{"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/make-batch","entry":"make_batch","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":20,"n_papers_ran":7,"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":19,"n_samples_ran":7,"n_samples_fingerprinted":1,"n_places":20,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":4,"unverified":12},"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":"2608.24460","paper":"/paper/arxiv-2608-24460","title":"Shortcut Before Circuit: Document Statistics Time In-Context Conflict Resolution","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"lyj20071013/Shortcut-Before-Circuit","path":"bench.py","file_url":"https://github.com/lyj20071013/Shortcut-Before-Circuit/blob/HEAD/bench.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":"9d0f0bf99fa3c165","mcp_get_code":{"code_sha256":"9d0f0bf99fa3c165"}},{"arxiv_id":"2607.22361","paper":"/paper/arxiv-2607-22361","title":"Indexing: the Beginning and the End","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"visho33/IndexRetrieval","path":"data.py","file_url":"https://github.com/visho33/IndexRetrieval/blob/HEAD/data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"711e77cf91407f79","mcp_get_code":{"code_sha256":"711e77cf91407f79"}},{"arxiv_id":"2605.00604","paper":"/paper/arxiv-2605-00604","title":"Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"russellwmy/affinity-is-not-enough","path":"prototype/ablation.py","file_url":"https://github.com/russellwmy/affinity-is-not-enough/blob/HEAD/prototype/ablation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"898413a5c9423425","mcp_get_code":{"code_sha256":"898413a5c9423425"}},{"arxiv_id":"2602.16967","paper":"/paper/arxiv-2602-16967","title":"Early-Warning Signals of Grokking via Loss-Landscape Geometry","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"skydancerosel/dyck_scan","path":"dyck/lm_pilot.py","file_url":"https://github.com/skydancerosel/dyck_scan/blob/HEAD/dyck/lm_pilot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d2e721359d2c66f3","mcp_get_code":{"code_sha256":"d2e721359d2c66f3"}},{"arxiv_id":"2509.20295","paper":"/paper/arxiv-2509-20295","title":"FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"Chhro123/fast-foreground-aware-anomaly-synthesis","path":"generate_with_mask_mvtec.py","file_url":"https://github.com/Chhro123/fast-foreground-aware-anomaly-synthesis/blob/HEAD/generate_with_mask_mvtec.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c23b317a405ae12c","mcp_get_code":{"code_sha256":"c23b317a405ae12c"}},{"arxiv_id":"2406.06196","paper":"/paper/lingoly-a-benchmark-of-olympiad-level","title":"LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages","date":"2024-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"am-bean/lingOly","path":"testing/code/benchmark_model.py","file_url":"https://github.com/am-bean/lingOly/blob/HEAD/testing/code/benchmark_model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"35d798e826fc7898","mcp_get_code":{"code_sha256":"35d798e826fc7898"}},{"arxiv_id":"2406.03946","paper":"/paper/a-probabilistic-approach-to-learning-the","title":"A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs","date":"2024-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"QUVA-Lab/partial-escnn","path":"networks/pointcnn.py","file_url":"https://github.com/QUVA-Lab/partial-escnn/blob/HEAD/networks/pointcnn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause-Clear","inline_ok":false,"code_sha256_prefix":"73fde12c36f4e5ba","mcp_get_code":{"code_sha256":"73fde12c36f4e5ba"}},{"arxiv_id":"2312.05767","paper":"/paper/anomalydiffusion-few-shot-anomaly-image","title":"AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model","date":"2023-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtuplayer/anomalydiffusion","path":"generate_mask.py","file_url":"https://github.com/sjtuplayer/anomalydiffusion/blob/HEAD/generate_mask.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c23b317a405ae12c","mcp_get_code":{"code_sha256":"c23b317a405ae12c"}},{"arxiv_id":"2310.19909","paper":"/paper/battle-of-the-backbones-a-large-scale","title":"Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"unveilx/slm-od-ml-comparison","path":"model/detr/code/mmdet_utils.py","file_url":"https://github.com/unveilx/slm-od-ml-comparison/blob/HEAD/model/detr/code/mmdet_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4aa50694be302159","mcp_get_code":{"code_sha256":"4aa50694be302159"}},{"arxiv_id":"2310.02553","paper":"/paper/full-atom-protein-pocket-design-via-iterative","title":"Full-Atom Protein Pocket Design via Iterative Refinement","date":"2023-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zaixizhang/FAIR","path":"models/data.py","file_url":"https://github.com/zaixizhang/FAIR/blob/HEAD/models/data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f8c753580565a015","mcp_get_code":{"code_sha256":"f8c753580565a015"}},{"arxiv_id":"2305.10973","paper":"/paper/drag-your-gan-interactive-point-based","title":"Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold","date":"2023-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/internchat","path":"iGPT/models/inpainting.py","file_url":"https://github.com/opengvlab/internchat/blob/HEAD/iGPT/models/inpainting.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":"eba3cd2d932aa5b3","mcp_get_code":{"code_sha256":"eba3cd2d932aa5b3"}},{"arxiv_id":"2207.06616","paper":"/paper/antibody-antigen-docking-and-design-via","title":"Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement","date":"2022-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wengong-jin/abdockgen","path":"bindgen/data.py","file_url":"https://github.com/wengong-jin/abdockgen/blob/HEAD/bindgen/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"701a4294608e3dc4","mcp_get_code":{"code_sha256":"701a4294608e3dc4"}},{"arxiv_id":"2206.05897","paper":"/paper/gradicon-approximate-diffeomorphisms-via","title":"$\\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency","date":"2022-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uncbiag/ICON","path":"training_scripts/gradICON/gradicon_knee_halfres_new.py","file_url":"https://github.com/uncbiag/ICON/blob/HEAD/training_scripts/gradICON/gradicon_knee_halfres_new.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"8af066c9ba1475cc","mcp_get_code":{"code_sha256":"8af066c9ba1475cc"}},{"arxiv_id":"2112.10752","paper":"/paper/high-resolution-image-synthesis-with-latent","title":"High-Resolution Image Synthesis with Latent Diffusion Models","date":"2021-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lorenzo-stacchio/Stable-Diffusion-Inpaint","path":"inpaint_utils.py","file_url":"https://github.com/lorenzo-stacchio/Stable-Diffusion-Inpaint/blob/HEAD/inpaint_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"af3d3fd0371ecf63","mcp_get_code":{"code_sha256":"af3d3fd0371ecf63"}},{"arxiv_id":"2010.10042","paper":"/paper/improving-factual-completeness-and","title":"Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation","date":"2020-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ysmiura/ifcc","path":"eval_prf.py","file_url":"https://github.com/ysmiura/ifcc/blob/HEAD/eval_prf.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1d5cd4a2a1c96249","mcp_get_code":{"code_sha256":"1d5cd4a2a1c96249"}},{"arxiv_id":"2010.03790","paper":"/paper/text-based-rl-agents-with-commonsense","title":"Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines","date":"2020-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/commonsense-rl","path":"games/dataset.py","file_url":"https://github.com/IBM/commonsense-rl/blob/HEAD/games/dataset.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":"d6f593cf555a535b","mcp_get_code":{"code_sha256":"d6f593cf555a535b"}},{"arxiv_id":"2006.08852","paper":"/paper/counterexample-guided-learning-of-monotonic","title":"Counterexample-Guided Learning of Monotonic Neural Networks","date":"2020-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AishwaryaSivaraman/COMET","path":"src/ModelCalls.py","file_url":"https://github.com/AishwaryaSivaraman/COMET/blob/HEAD/src/ModelCalls.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b557ba3cf03add5e","mcp_get_code":{"code_sha256":"b557ba3cf03add5e"}},{"arxiv_id":"1909.04120","paper":"/paper/span-selection-pre-training-for-question","title":"Span Selection Pre-training for Question Answering","date":"2019-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/span-selection-pretraining","path":"sspt/rc_data.py","file_url":"https://github.com/IBM/span-selection-pretraining/blob/HEAD/sspt/rc_data.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":"d8293148b27d48c6","mcp_get_code":{"code_sha256":"d8293148b27d48c6"}},{"arxiv_id":"1806.08409","paper":"/paper/end-to-end-audio-visual-scene-aware-dialog","title":"End-to-End Audio Visual Scene-Aware Dialog using Multimodal Attention-Based Video Features","date":"2018-06-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge","path":"AVSD_Baseline/Baseline/local/data_handler.py","file_url":"https://github.com/hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge/blob/HEAD/AVSD_Baseline/Baseline/local/data_handler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83b8b889613b52d2","mcp_get_code":{"code_sha256":"83b8b889613b52d2"}},{"arxiv_id":"1804.04235","paper":"/paper/adafactor-adaptive-learning-rates-with","title":"Adafactor: Adaptive Learning Rates with Sublinear Memory Cost","date":"2018-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arampacha/clip-rsicd","path":"run_clip_flax_tv.py","file_url":"https://github.com/arampacha/clip-rsicd/blob/HEAD/run_clip_flax_tv.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":"0219f41ee0de195f","mcp_get_code":{"code_sha256":"0219f41ee0de195f"}}]}