{"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/add","entry":"add","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":19,"n_papers_ran":12,"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":18,"n_samples_ran":11,"n_samples_fingerprinted":4,"n_places":19,"n_places_pointer_only":6,"by_status":{"ran_honours":2,"ran_violates":1,"ran_draft_wrong":1,"ran_fixture":2,"ran":5,"unverified":7},"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":"2601.21579","paper":"/paper/arxiv-2601-21579","title":"KromHC: Manifold-Constrained Hyper-Connections with Kronecker-Product Residual Matrices","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"wz1119/KromHC","path":"hyper_conn/Kromhc.py","file_url":"https://github.com/wz1119/KromHC/blob/HEAD/hyper_conn/Kromhc.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1dacda5180fc1b73","mcp_get_code":{"code_sha256":"1dacda5180fc1b73"}},{"arxiv_id":"2601.12124","paper":"/paper/arxiv-2601-12124","title":"SynQP: A Framework and Metrics for Evaluating the Quality and Privacy Risk of Synthetic Data","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"CAN-SYNH/SynQP","path":"Final_CTGAN_DP_PrivacyEval.py","file_url":"https://github.com/CAN-SYNH/SynQP/blob/HEAD/Final_CTGAN_DP_PrivacyEval.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b56815d2b256eabe","mcp_get_code":{"code_sha256":"b56815d2b256eabe"}},{"arxiv_id":"2510.24380","paper":"/paper/arxiv-2510-24380","title":"APEX: Approximate-but-Exhaustive Search for Ultra-Large Combinatorial Synthesis Libraries","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"NumerionLabs/apex","path":"apex/nn/scatter.py","file_url":"https://github.com/NumerionLabs/apex/blob/HEAD/apex/nn/scatter.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"2f6da2716aecc750","mcp_get_code":{"code_sha256":"2f6da2716aecc750"}},{"arxiv_id":"2412.04604","paper":"/paper/arc-prize-2024-technical-report","title":"ARC Prize 2024: Technical Report","date":"2024-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"michaelhodel/re-arc","path":"dsl.py","file_url":"https://github.com/michaelhodel/re-arc/blob/HEAD/dsl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"32b506d5ac9d3ef7","mcp_get_code":{"code_sha256":"32b506d5ac9d3ef7"}},{"arxiv_id":"2410.11061","paper":"/paper/learning-to-optimize-for-mixed-integer-non","title":"Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guarantees","date":"2024-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pnnl/neuromancer","path":"src/neuromancer/arg.py","file_url":"https://github.com/pnnl/neuromancer/blob/HEAD/src/neuromancer/arg.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"29a7dc8bf982359a","mcp_get_code":{"code_sha256":"29a7dc8bf982359a"}},{"arxiv_id":"2410.09542","paper":"/paper/mirage-evaluating-and-explaining-inductive","title":"MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models","date":"2024-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BugMakerzzz/mirage","path":"src/generate_data.py","file_url":"https://github.com/BugMakerzzz/mirage/blob/HEAD/src/generate_data.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ae6d6e516578392b","mcp_get_code":{"code_sha256":"ae6d6e516578392b"}},{"arxiv_id":"2406.02924","paper":"/paper/pruner-zero-evolving-symbolic-pruning-metric","title":"Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models","date":"2024-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pprp/pruner-zero","path":"lib/gptree.py","file_url":"https://github.com/pprp/pruner-zero/blob/HEAD/lib/gptree.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"28f088bf9d4cf797","mcp_get_code":{"code_sha256":"28f088bf9d4cf797"}},{"arxiv_id":"2404.04815","paper":"/paper/allo-a-programming-model-for-composable","title":"Allo: A Programming Model for Composable Accelerator Design","date":"2024-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cornell-zhang/allo","path":"allo/dsl.py","file_url":"https://github.com/cornell-zhang/allo/blob/HEAD/allo/dsl.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":"156caede4d5f8d82","mcp_get_code":{"code_sha256":"156caede4d5f8d82"}},{"arxiv_id":"2402.13144","paper":"/paper/neural-network-diffusion","title":"Neural Network Diffusion","date":"2024-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nus-hpc-ai-lab/neural-network-diffusion","path":"workspace/ensemble.py","file_url":"https://github.com/nus-hpc-ai-lab/neural-network-diffusion/blob/HEAD/workspace/ensemble.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1140e6a2186745ab","mcp_get_code":{"code_sha256":"1140e6a2186745ab"}},{"arxiv_id":"2402.01030","paper":"/paper/executable-code-actions-elicit-better-llm","title":"Executable Code Actions Elicit Better LLM Agents","date":"2024-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"langchain-ai/langgraph-codeact","path":"examples/math_example.py","file_url":"https://github.com/langchain-ai/langgraph-codeact/blob/HEAD/examples/math_example.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"084846ce6b55992b","mcp_get_code":{"code_sha256":"084846ce6b55992b"}},{"arxiv_id":"2310.04780","paper":"/paper/ipmix-label-preserving-data-augmentation-1","title":"IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers","date":"2023-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hzlsaber/IPMix","path":"cifar.py","file_url":"https://github.com/hzlsaber/IPMix/blob/HEAD/cifar.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cbb347ae0777187b","mcp_get_code":{"code_sha256":"cbb347ae0777187b"}},{"arxiv_id":"2303.11242","paper":"/paper/make-landscape-flatter-in-differentially","title":"Make Landscape Flatter in Differentially Private Federated Learning","date":"2023-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YMJS-Irfan/DP-FedSAM","path":"fedml_api/dpfedsam/dpfedsam_api.py","file_url":"https://github.com/YMJS-Irfan/DP-FedSAM/blob/HEAD/fedml_api/dpfedsam/dpfedsam_api.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5db9fffe698896fb","mcp_get_code":{"code_sha256":"5db9fffe698896fb"}},{"arxiv_id":"2302.12170","paper":"/paper/language-model-crossover-variation-through","title":"Language Model Crossover: Variation through Few-Shot Prompting","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"carperai/openelm","path":"src/openelm/benchmarks/benchmark_tinygp.py","file_url":"https://github.com/carperai/openelm/blob/HEAD/src/openelm/benchmarks/benchmark_tinygp.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1dacda5180fc1b73","mcp_get_code":{"code_sha256":"1dacda5180fc1b73"}},{"arxiv_id":"2209.06257","paper":"/paper/scimed-a-computational-framework-for-physics","title":"A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge","date":"2022-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lironsimon/scimed","path":"algo/ebs/eq_functions.py","file_url":"https://github.com/lironsimon/scimed/blob/HEAD/algo/ebs/eq_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"86d31a687f794a34","mcp_get_code":{"code_sha256":"86d31a687f794a34"}},{"arxiv_id":"2108.03673","paper":"/paper/recall-replay-based-continual-learning-in","title":"RECALL: Replay-based Continual Learning in Semantic Segmentation","date":"2021-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LTTM/RECALL","path":"model_resnet.py","file_url":"https://github.com/LTTM/RECALL/blob/HEAD/model_resnet.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a96ff10f26036567","mcp_get_code":{"code_sha256":"a96ff10f26036567"}},{"arxiv_id":"2107.03374","paper":"/paper/evaluating-large-language-models-trained-on","title":"Evaluating Large Language Models Trained on Code","date":"2021-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"openai/code-align-evals-data","path":"bad-solutions/add.py","file_url":"https://github.com/openai/code-align-evals-data/blob/HEAD/bad-solutions/add.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f6bcf31e58add54","mcp_get_code":{"code_sha256":"9f6bcf31e58add54"}},{"arxiv_id":"2102.07818","paper":"/paper/certified-robustness-to-programmable","title":"Certified Robustness to Programmable Transformations in LSTMs","date":"2021-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"foreverzyh/certified_lstms","path":"src/ibp.py","file_url":"https://github.com/foreverzyh/certified_lstms/blob/HEAD/src/ibp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"716e459ff7dd0927","mcp_get_code":{"code_sha256":"716e459ff7dd0927"}},{"arxiv_id":"1909.00986","paper":"/paper/certified-robustness-to-adversarial-word","title":"Certified Robustness to Adversarial Word Substitutions","date":"2019-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"robinjia/certified-word-sub","path":"src/ibp.py","file_url":"https://github.com/robinjia/certified-word-sub/blob/HEAD/src/ibp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab6c8d85177396c2","mcp_get_code":{"code_sha256":"ab6c8d85177396c2"}},{"arxiv_id":"2023.findings-acl.42","paper":null,"title":"arXiv:2023.findings-acl.42","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"JHL-HUST/EIBC-IBP","path":"src/ibp.py","file_url":"https://github.com/JHL-HUST/EIBC-IBP/blob/HEAD/src/ibp.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":"4bdf8236db18fee0","mcp_get_code":{"code_sha256":"4bdf8236db18fee0"}}]}