{"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/get-sim","entry":"get_sim","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":12,"n_papers_ran":6,"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":8,"n_samples_ran":3,"n_samples_fingerprinted":1,"n_places":12,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"unverified":5},"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":"2505.19650","paper":"/paper/modality-curation-building-universal","title":"Modality Curation: Building Universal Embeddings for Advanced Multimodal Information Retrieval","date":"2025-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"friedrichor/UNITE","path":"unite/model/criterions.py","file_url":"https://github.com/friedrichor/UNITE/blob/HEAD/unite/model/criterions.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":"b0ea61cfeeeb48e5","mcp_get_code":{"code_sha256":"b0ea61cfeeeb48e5"}},{"arxiv_id":"2410.19702","paper":"/paper/timesuite-improving-mllms-for-long-video","title":"TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning","date":"2024-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenGVLab/TimeSuite","path":"models/criterions.py","file_url":"https://github.com/OpenGVLab/TimeSuite/blob/HEAD/models/criterions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a475c4129bb51880","mcp_get_code":{"code_sha256":"a475c4129bb51880"}},{"arxiv_id":"2404.13948","paper":"/paper/typos-that-broke-the-rag-s-back-genetic","title":"Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations","date":"2024-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zomss/garag","path":"src/util.py","file_url":"https://github.com/zomss/garag/blob/HEAD/src/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e16e5d00c2166b7a","mcp_get_code":{"code_sha256":"e16e5d00c2166b7a"}},{"arxiv_id":"2404.02059","paper":"/paper/iisan-efficiently-adapting-multimodal","title":"IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gair-lab/iisan","path":"Code_Cached/model/model.py","file_url":"https://github.com/gair-lab/iisan/blob/HEAD/Code_Cached/model/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"42cf9ff13b24ef29","mcp_get_code":{"code_sha256":"42cf9ff13b24ef29"}},{"arxiv_id":"2403.10228","paper":"/paper/hawkeye-training-video-text-llms-for","title":"HawkEye: Training Video-Text LLMs for Grounding Text in Videos","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yellow-binary-tree/hawkeye","path":"models/criterions.py","file_url":"https://github.com/yellow-binary-tree/hawkeye/blob/HEAD/models/criterions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a475c4129bb51880","mcp_get_code":{"code_sha256":"a475c4129bb51880"}},{"arxiv_id":"2311.17005","paper":"/paper/mvbench-a-comprehensive-multi-modal-video","title":"MVBench: A Comprehensive Multi-modal Video Understanding Benchmark","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/ask-anything","path":"video_chat2/models/criterions.py","file_url":"https://github.com/opengvlab/ask-anything/blob/HEAD/video_chat2/models/criterions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a475c4129bb51880","mcp_get_code":{"code_sha256":"a475c4129bb51880"}},{"arxiv_id":"2310.17468","paper":"/paper/cross-modal-active-complementary-learning-1","title":"Cross-modal Active Complementary Learning with Self-refining Correspondence","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"42cf9ff13b24ef29","mcp_get_code":{"code_sha256":"42cf9ff13b24ef29"}},{"arxiv_id":"2304.10716","paper":"/paper/joint-token-pruning-and-squeezing-towards","title":"Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers","date":"2023-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megvii-research/TPS-CVPR2023","path":"torch_codebase/models/dynamic_vit.py","file_url":"https://github.com/megvii-research/TPS-CVPR2023/blob/HEAD/torch_codebase/models/dynamic_vit.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":"b31af37bba5ce554","mcp_get_code":{"code_sha256":"b31af37bba5ce554"}},{"arxiv_id":"2303.16058","paper":"/paper/unmasked-teacher-towards-training-efficient","title":"Unmasked Teacher: Towards Training-Efficient Video Foundation Models","date":"2023-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/unmasked_teacher","path":"multi_modality/models/criterions.py","file_url":"https://github.com/opengvlab/unmasked_teacher/blob/HEAD/multi_modality/models/criterions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"431de8c91d7b558f","mcp_get_code":{"code_sha256":"431de8c91d7b558f"}},{"arxiv_id":"2212.05698","paper":"/paper/modem-accelerating-visual-model-based","title":"MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations","date":"2022-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/modem","path":"tasks/mj_envs/mj_envs/envs/env_base.py","file_url":"https://github.com/facebookresearch/modem/blob/HEAD/tasks/mj_envs/mj_envs/envs/env_base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"e285ec81dccf7cb8","mcp_get_code":{"code_sha256":"e285ec81dccf7cb8"}},{"arxiv_id":"Cheng_VindLU_A_Recipe_for_Effective_Video-and-Language_Pretraining_CVPR_2023_paper","paper":null,"title":"arXiv:Cheng_VindLU_A_Recipe_for_Effective_Video-and-Language_Pretraining_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"klauscc/VindLU","path":"models/criterions.py","file_url":"https://github.com/klauscc/VindLU/blob/HEAD/models/criterions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"431de8c91d7b558f","mcp_get_code":{"code_sha256":"431de8c91d7b558f"}},{"arxiv_id":"2022.acl-long.159","paper":null,"title":"arXiv:2022.acl-long.159","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"thunlp/CrossET","path":"src/source/function.py","file_url":"https://github.com/thunlp/CrossET/blob/HEAD/src/source/function.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01f0124e21f3522e","mcp_get_code":{"code_sha256":"01f0124e21f3522e"}}]}