{"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/quantization/papers/5","list_of":"/task/quantization","task":"Quantization","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":5,"pages_in_order":50,"rows_per_page":100,"rows":[401,500],"of":4925,"counts":{"archive_papers_tagged":4925,"with_a_code_link":1596,"where_syntology_ran_a_sample":515,"not_listed_spam_title":0,"listed":4925,"listed_where_code_ran":515,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":452,"every_run_a_failure_of_syntologys_instrument":63,"listed_with_a_run_with_no_instrument_failure":452,"listed_every_run_a_failure_of_syntologys_instrument":63,"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/quantization","prev":"/task/quantization/papers/4","next":"/task/quantization/papers/6","papers":[{"url":"/paper/cauchy-schwarz-regularizers","slug":"cauchy-schwarz-regularizers","title":"Cauchy-Schwarz Regularizers","date":"2025-03-03","arxiv_id":"2503.01639","repositories_listed":1,"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/cauchy-schwarz-regularizers#ran","syntology_url":"https://syntology.ai/paper/2503.01639","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.01639"}},"official":{"repos":["iip-group/cs_regularizers"],"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/rsq-learning-from-important-tokens-leads-to","slug":"rsq-learning-from-important-tokens-leads-to","title":"RSQ: Learning from Important Tokens Leads to Better Quantized LLMs","date":"2025-03-03","arxiv_id":"2503.01820","repositories_listed":1,"syntology":null},{"url":"/paper/patient-level-anatomy-meets-scanning-level","slug":"patient-level-anatomy-meets-scanning-level","title":"Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model","date":"2025-03-02","arxiv_id":"2503.00908","repositories_listed":1,"syntology":null},{"url":"/paper/oscillation-reduced-mxfp4-training-for-vision","slug":"oscillation-reduced-mxfp4-training-for-vision","title":"Oscillation-Reduced MXFP4 Training for Vision Transformers","date":"2025-02-28","arxiv_id":"2502.20853","repositories_listed":1,"syntology":null},{"url":"/paper/towards-lossless-implicit-neural","slug":"towards-lossless-implicit-neural","title":"Towards Lossless Implicit Neural Representation via Bit Plane Decomposition","date":"2025-02-28","arxiv_id":"2502.21001","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/towards-lossless-implicit-neural#ran","syntology_url":"https://syntology.ai/paper/2502.21001","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.21001"}},"official":{"repos":["wookyounghan/losslessinr"],"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/unitok-a-unified-tokenizer-for-visual","slug":"unitok-a-unified-tokenizer-for-visual","title":"UniTok: A Unified Tokenizer for Visual Generation and Understanding","date":"2025-02-27","arxiv_id":"2502.20321","repositories_listed":1,"syntology":{"n":15,"n_ran":12,"n_constructed":9,"n_ran_checked":9,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"12 ran (of which 9 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/unitok-a-unified-tokenizer-for-visual#ran","syntology_url":"https://syntology.ai/paper/2502.20321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.20321"}},"official":{"repos":["foundationvision/unitok"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":9,"n_ran_no_instrument_failure":9,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/automatic-joint-structured-pruning-and","slug":"automatic-joint-structured-pruning-and","title":"Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression","date":"2025-02-23","arxiv_id":"2502.16638","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"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) · 4 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/automatic-joint-structured-pruning-and#ran","syntology_url":"https://syntology.ai/paper/2502.16638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.16638"}},"official":{"repos":["microsoft/geta"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/condiquant-condition-number-based-low-bit","slug":"condiquant-condition-number-based-low-bit","title":"CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution","date":"2025-02-21","arxiv_id":"2502.15478","repositories_listed":1,"syntology":null},{"url":"/paper/towards-economical-inference-enabling","slug":"towards-economical-inference-enabling","title":"Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs","date":"2025-02-20","arxiv_id":"2502.14837","repositories_listed":1,"syntology":{"n":15,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/towards-economical-inference-enabling#ran","syntology_url":"https://syntology.ai/paper/2502.14837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.14837"}},"official":{"repos":["JT-Ushio/MHA2MLA"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/train-small-infer-large-memory-efficient-lora","slug":"train-small-infer-large-memory-efficient-lora","title":"Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models","date":"2025-02-19","arxiv_id":"2502.13533","repositories_listed":1,"syntology":{"n":13,"n_ran":5,"n_constructed":4,"n_ran_checked":4,"n_instrument":1,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"5 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/train-small-infer-large-memory-efficient-lora#ran","syntology_url":"https://syntology.ai/paper/2502.13533","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.13533"}},"official":{"repos":["junzhang-zj/LoRAM"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":8,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/benchmarking-post-training-quantization-in","slug":"benchmarking-post-training-quantization-in","title":"Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis","date":"2025-02-18","arxiv_id":"2502.13178","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-the-impact-of-quantization","slug":"investigating-the-impact-of-quantization","title":"Investigating the Impact of Quantization Methods on the Safety and Reliability of Large Language Models","date":"2025-02-18","arxiv_id":"2502.15799","repositories_listed":1,"syntology":null},{"url":"/paper/ptq1-61-push-the-real-limit-of-extremely-low","slug":"ptq1-61-push-the-real-limit-of-extremely-low","title":"PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models","date":"2025-02-18","arxiv_id":"2502.13179","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-expert-predictions-in-moe-inference","slug":"accurate-expert-predictions-in-moe-inference","title":"Fate: Fast Edge Inference of Mixture-of-Experts Models via Cross-Layer Gate","date":"2025-02-17","arxiv_id":"2502.12224","repositories_listed":1,"syntology":null},{"url":"/paper/on-quantizing-neural-representation-for","slug":"on-quantizing-neural-representation-for","title":"On Quantizing Neural Representation for Variable-Rate Video Coding","date":"2025-02-17","arxiv_id":"2502.11729","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/on-quantizing-neural-representation-for#ran","syntology_url":"https://syntology.ai/paper/2502.11729","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.11729"}},"official":{"repos":["eric-qi/neuroquant"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/unveiling-environmental-impacts-of-large","slug":"unveiling-environmental-impacts-of-large","title":"Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View","date":"2025-02-16","arxiv_id":"2502.11256","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/unveiling-environmental-impacts-of-large#ran","syntology_url":"https://syntology.ai/paper/2502.11256","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.11256"}},"official":{"repos":["jojacola/fuel"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/calibquant-1-bit-kv-cache-quantization-for","slug":"calibquant-1-bit-kv-cache-quantization-for","title":"CalibQuant: 1-Bit KV Cache Quantization for Multimodal LLMs","date":"2025-02-15","arxiv_id":"2502.14882","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/calibquant-1-bit-kv-cache-quantization-for#ran","syntology_url":"https://syntology.ai/paper/2502.14882","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.14882"}},"official":{"repos":["insuhan/calibquant"],"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","unlocated"]}}},{"url":"/paper/cissir-beam-codebooks-with-self-interference","slug":"cissir-beam-codebooks-with-self-interference","title":"CISSIR: Beam Codebooks with Self-Interference Reduction Guarantees for Integrated Sensing and Communication Beyond 5G","date":"2025-02-14","arxiv_id":"2502.10371","repositories_listed":1,"syntology":null},{"url":"/paper/weighted-quantization-using-mmd-from-mean","slug":"weighted-quantization-using-mmd-from-mean","title":"Weighted quantization using MMD: From mean field to mean shift via gradient flows","date":"2025-02-14","arxiv_id":"2502.10600","repositories_listed":1,"syntology":null},{"url":"/paper/sq-gan-semantic-image-communications-using","slug":"sq-gan-semantic-image-communications-using","title":"SQ-GAN: Semantic Image Communications Using Masked Vector Quantization","date":"2025-02-13","arxiv_id":"2502.09520","repositories_listed":1,"syntology":null},{"url":"/paper/loss-landscape-analysis-for-reliable","slug":"loss-landscape-analysis-for-reliable","title":"Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing","date":"2025-02-12","arxiv_id":"2502.08355","repositories_listed":1,"syntology":null},{"url":"/paper/column-wise-quantization-of-weights-and","slug":"column-wise-quantization-of-weights-and","title":"Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory Accelerators","date":"2025-02-11","arxiv_id":"2502.07842","repositories_listed":1,"syntology":null},{"url":"/paper/grannite-enabling-high-performance-execution","slug":"grannite-enabling-high-performance-execution","title":"GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units","date":"2025-02-10","arxiv_id":"2502.06921","repositories_listed":1,"syntology":null},{"url":"/paper/indextts-an-industrial-level-controllable-and","slug":"indextts-an-industrial-level-controllable-and","title":"IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System","date":"2025-02-08","arxiv_id":"2502.05512","repositories_listed":1,"syntology":null},{"url":"/paper/physics-conditioned-diffusion-models-for","slug":"physics-conditioned-diffusion-models-for","title":"Physics-Conditioned Diffusion Models for Lattice Gauge Theory","date":"2025-02-08","arxiv_id":"2502.05504","repositories_listed":1,"syntology":null},{"url":"/paper/quest-stable-training-of-llms-with-1-bit","slug":"quest-stable-training-of-llms-with-1-bit","title":"QuEST: Stable Training of LLMs with 1-Bit Weights and Activations","date":"2025-02-07","arxiv_id":"2502.05003","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/quest-stable-training-of-llms-with-1-bit#ran","syntology_url":"https://syntology.ai/paper/2502.05003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.05003"}},"official":{"repos":["IST-DASLab/QuEST"],"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/kvtuner-sensitivity-aware-layer-wise-mixed","slug":"kvtuner-sensitivity-aware-layer-wise-mixed","title":"KVTuner: Sensitivity-Aware Layer-wise Mixed Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference","date":"2025-02-06","arxiv_id":"2502.04420","repositories_listed":1,"syntology":null},{"url":"/paper/bridle-generalized-self-supervised-learning","slug":"bridle-generalized-self-supervised-learning","title":"BRIDLE: Generalized Self-supervised Learning with Quantization","date":"2025-02-04","arxiv_id":"2502.02118","repositories_listed":1,"syntology":null},{"url":"/paper/paretoq-scaling-laws-in-extremely-low-bit-llm","slug":"paretoq-scaling-laws-in-extremely-low-bit-llm","title":"ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization","date":"2025-02-04","arxiv_id":"2502.02631","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":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/paretoq-scaling-laws-in-extremely-low-bit-llm#ran","syntology_url":"https://syntology.ai/paper/2502.02631","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.02631"}},"official":null}},{"url":"/paper/massive-values-in-self-attention-modules-are","slug":"massive-values-in-self-attention-modules-are","title":"Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding","date":"2025-02-03","arxiv_id":"2502.01563","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/massive-values-in-self-attention-modules-are#ran","syntology_url":"https://syntology.ai/paper/2502.01563","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.01563"}},"official":{"repos":["mingyuj666/rope_with_llm"],"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/nearly-lossless-adaptive-bit-switching","slug":"nearly-lossless-adaptive-bit-switching","title":"Nearly Lossless Adaptive Bit Switching","date":"2025-02-03","arxiv_id":"2502.01199","repositories_listed":1,"syntology":null},{"url":"/paper/qless-a-quantized-approach-for-data-valuation","slug":"qless-a-quantized-approach-for-data-valuation","title":"QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-Tuning","date":"2025-02-03","arxiv_id":"2502.01703","repositories_listed":1,"syntology":null},{"url":"/paper/cache-me-if-you-must-adaptive-key-value","slug":"cache-me-if-you-must-adaptive-key-value","title":"Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models","date":"2025-01-31","arxiv_id":"2501.19392","repositories_listed":1,"syntology":null},{"url":"/paper/visual-autoregressive-modeling-for-image","slug":"visual-autoregressive-modeling-for-image","title":"Visual Autoregressive Modeling for Image Super-Resolution","date":"2025-01-31","arxiv_id":"2501.18993","repositories_listed":1,"syntology":{"n":22,"n_ran":16,"n_constructed":0,"n_ran_checked":13,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":7,"phrase":"16 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; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/visual-autoregressive-modeling-for-image#ran","syntology_url":"https://syntology.ai/paper/2501.18993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.18993"}},"official":{"repos":["qyp2000/varsr"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/gaussiantoken-an-effective-image-tokenizer","slug":"gaussiantoken-an-effective-image-tokenizer","title":"GaussianToken: An Effective Image Tokenizer with 2D Gaussian Splatting","date":"2025-01-26","arxiv_id":"2501.15619","repositories_listed":1,"syntology":null},{"url":"/paper/ostquant-refining-large-language-model","slug":"ostquant-refining-large-language-model","title":"OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting","date":"2025-01-23","arxiv_id":"2501.13987","repositories_listed":1,"syntology":{"n":15,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 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; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/ostquant-refining-large-language-model#ran","syntology_url":"https://syntology.ai/paper/2501.13987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.13987"}},"official":{"repos":["brotherhappy/ostquant"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/quantized-spike-driven-transformer","slug":"quantized-spike-driven-transformer","title":"Quantized Spike-driven Transformer","date":"2025-01-23","arxiv_id":"2501.13492","repositories_listed":1,"syntology":{"n":25,"n_ran":13,"n_constructed":9,"n_ran_checked":13,"n_instrument":0,"n_unverified":12,"n_honours":2,"n_violates":0,"n_no_contract":11,"n_pointer_only":25,"phrase":"13 ran (of which 9 constructed an object rather than computing a result; 13 with no instrument failure: 2 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/quantized-spike-driven-transformer#ran","syntology_url":"https://syntology.ai/paper/2501.13492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.13492"}},"official":{"repos":["bollossom/qsd-transformer"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":9,"n_ran_no_instrument_failure":11,"n_unverified":12,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/ganq-gpu-adaptive-non-uniform-quantization","slug":"ganq-gpu-adaptive-non-uniform-quantization","title":"GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language Models","date":"2025-01-22","arxiv_id":"2501.12956","repositories_listed":1,"syntology":null},{"url":"/paper/lift-lightweight-fpga-tailored-3d-object","slug":"lift-lightweight-fpga-tailored-3d-object","title":"LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR data","date":"2025-01-19","arxiv_id":"2501.11159","repositories_listed":1,"syntology":null},{"url":"/paper/4bit-quantization-in-vector-embedding-for-rag","slug":"4bit-quantization-in-vector-embedding-for-rag","title":"4bit-Quantization in Vector-Embedding for RAG","date":"2025-01-17","arxiv_id":"2501.10534","repositories_listed":1,"syntology":null},{"url":"/paper/lossless-compression-of-vector-ids-for","slug":"lossless-compression-of-vector-ids-for","title":"Lossless Compression of Vector IDs for Approximate Nearest Neighbor Search","date":"2025-01-16","arxiv_id":"2501.10479","repositories_listed":1,"syntology":null},{"url":"/paper/d-2-dpm-dual-denoising-for-quantized","slug":"d-2-dpm-dual-denoising-for-quantized","title":"D$^2$-DPM: Dual Denoising for Quantized Diffusion Probabilistic Models","date":"2025-01-14","arxiv_id":"2501.08180","repositories_listed":1,"syntology":null},{"url":"/paper/discquant-a-quantization-method-for-neural","slug":"discquant-a-quantization-method-for-neural","title":"DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy Theory","date":"2025-01-11","arxiv_id":"2501.06417","repositories_listed":1,"syntology":null},{"url":"/paper/estimation-and-restoration-of-unknown","slug":"estimation-and-restoration-of-unknown","title":"Estimation and Restoration of Unknown Nonlinear Distortion using Diffusion","date":"2025-01-10","arxiv_id":"2501.05959","repositories_listed":1,"syntology":null},{"url":"/paper/kannolo-sweet-and-smooth-approximate-k","slug":"kannolo-sweet-and-smooth-approximate-k","title":"kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search","date":"2025-01-10","arxiv_id":"2501.06121","repositories_listed":1,"syntology":null},{"url":"/paper/neural-architecture-codesign-for-fast-physics","slug":"neural-architecture-codesign-for-fast-physics","title":"Neural Architecture Codesign for Fast Physics Applications","date":"2025-01-09","arxiv_id":"2501.05515","repositories_listed":1,"syntology":null},{"url":"/paper/dgq-distribution-aware-group-quantization-for","slug":"dgq-distribution-aware-group-quantization-for","title":"DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models","date":"2025-01-08","arxiv_id":"2501.04304","repositories_listed":1,"syntology":null},{"url":"/paper/qinco2-vector-compression-and-search-with","slug":"qinco2-vector-compression-and-search-with","title":"Qinco2: Vector Compression and Search with Improved Implicit Neural Codebooks","date":"2025-01-06","arxiv_id":"2501.03078","repositories_listed":1,"syntology":null},{"url":"/paper/the-power-of-negative-zero-datatype","slug":"the-power-of-negative-zero-datatype","title":"The Power of Negative Zero: Datatype Customization for Quantized Large Language Models","date":"2025-01-06","arxiv_id":"2501.04052","repositories_listed":1,"syntology":null},{"url":"/paper/halo-hadamard-assisted-lossless-optimization","slug":"halo-hadamard-assisted-lossless-optimization","title":"HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs","date":"2025-01-05","arxiv_id":"2501.02625","repositories_listed":1,"syntology":null},{"url":"/paper/remote-inference-over-dynamic-links-via","slug":"remote-inference-over-dynamic-links-via","title":"Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization","date":"2025-01-05","arxiv_id":"2501.02521","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-edge-ai-a-comprehensive-survey-on","slug":"optimizing-edge-ai-a-comprehensive-survey-on","title":"Optimizing Edge AI: A Comprehensive Survey on Data, Model, and System Strategies","date":"2025-01-04","arxiv_id":"2501.03265","repositories_listed":1,"syntology":null},{"url":"/paper/blockdialect-block-wise-fine-grained-mixed","slug":"blockdialect-block-wise-fine-grained-mixed","title":"BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference","date":"2025-01-02","arxiv_id":"2501.01144","repositories_listed":1,"syntology":null},{"url":"/paper/muq-self-supervised-music-representation","slug":"muq-self-supervised-music-representation","title":"MuQ: Self-Supervised Music Representation Learning with Mel Residual Vector Quantization","date":"2025-01-02","arxiv_id":"2501.01108","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":2,"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, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/muq-self-supervised-music-representation#ran","syntology_url":"https://syntology.ai/paper/2501.01108","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.01108"}},"official":{"repos":["tencent-ailab/muq"],"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/treelut-an-efficient-alternative-to-deep","slug":"treelut-an-efficient-alternative-to-deep","title":"TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees","date":"2025-01-02","arxiv_id":"2501.01511","repositories_listed":1,"syntology":null},{"url":"/paper/ptq4vm-post-training-quantization-for-visual","slug":"ptq4vm-post-training-quantization-for-visual","title":"PTQ4VM: Post-Training Quantization for Visual Mamba","date":"2024-12-29","arxiv_id":"2412.20386","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/mbq-modality-balanced-quantization-for-large","slug":"mbq-modality-balanced-quantization-for-large","title":"MBQ: Modality-Balanced Quantization for Large Vision-Language Models","date":"2024-12-27","arxiv_id":"2412.19509","repositories_listed":1,"syntology":null},{"url":"/paper/advanced-knowledge-transfer-refined-feature","slug":"advanced-knowledge-transfer-refined-feature","title":"Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge Computing","date":"2024-12-26","arxiv_id":"2412.19125","repositories_listed":1,"syntology":null},{"url":"/paper/an-automatic-graph-construction-framework","slug":"an-automatic-graph-construction-framework","title":"An Automatic Graph Construction Framework based on Large Language Models for Recommendation","date":"2024-12-24","arxiv_id":"2412.18241","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-vector-quantization-for","slug":"hierarchical-vector-quantization-for","title":"Hierarchical Vector Quantization for Unsupervised Action Segmentation","date":"2024-12-23","arxiv_id":"2412.17640","repositories_listed":1,"syntology":null},{"url":"/paper/highly-optimized-kernels-and-fine-grained","slug":"highly-optimized-kernels-and-fine-grained","title":"Highly Optimized Kernels and Fine-Grained Codebooks for LLM Inference on Arm CPUs","date":"2024-12-23","arxiv_id":"2501.00032","repositories_listed":1,"syntology":null},{"url":"/paper/the-hallurag-dataset-detecting-closed-domain","slug":"the-hallurag-dataset-detecting-closed-domain","title":"The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal States","date":"2024-12-22","arxiv_id":"2412.17056","repositories_listed":1,"syntology":null},{"url":"/paper/log-time-k-means-clustering-for-1d-data-novel","slug":"log-time-k-means-clustering-for-1d-data-novel","title":"Log-Time K-Means Clustering for 1D Data: Novel Approaches with Proof and Implementation","date":"2024-12-19","arxiv_id":"2412.15295","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/log-time-k-means-clustering-for-1d-data-novel#ran","syntology_url":"https://syntology.ai/paper/2412.15295","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.15295"}},"official":{"repos":["SyphonArch/flash1dkmeans"],"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/preventing-local-pitfalls-in-vector","slug":"preventing-local-pitfalls-in-vector","title":"Preventing Local Pitfalls in Vector Quantization via Optimal Transport","date":"2024-12-19","arxiv_id":"2412.15195","repositories_listed":1,"syntology":null},{"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/autoregressive-video-generation-without","slug":"autoregressive-video-generation-without","title":"Autoregressive Video Generation without Vector Quantization","date":"2024-12-18","arxiv_id":"2412.14169","repositories_listed":1,"syntology":{"n":26,"n_ran":19,"n_constructed":18,"n_ran_checked":19,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":0,"phrase":"19 ran (of which 18 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/autoregressive-video-generation-without#ran","syntology_url":"https://syntology.ai/paper/2412.14169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.14169"}},"official":{"repos":["baaivision/nova"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":18,"n_ran_no_instrument_failure":19,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/resq-mixed-precision-quantization-of-large","slug":"resq-mixed-precision-quantization-of-large","title":"ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals","date":"2024-12-18","arxiv_id":"2412.14363","repositories_listed":1,"syntology":null},{"url":"/paper/vidtok-a-versatile-and-open-source-video","slug":"vidtok-a-versatile-and-open-source-video","title":"VidTok: A Versatile and Open-Source Video Tokenizer","date":"2024-12-17","arxiv_id":"2412.13061","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/vidtok-a-versatile-and-open-source-video#ran","syntology_url":"https://syntology.ai/paper/2412.13061","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.13061"}},"official":{"repos":["microsoft/vidtok"],"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/fast-and-slow-gradient-approximation-for","slug":"fast-and-slow-gradient-approximation-for","title":"Fast and Slow Gradient Approximation for Binary Neural Network Optimization","date":"2024-12-16","arxiv_id":"2412.11777","repositories_listed":1,"syntology":null},{"url":"/paper/mpq-dm-mixed-precision-quantization-for","slug":"mpq-dm-mixed-precision-quantization-for","title":"MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models","date":"2024-12-16","arxiv_id":"2412.11549","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":2,"n_no_contract":4,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 2 violated, 4 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mpq-dm-mixed-precision-quantization-for#ran","syntology_url":"https://syntology.ai/paper/2412.11549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.11549"}},"official":{"repos":["cantbebetter2/mpq-dm"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/relation-guided-adversarial-learning-for-data","slug":"relation-guided-adversarial-learning-for-data","title":"Relation-Guided Adversarial Learning for Data-free Knowledge Transfer","date":"2024-12-16","arxiv_id":"2412.11380","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-quantization-aware-training-on","slug":"efficient-quantization-aware-training-on","title":"Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment","date":"2024-12-15","arxiv_id":"2412.11186","repositories_listed":1,"syntology":null},{"url":"/paper/tinysubnets-an-efficient-and-low-capacity","slug":"tinysubnets-an-efficient-and-low-capacity","title":"TinySubNets: An efficient and low capacity continual learning strategy","date":"2024-12-14","arxiv_id":"2412.10869","repositories_listed":1,"syntology":null},{"url":"/paper/cosyvoice-2-scalable-streaming-speech","slug":"cosyvoice-2-scalable-streaming-speech","title":"CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models","date":"2024-12-13","arxiv_id":"2412.10117","repositories_listed":1,"syntology":null},{"url":"/paper/scbench-a-kv-cache-centric-analysis-of-long","slug":"scbench-a-kv-cache-centric-analysis-of-long","title":"SCBench: A KV Cache-Centric Analysis of Long-Context Methods","date":"2024-12-13","arxiv_id":"2412.10319","repositories_listed":1,"syntology":null},{"url":"/paper/lexico-extreme-kv-cache-compression-via","slug":"lexico-extreme-kv-cache-compression-via","title":"Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries","date":"2024-12-12","arxiv_id":"2412.08890","repositories_listed":1,"syntology":null},{"url":"/paper/bidm-pushing-the-limit-of-quantization-for","slug":"bidm-pushing-the-limit-of-quantization-for","title":"BiDM: Pushing the Limit of Quantization for Diffusion Models","date":"2024-12-08","arxiv_id":"2412.05926","repositories_listed":1,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":6,"n_instrument":8,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":4,"n_pointer_only":16,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 2 honoured, 0 violated, 4 with no contract checked; 8 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/bidm-pushing-the-limit-of-quantization-for#ran","syntology_url":"https://syntology.ai/paper/2412.05926","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05926"}},"official":{"repos":["xingyu-zheng/bidm"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/temporally-compressed-3d-gaussian-splatting","slug":"temporally-compressed-3d-gaussian-splatting","title":"Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes","date":"2024-12-07","arxiv_id":"2412.05700","repositories_listed":1,"syntology":null},{"url":"/paper/apollo-sgd-like-memory-adamw-level","slug":"apollo-sgd-like-memory-adamw-level","title":"APOLLO: SGD-like Memory, AdamW-level Performance","date":"2024-12-06","arxiv_id":"2412.05270","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"phrase":"6 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; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/apollo-sgd-like-memory-adamw-level#ran","syntology_url":"https://syntology.ai/paper/2412.05270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.05270"}},"official":{"repos":["zhuhanqing/APOLLO"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/queen-quantized-efficient-encoding-of-dynamic","slug":"queen-quantized-efficient-encoding-of-dynamic","title":"QUEEN: QUantized Efficient ENcoding of Dynamic Gaussians for Streaming Free-viewpoint Videos","date":"2024-12-05","arxiv_id":"2412.04469","repositories_listed":1,"syntology":null},{"url":"/paper/scaling-image-tokenizers-with-grouped","slug":"scaling-image-tokenizers-with-grouped","title":"Scaling Image Tokenizers with Grouped Spherical Quantization","date":"2024-12-03","arxiv_id":"2412.02632","repositories_listed":1,"syntology":null},{"url":"/paper/improving-detail-in-pluralistic-image","slug":"improving-detail-in-pluralistic-image","title":"Improving Detail in Pluralistic Image Inpainting with Feature Dequantization","date":"2024-12-02","arxiv_id":"2412.01046","repositories_listed":1,"syntology":null},{"url":"/paper/reducing-inference-energy-consumption-using","slug":"reducing-inference-energy-consumption-using","title":"Reducing Inference Energy Consumption Using Dual Complementary CNNs","date":"2024-12-02","arxiv_id":"2412.01039","repositories_listed":1,"syntology":null},{"url":"/paper/rilq-rank-insensitive-lora-based-quantization","slug":"rilq-rank-insensitive-lora-based-quantization","title":"RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model Accuracy","date":"2024-12-02","arxiv_id":"2412.01129","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"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) · 3 unverified","sample_list":"/paper/rilq-rank-insensitive-lora-based-quantization#ran","syntology_url":"https://syntology.ai/paper/2412.01129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.01129"}},"official":{"repos":["aiha-lab/rilq"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/xq-gan-an-open-source-image-tokenization","slug":"xq-gan-an-open-source-image-tokenization","title":"XQ-GAN: An Open-source Image Tokenization Framework for Autoregressive Generation","date":"2024-12-02","arxiv_id":"2412.01762","repositories_listed":1,"syntology":null},{"url":"/paper/dfrot-achieving-outlier-free-and-massive","slug":"dfrot-achieving-outlier-free-and-massive","title":"DFRot: Achieving Outlier-Free and Massive Activation-Free for Rotated LLMs with Refined Rotation","date":"2024-12-01","arxiv_id":"2412.00648","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dfrot-achieving-outlier-free-and-massive#ran","syntology_url":"https://syntology.ai/paper/2412.00648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.00648"}},"official":{"repos":["jingyangxiang/dfrot"],"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/scaling-transformers-for-low-bitrate-high","slug":"scaling-transformers-for-low-bitrate-high","title":"Scaling Transformers for Low-Bitrate High-Quality Speech Coding","date":"2024-11-29","arxiv_id":"2411.19842","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":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) · 0 unverified","sample_list":"/paper/scaling-transformers-for-low-bitrate-high#ran","syntology_url":"https://syntology.ai/paper/2411.19842","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.19842"}},"official":{"repos":["Stability-AI/stable-codec"],"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/litevar-compressing-visual-autoregressive","slug":"litevar-compressing-visual-autoregressive","title":"LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and Quantization","date":"2024-11-26","arxiv_id":"2411.17178","repositories_listed":1,"syntology":null},{"url":"/paper/motionllama-a-unified-framework-for-motion","slug":"motionllama-a-unified-framework-for-motion","title":"MotionLLaMA: A Unified Framework for Motion Synthesis and Comprehension","date":"2024-11-26","arxiv_id":"2411.17335","repositories_listed":1,"syntology":null},{"url":"/paper/passionsr-post-training-quantization-with","slug":"passionsr-post-training-quantization-with","title":"PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution","date":"2024-11-26","arxiv_id":"2411.17106","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-video-face-enhancement-with","slug":"efficient-video-face-enhancement-with","title":"Efficient Video Face Enhancement with Enhanced Spatial-Temporal Consistency","date":"2024-11-25","arxiv_id":"2411.16468","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":11,"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) · 2 unverified","sample_list":"/paper/efficient-video-face-enhancement-with#ran","syntology_url":"https://syntology.ai/paper/2411.16468","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.16468"}},"official":{"repos":["dixin-lab/bfvr-stc"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/efficient-online-inference-of-vision","slug":"efficient-online-inference-of-vision","title":"Efficient Online Inference of Vision Transformers by Training-Free Tokenization","date":"2024-11-23","arxiv_id":"2411.15397","repositories_listed":1,"syntology":null},{"url":"/paper/quantization-without-tears","slug":"quantization-without-tears","title":"Quantization without Tears","date":"2024-11-21","arxiv_id":"2411.13918","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":3,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"8 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/quantization-without-tears#ran","syntology_url":"https://syntology.ai/paper/2411.13918","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.13918"}},"official":{"repos":["wujx2001/QwT"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/quantized-symbolic-time-series-approximation","slug":"quantized-symbolic-time-series-approximation","title":"Quantized symbolic time series approximation","date":"2024-11-20","arxiv_id":"2411.15209","repositories_listed":1,"syntology":null},{"url":"/paper/bitmod-bit-serial-mixture-of-datatype-llm","slug":"bitmod-bit-serial-mixture-of-datatype-llm","title":"BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration","date":"2024-11-18","arxiv_id":"2411.11745","repositories_listed":1,"syntology":null},{"url":"/paper/an-exploration-of-the-effect-of-quantisation","slug":"an-exploration-of-the-effect-of-quantisation","title":"An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2","date":"2024-11-15","arxiv_id":"2411.12758","repositories_listed":1,"syntology":null},{"url":"/paper/the-super-weight-in-large-language-models","slug":"the-super-weight-in-large-language-models","title":"The Super Weight in Large Language Models","date":"2024-11-11","arxiv_id":"2411.07191","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"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) · 4 unverified","sample_list":"/paper/the-super-weight-in-large-language-models#ran","syntology_url":"https://syntology.ai/paper/2411.07191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.07191"}},"official":{"repos":["mengxiayu/llmsuperweight"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/an-asymmetric-heuristic-for-trained-ternary","slug":"an-asymmetric-heuristic-for-trained-ternary","title":"An asymmetric heuristic for trained ternary quantization based on the statistics of the weights: an application to medical signal classification","date":"2024-11-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/expansion-quantization-network-an-efficient","slug":"expansion-quantization-network-an-efficient","title":"Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection Framework","date":"2024-11-09","arxiv_id":"2411.06160","repositories_listed":1,"syntology":null}],"record_sha256":"5581fe61f6c0a3553c404d5e858c41acdfc071139b2e8e1ea0c96fa99f3bd022","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}