{"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/planner","entry":"Planner","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":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":5,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":4},"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":"2506.13366","paper":"/paper/enhancing-goal-oriented-proactive-dialogue","title":"Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correction","date":"2025-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhyidi/CRC","path":"model/Planner.py","file_url":"https://github.com/zhyidi/CRC/blob/HEAD/model/Planner.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":"46145beb10b52410","mcp_get_code":{"code_sha256":"46145beb10b52410"}},{"arxiv_id":"2303.11166","paper":"/paper/imitating-graph-based-planning-with-goal","title":"Imitating Graph-Based Planning with Goal-Conditioned Policies","date":"2023-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junsu-kim97/pig","path":"planner/goal_plan.py","file_url":"https://github.com/junsu-kim97/pig/blob/HEAD/planner/goal_plan.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e14c1538dc4b9b2","mcp_get_code":{"code_sha256":"0e14c1538dc4b9b2"}},{"arxiv_id":"2302.01560","paper":"/paper/describe-explain-plan-and-select-interactive","title":"Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents","date":"2023-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"craftjarvis/mc-planner","path":"planner.py","file_url":"https://github.com/craftjarvis/mc-planner/blob/HEAD/planner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80f560faef552cc6","mcp_get_code":{"code_sha256":"80f560faef552cc6"}},{"arxiv_id":"2301.06015","paper":"/paper/diffusion-based-generation-optimization-and","title":"Diffusion-based Generation, Optimization, and Planning in 3D Scenes","date":"2023-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scenediffuser/Scene-Diffuser","path":"models/dm/ddpm.py","file_url":"https://github.com/scenediffuser/Scene-Diffuser/blob/HEAD/models/dm/ddpm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba8ebb3d3d0e9755","mcp_get_code":{"code_sha256":"ba8ebb3d3d0e9755"}},{"arxiv_id":"2110.13625","paper":"/paper/landmark-guided-subgoal-generation-in","title":"Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junsu-kim97/higl","path":"planner/goal_plan.py","file_url":"https://github.com/junsu-kim97/higl/blob/HEAD/planner/goal_plan.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c858dfe14c1b9209","mcp_get_code":{"code_sha256":"c858dfe14c1b9209"}}]}