{"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/2","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":32,"rows_per_page":100,"rows":[101,200],"of":3104,"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","prev":"/task/scheduling","next":"/task/scheduling/papers/3","papers":[{"url":"/paper/bqsched-a-non-intrusive-scheduler-for-batch","slug":"bqsched-a-non-intrusive-scheduler-for-batch","title":"BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement Learning","date":"2025-04-27","arxiv_id":"2504.19142","repositories_listed":1,"syntology":null},{"url":"/paper/swapped-logit-distillation-via-bi-level","slug":"swapped-logit-distillation-via-bi-level","title":"Swapped Logit Distillation via Bi-level Teacher Alignment","date":"2025-04-27","arxiv_id":"2504.20108","repositories_listed":1,"syntology":null},{"url":"/paper/stcl-curriculum-learning-strategies-for-deep","slug":"stcl-curriculum-learning-strategies-for-deep","title":"STCL:Curriculum learning Strategies for deep learning image steganography models","date":"2025-04-24","arxiv_id":"2504.17609","repositories_listed":1,"syntology":null},{"url":"/paper/splitwiser-efficient-lm-inference-with","slug":"splitwiser-efficient-lm-inference-with","title":"Splitwiser: Efficient LM inference with constrained resources","date":"2025-04-21","arxiv_id":"2505.03763","repositories_listed":1,"syntology":null},{"url":"/paper/greedy-restart-schedules-a-baseline-for","slug":"greedy-restart-schedules-a-baseline-for","title":"Greedy Restart Schedules: A Baseline for Dynamic Algorithm Selection on Numerical Black-box Optimization Problems","date":"2025-04-15","arxiv_id":"2504.11440","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-llm-inference-fluid-guided-online","slug":"optimizing-llm-inference-fluid-guided-online","title":"Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints","date":"2025-04-15","arxiv_id":"2504.11320","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-and-solving-an-integrated-periodic","slug":"modeling-and-solving-an-integrated-periodic","title":"Modeling and solving an integrated periodic vehicle routing and capacitated facility location problem in the context of solid waste collection","date":"2025-04-14","arxiv_id":"2504.10648","repositories_listed":1,"syntology":null},{"url":"/paper/interq-a-dqn-framework-for-optimal","slug":"interq-a-dqn-framework-for-optimal","title":"InterQ: A DQN Framework for Optimal Intermittent Control","date":"2025-04-12","arxiv_id":"2504.09035","repositories_listed":1,"syntology":null},{"url":"/paper/apt-serve-adaptive-request-scheduling-on","slug":"apt-serve-adaptive-request-scheduling-on","title":"Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving","date":"2025-04-10","arxiv_id":"2504.07494","repositories_listed":1,"syntology":null},{"url":"/paper/probability-estimation-and-scheduling","slug":"probability-estimation-and-scheduling","title":"Probability Estimation and Scheduling Optimization for Battery Swap Stations via LRU-Enhanced Genetic Algorithm and Dual-Factor Decision System","date":"2025-04-10","arxiv_id":"2504.07453","repositories_listed":1,"syntology":null},{"url":"/paper/hybrimoe-hybrid-cpu-gpu-scheduling-and-cache","slug":"hybrimoe-hybrid-cpu-gpu-scheduling-and-cache","title":"HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference","date":"2025-04-08","arxiv_id":"2504.05897","repositories_listed":1,"syntology":null},{"url":"/paper/l3gs-layered-3d-gaussian-splats-for-efficient","slug":"l3gs-layered-3d-gaussian-splats-for-efficient","title":"L3GS: Layered 3D Gaussian Splats for Efficient 3D Scene Delivery","date":"2025-04-07","arxiv_id":"2504.05517","repositories_listed":1,"syntology":null},{"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/b-gnn-a-robust-ensemble-approach-against","slug":"b-gnn-a-robust-ensemble-approach-against","title":"$β$-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation","date":"2025-03-26","arxiv_id":"2503.20630","repositories_listed":1,"syntology":null},{"url":"/paper/injecting-adrenaline-into-llm-serving","slug":"injecting-adrenaline-into-llm-serving","title":"Injecting Adrenaline into LLM Serving: Boosting Resource Utilization and Throughput via Attention Disaggregation","date":"2025-03-26","arxiv_id":"2503.20552","repositories_listed":1,"syntology":null},{"url":"/paper/mining-gym-a-configurable-rl-benchmarking","slug":"mining-gym-a-configurable-rl-benchmarking","title":"Mining-Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling","date":"2025-03-24","arxiv_id":"2503.19195","repositories_listed":1,"syntology":null},{"url":"/paper/mist-efficient-distributed-training-of-large","slug":"mist-efficient-distributed-training-of-large","title":"Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization","date":"2025-03-24","arxiv_id":"2503.19050","repositories_listed":1,"syntology":null},{"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/exploring-large-language-models-for-word","slug":"exploring-large-language-models-for-word","title":"Exploring Large Language Models for Word Games:Who is the Spy?","date":"2025-03-19","arxiv_id":"2503.15235","repositories_listed":1,"syntology":null},{"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/cost-effective-deep-learning-infrastructure","slug":"cost-effective-deep-learning-infrastructure","title":"Cost-effective Deep Learning Infrastructure with NVIDIA GPU","date":"2025-03-14","arxiv_id":"2503.11246","repositories_listed":1,"syntology":null},{"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/2503-00323","slug":"2503-00323","title":"FLStore: Efficient Federated Learning Storage for non-training workloads","date":"2025-03-01","arxiv_id":"2503.00323","repositories_listed":1,"syntology":null},{"url":"/paper/autohete-an-automatic-and-efficient","slug":"autohete-an-automatic-and-efficient","title":"AutoHete: An Automatic and Efficient Heterogeneous Training System for LLMs","date":"2025-02-27","arxiv_id":"2503.01890","repositories_listed":1,"syntology":null},{"url":"/paper/skippipe-partial-and-reordered-pipelining","slug":"skippipe-partial-and-reordered-pipelining","title":"SkipPipe: Partial and Reordered Pipelining Framework for Training LLMs in Heterogeneous Networks","date":"2025-02-27","arxiv_id":"2502.19913","repositories_listed":1,"syntology":null},{"url":"/paper/tripcraft-a-benchmark-for-spatio-temporally","slug":"tripcraft-a-benchmark-for-spatio-temporally","title":"TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning","date":"2025-02-27","arxiv_id":"2502.20508","repositories_listed":1,"syntology":null},{"url":"/paper/starjob-dataset-for-llm-driven-job-shop","slug":"starjob-dataset-for-llm-driven-job-shop","title":"Starjob: Dataset for LLM-Driven Job Shop Scheduling","date":"2025-02-26","arxiv_id":"2503.01877","repositories_listed":1,"syntology":null},{"url":"/paper/attentionengine-a-versatile-framework-for","slug":"attentionengine-a-versatile-framework-for","title":"AttentionEngine: A Versatile Framework for Efficient Attention Mechanisms on Diverse Hardware Platforms","date":"2025-02-21","arxiv_id":"2502.15349","repositories_listed":1,"syntology":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/blackout-difusco","slug":"blackout-difusco","title":"Blackout DIFUSCO","date":"2025-02-05","arxiv_id":"2502.05221","repositories_listed":1,"syntology":null},{"url":"/paper/e-3sfc-communication-efficient-federated","slug":"e-3sfc-communication-efficient-federated","title":"E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing","date":"2025-02-05","arxiv_id":"2502.03092","repositories_listed":1,"syntology":null},{"url":"/paper/the-surprising-agreement-between-convex","slug":"the-surprising-agreement-between-convex","title":"The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training","date":"2025-01-31","arxiv_id":"2501.18965","repositories_listed":1,"syntology":null},{"url":"/paper/genetic-algorithm-with-border-trades-gab","slug":"genetic-algorithm-with-border-trades-gab","title":"Genetic Algorithm with Innovative Chromosome Patterns in the Breeding Process","date":"2025-01-30","arxiv_id":"2501.18184","repositories_listed":1,"syntology":null},{"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/minimizing-queue-length-regret-for","slug":"minimizing-queue-length-regret-for","title":"Minimizing Queue Length Regret for Arbitrarily Varying Channels","date":"2025-01-23","arxiv_id":"2501.13551","repositories_listed":1,"syntology":null},{"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/cuasmrl-optimizing-gpu-sass-schedules-via","slug":"cuasmrl-optimizing-gpu-sass-schedules-via","title":"CuAsmRL: Optimizing GPU SASS Schedules via Deep Reinforcement Learning","date":"2025-01-14","arxiv_id":"2501.08071","repositories_listed":1,"syntology":null},{"url":"/paper/enhanced-sps-velocity-adaptive-scheme-access","slug":"enhanced-sps-velocity-adaptive-scheme-access","title":"Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networks","date":"2025-01-14","arxiv_id":"2501.08037","repositories_listed":1,"syntology":null},{"url":"/paper/codrivevlm-vlm-enhanced-urban-cooperative","slug":"codrivevlm-vlm-enhanced-urban-cooperative","title":"CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion Planning for Future Autonomous Mobility on Demand Systems","date":"2025-01-10","arxiv_id":"2501.06132","repositories_listed":1,"syntology":null},{"url":"/paper/dynamics-incorporated-modeling-framework-for","slug":"dynamics-incorporated-modeling-framework-for","title":"Dynamics-incorporated Modeling Framework for Stability Constrained Scheduling Under High-penetration of Renewable Energy","date":"2025-01-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/drl-based-medium-term-planning-of-renewable","slug":"drl-based-medium-term-planning-of-renewable","title":"DRL-Based Medium-Term Planning of Renewable-Integrated Self-Scheduling Cascaded Hydropower to Guide Wholesale Market Participation","date":"2025-01-08","arxiv_id":"2501.04839","repositories_listed":1,"syntology":null},{"url":"/paper/from-dense-to-sparse-event-response-for","slug":"from-dense-to-sparse-event-response-for","title":"From Dense to Sparse: Event Response for Enhanced Residential Load Forecasting","date":"2025-01-06","arxiv_id":"2501.02781","repositories_listed":1,"syntology":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/efficiently-serving-llm-reasoning-programs","slug":"efficiently-serving-llm-reasoning-programs","title":"Efficiently Serving LLM Reasoning Programs with Certaindex","date":"2024-12-30","arxiv_id":"2412.20993","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-large-language-model-acceleration","slug":"a-survey-on-large-language-model-acceleration","title":"A Survey on Large Language Model Acceleration based on KV Cache Management","date":"2024-12-27","arxiv_id":"2412.19442","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-aigc-services-with-latent-action","slug":"accelerating-aigc-services-with-latent-action","title":"Accelerating AIGC Services with Latent Action Diffusion Scheduling in Edge Networks","date":"2024-12-24","arxiv_id":"2412.18212","repositories_listed":1,"syntology":null},{"url":"/paper/brain-to-text-benchmark-24-lessons-learned","slug":"brain-to-text-benchmark-24-lessons-learned","title":"Brain-to-Text Benchmark '24: Lessons Learned","date":"2024-12-23","arxiv_id":"2412.17227","repositories_listed":1,"syntology":null},{"url":"/paper/power-and-fragmentation-aware-online","slug":"power-and-fragmentation-aware-online","title":"Power- and Fragmentation-aware Online Scheduling for GPU Datacenters","date":"2024-12-23","arxiv_id":"2412.17484","repositories_listed":1,"syntology":null},{"url":"/paper/towards-an-unsupervised-learning-scheme-for","slug":"towards-an-unsupervised-learning-scheme-for","title":"Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs","date":"2024-12-23","arxiv_id":"2412.17623","repositories_listed":1,"syntology":null},{"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/a-survey-on-inference-optimization-techniques","slug":"a-survey-on-inference-optimization-techniques","title":"A Survey on Inference Optimization Techniques for Mixture of Experts Models","date":"2024-12-18","arxiv_id":"2412.14219","repositories_listed":1,"syntology":null},{"url":"/paper/neural-combinatorial-optimization-for","slug":"neural-combinatorial-optimization-for","title":"Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems","date":"2024-12-18","arxiv_id":"2412.14052","repositories_listed":1,"syntology":null},{"url":"/paper/explicit-and-implicit-graduated-optimization","slug":"explicit-and-implicit-graduated-optimization","title":"Explicit and Implicit Graduated Optimization in Deep Neural Networks","date":"2024-12-16","arxiv_id":"2412.11501","repositories_listed":1,"syntology":null},{"url":"/paper/grid-visual-layout-generation","slug":"grid-visual-layout-generation","title":"Grid: Omni Visual Generation","date":"2024-12-14","arxiv_id":"2412.10718","repositories_listed":1,"syntology":null},{"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/srfs-parallel-processing-fault-tolerant-ros2","slug":"srfs-parallel-processing-fault-tolerant-ros2","title":"SRFS: Parallel Processing Fault-tolerant ROS2-based Flight Software for the Space Ranger Cubesat","date":"2024-12-11","arxiv_id":"2412.08164","repositories_listed":1,"syntology":null},{"url":"/paper/digital-transformation-in-the-water","slug":"digital-transformation-in-the-water","title":"Digital Transformation in the Water Distribution System based on the Digital Twins Concept","date":"2024-12-09","arxiv_id":"2412.06694","repositories_listed":1,"syntology":null},{"url":"/paper/fully-distributed-online-training-of-graph","slug":"fully-distributed-online-training-of-graph","title":"Fully Distributed Online Training of Graph Neural Networks in Networked Systems","date":"2024-12-08","arxiv_id":"2412.06105","repositories_listed":1,"syntology":null},{"url":"/paper/resource-adaptive-successive-doubling-for","slug":"resource-adaptive-successive-doubling-for","title":"Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems","date":"2024-12-03","arxiv_id":"2412.02729","repositories_listed":1,"syntology":null},{"url":"/paper/robomatrix-a-skill-centric-hierarchical","slug":"robomatrix-a-skill-centric-hierarchical","title":"RoboMatrix: A Skill-centric Hierarchical Framework for Scalable Robot Task Planning and Execution in Open-World","date":"2024-11-29","arxiv_id":"2412.00171","repositories_listed":1,"syntology":null},{"url":"/paper/combined-learning-of-linear-parameter-varying","slug":"combined-learning-of-linear-parameter-varying","title":"Combined Learning of Linear Parameter-Varying Models and Robust Control Invariant Sets","date":"2024-11-27","arxiv_id":"2411.18166","repositories_listed":1,"syntology":null},{"url":"/paper/fastswitch-optimizing-context-switching","slug":"fastswitch-optimizing-context-switching","title":"FastSwitch: Optimizing Context Switching Efficiency in Fairness-aware Large Language Model Serving","date":"2024-11-27","arxiv_id":"2411.18424","repositories_listed":1,"syntology":null},{"url":"/paper/dampening-parameter-distributional-shifts","slug":"dampening-parameter-distributional-shifts","title":"Dampening parameter distributional shifts under robust control and gain scheduling","date":"2024-11-25","arxiv_id":"2411.16566","repositories_listed":1,"syntology":null},{"url":"/paper/df-gnn-dynamic-fusion-framework-for-attention","slug":"df-gnn-dynamic-fusion-framework-for-attention","title":"DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs","date":"2024-11-25","arxiv_id":"2411.16127","repositories_listed":1,"syntology":null},{"url":"/paper/exptest-automating-learning-rate-searching","slug":"exptest-automating-learning-rate-searching","title":"ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks","date":"2024-11-25","arxiv_id":"2411.16975","repositories_listed":1,"syntology":null},{"url":"/paper/drl-based-optimization-for-aoi-and-energy","slug":"drl-based-optimization-for-aoi-and-energy","title":"DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV","date":"2024-11-20","arxiv_id":"2411.13104","repositories_listed":1,"syntology":null},{"url":"/paper/matrix-scheduling-of-qsr-dissipative-systems","slug":"matrix-scheduling-of-qsr-dissipative-systems","title":"Matrix-Scheduling of QSR-Dissipative Systems","date":"2024-11-20","arxiv_id":"2411.12955","repositories_listed":1,"syntology":null},{"url":"/paper/robust-planning-with-compound-llm","slug":"robust-planning-with-compound-llm","title":"Robust Planning with Compound LLM Architectures: An LLM-Modulo Approach","date":"2024-11-20","arxiv_id":"2411.14484","repositories_listed":1,"syntology":null},{"url":"/paper/whales-a-multi-agent-scheduling-dataset-for","slug":"whales-a-multi-agent-scheduling-dataset-for","title":"WHALES: A Multi-agent Scheduling Dataset for Enhanced Cooperation in Autonomous Driving","date":"2024-11-20","arxiv_id":"2411.13340","repositories_listed":1,"syntology":null},{"url":"/paper/topology-aware-preemptive-scheduling-for-co","slug":"topology-aware-preemptive-scheduling-for-co","title":"Topology-aware Preemptive Scheduling for Co-located LLM Workloads","date":"2024-11-18","arxiv_id":"2411.11560","repositories_listed":1,"syntology":null},{"url":"/paper/sauce-synchronous-and-asynchronous-user","slug":"sauce-synchronous-and-asynchronous-user","title":"SAUCE: Synchronous and Asynchronous User-Customizable Environment for Multi-Agent LLM Interaction","date":"2024-11-05","arxiv_id":"2411.03397","repositories_listed":1,"syntology":null},{"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/assessing-the-optimistic-bias-in-the-natural","slug":"assessing-the-optimistic-bias-in-the-natural","title":"Assessing the Optimistic Bias in the Natural Inflow Forecasts: A Call for Model Monitoring in Brazil","date":"2024-10-17","arxiv_id":"2410.13763","repositories_listed":1,"syntology":null},{"url":"/paper/meta-diffub-a-contextualized-sequence-to","slug":"meta-diffub-a-contextualized-sequence-to","title":"Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-Exploration","date":"2024-10-17","arxiv_id":"2410.13201","repositories_listed":1,"syntology":null},{"url":"/paper/boxr-body-and-head-motion-optimization","slug":"boxr-body-and-head-motion-optimization","title":"BOXR: Body and head motion Optimization framework for eXtended Reality","date":"2024-10-16","arxiv_id":"2410.13084","repositories_listed":1,"syntology":null},{"url":"/paper/theoretical-lower-bounds-for-the-oven","slug":"theoretical-lower-bounds-for-the-oven","title":"Theoretical Lower Bounds for the Oven Scheduling Problem","date":"2024-10-02","arxiv_id":"2410.01368","repositories_listed":1,"syntology":null},{"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/what-if-we-had-used-a-different-app-reliable","slug":"what-if-we-had-used-a-different-app-reliable","title":"What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems","date":"2024-09-30","arxiv_id":"2410.00150","repositories_listed":1,"syntology":null},{"url":"/paper/secure-control-systems-for-autonomous","slug":"secure-control-systems-for-autonomous","title":"Secure Control Systems for Autonomous Quadrotors against Cyber-Attacks","date":"2024-09-18","arxiv_id":"2409.11897","repositories_listed":1,"syntology":null},{"url":"/paper/offline-reinforcement-learning-for-learning","slug":"offline-reinforcement-learning-for-learning","title":"Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling","date":"2024-09-16","arxiv_id":"2409.10589","repositories_listed":1,"syntology":null},{"url":"/paper/proactive-and-reactive-constraint-programming","slug":"proactive-and-reactive-constraint-programming","title":"Proactive and Reactive Constraint Programming for Stochastic Project Scheduling with Maximal Time-Lags","date":"2024-09-13","arxiv_id":"2409.09107","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-probabilistic-planning-for-the","slug":"adaptive-probabilistic-planning-for-the","title":"Adaptive Probabilistic Planning for the Uncertain and Dynamic Orienteering Problem","date":"2024-09-09","arxiv_id":"2409.05545","repositories_listed":1,"syntology":null},{"url":"/paper/trace-cs-trustworthy-reasoning-for","slug":"trace-cs-trustworthy-reasoning-for","title":"TRACE-CS: A Synergistic Approach to Explainable Course Scheduling Using LLMs and Logic","date":"2024-09-05","arxiv_id":"2409.03671","repositories_listed":1,"syntology":null},{"url":"/paper/solving-integrated-process-planning-and","slug":"solving-integrated-process-planning-and","title":"Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning","date":"2024-09-02","arxiv_id":"2409.00968","repositories_listed":1,"syntology":null},{"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/fair-best-arm-identification-with-fixed","slug":"fair-best-arm-identification-with-fixed","title":"Fair Best Arm Identification with Fixed Confidence","date":"2024-08-30","arxiv_id":"2408.17313","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-llm-scheduling-by-learning-to-rank","slug":"efficient-llm-scheduling-by-learning-to-rank","title":"Efficient LLM Scheduling by Learning to Rank","date":"2024-08-28","arxiv_id":"2408.15792","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 3 unverified","sample_list":"/paper/efficient-llm-scheduling-by-learning-to-rank#ran","syntology_url":"https://syntology.ai/paper/2408.15792","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.15792"}},"official":{"repos":["hao-ai-lab/vllm-ltr"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"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/a-population-to-individual-tuning-framework","slug":"a-population-to-individual-tuning-framework","title":"A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction","date":"2024-08-19","arxiv_id":"2408.09815","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-explore-for-stochastic-gradient","slug":"learning-to-explore-for-stochastic-gradient","title":"Learning to Explore for Stochastic Gradient MCMC","date":"2024-08-17","arxiv_id":"2408.09140","repositories_listed":1,"syntology":null},{"url":"/paper/flashgs-efficient-3d-gaussian-splatting-for","slug":"flashgs-efficient-3d-gaussian-splatting-for","title":"FlashGS: Efficient 3D Gaussian Splatting for Large-scale and High-resolution Rendering","date":"2024-08-15","arxiv_id":"2408.07967","repositories_listed":1,"syntology":null},{"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/palantir-towards-efficient-super-resolution","slug":"palantir-towards-efficient-super-resolution","title":"Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live Streaming","date":"2024-08-12","arxiv_id":"2408.06152","repositories_listed":1,"syntology":null},{"url":"/paper/passivity-based-gain-scheduled-control-with","slug":"passivity-based-gain-scheduled-control-with","title":"Passivity-Based Gain-Scheduled Control with Scheduling Matrices","date":"2024-08-12","arxiv_id":"2408.06476","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-evolving-strategy-for-automatic","slug":"collaborative-evolving-strategy-for-automatic","title":"Collaborative Evolving Strategy for Automatic Data-Centric Development","date":"2024-07-26","arxiv_id":"2407.18690","repositories_listed":1,"syntology":null},{"url":"/paper/take-a-step-and-reconsider-sequence-decoding","slug":"take-a-step-and-reconsider-sequence-decoding","title":"Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial Optimization","date":"2024-07-24","arxiv_id":"2407.17206","repositories_listed":1,"syntology":null}],"record_sha256":"7c240a72b1514b628c2cdcc0a8acc528548d9eb20cfa5c690c079bbb3bf258c5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}