{"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/pad-to-length","entry":"pad_to_length","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":18,"n_papers_ran":5,"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":15,"n_samples_ran":5,"n_samples_fingerprinted":3,"n_places":18,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":1,"ran":3,"unverified":10},"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":"2603.24422","paper":"/paper/arxiv-2603-24422","title":"OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"benchen4395/onesearch-family","path":"rlhf/listwisedpo.py","file_url":"https://github.com/benchen4395/onesearch-family/blob/HEAD/rlhf/listwisedpo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"21ba0273539ebdcc","mcp_get_code":{"code_sha256":"21ba0273539ebdcc"}},{"arxiv_id":"2505.13000","paper":"/paper/dualcodec-a-low-frame-rate-semantically-1","title":"DualCodec: A Low-Frame-Rate, Semantically-Enhanced Neural Audio Codec for Speech Generation","date":null,"month_inferred_from_arxiv_id":"2025-05","title_source":"archive","repo":"jiaqili3/DualCodec","path":"dualcodec/model_codec/dac_model.py","file_url":"https://github.com/jiaqili3/DualCodec/blob/HEAD/dualcodec/model_codec/dac_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d548fecaa09ac280","mcp_get_code":{"code_sha256":"d548fecaa09ac280"}},{"arxiv_id":"2412.09243","paper":"/paper/sprec-leveraging-self-play-to-debias","title":"SPRec: Leveraging Self-Play to Debias Preference Alignment for Large Language Model-based Recommendations","date":"2024-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"regionch/sprec","path":"baselines/DMPO/utils.py","file_url":"https://github.com/regionch/sprec/blob/HEAD/baselines/DMPO/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ea8ede0d54bd8225","mcp_get_code":{"code_sha256":"ea8ede0d54bd8225"}},{"arxiv_id":"2412.03187","paper":"/paper/weighted-reward-preference-optimization-for","title":"Weighted-Reward Preference Optimization for Implicit Model Fusion","date":"2024-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"slit-ai/wrpo","path":"scripts/wrpo_trainer.py","file_url":"https://github.com/slit-ai/wrpo/blob/HEAD/scripts/wrpo_trainer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"030b2d5e6e147a1d","mcp_get_code":{"code_sha256":"030b2d5e6e147a1d"}},{"arxiv_id":"2411.07121","paper":"/paper/decoding-visual-experience-and-mapping","title":"Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation Models","date":"2024-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ppwangyc/wave","path":"src/dataset/dataset.py","file_url":"https://github.com/ppwangyc/wave/blob/HEAD/src/dataset/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d080c3d4fd4bd8d","mcp_get_code":{"code_sha256":"8d080c3d4fd4bd8d"}},{"arxiv_id":"2411.02442","paper":"/paper/todo-enhancing-llm-alignment-with-ternary","title":"TODO: Enhancing LLM Alignment with Ternary Preferences","date":"2024-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"XXares/TODO","path":"utils/dpo_tie_trainer_eval.py","file_url":"https://github.com/XXares/TODO/blob/HEAD/utils/dpo_tie_trainer_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"39e3cf1d3ab26978","mcp_get_code":{"code_sha256":"39e3cf1d3ab26978"}},{"arxiv_id":"2410.22891","paper":"/paper/vpo-leveraging-the-number-of-votes-in","title":"VPO: Leveraging the Number of Votes in Preference Optimization","date":"2024-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ku-dmlab/vpo","path":"utils.py","file_url":"https://github.com/ku-dmlab/vpo/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":"513a6b6aa9a3c83e","mcp_get_code":{"code_sha256":"513a6b6aa9a3c83e"}},{"arxiv_id":"2410.20247","paper":"/paper/model-equality-testing-which-model-is-this","title":"Model Equality Testing: Which Model Is This API Serving?","date":"2024-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"i-gao/model-equality-testing","path":"model_equality_testing/src/utils.py","file_url":"https://github.com/i-gao/model-equality-testing/blob/HEAD/model_equality_testing/src/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4d86fb786ab0c1dd","mcp_get_code":{"code_sha256":"4d86fb786ab0c1dd"}},{"arxiv_id":"2410.17578","paper":"/paper/mm-eval-a-multilingual-meta-evaluation","title":"MM-Eval: A Multilingual Meta-Evaluation Benchmark for LLM-as-a-Judge and Reward Models","date":"2024-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guijinSON/MM-Eval","path":"rewardbench/dpo.py","file_url":"https://github.com/guijinSON/MM-Eval/blob/HEAD/rewardbench/dpo.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":"39e3cf1d3ab26978","mcp_get_code":{"code_sha256":"39e3cf1d3ab26978"}},{"arxiv_id":"2406.09215","paper":"/paper/on-softmax-direct-preference-optimization-for","title":"On Softmax Direct Preference Optimization for Recommendation","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenyuxin1999/S-DPO","path":"trainer/utils.py","file_url":"https://github.com/chenyuxin1999/S-DPO/blob/HEAD/trainer/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ea8ede0d54bd8225","mcp_get_code":{"code_sha256":"ea8ede0d54bd8225"}},{"arxiv_id":"2405.11165","paper":"/paper/automated-multi-level-preference-for-mllms","title":"Automated Multi-level Preference for MLLMs","date":"2024-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"takomc/amp","path":"models/utils.py","file_url":"https://github.com/takomc/amp/blob/HEAD/models/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"feb20052f9ff6506","mcp_get_code":{"code_sha256":"feb20052f9ff6506"}},{"arxiv_id":"2402.01306","paper":"/paper/kto-model-alignment-as-prospect-theoretic","title":"KTO: Model Alignment as Prospect Theoretic Optimization","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"contextualai/halos","path":"train/utils.py","file_url":"https://github.com/contextualai/halos/blob/HEAD/train/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":"38bf6f7230fcc687","mcp_get_code":{"code_sha256":"38bf6f7230fcc687"}},{"arxiv_id":"2307.03214","paper":"/paper/preadd-prefix-adaptive-decoding-for","title":"PREADD: Prefix-Adaptive Decoding for Controlled Text Generation","date":"2023-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jonnypei/acl23-preadd","path":"methods/fudge/util.py","file_url":"https://github.com/jonnypei/acl23-preadd/blob/HEAD/methods/fudge/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bb59f1498141e958","mcp_get_code":{"code_sha256":"bb59f1498141e958"}},{"arxiv_id":"2306.13643","paper":"/paper/lightglue-local-feature-matching-at-light","title":"LightGlue: Local Feature Matching at Light Speed","date":"2023-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvg/LightGlue","path":"lightglue/lightglue.py","file_url":"https://github.com/cvg/LightGlue/blob/HEAD/lightglue/lightglue.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8751587f16cda36c","mcp_get_code":{"code_sha256":"8751587f16cda36c"}},{"arxiv_id":"2305.20009","paper":"/paper/protein-design-with-guided-discrete-diffusion-1","title":"Protein Design with Guided Discrete Diffusion","date":"2023-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ngruver/nos","path":"seq_models/data.py","file_url":"https://github.com/ngruver/nos/blob/HEAD/seq_models/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8c3f4d642b502689","mcp_get_code":{"code_sha256":"8c3f4d642b502689"}},{"arxiv_id":"2106.01098","paper":"/paper/evaluation-metrics-for-graph-generative","title":"Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions","date":"2021-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"borgwardtlab/ggme","path":"src/metrics/utils.py","file_url":"https://github.com/borgwardtlab/ggme/blob/HEAD/src/metrics/utils.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":"323380a27091cb1b","mcp_get_code":{"code_sha256":"323380a27091cb1b"}},{"arxiv_id":"1607.04606","paper":"/paper/enriching-word-vectors-with-subword","title":"Enriching Word Vectors with Subword Information","date":"2016-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dbaumgarten/FToDTF","path":"ftodtf/input.py","file_url":"https://github.com/dbaumgarten/FToDTF/blob/HEAD/ftodtf/input.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":"fa43a85dd995968e","mcp_get_code":{"code_sha256":"fa43a85dd995968e"}},{"arxiv_id":"2025.findings-acl.250","paper":null,"title":"arXiv:2025.findings-acl.250","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Lanyu0303/KPO","path":"utils/trainer_utils.py","file_url":"https://github.com/Lanyu0303/KPO/blob/HEAD/utils/trainer_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ea8ede0d54bd8225","mcp_get_code":{"code_sha256":"ea8ede0d54bd8225"}}]}