{"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/scheduling/papers/ran/1","list_of":"/task/scheduling","task":"Scheduling","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":2,"rows_per_page":100,"rows":[1,100],"of":107,"counts":{"archive_papers_tagged":3104,"with_a_code_link":574,"where_syntology_ran_a_sample":107,"not_listed_spam_title":0,"listed":3104,"listed_where_code_ran":107,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":94,"every_run_a_failure_of_syntologys_instrument":13,"listed_with_a_run_with_no_instrument_failure":94,"listed_every_run_a_failure_of_syntologys_instrument":13,"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/scheduling/papers/ran/1","prev":null,"next":"/task/scheduling/papers/ran/2","papers":[{"url":"/paper/plan-for-speed-dilated-scheduling-for-masked","slug":"plan-for-speed-dilated-scheduling-for-masked","title":"Plan for Speed -- Dilated Scheduling for Masked Diffusion Language Models","date":"2025-06-23","arxiv_id":"2506.19037","repositories_listed":0,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/plan-for-speed-dilated-scheduling-for-masked#ran","syntology_url":"https://syntology.ai/paper/2506.19037","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.19037"}},"official":null}},{"url":"/paper/ale-bench-a-benchmark-for-long-horizon","slug":"ale-bench-a-benchmark-for-long-horizon","title":"ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering","date":"2025-06-10","arxiv_id":"2506.09050","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/ale-bench-a-benchmark-for-long-horizon#ran","syntology_url":"https://syntology.ai/paper/2506.09050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.09050"}},"official":{"repos":["sakanaai/ale-bench"],"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/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/gates-cost-aware-dynamic-workflow-scheduling","slug":"gates-cost-aware-dynamic-workflow-scheduling","title":"GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy","date":"2025-05-18","arxiv_id":"2505.12355","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":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) · 2 unverified","sample_list":"/paper/gates-cost-aware-dynamic-workflow-scheduling#ran","syntology_url":"https://syntology.ai/paper/2505.12355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.12355"}},"official":{"repos":["yashen998/gates"],"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/piloting-structure-based-drug-design-via","slug":"piloting-structure-based-drug-design-via","title":"Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule","date":"2025-05-12","arxiv_id":"2505.07286","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/piloting-structure-based-drug-design-via#ran","syntology_url":"https://syntology.ai/paper/2505.07286","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.07286"}},"official":{"repos":["algomole/molcraft"],"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/enhanced-diffusion-sampling-via-extrapolation","slug":"enhanced-diffusion-sampling-via-extrapolation","title":"Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutions","date":"2025-04-02","arxiv_id":"2504.01855","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/enhanced-diffusion-sampling-via-extrapolation#ran","syntology_url":"https://syntology.ai/paper/2504.01855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.01855"}},"official":{"repos":["jin01020/rx-dpm"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/autoeval-autonomous-evaluation-of-generalist","slug":"autoeval-autonomous-evaluation-of-generalist","title":"AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World","date":"2025-03-31","arxiv_id":"2503.24278","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/autoeval-autonomous-evaluation-of-generalist#ran","syntology_url":"https://syntology.ai/paper/2503.24278","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.24278"}},"official":{"repos":["zhouzypaul/auto_eval"],"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/temple-temporal-preference-learning-of-video","slug":"temple-temporal-preference-learning-of-video","title":"TEMPLE:Temporal Preference Learning of Video LLMs via Difficulty Scheduling and Pre-SFT Alignment","date":"2025-03-21","arxiv_id":"2503.16929","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/temple-temporal-preference-learning-of-video#ran","syntology_url":"https://syntology.ai/paper/2503.16929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.16929"}},"official":{"repos":["lscpku/temple"],"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/skyladder-better-and-faster-pretraining-via","slug":"skyladder-better-and-faster-pretraining-via","title":"SkyLadder: Better and Faster Pretraining via Context Window Scheduling","date":"2025-03-19","arxiv_id":"2503.15450","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/skyladder-better-and-faster-pretraining-via#ran","syntology_url":"https://syntology.ai/paper/2503.15450","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.15450"}},"official":{"repos":["sail-sg/skyladder"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/flowtok-flowing-seamlessly-across-text-and","slug":"flowtok-flowing-seamlessly-across-text-and","title":"FlowTok: Flowing Seamlessly Across Text and Image Tokens","date":"2025-03-13","arxiv_id":"2503.10772","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":6,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 6 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) · 3 unverified; every one of the 6 samples that ran constructed an object rather than computing a result","sample_list":"/paper/flowtok-flowing-seamlessly-across-text-and#ran","syntology_url":"https://syntology.ai/paper/2503.10772","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.10772"}},"official":{"repos":["bytedance/1d-tokenizer"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-combinatorial-optimization-via","slug":"neural-combinatorial-optimization-via","title":"Neural Combinatorial Optimization via Preference Optimization","date":"2025-03-10","arxiv_id":"2503.07580","repositories_listed":0,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":9,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":2,"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) · 2 unverified","sample_list":"/paper/neural-combinatorial-optimization-via#ran","syntology_url":"https://syntology.ai/paper/2503.07580","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.07580"}},"official":null}},{"url":"/paper/learning-guided-rolling-horizon-optimization","slug":"learning-guided-rolling-horizon-optimization","title":"Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling","date":"2025-02-18","arxiv_id":"2502.15791","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-guided-rolling-horizon-optimization#ran","syntology_url":"https://syntology.ai/paper/2502.15791","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.15791"}},"official":{"repos":["mit-wu-lab/l-rho"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/streaming-video-understanding-and-multi-round","slug":"streaming-video-understanding-and-multi-round","title":"Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge","date":"2025-01-23","arxiv_id":"2501.13468","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/streaming-video-understanding-and-multi-round#ran","syntology_url":"https://syntology.ai/paper/2501.13468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.13468"}},"official":{"repos":["hmxiong/streamchat"],"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/an-efficient-diffusion-based-non","slug":"an-efficient-diffusion-based-non","title":"An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem","date":"2025-01-23","arxiv_id":"2501.13767","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/an-efficient-diffusion-based-non#ran","syntology_url":"https://syntology.ai/paper/2501.13767","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.13767"}},"official":{"repos":["deitsp/deitsp"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hygen-efficient-llm-serving-via-elastic","slug":"hygen-efficient-llm-serving-via-elastic","title":"HyGen: Efficient LLM Serving via Elastic Online-Offline Request Co-location","date":"2025-01-15","arxiv_id":"2501.14808","repositories_listed":0,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/hygen-efficient-llm-serving-via-elastic#ran","syntology_url":"https://syntology.ai/paper/2501.14808","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.14808"}},"official":null}},{"url":"/paper/flashinfer-efficient-and-customizable","slug":"flashinfer-efficient-and-customizable","title":"FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving","date":"2025-01-02","arxiv_id":"2501.01005","repositories_listed":1,"syntology":{"n":16,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":11,"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) · 11 unverified","sample_list":"/paper/flashinfer-efficient-and-customizable#ran","syntology_url":"https://syntology.ai/paper/2501.01005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.01005"}},"official":{"repos":["flashinfer-ai/flashinfer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-agent-reinforcement-learning-for-24","slug":"multi-agent-reinforcement-learning-for-24","title":"Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems","date":"2024-12-20","arxiv_id":"2412.15573","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-agent-reinforcement-learning-for-24#ran","syntology_url":"https://syntology.ai/paper/2412.15573","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.15573"}},"official":{"repos":["Rainlabuw/rl-enabled-distributed-assignment"],"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/from-allies-to-adversaries-manipulating-llm","slug":"from-allies-to-adversaries-manipulating-llm","title":"From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection","date":"2024-12-13","arxiv_id":"2412.10198","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"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) · 2 unverified","sample_list":"/paper/from-allies-to-adversaries-manipulating-llm#ran","syntology_url":"https://syntology.ai/paper/2412.10198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.10198"}},"official":{"repos":["anonymous-lgtm/toolcommander"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unveiling-redundancy-in-diffusion","slug":"unveiling-redundancy-in-diffusion","title":"Unveiling Redundancy in Diffusion Transformers (DiTs): A Systematic Study","date":"2024-11-18","arxiv_id":"2411.13588","repositories_listed":2,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/unveiling-redundancy-in-diffusion#ran","syntology_url":"https://syntology.ai/paper/2411.13588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.13588"}},"official":{"repos":["xdit-project/ditcacheanalysis"],"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/neo-saving-gpu-memory-crisis-with-cpu","slug":"neo-saving-gpu-memory-crisis-with-cpu","title":"NEO: Saving GPU Memory Crisis with CPU Offloading for Online LLM Inference","date":"2024-11-02","arxiv_id":"2411.01142","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/neo-saving-gpu-memory-crisis-with-cpu#ran","syntology_url":"https://syntology.ai/paper/2411.01142","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.01142"}},"official":null}},{"url":"/paper/how-does-critical-batch-size-scale-in-pre","slug":"how-does-critical-batch-size-scale-in-pre","title":"How Does Critical Batch Size Scale in Pre-training?","date":"2024-10-29","arxiv_id":"2410.21676","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/how-does-critical-batch-size-scale-in-pre#ran","syntology_url":"https://syntology.ai/paper/2410.21676","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.21676"}},"official":{"repos":["hlzhang109/critical-batch-size"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/read-me-refactorizing-llms-as-router","slug":"read-me-refactorizing-llms-as-router","title":"Read-ME: Refactorizing LLMs as Router-Decoupled Mixture of Experts with System Co-Design","date":"2024-10-24","arxiv_id":"2410.19123","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":16,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/read-me-refactorizing-llms-as-router#ran","syntology_url":"https://syntology.ai/paper/2410.19123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.19123"}},"official":{"repos":["vita-group/read-me"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/one-step-diffusion-via-shortcut-models","slug":"one-step-diffusion-via-shortcut-models","title":"One Step Diffusion via Shortcut Models","date":"2024-10-16","arxiv_id":"2410.12557","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/one-step-diffusion-via-shortcut-models#ran","syntology_url":"https://syntology.ai/paper/2410.12557","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.12557"}},"official":{"repos":["kvfrans/shortcut-models"],"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":["listed","official"]}}},{"url":"/paper/planning-in-strawberry-fields-evaluating-and","slug":"planning-in-strawberry-fields-evaluating-and","title":"Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities of LRM o1","date":"2024-10-03","arxiv_id":"2410.02162","repositories_listed":2,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/planning-in-strawberry-fields-evaluating-and#ran","syntology_url":"https://syntology.ai/paper/2410.02162","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.02162"}},"official":null}},{"url":"/paper/agent-oriented-planning-in-multi-agent","slug":"agent-oriented-planning-in-multi-agent","title":"Agent-Oriented Planning in Multi-Agent Systems","date":"2024-10-03","arxiv_id":"2410.02189","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/agent-oriented-planning-in-multi-agent#ran","syntology_url":"https://syntology.ai/paper/2410.02189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.02189"}},"official":{"repos":["lalaliat/agent-oriented-planning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/layerkv-optimizing-large-language-model","slug":"layerkv-optimizing-large-language-model","title":"LayerKV: Optimizing Large Language Model Serving with Layer-wise KV Cache Management","date":"2024-10-01","arxiv_id":"2410.00428","repositories_listed":1,"syntology":{"n":14,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/layerkv-optimizing-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2410.00428","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.00428"}},"official":null}},{"url":"/paper/parco-learning-parallel-autoregressive","slug":"parco-learning-parallel-autoregressive","title":"Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization","date":"2024-09-05","arxiv_id":"2409.03811","repositories_listed":2,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/parco-learning-parallel-autoregressive#ran","syntology_url":"https://syntology.ai/paper/2409.03811","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.03811"}},"official":{"repos":["ai4co/parco"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/adanat-exploring-adaptive-policy-for-token","slug":"adanat-exploring-adaptive-policy-for-token","title":"AdaNAT: Exploring Adaptive Policy for Token-Based Image Generation","date":"2024-08-31","arxiv_id":"2409.00342","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":9,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":8,"n_pointer_only":15,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/adanat-exploring-adaptive-policy-for-token#ran","syntology_url":"https://syntology.ai/paper/2409.00342","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.00342"}},"official":{"repos":["leaplabthu/adanat"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/marlin-mixed-precision-auto-regressive","slug":"marlin-mixed-precision-auto-regressive","title":"MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models","date":"2024-08-21","arxiv_id":"2408.11743","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/marlin-mixed-precision-auto-regressive#ran","syntology_url":"https://syntology.ai/paper/2408.11743","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.11743"}},"official":{"repos":["ist-daslab/marlin","ist-daslab/sparse-marlin"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/megafusion-extend-diffusion-models-towards","slug":"megafusion-extend-diffusion-models-towards","title":"MegaFusion: Extend Diffusion Models towards Higher-resolution Image Generation without Further Tuning","date":"2024-08-20","arxiv_id":"2408.11001","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/megafusion-extend-diffusion-models-towards#ran","syntology_url":"https://syntology.ai/paper/2408.11001","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.11001"}},"official":{"repos":["haoningwu3639/MegaFusion"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sustaindc-benchmarking-for-sustainable-data","slug":"sustaindc-benchmarking-for-sustainable-data","title":"SustainDC: Benchmarking for Sustainable Data Center Control","date":"2024-08-14","arxiv_id":"2408.07841","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/sustaindc-benchmarking-for-sustainable-data#ran","syntology_url":"https://syntology.ai/paper/2408.07841","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.07841"}},"official":{"repos":["hewlettpackard/dc-rl"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/llms-can-schedule","slug":"llms-can-schedule","title":"LLMs can Schedule","date":"2024-08-13","arxiv_id":"2408.06993","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":7,"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/llms-can-schedule#ran","syntology_url":"https://syntology.ai/paper/2408.06993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.06993"}},"official":{"repos":["starjob42/datasetjsp"],"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/self-guided-generation-of-minority-samples","slug":"self-guided-generation-of-minority-samples","title":"Self-Guided Generation of Minority Samples Using Diffusion Models","date":"2024-07-16","arxiv_id":"2407.11555","repositories_listed":1,"syntology":{"n":18,"n_ran":15,"n_constructed":0,"n_ran_checked":9,"n_instrument":6,"n_unverified":3,"n_honours":3,"n_violates":0,"n_no_contract":6,"n_pointer_only":12,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 3 honoured, 0 violated, 6 with no contract checked; 6 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/self-guided-generation-of-minority-samples#ran","syntology_url":"https://syntology.ai/paper/2407.11555","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11555"}},"official":{"repos":["soobin-um/sg-minority"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/rodinhd-high-fidelity-3d-avatar-generation","slug":"rodinhd-high-fidelity-3d-avatar-generation","title":"RodinHD: High-Fidelity 3D Avatar Generation with Diffusion Models","date":"2024-07-09","arxiv_id":"2407.06938","repositories_listed":1,"syntology":{"n":13,"n_ran":5,"n_constructed":2,"n_ran_checked":3,"n_instrument":2,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":13,"phrase":"5 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; 2 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/rodinhd-high-fidelity-3d-avatar-generation#ran","syntology_url":"https://syntology.ai/paper/2407.06938","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.06938"}},"official":null}},{"url":"/paper/goal-a-generalist-combinatorial-optimization","slug":"goal-a-generalist-combinatorial-optimization","title":"GOAL: A Generalist Combinatorial Optimization Agent Learning","date":"2024-06-21","arxiv_id":"2406.15079","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"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) · 3 unverified","sample_list":"/paper/goal-a-generalist-combinatorial-optimization#ran","syntology_url":"https://syntology.ai/paper/2406.15079","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.15079"}},"official":{"repos":["naver/goal-co"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/powerinfer-2-fast-large-language-model","slug":"powerinfer-2-fast-large-language-model","title":"PowerInfer-2: Fast Large Language Model Inference on a Smartphone","date":"2024-06-10","arxiv_id":"2406.06282","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"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) · 2 unverified","sample_list":"/paper/powerinfer-2-fast-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2406.06282","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.06282"}},"official":null}},{"url":"/paper/helix-distributed-serving-of-large-language","slug":"helix-distributed-serving-of-large-language","title":"Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow","date":"2024-06-03","arxiv_id":"2406.01566","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/helix-distributed-serving-of-large-language#ran","syntology_url":"https://syntology.ai/paper/2406.01566","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01566"}},"official":{"repos":["Thesys-lab/Helix-ASPLOS25"],"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/reinforcement-learning-1","slug":"reinforcement-learning-1","title":"Reinforcement learning","date":"2024-05-16","arxiv_id":"2405.10369","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/reinforcement-learning-1#ran","syntology_url":"https://syntology.ai/paper/2405.10369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.10369"}},"official":{"repos":["sarodyatawatta/hintrl"],"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/vidur-a-large-scale-simulation-framework-for","slug":"vidur-a-large-scale-simulation-framework-for","title":"Vidur: A Large-Scale Simulation Framework For LLM Inference","date":"2024-05-08","arxiv_id":"2405.05465","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/vidur-a-large-scale-simulation-framework-for#ran","syntology_url":"https://syntology.ai/paper/2405.05465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.05465"}},"official":{"repos":["microsoft/vidur"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/preble-efficient-distributed-prompt","slug":"preble-efficient-distributed-prompt","title":"Preble: Efficient Distributed Prompt Scheduling for LLM Serving","date":"2024-05-08","arxiv_id":"2407.00023","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":0,"n_no_contract":2,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/preble-efficient-distributed-prompt#ran","syntology_url":"https://syntology.ai/paper/2407.00023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.00023"}},"official":{"repos":["wuklab/preble"],"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/workbench-a-benchmark-dataset-for-agents-in-a","slug":"workbench-a-benchmark-dataset-for-agents-in-a","title":"WorkBench: a Benchmark Dataset for Agents in a Realistic Workplace Setting","date":"2024-05-01","arxiv_id":"2405.00823","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/workbench-a-benchmark-dataset-for-agents-in-a#ran","syntology_url":"https://syntology.ai/paper/2405.00823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.00823"}},"official":{"repos":["olly-styles/workbench"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/flagvne-a-flexible-and-generalizable","slug":"flagvne-a-flexible-and-generalizable","title":"FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation","date":"2024-04-19","arxiv_id":"2404.12633","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"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) · 3 unverified","sample_list":"/paper/flagvne-a-flexible-and-generalizable#ran","syntology_url":"https://syntology.ai/paper/2404.12633","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.12633"}},"official":{"repos":["GeminiLight/flag-vne"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-interactive-llm-serving-with-proxy","slug":"efficient-interactive-llm-serving-with-proxy","title":"Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction","date":"2024-04-12","arxiv_id":"2404.08509","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/efficient-interactive-llm-serving-with-proxy#ran","syntology_url":"https://syntology.ai/paper/2404.08509","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.08509"}},"official":{"repos":["james-qiuhaoran/llm-serving-with-proxy-models"],"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/self-improvement-for-neural-combinatorial","slug":"self-improvement-for-neural-combinatorial","title":"Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement","date":"2024-03-22","arxiv_id":"2403.15180","repositories_listed":1,"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":5,"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/self-improvement-for-neural-combinatorial#ran","syntology_url":"https://syntology.ai/paper/2403.15180","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.15180"}},"official":{"repos":["grimmlab/gumbeldore"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/taming-throughput-latency-tradeoff-in-llm","slug":"taming-throughput-latency-tradeoff-in-llm","title":"Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve","date":"2024-03-04","arxiv_id":"2403.02310","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/taming-throughput-latency-tradeoff-in-llm#ran","syntology_url":"https://syntology.ai/paper/2403.02310","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.02310"}},"official":{"repos":["microsoft/sarathi-serve"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/gradient-based-discrete-sampling-with","slug":"gradient-based-discrete-sampling-with","title":"Gradient-based Discrete Sampling with Automatic Cyclical Scheduling","date":"2024-02-27","arxiv_id":"2402.17699","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":16,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/gradient-based-discrete-sampling-with#ran","syntology_url":"https://syntology.ai/paper/2402.17699","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17699"}},"official":{"repos":["patrickpynadath1/automatic_cyclical_sampling"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/serverlessllm-locality-enhanced-serverless","slug":"serverlessllm-locality-enhanced-serverless","title":"ServerlessLLM: Low-Latency Serverless Inference for Large Language Models","date":"2024-01-25","arxiv_id":"2401.14351","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/serverlessllm-locality-enhanced-serverless#ran","syntology_url":"https://syntology.ai/paper/2401.14351","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14351"}},"official":{"repos":["serverlessllm/serverlessllm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/organa-a-robotic-assistant-for-automated","slug":"organa-a-robotic-assistant-for-automated","title":"ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization","date":"2024-01-13","arxiv_id":"2401.06949","repositories_listed":1,"syntology":{"n":18,"n_ran":16,"n_constructed":0,"n_ran_checked":16,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":16,"n_pointer_only":18,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/organa-a-robotic-assistant-for-automated#ran","syntology_url":"https://syntology.ai/paper/2401.06949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.06949"}},"official":{"repos":["ac-rad/organa"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":16,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/fairness-in-serving-large-language-models","slug":"fairness-in-serving-large-language-models","title":"Fairness in Serving Large Language Models","date":"2023-12-31","arxiv_id":"2401.00588","repositories_listed":2,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/fairness-in-serving-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2401.00588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.00588"}},"official":{"repos":["ying1123/vtc-artifact"],"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":["listed","official"]}}},{"url":"/paper/temperature-balancing-layer-wise-weight-1","slug":"temperature-balancing-layer-wise-weight-1","title":"Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training","date":"2023-12-01","arxiv_id":"2312.00359","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/temperature-balancing-layer-wise-weight-1#ran","syntology_url":"https://syntology.ai/paper/2312.00359","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.00359"}},"official":{"repos":["yefanzhou/tempbalance"],"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/rethinking-and-benchmarking-predict-then","slug":"rethinking-and-benchmarking-predict-then","title":"Benchmarking PtO and PnO Methods in the Predictive Combinatorial Optimization Regime","date":"2023-11-13","arxiv_id":"2311.07633","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/rethinking-and-benchmarking-predict-then#ran","syntology_url":"https://syntology.ai/paper/2311.07633","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.07633"}},"official":{"repos":["thinklab-sjtu/predictiveco-benchmark"],"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/combinatorial-optimization-with-policy","slug":"combinatorial-optimization-with-policy","title":"Combinatorial Optimization with Policy Adaptation using Latent Space Search","date":"2023-11-13","arxiv_id":"2311.13569","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"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) · 2 unverified","sample_list":"/paper/combinatorial-optimization-with-policy#ran","syntology_url":"https://syntology.ai/paper/2311.13569","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13569"}},"official":{"repos":["instadeepai/compass"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/controlllm-augment-language-models-with-tools","slug":"controlllm-augment-language-models-with-tools","title":"ControlLLM: Augment Language Models with Tools by Searching on Graphs","date":"2023-10-26","arxiv_id":"2310.17796","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":3,"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/controlllm-augment-language-models-with-tools#ran","syntology_url":"https://syntology.ai/paper/2310.17796","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17796"}},"official":{"repos":["opengvlab/controlllm"],"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/imitation-learning-from-observation-with","slug":"imitation-learning-from-observation-with","title":"Imitation Learning from Observation with Automatic Discount Scheduling","date":"2023-10-11","arxiv_id":"2310.07433","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 1 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/imitation-learning-from-observation-with#ran","syntology_url":"https://syntology.ai/paper/2310.07433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07433"}},"official":null}},{"url":"/paper/when-why-and-how-much-adaptive-learning-rate","slug":"when-why-and-how-much-adaptive-learning-rate","title":"Optimal Linear Decay Learning Rate Schedules and Further Refinements","date":"2023-10-11","arxiv_id":"2310.07831","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/when-why-and-how-much-adaptive-learning-rate#ran","syntology_url":"https://syntology.ai/paper/2310.07831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.07831"}},"official":{"repos":["facebookresearch/adaptive_scheduling"],"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/latency-aware-unified-dynamic-networks-for","slug":"latency-aware-unified-dynamic-networks-for","title":"Latency-aware Unified Dynamic Networks for Efficient Image Recognition","date":"2023-08-30","arxiv_id":"2308.15949","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"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; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/latency-aware-unified-dynamic-networks-for#ran","syntology_url":"https://syntology.ai/paper/2308.15949","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.15949"}},"official":{"repos":["leaplabthu/laudnet"],"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/job-shop-scheduling-benchmark-environments","slug":"job-shop-scheduling-benchmark-environments","title":"Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods","date":"2023-08-24","arxiv_id":"2308.12794","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":0,"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/job-shop-scheduling-benchmark-environments#ran","syntology_url":"https://syntology.ai/paper/2308.12794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12794"}},"official":{"repos":["ai-for-decision-making-tue/job_shop_scheduling_benchmark"],"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/an-end-to-end-reinforcement-learning-approach","slug":"an-end-to-end-reinforcement-learning-approach","title":"An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint Programming","date":"2023-06-09","arxiv_id":"2306.05747","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/an-end-to-end-reinforcement-learning-approach#ran","syntology_url":"https://syntology.ai/paper/2306.05747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05747"}},"official":{"repos":["ingambe/End2End-Job-Shop-Scheduling-CP"],"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/plasma-making-small-language-models-better","slug":"plasma-making-small-language-models-better","title":"PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning","date":"2023-05-31","arxiv_id":"2305.19472","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/plasma-making-small-language-models-better#ran","syntology_url":"https://syntology.ai/paper/2305.19472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19472"}},"official":{"repos":["allenai/plasma"],"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/fedzero-leveraging-renewable-excess-energy-in","slug":"fedzero-leveraging-renewable-excess-energy-in","title":"FedZero: Leveraging Renewable Excess Energy in Federated Learning","date":"2023-05-24","arxiv_id":"2305.15092","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/fedzero-leveraging-renewable-excess-energy-in#ran","syntology_url":"https://syntology.ai/paper/2305.15092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15092"}},"official":{"repos":["dos-group/fedzero"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rmmdet-road-side-multitype-and-multigroup","slug":"rmmdet-road-side-multitype-and-multigroup","title":"RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving","date":"2023-03-09","arxiv_id":"2303.05203","repositories_listed":2,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/rmmdet-road-side-multitype-and-multigroup#ran","syntology_url":"https://syntology.ai/paper/2303.05203","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.05203"}},"official":{"repos":["OrangeSodahub/CRLFnet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-airport-ground-handling","slug":"neural-airport-ground-handling","title":"Neural Airport Ground Handling","date":"2023-03-04","arxiv_id":"2303.02442","repositories_listed":1,"syntology":{"n":10,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"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) · 8 unverified","sample_list":"/paper/neural-airport-ground-handling#ran","syntology_url":"https://syntology.ai/paper/2303.02442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.02442"}},"official":{"repos":["royalskye/agh"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/a-unified-framework-for-soft-threshold","slug":"a-unified-framework-for-soft-threshold","title":"A Unified Framework for Soft Threshold Pruning","date":"2023-02-25","arxiv_id":"2302.13019","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-unified-framework-for-soft-threshold#ran","syntology_url":"https://syntology.ai/paper/2302.13019","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.13019"}},"official":{"repos":["yanqi-chen/lats"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/decoupled-model-schedule-for-deep-learning","slug":"decoupled-model-schedule-for-deep-learning","title":"Slapo: A Schedule Language for Progressive Optimization of Large Deep Learning Model Training","date":"2023-02-16","arxiv_id":"2302.08005","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/decoupled-model-schedule-for-deep-learning#ran","syntology_url":"https://syntology.ai/paper/2302.08005","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.08005"}},"official":{"repos":["awslabs/slapo"],"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/learning-to-simulate-daily-activities-via","slug":"learning-to-simulate-daily-activities-via","title":"Learning to Simulate Daily Activities via Modeling Dynamic Human Needs","date":"2023-02-09","arxiv_id":"2302.10897","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/learning-to-simulate-daily-activities-via#ran","syntology_url":"https://syntology.ai/paper/2302.10897","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.10897"}},"official":{"repos":["tsinghua-fib-lab/sand"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-importance-of-noise-scheduling-for","slug":"on-the-importance-of-noise-scheduling-for","title":"On the Importance of Noise Scheduling for Diffusion Models","date":"2023-01-26","arxiv_id":"2301.10972","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-the-importance-of-noise-scheduling-for#ran","syntology_url":"https://syntology.ai/paper/2301.10972","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.10972"}},"official":null}},{"url":"/paper/evosax-jax-based-evolution-strategies","slug":"evosax-jax-based-evolution-strategies","title":"evosax: JAX-based Evolution Strategies","date":"2022-12-08","arxiv_id":"2212.04180","repositories_listed":2,"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/evosax-jax-based-evolution-strategies#ran","syntology_url":"https://syntology.ai/paper/2212.04180","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.04180"}},"official":{"repos":["roberttlange/evosax","adaptive-intelligent-robotics/qdax"],"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/end-to-end-stochastic-optimization-with","slug":"end-to-end-stochastic-optimization-with","title":"End-to-End Stochastic Optimization with Energy-Based Model","date":"2022-11-25","arxiv_id":"2211.13837","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/end-to-end-stochastic-optimization-with#ran","syntology_url":"https://syntology.ai/paper/2211.13837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.13837"}},"official":{"repos":["Lingkai-Kong/SO-EBM"],"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/algorithms-with-prediction-portfolios","slug":"algorithms-with-prediction-portfolios","title":"Algorithms with Prediction Portfolios","date":"2022-10-22","arxiv_id":"2210.12438","repositories_listed":1,"syntology":{"n":12,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/algorithms-with-prediction-portfolios#ran","syntology_url":"https://syntology.ai/paper/2210.12438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12438"}},"official":{"repos":["tlavastida/predictionportfolios"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hidet-task-mapping-programming-paradigm-for","slug":"hidet-task-mapping-programming-paradigm-for","title":"Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs","date":"2022-10-18","arxiv_id":"2210.09603","repositories_listed":2,"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":0,"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/hidet-task-mapping-programming-paradigm-for#ran","syntology_url":"https://syntology.ai/paper/2210.09603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.09603"}},"official":{"repos":["hidet-org/hidet","yaoyaoding/hidet-artifacts"],"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/latency-aware-spatial-wise-dynamic-networks","slug":"latency-aware-spatial-wise-dynamic-networks","title":"Latency-aware Spatial-wise Dynamic Networks","date":"2022-10-12","arxiv_id":"2210.06223","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":2,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"5 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/latency-aware-spatial-wise-dynamic-networks#ran","syntology_url":"https://syntology.ai/paper/2210.06223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.06223"}},"official":{"repos":["leaplabthu/lasnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/winner-takes-it-all-training-performant-rl-1","slug":"winner-takes-it-all-training-performant-rl-1","title":"Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization","date":"2022-10-07","arxiv_id":"2210.03475","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/winner-takes-it-all-training-performant-rl-1#ran","syntology_url":"https://syntology.ai/paper/2210.03475","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.03475"}},"official":null}},{"url":"/paper/supplementing-recurrent-neural-networks-with","slug":"supplementing-recurrent-neural-networks-with","title":"Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization Problems","date":"2022-07-17","arxiv_id":"2207.08189","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/supplementing-recurrent-neural-networks-with#ran","syntology_url":"https://syntology.ai/paper/2207.08189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08189"}},"official":{"repos":["rnn-vca-co/rnn-vca-co"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/decentralized-training-of-foundation-models","slug":"decentralized-training-of-foundation-models","title":"Decentralized Training of Foundation Models in Heterogeneous Environments","date":"2022-06-02","arxiv_id":"2206.01288","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":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) · 2 unverified","sample_list":"/paper/decentralized-training-of-foundation-models#ran","syntology_url":"https://syntology.ai/paper/2206.01288","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.01288"}},"official":{"repos":["DS3Lab/DT-FM"],"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/static-scheduling-with-predictions-learned","slug":"static-scheduling-with-predictions-learned","title":"On Preemption and Learning in Stochastic Scheduling","date":"2022-05-31","arxiv_id":"2205.15695","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/static-scheduling-with-predictions-learned#ran","syntology_url":"https://syntology.ai/paper/2205.15695","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15695"}},"official":{"repos":["hugorichard/ml4a-scheduling"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unigdd-a-unified-generative-framework-for","slug":"unigdd-a-unified-generative-framework-for","title":"UniGDD: A Unified Generative Framework for Goal-Oriented Document-Grounded Dialogue","date":"2022-04-16","arxiv_id":"2204.07770","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unigdd-a-unified-generative-framework-for#ran","syntology_url":"https://syntology.ai/paper/2204.07770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07770"}},"official":{"repos":["gao-xiao-bai/UniGDD"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/tgl-a-general-framework-for-temporal-gnn","slug":"tgl-a-general-framework-for-temporal-gnn","title":"TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs","date":"2022-03-28","arxiv_id":"2203.14883","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"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) · 3 unverified","sample_list":"/paper/tgl-a-general-framework-for-temporal-gnn#ran","syntology_url":"https://syntology.ai/paper/2203.14883","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14883"}},"official":{"repos":["amazon-research/tgl"],"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":["listed","official"]}}},{"url":"/paper/adeada-adaptive-density-aware-active-domain","slug":"adeada-adaptive-density-aware-active-domain","title":"D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation","date":"2022-02-14","arxiv_id":"2202.06484","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adeada-adaptive-density-aware-active-domain#ran","syntology_url":"https://syntology.ai/paper/2202.06484","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.06484"}},"official":{"repos":["tsunghan-wu/d2ada"],"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/reusing-auto-schedules-for-efficient-dnn","slug":"reusing-auto-schedules-for-efficient-dnn","title":"Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation","date":"2022-01-14","arxiv_id":"2201.05587","repositories_listed":1,"syntology":{"n":12,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"9 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/reusing-auto-schedules-for-efficient-dnn#ran","syntology_url":"https://syntology.ai/paper/2201.05587","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.05587"}},"official":{"repos":["giclab/transfer-tuning"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/elegantrl-podracer-scalable-and-elastic","slug":"elegantrl-podracer-scalable-and-elastic","title":"ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning","date":"2021-12-11","arxiv_id":"2112.05923","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/elegantrl-podracer-scalable-and-elastic#ran","syntology_url":"https://syntology.ai/paper/2112.05923","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.05923"}},"official":{"repos":["ai4finance-foundation/elegantrl"],"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/automated-learning-rate-scheduler-for-large","slug":"automated-learning-rate-scheduler-for-large","title":"Automated Learning Rate Scheduler for Large-batch Training","date":"2021-07-13","arxiv_id":"2107.05855","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":3,"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 3 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; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/automated-learning-rate-scheduler-for-large#ran","syntology_url":"https://syntology.ai/paper/2107.05855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.05855"}},"official":{"repos":["kakaobrain/autowu"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/chacha-for-online-automl","slug":"chacha-for-online-automl","title":"ChaCha for Online AutoML","date":"2021-06-09","arxiv_id":"2106.04815","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":1,"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, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/chacha-for-online-automl#ran","syntology_url":"https://syntology.ai/paper/2106.04815","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04815"}},"official":{"repos":["microsoft/FLAML"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/a-bi-level-framework-for-learning-to-solve","slug":"a-bi-level-framework-for-learning-to-solve","title":"A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs","date":"2021-06-09","arxiv_id":"2106.04927","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/a-bi-level-framework-for-learning-to-solve#ran","syntology_url":"https://syntology.ai/paper/2106.04927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04927"}},"official":{"repos":["thinklab-sjtu/ppo-bihyb"],"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/structured-convolutional-kernel-networks-for","slug":"structured-convolutional-kernel-networks-for","title":"Structured Convolutional Kernel Networks for Airline Crew Scheduling","date":"2021-05-25","arxiv_id":"2105.11646","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/structured-convolutional-kernel-networks-for#ran","syntology_url":"https://syntology.ai/paper/2105.11646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.11646"}},"official":{"repos":["Yaakoubi/Struct-CKN"],"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/batch-bayesian-optimization-on-permutations","slug":"batch-bayesian-optimization-on-permutations","title":"Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel","date":"2021-02-26","arxiv_id":"2102.13382","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/batch-bayesian-optimization-on-permutations#ran","syntology_url":"https://syntology.ai/paper/2102.13382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.13382"}},"official":{"repos":["changyong-oh/law2order"],"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/learning-to-dispatch-for-job-shop-scheduling","slug":"learning-to-dispatch-for-job-shop-scheduling","title":"Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning","date":"2020-10-23","arxiv_id":"2010.12367","repositories_listed":4,"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":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) · 2 unverified","sample_list":"/paper/learning-to-dispatch-for-job-shop-scheduling#ran","syntology_url":"https://syntology.ai/paper/2010.12367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12367"}},"official":{"repos":["zcajiayin/L2D"],"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/difftune-optimizing-cpu-simulator-parameters","slug":"difftune-optimizing-cpu-simulator-parameters","title":"DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates","date":"2020-10-08","arxiv_id":"2010.04017","repositories_listed":2,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/difftune-optimizing-cpu-simulator-parameters#ran","syntology_url":"https://syntology.ai/paper/2010.04017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.04017"}},"official":{"repos":["ithemal/DiffTune"],"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/lemma-a-multi-view-dataset-for-learning-multi","slug":"lemma-a-multi-view-dataset-for-learning-multi","title":"LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities","date":"2020-07-31","arxiv_id":"2007.15781","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/lemma-a-multi-view-dataset-for-learning-multi#ran","syntology_url":"https://syntology.ai/paper/2007.15781","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.15781"}},"official":{"repos":["Buzz-Beater/LEMMA"],"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/learn-to-use-future-information-in","slug":"learn-to-use-future-information-in","title":"Temporally Correlated Task Scheduling for Sequence Learning","date":"2020-07-10","arxiv_id":"2007.05290","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"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 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learn-to-use-future-information-in#ran","syntology_url":"https://syntology.ai/paper/2007.05290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05290"}},"official":{"repos":["microsoft/qlib"],"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":["named_in_paper"]}}},{"url":"/paper/node-classification-on-graphs-with-few-shot","slug":"node-classification-on-graphs-with-few-shot","title":"Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding","date":"2020-07-06","arxiv_id":"2007.02914","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"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) · 2 unverified","sample_list":"/paper/node-classification-on-graphs-with-few-shot#ran","syntology_url":"https://syntology.ai/paper/2007.02914","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02914"}},"official":{"repos":["llan-ml/MetaTNE"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/compositional-video-synthesis-with-action","slug":"compositional-video-synthesis-with-action","title":"Compositional Video Synthesis with Action Graphs","date":"2020-06-27","arxiv_id":"2006.15327","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/compositional-video-synthesis-with-action#ran","syntology_url":"https://syntology.ai/paper/2006.15327","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.15327"}},"official":{"repos":["roeiherz/AG2Video"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-loss-landscape-of-adversarial-training","slug":"on-the-loss-landscape-of-adversarial-training","title":"On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them","date":"2020-06-15","arxiv_id":"2006.08403","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/on-the-loss-landscape-of-adversarial-training#ran","syntology_url":"https://syntology.ai/paper/2006.08403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.08403"}},"official":{"repos":["liuchen11/AdversaryLossLandscape"],"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/mnn-a-universal-and-efficient-inference","slug":"mnn-a-universal-and-efficient-inference","title":"MNN: A Universal and Efficient Inference Engine","date":"2020-02-27","arxiv_id":"2002.12418","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/mnn-a-universal-and-efficient-inference#ran","syntology_url":"https://syntology.ai/paper/2002.12418","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.12418"}},"official":{"repos":["alibaba/MNN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/variational-learning-with-disentanglement","slug":"variational-learning-with-disentanglement","title":"Variational Learning with Disentanglement-PyTorch","date":"2019-12-11","arxiv_id":"1912.05184","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/variational-learning-with-disentanglement#ran","syntology_url":"https://syntology.ai/paper/1912.05184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.05184"}},"official":{"repos":["amir-abdi/disentanglement-pytorch"],"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/smart-predict-and-optimize-for-hard","slug":"smart-predict-and-optimize-for-hard","title":"Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems","date":"2019-11-22","arxiv_id":"1911.10092","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":7,"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) · 7 unverified","sample_list":"/paper/smart-predict-and-optimize-for-hard#ran","syntology_url":"https://syntology.ai/paper/1911.10092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.10092"}},"official":{"repos":["JayMan91/aaai_predit_then_optimize"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/scheduling-the-learning-rate-via-1","slug":"scheduling-the-learning-rate-via-1","title":"MARTHE: Scheduling the Learning Rate Via Online Hypergradients","date":"2019-10-18","arxiv_id":"1910.08525","repositories_listed":1,"syntology":{"n":7,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"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) · 5 unverified","sample_list":"/paper/scheduling-the-learning-rate-via-1#ran","syntology_url":"https://syntology.ai/paper/1910.08525","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.08525"}},"official":{"repos":["awslabs/adatune"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/personalized-apprenticeship-learning-from","slug":"personalized-apprenticeship-learning-from","title":"Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations","date":"2019-06-14","arxiv_id":"1906.06397","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":0,"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/personalized-apprenticeship-learning-from#ran","syntology_url":"https://syntology.ai/paper/1906.06397","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.06397"}},"official":{"repos":["core-robotics-lab/personalized_neural_trees"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/das3h-modeling-student-learning-and","slug":"das3h-modeling-student-learning-and","title":"DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills","date":"2019-05-14","arxiv_id":"1905.06873","repositories_listed":4,"syntology":{"n":22,"n_ran":15,"n_constructed":0,"n_ran_checked":13,"n_instrument":2,"n_unverified":7,"n_honours":1,"n_violates":2,"n_no_contract":10,"n_pointer_only":10,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 1 honoured, 2 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/das3h-modeling-student-learning-and#ran","syntology_url":"https://syntology.ai/paper/1905.06873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.06873"}},"official":{"repos":["BenoitChoffin/das3h"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"url":"/paper/dynamic-mini-batch-sgd-for-elastic","slug":"dynamic-mini-batch-sgd-for-elastic","title":"Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources","date":"2019-04-26","arxiv_id":"1904.12043","repositories_listed":2,"syntology":{"n":20,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":14,"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) · 7 unverified","sample_list":"/paper/dynamic-mini-batch-sgd-for-elastic#ran","syntology_url":"https://syntology.ai/paper/1904.12043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.12043"}},"official":null}},{"url":"/paper/neurocore-guiding-cdcl-with-unsat-core","slug":"neurocore-guiding-cdcl-with-unsat-core","title":"Guiding High-Performance SAT Solvers with Unsat-Core Predictions","date":"2019-03-12","arxiv_id":"1903.04671","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/neurocore-guiding-cdcl-with-unsat-core#ran","syntology_url":"https://syntology.ai/paper/1903.04671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.04671"}},"official":{"repos":["dselsam/neurocore-public"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}}],"record_sha256":"9cc0016dc4f7e16fc0eebc9bc1cbb70440587ef227cbd0e66d208d01845a2027","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}