{"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/topological-sort","entry":"topological_sort","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":4,"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":4,"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":1,"ran":3,"unverified":1},"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":"2502.04510","paper":"/paper/heterogeneous-swarms-jointly-optimizing-model","title":"Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems","date":"2025-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BunsenFeng/heterogeneous_swarm","path":"search.py","file_url":"https://github.com/BunsenFeng/heterogeneous_swarm/blob/HEAD/search.py","status":"ran_fixture","verification_level":1,"contract_check":"TIMEOUT","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9e81861a2b61b612","mcp_get_code":{"code_sha256":"9e81861a2b61b612"}},{"arxiv_id":"2410.01215","paper":"/paper/from-code-to-correctness-closing-the-last","title":"From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging","date":"2024-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YerbaPage/MGDebugger","path":"src/utils.py","file_url":"https://github.com/YerbaPage/MGDebugger/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a37aeb14211cb8a6","mcp_get_code":{"code_sha256":"a37aeb14211cb8a6"}},{"arxiv_id":"2406.17681","paper":"/paper/varbench-robust-language-model-benchmarking","title":"VarBench: Robust Language Model Benchmarking Through Dynamic Variable Perturbation","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qbetterk/VarBench","path":"base/utils.py","file_url":"https://github.com/qbetterk/VarBench/blob/HEAD/base/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e000a47a30f4f0e","mcp_get_code":{"code_sha256":"1e000a47a30f4f0e"}},{"arxiv_id":"2405.05465","paper":"/paper/vidur-a-large-scale-simulation-framework-for","title":"Vidur: A Large-Scale Simulation Framework For LLM Inference","date":"2024-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/vidur","path":"vidur/config/flat_dataclass.py","file_url":"https://github.com/microsoft/vidur/blob/HEAD/vidur/config/flat_dataclass.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5937c68b088c58dd","mcp_get_code":{"code_sha256":"5937c68b088c58dd"}},{"arxiv_id":"2206.05871","paper":"/paper/causal-inference-based-root-cause-analysis","title":"Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition","date":"2022-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"netmanaiops/circa","path":"circa/utils.py","file_url":"https://github.com/netmanaiops/circa/blob/HEAD/circa/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":"42d0e5e5d6d4684c","mcp_get_code":{"code_sha256":"42d0e5e5d6d4684c"}}]}