{"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/read-lines","entry":"read_lines","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":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":21,"n_samples_ran":7,"n_samples_fingerprinted":0,"n_places":21,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":1,"ran":4,"unverified":14},"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":"2607.10569","paper":"/paper/arxiv-2607-10569","title":"When Does Restricting a Coding Agent to execute_code Help? A Regime × Agent-Design Ablation","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"hyang0129/onlycodes","path":"exec_server/codebox.py","file_url":"https://github.com/hyang0129/onlycodes/blob/HEAD/exec_server/codebox.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1988c67e76cdf7fc","mcp_get_code":{"code_sha256":"1988c67e76cdf7fc"}},{"arxiv_id":"2606.29024","paper":"/paper/arxiv-2606-29024","title":"Conversational Domain Adaptation of IndicTrans2 across 21 Indic Languages via Experience Replay and Model Soups","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Aditya-PS-05/indictrans2-conversational","path":"src/eval/build_eval_sheet.py","file_url":"https://github.com/Aditya-PS-05/indictrans2-conversational/blob/HEAD/src/eval/build_eval_sheet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5bcee79ee725d399","mcp_get_code":{"code_sha256":"5bcee79ee725d399"}},{"arxiv_id":"2601.11930","paper":"/paper/arxiv-2601-11930","title":"SupScene: Scene-Structured Overlap Supervision for Image Retrieval in Unconstrained SfM","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Suxilan/SupScene","path":"supscene/datasets/scenegraph.py","file_url":"https://github.com/Suxilan/SupScene/blob/HEAD/supscene/datasets/scenegraph.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":"92deb9e160910a3d","mcp_get_code":{"code_sha256":"92deb9e160910a3d"}},{"arxiv_id":"2502.09416","paper":"/paper/rethinking-evaluation-metrics-for-grammatical","title":"Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use a Different Evaluation Process than Human?","date":"2025-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gotutiyan/gec-metrics","path":"src/gec_metrics/cli/evaluate.py","file_url":"https://github.com/gotutiyan/gec-metrics/blob/HEAD/src/gec_metrics/cli/evaluate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d8873cab2939d4e3","mcp_get_code":{"code_sha256":"d8873cab2939d4e3"}},{"arxiv_id":"2502.09416","paper":"/paper/rethinking-evaluation-metrics-for-grammatical","title":"Rethinking Evaluation Metrics for Grammatical Error Correction: Why Use a Different Evaluation Process than Human?","date":"2025-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gotutiyan/gec-metrics","path":"src/gec_metrics/meta_eval/utils.py","file_url":"https://github.com/gotutiyan/gec-metrics/blob/HEAD/src/gec_metrics/meta_eval/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7bc198cd024f4ed8","mcp_get_code":{"code_sha256":"7bc198cd024f4ed8"}},{"arxiv_id":"2403.00758","paper":"/paper/mitigating-reversal-curse-via-semantic-aware","title":"Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"beeevita/SPT","path":"raw_data/reverse_experiments/extract_prefix.py","file_url":"https://github.com/beeevita/SPT/blob/HEAD/raw_data/reverse_experiments/extract_prefix.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4824669ee63eb39a","mcp_get_code":{"code_sha256":"4824669ee63eb39a"}},{"arxiv_id":"2402.01887","paper":"/paper/on-f-divergence-principled-domain-adaptation","title":"On $f$-Divergence Principled Domain Adaptation: An Improved Framework","date":"2024-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuml/CDAN","path":"tensorflow/prep.py","file_url":"https://github.com/thuml/CDAN/blob/HEAD/tensorflow/prep.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0cb72c021b9db859","mcp_get_code":{"code_sha256":"0cb72c021b9db859"}},{"arxiv_id":"2309.08532","paper":"/paper/connecting-large-language-models-with","title":"EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers","date":"2023-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"beeevita/EvoPrompt","path":"evoluter.py","file_url":"https://github.com/beeevita/EvoPrompt/blob/HEAD/evoluter.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0e0098507bc5fbe9","mcp_get_code":{"code_sha256":"0e0098507bc5fbe9"}},{"arxiv_id":"2306.11825","paper":"/paper/on-compositionality-and-improved-training-of","title":"DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models","date":"2023-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pluslabnlp/dinado","path":"evaluation/eval_kit/measure_scores.py","file_url":"https://github.com/pluslabnlp/dinado/blob/HEAD/evaluation/eval_kit/measure_scores.py","status":"ran_draft_wrong","verification_level":2,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"092cd75165a8db61","mcp_get_code":{"code_sha256":"092cd75165a8db61"}},{"arxiv_id":"2210.13210","paper":"/paper/mutual-information-alleviates-hallucinations","title":"Mutual Information Alleviates Hallucinations in Abstractive Summarization","date":"2022-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vanderpoelliam/cpmi","path":"src/detect_hallucination/process_labels.py","file_url":"https://github.com/vanderpoelliam/cpmi/blob/HEAD/src/detect_hallucination/process_labels.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70c754919b846528","mcp_get_code":{"code_sha256":"70c754919b846528"}},{"arxiv_id":"2210.13210","paper":"/paper/mutual-information-alleviates-hallucinations","title":"Mutual Information Alleviates Hallucinations in Abstractive Summarization","date":"2022-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vanderpoelliam/cpmi","path":"src/factCC/generate_json_data.py","file_url":"https://github.com/vanderpoelliam/cpmi/blob/HEAD/src/factCC/generate_json_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5685319232b466bb","mcp_get_code":{"code_sha256":"5685319232b466bb"}},{"arxiv_id":"2107.10821","paper":"/paper/to-ship-or-not-to-ship-an-extensive","title":"To Ship or Not to Ship: An Extensive Evaluation of Automatic Metrics for Machine Translation","date":"2021-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"unbabel/mt-telescope","path":"telescope/utils.py","file_url":"https://github.com/unbabel/mt-telescope/blob/HEAD/telescope/utils.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":"b747bf1b0cf69e35","mcp_get_code":{"code_sha256":"b747bf1b0cf69e35"}},{"arxiv_id":"2104.10064","paper":"/paper/style-aware-normalized-loss-for-improving","title":"Style-Aware Normalized Loss for Improving Arbitrary Style Transfer","date":"2021-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"inejc/painters","path":"painters/utils.py","file_url":"https://github.com/inejc/painters/blob/HEAD/painters/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d9397cebf67848e7","mcp_get_code":{"code_sha256":"d9397cebf67848e7"}},{"arxiv_id":"2104.09952","paper":"/paper/mgsampler-an-explainable-sampling-strategy","title":"MGSampler: An Explainable Sampling Strategy for Video Action Recognition","date":"2021-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MCG-NJU/MGSampler","path":"generate_feature_diff.py","file_url":"https://github.com/MCG-NJU/MGSampler/blob/HEAD/generate_feature_diff.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":"8371a0fe16f8eaeb","mcp_get_code":{"code_sha256":"8371a0fe16f8eaeb"}},{"arxiv_id":"2005.08526","paper":"/paper/unconditional-audio-generation-with","title":"Unconditional Audio Generation with Generative Adversarial Networks and Cycle Regularization","date":"2020-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ciaua/unagan","path":"src/training_manager.py","file_url":"https://github.com/ciaua/unagan/blob/HEAD/src/training_manager.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b400f05ed46f9f4","mcp_get_code":{"code_sha256":"2b400f05ed46f9f4"}},{"arxiv_id":"1909.04076","paper":"/paper/counterfactual-story-reasoning-and-generation","title":"Counterfactual Story Reasoning and Generation","date":"2019-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qkaren/Counterfactual-StoryRW","path":"evaluate.py","file_url":"https://github.com/qkaren/Counterfactual-StoryRW/blob/HEAD/evaluate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"320dc467a5162394","mcp_get_code":{"code_sha256":"320dc467a5162394"}},{"arxiv_id":"1908.05739","paper":"/paper/abductive-commonsense-reasoning","title":"Abductive Commonsense Reasoning","date":"2019-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/abductive-commonsense-reasoning","path":"utils/file_utils.py","file_url":"https://github.com/allenai/abductive-commonsense-reasoning/blob/HEAD/utils/file_utils.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":"7e775841119597bd","mcp_get_code":{"code_sha256":"7e775841119597bd"}},{"arxiv_id":"1902.08858","paper":"/paper/rethinking-action-spaces-for-reinforcement","title":"Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models","date":"2019-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snakeztc/NeuralDialog-LaRL","path":"FB/data.py","file_url":"https://github.com/snakeztc/NeuralDialog-LaRL/blob/HEAD/FB/data.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":"4f6d65cb0a38293a","mcp_get_code":{"code_sha256":"4f6d65cb0a38293a"}},{"arxiv_id":"aaai_21290","paper":null,"title":"arXiv:aaai_21290","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jiangjiechen/EDUCAT","path":"src/eval_client/metrics.py","file_url":"https://github.com/jiangjiechen/EDUCAT/blob/HEAD/src/eval_client/metrics.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":"c58c04a907e2427a","mcp_get_code":{"code_sha256":"c58c04a907e2427a"}},{"arxiv_id":"2024.findings-acl.148","paper":null,"title":"arXiv:2024.findings-acl.148","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"HillZhang1999/MuCGEC","path":"models/seq2seq-based-CGEC/utils.py","file_url":"https://github.com/HillZhang1999/MuCGEC/blob/HEAD/models/seq2seq-based-CGEC/utils.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":"5431270e7aba8ae2","mcp_get_code":{"code_sha256":"5431270e7aba8ae2"}},{"arxiv_id":"2022.emnlp-main.663","paper":null,"title":"arXiv:2022.emnlp-main.663","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mcao516/rej-summ","path":"preprocessing.py","file_url":"https://github.com/mcao516/rej-summ/blob/HEAD/preprocessing.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"600c667aff3fb774","mcp_get_code":{"code_sha256":"600c667aff3fb774"}}]}