{"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-torch-dtype","entry":"get_torch_dtype","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":8,"n_papers_ran":3,"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":8,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"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":"2605.22644","paper":"/paper/arxiv-2605-22644","title":"Why SGD is not Brownian Motion: A New Perspective on Stochastic Dynamics","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"brain-lab-research/SGDiffusion","path":"src/datamodelopt/core/config.py","file_url":"https://github.com/brain-lab-research/SGDiffusion/blob/HEAD/src/datamodelopt/core/config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cb3865cbb3050b3c","mcp_get_code":{"code_sha256":"cb3865cbb3050b3c"}},{"arxiv_id":"2604.07035","paper":"/paper/arxiv-2604-07035","title":"Unified Deployment-Aware Evaluation of Open Reasoning Language Models","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"mkboch/UDAE","path":"models/loader.py","file_url":"https://github.com/mkboch/UDAE/blob/HEAD/models/loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"92e0dae95ed263ab","mcp_get_code":{"code_sha256":"92e0dae95ed263ab"}},{"arxiv_id":"2602.24040","paper":"/paper/arxiv-2602-24040","title":"RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"lasgroup/rewarduq","path":"src/rewarduq/utils/models.py","file_url":"https://github.com/lasgroup/rewarduq/blob/HEAD/src/rewarduq/utils/models.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":"1bdb717c20c65159","mcp_get_code":{"code_sha256":"1bdb717c20c65159"}},{"arxiv_id":"2412.13341","paper":"/paper/concept-rot-poisoning-concepts-in-large","title":"Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing","date":"2024-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"keltin13/concept-rot","path":"dsets/generate_concept_dataset.py","file_url":"https://github.com/keltin13/concept-rot/blob/HEAD/dsets/generate_concept_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6692d23bb78268d1","mcp_get_code":{"code_sha256":"6692d23bb78268d1"}},{"arxiv_id":"2412.09013","paper":"/paper/arbitrary-steps-image-super-resolution-via","title":"Arbitrary-steps Image Super-resolution via Diffusion Inversion","date":"2024-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zsyoaoa/invsr","path":"sampler_invsr.py","file_url":"https://github.com/zsyoaoa/invsr/blob/HEAD/sampler_invsr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7e98ca1772763208","mcp_get_code":{"code_sha256":"7e98ca1772763208"}},{"arxiv_id":"2404.12096","paper":"/paper/longembed-extending-embedding-models-for-long","title":"LongEmbed: Extending Embedding Models for Long Context Retrieval","date":"2024-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dwzhu-pku/longembed","path":"src/model_utils.py","file_url":"https://github.com/dwzhu-pku/longembed/blob/HEAD/src/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":"5504df730c582a6e","mcp_get_code":{"code_sha256":"5504df730c582a6e"}},{"arxiv_id":"2310.00752","paper":"/paper/tigerscore-towards-building-explainable","title":"TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks","date":"2023-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TIGER-AI-Lab/TIGERScore","path":"tigerscore/candidates_generation/generate_candidates.py","file_url":"https://github.com/TIGER-AI-Lab/TIGERScore/blob/HEAD/tigerscore/candidates_generation/generate_candidates.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"81990a380398703e","mcp_get_code":{"code_sha256":"81990a380398703e"}},{"arxiv_id":"2306.02561","paper":"/paper/llm-blender-ensembling-large-language-models","title":"LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion","date":"2023-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuchenlin/LLM-Blender","path":"llm_blender/blender/blender_utils.py","file_url":"https://github.com/yuchenlin/LLM-Blender/blob/HEAD/llm_blender/blender/blender_utils.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":"81990a380398703e","mcp_get_code":{"code_sha256":"81990a380398703e"}}]}