{"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/is-equal","entry":"is_equal","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":8,"n_samples_ran":4,"n_samples_fingerprinted":3,"n_places":9,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"unverified":4},"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":"2502.02827","paper":"/paper/coffe-a-code-efficiency-benchmark-for-code","title":"COFFE: A Code Efficiency Benchmark for Code Generation","date":null,"month_inferred_from_arxiv_id":"2025-02","title_source":"archive","repo":"JohnnyPeng18/Coffe","path":"coffe/code_execution.py","file_url":"https://github.com/JohnnyPeng18/Coffe/blob/HEAD/coffe/code_execution.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":"0bf2e66e104d3db2","mcp_get_code":{"code_sha256":"0bf2e66e104d3db2"}},{"arxiv_id":"2408.04259","paper":"/paper/efficientrag-efficient-retriever-for-multi","title":"EfficientRAG: Efficient Retriever for Multi-Hop Question Answering","date":"2024-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nil-zhuang/efficientrag-official","path":"src/data_synthesize/negative_token_extraction.py","file_url":"https://github.com/nil-zhuang/efficientrag-official/blob/HEAD/src/data_synthesize/negative_token_extraction.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7da9282cb2dcf1ab","mcp_get_code":{"code_sha256":"7da9282cb2dcf1ab"}},{"arxiv_id":"2407.02490","paper":"/paper/minference-1-0-accelerating-pre-filling-for","title":"MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/LLMLingua","path":"experiments/llmlingua2/data_collection/label_word.py","file_url":"https://github.com/microsoft/LLMLingua/blob/HEAD/experiments/llmlingua2/data_collection/label_word.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7da9282cb2dcf1ab","mcp_get_code":{"code_sha256":"7da9282cb2dcf1ab"}},{"arxiv_id":"2407.02490","paper":"/paper/minference-1-0-accelerating-pre-filling-for","title":"MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/LLMLingua","path":"experiments/securitylingua/label_word.py","file_url":"https://github.com/microsoft/LLMLingua/blob/HEAD/experiments/securitylingua/label_word.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5c2c5ab6b6d57f48","mcp_get_code":{"code_sha256":"5c2c5ab6b6d57f48"}},{"arxiv_id":"2406.05673","paper":"/paper/flow-of-reasoning-efficient-training-of-llm","title":"Flow of Reasoning:Training LLMs for Divergent Problem Solving with Minimal Examples","date":"2024-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Yu-Fangxu/FoR","path":"GSM8K/lightning_module_selection.py","file_url":"https://github.com/Yu-Fangxu/FoR/blob/HEAD/GSM8K/lightning_module_selection.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"327e54ca69883f06","mcp_get_code":{"code_sha256":"327e54ca69883f06"}},{"arxiv_id":"2401.05507","paper":"/paper/infiagent-dabench-evaluating-agents-on-data","title":"InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks","date":"2024-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"infiagent/infiagent","path":"examples/DA-Agent/eval_closed_form.py","file_url":"https://github.com/infiagent/infiagent/blob/HEAD/examples/DA-Agent/eval_closed_form.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b2245c8f5cbe069d","mcp_get_code":{"code_sha256":"b2245c8f5cbe069d"}},{"arxiv_id":"1811.03194","paper":"/paper/ad-versarial-perceptual-ad-blocking-meets","title":"AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning","date":"2018-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ftramer/ad-versarial","path":"element-frame-based/exact_match.py","file_url":"https://github.com/ftramer/ad-versarial/blob/HEAD/element-frame-based/exact_match.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f7a281adc4fb2f95","mcp_get_code":{"code_sha256":"f7a281adc4fb2f95"}},{"arxiv_id":"1811.01255","paper":"/paper/adjoint-method-and-inverse-design-for","title":"Adjoint method and inverse design for nonlinear nanophotonic devices","date":null,"month_inferred_from_arxiv_id":"2018-11","title_source":"archive","repo":"fancompute/angler","path":"angler/linalg.py","file_url":"https://github.com/fancompute/angler/blob/HEAD/angler/linalg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6ef0316ccb7b56e1","mcp_get_code":{"code_sha256":"6ef0316ccb7b56e1"}},{"arxiv_id":"2025.findings-acl.964","paper":null,"title":"arXiv:2025.findings-acl.964","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"QwenLM/Qwen2.5-VL","path":"evaluation/MathVision/eval_utils.py","file_url":"https://github.com/QwenLM/Qwen2.5-VL/blob/HEAD/evaluation/MathVision/eval_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":"4d07ee4b13028c58","mcp_get_code":{"code_sha256":"4d07ee4b13028c58"}}]}