{"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-problem","entry":"get_problem","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":8,"n_papers_ran":4,"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":7,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":1,"ran":2,"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":"2608.15073","paper":"/paper/arxiv-2608-15073","title":"BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"rosenyu304/BOCoDe","path":"bocode/registry.py","file_url":"https://github.com/rosenyu304/BOCoDe/blob/HEAD/bocode/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f110d1d19300815","mcp_get_code":{"code_sha256":"7f110d1d19300815"}},{"arxiv_id":"2606.02909","paper":"/paper/arxiv-2606-02909","title":"Scalable Derivative Gaussian Processes via Exact Gradient Reduction","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"hseung88/tera","path":"src/bo_experiments/benchmarks.py","file_url":"https://github.com/hseung88/tera/blob/HEAD/src/bo_experiments/benchmarks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2ab4d3e86172d1ea","mcp_get_code":{"code_sha256":"2ab4d3e86172d1ea"}},{"arxiv_id":"2408.11743","paper":"/paper/marlin-mixed-precision-auto-regressive","title":"MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models","date":"2024-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ist-daslab/sparse-marlin","path":"bench_2_4.py","file_url":"https://github.com/ist-daslab/sparse-marlin/blob/HEAD/bench_2_4.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7243559c0e01f359","mcp_get_code":{"code_sha256":"7243559c0e01f359"}},{"arxiv_id":"2405.14854","paper":"/paper/terdit-ternary-diffusion-models-with","title":"TerDiT: Ternary Diffusion Models with Transformers","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lucky-Lance/TerDiT","path":"fairscale/benchmarks/oss.py","file_url":"https://github.com/Lucky-Lance/TerDiT/blob/HEAD/fairscale/benchmarks/oss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d457115aa40f2a97","mcp_get_code":{"code_sha256":"d457115aa40f2a97"}},{"arxiv_id":"2402.11443","paper":"/paper/benchmark-self-evolving-a-multi-agent","title":"Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation","date":"2024-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nanshineloong/self-evolving-benchmark","path":"gen_dataset.py","file_url":"https://github.com/nanshineloong/self-evolving-benchmark/blob/HEAD/gen_dataset.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"77f7f2e99f715477","mcp_get_code":{"code_sha256":"77f7f2e99f715477"}},{"arxiv_id":"2104.11667","paper":"/paper/scalable-and-flexible-deep-bayesian","title":"Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure","date":"2021-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samuelkim314/DeepBO","path":"opt_scatter.py","file_url":"https://github.com/samuelkim314/DeepBO/blob/HEAD/opt_scatter.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8084772e161bccc4","mcp_get_code":{"code_sha256":"8084772e161bccc4"}},{"arxiv_id":"1706.01338","paper":"/paper/understanding-the-learned-iterative-soft","title":"Understanding the Learned Iterative Soft Thresholding Algorithm with matrix factorization","date":"2017-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tomMoral/AdaptiveOptim","path":"NIPS_figures.py","file_url":"https://github.com/tomMoral/AdaptiveOptim/blob/HEAD/NIPS_figures.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d8eb74aaf0c1af20","mcp_get_code":{"code_sha256":"d8eb74aaf0c1af20"}},{"arxiv_id":"1609.00285","paper":"/paper/understanding-trainable-sparse-coding-via","title":"Understanding Trainable Sparse Coding via Matrix Factorization","date":"2016-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"d8eb74aaf0c1af20","mcp_get_code":{"code_sha256":"d8eb74aaf0c1af20"}}]}