{"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/18","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":18,"pages_in_order":143,"rows_per_page":100,"rows":[1701,1800],"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/17","next":"/task/retrieval/papers/19","papers":[{"url":"/paper/h-star-llm-driven-hybrid-sql-text-adaptive","slug":"h-star-llm-driven-hybrid-sql-text-adaptive","title":"H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables","date":"2024-06-29","arxiv_id":"2407.05952","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/h-star-llm-driven-hybrid-sql-text-adaptive#ran","syntology_url":"https://syntology.ai/paper/2407.05952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05952"}},"official":{"repos":["nikhilsab/h-star"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/candy-a-benchmark-for-continuous-approximate","slug":"candy-a-benchmark-for-continuous-approximate","title":"CANDY: A Benchmark for Continuous Approximate Nearest Neighbor Search with Dynamic Data Ingestion","date":"2024-06-28","arxiv_id":"2406.19651","repositories_listed":1,"syntology":null},{"url":"/paper/learning-interpretable-legal-case-retrieval","slug":"learning-interpretable-legal-case-retrieval","title":"Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation","date":"2024-06-28","arxiv_id":"2406.19760","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 1 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; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/learning-interpretable-legal-case-retrieval#ran","syntology_url":"https://syntology.ai/paper/2406.19760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.19760"}},"official":{"repos":["ChenlongDeng/KELLER"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-surprisingly-simple-yet-effective-multi","slug":"a-surprisingly-simple-yet-effective-multi","title":"A Surprisingly Simple yet Effective Multi-Query Rewriting Method for Conversational Passage Retrieval","date":"2024-06-27","arxiv_id":"2406.18960","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-course-recommendations-with-t5","slug":"efficient-course-recommendations-with-t5","title":"Efficient course recommendations with T5-based ranking and summarization","date":"2024-06-27","arxiv_id":"2406.19018","repositories_listed":1,"syntology":null},{"url":"/paper/from-artificial-needles-to-real-haystacks","slug":"from-artificial-needles-to-real-haystacks","title":"From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data","date":"2024-06-27","arxiv_id":"2406.19292","repositories_listed":1,"syntology":null},{"url":"/paper/learning-retrieval-augmentation-for","slug":"learning-retrieval-augmentation-for","title":"Learning Retrieval Augmentation for Personalized Dialogue Generation","date":"2024-06-27","arxiv_id":"2406.18847","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/learning-retrieval-augmentation-for#ran","syntology_url":"https://syntology.ai/paper/2406.18847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.18847"}},"official":{"repos":["hqsiswiliam/lapdog"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/seakr-self-aware-knowledge-retrieval-for","slug":"seakr-self-aware-knowledge-retrieval-for","title":"SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation","date":"2024-06-27","arxiv_id":"2406.19215","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":12,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/seakr-self-aware-knowledge-retrieval-for#ran","syntology_url":"https://syntology.ai/paper/2406.19215","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.19215"}},"official":{"repos":["thu-keg/seakr"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/trustuqa-a-trustful-framework-for-unified","slug":"trustuqa-a-trustful-framework-for-unified","title":"TrustUQA: A Trustful Framework for Unified Structured Data Question Answering","date":"2024-06-27","arxiv_id":"2406.18916","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":11,"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) · 0 unverified","sample_list":"/paper/trustuqa-a-trustful-framework-for-unified#ran","syntology_url":"https://syntology.ai/paper/2406.18916","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.18916"}},"official":{"repos":["zjukg/trustuqa"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-the-consistency-in-cross-lingual","slug":"improving-the-consistency-in-cross-lingual","title":"Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning","date":"2024-06-26","arxiv_id":"2406.18254","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-graph-enhanced-retrieval-augmented","slug":"knowledge-graph-enhanced-retrieval-augmented","title":"Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis","date":"2024-06-26","arxiv_id":"2406.18114","repositories_listed":1,"syntology":null},{"url":"/paper/the-surprising-effectiveness-of-multimodal","slug":"the-surprising-effectiveness-of-multimodal","title":"The Surprising Effectiveness of Multimodal Large Language Models for Video Moment Retrieval","date":"2024-06-26","arxiv_id":"2406.18113","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/the-surprising-effectiveness-of-multimodal#ran","syntology_url":"https://syntology.ai/paper/2406.18113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.18113"}},"official":{"repos":["sudo-Boris/mr-Blip"],"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/understand-what-llm-needs-dual-preference","slug":"understand-what-llm-needs-dual-preference","title":"Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation","date":"2024-06-26","arxiv_id":"2406.18676","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/understand-what-llm-needs-dual-preference#ran","syntology_url":"https://syntology.ai/paper/2406.18676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.18676"}},"official":{"repos":["dongguanting/dpa-rag"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-tool-retrieval-with-iterative","slug":"enhancing-tool-retrieval-with-iterative","title":"Enhancing Tool Retrieval with Iterative Feedback from Large Language Models","date":"2024-06-25","arxiv_id":"2406.17465","repositories_listed":1,"syntology":null},{"url":"/paper/entropy-based-decoding-for-retrieval","slug":"entropy-based-decoding-for-retrieval","title":"Entropy-Based Decoding for Retrieval-Augmented Large Language Models","date":"2024-06-25","arxiv_id":"2406.17519","repositories_listed":1,"syntology":null},{"url":"/paper/graphsnapshot-graph-machine-learning","slug":"graphsnapshot-graph-machine-learning","title":"GraphSnapShot: Caching Local Structure for Fast Graph Learning","date":"2024-06-25","arxiv_id":"2406.17918","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/graphsnapshot-graph-machine-learning#ran","syntology_url":"https://syntology.ai/paper/2406.17918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17918"}},"official":{"repos":["noakliu/graphsnapshot"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lumberchunker-long-form-narrative-document","slug":"lumberchunker-long-form-narrative-document","title":"LumberChunker: Long-Form Narrative Document Segmentation","date":"2024-06-25","arxiv_id":"2406.17526","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lumberchunker-long-form-narrative-document#ran","syntology_url":"https://syntology.ai/paper/2406.17526","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17526"}},"official":{"repos":["joaodsmarques/lumberchunker"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/retrieval-augmented-instruction-tuning-for","slug":"retrieval-augmented-instruction-tuning-for","title":"Retrieval Augmented Instruction Tuning for Open NER with Large Language Models","date":"2024-06-25","arxiv_id":"2406.17305","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-style-in-context-learning-for-few","slug":"retrieval-style-in-context-learning-for-few","title":"Retrieval-style In-Context Learning for Few-shot Hierarchical Text Classification","date":"2024-06-25","arxiv_id":"2406.17534","repositories_listed":1,"syntology":null},{"url":"/paper/clerc-a-dataset-for-legal-case-retrieval-and","slug":"clerc-a-dataset-for-legal-case-retrieval-and","title":"CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation","date":"2024-06-24","arxiv_id":"2406.17186","repositories_listed":1,"syntology":null},{"url":"/paper/dexter-a-benchmark-for-open-domain-complex","slug":"dexter-a-benchmark-for-open-domain-complex","title":"DEXTER: A Benchmark for open-domain Complex Question Answering using LLMs","date":"2024-06-24","arxiv_id":"2406.17158","repositories_listed":1,"syntology":null},{"url":"/paper/large-language-models-are-cross-lingual","slug":"large-language-models-are-cross-lingual","title":"Large Language Models Are Cross-Lingual Knowledge-Free Reasoners","date":"2024-06-24","arxiv_id":"2406.16655","repositories_listed":1,"syntology":null},{"url":"/paper/one-thousand-and-one-pairs-a-novel-challenge","slug":"one-thousand-and-one-pairs-a-novel-challenge","title":"One Thousand and One Pairs: A \"novel\" challenge for long-context language models","date":"2024-06-24","arxiv_id":"2406.16264","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-laws-for-linear-complexity-language","slug":"scaling-laws-for-linear-complexity-language","title":"Scaling Laws for Linear Complexity Language Models","date":"2024-06-24","arxiv_id":"2406.16690","repositories_listed":1,"syntology":null},{"url":"/paper/training-free-exponential-extension-of","slug":"training-free-exponential-extension-of","title":"Training-Free Exponential Context Extension via Cascading KV Cache","date":"2024-06-24","arxiv_id":"2406.17808","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/training-free-exponential-extension-of#ran","syntology_url":"https://syntology.ai/paper/2406.17808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17808"}},"official":{"repos":["jeffwillette/cascading_kv_cache"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-all-mlp-sequence-modeling-architecture","slug":"an-all-mlp-sequence-modeling-architecture","title":"An All-MLP Sequence Modeling Architecture That Excels at Copying","date":"2024-06-23","arxiv_id":"2406.16168","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/an-all-mlp-sequence-modeling-architecture#ran","syntology_url":"https://syntology.ai/paper/2406.16168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.16168"}},"official":{"repos":["kerner-lab/causal-relation-networks"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/breaking-the-frame-image-retrieval-by-visual","slug":"breaking-the-frame-image-retrieval-by-visual","title":"Breaking the Frame: Visual Place Recognition by Overlap Prediction","date":"2024-06-23","arxiv_id":"2406.16204","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"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) · 1 unverified","sample_list":"/paper/breaking-the-frame-image-retrieval-by-visual#ran","syntology_url":"https://syntology.ai/paper/2406.16204","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.16204"}},"official":{"repos":["weitong8591/vop"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-commentary-strategies-for-imperfect","slug":"enhancing-commentary-strategies-for-imperfect","title":"Enhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentary","date":"2024-06-23","arxiv_id":"2406.17807","repositories_listed":1,"syntology":null},{"url":"/paper/fs-rag-a-frame-semantics-based-approach-for","slug":"fs-rag-a-frame-semantics-based-approach-for","title":"FS-RAG: A Frame Semantics Based Approach for Improved Factual Accuracy in Large Language Models","date":"2024-06-23","arxiv_id":"2406.16167","repositories_listed":1,"syntology":null},{"url":"/paper/preference-tuning-for-toxicity-mitigation","slug":"preference-tuning-for-toxicity-mitigation","title":"Preference Tuning For Toxicity Mitigation Generalizes Across Languages","date":"2024-06-23","arxiv_id":"2406.16235","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/preference-tuning-for-toxicity-mitigation#ran","syntology_url":"https://syntology.ai/paper/2406.16235","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.16235"}},"official":{"repos":["batsresearch/cross-lingual-detox"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-tale-of-trust-and-accuracy-base-vs-instruct","slug":"a-tale-of-trust-and-accuracy-base-vs-instruct","title":"A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems","date":"2024-06-21","arxiv_id":"2406.14972","repositories_listed":1,"syntology":null},{"url":"/paper/care-a-benchmark-suite-for-the-classification","slug":"care-a-benchmark-suite-for-the-classification","title":"CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes","date":"2024-06-21","arxiv_id":"2406.15669","repositories_listed":1,"syntology":null},{"url":"/paper/r-b-rhythm-and-brain-cross-subject-decoding","slug":"r-b-rhythm-and-brain-cross-subject-decoding","title":"R&B -- Rhythm and Brain: Cross-subject Decoding of Music from Human Brain Activity","date":"2024-06-21","arxiv_id":"2406.15537","repositories_listed":1,"syntology":null},{"url":"/paper/retrieval-augmented-zero-shot-text","slug":"retrieval-augmented-zero-shot-text","title":"Retrieval Augmented Zero-Shot Text Classification","date":"2024-06-21","arxiv_id":"2406.15241","repositories_listed":1,"syntology":null},{"url":"/paper/uda-a-benchmark-suite-for-retrieval-augmented","slug":"uda-a-benchmark-suite-for-retrieval-augmented","title":"UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document Analysis","date":"2024-06-21","arxiv_id":"2406.15187","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/uda-a-benchmark-suite-for-retrieval-augmented#ran","syntology_url":"https://syntology.ai/paper/2406.15187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.15187"}},"official":{"repos":["qinchuanhui/uda-benchmark"],"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","unlocated"]}}},{"url":"/paper/augmenting-query-and-passage-for-retrieval","slug":"augmenting-query-and-passage-for-retrieval","title":"QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMs","date":"2024-06-20","arxiv_id":"2406.14277","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/augmenting-query-and-passage-for-retrieval#ran","syntology_url":"https://syntology.ai/paper/2406.14277","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14277"}},"official":{"repos":["kmswin1/qpaug"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/coderag-bench-can-retrieval-augment-code","slug":"coderag-bench-can-retrieval-augment-code","title":"CodeRAG-Bench: Can Retrieval Augment Code Generation?","date":"2024-06-20","arxiv_id":"2406.14497","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/coderag-bench-can-retrieval-augment-code#ran","syntology_url":"https://syntology.ai/paper/2406.14497","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14497"}},"official":{"repos":["code-rag-bench/code-rag-bench"],"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/diras-efficient-llm-assisted-annotation-of","slug":"diras-efficient-llm-assisted-annotation-of","title":"DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation","date":"2024-06-20","arxiv_id":"2406.14162","repositories_listed":1,"syntology":null},{"url":"/paper/eager-two-stream-generative-recommender-with-1","slug":"eager-two-stream-generative-recommender-with-1","title":"EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration","date":"2024-06-20","arxiv_id":"2406.14017","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/eager-two-stream-generative-recommender-with-1#ran","syntology_url":"https://syntology.ai/paper/2406.14017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14017"}},"official":{"repos":["yewzz/EAGER"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluating-rag-fusion-with-ragelo-an","slug":"evaluating-rag-fusion-with-ragelo-an","title":"Evaluating RAG-Fusion with RAGElo: an Automated Elo-based Framework","date":"2024-06-20","arxiv_id":"2406.14783","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/evaluating-rag-fusion-with-ragelo-an#ran","syntology_url":"https://syntology.ai/paper/2406.14783","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14783"}},"official":{"repos":["zetaalphavector/ragelo"],"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/insights-into-llm-long-context-failures-when","slug":"insights-into-llm-long-context-failures-when","title":"Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell","date":"2024-06-20","arxiv_id":"2406.14673","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/insights-into-llm-long-context-failures-when#ran","syntology_url":"https://syntology.ai/paper/2406.14673","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14673"}},"official":{"repos":["TaiMingLu/know-dont-tell"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-plan-for-retrieval-augmented","slug":"learning-to-plan-for-retrieval-augmented","title":"Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs","date":"2024-06-20","arxiv_id":"2406.14282","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/learning-to-plan-for-retrieval-augmented#ran","syntology_url":"https://syntology.ai/paper/2406.14282","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14282"}},"official":{"repos":["zjukg/lpkg"],"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/can-long-context-language-models-subsume","slug":"can-long-context-language-models-subsume","title":"Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?","date":"2024-06-19","arxiv_id":"2406.13121","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":0,"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/can-long-context-language-models-subsume#ran","syntology_url":"https://syntology.ai/paper/2406.13121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13121"}},"official":{"repos":["google-deepmind/loft"],"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/clip-branches-interactive-fine-tuning-for","slug":"clip-branches-interactive-fine-tuning-for","title":"CLIP-Branches: Interactive Fine-Tuning for Text-Image Retrieval","date":"2024-06-19","arxiv_id":"2406.13322","repositories_listed":1,"syntology":null},{"url":"/paper/instructrag-instructing-retrieval-augmented","slug":"instructrag-instructing-retrieval-augmented","title":"InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales","date":"2024-06-19","arxiv_id":"2406.13629","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/instructrag-instructing-retrieval-augmented#ran","syntology_url":"https://syntology.ai/paper/2406.13629","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13629"}},"official":{"repos":["weizhepei/instructrag"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/model-internals-based-answer-attribution-for","slug":"model-internals-based-answer-attribution-for","title":"Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13663","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":0,"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/model-internals-based-answer-attribution-for#ran","syntology_url":"https://syntology.ai/paper/2406.13663","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13663"}},"official":{"repos":["betswish/mirage"],"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/multi-meta-rag-improving-rag-for-multi-hop","slug":"multi-meta-rag-improving-rag-for-multi-hop","title":"Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted Metadata","date":"2024-06-19","arxiv_id":"2406.13213","repositories_listed":1,"syntology":null},{"url":"/paper/r-2ag-incorporating-retrieval-information","slug":"r-2ag-incorporating-retrieval-information","title":"R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13249","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/r-2ag-incorporating-retrieval-information#ran","syntology_url":"https://syntology.ai/paper/2406.13249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.13249"}},"official":{"repos":["yefd/RRAG"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/stackrag-agent-improving-developer-answers","slug":"stackrag-agent-improving-developer-answers","title":"StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13840","repositories_listed":1,"syntology":null},{"url":"/paper/synchronous-faithfulness-monitoring-for","slug":"synchronous-faithfulness-monitoring-for","title":"Synchronous Faithfulness Monitoring for Trustworthy Retrieval-Augmented Generation","date":"2024-06-19","arxiv_id":"2406.13692","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-multimodal-framework-for-remote","slug":"towards-a-multimodal-framework-for-remote","title":"Towards a multimodal framework for remote sensing image change retrieval and captioning","date":"2024-06-19","arxiv_id":"2406.13424","repositories_listed":1,"syntology":null},{"url":"/paper/defending-against-social-engineering-attacks","slug":"defending-against-social-engineering-attacks","title":"Defending Against Social Engineering Attacks in the Age of LLMs","date":"2024-06-18","arxiv_id":"2406.12263","repositories_listed":1,"syntology":null},{"url":"/paper/planrag-a-plan-then-retrieval-augmented","slug":"planrag-a-plan-then-retrieval-augmented","title":"PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers","date":"2024-06-18","arxiv_id":"2406.12430","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/planrag-a-plan-then-retrieval-augmented#ran","syntology_url":"https://syntology.ai/paper/2406.12430","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12430"}},"official":{"repos":["myeon9h/planrag"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/symmetric-multi-similarity-loss-for-epic","slug":"symmetric-multi-similarity-loss-for-epic","title":"Symmetric Multi-Similarity Loss for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2024","date":"2024-06-18","arxiv_id":"2406.12256","repositories_listed":1,"syntology":null},{"url":"/paper/unified-active-retrieval-for-retrieval","slug":"unified-active-retrieval-for-retrieval","title":"Unified Active Retrieval for Retrieval Augmented Generation","date":"2024-06-18","arxiv_id":"2406.12534","repositories_listed":1,"syntology":null},{"url":"/paper/avatar-optimizing-llm-agents-for-tool","slug":"avatar-optimizing-llm-agents-for-tool","title":"AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning","date":"2024-06-17","arxiv_id":"2406.11200","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/avatar-optimizing-llm-agents-for-tool#ran","syntology_url":"https://syntology.ai/paper/2406.11200","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11200"}},"official":{"repos":["zou-group/avatar"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/citrus-chunked-instruction-aware-state","slug":"citrus-chunked-instruction-aware-state","title":"CItruS: Chunked Instruction-aware State Eviction for Long Sequence Modeling","date":"2024-06-17","arxiv_id":"2406.12018","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":10,"phrase":"5 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; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/citrus-chunked-instruction-aware-state#ran","syntology_url":"https://syntology.ai/paper/2406.12018","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12018"}},"official":{"repos":["ybai-nlp/CItruS"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/cram-credibility-aware-attention-modification","slug":"cram-credibility-aware-attention-modification","title":"CrAM: Credibility-Aware Attention Modification in LLMs for Combating Misinformation in RAG","date":"2024-06-17","arxiv_id":"2406.11497","repositories_listed":1,"syntology":null},{"url":"/paper/dtgb-a-comprehensive-benchmark-for-dynamic","slug":"dtgb-a-comprehensive-benchmark-for-dynamic","title":"DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs","date":"2024-06-17","arxiv_id":"2406.12072","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/dtgb-a-comprehensive-benchmark-for-dynamic#ran","syntology_url":"https://syntology.ai/paper/2406.12072","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12072"}},"official":{"repos":["zjs123/DTGB"],"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/duoduo-clip-efficient-3d-understanding-with","slug":"duoduo-clip-efficient-3d-understanding-with","title":"Duoduo CLIP: Efficient 3D Understanding with Multi-View Images","date":"2024-06-17","arxiv_id":"2406.11579","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/duoduo-clip-efficient-3d-understanding-with#ran","syntology_url":"https://syntology.ai/paper/2406.11579","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11579"}},"official":{"repos":["3dlg-hcvc/DuoduoCLIP"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/evaluating-the-efficacy-of-open-source-llms","slug":"evaluating-the-efficacy-of-open-source-llms","title":"Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability","date":"2024-06-17","arxiv_id":"2406.11424","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-recognition-via-stage-wise-augmented","slug":"few-shot-recognition-via-stage-wise-augmented","title":"Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuning","date":"2024-06-17","arxiv_id":"2406.11148","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/few-shot-recognition-via-stage-wise-augmented#ran","syntology_url":"https://syntology.ai/paper/2406.11148","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11148"}},"official":{"repos":["tian1327/swat"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/language-modeling-with-editable-external","slug":"language-modeling-with-editable-external","title":"Language Modeling with Editable External Knowledge","date":"2024-06-17","arxiv_id":"2406.11830","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":2,"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/language-modeling-with-editable-external#ran","syntology_url":"https://syntology.ai/paper/2406.11830","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11830"}},"official":{"repos":["belindal/erase"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/multimodal-needle-in-a-haystack-benchmarking","slug":"multimodal-needle-in-a-haystack-benchmarking","title":"Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models","date":"2024-06-17","arxiv_id":"2406.11230","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/multimodal-needle-in-a-haystack-benchmarking#ran","syntology_url":"https://syntology.ai/paper/2406.11230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11230"}},"official":{"repos":["wang-ml-lab/multimodal-needle-in-a-haystack"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/r-eval-a-unified-toolkit-for-evaluating","slug":"r-eval-a-unified-toolkit-for-evaluating","title":"R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models","date":"2024-06-17","arxiv_id":"2406.11681","repositories_listed":1,"syntology":null},{"url":"/paper/satyrn-a-platform-for-analytics-augmented","slug":"satyrn-a-platform-for-analytics-augmented","title":"Satyrn: A Platform for Analytics Augmented Generation","date":"2024-06-17","arxiv_id":"2406.12069","repositories_listed":1,"syntology":null},{"url":"/paper/textit-refiner-restructure-retrieval-content","slug":"textit-refiner-restructure-retrieval-content","title":"Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities","date":"2024-06-17","arxiv_id":"2406.11357","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/textit-refiner-restructure-retrieval-content#ran","syntology_url":"https://syntology.ai/paper/2406.11357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.11357"}},"official":{"repos":["allen-li1231/refiner-rag"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/curating-stopwords-in-marathi-a-tf-idf","slug":"curating-stopwords-in-marathi-a-tf-idf","title":"Curating Stopwords in Marathi: A TF-IDF Approach for Improved Text Analysis and Information Retrieval","date":"2024-06-16","arxiv_id":"2406.11029","repositories_listed":1,"syntology":null},{"url":"/paper/raemollm-retrieval-augmented-llms-for-cross","slug":"raemollm-retrieval-augmented-llms-for-cross","title":"RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning based on Emotional Information","date":"2024-06-16","arxiv_id":"2406.11093","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-cosine-similarity-via-normalized","slug":"revisiting-cosine-similarity-via-normalized","title":"Revisiting Cosine Similarity via Normalized ICA-transformed Embeddings","date":"2024-06-16","arxiv_id":"2406.10984","repositories_listed":1,"syntology":null},{"url":"/paper/bivlc-extending-vision-language","slug":"bivlc-extending-vision-language","title":"BiVLC: Extending Vision-Language Compositionality Evaluation with Text-to-Image Retrieval","date":"2024-06-14","arxiv_id":"2406.09952","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/bivlc-extending-vision-language#ran","syntology_url":"https://syntology.ai/paper/2406.09952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09952"}},"official":{"repos":["imirandam/bivlc"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/climretrieve-a-benchmarking-dataset-for","slug":"climretrieve-a-benchmarking-dataset-for","title":"ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures","date":"2024-06-14","arxiv_id":"2406.09818","repositories_listed":1,"syntology":null},{"url":"/paper/hiro-hierarchical-information-retrieval","slug":"hiro-hierarchical-information-retrieval","title":"HIRO: Hierarchical Information Retrieval Optimization","date":"2024-06-14","arxiv_id":"2406.09979","repositories_listed":1,"syntology":null},{"url":"/paper/neural-concept-binder","slug":"neural-concept-binder","title":"Neural Concept Binder","date":"2024-06-14","arxiv_id":"2406.09949","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":0,"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/neural-concept-binder#ran","syntology_url":"https://syntology.ai/paper/2406.09949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09949"}},"official":{"repos":["ml-research/neuralconceptbinder"],"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/common-and-rare-fundus-diseases","slug":"common-and-rare-fundus-diseases","title":"Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model","date":"2024-06-13","arxiv_id":"2406.09317","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/common-and-rare-fundus-diseases#ran","syntology_url":"https://syntology.ai/paper/2406.09317","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.09317"}},"official":{"repos":["LooKing9218/RetiZero"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/explore-the-limits-of-omni-modal-pretraining","slug":"explore-the-limits-of-omni-modal-pretraining","title":"Explore the Limits of Omni-modal Pretraining at Scale","date":"2024-06-13","arxiv_id":"2406.09412","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-the-spectrum-of-visio-linguistic","slug":"exploring-the-spectrum-of-visio-linguistic","title":"Exploring the Spectrum of Visio-Linguistic Compositionality and Recognition","date":"2024-06-13","arxiv_id":"2406.09388","repositories_listed":1,"syntology":null},{"url":"/paper/reducing-task-discrepancy-of-text-encoders","slug":"reducing-task-discrepancy-of-text-encoders","title":"An Efficient Post-hoc Framework for Reducing Task Discrepancy of Text Encoders for Composed Image Retrieval","date":"2024-06-13","arxiv_id":"2406.09188","repositories_listed":1,"syntology":null},{"url":"/paper/openobj-open-vocabulary-object-level-neural","slug":"openobj-open-vocabulary-object-level-neural","title":"OpenObj: Open-Vocabulary Object-Level Neural Radiance Fields with Fine-Grained Understanding","date":"2024-06-12","arxiv_id":"2406.08009","repositories_listed":1,"syntology":null},{"url":"/paper/benchmarking-vision-language-contrastive","slug":"benchmarking-vision-language-contrastive","title":"Benchmarking Vision-Language Contrastive Methods for Medical Representation Learning","date":"2024-06-11","arxiv_id":"2406.07450","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-language-gaps-in-audio-text","slug":"bridging-language-gaps-in-audio-text","title":"Bridging Language Gaps in Audio-Text Retrieval","date":"2024-06-11","arxiv_id":"2406.07012","repositories_listed":1,"syntology":null},{"url":"/paper/limited-out-of-context-knowledge-reasoning-in","slug":"limited-out-of-context-knowledge-reasoning-in","title":"Large Language Models are Limited in Out-of-Context Knowledge Reasoning","date":"2024-06-11","arxiv_id":"2406.07393","repositories_listed":1,"syntology":null},{"url":"/paper/miners-multilingual-language-models-as","slug":"miners-multilingual-language-models-as","title":"MINERS: Multilingual Language Models as Semantic Retrievers","date":"2024-06-11","arxiv_id":"2406.07424","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/miners-multilingual-language-models-as#ran","syntology_url":"https://syntology.ai/paper/2406.07424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.07424"}},"official":{"repos":["gentaiscool/miners"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/needle-in-a-multimodal-haystack","slug":"needle-in-a-multimodal-haystack","title":"Needle In A Multimodal Haystack","date":"2024-06-11","arxiv_id":"2406.07230","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":12,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/needle-in-a-multimodal-haystack#ran","syntology_url":"https://syntology.ai/paper/2406.07230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.07230"}},"official":{"repos":["opengvlab/mm-niah"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/rs-agent-automating-remote-sensing-tasks","slug":"rs-agent-automating-remote-sensing-tasks","title":"RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent","date":"2024-06-11","arxiv_id":"2406.07089","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/rs-agent-automating-remote-sensing-tasks#ran","syntology_url":"https://syntology.ai/paper/2406.07089","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.07089"}},"official":{"repos":["intellisensing/rs-agent"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/scholarly-question-answering-using-large","slug":"scholarly-question-answering-using-large","title":"Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway","date":"2024-06-11","arxiv_id":"2406.07257","repositories_listed":1,"syntology":null},{"url":"/paper/which-country-is-this-automatic-country","slug":"which-country-is-this-automatic-country","title":"Which Country Is This? Automatic Country Ranking of Street View Photos","date":"2024-06-11","arxiv_id":"2406.07227","repositories_listed":1,"syntology":null},{"url":"/paper/autosurvey-large-language-models-can","slug":"autosurvey-large-language-models-can","title":"AutoSurvey: Large Language Models Can Automatically Write Surveys","date":"2024-06-10","arxiv_id":"2406.10252","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/autosurvey-large-language-models-can#ran","syntology_url":"https://syntology.ai/paper/2406.10252","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.10252"}},"official":{"repos":["autosurveys/autosurvey"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-k-nearest-neighbor-machine","slug":"efficient-k-nearest-neighbor-machine","title":"Efficient k-Nearest-Neighbor Machine Translation with Dynamic Retrieval","date":"2024-06-10","arxiv_id":"2406.06073","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-context-compression-efficiently","slug":"recurrent-context-compression-efficiently","title":"Recurrent Context Compression: Efficiently Expanding the Context Window of LLM","date":"2024-06-10","arxiv_id":"2406.06110","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/recurrent-context-compression-efficiently#ran","syntology_url":"https://syntology.ai/paper/2406.06110","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.06110"}},"official":{"repos":["WUHU-G/RCC_Transformer"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/umbrela-umbrela-is-the-open-source","slug":"umbrela-umbrela-is-the-open-source","title":"UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance Assessor","date":"2024-06-10","arxiv_id":"2406.06519","repositories_listed":1,"syntology":null},{"url":"/paper/growover-how-can-llms-adapt-to-growing-real","slug":"growover-how-can-llms-adapt-to-growing-real","title":"GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?","date":"2024-06-09","arxiv_id":"2406.05606","repositories_listed":1,"syntology":null},{"url":"/paper/hello-again-llm-powered-personalized-agent","slug":"hello-again-llm-powered-personalized-agent","title":"Hello Again! LLM-powered Personalized Agent for Long-term Dialogue","date":"2024-06-09","arxiv_id":"2406.05925","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/hello-again-llm-powered-personalized-agent#ran","syntology_url":"https://syntology.ai/paper/2406.05925","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05925"}},"official":{"repos":["leolee99/ld-agent"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/re-rag-improving-open-domain-qa-performance","slug":"re-rag-improving-open-domain-qa-performance","title":"RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation","date":"2024-06-09","arxiv_id":"2406.05794","repositories_listed":1,"syntology":null},{"url":"/paper/corpus-poisoning-via-approximate-greedy","slug":"corpus-poisoning-via-approximate-greedy","title":"Corpus Poisoning via Approximate Greedy Gradient Descent","date":"2024-06-07","arxiv_id":"2406.05087","repositories_listed":1,"syntology":null},{"url":"/paper/diving-deep-into-the-motion-representation-of","slug":"diving-deep-into-the-motion-representation-of","title":"Diving Deep into the Motion Representation of Video-Text Models","date":"2024-06-07","arxiv_id":"2406.05075","repositories_listed":1,"syntology":null},{"url":"/paper/pqpp-a-joint-benchmark-for-text-to-image","slug":"pqpp-a-joint-benchmark-for-text-to-image","title":"PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction","date":"2024-06-07","arxiv_id":"2406.04746","repositories_listed":1,"syntology":null},{"url":"/paper/the-unmet-promise-of-synthetic-training","slug":"the-unmet-promise-of-synthetic-training","title":"The Unmet Promise of Synthetic Training Images: Using Retrieved Real Images Performs Better","date":"2024-06-07","arxiv_id":"2406.05184","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":6,"phrase":"5 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; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/the-unmet-promise-of-synthetic-training#ran","syntology_url":"https://syntology.ai/paper/2406.05184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.05184"}},"official":{"repos":["scottgeng00/unmet-promise"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/measuring-and-addressing-indexical-bias-in","slug":"measuring-and-addressing-indexical-bias-in","title":"Measuring and Addressing Indexical Bias in Information Retrieval","date":"2024-06-06","arxiv_id":"2406.04298","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/measuring-and-addressing-indexical-bias-in#ran","syntology_url":"https://syntology.ai/paper/2406.04298","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.04298"}},"official":{"repos":["SALT-NLP/pair"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-importance-of-reasoning-for-context","slug":"on-the-importance-of-reasoning-for-context","title":"On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing","date":"2024-06-06","arxiv_id":"2406.04464","repositories_listed":1,"syntology":null}],"record_sha256":"cb8445d2e0e83acbe5d4d189a8c1e9a6fa9c67be614caca771e2c6e86d0e4be5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}