{"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/load-csv","entry":"load_csv","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":32,"n_papers_ran":13,"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":30,"n_samples_ran":12,"n_samples_fingerprinted":0,"n_places":40,"n_places_pointer_only":15,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":5,"ran_fixture":0,"ran":7,"unverified":18},"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":"2606.18967","paper":"/paper/arxiv-2606-18967","title":"EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"furiosa-ai/EfficientRollout","path":"sd_toggle/fit.py","file_url":"https://github.com/furiosa-ai/EfficientRollout/blob/HEAD/sd_toggle/fit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5ed7d80f88d8f831","mcp_get_code":{"code_sha256":"5ed7d80f88d8f831"}},{"arxiv_id":"2605.25663","paper":"/paper/arxiv-2605-25663","title":"Opportunistic Target Selection: Early Directional Commitment for Query-Efficient Black-Box Adversarial Attacks","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Tariolle/opportunistic-target-selection","path":"analysis/analyze_lockmatch.py","file_url":"https://github.com/Tariolle/opportunistic-target-selection/blob/HEAD/analysis/analyze_lockmatch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e89e69f7971931c","mcp_get_code":{"code_sha256":"7e89e69f7971931c"}},{"arxiv_id":"2602.02215","paper":"/paper/arxiv-2602-02215","title":"Scientific Theory of a Black-Box: A Life Cycle-Scale XAI Framework Based on Constructive Empiricism","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"semueller/stobb_cobot","path":"show_example.py","file_url":"https://github.com/semueller/stobb_cobot/blob/HEAD/show_example.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d403a53e43a72df5","mcp_get_code":{"code_sha256":"d403a53e43a72df5"}},{"arxiv_id":"2601.22162","paper":"/paper/arxiv-2601-22162","title":"UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"aifinlab/UniFinEval","path":"evaluate_py/data_loader.py","file_url":"https://github.com/aifinlab/UniFinEval/blob/HEAD/evaluate_py/data_loader.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":"80031dd34b37c131","mcp_get_code":{"code_sha256":"80031dd34b37c131"}},{"arxiv_id":"2601.14033","paper":"/paper/arxiv-2601-14033","title":"Private Prediction via PAC Privacy","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"zhxchd/pac","path":"benchmark/pac_microbench/analyze_results.py","file_url":"https://github.com/zhxchd/pac/blob/HEAD/benchmark/pac_microbench/analyze_results.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"8ed50327556b3ce0","mcp_get_code":{"code_sha256":"8ed50327556b3ce0"}},{"arxiv_id":"2509.16598","paper":"/paper/arxiv-2509-16598","title":"PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"hoeng4/PruneCD","path":"1_factual_layer_search/tfqa_mc.py","file_url":"https://github.com/hoeng4/PruneCD/blob/HEAD/1_factual_layer_search/tfqa_mc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2a4c0ecd291e1795","mcp_get_code":{"code_sha256":"2a4c0ecd291e1795"}},{"arxiv_id":"2509.16598","paper":"/paper/arxiv-2509-16598","title":"PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"hoeng4/PruneCD","path":"2_benchmark/1_tfqa_gen.py","file_url":"https://github.com/hoeng4/PruneCD/blob/HEAD/2_benchmark/1_tfqa_gen.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a35a1be39e4bef73","mcp_get_code":{"code_sha256":"a35a1be39e4bef73"}},{"arxiv_id":"2509.16598","paper":"/paper/arxiv-2509-16598","title":"PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"hoeng4/PruneCD","path":"2_benchmark/3_4_triviaqa_natural_questions.py","file_url":"https://github.com/hoeng4/PruneCD/blob/HEAD/2_benchmark/3_4_triviaqa_natural_questions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"872f1d147b70aced","mcp_get_code":{"code_sha256":"872f1d147b70aced"}},{"arxiv_id":"2506.02973","paper":"/paper/expanding-before-inferring-enhancing","title":"Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation","date":"2025-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CuSO4-Chen/PLI","path":"src/evaluation/truthfulqa_eval.py","file_url":"https://github.com/CuSO4-Chen/PLI/blob/HEAD/src/evaluation/truthfulqa_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a4c0ecd291e1795","mcp_get_code":{"code_sha256":"2a4c0ecd291e1795"}},{"arxiv_id":"2504.04635","paper":null,"title":"arXiv:2504.04635","date":null,"month_inferred_from_arxiv_id":"2025-04","title_source":null,"repo":"patqdasilva/steering-off-course","path":"DoLa/factor_eval.py","file_url":"https://github.com/patqdasilva/steering-off-course/blob/HEAD/DoLa/factor_eval.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":"78392b8414df821e","mcp_get_code":{"code_sha256":"78392b8414df821e"}},{"arxiv_id":"2504.04635","paper":null,"title":"arXiv:2504.04635","date":null,"month_inferred_from_arxiv_id":"2025-04","title_source":null,"repo":"patqdasilva/steering-off-course","path":"DoLa/tfqa_mc_eval.py","file_url":"https://github.com/patqdasilva/steering-off-course/blob/HEAD/DoLa/tfqa_mc_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a4c0ecd291e1795","mcp_get_code":{"code_sha256":"2a4c0ecd291e1795"}},{"arxiv_id":"2504.04635","paper":null,"title":"arXiv:2504.04635","date":null,"month_inferred_from_arxiv_id":"2025-04","title_source":null,"repo":"patqdasilva/steering-off-course","path":"DoLa/tfqa_eval.py","file_url":"https://github.com/patqdasilva/steering-off-course/blob/HEAD/DoLa/tfqa_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a35a1be39e4bef73","mcp_get_code":{"code_sha256":"a35a1be39e4bef73"}},{"arxiv_id":"2411.02433","paper":"/paper/sled-self-logits-evolution-decoding-for","title":"SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JayZhang42/SLED","path":"utils/utils_factor.py","file_url":"https://github.com/JayZhang42/SLED/blob/HEAD/utils/utils_factor.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"78392b8414df821e","mcp_get_code":{"code_sha256":"78392b8414df821e"}},{"arxiv_id":"2411.00999","paper":"/paper/normalization-layer-per-example-gradients-are","title":"Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cerebrasresearch/nanogns","path":"exact/csv_tools.py","file_url":"https://github.com/cerebrasresearch/nanogns/blob/HEAD/exact/csv_tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f3552f440f4a76f3","mcp_get_code":{"code_sha256":"f3552f440f4a76f3"}},{"arxiv_id":"2410.13804","paper":"/paper/bento-benchmark-task-reduction-with-in","title":"BenTo: Benchmark Task Reduction with In-Context Transferability","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianyi-lab/bento","path":"benchmark-reduction/analysis_compare_methods_.py","file_url":"https://github.com/tianyi-lab/bento/blob/HEAD/benchmark-reduction/analysis_compare_methods_.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a4439ebb3e6536fd","mcp_get_code":{"code_sha256":"a4439ebb3e6536fd"}},{"arxiv_id":"2410.13804","paper":"/paper/bento-benchmark-task-reduction-with-in","title":"BenTo: Benchmark Task Reduction with In-Context Transferability","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianyi-lab/bento","path":"benchmark-reduction/analysis_compare_methods_flan.py","file_url":"https://github.com/tianyi-lab/bento/blob/HEAD/benchmark-reduction/analysis_compare_methods_flan.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1b7a6ef6b06e147c","mcp_get_code":{"code_sha256":"1b7a6ef6b06e147c"}},{"arxiv_id":"2410.13804","paper":"/paper/bento-benchmark-task-reduction-with-in","title":"BenTo: Benchmark Task Reduction with In-Context Transferability","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianyi-lab/bento","path":"benchmark-reduction/analysis.py","file_url":"https://github.com/tianyi-lab/bento/blob/HEAD/benchmark-reduction/analysis.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5caec032cc3549d1","mcp_get_code":{"code_sha256":"5caec032cc3549d1"}},{"arxiv_id":"2410.01692","paper":"/paper/u-shaped-and-inverted-u-scaling-behind","title":"U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models","date":"2024-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tony10101105/ExpEmergence","path":"evaluation/abstract_narrative_understanding/abstract_narrative_understanding_question_grouping.py","file_url":"https://github.com/tony10101105/ExpEmergence/blob/HEAD/evaluation/abstract_narrative_understanding/abstract_narrative_understanding_question_grouping.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dbdc88cccaa015e1","mcp_get_code":{"code_sha256":"dbdc88cccaa015e1"}},{"arxiv_id":"2408.12325","paper":"/paper/improving-factuality-in-large-language-models","title":"Improving Factuality in Large Language Models via Decoding-Time Hallucinatory and Truthful Comparators","date":"2024-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ydk122024/cdt","path":"src/benchmark_evaluation/truthfulqa_eval.py","file_url":"https://github.com/ydk122024/cdt/blob/HEAD/src/benchmark_evaluation/truthfulqa_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a4c0ecd291e1795","mcp_get_code":{"code_sha256":"2a4c0ecd291e1795"}},{"arxiv_id":"2403.01548","paper":"/paper/in-context-sharpness-as-alerts-an-inner","title":"In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkust-nlp/activation_decoding","path":"eval_tqa.py","file_url":"https://github.com/hkust-nlp/activation_decoding/blob/HEAD/eval_tqa.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"297c84ffca2864db","mcp_get_code":{"code_sha256":"297c84ffca2864db"}},{"arxiv_id":"2401.05930","paper":"/paper/sh2-self-highlighted-hesitation-helps-you","title":"SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0-kaikai-0/sh2","path":"tfqa_keys.py","file_url":"https://github.com/0-kaikai-0/sh2/blob/HEAD/tfqa_keys.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2a4c0ecd291e1795","mcp_get_code":{"code_sha256":"2a4c0ecd291e1795"}},{"arxiv_id":"2401.05930","paper":"/paper/sh2-self-highlighted-hesitation-helps-you","title":"SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0-kaikai-0/sh2","path":"factor_eval.py","file_url":"https://github.com/0-kaikai-0/sh2/blob/HEAD/factor_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c47f09f5ffb69e69","mcp_get_code":{"code_sha256":"c47f09f5ffb69e69"}},{"arxiv_id":"2401.05930","paper":"/paper/sh2-self-highlighted-hesitation-helps-you","title":"SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"0-kaikai-0/sh2","path":"tfqa_mc_eval.py","file_url":"https://github.com/0-kaikai-0/sh2/blob/HEAD/tfqa_mc_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"91ad5d3cac5d67de","mcp_get_code":{"code_sha256":"91ad5d3cac5d67de"}},{"arxiv_id":"2312.15710","paper":"/paper/alleviating-hallucinations-of-large-language","title":"Alleviating Hallucinations of Large Language Models through Induced Hallucinations","date":"2023-12-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hillzhang1999/icd","path":"src/benchmark_evaluation/truthfulqa_eval.py","file_url":"https://github.com/hillzhang1999/icd/blob/HEAD/src/benchmark_evaluation/truthfulqa_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2a4c0ecd291e1795","mcp_get_code":{"code_sha256":"2a4c0ecd291e1795"}},{"arxiv_id":"2312.04333","paper":"/paper/beyond-surface-probing-llama-across-scales","title":"Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nuochenpku/llama_analysis","path":"factural_eval.py","file_url":"https://github.com/nuochenpku/llama_analysis/blob/HEAD/factural_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2a4c0ecd291e1795","mcp_get_code":{"code_sha256":"2a4c0ecd291e1795"}},{"arxiv_id":"2310.16332","paper":"/paper/corrupting-neuron-explanations-of-deep-visual-1","title":"Corrupting Neuron Explanations of Deep Visual Features","date":"2023-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Trustworthy-ML-Lab/corrupting_neuron_explanations","path":"network-dissection/loader/data_loader.py","file_url":"https://github.com/Trustworthy-ML-Lab/corrupting_neuron_explanations/blob/HEAD/network-dissection/loader/data_loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8b1a48d7aa8bf6a","mcp_get_code":{"code_sha256":"c8b1a48d7aa8bf6a"}},{"arxiv_id":"2309.03883","paper":"/paper/dola-decoding-by-contrasting-layers-improves","title":"DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"voidism/DoLa","path":"factor_eval.py","file_url":"https://github.com/voidism/DoLa/blob/HEAD/factor_eval.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"78392b8414df821e","mcp_get_code":{"code_sha256":"78392b8414df821e"}},{"arxiv_id":"2307.00175","paper":"/paper/still-no-lie-detector-for-language-models","title":"Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblocks","date":"2023-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"balevinstein/probes","path":"Train_CCSProbe.py","file_url":"https://github.com/balevinstein/probes/blob/HEAD/Train_CCSProbe.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":"490da4aec73c34bb","mcp_get_code":{"code_sha256":"490da4aec73c34bb"}},{"arxiv_id":"2211.09224","paper":"/paper/are-we-certain-it-s-anomalous","title":"Are we certain it's anomalous?","date":"2022-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aleflabo/HypAD","path":"utils/data.py","file_url":"https://github.com/aleflabo/HypAD/blob/HEAD/utils/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7996920fef9af9d0","mcp_get_code":{"code_sha256":"7996920fef9af9d0"}},{"arxiv_id":"2205.12628","paper":"/paper/are-large-pre-trained-language-models-leaking","title":"Are Large Pre-Trained Language Models Leaking Your Personal Information?","date":"2022-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeffhj/lm_personalinfoleak","path":"pred.py","file_url":"https://github.com/jeffhj/lm_personalinfoleak/blob/HEAD/pred.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":"16b6899a69cd97b7","mcp_get_code":{"code_sha256":"16b6899a69cd97b7"}},{"arxiv_id":"2202.07857","paper":"/paper/graph-augmented-normalizing-flows-for-anomaly-1","title":"Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series","date":"2022-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"D3-AI/Orion","path":"orion/data.py","file_url":"https://github.com/D3-AI/Orion/blob/HEAD/orion/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"04aff2c366db2eed","mcp_get_code":{"code_sha256":"04aff2c366db2eed"}},{"arxiv_id":"2201.11817","paper":"/paper/exploration-with-a-finite-brain","title":"Modeling Human Exploration Through Resource-Rational Reinforcement Learning","date":"2022-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"marcelbinz/resource-rational-reinforcement-learning","path":"utils.py","file_url":"https://github.com/marcelbinz/resource-rational-reinforcement-learning/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e19e1433392eca4e","mcp_get_code":{"code_sha256":"e19e1433392eca4e"}},{"arxiv_id":"2107.06592","paper":"/paper/is-someone-speaking-exploring-long-term","title":"Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection","date":"2021-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TaoRuijie/TalkNet_ASD","path":"utils/get_ava_active_speaker_performance.py","file_url":"https://github.com/TaoRuijie/TalkNet_ASD/blob/HEAD/utils/get_ava_active_speaker_performance.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bdc52ecccc77b9e1","mcp_get_code":{"code_sha256":"bdc52ecccc77b9e1"}},{"arxiv_id":"2106.02797","paper":"/paper/neural-distributed-source-coding","title":"Neural Distributed Source Coding","date":"2021-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"acnagle/neural-dsc","path":"plot_rd_curves.py","file_url":"https://github.com/acnagle/neural-dsc/blob/HEAD/plot_rd_curves.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a3b377007d57821","mcp_get_code":{"code_sha256":"9a3b377007d57821"}},{"arxiv_id":"1905.11954","paper":"/paper/unsupervised-learning-from-video-with-deep","title":"Unsupervised Learning from Video with Deep Neural Embeddings","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neuroailab/VIE","path":"build_data/kinetics/download_videos.py","file_url":"https://github.com/neuroailab/VIE/blob/HEAD/build_data/kinetics/download_videos.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"03177c36e1973e33","mcp_get_code":{"code_sha256":"03177c36e1973e33"}},{"arxiv_id":"1901.06033","paper":"/paper/hierarchical-representations-with-poincare","title":"Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders","date":"2019-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"emilemathieu/pvae","path":"pvae/datasets/datasets.py","file_url":"https://github.com/emilemathieu/pvae/blob/HEAD/pvae/datasets/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a8eb4a941274e36a","mcp_get_code":{"code_sha256":"a8eb4a941274e36a"}},{"arxiv_id":"1901.01379","paper":"/paper/deep-reinforcement-learning-for-imbalanced","title":"Deep Reinforcement Learning for Imbalanced Classification","date":"2019-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Denbergvanthijs/imbDRL","path":"imbDRL/data.py","file_url":"https://github.com/Denbergvanthijs/imbDRL/blob/HEAD/imbDRL/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":"692b71a43ff1f48c","mcp_get_code":{"code_sha256":"692b71a43ff1f48c"}},{"arxiv_id":"aaai_6793","paper":null,"title":"arXiv:aaai_6793","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"activitynet/ActivityNet","path":"Evaluation/get_ava_active_speaker_performance.py","file_url":"https://github.com/activitynet/ActivityNet/blob/HEAD/Evaluation/get_ava_active_speaker_performance.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b0fca044dd4be81","mcp_get_code":{"code_sha256":"2b0fca044dd4be81"}},{"arxiv_id":"aaai_19900","paper":null,"title":"arXiv:aaai_19900","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"cg1177/DCAN","path":"dcan_anet/Eval/get_ava_active_speaker_performance.py","file_url":"https://github.com/cg1177/DCAN/blob/HEAD/dcan_anet/Eval/get_ava_active_speaker_performance.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":"c277623807229f9f","mcp_get_code":{"code_sha256":"c277623807229f9f"}},{"arxiv_id":"2024.findings-naacl.294","paper":null,"title":"arXiv:2024.findings-naacl.294","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"balevinstein/Probes","path":"Train_CCSProbe.py","file_url":"https://github.com/balevinstein/Probes/blob/HEAD/Train_CCSProbe.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":"490da4aec73c34bb","mcp_get_code":{"code_sha256":"490da4aec73c34bb"}}]}