{"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/10","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":10,"pages_in_order":143,"rows_per_page":100,"rows":[901,1000],"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/9","next":"/task/retrieval/papers/11","papers":[{"url":"/paper/poisonarena-uncovering-competing-poisoning","slug":"poisonarena-uncovering-competing-poisoning","title":"PoisonArena: Uncovering Competing Poisoning Attacks in Retrieval-Augmented Generation","date":"2025-05-18","arxiv_id":"2505.12574","repositories_listed":1,"syntology":null},{"url":"/paper/rebalancing-contrastive-alignment-with","slug":"rebalancing-contrastive-alignment-with","title":"Contrastive Alignment with Semantic Gap-Aware Corrections in Text-Video Retrieval","date":"2025-05-18","arxiv_id":"2505.12499","repositories_listed":1,"syntology":null},{"url":"/paper/demystifying-and-enhancing-the-efficiency-of","slug":"demystifying-and-enhancing-the-efficiency-of","title":"Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents","date":"2025-05-17","arxiv_id":"2505.12065","repositories_listed":1,"syntology":null},{"url":"/paper/elite-embedding-less-retrieval-with-iterative","slug":"elite-embedding-less-retrieval-with-iterative","title":"ELITE: Embedding-Less retrieval with Iterative Text Exploration","date":"2025-05-17","arxiv_id":"2505.11908","repositories_listed":1,"syntology":null},{"url":"/paper/neuro-symbolic-query-compiler","slug":"neuro-symbolic-query-compiler","title":"Neuro-Symbolic Query Compiler","date":"2025-05-17","arxiv_id":"2505.11932","repositories_listed":1,"syntology":null},{"url":"/paper/2505-11180","slug":"2505-11180","title":"mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs","date":"2025-05-16","arxiv_id":"2505.11180","repositories_listed":1,"syntology":null},{"url":"/paper/2505-11277","slug":"2505-11277","title":"Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs","date":"2025-05-16","arxiv_id":"2505.11277","repositories_listed":1,"syntology":null},{"url":"/paper/2505-11388","slug":"2505-11388","title":"The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems","date":"2025-05-16","arxiv_id":"2505.11388","repositories_listed":1,"syntology":null},{"url":"/paper/finetune-rag-fine-tuning-language-models-to","slug":"finetune-rag-fine-tuning-language-models-to","title":"Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation","date":"2025-05-16","arxiv_id":"2505.10792","repositories_listed":1,"syntology":null},{"url":"/paper/do-rag-a-domain-specific-qa-framework-using","slug":"do-rag-a-domain-specific-qa-framework-using","title":"DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation","date":"2025-05-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-document-refinement-for-long","slug":"hierarchical-document-refinement-for-long","title":"Hierarchical Document Refinement for Long-context Retrieval-augmented Generation","date":"2025-05-15","arxiv_id":"2505.10413","repositories_listed":1,"syntology":null},{"url":"/paper/personalizing-large-language-models-using","slug":"personalizing-large-language-models-using","title":"Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph","date":"2025-05-15","arxiv_id":"2505.09945","repositories_listed":1,"syntology":null},{"url":"/paper/scent-of-knowledge-optimizing-search-enhanced","slug":"scent-of-knowledge-optimizing-search-enhanced","title":"Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging","date":"2025-05-14","arxiv_id":"2505.09316","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scent-of-knowledge-optimizing-search-enhanced#ran","syntology_url":"https://syntology.ai/paper/2505.09316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.09316"}},"official":{"repos":["qhjqhj00/inforage"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dynamicrag-leveraging-outputs-of-large","slug":"dynamicrag-leveraging-outputs-of-large","title":"DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation","date":"2025-05-12","arxiv_id":"2505.07233","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-and-reproducible-biomedical","slug":"efficient-and-reproducible-biomedical","title":"Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation","date":"2025-05-12","arxiv_id":"2505.07917","repositories_listed":1,"syntology":null},{"url":"/paper/grada-graph-based-reranker-against","slug":"grada-graph-based-reranker-against","title":"GRADA: Graph-based Reranker against Adversarial Documents Attack","date":"2025-05-12","arxiv_id":"2505.07546","repositories_listed":1,"syntology":null},{"url":"/paper/onprem-llm-a-privacy-conscious-document","slug":"onprem-llm-a-privacy-conscious-document","title":"OnPrem.LLM: A Privacy-Conscious Document Intelligence Toolkit","date":"2025-05-12","arxiv_id":"2505.07672","repositories_listed":1,"syntology":null},{"url":"/paper/pre-training-vs-fine-tuning-a-reproducibility","slug":"pre-training-vs-fine-tuning-a-reproducibility","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","date":"2025-05-12","arxiv_id":"2505.07166","repositories_listed":1,"syntology":null},{"url":"/paper/recdap-relation-based-conditional-diffusion","slug":"recdap-relation-based-conditional-diffusion","title":"ReCDAP: Relation-Based Conditional Diffusion with Attention Pooling for Few-Shot Knowledge Graph Completion","date":"2025-05-12","arxiv_id":"2505.07171","repositories_listed":1,"syntology":null},{"url":"/paper/reinforced-internal-external-knowledge","slug":"reinforced-internal-external-knowledge","title":"Reinforced Internal-External Knowledge Synergistic Reasoning for Efficient Adaptive Search Agent","date":"2025-05-12","arxiv_id":"2505.07596","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/reinforced-internal-external-knowledge#ran","syntology_url":"https://syntology.ai/paper/2505.07596","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.07596"}},"official":null}},{"url":"/paper/reproducibility-replicability-and-insights","slug":"reproducibility-replicability-and-insights","title":"Reproducibility, Replicability, and Insights into Visual Document Retrieval with Late Interaction","date":"2025-05-12","arxiv_id":"2505.07730","repositories_listed":1,"syntology":null},{"url":"/paper/macrag-compress-slice-and-scale-up-for-multi","slug":"macrag-compress-slice-and-scale-up-for-multi","title":"MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAG","date":"2025-05-10","arxiv_id":"2505.06569","repositories_listed":1,"syntology":null},{"url":"/paper/neoqa-evidence-based-question-answering-with","slug":"neoqa-evidence-based-question-answering-with","title":"NeoQA: Evidence-based Question Answering with Generated News Events","date":"2025-05-09","arxiv_id":"2505.05949","repositories_listed":1,"syntology":null},{"url":"/paper/fg-clip-fine-grained-visual-and-textual","slug":"fg-clip-fine-grained-visual-and-textual","title":"FG-CLIP: Fine-Grained Visual and Textual Alignment","date":"2025-05-08","arxiv_id":"2505.05071","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fg-clip-fine-grained-visual-and-textual#ran","syntology_url":"https://syntology.ai/paper/2505.05071","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.05071"}},"official":{"repos":["360cvgroup/fg-clip"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lsrp-a-leader-subordinate-retrieval-framework","slug":"lsrp-a-leader-subordinate-retrieval-framework","title":"LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device Collaboration","date":"2025-05-08","arxiv_id":"2505.05031","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-augmented-time-series-forecasting-1","slug":"retrieval-augmented-time-series-forecasting-1","title":"Retrieval Augmented Time Series Forecasting","date":"2025-05-07","arxiv_id":"2505.04163","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/retrieval-augmented-time-series-forecasting-1#ran","syntology_url":"https://syntology.ai/paper/2505.04163","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.04163"}},"official":{"repos":["archon159/RAFT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/zerosearch-incentivize-the-search-capability","slug":"zerosearch-incentivize-the-search-capability","title":"ZeroSearch: Incentivize the Search Capability of LLMs without Searching","date":"2025-05-07","arxiv_id":"2505.04588","repositories_listed":1,"syntology":{"n":15,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":8,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/zerosearch-incentivize-the-search-capability#ran","syntology_url":"https://syntology.ai/paper/2505.04588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.04588"}},"official":{"repos":["alibaba-nlp/zerosearch"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/multimodal-benchmarking-and-recommendation-of","slug":"multimodal-benchmarking-and-recommendation-of","title":"Multimodal Benchmarking and Recommendation of Text-to-Image Generation Models","date":"2025-05-06","arxiv_id":"2505.04650","repositories_listed":1,"syntology":null},{"url":"/paper/rational-retrieval-acts-leveraging-pragmatic","slug":"rational-retrieval-acts-leveraging-pragmatic","title":"Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval","date":"2025-05-06","arxiv_id":"2505.03676","repositories_listed":1,"syntology":null},{"url":"/paper/seeing-the-abstract-translating-the-abstract","slug":"seeing-the-abstract-translating-the-abstract","title":"Seeing the Abstract: Translating the Abstract Language for Vision Language Models","date":"2025-05-06","arxiv_id":"2505.03242","repositories_listed":1,"syntology":null},{"url":"/paper/direct-retrieval-augmented-optimization","slug":"direct-retrieval-augmented-optimization","title":"Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models","date":"2025-05-05","arxiv_id":"2505.03075","repositories_listed":1,"syntology":null},{"url":"/paper/knowing-you-don-t-know-learning-when-to","slug":"knowing-you-don-t-know-learning-when-to","title":"Knowing You Don't Know: Learning When to Continue Search in Multi-round RAG through Self-Practicing","date":"2025-05-05","arxiv_id":"2505.02811","repositories_listed":1,"syntology":null},{"url":"/paper/teda-boosting-vision-lanuage-models-for-zero","slug":"teda-boosting-vision-lanuage-models-for-zero","title":"TeDA: Boosting Vision-Lanuage Models for Zero-Shot 3D Object Retrieval via Testing-time Distribution Alignment","date":"2025-05-05","arxiv_id":"2505.02325","repositories_listed":1,"syntology":null},{"url":"/paper/tevatron-2-0-unified-document-retrieval","slug":"tevatron-2-0-unified-document-retrieval","title":"Tevatron 2.0: Unified Document Retrieval Toolkit across Scale, Language, and Modality","date":"2025-05-05","arxiv_id":"2505.02466","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-legal-structure-in-retrieval","slug":"incorporating-legal-structure-in-retrieval","title":"Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair Use","date":"2025-05-04","arxiv_id":"2505.02164","repositories_listed":1,"syntology":null},{"url":"/paper/interpreting-multilingual-and-document-length","slug":"interpreting-multilingual-and-document-length","title":"Interpreting Multilingual and Document-Length Sensitive Relevance Computations in Neural Retrieval Models through Axiomatic Causal Interventions","date":"2025-05-04","arxiv_id":"2505.02154","repositories_listed":1,"syntology":null},{"url":"/paper/obd-finder-explainable-coarse-to-fine-text","slug":"obd-finder-explainable-coarse-to-fine-text","title":"OBD-Finder: Explainable Coarse-to-Fine Text-Centric Oracle Bone Duplicates Discovery","date":"2025-05-04","arxiv_id":"2505.03836","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-task-arithmetic-for-zero-shot","slug":"investigating-task-arithmetic-for-zero-shot","title":"Investigating Task Arithmetic for Zero-Shot Information Retrieval","date":"2025-05-01","arxiv_id":"2505.00649","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-conversational-search-via-topical","slug":"efficient-conversational-search-via-topical","title":"Efficient Conversational Search via Topical Locality in Dense Retrieval","date":"2025-04-30","arxiv_id":"2504.21507","repositories_listed":1,"syntology":null},{"url":"/paper/talk-before-you-retrieve-agent-led","slug":"talk-before-you-retrieve-agent-led","title":"Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA","date":"2025-04-30","arxiv_id":"2504.21252","repositories_listed":1,"syntology":null},{"url":"/paper/information-retrieval-in-the-age-of","slug":"information-retrieval-in-the-age-of","title":"Information Retrieval in the Age of Generative AI: The RGB Model","date":"2025-04-29","arxiv_id":"2504.20610","repositories_listed":1,"syntology":null},{"url":"/paper/universalrag-retrieval-augmented-generation","slug":"universalrag-retrieval-augmented-generation","title":"UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities","date":"2025-04-29","arxiv_id":"2504.20734","repositories_listed":1,"syntology":null},{"url":"/paper/reconstructing-context-evaluating-advanced","slug":"reconstructing-context-evaluating-advanced","title":"Reconstructing Context: Evaluating Advanced Chunking Strategies for Retrieval-Augmented Generation","date":"2025-04-28","arxiv_id":"2504.19754","repositories_listed":1,"syntology":null},{"url":"/paper/treehop-generate-and-filter-next-query","slug":"treehop-generate-and-filter-next-query","title":"TreeHop: Generate and Filter Next Query Embeddings Efficiently for Multi-hop Question Answering","date":"2025-04-28","arxiv_id":"2504.20114","repositories_listed":1,"syntology":null},{"url":"/paper/browsecomp-zh-benchmarking-web-browsing","slug":"browsecomp-zh-benchmarking-web-browsing","title":"BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese","date":"2025-04-27","arxiv_id":"2504.19314","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/browsecomp-zh-benchmarking-web-browsing#ran","syntology_url":"https://syntology.ai/paper/2504.19314","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.19314"}},"official":{"repos":["palin2018/browsecomp-zh"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-speech-to-speech-dialogue-modeling","slug":"enhancing-speech-to-speech-dialogue-modeling","title":"Enhancing Speech-to-Speech Dialogue Modeling with End-to-End Retrieval-Augmented Generation","date":"2025-04-27","arxiv_id":"2505.00028","repositories_listed":1,"syntology":null},{"url":"/paper/camel-cross-modality-adaptive-meta-learning","slug":"camel-cross-modality-adaptive-meta-learning","title":"CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person Retrieval","date":"2025-04-26","arxiv_id":"2504.18782","repositories_listed":1,"syntology":null},{"url":"/paper/smartfinrag-interactive-modularized-financial","slug":"smartfinrag-interactive-modularized-financial","title":"SMARTFinRAG: Interactive Modularized Financial RAG Benchmark","date":"2025-04-25","arxiv_id":"2504.18024","repositories_listed":1,"syntology":null},{"url":"/paper/alignrag-an-adaptable-framework-for-resolving","slug":"alignrag-an-adaptable-framework-for-resolving","title":"AlignRAG: Leveraging Critique Learning for Evidence-Sensitive Retrieval-Augmented Reasoning","date":"2025-04-21","arxiv_id":"2504.14858","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/alignrag-an-adaptable-framework-for-resolving#ran","syntology_url":"https://syntology.ai/paper/2504.14858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.14858"}},"official":{"repos":["qqw-ing/rag-reasonalignment"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/efficient-document-retrieval-with-g-retriever","slug":"efficient-document-retrieval-with-g-retriever","title":"Efficient Document Retrieval with G-Retriever","date":"2025-04-21","arxiv_id":"2504.14955","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-ell-0-sparsification-for-inference","slug":"exploring-ell-0-sparsification-for-inference","title":"Exploring $\\ell_0$ Sparsification for Inference-free Sparse Retrievers","date":"2025-04-21","arxiv_id":"2504.14839","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/exploring-ell-0-sparsification-for-inference#ran","syntology_url":"https://syntology.ai/paper/2504.14839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.14839"}},"official":{"repos":["zhichao-aws/opensearch-sparse-model-tuning-sample"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/retrieval-augmented-generation-evaluation-in","slug":"retrieval-augmented-generation-evaluation-in","title":"Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey","date":"2025-04-21","arxiv_id":"2504.14891","repositories_listed":1,"syntology":null},{"url":"/paper/template-based-financial-report-generation-in","slug":"template-based-financial-report-generation-in","title":"Template-Based Financial Report Generation in Agentic and Decomposed Information Retrieval","date":"2025-04-19","arxiv_id":"2504.14233","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-automatic-cad-annotations-for","slug":"leveraging-automatic-cad-annotations-for","title":"Leveraging Automatic CAD Annotations for Supervised Learning in 3D Scene Understanding","date":"2025-04-18","arxiv_id":"2504.13580","repositories_listed":1,"syntology":null},{"url":"/paper/building-russian-benchmark-for-evaluation-of","slug":"building-russian-benchmark-for-evaluation-of","title":"Building Russian Benchmark for Evaluation of Information Retrieval Models","date":"2025-04-17","arxiv_id":"2504.12879","repositories_listed":1,"syntology":null},{"url":"/paper/can-llms-reason-over-extended-multilingual","slug":"can-llms-reason-over-extended-multilingual","title":"Can LLMs reason over extended multilingual contexts? Towards long-context evaluation beyond retrieval and haystacks","date":"2025-04-17","arxiv_id":"2504.12845","repositories_listed":1,"syntology":null},{"url":"/paper/cdf-rag-causal-dynamic-feedback-for-adaptive","slug":"cdf-rag-causal-dynamic-feedback-for-adaptive","title":"CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation","date":"2025-04-17","arxiv_id":"2504.12560","repositories_listed":1,"syntology":null},{"url":"/paper/estimating-optimal-context-length-for-hybrid","slug":"estimating-optimal-context-length-for-hybrid","title":"Estimating Optimal Context Length for Hybrid Retrieval-augmented Multi-document Summarization","date":"2025-04-17","arxiv_id":"2504.12972","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-augmented-generation-with-3","slug":"retrieval-augmented-generation-with-3","title":"Retrieval-Augmented Generation with Conflicting Evidence","date":"2025-04-17","arxiv_id":"2504.13079","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/retrieval-augmented-generation-with-3#ran","syntology_url":"https://syntology.ai/paper/2504.13079","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.13079"}},"official":{"repos":["hannight/ramdocs"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-lossless-token-pruning-in-late","slug":"towards-lossless-token-pruning-in-late","title":"Towards Lossless Token Pruning in Late-Interaction Retrieval Models","date":"2025-04-17","arxiv_id":"2504.12778","repositories_listed":1,"syntology":null},{"url":"/paper/a-human-ai-comparative-analysis-of-prompt","slug":"a-human-ai-comparative-analysis-of-prompt","title":"A Human-AI Comparative Analysis of Prompt Sensitivity in LLM-Based Relevance Judgment","date":"2025-04-16","arxiv_id":"2504.12408","repositories_listed":1,"syntology":null},{"url":"/paper/learning-compatible-multi-prize-subnetworks","slug":"learning-compatible-multi-prize-subnetworks","title":"Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval","date":"2025-04-16","arxiv_id":"2504.11879","repositories_listed":1,"syntology":null},{"url":"/paper/neighbor-based-feature-and-index-enhancement","slug":"neighbor-based-feature-and-index-enhancement","title":"Neighbor-Based Feature and Index Enhancement for Person Re-Identification","date":"2025-04-16","arxiv_id":"2504.11798","repositories_listed":1,"syntology":null},{"url":"/paper/ai2-scholar-qa-organized-literature-synthesis","slug":"ai2-scholar-qa-organized-literature-synthesis","title":"Ai2 Scholar QA: Organized Literature Synthesis with Attribution","date":"2025-04-15","arxiv_id":"2504.10861","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ai2-scholar-qa-organized-literature-synthesis#ran","syntology_url":"https://syntology.ai/paper/2504.10861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.10861"}},"official":{"repos":["allenai/ai2-scholarqa-lib"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/reinforcing-compositional-retrieval","slug":"reinforcing-compositional-retrieval","title":"Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts","date":"2025-04-15","arxiv_id":"2504.11420","repositories_listed":1,"syntology":null},{"url":"/paper/towards-efficient-partially-relevant-video","slug":"towards-efficient-partially-relevant-video","title":"Towards Efficient Partially Relevant Video Retrieval with Active Moment Discovering","date":"2025-04-15","arxiv_id":"2504.10920","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-efficient-partially-relevant-video#ran","syntology_url":"https://syntology.ai/paper/2504.10920","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.10920"}},"official":{"repos":["songpipi/amdnet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/visual-re-ranking-with-non-visual-side","slug":"visual-re-ranking-with-non-visual-side","title":"Visual Re-Ranking with Non-Visual Side Information","date":"2025-04-15","arxiv_id":"2504.11134","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-of-personalization-from-rag-to-agent","slug":"a-survey-of-personalization-from-rag-to-agent","title":"A Survey of Personalization: From RAG to Agent","date":"2025-04-14","arxiv_id":"2504.10147","repositories_listed":1,"syntology":null},{"url":"/paper/focus-on-local-finding-reliable-1","slug":"focus-on-local-finding-reliable-1","title":"Focus on Local: Finding Reliable Discriminative Regions for Visual Place Recognition","date":"2025-04-14","arxiv_id":"2504.09881","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/focus-on-local-finding-reliable-1#ran","syntology_url":"https://syntology.ai/paper/2504.09881","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.09881"}},"official":{"repos":["chenshunpeng/FoL"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rakg-document-level-retrieval-augmented","slug":"rakg-document-level-retrieval-augmented","title":"RAKG:Document-level Retrieval Augmented Knowledge Graph Construction","date":"2025-04-14","arxiv_id":"2504.09823","repositories_listed":1,"syntology":null},{"url":"/paper/up-person-unified-parameter-efficient","slug":"up-person-unified-parameter-efficient","title":"UP-Person: Unified Parameter-Efficient Transfer Learning for Text-based Person Retrieval","date":"2025-04-14","arxiv_id":"2504.10084","repositories_listed":1,"syntology":null},{"url":"/paper/hm-rag-hierarchical-multi-agent-multimodal","slug":"hm-rag-hierarchical-multi-agent-multimodal","title":"HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation","date":"2025-04-13","arxiv_id":"2504.12330","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-sparse-phase-retrieval-via-a-quasi","slug":"optimal-sparse-phase-retrieval-via-a-quasi","title":"Optimal sparse phase retrieval via a quasi-Bayesian approach","date":"2025-04-13","arxiv_id":"2504.09509","repositories_listed":1,"syntology":null},{"url":"/paper/pneuma-leveraging-llms-for-tabular-data","slug":"pneuma-leveraging-llms-for-tabular-data","title":"Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System","date":"2025-04-12","arxiv_id":"2504.09207","repositories_listed":1,"syntology":null},{"url":"/paper/onset-ontology-and-semantic-exploration","slug":"onset-ontology-and-semantic-exploration","title":"OnSET: Ontology and Semantic Exploration Toolkit","date":"2025-04-11","arxiv_id":"2504.08373","repositories_listed":1,"syntology":null},{"url":"/paper/out-of-style-rag-s-fragility-to-linguistic","slug":"out-of-style-rag-s-fragility-to-linguistic","title":"Out of Style: RAG's Fragility to Linguistic Variation","date":"2025-04-11","arxiv_id":"2504.08231","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/out-of-style-rag-s-fragility-to-linguistic#ran","syntology_url":"https://syntology.ai/paper/2504.08231","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.08231"}},"official":{"repos":["springcty/rag-fragility-to-linguistic-variation"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rag-vr-leveraging-retrieval-augmented","slug":"rag-vr-leveraging-retrieval-augmented","title":"RAG-VR: Leveraging Retrieval-Augmented Generation for 3D Question Answering in VR Environments","date":"2025-04-11","arxiv_id":"2504.08256","repositories_listed":1,"syntology":null},{"url":"/paper/the-other-side-of-the-coin-exploring-fairness","slug":"the-other-side-of-the-coin-exploring-fairness","title":"The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented Generation","date":"2025-04-11","arxiv_id":"2504.12323","repositories_listed":1,"syntology":null},{"url":"/paper/a-system-for-comprehensive-assessment-of-rag","slug":"a-system-for-comprehensive-assessment-of-rag","title":"A System for Comprehensive Assessment of RAG Frameworks","date":"2025-04-10","arxiv_id":"2504.07803","repositories_listed":1,"syntology":null},{"url":"/paper/mrd-rag-enhancing-medical-diagnosis-with","slug":"mrd-rag-enhancing-medical-diagnosis-with","title":"MRD-RAG: Enhancing Medical Diagnosis with Multi-Round Retrieval-Augmented Generation","date":"2025-04-10","arxiv_id":"2504.07724","repositories_listed":1,"syntology":null},{"url":"/paper/plan-and-refine-diverse-and-comprehensive","slug":"plan-and-refine-diverse-and-comprehensive","title":"Plan-and-Refine: Diverse and Comprehensive Retrieval-Augmented Generation","date":"2025-04-10","arxiv_id":"2504.07794","repositories_listed":1,"syntology":null},{"url":"/paper/reanimator-reanimate-retrieval-test","slug":"reanimator-reanimate-retrieval-test","title":"REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources","date":"2025-04-10","arxiv_id":"2504.07584","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-augmented-generation-with-2","slug":"retrieval-augmented-generation-with-2","title":"Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation","date":"2025-04-08","arxiv_id":"2504.05731","repositories_listed":1,"syntology":null},{"url":"/paper/to-match-or-not-to-match-revisiting-image","slug":"to-match-or-not-to-match-revisiting-image","title":"To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition","date":"2025-04-08","arxiv_id":"2504.06116","repositories_listed":1,"syntology":null},{"url":"/paper/collab-rag-boosting-retrieval-augmented","slug":"collab-rag-boosting-retrieval-augmented","title":"Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration","date":"2025-04-07","arxiv_id":"2504.04915","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/collab-rag-boosting-retrieval-augmented#ran","syntology_url":"https://syntology.ai/paper/2504.04915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.04915"}},"official":{"repos":["ritaranx/collab-rag"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/graphraft-retrieval-augmented-fine-tuning-for","slug":"graphraft-retrieval-augmented-fine-tuning-for","title":"GraphRAFT: Retrieval Augmented Fine-Tuning for Knowledge Graphs on Graph Databases","date":"2025-04-07","arxiv_id":"2504.05478","repositories_listed":1,"syntology":null},{"url":"/paper/tc-mgc-text-conditioned-multi-grained","slug":"tc-mgc-text-conditioned-multi-grained","title":"TC-MGC: Text-Conditioned Multi-Grained Contrastive Learning for Text-Video Retrieval","date":"2025-04-07","arxiv_id":"2504.04707","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tc-mgc-text-conditioned-multi-grained#ran","syntology_url":"https://syntology.ai/paper/2504.04707","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.04707"}},"official":{"repos":["jingxiaolun/tc-mgc"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/autossvh-exploring-automated-frame-sampling","slug":"autossvh-exploring-automated-frame-sampling","title":"AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing","date":"2025-04-04","arxiv_id":"2504.03587","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":6,"n_ran_checked":8,"n_instrument":1,"n_unverified":5,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":14,"phrase":"9 ran (of which 6 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/autossvh-exploring-automated-frame-sampling#ran","syntology_url":"https://syntology.ai/paper/2504.03587","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.03587"}},"official":{"repos":["EliSpectre/CVPR25-AutoSSVH"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":6,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-dynamic-clustering-based-document","slug":"efficient-dynamic-clustering-based-document","title":"Efficient Dynamic Clustering-Based Document Compression for Retrieval-Augmented-Generation","date":"2025-04-04","arxiv_id":"2504.03165","repositories_listed":1,"syntology":null},{"url":"/paper/generative-ai-enhanced-financial-risk","slug":"generative-ai-enhanced-financial-risk","title":"Generative AI Enhanced Financial Risk Management Information Retrieval","date":"2025-04-04","arxiv_id":"2504.06293","repositories_listed":1,"syntology":null},{"url":"/paper/talk2x-an-open-source-toolkit-facilitating","slug":"talk2x-an-open-source-toolkit-facilitating","title":"Talk2X -- An Open-Source Toolkit Facilitating Deployment of LLM-Powered Chatbots on the Web","date":"2025-04-04","arxiv_id":"2504.03343","repositories_listed":1,"syntology":null},{"url":"/paper/cascade-your-datasets-for-cross-mode","slug":"cascade-your-datasets-for-cross-mode","title":"CASCADE Your Datasets for Cross-Mode Knowledge Retrieval of Language Models","date":"2025-04-02","arxiv_id":"2504.01450","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-constant-space-multi-vector","slug":"efficient-constant-space-multi-vector","title":"Efficient Constant-Space Multi-Vector Retrieval","date":"2025-04-02","arxiv_id":"2504.01818","repositories_listed":1,"syntology":null},{"url":"/paper/large-legal-retrieval-augmented-generation","slug":"large-legal-retrieval-augmented-generation","title":"LARGE: Legal Retrieval Augmented Generation Evaluation Tool","date":"2025-04-02","arxiv_id":"2504.01840","repositories_listed":1,"syntology":null},{"url":"/paper/prophet-an-inferable-future-forecasting","slug":"prophet-an-inferable-future-forecasting","title":"PROPHET: An Inferable Future Forecasting Benchmark with Causal Intervened Likelihood Estimation","date":"2025-04-02","arxiv_id":"2504.01509","repositories_listed":1,"syntology":null},{"url":"/paper/beyond-quacking-deep-integration-of-language","slug":"beyond-quacking-deep-integration-of-language","title":"Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB","date":"2025-04-01","arxiv_id":"2504.01157","repositories_listed":1,"syntology":null},{"url":"/paper/wikivideo-article-generation-from-multiple","slug":"wikivideo-article-generation-from-multiple","title":"WikiVideo: Article Generation from Multiple Videos","date":"2025-04-01","arxiv_id":"2504.00939","repositories_listed":1,"syntology":null},{"url":"/paper/better-wit-than-wealth-dynamic-parametric","slug":"better-wit-than-wealth-dynamic-parametric","title":"Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement","date":"2025-03-31","arxiv_id":"2503.23895","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/better-wit-than-wealth-dynamic-parametric#ran","syntology_url":"https://syntology.ai/paper/2503.23895","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.23895"}},"official":{"repos":["trae1oung/dyprag"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/combining-query-performance-predictors-a","slug":"combining-query-performance-predictors-a","title":"Combining Query Performance Predictors: A Reproducibility Study","date":"2025-03-31","arxiv_id":"2503.24251","repositories_listed":1,"syntology":null},{"url":"/paper/interactivesurvey-an-llm-based-personalized","slug":"interactivesurvey-an-llm-based-personalized","title":"InteractiveSurvey: An LLM-based Personalized and Interactive Survey Paper Generation System","date":"2025-03-31","arxiv_id":"2504.08762","repositories_listed":1,"syntology":null}],"record_sha256":"15ebe1ec1e234f9c55f010ec336d22065d81221405d1e62c3fcbaefbd4a30f0f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}