{"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/compress","entry":"compress","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":35,"n_papers_ran":9,"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":11,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":35,"n_places_pointer_only":13,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":1,"ran":2,"unverified":7},"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":"2605.28589","paper":"/paper/arxiv-2605-28589","title":"Thinned Mean Field Langevin Dynamics","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"hudsonchen/thinned_mfld","path":"utils/kt.py","file_url":"https://github.com/hudsonchen/thinned_mfld/blob/HEAD/utils/kt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b8179bb351b7af3d","mcp_get_code":{"code_sha256":"b8179bb351b7af3d"}},{"arxiv_id":"2604.17897","paper":"/paper/arxiv-2604-17897","title":"LoReC: Rethinking Large Language Models for Graph Data Analysis","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"Git-King-Zhan/LoReC","path":"lorec-gpt/graphgpt/model/compression.py","file_url":"https://github.com/Git-King-Zhan/LoReC/blob/HEAD/lorec-gpt/graphgpt/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2603.21461","paper":"/paper/arxiv-2603-21461","title":"DSPA: Dynamic SAE Steering for Data-Efficient Preference Alignment","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"lm-sys/FastChat","path":"fastchat/model/compression.py","file_url":"https://github.com/lm-sys/FastChat/blob/HEAD/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2601.06787","paper":"/paper/arxiv-2601-06787","title":"Garbage Attention in Large Language Models: <BOS> Sink Heads and Sink-aware Pruning","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"CASIA-LMC-Lab/FLAP","path":"lib/prune.py","file_url":"https://github.com/CASIA-LMC-Lab/FLAP/blob/HEAD/lib/prune.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1e3629d8fc0ca6c7","mcp_get_code":{"code_sha256":"1e3629d8fc0ca6c7"}},{"arxiv_id":"2409.17836","paper":"/paper/language-models-as-zero-shot-lossless","title":"Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models","date":"2024-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hui-po-wang/LM-GC","path":"compressors/png.py","file_url":"https://github.com/hui-po-wang/LM-GC/blob/HEAD/compressors/png.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a97701702e1a2e67","mcp_get_code":{"code_sha256":"a97701702e1a2e67"}},{"arxiv_id":"2406.14670","paper":"/paper/exploring-design-choices-for-building","title":"Exploring Design Choices for Building Language-Specific LLMs","date":"2024-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"atutej/token-language-adaptation","path":"FastChat/fastchat/model/compression.py","file_url":"https://github.com/atutej/token-language-adaptation/blob/HEAD/FastChat/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2406.04845","paper":"/paper/fedllm-bench-realistic-benchmarks-for","title":"FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models","date":"2024-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rui-ye/fedllm-bench","path":"compression.py","file_url":"https://github.com/rui-ye/fedllm-bench/blob/HEAD/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2404.12290","paper":"/paper/debiased-distribution-compression","title":"Debiased Distribution Compression","date":"2024-04-18","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":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"9a913678c56785e8","mcp_get_code":{"code_sha256":"9a913678c56785e8"}},{"arxiv_id":"2403.07714","paper":"/paper/stabletoolbench-towards-stable-large-scale","title":"StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openbmb/toolbench","path":"toolbench/model/compression.py","file_url":"https://github.com/openbmb/toolbench/blob/HEAD/toolbench/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"386536152177a379","mcp_get_code":{"code_sha256":"386536152177a379"}},{"arxiv_id":"2403.04132","paper":"/paper/chatbot-arena-an-open-platform-for-evaluating","title":"Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BirgerMoell/SwedishLLMBenchmark","path":"fastchat/model/compression.py","file_url":"https://github.com/BirgerMoell/SwedishLLMBenchmark/blob/HEAD/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2403.02502","paper":"/paper/trial-and-error-exploration-based-trajectory","title":"Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents","date":"2024-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yifan-song793/eto","path":"fastchat/model/compression.py","file_url":"https://github.com/yifan-song793/eto/blob/HEAD/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2403.00813","paper":"/paper/urbangpt-spatio-temporal-large-language","title":"UrbanGPT: Spatio-Temporal Large Language Models","date":"2024-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkuds/urbangpt","path":"urbangpt/model/compression.py","file_url":"https://github.com/hkuds/urbangpt/blob/HEAD/urbangpt/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2402.16302","paper":"/paper/graph-diffusion-policy-optimization","title":"Graph Diffusion Policy Optimization","date":"2024-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/gdpo","path":"model/diffusion_discrete.py","file_url":"https://github.com/sail-sg/gdpo/blob/HEAD/model/diffusion_discrete.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4d13d6ce3ccd0cc1","mcp_get_code":{"code_sha256":"4d13d6ce3ccd0cc1"}},{"arxiv_id":"2402.11746","paper":"/paper/language-models-are-homer-simpson-safety-re","title":"Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"declare-lab/red-instruct","path":"starling_training/fastchat/model/compression.py","file_url":"https://github.com/declare-lab/red-instruct/blob/HEAD/starling_training/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2402.02130","paper":"/paper/rendering-graphs-for-graph-reasoning-in","title":"GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning","date":"2024-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WEIYanbin1999/GITA","path":"fastchat/model/compression.py","file_url":"https://github.com/WEIYanbin1999/GITA/blob/HEAD/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2401.08326","paper":"/paper/rotbench-a-multi-level-benchmark-for","title":"RoTBench: A Multi-Level Benchmark for Evaluating the Robustness of Large Language Models in Tool Learning","date":"2024-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Junjie-Ye/RoTBench","path":"Code/model/compression.py","file_url":"https://github.com/Junjie-Ye/RoTBench/blob/HEAD/Code/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"386536152177a379","mcp_get_code":{"code_sha256":"386536152177a379"}},{"arxiv_id":"2312.00401","paper":"/paper/viotgpt-learning-to-schedule-vision-tools","title":"VIoTGPT: Learning to Schedule Vision Tools in LLMs towards Intelligent Video Internet of Things","date":"2023-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhongyy/viotgpt","path":"train/compression.py","file_url":"https://github.com/zhongyy/viotgpt/blob/HEAD/train/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"386536152177a379","mcp_get_code":{"code_sha256":"386536152177a379"}},{"arxiv_id":"2310.19740","paper":"/paper/collaborative-evaluation-exploring-the","title":"Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qtli/coeval","path":"webapp/compression.py","file_url":"https://github.com/qtli/coeval/blob/HEAD/webapp/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2310.17631","paper":"/paper/judgelm-fine-tuned-large-language-models-are","title":"JudgeLM: Fine-tuned Large Language Models are Scalable Judges","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hitz-zentroa/eval-MCG-COLING-2025","path":"evaluation/JudgeLM-main/judgelm/model/compression.py","file_url":"https://github.com/hitz-zentroa/eval-MCG-COLING-2025/blob/HEAD/evaluation/JudgeLM-main/judgelm/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2310.13023","paper":"/paper/graphgpt-graph-instruction-tuning-for-large","title":"GraphGPT: Graph Instruction Tuning for Large Language Models","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HKUDS/GraphGPT","path":"graphgpt/model/compression.py","file_url":"https://github.com/HKUDS/GraphGPT/blob/HEAD/graphgpt/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2309.10668","paper":"/paper/language-modeling-is-compression","title":"Language Modeling Is Compression","date":"2023-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-deepmind/language_modeling_is_compression","path":"compressors/png.py","file_url":"https://github.com/google-deepmind/language_modeling_is_compression/blob/HEAD/compressors/png.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a97701702e1a2e67","mcp_get_code":{"code_sha256":"a97701702e1a2e67"}},{"arxiv_id":"2307.12981","paper":"/paper/3d-llm-injecting-the-3d-world-into-large","title":"3D-LLM: Injecting the 3D World into Large Language Models","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"umass-foundation-model/3d-llm","path":"3DLanguage_data/ChatCaptioner_based/quetion_model.py","file_url":"https://github.com/umass-foundation-model/3d-llm/blob/HEAD/3DLanguage_data/ChatCaptioner_based/quetion_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2304.10453","paper":"/paper/phoenix-democratizing-chatgpt-across","title":"Phoenix: Democratizing ChatGPT across Languages","date":"2023-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"freedomintelligence/llmzoo","path":"llmzoo/deploy/webapp/compression.py","file_url":"https://github.com/freedomintelligence/llmzoo/blob/HEAD/llmzoo/deploy/webapp/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2303.06865","paper":"/paper/high-throughput-generative-inference-of-large","title":"FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU","date":"2023-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fminference/flexgen","path":"flexllmgen/compression.py","file_url":"https://github.com/fminference/flexgen/blob/HEAD/flexllmgen/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2301.05974","paper":"/paper/compress-then-test-powerful-kernel-testing-in","title":"Compress Then Test: Powerful Kernel Testing in Near-linear Time","date":"2023-01-14","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":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"9a913678c56785e8","mcp_get_code":{"code_sha256":"9a913678c56785e8"}},{"arxiv_id":"2112.05682","paper":"/paper/self-attention-does-not-need-o-n-2-memory","title":"Self-attention Does Not Need $O(n^2)$ Memory","date":"2021-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stability-ai/fastchat","path":"fastchat/model/compression.py","file_url":"https://github.com/stability-ai/fastchat/blob/HEAD/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2111.07941","paper":"/paper/distribution-compression-in-near-linear-time-1","title":"Distribution Compression in Near-linear Time","date":"2021-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/goodpoints","path":"goodpoints/compress.py","file_url":"https://github.com/microsoft/goodpoints/blob/HEAD/goodpoints/compress.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a913678c56785e8","mcp_get_code":{"code_sha256":"9a913678c56785e8"}},{"arxiv_id":"2110.01593","paper":"/paper/generalized-kernel-thinning","title":"Generalized Kernel Thinning","date":"2021-10-04","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":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"9a913678c56785e8","mcp_get_code":{"code_sha256":"9a913678c56785e8"}},{"arxiv_id":"2105.06138","paper":"/paper/unsupervised-hashing-with-contrastive","title":"Unsupervised Hashing with Contrastive Information Bottleneck","date":"2021-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qiuzx2/CIBHash","path":"model/CIBHash.py","file_url":"https://github.com/qiuzx2/CIBHash/blob/HEAD/model/CIBHash.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9d8f8e1077379f49","mcp_get_code":{"code_sha256":"9d8f8e1077379f49"}},{"arxiv_id":"2105.05842","paper":"/paper/kernel-thinning","title":"Kernel Thinning","date":"2021-05-12","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":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"9a913678c56785e8","mcp_get_code":{"code_sha256":"9a913678c56785e8"}},{"arxiv_id":"1912.09522","paper":"/paper/contextual-outlier-detection-in-continuous","title":"Event Outlier Detection in Continuous Time","date":"2019-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"siqil/CPPOD","path":"summarize_results_std.py","file_url":"https://github.com/siqil/CPPOD/blob/HEAD/summarize_results_std.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2889e16a4eb04885","mcp_get_code":{"code_sha256":"2889e16a4eb04885"}},{"arxiv_id":"1905.08494","paper":"/paper/deep-signatures","title":"Deep Signature Transforms","date":"2019-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anh-tong/signax","path":"src/signax/utils.py","file_url":"https://github.com/anh-tong/signax/blob/HEAD/src/signax/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"240a22a9a6f347bd","mcp_get_code":{"code_sha256":"240a22a9a6f347bd"}},{"arxiv_id":"1903.08548","paper":"/paper/learning-convolutional-transforms-for-lossy","title":"Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression","date":"2019-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mauriceqch/pcc_geo_cnn","path":"src/compress.py","file_url":"https://github.com/mauriceqch/pcc_geo_cnn/blob/HEAD/src/compress.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68ecd4d88b382314","mcp_get_code":{"code_sha256":"68ecd4d88b382314"}},{"arxiv_id":"2025.emnlp-main.1337","paper":null,"title":"arXiv:2025.emnlp-main.1337","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"RazvanDu/CopySpec","path":"FastChat/fastchat/model/compression.py","file_url":"https://github.com/RazvanDu/CopySpec/blob/HEAD/FastChat/fastchat/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}},{"arxiv_id":"2024.findings-emnlp.559","paper":null,"title":"arXiv:2024.findings-emnlp.559","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hitz-zentroa/cn-eval","path":"evaluation/JudgeLM-main/judgelm/model/compression.py","file_url":"https://github.com/hitz-zentroa/cn-eval/blob/HEAD/evaluation/JudgeLM-main/judgelm/model/compression.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"80dde39da1ba3708","mcp_get_code":{"code_sha256":"80dde39da1ba3708"}}]}