{"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/rgetattr","entry":"rgetattr","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":22,"n_papers_ran":2,"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":7,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":22,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"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":"2409.12822","paper":"/paper/language-models-learn-to-mislead-humans-via","title":"Language Models Learn to Mislead Humans via RLHF","date":"2024-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiaxin-wen/misleadlm","path":"trlx/utils/modeling.py","file_url":"https://github.com/jiaxin-wen/misleadlm/blob/HEAD/trlx/utils/modeling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc2499cfa7162602","mcp_get_code":{"code_sha256":"fc2499cfa7162602"}},{"arxiv_id":"2407.10964","paper":"/paper/no-train-all-gain-self-supervised-gradients","title":"No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations","date":"2024-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"waltersimoncini/fungivision","path":"fungivision/gradients/base_extractor.py","file_url":"https://github.com/waltersimoncini/fungivision/blob/HEAD/fungivision/gradients/base_extractor.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":"7adb31904977e81b","mcp_get_code":{"code_sha256":"7adb31904977e81b"}},{"arxiv_id":"2406.17563","paper":"/paper/multi-property-steering-of-large-language","title":"Multi-property Steering of Large Language Models with Dynamic Activation Composition","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"danielsc4/dynamic-activation-composition","path":"src/utils/model_utils.py","file_url":"https://github.com/danielsc4/dynamic-activation-composition/blob/HEAD/src/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2405.14852","paper":"/paper/pv-tuning-beyond-straight-through-estimation","title":"PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vahe1994/aqlm","path":"convert_legacy_model_format.py","file_url":"https://github.com/vahe1994/aqlm/blob/HEAD/convert_legacy_model_format.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":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2403.05612","paper":"/paper/unfamiliar-finetuning-examples-control-how","title":"Unfamiliar Finetuning Examples Control How Language Models Hallucinate","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"katiekang1998/llm_hallucinations","path":"trlx/utils/modeling.py","file_url":"https://github.com/katiekang1998/llm_hallucinations/blob/HEAD/trlx/utils/modeling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fc2499cfa7162602","mcp_get_code":{"code_sha256":"fc2499cfa7162602"}},{"arxiv_id":"2401.06197","paper":"/paper/efficient-deformable-convnets-rethinking","title":"Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/dcnv4","path":"classification/extract_feature.py","file_url":"https://github.com/opengvlab/dcnv4/blob/HEAD/classification/extract_feature.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2401.04679","paper":"/paper/rosa-accurate-parameter-efficient-fine-tuning","title":"RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation","date":"2024-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ist-daslab/rosa","path":"llmfoundry/models/hf/hf_fsdp.py","file_url":"https://github.com/ist-daslab/rosa/blob/HEAD/llmfoundry/models/hf/hf_fsdp.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":"7383beb2cec34bf1","mcp_get_code":{"code_sha256":"7383beb2cec34bf1"}},{"arxiv_id":"2311.16914","paper":"/paper/brain-id-learning-robust-feature","title":"Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"peirong26/Brain-ID","path":"utils/config.py","file_url":"https://github.com/peirong26/Brain-ID/blob/HEAD/utils/config.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":"f6c7280f75271482","mcp_get_code":{"code_sha256":"f6c7280f75271482"}},{"arxiv_id":"2311.12871","paper":"/paper/an-embodied-generalist-agent-in-3d-world","title":"An Embodied Generalist Agent in 3D World","date":"2023-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"embodied-generalist/embodied-generalist","path":"common/misc.py","file_url":"https://github.com/embodied-generalist/embodied-generalist/blob/HEAD/common/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2309.17179","paper":"/paper/alphazero-like-tree-search-can-guide-large","title":"Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training","date":"2023-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"waterhorse1/llm_tree_search","path":"tsllm/model/utils.py","file_url":"https://github.com/waterhorse1/llm_tree_search/blob/HEAD/tsllm/model/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fc2499cfa7162602","mcp_get_code":{"code_sha256":"fc2499cfa7162602"}},{"arxiv_id":"2306.02231","paper":"/paper/fine-tuning-language-models-with-advantage","title":"Fine-Tuning Language Models with Advantage-Induced Policy Alignment","date":"2023-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/rlhf-apa","path":"trlx/utils/modeling.py","file_url":"https://github.com/microsoft/rlhf-apa/blob/HEAD/trlx/utils/modeling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc2499cfa7162602","mcp_get_code":{"code_sha256":"fc2499cfa7162602"}},{"arxiv_id":"2305.13417","paper":"/paper/interpreting-transformer-s-attention-dynamic","title":"VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shacharkz/visit-visualizing-transformers","path":"utils.py","file_url":"https://github.com/shacharkz/visit-visualizing-transformers/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2304.04858","paper":"/paper/simulated-annealing-in-early-layers-leads-to","title":"Simulated Annealing in Early Layers Leads to Better Generalization","date":"2023-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2212.04501","paper":"/paper/learning-video-representations-from-large","title":"Learning Video Representations from Large Language Models","date":"2022-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/lavila","path":"lavila/models/utils.py","file_url":"https://github.com/facebookresearch/lavila/blob/HEAD/lavila/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2212.04356","paper":"/paper/robust-speech-recognition-via-large-scale-1","title":"Robust Speech Recognition via Large-Scale Weak Supervision","date":"2022-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"robflynnyh/long-context-asr","path":"exp/run_launcher.py","file_url":"https://github.com/robflynnyh/long-context-asr/blob/HEAD/exp/run_launcher.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":"bdbf18d0ee9006f7","mcp_get_code":{"code_sha256":"bdbf18d0ee9006f7"}},{"arxiv_id":"2211.05778","paper":"/paper/internimage-exploring-large-scale-vision","title":"InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions","date":"2022-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/internimage","path":"classification/extract_feature.py","file_url":"https://github.com/opengvlab/internimage/blob/HEAD/classification/extract_feature.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2209.02952","paper":"/paper/bifuse-self-supervised-and-efficient-bi","title":"BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation","date":"2022-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fuenwang/bifusev2","path":"BiFusev2/Tools.py","file_url":"https://github.com/fuenwang/bifusev2/blob/HEAD/BiFusev2/Tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2203.04036","paper":"/paper/styleheat-one-shot-high-resolution-editable","title":"StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN","date":"2022-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FeiiYin/StyleHEAT","path":"configs/config.py","file_url":"https://github.com/FeiiYin/StyleHEAT/blob/HEAD/configs/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f6c7280f75271482","mcp_get_code":{"code_sha256":"f6c7280f75271482"}},{"arxiv_id":"1912.04616","paper":"/paper/openbiolink-a-resource-and-benchmarking","title":"OpenBioLink: A benchmarking framework for large-scale biomedical link prediction","date":"2019-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenBioLink/OpenBioLink","path":"src/openbiolink/utils.py","file_url":"https://github.com/OpenBioLink/OpenBioLink/blob/HEAD/src/openbiolink/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"96f40fb9f1e9aa6b","mcp_get_code":{"code_sha256":"96f40fb9f1e9aa6b"}},{"arxiv_id":"aaai_26699","paper":null,"title":"arXiv:aaai_26699","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"fuenwang/MixFairFace","path":"MixFairFace/Tools.py","file_url":"https://github.com/fuenwang/MixFairFace/blob/HEAD/MixFairFace/Tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"Liu_Revealing_Key_Details_to_See_Differences_A_Novel_Prototypical_Perspective_CVPR_2025_paper","paper":null,"title":"arXiv:Liu_Revealing_Key_Details_to_See_Differences_A_Novel_Prototypical_Perspective_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"firework8/ProtoGCN","path":"protogcn/core/hooks.py","file_url":"https://github.com/firework8/ProtoGCN/blob/HEAD/protogcn/core/hooks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44925cf3831bfe79","mcp_get_code":{"code_sha256":"44925cf3831bfe79"}},{"arxiv_id":"2025.findings-acl.229","paper":null,"title":"arXiv:2025.findings-acl.229","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"CarperAI/trlx","path":"trlx/utils/modeling.py","file_url":"https://github.com/CarperAI/trlx/blob/HEAD/trlx/utils/modeling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fc2499cfa7162602","mcp_get_code":{"code_sha256":"fc2499cfa7162602"}}]}