{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/code/lp-loss","entry":"lp_loss","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":20,"n_papers_ran":18,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":9,"n_samples_ran":7,"n_samples_fingerprinted":7,"n_places":21,"n_places_pointer_only":9,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":3,"ran":1,"unverified":2},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2606.25285","paper":"/paper/arxiv-2606-25285","title":"EPTS: Elastic Post-Training Sparsity for Efficient Large Language Model Compression","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"xuke225/EPTS","path":"utils/reconstruction.py","file_url":"https://github.com/xuke225/EPTS/blob/HEAD/utils/reconstruction.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8fcf6c2e205ea12b","mcp_get_code":{"code_sha256":"8fcf6c2e205ea12b"}},{"arxiv_id":"2510.10467","paper":"/paper/arxiv-2510-10467","title":"AnyBCQ: Hardware Efficient Flexible Binary-Coded Quantization for Multi-Precision LLMs","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"naver-aics/anybcq","path":"anybcq/quantization/anybcq.py","file_url":"https://github.com/naver-aics/anybcq/blob/HEAD/anybcq/quantization/anybcq.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ea5af2ff88120865","mcp_get_code":{"code_sha256":"ea5af2ff88120865"}},{"arxiv_id":"2412.11549","paper":"/paper/mpq-dm-mixed-precision-quantization-for","title":"MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models","date":"2024-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cantbebetter2/mpq-dm","path":"quant_scripts/quant_layer.py","file_url":"https://github.com/cantbebetter2/mpq-dm/blob/HEAD/quant_scripts/quant_layer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}},{"arxiv_id":"2412.07268","paper":"/paper/ptsbench-a-comprehensive-post-training","title":"PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models","date":"2024-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"modeltc/msbench","path":"msbench/advanced_pts.py","file_url":"https://github.com/modeltc/msbench/blob/HEAD/msbench/advanced_pts.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"46008b787459aa50","mcp_get_code":{"code_sha256":"46008b787459aa50"}},{"arxiv_id":"2410.21759","paper":"/paper/intlora-integral-low-rank-adaptation-of","title":"IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models","date":"2024-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csguoh/intlora","path":"utils/intlora_shift.py","file_url":"https://github.com/csguoh/intlora/blob/HEAD/utils/intlora_shift.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8fcf6c2e205ea12b","mcp_get_code":{"code_sha256":"8fcf6c2e205ea12b"}},{"arxiv_id":"2405.16005","paper":"/paper/ptq4dit-post-training-quantization-for","title":"PTQ4DiT: Post-training Quantization for Diffusion Transformers","date":"2024-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}},{"arxiv_id":"2405.03144","paper":"/paper/ptq4sam-post-training-quantization-for","title":"PTQ4SAM: Post-Training Quantization for Segment Anything","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chengtao-lv/PTQ4SAM","path":"ptq4sam/solver/recon.py","file_url":"https://github.com/chengtao-lv/PTQ4SAM/blob/HEAD/ptq4sam/solver/recon.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8003cb7a5176e497","mcp_get_code":{"code_sha256":"8003cb7a5176e497"}},{"arxiv_id":"2403.19140","paper":"/paper/qncd-quantization-noise-correction-for","title":"QNCD: Quantization Noise Correction for Diffusion Models","date":"2024-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huanpengchu/qncd","path":"quant/quant_layer.py","file_url":"https://github.com/huanpengchu/qncd/blob/HEAD/quant/quant_layer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}},{"arxiv_id":"2402.03666","paper":"/paper/quest-low-bit-diffusion-model-quantization","title":"QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hatchetProject/QuEST","path":"qdiff/quant_layer.py","file_url":"https://github.com/hatchetProject/QuEST/blob/HEAD/qdiff/quant_layer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}},{"arxiv_id":"2311.06322","paper":"/paper/post-training-quantization-with-progressive","title":"Post-training Quantization for Text-to-Image Diffusion Models with Progressive Calibration and Activation Relaxing","date":"2023-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsa18/PCR","path":"quantization_tools/quantization/loss.py","file_url":"https://github.com/tsa18/PCR/blob/HEAD/quantization_tools/quantization/loss.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}},{"arxiv_id":"2310.03270","paper":"/paper/efficientdm-efficient-quantization-aware-fine","title":"EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ThisisBillhe/EfficientDM","path":"quant_scripts/quant_layer.py","file_url":"https://github.com/ThisisBillhe/EfficientDM/blob/HEAD/quant_scripts/quant_layer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}},{"arxiv_id":"2308.07650","paper":"/paper/eq-net-elastic-quantization-neural-networks","title":"EQ-Net: Elastic Quantization Neural Networks","date":"2023-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuke225/EQ-Net","path":"module/base_uaq.py","file_url":"https://github.com/xuke225/EQ-Net/blob/HEAD/module/base_uaq.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}},{"arxiv_id":"2308.07650","paper":"/paper/eq-net-elastic-quantization-neural-networks","title":"EQ-Net: Elastic Quantization Neural Networks","date":"2023-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuke225/EQ-Net","path":"module/base_lsq.py","file_url":"https://github.com/xuke225/EQ-Net/blob/HEAD/module/base_lsq.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dc5d39ef0041492d","mcp_get_code":{"code_sha256":"dc5d39ef0041492d"}},{"arxiv_id":"2305.18723","paper":"/paper/towards-accurate-data-free-quantization-for","title":"Towards Accurate Post-training Quantization for Diffusion Models","date":"2023-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"changyuanwang17/apq-dm","path":"utils/quant_util.py","file_url":"https://github.com/changyuanwang17/apq-dm/blob/HEAD/utils/quant_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}},{"arxiv_id":"2203.05740","paper":"/paper/qdrop-randomly-dropping-quantization-for-1","title":"QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization","date":"2022-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wimh966/QDrop","path":"qdrop/solver/recon.py","file_url":"https://github.com/wimh966/QDrop/blob/HEAD/qdrop/solver/recon.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8003cb7a5176e497","mcp_get_code":{"code_sha256":"8003cb7a5176e497"}},{"arxiv_id":"2201.00058","paper":"/paper/representation-topology-divergence-a-method-1","title":"Representation Topology Divergence: A Method for Comparing Neural Network Representations","date":"2021-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nikitabalabin/topdis","path":"BetaTCVAE_TopDis/rtd.py","file_url":"https://github.com/nikitabalabin/topdis/blob/HEAD/BetaTCVAE_TopDis/rtd.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"230251fbc53d99a1","mcp_get_code":{"code_sha256":"230251fbc53d99a1"}},{"arxiv_id":"2106.06984","paper":"/paper/a-free-lunch-from-ann-towards-efficient","title":"A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration","date":"2021-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yhhhli/SNN_Calibration","path":"models/spiking_layer.py","file_url":"https://github.com/yhhhli/SNN_Calibration/blob/HEAD/models/spiking_layer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54ab1b5bf761f953","mcp_get_code":{"code_sha256":"54ab1b5bf761f953"}},{"arxiv_id":"1511.05440","paper":"/paper/deep-multi-scale-video-prediction-beyond-mean","title":"Deep multi-scale video prediction beyond mean square error","date":"2015-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dyelax/Adversarial_Video_Generation","path":"Code/loss_functions.py","file_url":"https://github.com/dyelax/Adversarial_Video_Generation/blob/HEAD/Code/loss_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"98805789ae7fe83c","mcp_get_code":{"code_sha256":"98805789ae7fe83c"}},{"arxiv_id":"Zhang_AHCPTQ_Accurate_and_Hardware-Compatible_Post-Training_Quantization_for_Segment_Anything_Model_ICCV_2025_paper","paper":null,"title":"arXiv:Zhang_AHCPTQ_Accurate_and_Hardware-Compatible_Post-Training_Quantization_for_Segment_Anything_Model_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Keio-CSG/AHCPTQ","path":"ahcptq/solver/recon.py","file_url":"https://github.com/Keio-CSG/AHCPTQ/blob/HEAD/ahcptq/solver/recon.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8003cb7a5176e497","mcp_get_code":{"code_sha256":"8003cb7a5176e497"}},{"arxiv_id":"Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper","paper":null,"title":"arXiv:Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hustvl/PD-Quant","path":"quant/quant_layer.py","file_url":"https://github.com/hustvl/PD-Quant/blob/HEAD/quant/quant_layer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}},{"arxiv_id":"Li_RepQ-ViT_Scale_Reparameterization_for_Post-Training_Quantization_of_Vision_Transformers_ICCV_2023_paper","paper":null,"title":"arXiv:Li_RepQ-ViT_Scale_Reparameterization_for_Post-Training_Quantization_of_Vision_Transformers_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zkkli/RepQ-ViT","path":"classification/quant/quantizer.py","file_url":"https://github.com/zkkli/RepQ-ViT/blob/HEAD/classification/quant/quantizer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"08c6ea7576220a13","mcp_get_code":{"code_sha256":"08c6ea7576220a13"}}]}