{"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/get-options","entry":"get_options","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":25,"n_papers_ran":7,"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":24,"n_samples_ran":6,"n_samples_fingerprinted":0,"n_places":27,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":3,"unverified":18},"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":"2606.30680","paper":"/paper/arxiv-2606-30680","title":"Locker-based Truck-Drone Routing with Integrated Considerations of Pickups, Deliveries, and No-Fly Zones","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"wouterkool/attention-learn-to-route","path":"options.py","file_url":"https://github.com/wouterkool/attention-learn-to-route/blob/HEAD/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0f6f8675d8280c4f","mcp_get_code":{"code_sha256":"0f6f8675d8280c4f"}},{"arxiv_id":"2604.16581","paper":"/paper/arxiv-2604-16581","title":"NCO4CVRP: Neural Combinatorial Optimization for the Capacitated Vehicle Routing Problem","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"CIAM-Group/NCO_code","path":"multi-objective/MORAM/options.py","file_url":"https://github.com/CIAM-Group/NCO_code/blob/HEAD/multi-objective/MORAM/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7661feb5a9c80c6b","mcp_get_code":{"code_sha256":"7661feb5a9c80c6b"}},{"arxiv_id":"2505.13025","paper":"/paper/libog-lifelong-learning-for-black-box","title":"LiBOG: Lifelong Learning for Black-Box Optimizer Generation","date":"2025-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"peijy/libog","path":"options.py","file_url":"https://github.com/peijy/libog/blob/HEAD/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7418cf1e80d665bc","mcp_get_code":{"code_sha256":"7418cf1e80d665bc"}},{"arxiv_id":"2505.02010","paper":"/paper/meta-black-box-optimization-through-offline-q","title":"Meta-Black-Box-Optimization through Offline Q-function Learning","date":"2025-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"metaevo/q-mamba","path":"config.py","file_url":"https://github.com/metaevo/q-mamba/blob/HEAD/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"640375203ce8920b","mcp_get_code":{"code_sha256":"640375203ce8920b"}},{"arxiv_id":"2505.02010","paper":"/paper/meta-black-box-optimization-through-offline-q","title":"Meta-Black-Box-Optimization through Offline Q-function Learning","date":"2025-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"metaevo/q-mamba","path":"options.py","file_url":"https://github.com/metaevo/q-mamba/blob/HEAD/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"66a1e55cf718accd","mcp_get_code":{"code_sha256":"66a1e55cf718accd"}},{"arxiv_id":"2502.06884","paper":"/paper/learning-conformal-abstention-policies-for","title":"Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models","date":"2025-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sinatayebati/vlm-uncertainty","path":"data_utils/common_utils.py","file_url":"https://github.com/sinatayebati/vlm-uncertainty/blob/HEAD/data_utils/common_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ec8353674842469","mcp_get_code":{"code_sha256":"7ec8353674842469"}},{"arxiv_id":"2411.16721","paper":"/paper/steering-away-from-harm-an-adaptive-approach","title":"Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreaks","date":"2024-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ASTRAL-Group/ASTRA","path":"utility_eval/minigpt_mmbench.py","file_url":"https://github.com/ASTRAL-Group/ASTRA/blob/HEAD/utility_eval/minigpt_mmbench.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":"2c5fc2384c283cdb","mcp_get_code":{"code_sha256":"2c5fc2384c283cdb"}},{"arxiv_id":"2410.21066","paper":"/paper/learning-to-handle-complex-constraints-for","title":"Learning to Handle Complex Constraints for Vehicle Routing Problems","date":"2024-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jieyibi/PIP-constraint","path":"AM+PIP/options.py","file_url":"https://github.com/jieyibi/PIP-constraint/blob/HEAD/AM%2BPIP/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"248273aac0ceb091","mcp_get_code":{"code_sha256":"248273aac0ceb091"}},{"arxiv_id":"2410.04139","paper":"/paper/from-reading-to-compressing-exploring-the","title":"From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression","date":"2024-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eunseongc/r2c","path":"src_fid/options.py","file_url":"https://github.com/eunseongc/r2c/blob/HEAD/src_fid/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"966db28850700a6a","mcp_get_code":{"code_sha256":"966db28850700a6a"}},{"arxiv_id":"2403.20331","paper":"/paper/unsolvable-problem-detection-evaluating","title":"Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"atsumiyai/upd","path":"vlms/cogvlm/cogvlm_vqa_updbench.py","file_url":"https://github.com/atsumiyai/upd/blob/HEAD/vlms/cogvlm/cogvlm_vqa_updbench.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6c59a594c6d59480","mcp_get_code":{"code_sha256":"6c59a594c6d59480"}},{"arxiv_id":"2403.06095","paper":"/paper/repohyper-better-context-retrieval-is-all-you","title":"RepoHyper: Search-Expand-Refine on Semantic Graphs for Repository-Level Code Completion","date":"2024-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fsoft-ai4code/repohyper","path":"src/train/codet5_arg_parser.py","file_url":"https://github.com/fsoft-ai4code/repohyper/blob/HEAD/src/train/codet5_arg_parser.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":"c97f421638696408","mcp_get_code":{"code_sha256":"c97f421638696408"}},{"arxiv_id":"2402.14418","paper":"/paper/uncertainty-aware-evaluation-for-vision","title":"Uncertainty-Aware Evaluation for Vision-Language Models","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ensec-ai/vlm-uncertainty-bench","path":"data_utils/common_utils.py","file_url":"https://github.com/ensec-ai/vlm-uncertainty-bench/blob/HEAD/data_utils/common_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ec8353674842469","mcp_get_code":{"code_sha256":"7ec8353674842469"}},{"arxiv_id":"2402.05391","paper":"/paper/knowledge-graphs-meet-multi-modal-learning-a","title":"Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey","date":"2024-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hackerchenzhuo/LaKo","path":"src/options.py","file_url":"https://github.com/hackerchenzhuo/LaKo/blob/HEAD/src/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b4e32355db15fe9f","mcp_get_code":{"code_sha256":"b4e32355db15fe9f"}},{"arxiv_id":"2402.02503","paper":"/paper/gerea-question-aware-prompt-captions-for","title":"GeReA: Question-Aware Prompt Captions for Knowledge-based Visual Question Answering","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"upper9527/gerea","path":"src/options.py","file_url":"https://github.com/upper9527/gerea/blob/HEAD/src/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4c076a246978c67e","mcp_get_code":{"code_sha256":"4c076a246978c67e"}},{"arxiv_id":"2402.02355","paper":"/paper/symbol-generating-flexible-black-box","title":"Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning","date":"2024-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GMC-DRL/Symbol","path":"options.py","file_url":"https://github.com/GMC-DRL/Symbol/blob/HEAD/options.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d1e201bc39eefb0c","mcp_get_code":{"code_sha256":"d1e201bc39eefb0c"}},{"arxiv_id":"2401.16979","paper":"/paper/re3val-reinforced-and-reranked-generative","title":"Re3val: Reinforced and Reranked Generative Retrieval","date":"2024-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/FiD","path":"src/options.py","file_url":"https://github.com/facebookresearch/FiD/blob/HEAD/src/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c97f421638696408","mcp_get_code":{"code_sha256":"c97f421638696408"}},{"arxiv_id":"2310.18264","paper":"/paper/learning-to-search-feasible-and-infeasible-1","title":"Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt","date":"2023-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yining043/NeuOpt","path":"options.py","file_url":"https://github.com/yining043/NeuOpt/blob/HEAD/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be653fe85fcebca5","mcp_get_code":{"code_sha256":"be653fe85fcebca5"}},{"arxiv_id":"2303.02442","paper":"/paper/neural-airport-ground-handling","title":"Neural Airport Ground Handling","date":"2023-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"royalskye/agh","path":"Construction_based/options.py","file_url":"https://github.com/royalskye/agh/blob/HEAD/Construction_based/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c2c72575dcd069f3","mcp_get_code":{"code_sha256":"c2c72575dcd069f3"}},{"arxiv_id":"2205.13225","paper":"/paper/collaborative-distillation-meta-learning-for","title":"DevFormer: A Symmetric Transformer for Context-Aware Device Placement","date":"2022-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kaist-silab/devformer","path":"src/options.py","file_url":"https://github.com/kaist-silab/devformer/blob/HEAD/src/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"082646223bfd2be7","mcp_get_code":{"code_sha256":"082646223bfd2be7"}},{"arxiv_id":"2112.08688","paper":"/paper/evidentiality-guided-generation-for-knowledge","title":"Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks","date":"2021-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akariasai/evidentiality_qa","path":"evi_gen/src/options.py","file_url":"https://github.com/akariasai/evidentiality_qa/blob/HEAD/evi_gen/src/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c4b02a23802ff58","mcp_get_code":{"code_sha256":"9c4b02a23802ff58"}},{"arxiv_id":"2106.02382","paper":"/paper/annotation-curricula-to-implicitly-train-non","title":"Annotation Curricula to Implicitly Train Non-Expert Annotators","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ukplab/annotation-curriculum","path":"user_study/model_external.py","file_url":"https://github.com/ukplab/annotation-curriculum/blob/HEAD/user_study/model_external.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":"05bc13270d4df3b1","mcp_get_code":{"code_sha256":"05bc13270d4df3b1"}},{"arxiv_id":"2006.07054","paper":"/paper/learning-tsp-requires-rethinking","title":"Learning the Travelling Salesperson Problem Requires Rethinking Generalization","date":"2020-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaitjo/learning-tsp","path":"options.py","file_url":"https://github.com/chaitjo/learning-tsp/blob/HEAD/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"879b035fee7c0b04","mcp_get_code":{"code_sha256":"879b035fee7c0b04"}},{"arxiv_id":"2006.07054","paper":"/paper/learning-tsp-requires-rethinking","title":"Learning the Travelling Salesperson Problem Requires Rethinking Generalization","date":"2020-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaitjo/learning-paradigms-for-tsp","path":"options.py","file_url":"https://github.com/chaitjo/learning-paradigms-for-tsp/blob/HEAD/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"182bb7168c89e297","mcp_get_code":{"code_sha256":"182bb7168c89e297"}},{"arxiv_id":"1907.01197","paper":"/paper/treant-training-evasion-aware-decision-trees","title":"Treant: Training Evasion-Aware Decision Trees","date":"2019-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gtolomei/treant","path":"src/train_robust_forest.py","file_url":"https://github.com/gtolomei/treant/blob/HEAD/src/train_robust_forest.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":"a25a5ed3cc5c21bb","mcp_get_code":{"code_sha256":"a25a5ed3cc5c21bb"}},{"arxiv_id":"1803.10081","paper":"/paper/deepjdot-deep-joint-distribution-optimal","title":"DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation","date":"2018-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"asahi417/DeepDomainAdaptation","path":"random/script_check_tfrecord.py","file_url":"https://github.com/asahi417/DeepDomainAdaptation/blob/HEAD/random/script_check_tfrecord.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e615321f051e8f6f","mcp_get_code":{"code_sha256":"e615321f051e8f6f"}},{"arxiv_id":"1803.08475","paper":"/paper/attention-learn-to-solve-routing-problems","title":"Attention, Learn to Solve Routing Problems!","date":"2018-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"theresearchai/vehicle_routing_rl_2","path":"options.py","file_url":"https://github.com/theresearchai/vehicle_routing_rl_2/blob/HEAD/options.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0f6f8675d8280c4f","mcp_get_code":{"code_sha256":"0f6f8675d8280c4f"}},{"arxiv_id":"1706.06691","paper":"/paper/interpretable-predictions-of-tree-based","title":"Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking","date":"2017-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gtolomei/ml-feature-tweaking","path":"tweak_features.py","file_url":"https://github.com/gtolomei/ml-feature-tweaking/blob/HEAD/tweak_features.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":"717f20e52d2d6ad6","mcp_get_code":{"code_sha256":"717f20e52d2d6ad6"}}]}