{"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/squared-error","entry":"squared_error","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":9,"n_papers_ran":6,"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":4,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":9,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"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":"2506.05188","paper":"/paper/2506-05188","title":"Counterfactual reasoning: an analysis of in-context emergence","date":"2025-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"moxmiller/counterfactual-reasoning","path":"src/tasks.py","file_url":"https://github.com/moxmiller/counterfactual-reasoning/blob/HEAD/src/tasks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"348d63a78eb4cb94","mcp_get_code":{"code_sha256":"348d63a78eb4cb94"}},{"arxiv_id":"2410.05448","paper":"/paper/task-diversity-shortens-the-icl-plateau","title":"Task Diversity Shortens the ICL Plateau","date":"2024-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sehyunkwon/task-diversity-icl","path":"src/continuous_task.py","file_url":"https://github.com/sehyunkwon/task-diversity-icl/blob/HEAD/src/continuous_task.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"348d63a78eb4cb94","mcp_get_code":{"code_sha256":"348d63a78eb4cb94"}},{"arxiv_id":"2407.19346","paper":"/paper/polynomial-regression-as-a-task-for","title":"Polynomial Regression as a Task for Understanding In-context Learning Through Finetuning and Alignment","date":"2024-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dtsip/in-context-learning","path":"src/tasks.py","file_url":"https://github.com/dtsip/in-context-learning/blob/HEAD/src/tasks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"348d63a78eb4cb94","mcp_get_code":{"code_sha256":"348d63a78eb4cb94"}},{"arxiv_id":"2404.18191","paper":"/paper/exploring-the-robustness-of-in-context","title":"Exploring the Robustness of In-Context Learning with Noisy Labels","date":"2024-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"InezYu0928/in-context-learning","path":"src/tasks.py","file_url":"https://github.com/InezYu0928/in-context-learning/blob/HEAD/src/tasks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"348d63a78eb4cb94","mcp_get_code":{"code_sha256":"348d63a78eb4cb94"}},{"arxiv_id":"2402.04248","paper":"/paper/can-mamba-learn-how-to-learn-a-comparative","title":"Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks","date":"2024-02-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"krafton-ai/mambaformer-icl","path":"src/tasks.py","file_url":"https://github.com/krafton-ai/mambaformer-icl/blob/HEAD/src/tasks.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":"348d63a78eb4cb94","mcp_get_code":{"code_sha256":"348d63a78eb4cb94"}},{"arxiv_id":"2310.17086","paper":"/paper/transformers-learn-higher-order-optimization","title":"Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeqingFu/transformers-icl-second-order","path":"src/tasks.py","file_url":"https://github.com/DeqingFu/transformers-icl-second-order/blob/HEAD/src/tasks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"348d63a78eb4cb94","mcp_get_code":{"code_sha256":"348d63a78eb4cb94"}},{"arxiv_id":"2307.00142","paper":"/paper/buildingsbench-a-large-scale-dataset-of-900k-1","title":"BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting","date":"2023-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NREL/BuildingsBench","path":"buildings_bench/evaluation/metrics.py","file_url":"https://github.com/NREL/BuildingsBench/blob/HEAD/buildings_bench/evaluation/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"97e0d2619679e5a7","mcp_get_code":{"code_sha256":"97e0d2619679e5a7"}},{"arxiv_id":"2104.01677","paper":"/paper/a-contrastive-rule-for-meta-learning","title":"A contrastive rule for meta-learning","date":"2021-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"smonsays/contrastive-meta-learning","path":"bandit/energy/loss.py","file_url":"https://github.com/smonsays/contrastive-meta-learning/blob/HEAD/bandit/energy/loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a0da98175e2951e","mcp_get_code":{"code_sha256":"9a0da98175e2951e"}},{"arxiv_id":"1703.06697","paper":"/paper/timbre-analysis-of-music-audio-signals-with","title":"Timbre Analysis of Music Audio Signals with Convolutional Neural Networks","date":null,"month_inferred_from_arxiv_id":"2017-03","title_source":"archive","repo":"jordipons/EUSIPCO2017","path":"src/lasagne/objectives.py","file_url":"https://github.com/jordipons/EUSIPCO2017/blob/HEAD/src/lasagne/objectives.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5e68226cfc99fbe6","mcp_get_code":{"code_sha256":"5e68226cfc99fbe6"}}]}