{"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/mean","entry":"mean","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":84,"n_papers_ran":53,"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":44,"n_samples_ran":21,"n_samples_fingerprinted":14,"n_places":85,"n_places_pointer_only":29,"by_status":{"ran_honours":2,"ran_violates":9,"ran_draft_wrong":0,"ran_fixture":4,"ran":6,"unverified":23},"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":"2609.03430","paper":"/paper/arxiv-2609-03430","title":"Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"SalesforceAIResearch/Random-Attention","path":"kvcompress/analysis/analyze_union.py","file_url":"https://github.com/SalesforceAIResearch/Random-Attention/blob/HEAD/kvcompress/analysis/analyze_union.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":"2971e9e45e0853ea","mcp_get_code":{"code_sha256":"2971e9e45e0853ea"}},{"arxiv_id":"2607.26648","paper":"/paper/arxiv-2607-26648","title":"The Sparsity Ceiling: Where Spiking Networks Canand Cannot-Trade Activity for Energy","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"zeyuyuyu/sparsity-ceiling","path":"agg_copy.py","file_url":"https://github.com/zeyuyuyu/sparsity-ceiling/blob/HEAD/agg_copy.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0479fb5f08e12e14","mcp_get_code":{"code_sha256":"0479fb5f08e12e14"}},{"arxiv_id":"2606.16535","paper":"/paper/arxiv-2606-16535","title":"Assessing Reliability of Symbol Detection in Concept Bottleneck Models","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Fuminides/cbm_sanity","path":"src/symbol_sanity/multihead.py","file_url":"https://github.com/Fuminides/cbm_sanity/blob/HEAD/src/symbol_sanity/multihead.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"21eab06f5c9e22db","mcp_get_code":{"code_sha256":"21eab06f5c9e22db"}},{"arxiv_id":"2605.09623","paper":"/paper/arxiv-2605-09623","title":"Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum Akuen Akoi Deng ⋆[0009-0007-6228-3340] , Eimantas Butkus ⋆[0009-0001-5647-0779] , Alfreds Lapkovskis [0009-0003-4424-949X] , and","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Akuien/DNN-partitioning-and-Oflloading-framework-REAP","path":"adaptive-framework/split_infer.py","file_url":"https://github.com/Akuien/DNN-partitioning-and-Oflloading-framework-REAP/blob/HEAD/adaptive-framework/split_infer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"63da45ffd1618ec0","mcp_get_code":{"code_sha256":"63da45ffd1618ec0"}},{"arxiv_id":"2605.00855","paper":"/paper/arxiv-2605-00855","title":"An Efficient Spatial Branch-and-Bound Algorithm for Global Optimization of Gaussian Process Posterior Mean Functions","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"PaulsonLab/PALM-Mean","path":"src/UpperBound.py","file_url":"https://github.com/PaulsonLab/PALM-Mean/blob/HEAD/src/UpperBound.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a3adb1a3bb19e20","mcp_get_code":{"code_sha256":"3a3adb1a3bb19e20"}},{"arxiv_id":"2604.19457","paper":"/paper/arxiv-2604-19457","title":"Four-Axis Decision Alignment for Long-Horizon Enterprise AI Agents","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"vasundras/decision-alignment-long-horizon-agents","path":"pilot/run_dpm.py","file_url":"https://github.com/vasundras/decision-alignment-long-horizon-agents/blob/HEAD/pilot/run_dpm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2971e9e45e0853ea","mcp_get_code":{"code_sha256":"2971e9e45e0853ea"}},{"arxiv_id":"2604.07937","paper":"/paper/arxiv-2604-07937","title":"HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"XMUDeepLIT/HCRE","path":"inference/utils.py","file_url":"https://github.com/XMUDeepLIT/HCRE/blob/HEAD/inference/utils.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e8b6cf1e9387fc9a","mcp_get_code":{"code_sha256":"e8b6cf1e9387fc9a"}},{"arxiv_id":"2601.21963","paper":"/paper/arxiv-2601-21963","title":"Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"aloth/verification-crisis","path":"analysis/recompute_published_statistics.py","file_url":"https://github.com/aloth/verification-crisis/blob/HEAD/analysis/recompute_published_statistics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a5a6fcfb0296e11b","mcp_get_code":{"code_sha256":"a5a6fcfb0296e11b"}},{"arxiv_id":"2601.16503","paper":"/paper/arxiv-2601-16503","title":"MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicine","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"hendrycks/test","path":"calib_tools.py","file_url":"https://github.com/hendrycks/test/blob/HEAD/calib_tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0af29c87aa4bd6a8","mcp_get_code":{"code_sha256":"0af29c87aa4bd6a8"}},{"arxiv_id":"2601.02682","paper":"/paper/arxiv-2601-02682","title":"Topology-Independent Robustness of the Weighted Mean under Label Poisoning Attacks in Heterogeneous Decentralized Learning","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"pengj97/DLPA","path":"ByrdLab/aggregation.py","file_url":"https://github.com/pengj97/DLPA/blob/HEAD/ByrdLab/aggregation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e8cd9a6e01e497b4","mcp_get_code":{"code_sha256":"e8cd9a6e01e497b4"}},{"arxiv_id":"2510.24380","paper":"/paper/arxiv-2510-24380","title":"APEX: Approximate-but-Exhaustive Search for Ultra-Large Combinatorial Synthesis Libraries","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"NumerionLabs/apex","path":"apex/nn/scatter.py","file_url":"https://github.com/NumerionLabs/apex/blob/HEAD/apex/nn/scatter.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d27ad721d104ef3f","mcp_get_code":{"code_sha256":"d27ad721d104ef3f"}},{"arxiv_id":"2508.20718","paper":"/paper/arxiv-2508-20718","title":"Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"ryehr/Consistency","path":"src/tokenization_consistency/metrics.py","file_url":"https://github.com/ryehr/Consistency/blob/HEAD/src/tokenization_consistency/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ef553fc5d98847b","mcp_get_code":{"code_sha256":"4ef553fc5d98847b"}},{"arxiv_id":"2504.18397","paper":"/paper/unsupervised-visual-chain-of-thought","title":"Unsupervised Visual Chain-of-Thought Reasoning via Preference Optimization","date":"2025-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"e8b6cf1e9387fc9a","mcp_get_code":{"code_sha256":"e8b6cf1e9387fc9a"}},{"arxiv_id":"2504.08165","paper":"/paper/findings-of-the-babylm-challenge-sample","title":"Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora","date":"2025-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"babylm/evaluation-pipeline","path":"lm_eval/api/metric.py","file_url":"https://github.com/babylm/evaluation-pipeline/blob/HEAD/lm_eval/api/metric.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2503.13914","paper":"/paper/psa-ssl-pose-and-size-aware-self-supervised","title":"PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point Clouds","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TRAILab/PSA-SSL","path":"criterions/lovasz_softmax.py","file_url":"https://github.com/TRAILab/PSA-SSL/blob/HEAD/criterions/lovasz_softmax.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80b5a150c6e092f7","mcp_get_code":{"code_sha256":"80b5a150c6e092f7"}},{"arxiv_id":"2501.06252","paper":"/paper/text-transformer-2-self-adaptive-llms","title":"Transformer-Squared: Self-adaptive LLMs","date":"2025-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SakanaAI/self-adaptive-llms","path":"tasks/cls.py","file_url":"https://github.com/SakanaAI/self-adaptive-llms/blob/HEAD/tasks/cls.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":"d00162523239ac86","mcp_get_code":{"code_sha256":"d00162523239ac86"}},{"arxiv_id":"2409.06305","paper":"/paper/high-performance-few-shot-segmentation-with","title":"High-Performance Few-Shot Segmentation with Foundation Models: An Empirical Study","date":"2024-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dut-csj/foundationfss","path":"common/utils.py","file_url":"https://github.com/dut-csj/foundationfss/blob/HEAD/common/utils.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":"2fbd545597570f15","mcp_get_code":{"code_sha256":"2fbd545597570f15"}},{"arxiv_id":"2408.10419","paper":"/paper/second-order-forward-mode-automatic","title":"Second-Order Forward-Mode Automatic Differentiation for Optimization","date":"2024-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sri-csl/fomoh","path":"src/fomoh/nn.py","file_url":"https://github.com/sri-csl/fomoh/blob/HEAD/src/fomoh/nn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"68aa86b28a69f9b3","mcp_get_code":{"code_sha256":"68aa86b28a69f9b3"}},{"arxiv_id":"2407.14985","paper":"/paper/generalization-v-s-memorization-tracing","title":"Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data","date":"2024-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EleutherAI/the-pile","path":"processing_scripts/lang_len_analysis_pass2.py","file_url":"https://github.com/EleutherAI/the-pile/blob/HEAD/processing_scripts/lang_len_analysis_pass2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0479fb5f08e12e14","mcp_get_code":{"code_sha256":"0479fb5f08e12e14"}},{"arxiv_id":"2407.10542","paper":"/paper/3d-geometric-shape-assembly-via-efficient","title":"3D Geometric Shape Assembly via Efficient Point Cloud Matching","date":"2024-07-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NahyukLEE/pmtr","path":"common/utils.py","file_url":"https://github.com/NahyukLEE/pmtr/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fbd545597570f15","mcp_get_code":{"code_sha256":"2fbd545597570f15"}},{"arxiv_id":"2407.08632","paper":"/paper/generalization-error-matters-in-decentralized","title":"Generalization Error Matters in Decentralized Learning Under Byzantine Attacks","date":"2024-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haoxiangye/BRDSGD-GE","path":"BRDSGD-GE/ByrdLab/aggregation.py","file_url":"https://github.com/haoxiangye/BRDSGD-GE/blob/HEAD/BRDSGD-GE/ByrdLab/aggregation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e8cd9a6e01e497b4","mcp_get_code":{"code_sha256":"e8cd9a6e01e497b4"}},{"arxiv_id":"2406.11813","paper":"/paper/how-do-large-language-models-acquire-factual","title":"How Do Large Language Models Acquire Factual Knowledge During Pretraining?","date":"2024-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kaistai/factual-knowledge-acquisition","path":"analysis/ppl_analysis.py","file_url":"https://github.com/kaistai/factual-knowledge-acquisition/blob/HEAD/analysis/ppl_analysis.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"63cf7ec10b164550","mcp_get_code":{"code_sha256":"63cf7ec10b164550"}},{"arxiv_id":"2406.10594","paper":"/paper/blockpruner-fine-grained-pruning-for-large","title":"BlockPruner: Fine-grained Pruning for Large Language Models","date":"2024-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MrGGLS/BlockPruner","path":"lm_eval/lm_eval/metrics.py","file_url":"https://github.com/MrGGLS/BlockPruner/blob/HEAD/lm_eval/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2406.01658","paper":"/paper/proxy-denoising-for-source-free-domain","title":"Proxy Denoising for Source-Free Domain Adaptation","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tntek/source-free-domain-adaptation","path":"src/utils/utils.py","file_url":"https://github.com/tntek/source-free-domain-adaptation/blob/HEAD/src/utils/utils.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6add5f42d9d96584","mcp_get_code":{"code_sha256":"6add5f42d9d96584"}},{"arxiv_id":"2405.15265","paper":"/paper/cross-domain-few-shot-semantic-segmentation-1","title":"Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChenJiayi68/DMTNet","path":"common/utils.py","file_url":"https://github.com/ChenJiayi68/DMTNet/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fbd545597570f15","mcp_get_code":{"code_sha256":"2fbd545597570f15"}},{"arxiv_id":"2404.01365","paper":"/paper/prompt-prompted-mixture-of-experts-for","title":"Prompt-prompted Adaptive Structured Pruning for Efficient LLM Generation","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hdong920/griffin","path":"src/lm_eval/metrics.py","file_url":"https://github.com/hdong920/griffin/blob/HEAD/src/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2403.13187","paper":"/paper/evolutionary-optimization-of-model-merging","title":"Evolutionary Optimization of Model Merging Recipes","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sakanaai/evolutionary-model-merge","path":"evomerge/eval/metrics.py","file_url":"https://github.com/sakanaai/evolutionary-model-merge/blob/HEAD/evomerge/eval/metrics.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"63cf7ec10b164550","mcp_get_code":{"code_sha256":"63cf7ec10b164550"}},{"arxiv_id":"2403.03031","paper":"/paper/learning-to-use-tools-via-cooperative-and","title":"Learning to Use Tools via Cooperative and Interactive Agents","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shizhl/coagents","path":"utilize/utilze.py","file_url":"https://github.com/shizhl/coagents/blob/HEAD/utilize/utilze.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e630822f7b9d4693","mcp_get_code":{"code_sha256":"e630822f7b9d4693"}},{"arxiv_id":"2403.01244","paper":"/paper/mitigating-catastrophic-forgetting-in-large","title":"Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeepLearnXMU/SSR","path":"mmlu_test/calib_tools.py","file_url":"https://github.com/DeepLearnXMU/SSR/blob/HEAD/mmlu_test/calib_tools.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":"0af29c87aa4bd6a8","mcp_get_code":{"code_sha256":"0af29c87aa4bd6a8"}},{"arxiv_id":"2402.17726","paper":"/paper/vrp-sam-sam-with-visual-reference-prompt","title":"VRP-SAM: SAM with Visual Reference Prompt","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"syp2ysy/vrp-sam","path":"common/utils.py","file_url":"https://github.com/syp2ysy/vrp-sam/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fbd545597570f15","mcp_get_code":{"code_sha256":"2fbd545597570f15"}},{"arxiv_id":"2402.17614","paper":"/paper/adapt-before-comparison-a-new-perspective-on","title":"Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation","date":"2024-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vision-kek/abcdfss","path":"utils/commonutils.py","file_url":"https://github.com/vision-kek/abcdfss/blob/HEAD/utils/commonutils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fbd545597570f15","mcp_get_code":{"code_sha256":"2fbd545597570f15"}},{"arxiv_id":"2402.16775","paper":"/paper/a-comprehensive-evaluation-of-quantization","title":"A Comprehensive Evaluation of Quantization Strategies for Large Language Models","date":"2024-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cordercorder/quant_eval","path":"quant/SpQR/lm-evaluation-harness/lm_eval/metrics.py","file_url":"https://github.com/cordercorder/quant_eval/blob/HEAD/quant/SpQR/lm-evaluation-harness/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2402.12192","paper":"/paper/pan-mamba-effective-pan-sharpening-with-state","title":"Pan-Mamba: Effective pan-sharpening with State Space Model","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexhe101/pan-mamba","path":"pan-sharpening/model/thops.py","file_url":"https://github.com/alexhe101/pan-mamba/blob/HEAD/pan-sharpening/model/thops.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e5786fcae2918427","mcp_get_code":{"code_sha256":"e5786fcae2918427"}},{"arxiv_id":"2402.09398","paper":"/paper/get-more-with-less-synthesizing-recurrence","title":"Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference","date":"2024-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hdong920/less","path":"src/lm_eval/metrics.py","file_url":"https://github.com/hdong920/less/blob/HEAD/src/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2402.05099","paper":"/paper/hydragen-high-throughput-llm-inference-with","title":"Hydragen: High-Throughput LLM Inference with Shared Prefixes","date":"2024-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jordan-benjamin/hydragen","path":"hydragen/utils.py","file_url":"https://github.com/jordan-benjamin/hydragen/blob/HEAD/hydragen/utils.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":"0479fb5f08e12e14","mcp_get_code":{"code_sha256":"0479fb5f08e12e14"}},{"arxiv_id":"2401.12452","paper":"/paper/self-supervised-learning-of-lidar-3d-point","title":"Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration","date":"2024-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"ed2a67a7b19a5c07","mcp_get_code":{"code_sha256":"ed2a67a7b19a5c07"}},{"arxiv_id":"2312.15731","paper":"/paper/adaptive-fss-a-novel-few-shot-segmentation","title":"Adaptive FSS: A Novel Few-Shot Segmentation Framework via Prototype Enhancement","date":"2023-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingw193/adaptive_fss","path":"common/utils.py","file_url":"https://github.com/jingw193/adaptive_fss/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fbd545597570f15","mcp_get_code":{"code_sha256":"2fbd545597570f15"}},{"arxiv_id":"2311.17491","paper":"/paper/spherical-frustum-sparse-convolution-network","title":"Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation","date":"2023-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IRMVLab/SFCNet","path":"SFCNet/network/Lovasz_Softmax.py","file_url":"https://github.com/IRMVLab/SFCNet/blob/HEAD/SFCNet/network/Lovasz_Softmax.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80b5a150c6e092f7","mcp_get_code":{"code_sha256":"80b5a150c6e092f7"}},{"arxiv_id":"2311.08711","paper":"/paper/plug-leveraging-pivot-language-in-cross","title":"PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning","date":"2023-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ytyz1307zzh/plug","path":"src/translation/translate_together_chatgpt.py","file_url":"https://github.com/ytyz1307zzh/plug/blob/HEAD/src/translation/translate_together_chatgpt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1010d5fc3c98b8e5","mcp_get_code":{"code_sha256":"1010d5fc3c98b8e5"}},{"arxiv_id":"2311.07911","paper":"/paper/instruction-following-evaluation-for-large","title":"Instruction-Following Evaluation for Large Language Models","date":"2023-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"josejg/instruction_following_eval","path":"instruction_following_eval/evaluation.py","file_url":"https://github.com/josejg/instruction_following_eval/blob/HEAD/instruction_following_eval/evaluation.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":"05d583bc67660802","mcp_get_code":{"code_sha256":"05d583bc67660802"}},{"arxiv_id":"2310.20708","paper":"/paper/unexpected-improvements-to-expected","title":"Unexpected Improvements to Expected Improvement for Bayesian Optimization","date":"2023-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"omerronen/scales","path":"les/analysis/opt_results.py","file_url":"https://github.com/omerronen/scales/blob/HEAD/les/analysis/opt_results.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fefc89892209bb98","mcp_get_code":{"code_sha256":"fefc89892209bb98"}},{"arxiv_id":"2310.17281","paper":"/paper/bevcontrast-self-supervision-in-bev-space-for","title":"BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds","date":"2023-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valeoai/bevcontrast","path":"downstream/train_downstream_semseg.py","file_url":"https://github.com/valeoai/bevcontrast/blob/HEAD/downstream/train_downstream_semseg.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ed2a67a7b19a5c07","mcp_get_code":{"code_sha256":"ed2a67a7b19a5c07"}},{"arxiv_id":"2310.05620","paper":"/paper/laiw-a-chinese-legal-large-language-models","title":"LAiW: A Chinese Legal Large Language Models Benchmark","date":"2023-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dai-shen/laiw","path":"src/financial-evaluation/lm_eval/metrics.py","file_url":"https://github.com/dai-shen/laiw/blob/HEAD/src/financial-evaluation/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2308.15291","paper":"/paper/towards-quantitative-precision-for-ecg","title":"Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata","date":"2023-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tmehari/ssm_ecg","path":"code/train_ecg_model.py","file_url":"https://github.com/tmehari/ssm_ecg/blob/HEAD/code/train_ecg_model.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76ec45b4b876760c","mcp_get_code":{"code_sha256":"76ec45b4b876760c"}},{"arxiv_id":"2308.13469","paper":"/paper/restnet-boosting-cross-domain-few-shot","title":"RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network","date":"2023-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bupt-ai-cz/restnet","path":"common/utils.py","file_url":"https://github.com/bupt-ai-cz/restnet/blob/HEAD/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fbd545597570f15","mcp_get_code":{"code_sha256":"2fbd545597570f15"}},{"arxiv_id":"2307.16212","paper":"/paper/robust-multi-agent-reinforcement-learning-3","title":"Robust Multi-Agent Reinforcement Learning with State Uncertainty","date":"2023-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sihongho/robust_marl_with_state_uncertainty","path":"RMA-AC/maddpg/common/tf_util.py","file_url":"https://github.com/sihongho/robust_marl_with_state_uncertainty/blob/HEAD/RMA-AC/maddpg/common/tf_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8ebadf88476f8efe","mcp_get_code":{"code_sha256":"8ebadf88476f8efe"}},{"arxiv_id":"2307.16039","paper":"/paper/okapi-instruction-tuned-large-language-models","title":"Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback","date":"2023-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nlp-uoregon/mlmm-evaluation","path":"lm_eval/metrics.py","file_url":"https://github.com/nlp-uoregon/mlmm-evaluation/blob/HEAD/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2306.09200","paper":"/paper/chessgpt-bridging-policy-learning-and-1","title":"ChessGPT: Bridging Policy Learning and Language Modeling","date":"2023-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"waterhorse1/ChessGPT","path":"eval/eval_clip/eval_clip_checkmate_in_one.py","file_url":"https://github.com/waterhorse1/ChessGPT/blob/HEAD/eval/eval_clip/eval_clip_checkmate_in_one.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2305.14493","paper":"/paper/prompt-position-really-matters-in-few-shot","title":"Do prompt positions really matter?","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"milliemaoo/prompt-position","path":"lm_eval/metrics.py","file_url":"https://github.com/milliemaoo/prompt-position/blob/HEAD/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2304.11379","paper":"/paper/lidar2map-in-defense-of-lidar-based-semantic","title":"LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation","date":"2023-04-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songw-zju/LiDAR2Map","path":"map/model/loss/Lovasz_Softmax.py","file_url":"https://github.com/songw-zju/LiDAR2Map/blob/HEAD/map/model/loss/Lovasz_Softmax.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80b5a150c6e092f7","mcp_get_code":{"code_sha256":"80b5a150c6e092f7"}},{"arxiv_id":"2303.17003","paper":"/paper/evaluating-gpt-3-5-and-gpt-4-models-on","title":"Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"piresramon/gpt-4-enem","path":"lm_eval/metrics.py","file_url":"https://github.com/piresramon/gpt-4-enem/blob/HEAD/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2303.05203","paper":"/paper/rmmdet-road-side-multitype-and-multigroup","title":"RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving","date":"2023-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OrangeSodahub/CRLFnet","path":"src/site_model/src/LidCamFusion/vis.py","file_url":"https://github.com/OrangeSodahub/CRLFnet/blob/HEAD/src/site_model/src/LidCamFusion/vis.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b38eacd724df1456","mcp_get_code":{"code_sha256":"b38eacd724df1456"}},{"arxiv_id":"2301.04330","paper":"/paper/fast-kinodynamic-planning-on-the-constraint","title":"Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks","date":"2023-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pkicki/cnp-b","path":"plots_and_stats/plot_air_hockey_hitting_real.py","file_url":"https://github.com/pkicki/cnp-b/blob/HEAD/plots_and_stats/plot_air_hockey_hitting_real.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b433888057f05208","mcp_get_code":{"code_sha256":"b433888057f05208"}},{"arxiv_id":"2301.04330","paper":"/paper/fast-kinodynamic-planning-on-the-constraint","title":"Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks","date":"2023-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pkicki/cnp-b","path":"plots_and_stats/plot_air_hockey_hitting_simulation.py","file_url":"https://github.com/pkicki/cnp-b/blob/HEAD/plots_and_stats/plot_air_hockey_hitting_simulation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a8ae03c9567b7ec5","mcp_get_code":{"code_sha256":"a8ae03c9567b7ec5"}},{"arxiv_id":"2212.11702","paper":"/paper/robust-meta-representation-learning-via","title":"Robust Meta-Representation Learning via Global Label Inference and Classification","date":"2022-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isakfalk/mela","path":"meta_learner.py","file_url":"https://github.com/isakfalk/mela/blob/HEAD/meta_learner.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2212.02705","paper":"/paper/what-is-the-solution-for-state-adversarial","title":"What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?","date":"2022-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"susanbao/rmarl_code","path":"rmarl/maddpg/common/tf_util.py","file_url":"https://github.com/susanbao/rmarl_code/blob/HEAD/rmarl/maddpg/common/tf_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"8ebadf88476f8efe","mcp_get_code":{"code_sha256":"8ebadf88476f8efe"}},{"arxiv_id":"2210.01784","paper":"/paper/coarse3d-class-prototypes-for-contrastive","title":"COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation","date":"2022-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cv-rits/coarse3d","path":"pc_processor/loss/lovasz_softmax.py","file_url":"https://github.com/cv-rits/coarse3d/blob/HEAD/pc_processor/loss/lovasz_softmax.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":"bba5ee8fc4344cc0","mcp_get_code":{"code_sha256":"bba5ee8fc4344cc0"}},{"arxiv_id":"2207.08867","paper":"/paper/mctensor-a-high-precision-deep-learning","title":"MCTensor: A High-Precision Deep Learning Library with Multi-Component Floating-Point","date":"2022-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ydtydr/mctensor","path":"src/MCTensor/MCTensor.py","file_url":"https://github.com/ydtydr/mctensor/blob/HEAD/src/MCTensor/MCTensor.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"27f65c89e631de91","mcp_get_code":{"code_sha256":"27f65c89e631de91"}},{"arxiv_id":"2207.01115","paper":"/paper/usher-unbiased-sampling-for-hindsight","title":"USHER: Unbiased Sampling for Hindsight Experience Replay","date":"2022-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"schrammlb2/USHER_Implementation","path":"discrete_usher/clean_q_implementation.py","file_url":"https://github.com/schrammlb2/USHER_Implementation/blob/HEAD/discrete_usher/clean_q_implementation.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e8b6cf1e9387fc9a","mcp_get_code":{"code_sha256":"e8b6cf1e9387fc9a"}},{"arxiv_id":"2110.08988","paper":"/paper/feanet-feature-enhanced-attention-network-for","title":"FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation","date":"2021-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"matrixgame2018/FEANet","path":"loss_hub/losses/lovasz.py","file_url":"https://github.com/matrixgame2018/FEANet/blob/HEAD/loss_hub/losses/lovasz.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ab905d7b509bc70c","mcp_get_code":{"code_sha256":"ab905d7b509bc70c"}},{"arxiv_id":"2110.04374","paper":"/paper/a-few-more-examples-may-be-worth-billions-of","title":"A Few More Examples May Be Worth Billions of Parameters","date":"2021-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuvalkirstain/lm-evaluation-harness","path":"lm_eval/metrics.py","file_url":"https://github.com/yuvalkirstain/lm-evaluation-harness/blob/HEAD/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}},{"arxiv_id":"2109.04096","paper":"/paper/a-three-stage-learning-framework-for-low","title":"A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation","date":"2021-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neukg/kat-tslf","path":"dial/metrics.py","file_url":"https://github.com/neukg/kat-tslf/blob/HEAD/dial/metrics.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e8b6cf1e9387fc9a","mcp_get_code":{"code_sha256":"e8b6cf1e9387fc9a"}},{"arxiv_id":"2108.05301","paper":"/paper/hierarchical-conditional-flow-a-unified","title":"Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling","date":"2021-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingyunliang/hcflow","path":"codes/models/modules/HCFlowNet_SR_arch.py","file_url":"https://github.com/jingyunliang/hcflow/blob/HEAD/codes/models/modules/HCFlowNet_SR_arch.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e5786fcae2918427","mcp_get_code":{"code_sha256":"e5786fcae2918427"}},{"arxiv_id":"2106.04180","paper":"/paper/image2point-3d-point-cloud-understanding-with","title":"Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenfengxu714/image2point","path":"core/criterions.py","file_url":"https://github.com/chenfengxu714/image2point/blob/HEAD/core/criterions.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"80b5a150c6e092f7","mcp_get_code":{"code_sha256":"80b5a150c6e092f7"}},{"arxiv_id":"2105.06022","paper":"/paper/principled-exploration-via-optimistic","title":"Principled Exploration via Optimistic Bootstrapping and Backward Induction","date":"2021-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rrmenon10/Bootstrapped-DQN","path":"baselines/common/tf_util.py","file_url":"https://github.com/rrmenon10/Bootstrapped-DQN/blob/HEAD/baselines/common/tf_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"3dc7f82ede7de679","mcp_get_code":{"code_sha256":"3dc7f82ede7de679"}},{"arxiv_id":"2104.01541","paper":"/paper/attention-back-end-for-automatic-speaker","title":"Attention Back-end for Automatic Speaker Verification with Multiple Enrollment Utterances","date":"2021-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nii-yamagishilab/Attention_Backend_for_ASV","path":"model.py","file_url":"https://github.com/nii-yamagishilab/Attention_Backend_for_ASV/blob/HEAD/model.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":"74f233e6392c3134","mcp_get_code":{"code_sha256":"74f233e6392c3134"}},{"arxiv_id":"2009.12789","paper":"/paper/learning-optimal-representations-with-the","title":"Learning Optimal Representations with the Decodable Information Bottleneck","date":"2020-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YannDubs/Mini_Decodable_Information_Bottleneck","path":"dib.py","file_url":"https://github.com/YannDubs/Mini_Decodable_Information_Bottleneck/blob/HEAD/dib.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"65f37b6c0e79c62c","mcp_get_code":{"code_sha256":"65f37b6c0e79c62c"}},{"arxiv_id":"2009.03300","paper":"/paper/measuring-massive-multitask-language","title":"Measuring Massive Multitask Language Understanding","date":"2020-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ollmer/mmlu","path":"calib_tools.py","file_url":"https://github.com/ollmer/mmlu/blob/HEAD/calib_tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0af29c87aa4bd6a8","mcp_get_code":{"code_sha256":"0af29c87aa4bd6a8"}},{"arxiv_id":"2008.08115","paper":"/paper/tide-a-general-toolbox-for-identifying-object","title":"TIDE: A General Toolbox for Identifying Object Detection Errors","date":"2020-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dbolya/tide","path":"tidecv/functions.py","file_url":"https://github.com/dbolya/tide/blob/HEAD/tidecv/functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"caf6f29a56ca01cb","mcp_get_code":{"code_sha256":"caf6f29a56ca01cb"}},{"arxiv_id":"2007.11471","paper":"/paper/compressing-invariant-manifolds-in-neural","title":"Geometric compression of invariant manifolds in neural nets","date":"2020-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mariogeiger/feature_lazy","path":"exp/feature_lazy.py","file_url":"https://github.com/mariogeiger/feature_lazy/blob/HEAD/exp/feature_lazy.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8ae298bcdfa4ffe2","mcp_get_code":{"code_sha256":"8ae298bcdfa4ffe2"}},{"arxiv_id":"2007.10587","paper":"/paper/learning-to-compose-hypercolumns-for-visual","title":"Learning to Compose Hypercolumns for Visual Correspondence","date":"2020-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juhongm999/dhpf","path":"common/utils.py","file_url":"https://github.com/juhongm999/dhpf/blob/HEAD/common/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":"25f5aa52cfed5eda","mcp_get_code":{"code_sha256":"25f5aa52cfed5eda"}},{"arxiv_id":"2007.01332","paper":"/paper/meta-learning-stationary-stochastic-process","title":"Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes","date":"2020-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YannDubs/Neural-Process-Family","path":"utils/helpers.py","file_url":"https://github.com/YannDubs/Neural-Process-Family/blob/HEAD/utils/helpers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ba480fa4f850c03","mcp_get_code":{"code_sha256":"1ba480fa4f850c03"}},{"arxiv_id":"1909.13584","paper":"/paper/interpretations-are-useful-penalizing","title":"Interpretations are useful: penalizing explanations to align neural networks with prior knowledge","date":"2019-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"csinva/local-vae","path":"lib/disentangling-vae/disvae/training.py","file_url":"https://github.com/csinva/local-vae/blob/HEAD/lib/disentangling-vae/disvae/training.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ef0809bca2dd8069","mcp_get_code":{"code_sha256":"ef0809bca2dd8069"}},{"arxiv_id":"1907.06173","paper":"/paper/the-fast-algorithm-for-submodular","title":"The FAST Algorithm for Submodular Maximization","date":"2019-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luccote/msm-primal-dual","path":"visuals/objective_visualizer.py","file_url":"https://github.com/luccote/msm-primal-dual/blob/HEAD/visuals/objective_visualizer.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f8bab31130ea6b93","mcp_get_code":{"code_sha256":"f8bab31130ea6b93"}},{"arxiv_id":"1906.08878","paper":"/paper/bayesian-optimisation-over-multiple","title":"Bayesian Optimisation over Multiple Continuous and Categorical Inputs","date":"2019-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rubinxin/CoCaBO_code","path":"utils/probability.py","file_url":"https://github.com/rubinxin/CoCaBO_code/blob/HEAD/utils/probability.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d129b821885a91c","mcp_get_code":{"code_sha256":"1d129b821885a91c"}},{"arxiv_id":"1803.00933","paper":"/paper/distributed-prioritized-experience-replay","title":"Distributed Prioritized Experience Replay","date":"2018-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uber-research/ape-x","path":"tf_util.py","file_url":"https://github.com/uber-research/ape-x/blob/HEAD/tf_util.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":"3dc7f82ede7de679","mcp_get_code":{"code_sha256":"3dc7f82ede7de679"}},{"arxiv_id":"1803.00653","paper":"/paper/semi-parametric-topological-memory-for","title":"Semi-parametric Topological Memory for Navigation","date":"2018-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nsavinov/SPTM","path":"src/common/util.py","file_url":"https://github.com/nsavinov/SPTM/blob/HEAD/src/common/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1eb55fc82e80bf58","mcp_get_code":{"code_sha256":"1eb55fc82e80bf58"}},{"arxiv_id":"1710.00814","paper":"/paper/detecting-adversarial-attacks-on-neural","title":"Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight","date":"2017-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yenchenlin/rl-attack-detection","path":"baselines/common/tf_util.py","file_url":"https://github.com/yenchenlin/rl-attack-detection/blob/HEAD/baselines/common/tf_util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"3dc7f82ede7de679","mcp_get_code":{"code_sha256":"3dc7f82ede7de679"}},{"arxiv_id":"1706.02275","paper":"/paper/multi-agent-actor-critic-for-mixed","title":"Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments","date":"2017-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baoqianwang/iros22_darl1n","path":"maddpg_o/maddpg_local/trainer/maddpg.py","file_url":"https://github.com/baoqianwang/iros22_darl1n/blob/HEAD/maddpg_o/maddpg_local/trainer/maddpg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8ebadf88476f8efe","mcp_get_code":{"code_sha256":"8ebadf88476f8efe"}},{"arxiv_id":"1703.07737","paper":"/paper/in-defense-of-the-triplet-loss-for-person-re","title":"In Defense of the Triplet Loss for Person Re-Identification","date":"2017-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AsuradaYuci/tripletreid-zhushi","path":"aggregators.py","file_url":"https://github.com/AsuradaYuci/tripletreid-zhushi/blob/HEAD/aggregators.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d7c7885af720b02","mcp_get_code":{"code_sha256":"3d7c7885af720b02"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shanglianlm0525/CvPytorch","path":"src/models/unet.py","file_url":"https://github.com/shanglianlm0525/CvPytorch/blob/HEAD/src/models/unet.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80b5a150c6e092f7","mcp_get_code":{"code_sha256":"80b5a150c6e092f7"}},{"arxiv_id":"openreview_g9G7qyAzki","paper":null,"title":"arXiv:openreview_g9G7qyAzki","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"The-Inscrutable-X/CalibratedModelAgnosticCorrectness","path":"utils/calib_tools.py","file_url":"https://github.com/The-Inscrutable-X/CalibratedModelAgnosticCorrectness/blob/HEAD/utils/calib_tools.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":"0af29c87aa4bd6a8","mcp_get_code":{"code_sha256":"0af29c87aa4bd6a8"}},{"arxiv_id":"aaai_28395","paper":null,"title":"arXiv:aaai_28395","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yanzq95/SGNet","path":"thops.py","file_url":"https://github.com/yanzq95/SGNet/blob/HEAD/thops.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e5786fcae2918427","mcp_get_code":{"code_sha256":"e5786fcae2918427"}},{"arxiv_id":"aaai_16202","paper":null,"title":"arXiv:aaai_16202","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"3dpose/GnTCN","path":"util/evaluate.py","file_url":"https://github.com/3dpose/GnTCN/blob/HEAD/util/evaluate.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"63cf7ec10b164550","mcp_get_code":{"code_sha256":"63cf7ec10b164550"}},{"arxiv_id":"2025.naacl-long.237","paper":null,"title":"arXiv:2025.naacl-long.237","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"nbasyl/LLM-FP4","path":"lm_eval/metrics.py","file_url":"https://github.com/nbasyl/LLM-FP4/blob/HEAD/lm_eval/metrics.py","status":"ran_violates","verification_level":2,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0928f497e20fb443","mcp_get_code":{"code_sha256":"0928f497e20fb443"}}]}