{"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/synthesizer","entry":"Synthesizer","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":5,"n_papers_ran":2,"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":5,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":3},"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":"2502.18017","paper":"/paper/vidorag-visual-document-retrieval-augmented","title":"ViDoRAG: Visual Document Retrieval-Augmented Generation via Dynamic Iterative Reasoning Agents","date":"2025-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Alibaba-NLP/ViDoRAG","path":"vidorag_agents.py","file_url":"https://github.com/Alibaba-NLP/ViDoRAG/blob/HEAD/vidorag_agents.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"65c25bf6cf01ae91","mcp_get_code":{"code_sha256":"65c25bf6cf01ae91"}},{"arxiv_id":"2405.00984","paper":"/paper/free-faster-and-better-data-free-meta","title":"FREE: Faster and Better Data-Free Meta-Learning","date":"2024-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"walkerworldpeace/free","path":"synthesis/task_recovery.py","file_url":"https://github.com/walkerworldpeace/free/blob/HEAD/synthesis/task_recovery.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":"050d036386d5e99b","mcp_get_code":{"code_sha256":"050d036386d5e99b"}},{"arxiv_id":"2205.11158","paper":"/paper/qekd-query-efficient-and-data-free-knowledge","title":"IDEAL: Query-Efficient Data-Free Learning from Black-box Models","date":"2022-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SonyResearch/IDEAL","path":"ideal.py","file_url":"https://github.com/SonyResearch/IDEAL/blob/HEAD/ideal.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"61fe0765e1db67f8","mcp_get_code":{"code_sha256":"61fe0765e1db67f8"}},{"arxiv_id":"2110.14513","paper":"/paper/neural-analysis-and-synthesis-reconstructing","title":"Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations","date":"2021-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"revsic/torch-nansy","path":"nansy/synthesizer.py","file_url":"https://github.com/revsic/torch-nansy/blob/HEAD/nansy/synthesizer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d2d2661a86323397","mcp_get_code":{"code_sha256":"d2d2661a86323397"}},{"arxiv_id":"2103.07854","paper":"/paper/three-steps-to-multimodal-trajectory","title":"Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis","date":"2021-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ApeironY/PCCSNet","path":"PCCSNet/models/components.py","file_url":"https://github.com/ApeironY/PCCSNet/blob/HEAD/PCCSNet/models/components.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"500cb1c39abba542","mcp_get_code":{"code_sha256":"500cb1c39abba542"}}]}