{"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/symlog","entry":"symlog","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":24,"n_papers_ran":21,"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":13,"n_samples_ran":10,"n_samples_fingerprinted":10,"n_places":24,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":4,"ran_fixture":1,"ran":4,"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":"2605.26419","paper":"/paper/arxiv-2605-26419","title":"Amortized Factor Inference Networks for Posterior Inference","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"joohwanko/AFINs","path":"src/afin/model.py","file_url":"https://github.com/joohwanko/AFINs/blob/HEAD/src/afin/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9d518e211efa7b47","mcp_get_code":{"code_sha256":"9d518e211efa7b47"}},{"arxiv_id":"2605.06407","paper":"/paper/arxiv-2605-06407","title":"WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"yanghaha0908/WavCube","path":"vocos/modules.py","file_url":"https://github.com/yanghaha0908/WavCube/blob/HEAD/vocos/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3acb919a94ac1a19","mcp_get_code":{"code_sha256":"3acb919a94ac1a19"}},{"arxiv_id":"2603.18202","paper":"/paper/arxiv-2603-18202","title":"R2-Dreamer: Redundancy-Reduced World Models without Decoders or Augmentation","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"NM512/r2dreamer","path":"dreamer.py","file_url":"https://github.com/NM512/r2dreamer/blob/HEAD/dreamer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b6d53c21daa4c865","mcp_get_code":{"code_sha256":"b6d53c21daa4c865"}},{"arxiv_id":"2601.19336","paper":"/paper/arxiv-2601-19336","title":"From Observations to Events: Event-Aware World Model for Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"MarquisDarwin/EAWM","path":"EADream/tools.py","file_url":"https://github.com/MarquisDarwin/EAWM/blob/HEAD/EADream/tools.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"3e346b9b00ea8169","mcp_get_code":{"code_sha256":"3e346b9b00ea8169"}},{"arxiv_id":"2510.04280","paper":"/paper/arxiv-2510-04280","title":"A KL-regularization Framework for Learning to Plan with Adaptive Priors","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"alvaro-serra/pompc","path":"pompc/common/util.py","file_url":"https://github.com/alvaro-serra/pompc/blob/HEAD/pompc/common/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"89c9792c5bbea8e1","mcp_get_code":{"code_sha256":"89c9792c5bbea8e1"}},{"arxiv_id":"2506.02612","paper":"/paper/simple-good-fast-self-supervised-world-models","title":"Simple, Good, Fast: Self-Supervised World Models Free of Baggage","date":"2025-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jrobine/sgf","path":"src/wm.py","file_url":"https://github.com/jrobine/sgf/blob/HEAD/src/wm.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d89a7360f2557af3","mcp_get_code":{"code_sha256":"d89a7360f2557af3"}},{"arxiv_id":"2503.00653","paper":"/paper/discrete-codebook-world-models-for-continuous","title":"Discrete Codebook World Models for Continuous Control","date":"2025-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aidanscannell/dcmpc","path":"utils/helper.py","file_url":"https://github.com/aidanscannell/dcmpc/blob/HEAD/utils/helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7fb5eb47c3b273d5","mcp_get_code":{"code_sha256":"7fb5eb47c3b273d5"}},{"arxiv_id":"2410.08893","paper":"/paper/drama-mamba-enabled-model-based-reinforcement","title":"Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient","date":"2024-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"realwenlongwang/Drama","path":"sub_models/functions_losses.py","file_url":"https://github.com/realwenlongwang/Drama/blob/HEAD/sub_models/functions_losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c5f6f10f82106e10","mcp_get_code":{"code_sha256":"c5f6f10f82106e10"}},{"arxiv_id":"2408.16532","paper":"/paper/wavtokenizer-an-efficient-acoustic-discrete","title":"WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling","date":"2024-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jishengpeng/wavtokenizer","path":"decoder/modules.py","file_url":"https://github.com/jishengpeng/wavtokenizer/blob/HEAD/decoder/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3acb919a94ac1a19","mcp_get_code":{"code_sha256":"3acb919a94ac1a19"}},{"arxiv_id":"2407.05361","paper":"/paper/emilia-an-extensive-multilingual-and-diverse","title":"Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation","date":"2024-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"open-mmlab/Amphion","path":"models/codec/amphion_codec/vocos.py","file_url":"https://github.com/open-mmlab/Amphion/blob/HEAD/models/codec/amphion_codec/vocos.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3acb919a94ac1a19","mcp_get_code":{"code_sha256":"3acb919a94ac1a19"}},{"arxiv_id":"2406.18043","paper":"/paper/multimodal-foundation-world-models-for","title":"GenRL: Multimodal-foundation world models for generalization in embodied agents","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mazpie/genrl","path":"agent/dreamer_utils.py","file_url":"https://github.com/mazpie/genrl/blob/HEAD/agent/dreamer_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e346b9b00ea8169","mcp_get_code":{"code_sha256":"3e346b9b00ea8169"}},{"arxiv_id":"2406.00392","paper":"/paper/artificial-generational-intelligence-cultural","title":"Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning","date":"2024-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"flairox/cultural-accumulation","path":"goal_seq/train_oracle_s5.py","file_url":"https://github.com/flairox/cultural-accumulation/blob/HEAD/goal_seq/train_oracle_s5.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bb06b4458359de80","mcp_get_code":{"code_sha256":"bb06b4458359de80"}},{"arxiv_id":"2405.11273","paper":"/paper/uni-moe-scaling-unified-multimodal-llms-with","title":"Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts","date":"2024-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hitsz-tmg/umoe-scaling-unified-multimodal-llms","path":"Uni-MoE-2/decoder/modules.py","file_url":"https://github.com/hitsz-tmg/umoe-scaling-unified-multimodal-llms/blob/HEAD/Uni-MoE-2/decoder/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3acb919a94ac1a19","mcp_get_code":{"code_sha256":"3acb919a94ac1a19"}},{"arxiv_id":"2403.09859","paper":"/paper/mamba-an-effective-world-model-approach-for","title":"MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning","date":"2024-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zoharri/mamba","path":"tools.py","file_url":"https://github.com/zoharri/mamba/blob/HEAD/tools.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e346b9b00ea8169","mcp_get_code":{"code_sha256":"3e346b9b00ea8169"}},{"arxiv_id":"2402.12208","paper":"/paper/language-codec-reducing-the-gaps-between","title":"Language-Codec: Bridging Discrete Codec Representations and Speech Language Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jishengpeng/languagecodec","path":"languagecodec_decoder/modules.py","file_url":"https://github.com/jishengpeng/languagecodec/blob/HEAD/languagecodec_decoder/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3acb919a94ac1a19","mcp_get_code":{"code_sha256":"3acb919a94ac1a19"}},{"arxiv_id":"2310.17805","paper":"/paper/reward-scale-robustness-for-proximal-policy","title":"Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RyanNavillus/PPO-v3","path":"ppo_v3/ppo_envpool_tricks.py","file_url":"https://github.com/RyanNavillus/PPO-v3/blob/HEAD/ppo_v3/ppo_envpool_tricks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2898f3c58f169917","mcp_get_code":{"code_sha256":"2898f3c58f169917"}},{"arxiv_id":"2310.09615","paper":"/paper/storm-efficient-stochastic-transformer-based-1","title":"STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning","date":"2023-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weipu-zhang/storm","path":"sub_models/functions_losses.py","file_url":"https://github.com/weipu-zhang/storm/blob/HEAD/sub_models/functions_losses.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c5f6f10f82106e10","mcp_get_code":{"code_sha256":"c5f6f10f82106e10"}},{"arxiv_id":"2310.05167","paper":"/paper/hieros-hierarchical-imagination-on-structured","title":"Hieros: Hierarchical Imagination on Structured State Space Sequence World Models","date":"2023-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Snagnar/Hieros","path":"hieros/tools.py","file_url":"https://github.com/Snagnar/Hieros/blob/HEAD/hieros/tools.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e346b9b00ea8169","mcp_get_code":{"code_sha256":"3e346b9b00ea8169"}},{"arxiv_id":"2308.12270","paper":"/paper/language-reward-modulation-for-pretraining","title":"Language Reward Modulation for Pretraining Reinforcement Learning","date":"2023-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ademiadeniji/lamp","path":"common/dists.py","file_url":"https://github.com/ademiadeniji/lamp/blob/HEAD/common/dists.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6ed5bbdae5b17591","mcp_get_code":{"code_sha256":"6ed5bbdae5b17591"}},{"arxiv_id":"2306.01896","paper":"/paper/tackling-unbounded-state-spaces-in-continuing","title":"Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces","date":"2023-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"badger-rl/stop","path":"nmodel.py","file_url":"https://github.com/badger-rl/stop/blob/HEAD/nmodel.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"69b48660ab8e75d9","mcp_get_code":{"code_sha256":"69b48660ab8e75d9"}},{"arxiv_id":"2306.00814","paper":"/paper/vocos-closing-the-gap-between-time-domain-and","title":"Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gemelo-ai/vocos","path":"vocos/modules.py","file_url":"https://github.com/gemelo-ai/vocos/blob/HEAD/vocos/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3acb919a94ac1a19","mcp_get_code":{"code_sha256":"3acb919a94ac1a19"}},{"arxiv_id":"2301.04104","paper":"/paper/mastering-diverse-domains-through-world","title":"Mastering Diverse Domains through World Models","date":"2023-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nm512/dreamerv3-torch","path":"dreamer.py","file_url":"https://github.com/nm512/dreamerv3-torch/blob/HEAD/dreamer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e346b9b00ea8169","mcp_get_code":{"code_sha256":"3e346b9b00ea8169"}},{"arxiv_id":"2210.13702","paper":"/paper/dextreme-transfer-of-agile-in-hand","title":"DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality","date":"2022-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Denys88/rl_games","path":"rl_games/algos_torch/layers.py","file_url":"https://github.com/Denys88/rl_games/blob/HEAD/rl_games/algos_torch/layers.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e346b9b00ea8169","mcp_get_code":{"code_sha256":"3e346b9b00ea8169"}},{"arxiv_id":"1912.12482","paper":"/paper/slm-lab-a-comprehensive-benchmark-and-modular-1","title":"SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning","date":"2019-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kengz/SLM-Lab","path":"slm_lab/lib/math_util.py","file_url":"https://github.com/kengz/SLM-Lab/blob/HEAD/slm_lab/lib/math_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bb21890c6aa0c40e","mcp_get_code":{"code_sha256":"bb21890c6aa0c40e"}}]}