{"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/16k/papers/ran/1","list_of":"/task/16k","task":"16k","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":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,34],"of":34,"counts":{"archive_papers_tagged":146,"with_a_code_link":87,"where_syntology_ran_a_sample":34,"not_listed_spam_title":0,"listed":146,"listed_where_code_ran":34,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":24,"every_run_a_failure_of_syntologys_instrument":10,"listed_with_a_run_with_no_instrument_failure":24,"listed_every_run_a_failure_of_syntologys_instrument":10,"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/16k/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/mstar-box-free-multi-query-scene-text","slug":"mstar-box-free-multi-query-scene-text","title":"MSTAR: Box-free Multi-query Scene Text Retrieval with Attention Recycling","date":"2025-06-12","arxiv_id":"2506.10609","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/mstar-box-free-multi-query-scene-text#ran","syntology_url":"https://syntology.ai/paper/2506.10609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.10609"}},"official":{"repos":["yingift/mstar"],"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/flashdmoe-fast-distributed-moe-in-a-single","slug":"flashdmoe-fast-distributed-moe-in-a-single","title":"FlashDMoE: Fast Distributed MoE in a Single Kernel","date":"2025-06-05","arxiv_id":"2506.04667","repositories_listed":2,"syntology":{"n":7,"n_ran":3,"n_constructed":2,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 2 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) · 4 unverified","sample_list":"/paper/flashdmoe-fast-distributed-moe-in-a-single#ran","syntology_url":"https://syntology.ai/paper/2506.04667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.04667"}},"official":{"repos":["osayamenja/aristos","osayamenja/kleos"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/monarchattention-zero-shot-conversion-to-fast","slug":"monarchattention-zero-shot-conversion-to-fast","title":"MonarchAttention: Zero-Shot Conversion to Fast, Hardware-Aware Structured Attention","date":"2025-05-24","arxiv_id":"2505.18698","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/monarchattention-zero-shot-conversion-to-fast#ran","syntology_url":"https://syntology.ai/paper/2505.18698","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.18698"}},"official":{"repos":["cjyaras/monarch-attention"],"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/m-extending-memoryllm-with-scalable-long-term","slug":"m-extending-memoryllm-with-scalable-long-term","title":"M+: Extending MemoryLLM with Scalable Long-Term Memory","date":"2025-02-01","arxiv_id":"2502.00592","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":2,"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/m-extending-memoryllm-with-scalable-long-term#ran","syntology_url":"https://syntology.ai/paper/2502.00592","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.00592"}},"official":{"repos":["wangyu-ustc/memoryllm"],"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","unlocated"]}}},{"url":"/paper/mitigating-hallucinations-in-large-vision-3","slug":"mitigating-hallucinations-in-large-vision-3","title":"Mitigating Hallucinations in Large Vision-Language Models via DPO: On-Policy Data Hold the Key","date":"2025-01-16","arxiv_id":"2501.09695","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":16,"phrase":"11 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; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/mitigating-hallucinations-in-large-vision-3#ran","syntology_url":"https://syntology.ai/paper/2501.09695","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.09695"}},"official":{"repos":["zhyang2226/opa-dpo"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/retrieval-or-global-context-understanding-on","slug":"retrieval-or-global-context-understanding-on","title":"Retrieval or Global Context Understanding? On Many-Shot In-Context Learning for Long-Context Evaluation","date":"2024-11-11","arxiv_id":"2411.07130","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"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) · 2 unverified","sample_list":"/paper/retrieval-or-global-context-understanding-on#ran","syntology_url":"https://syntology.ai/paper/2411.07130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.07130"}},"official":{"repos":["launchnlp/ManyICLBench"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/denial-of-service-poisoning-attacks-against","slug":"denial-of-service-poisoning-attacks-against","title":"Denial-of-Service Poisoning Attacks against Large Language Models","date":"2024-10-14","arxiv_id":"2410.10760","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":9,"phrase":"3 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; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/denial-of-service-poisoning-attacks-against#ran","syntology_url":"https://syntology.ai/paper/2410.10760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10760"}},"official":{"repos":["sail-sg/p-dos"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-fourier-modelling-a-highly-compact","slug":"neural-fourier-modelling-a-highly-compact","title":"Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysis","date":"2024-10-07","arxiv_id":"2410.04703","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":1,"n_no_contract":3,"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, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-fourier-modelling-a-highly-compact#ran","syntology_url":"https://syntology.ai/paper/2410.04703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04703"}},"official":{"repos":["minkiml/NFM"],"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/extending-context-window-of-large-language-2","slug":"extending-context-window-of-large-language-2","title":"Extending Context Window of Large Language Models from a Distributional Perspective","date":"2024-10-02","arxiv_id":"2410.01490","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":1,"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/extending-context-window-of-large-language-2#ran","syntology_url":"https://syntology.ai/paper/2410.01490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.01490"}},"official":{"repos":["1180301012/DPRoPE"],"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/spinning-the-golden-thread-benchmarking-long","slug":"spinning-the-golden-thread-benchmarking-long","title":"LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs","date":"2024-09-03","arxiv_id":"2409.02076","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"phrase":"8 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spinning-the-golden-thread-benchmarking-long#ran","syntology_url":"https://syntology.ai/paper/2409.02076","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.02076"}},"official":{"repos":["mozhu621/SGT","mozhu621/longgenbench"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/linfusion-1-gpu-1-minute-16k-image","slug":"linfusion-1-gpu-1-minute-16k-image","title":"LinFusion: 1 GPU, 1 Minute, 16K Image","date":"2024-09-03","arxiv_id":"2409.02097","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/linfusion-1-gpu-1-minute-16k-image#ran","syntology_url":"https://syntology.ai/paper/2409.02097","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.02097"}},"official":{"repos":["huage001/linfusion"],"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/1-5-pints-technical-report-pretraining-in","slug":"1-5-pints-technical-report-pretraining-in","title":"1.5-Pints Technical Report: Pretraining in Days, Not Months -- Your Language Model Thrives on Quality Data","date":"2024-08-07","arxiv_id":"2408.03506","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/1-5-pints-technical-report-pretraining-in#ran","syntology_url":"https://syntology.ai/paper/2408.03506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.03506"}},"official":{"repos":["Pints-AI/1.5-Pints"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/spacejam-a-lightweight-and-regularization","slug":"spacejam-a-lightweight-and-regularization","title":"SpaceJAM: a Lightweight and Regularization-free Method for Fast Joint Alignment of Images","date":"2024-07-16","arxiv_id":"2407.11850","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/spacejam-a-lightweight-and-regularization#ran","syntology_url":"https://syntology.ai/paper/2407.11850","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11850"}},"official":{"repos":["BGU-CS-VIL/SpaceJAM"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-learn-at-test-time-rnns-with","slug":"learning-to-learn-at-test-time-rnns-with","title":"Learning to (Learn at Test Time): RNNs with Expressive Hidden States","date":"2024-07-05","arxiv_id":"2407.04620","repositories_listed":3,"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":1,"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/learning-to-learn-at-test-time-rnns-with#ran","syntology_url":"https://syntology.ai/paper/2407.04620","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.04620"}},"official":{"repos":["test-time-training/ttt-lm-pytorch"],"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","unlocated"]}}},{"url":"/paper/llasa-large-multimodal-agent-for-human","slug":"llasa-large-multimodal-agent-for-human","title":"LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone Sensors","date":"2024-06-20","arxiv_id":"2406.14498","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/llasa-large-multimodal-agent-for-human#ran","syntology_url":"https://syntology.ai/paper/2406.14498","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14498"}},"official":{"repos":["bashlab/llasa"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/csrt-evaluation-and-analysis-of-llms-using","slug":"csrt-evaluation-and-analysis-of-llms-using","title":"Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding","date":"2024-06-17","arxiv_id":"2406.15481","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":2,"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/csrt-evaluation-and-analysis-of-llms-using#ran","syntology_url":"https://syntology.ai/paper/2406.15481","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.15481"}},"official":{"repos":["haneul-yoo/csrt"],"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/snapkv-llm-knows-what-you-are-looking-for","slug":"snapkv-llm-knows-what-you-are-looking-for","title":"SnapKV: LLM Knows What You are Looking for Before Generation","date":"2024-04-22","arxiv_id":"2404.14469","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":3,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/snapkv-llm-knows-what-you-are-looking-for#ran","syntology_url":"https://syntology.ai/paper/2404.14469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14469"}},"official":{"repos":["fasterdecoding/snapkv"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/long-form-factuality-in-large-language-models","slug":"long-form-factuality-in-large-language-models","title":"Long-form factuality in large language models","date":"2024-03-27","arxiv_id":"2403.18802","repositories_listed":3,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/long-form-factuality-in-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2403.18802","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.18802"}},"official":{"repos":["google-deepmind/long-form-factuality"],"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":["listed","official"]}}},{"url":"/paper/hydragen-high-throughput-llm-inference-with","slug":"hydragen-high-throughput-llm-inference-with","title":"Hydragen: High-Throughput LLM Inference with Shared Prefixes","date":"2024-02-07","arxiv_id":"2402.05099","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/hydragen-high-throughput-llm-inference-with#ran","syntology_url":"https://syntology.ai/paper/2402.05099","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05099"}},"official":{"repos":["jordan-benjamin/hydragen"],"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/lv-eval-a-balanced-long-context-benchmark","slug":"lv-eval-a-balanced-long-context-benchmark","title":"LV-Eval: A Balanced Long-Context Benchmark with 5 Length Levels Up to 256K","date":"2024-02-06","arxiv_id":"2402.05136","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":1,"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/lv-eval-a-balanced-long-context-benchmark#ran","syntology_url":"https://syntology.ai/paper/2402.05136","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.05136"}},"official":{"repos":["infinigence/lveval"],"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","unlocated"]}}},{"url":"/paper/longbench-a-bilingual-multitask-benchmark-for","slug":"longbench-a-bilingual-multitask-benchmark-for","title":"LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding","date":"2023-08-28","arxiv_id":"2308.14508","repositories_listed":3,"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/longbench-a-bilingual-multitask-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2308.14508","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.14508"}},"official":{"repos":["thudm/longbench"],"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/code-llama-open-foundation-models-for-code","slug":"code-llama-open-foundation-models-for-code","title":"Code Llama: Open Foundation Models for Code","date":"2023-08-24","arxiv_id":"2308.12950","repositories_listed":2,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/code-llama-open-foundation-models-for-code#ran","syntology_url":"https://syntology.ai/paper/2308.12950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12950"}},"official":{"repos":["facebookresearch/codellama"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/giraffe-adventures-in-expanding-context","slug":"giraffe-adventures-in-expanding-context","title":"Giraffe: Adventures in Expanding Context Lengths in LLMs","date":"2023-08-21","arxiv_id":"2308.10882","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":4,"phrase":"10 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/giraffe-adventures-in-expanding-context#ran","syntology_url":"https://syntology.ai/paper/2308.10882","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.10882"}},"official":{"repos":["abacusai/long-context"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/llavar-enhanced-visual-instruction-tuning-for","slug":"llavar-enhanced-visual-instruction-tuning-for","title":"LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding","date":"2023-06-29","arxiv_id":"2306.17107","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/llavar-enhanced-visual-instruction-tuning-for#ran","syntology_url":"https://syntology.ai/paper/2306.17107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.17107"}},"official":{"repos":["SALT-NLP/LLaVAR"],"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":["listed","official","unlocated"]}}},{"url":"/paper/the-elm-neuron-an-efficient-and-expressive","slug":"the-elm-neuron-an-efficient-and-expressive","title":"The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks","date":"2023-06-14","arxiv_id":"2306.16922","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/the-elm-neuron-an-efficient-and-expressive#ran","syntology_url":"https://syntology.ai/paper/2306.16922","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.16922"}},"official":{"repos":["AaronSpieler/elmneuron"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/circle-color-invariant-representation","slug":"circle-color-invariant-representation","title":"CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions","date":"2022-08-29","arxiv_id":"2208.13528","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":1,"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/circle-color-invariant-representation#ran","syntology_url":"https://syntology.ai/paper/2208.13528","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.13528"}},"official":{"repos":["arezou-pakzad/circle"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/flashattention-fast-and-memory-efficient","slug":"flashattention-fast-and-memory-efficient","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","date":"2022-05-27","arxiv_id":"2205.14135","repositories_listed":13,"syntology":{"n":30,"n_ran":24,"n_constructed":2,"n_ran_checked":18,"n_instrument":6,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":17,"n_pointer_only":1,"phrase":"24 ran (of which 2 constructed an object rather than computing a result; 18 with no instrument failure: 0 honoured, 1 violated, 17 with no contract checked; 6 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/flashattention-fast-and-memory-efficient#ran","syntology_url":"https://syntology.ai/paper/2205.14135","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.14135"}},"official":{"repos":["dao-ailab/flash-attention"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text","listed"]}}},{"url":"/paper/hierarchical-nearest-neighbor-graph-embedding","slug":"hierarchical-nearest-neighbor-graph-embedding","title":"Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction","date":"2022-03-24","arxiv_id":"2203.12997","repositories_listed":1,"syntology":{"n":19,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/hierarchical-nearest-neighbor-graph-embedding#ran","syntology_url":"https://syntology.ai/paper/2203.12997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12997"}},"official":{"repos":["koulakis/h-nne"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/long-range-arena-a-benchmark-for-efficient-1","slug":"long-range-arena-a-benchmark-for-efficient-1","title":"Long Range Arena: A Benchmark for Efficient Transformers","date":"2020-11-08","arxiv_id":"2011.04006","repositories_listed":5,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":2,"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; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/long-range-arena-a-benchmark-for-efficient-1#ran","syntology_url":"https://syntology.ai/paper/2011.04006","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.04006"}},"official":{"repos":["google-research/long-range-arena"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/smyrf-efficient-attention-using-asymmetric","slug":"smyrf-efficient-attention-using-asymmetric","title":"SMYRF: Efficient Attention using Asymmetric Clustering","date":"2020-10-11","arxiv_id":"2010.05315","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":3,"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/smyrf-efficient-attention-using-asymmetric#ran","syntology_url":"https://syntology.ai/paper/2010.05315","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.05315"}},"official":{"repos":["giannisdaras/smyrf"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/morphocluster-efficient-annotation-of","slug":"morphocluster-efficient-annotation-of","title":"MorphoCluster: Efficient Annotation of Plankton images by Clustering","date":"2020-05-04","arxiv_id":"2005.01595","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/morphocluster-efficient-annotation-of#ran","syntology_url":"https://syntology.ai/paper/2005.01595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.01595"}},"official":null}},{"url":"/paper/classifying-the-classifier-dissecting-the","slug":"classifying-the-classifier-dissecting-the","title":"Classifying the classifier: dissecting the weight space of neural networks","date":"2020-02-13","arxiv_id":"2002.05688","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":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) · 1 unverified","sample_list":"/paper/classifying-the-classifier-dissecting-the#ran","syntology_url":"https://syntology.ai/paper/2002.05688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.05688"}},"official":{"repos":["gabrieleilertsen/nws"],"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/tabfact-a-large-scale-dataset-for-table-based","slug":"tabfact-a-large-scale-dataset-for-table-based","title":"TabFact: A Large-scale Dataset for Table-based Fact Verification","date":"2019-09-05","arxiv_id":"1909.02164","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":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) · 1 unverified","sample_list":"/paper/tabfact-a-large-scale-dataset-for-table-based#ran","syntology_url":"https://syntology.ai/paper/1909.02164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.02164"}},"official":{"repos":["wenhuchen/Table-Fact-Checking"],"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","unlocated"]}}},{"url":"/paper/visual-semantic-role-labeling","slug":"visual-semantic-role-labeling","title":"Visual Semantic Role Labeling","date":"2015-05-17","arxiv_id":"1505.04474","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/visual-semantic-role-labeling#ran","syntology_url":"https://syntology.ai/paper/1505.04474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1505.04474"}},"official":null}}],"record_sha256":"5b708c8bde58ec05599af4775ed843f900e367d592a24002ea92b1f01cf6783e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}