{"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/allgather","entry":"AllGather","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":13,"n_papers_ran":11,"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":13,"n_samples_ran":11,"n_samples_fingerprinted":2,"n_places":13,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":11,"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":"2605.19374","paper":"/paper/arxiv-2605-19374","title":"Concept-Guided Noisy Negative Suppression for Zero-Shot Classification and Grounding of Chest X-Ray Findings","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"DopamineLcy/conns","path":"conns/loss.py","file_url":"https://github.com/DopamineLcy/conns/blob/HEAD/conns/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2e136285a741a20a","mcp_get_code":{"code_sha256":"2e136285a741a20a"}},{"arxiv_id":"2601.18190","paper":"/paper/arxiv-2601-18190","title":"Multi-Perspective Subimage CLIP with Keyword Guidance for Remote Sensing Image-Text Retrieval","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Lcrucial1f/MPS-CLIP","path":"models/mpsclip.py","file_url":"https://github.com/Lcrucial1f/MPS-CLIP/blob/HEAD/models/mpsclip.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":"ca8a9f13e020ca92","mcp_get_code":{"code_sha256":"ca8a9f13e020ca92"}},{"arxiv_id":"2502.11401","paper":"/paper/following-the-autoregressive-nature-of-llm","title":"Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment","date":"2025-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TrustedLLM/AutoRegEmbed","path":"src/modeling/modeling_autoregembed.py","file_url":"https://github.com/TrustedLLM/AutoRegEmbed/blob/HEAD/src/modeling/modeling_autoregembed.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"953781d84ef8e8ee","mcp_get_code":{"code_sha256":"953781d84ef8e8ee"}},{"arxiv_id":"2410.00263","paper":"/paper/procedure-aware-surgical-video-language","title":"Procedure-Aware Surgical Video-language Pretraining with Hierarchical Knowledge Augmentation","date":"2024-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"camma-public/peskavlp","path":"codes/engines/engine_phase_video_hierarchy.py","file_url":"https://github.com/camma-public/peskavlp/blob/HEAD/codes/engines/engine_phase_video_hierarchy.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8ca1275e7e3d3105","mcp_get_code":{"code_sha256":"8ca1275e7e3d3105"}},{"arxiv_id":"2404.18253","paper":"/paper/efficient-remote-sensing-with-harmonized","title":"Efficient Remote Sensing with Harmonized Transfer Learning and Modality Alignment","date":"2024-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seekerhuang/harma","path":"models/model_retrieval.py","file_url":"https://github.com/seekerhuang/harma/blob/HEAD/models/model_retrieval.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":"3d8d0c7edd3e4998","mcp_get_code":{"code_sha256":"3d8d0c7edd3e4998"}},{"arxiv_id":"2310.14652","paper":"/paper/invariant-feature-regularization-for-fair-1","title":"Invariant Feature Regularization for Fair Face Recognition","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"panasonicconnect/invreg","path":"utils/utils_invreg.py","file_url":"https://github.com/panasonicconnect/invreg/blob/HEAD/utils/utils_invreg.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1736503522b025e1","mcp_get_code":{"code_sha256":"1736503522b025e1"}},{"arxiv_id":"2310.12692","paper":"/paper/representation-learning-via-consistent-2","title":"Representation Learning via Consistent Assignment of Views over Random Partitions","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sthalles/carp","path":"modules/carp_loss.py","file_url":"https://github.com/sthalles/carp/blob/HEAD/modules/carp_loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b72b086ec0bbd4c5","mcp_get_code":{"code_sha256":"b72b086ec0bbd4c5"}},{"arxiv_id":"2308.00951","paper":"/paper/from-sparse-to-soft-mixtures-of-experts","title":"From Sparse to Soft Mixtures of Experts","date":"2023-08-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/soft-moe-pytorch","path":"soft_moe_pytorch/soft_moe.py","file_url":"https://github.com/lucidrains/soft-moe-pytorch/blob/HEAD/soft_moe_pytorch/soft_moe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f86d2cc1b619c3a7","mcp_get_code":{"code_sha256":"f86d2cc1b619c3a7"}},{"arxiv_id":"2305.17530","paper":"/paper/pumer-pruning-and-merging-tokens-for","title":"PuMer: Pruning and Merging Tokens for Efficient Vision Language Models","date":"2023-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csarron/pumer","path":"src/pumer/model/pruner.py","file_url":"https://github.com/csarron/pumer/blob/HEAD/src/pumer/model/pruner.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01294f03cdd7b3b4","mcp_get_code":{"code_sha256":"01294f03cdd7b3b4"}},{"arxiv_id":"2303.14865","paper":"/paper/revisiting-multimodal-representation-in","title":"Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuxiaochen1103/fdt","path":"prototype/model/clip_fdt.py","file_url":"https://github.com/yuxiaochen1103/fdt/blob/HEAD/prototype/model/clip_fdt.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":"c64326f5874bc072","mcp_get_code":{"code_sha256":"c64326f5874bc072"}},{"arxiv_id":"2303.14369","paper":"/paper/video-text-as-game-players-hierarchical","title":"Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning","date":"2023-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jpthu17/dicosa","path":"tvr/models/modeling.py","file_url":"https://github.com/jpthu17/dicosa/blob/HEAD/tvr/models/modeling.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":"72bb773921596f80","mcp_get_code":{"code_sha256":"72bb773921596f80"}},{"arxiv_id":"2204.02311","paper":"/paper/palm-scaling-language-modeling-with-pathways-1","title":"PaLM: Scaling Language Modeling with Pathways","date":"2022-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/CoCa-pytorch","path":"coca_pytorch/coca_pytorch.py","file_url":"https://github.com/lucidrains/CoCa-pytorch/blob/HEAD/coca_pytorch/coca_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54e85922a73fb785","mcp_get_code":{"code_sha256":"54e85922a73fb785"}},{"arxiv_id":"2104.13963","paper":"/paper/semi-supervised-learning-of-visual-features","title":"Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples","date":"2021-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/suncet","path":"src/losses.py","file_url":"https://github.com/facebookresearch/suncet/blob/HEAD/src/losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f68fac75925a8996","mcp_get_code":{"code_sha256":"f68fac75925a8996"}}]}