{"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/none-or-str","entry":"none_or_str","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":23,"n_papers_ran":0,"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":4,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":23,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":4},"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":"2605.19811","paper":"/paper/arxiv-2605-19811","title":"LionMuon: Alternating Spectral and Sign Descent for Efficient Training","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"brain-lab-research/lion-muon","path":"src/config/base.py","file_url":"https://github.com/brain-lab-research/lion-muon/blob/HEAD/src/config/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2605.06615","paper":"/paper/arxiv-2605-06615","title":"When and Why SignSGD Outperforms SGD: A Theoretical Study Based on ℓ 1 -norm Lower Bounds","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Dingzhen230/SignSGD_Outperforms_SGD","path":"nanoGPT_expt/src/config/base.py","file_url":"https://github.com/Dingzhen230/SignSGD_Outperforms_SGD/blob/HEAD/nanoGPT_expt/src/config/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2604.00536","paper":"/paper/arxiv-2604-00536","title":"Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"FanZT6/OptimSyn","path":"training/influence/warmup/data_arguments.py","file_url":"https://github.com/FanZT6/OptimSyn/blob/HEAD/training/influence/warmup/data_arguments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"12b6473bde25b1dd","mcp_get_code":{"code_sha256":"12b6473bde25b1dd"}},{"arxiv_id":"2602.08351","paper":"/paper/arxiv-2602-08351","title":"The Chicken and Egg Dilemma: Co-optimizing Data and Model Configurations for LLMs","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"princeton-nlp/LESS","path":"less/train/data_arguments.py","file_url":"https://github.com/princeton-nlp/LESS/blob/HEAD/less/train/data_arguments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"12b6473bde25b1dd","mcp_get_code":{"code_sha256":"12b6473bde25b1dd"}},{"arxiv_id":"2602.07425","paper":"/paper/arxiv-2602-07425","title":"Sign-Based Optimizers Are Effective Under Heavy-Tailed Noise","date":"2026-02-07","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"Dingzhen230/Heavy-tailed-Noise-in-LLMs","path":"src/config/base.py","file_url":"https://github.com/Dingzhen230/Heavy-tailed-Noise-in-LLMs/blob/HEAD/src/config/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2507.09846","paper":null,"title":"arXiv:2507.09846","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"epfml/llm-baselines","path":"src/config/base.py","file_url":"https://github.com/epfml/llm-baselines/blob/HEAD/src/config/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2503.21758","paper":"/paper/lumina-image-2-0-a-unified-and-efficient","title":"Lumina-Image 2.0: A Unified and Efficient Image Generative Framework","date":"2025-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Alpha-VLLM/Lumina-Image-2.0","path":"sample.py","file_url":"https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/HEAD/sample.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":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2412.17288","paper":"/paper/multi-modal-grounded-planning-and-efficient","title":"Multi-Modal Grounded Planning and Efficient Replanning For Learning Embodied Agents with A Few Examples","date":"2024-12-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snumprlab/flare","path":"models/instructions_processed_LP/ALFRED_task_helper.py","file_url":"https://github.com/snumprlab/flare/blob/HEAD/models/instructions_processed_LP/ALFRED_task_helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3ad8e7f1396f4717","mcp_get_code":{"code_sha256":"3ad8e7f1396f4717"}},{"arxiv_id":"2411.04168","paper":"/paper/dimsum-diffusion-mamba-a-scalable-and-unified","title":"DiMSUM: Diffusion Mamba -- A Scalable and Unified Spatial-Frequency Method for Image Generation","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vinairesearch/dimsum","path":"dimsum/sample.py","file_url":"https://github.com/vinairesearch/dimsum/blob/HEAD/dimsum/sample.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2409.18128","paper":"/paper/flowturbo-towards-real-time-flow-based-image","title":"FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity Refiner","date":"2024-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shiml20/FlowTurbo","path":"train_utils.py","file_url":"https://github.com/shiml20/FlowTurbo/blob/HEAD/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2409.15278","paper":"/paper/pixwizard-versatile-image-to-image-visual","title":"PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions","date":"2024-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"afeng-x/pixwizard","path":"sample_pixwizard.py","file_url":"https://github.com/afeng-x/pixwizard/blob/HEAD/sample_pixwizard.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2408.05710","paper":"/paper/efficient-diffusion-transformer-with-step","title":"Efficient Diffusion Transformer with Step-wise Dynamic Attention Mediators","date":"2024-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/attention-mediators","path":"attention_mediator/train_utils.py","file_url":"https://github.com/leaplabthu/attention-mediators/blob/HEAD/attention_mediator/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2408.02657","paper":"/paper/2408-02657","title":"Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining","date":"2024-08-05","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":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2407.15235","paper":"/paper/tagcos-task-agnostic-gradient-clustered","title":"TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data","date":"2024-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"2003pro/tagcos","path":"train/data_arguments.py","file_url":"https://github.com/2003pro/tagcos/blob/HEAD/train/data_arguments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"12b6473bde25b1dd","mcp_get_code":{"code_sha256":"12b6473bde25b1dd"}},{"arxiv_id":"2402.12376","paper":"/paper/fit-flexible-vision-transformer-for-diffusion","title":"FiT: Flexible Vision Transformer for Diffusion Model","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"whlzy/fit","path":"fit/utils/sit_eval_utils.py","file_url":"https://github.com/whlzy/fit/blob/HEAD/fit/utils/sit_eval_utils.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":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2402.04333","paper":"/paper/less-selecting-influential-data-for-targeted","title":"LESS: Selecting Influential Data for Targeted Instruction Tuning","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-nlp/less","path":"less/train/data_arguments.py","file_url":"https://github.com/princeton-nlp/less/blob/HEAD/less/train/data_arguments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"12b6473bde25b1dd","mcp_get_code":{"code_sha256":"12b6473bde25b1dd"}},{"arxiv_id":"2401.08740","paper":"/paper/sit-exploring-flow-and-diffusion-based","title":"SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers","date":"2024-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"willisma/sit","path":"train_utils.py","file_url":"https://github.com/willisma/sit/blob/HEAD/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2305.18010","paper":"/paper/test-time-adaptation-with-clip-reward-for","title":"Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models","date":"2023-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mzhaoshuai/RLCF","path":"TPT/params.py","file_url":"https://github.com/mzhaoshuai/RLCF/blob/HEAD/TPT/params.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":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2305.03533","paper":"/paper/challenging-interferometric-imaging-machine","title":"Challenging interferometric imaging: Machine learning-based source localization from uv-plane observations","date":null,"month_inferred_from_arxiv_id":"2023-05","title_source":"archive","repo":"tarano/ml-based_source_localization_from_uv-plane","path":"src/libs/utils.py","file_url":"https://github.com/tarano/ml-based_source_localization_from_uv-plane/blob/HEAD/src/libs/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"2109.11251","paper":"/paper/trust-region-policy-optimisation-in-multi","title":"Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning","date":"2021-09-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eduardosebastianrodriguez/phmarl","path":"parse_args.py","file_url":"https://github.com/eduardosebastianrodriguez/phmarl/blob/HEAD/parse_args.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"1909.10893","paper":"/paper/recurrent-independent-mechanisms","title":"Recurrent Independent Mechanisms","date":"2019-09-24","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":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}},{"arxiv_id":"1810.04805","paper":"/paper/bert-pre-training-of-deep-bidirectional","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","date":"2018-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anton-bushuiev/ppiformer","path":"ppiformer/utils/typing.py","file_url":"https://github.com/anton-bushuiev/ppiformer/blob/HEAD/ppiformer/utils/typing.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c44e3a6568479588","mcp_get_code":{"code_sha256":"c44e3a6568479588"}},{"arxiv_id":"1810.01845","paper":"/paper/task-oriented-hand-motion-retargeting-for","title":"Task-Oriented Hand Motion Retargeting for Dexterous Manipulation Imitation","date":"2018-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DaphneAntotsiou/task-oriented-hand-retargeting","path":"replay_trajectories.py","file_url":"https://github.com/DaphneAntotsiou/task-oriented-hand-retargeting/blob/HEAD/replay_trajectories.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"2fc6fdb85f13dc41","mcp_get_code":{"code_sha256":"2fc6fdb85f13dc41"}}]}