecture with Generalized Cross-Attention","date":"2025-01-01","arxiv_id":"2501.00823","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-zero-shot-composed-image-retrieval","title":"Generative Zero-Shot Composed Image Retrieval","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"horus-multimodal-large-language-models","title":"HORUS: Multimodal Large Language Models Framework for Video Retrieval at VBS 2025","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-dense-knowledge-alignment-into","title":"Incorporating Dense Knowledge Alignment into Unified Multimodal Representation Models","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-noisy-triplet-correspondence","title":"Learning with Noisy Triplet Correspondence for Composed Image Retrieval","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prompthash-affinity-prompted-collaborative-1","title":"PromptHash:Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"retaining-knowledge-and-enhancing-long-text","title":"Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-noisy-video-text-retrieval-via","title":"Rethinking Noisy Video-Text Retrieval via Relation-aware Alignment","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"shift-the-lens-environment-aware-unsupervised","title":"Shift the Lens: Environment-Aware Unsupervised Camouflaged Object Detection","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-natural-language-based-document-image","title":"Towards Natural Language-Based Document Image Retrieval: New Dataset and Benchmark","date":"2025-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-overview-and-discussion-on-using-large","title":"An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems","date":"2024-12-31","arxiv_id":"2501.00562","repositories_listed":0,"syntology":null},{"url":null,"slug":"cancerkg-org-a-web-scale-interactive","title":"CancerKG.ORG A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care","date":"2024-12-31","arxiv_id":"2501.00223","repositories_listed":0,"syntology":null},{"url":null,"slug":"chunk-distilled-language-modeling","title":"Chunk-Distilled Language Modeling","date":"2024-12-31","arxiv_id":"2501.00343","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-video-text-retrieval-a-new","title":"CaReBench: A Fine-Grained Benchmark for Video Captioning and Retrieval","date":"2024-12-31","arxiv_id":"2501.00513","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowra-knowledge-retrieval-augmented-method","title":"KnowRA: Knowledge Retrieval Augmented Method for Document-level Relation Extraction with Comprehensive Reasoning Abilities","date":"2024-12-31","arxiv_id":"2501.00571","repositories_listed":0,"syntology":null},{"url":null,"slug":"main-rag-multi-agent-filtering-retrieval","title":"MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation","date":"2024-12-31","arxiv_id":"2501.00332","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-with-graphs","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","date":"2024-12-31","arxiv_id":"2501.00309","repositories_listed":0,"syntology":null},{"url":null,"slug":"acl-rlg-a-dataset-for-reading-list-generation","title":"ACL-rlg: A Dataset for Reading List Generation","date":"2024-12-30","arxiv_id":"2502.15692","repositories_listed":0,"syntology":null},{"url":null,"slug":"edgerag-online-indexed-rag-for-edge-devices","title":"EdgeRAG: Online-Indexed RAG for Edge Devices","date":"2024-12-30","arxiv_id":"2412.21023","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-multimodal-rag-llm-for-accurate","title":"Enhanced Multimodal RAG-LLM for Accurate Visual Question Answering","date":"2024-12-30","arxiv_id":"2412.20927","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-based-audio-retrieval-with-co","title":"Language-based Audio Retrieval with Co-Attention Networks","date":"2024-12-30","arxiv_id":"2412.20914","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-augmented-generation-for-mobile","title":"Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model","date":"2024-12-30","arxiv_id":"2412.20820","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-text-classification-pipeline-starting","title":"The Text Classification Pipeline: Starting Shallow going Deeper","date":"2024-12-30","arxiv_id":"2501.00174","repositories_listed":0,"syntology":null},{"url":null,"slug":"timeraf-retrieval-augmented-foundation-model","title":"TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting","date":"2024-12-30","arxiv_id":"2412.20810","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-dense-retrieval-with","title":"Unsupervised dense retrieval with conterfactual contrastive learning","date":"2024-12-30","arxiv_id":"2412.20756","repositories_listed":0,"syntology":null},{"url":null,"slug":"glilem-leveraging-gliner-for-contextualized","title":"GliLem: Leveraging GliNER for Contextualized Lemmatization in Estonian","date":"2024-12-29","arxiv_id":"2412.20597","repositories_listed":0,"syntology":null},{"url":null,"slug":"fashionfae-fine-grained-attributes-enhanced","title":"FashionFAE: Fine-grained Attributes Enhanced Fashion Vision-Language Pre-training","date":"2024-12-28","arxiv_id":"2412.19997","repositories_listed":0,"syntology":null},{"url":null,"slug":"staykate-hybrid-in-context-example-selection","title":"STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains","date":"2024-12-28","arxiv_id":"2412.20043","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-multi-stage-language-models-for","title":"Optimizing Multi-Stage Language Models for Effective Text Retrieval","date":"2024-12-26","arxiv_id":"2412.19265","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-residual-for-multimodal-unified","title":"Semantic Residual for Multimodal Unified Discrete Representation","date":"2024-12-26","arxiv_id":"2412.19128","repositories_listed":0,"syntology":null},{"url":null,"slug":"for-finetuning-for-object-level-open","title":"FOR: Finetuning for Object Level Open Vocabulary Image Retrieval","date":"2024-12-25","arxiv_id":"2412.18806","repositories_listed":0,"syntology":null},{"url":null,"slug":"research-experiment-on-multi-model-comparison","title":"Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks","date":"2024-12-25","arxiv_id":"2412.18908","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynagrag-improving-language-understanding-and","title":"DynaGRAG | Exploring the Topology of Information for Advancing Language Understanding and Generation in Graph Retrieval-Augmented Generation","date":"2024-12-24","arxiv_id":"2412.18644","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-long-context-language-model","title":"Efficient Long Context Language Model Retrieval with Compression","date":"2024-12-24","arxiv_id":"2412.18232","repositories_listed":0,"syntology":null},{"url":null,"slug":"gear-graph-enhanced-agent-for-retrieval","title":"GeAR: Graph-enhanced Agent for Retrieval-augmented Generation","date":"2024-12-24","arxiv_id":"2412.18431","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-factuality-with-explicit-working","title":"Improving Factuality with Explicit Working Memory","date":"2024-12-24","arxiv_id":"2412.18069","repositories_listed":0,"syntology":null},{"url":null,"slug":"molly-making-large-language-model-agents","title":"Molly: Making Large Language Model Agents Solve Python Problem More Logically","date":"2024-12-24","arxiv_id":"2412.18093","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-auto-association-with-optimal-bayesian","title":"Neural auto-association with optimal Bayesian learning","date":"2024-12-24","arxiv_id":"2412.18349","repositories_listed":0,"syntology":null},{"url":null,"slug":"pirates-of-the-rag-adaptively-attacking-llms","title":"Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases","date":"2024-12-24","arxiv_id":"2412.18295","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-query-optimization-in-large","title":"A Survey of Query Optimization in Large Language Models","date":"2024-12-23","arxiv_id":"2412.17558","repositories_listed":0,"syntology":null},{"url":null,"slug":"cobra-combinatorial-retrieval-augmentation","title":"COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation","date":"2024-12-23","arxiv_id":"2412.17684","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrato360-2-0-a-document-and-database","title":"Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents","date":"2024-12-23","arxiv_id":"2412.17942","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-multi-agent-orchestration-and","title":"Dynamic Multi-Agent Orchestration and Retrieval for Multi-Source Question-Answer Systems using Large Language Models","date":"2024-12-23","arxiv_id":"2412.17964","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-memory-retrieval-to-enhance-llm","title":"Leveraging Memory Retrieval to Enhance LLM-based Generative Recommendation","date":"2024-12-23","arxiv_id":"2412.17593","repositories_listed":0,"syntology":null},{"url":null,"slug":"ragonite-iterative-retrieval-on-induced","title":"RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG","date":"2024-12-23","arxiv_id":"2412.17690","repositories_listed":0,"syntology":null},{"url":null,"slug":"syneg-llm-driven-synthetic-hard-negatives-for","title":"SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval","date":"2024-12-23","arxiv_id":"2412.17250","repositories_listed":0,"syntology":null},{"url":null,"slug":"gme-improving-universal-multimodal-retrieval","title":"GME: Improving Universal Multimodal Retrieval by Multimodal LLMs","date":"2024-12-22","arxiv_id":"2412.16855","repositories_listed":0,"syntology":null},{"url":null,"slug":"alzheimerrag-multimodal-retrieval-augmented","title":"AlzheimerRAG: Multimodal Retrieval Augmented Generation for PubMed articles","date":"2024-12-21","arxiv_id":"2412.16701","repositories_listed":0,"syntology":null},{"url":null,"slug":"formal-language-knowledge-corpus-for","title":"Formal Language Knowledge Corpus for Retrieval Augmented Generation","date":"2024-12-21","arxiv_id":"2412.16689","repositories_listed":0,"syntology":null},{"url":null,"slug":"speech-retrieval-augmented-generation-without","title":"Speech Retrieval-Augmented Generation without Automatic Speech Recognition","date":"2024-12-21","arxiv_id":"2412.16500","repositories_listed":0,"syntology":null},{"url":null,"slug":"timerag-boosting-llm-time-series-forecasting","title":"TimeRAG: BOOSTING LLM Time Series Forecasting via Retrieval-Augmented Generation","date":"2024-12-21","arxiv_id":"2412.16643","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybgrag-hybrid-retrieval-augmented-generation","title":"HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases","date":"2024-12-20","arxiv_id":"2412.16311","repositories_listed":0,"syntology":null},{"url":null,"slug":"mrag-a-modular-retrieval-framework-for-time","title":"MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering","date":"2024-12-20","arxiv_id":"2412.15540","repositories_listed":0,"syntology":null},{"url":null,"slug":"polysmart-and-vireo-trecvid-2024-ad-hoc-video","title":"PolySmart and VIREO @ TRECVid 2024 Ad-hoc Video Search","date":"2024-12-20","arxiv_id":"2412.15494","repositories_listed":0,"syntology":null},{"url":null,"slug":"polysmart-trecvid-2024-medical-video-question","title":"PolySmart @ TRECVid 2024 Medical Video Question Answering","date":"2024-12-20","arxiv_id":"2412.15514","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-retrieval-augmented-generation-framework","title":"A Retrieval-Augmented Generation Framework for Academic Literature Navigation in Data Science","date":"2024-12-19","arxiv_id":"2412.15404","repositories_listed":0,"syntology":null},{"url":null,"slug":"balanced-gradient-sample-retrieval-for","title":"Balanced Gradient Sample Retrieval for Enhanced Knowledge Retention in Proxy-based Continual Learning","date":"2024-12-19","arxiv_id":"2412.14430","repositories_listed":0,"syntology":null},{"url":null,"slug":"cord-balancing-consistency-and-rank","title":"CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation","date":"2024-12-19","arxiv_id":"2412.14581","repositories_listed":0,"syntology":null},{"url":null,"slug":"dehallucinating-parallel-context-extension","title":"Dehallucinating Parallel Context Extension for Retrieval-Augmented Generation","date":"2024-12-19","arxiv_id":"2412.14905","repositories_listed":0,"syntology":null},{"url":null,"slug":"eclipse-contrastive-dimension-importance","title":"ECLIPSE: Contrastive Dimension Importance Estimation with Pseudo-Irrelevance Feedback for Dense Retrieval","date":"2024-12-19","arxiv_id":"2412.14967","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-multimodal-reasoning-via-active","title":"Progressive Multimodal Reasoning via Active Retrieval","date":"2024-12-19","arxiv_id":"2412.14835","repositories_listed":0,"syntology":null},{"url":null,"slug":"query-pipeline-optimization-for-cancer","title":"Query pipeline optimization for cancer patient question answering systems","date":"2024-12-19","arxiv_id":"2412.14751","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-then-refine-a-dynamic-framework-for","title":"Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability","date":"2024-12-19","arxiv_id":"2412.15101","repositories_listed":0,"syntology":null},{"url":null,"slug":"sketch-structured-knowledge-enhanced-text","title":"SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval","date":"2024-12-19","arxiv_id":"2412.15443","repositories_listed":0,"syntology":null},{"url":null,"slug":"visa-retrieval-augmented-generation-with","title":"VISA: Retrieval Augmented Generation with Visual Source Attribution","date":"2024-12-19","arxiv_id":"2412.14457","repositories_listed":0,"syntology":null},{"url":"/paper/advanced-reasoning-and-transformation-engine","slug":"advanced-reasoning-and-transformation-engine","title":"ARTEMIS-DA: An Advanced Reasoning and Transformation Engine for Multi-Step Insight Synthesis in Data Analytics","date":"2024-12-18","arxiv_id":"2412.14146","repositories_listed":0,"syntology":null},{"url":null,"slug":"crm-retrieval-model-with-controllable","title":"CRM: Retrieval Model with Controllable Condition","date":"2024-12-18","arxiv_id":"2412.13844","repositories_listed":0,"syntology":null},{"url":null,"slug":"denoising-nearest-neighbor-graph-via","title":"Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning","date":"2024-12-18","arxiv_id":"2412.13875","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-and-rag-integration-a","title":"Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models","date":"2024-12-18","arxiv_id":"2412.13720","repositories_listed":0,"syntology":null},{"url":null,"slug":"llms-can-realize-combinatorial-creativity","title":"LLMs can realize combinatorial creativity: generating creative ideas via LLMs for scientific research","date":"2024-12-18","arxiv_id":"2412.14141","repositories_listed":0,"syntology":null},{"url":null,"slug":"maybe-you-are-looking-for-croqs-cross-modal","title":"Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval","date":"2024-12-18","arxiv_id":"2412.13834","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-ophthalingua-a-multilingual-benchmark","title":"Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs","date":"2024-12-18","arxiv_id":"2412.14304","repositories_listed":0,"syntology":null}],"record_sha256":"94c8158f90c3bfcf7127213e91866897c79bd23a6f4f45448c09b845f508c825","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}