{"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/greedy-search","entry":"greedy_search","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":7,"n_papers_ran":1,"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":6,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"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":"2607.10661","paper":"/paper/arxiv-2607-10661","title":"Unlocking Parallelism in Autoregressive Language Models via Speculative Decoding with Progressive Tree Drafting","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"MINE-USTC/PTD","path":"tree_draft/decode_strategy/autoregressive.py","file_url":"https://github.com/MINE-USTC/PTD/blob/HEAD/tree_draft/decode_strategy/autoregressive.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":"d509548acb6dcc24","mcp_get_code":{"code_sha256":"d509548acb6dcc24"}},{"arxiv_id":"2502.06130","paper":"/paper/self-correcting-decoding-with-generative","title":"Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models","date":"2025-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangce01/degf","path":"degf_utils/degf_sample.py","file_url":"https://github.com/zhangce01/degf/blob/HEAD/degf_utils/degf_sample.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"128612930e882fac","mcp_get_code":{"code_sha256":"128612930e882fac"}},{"arxiv_id":"2410.02745","paper":"/paper/avg-llava-a-large-multimodal-model-with","title":"AVG-LLaVA: A Large Multimodal Model with Adaptive Visual Granularity","date":"2024-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deeplearnxmu/avg-llava","path":"llava/generate_utils.py","file_url":"https://github.com/deeplearnxmu/avg-llava/blob/HEAD/llava/generate_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":"6c3e7a3c3b9dba43","mcp_get_code":{"code_sha256":"6c3e7a3c3b9dba43"}},{"arxiv_id":"2402.14874","paper":"/paper/distillation-contrastive-decoding-improving","title":"Distillation Contrastive Decoding: Improving LLMs Reasoning with Contrastive Decoding and Distillation","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pphuc25/distil-cd","path":"src/dcd/generate.py","file_url":"https://github.com/pphuc25/distil-cd/blob/HEAD/src/dcd/generate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad7789fd83efa143","mcp_get_code":{"code_sha256":"ad7789fd83efa143"}},{"arxiv_id":"2110.15797","paper":"/paper/discovering-non-monotonic-autoregressive","title":"Discovering Non-monotonic Autoregressive Orderings with Variational Inference","date":"2021-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xuanlinli17/autoregressive_inference","path":"voi/algorithms/greedy_search.py","file_url":"https://github.com/xuanlinli17/autoregressive_inference/blob/HEAD/voi/algorithms/greedy_search.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f6c9fbef829da96","mcp_get_code":{"code_sha256":"6f6c9fbef829da96"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"demelin/nematode","path":"codebase/transformer.py","file_url":"https://github.com/demelin/nematode/blob/HEAD/codebase/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d6c7992b8eee6f31","mcp_get_code":{"code_sha256":"d6c7992b8eee6f31"}},{"arxiv_id":"2025.findings-acl.865","paper":null,"title":"arXiv:2025.findings-acl.865","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"DeepLearnXMU/AVG-LLaVA","path":"llava/generate_utils.py","file_url":"https://github.com/DeepLearnXMU/AVG-LLaVA/blob/HEAD/llava/generate_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":"6c3e7a3c3b9dba43","mcp_get_code":{"code_sha256":"6c3e7a3c3b9dba43"}}]}