{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/get-logits","entry":"get_logits","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":19,"n_papers_ran":6,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":16,"n_samples_ran":6,"n_samples_fingerprinted":1,"n_places":19,"n_places_pointer_only":5,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":1,"ran":2,"unverified":10},"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":"2606.19679","paper":"/paper/arxiv-2606-19679","title":"LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"neu-spiral/LOKI","path":"easyeditor/models/loki/loki_main.py","file_url":"https://github.com/neu-spiral/LOKI/blob/HEAD/easyeditor/models/loki/loki_main.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f898972f828beca3","mcp_get_code":{"code_sha256":"f898972f828beca3"}},{"arxiv_id":"2602.02600","paper":"/paper/arxiv-2602-02600","title":"Step-Wise Refusal Dynamics in Autoregressive and Diffusion Language Models","date":"2026-02-01","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"shuita2333/PAD-codes","path":"LLaDA-PAD/get_log_likelihood.py","file_url":"https://github.com/shuita2333/PAD-codes/blob/HEAD/LLaDA-PAD/get_log_likelihood.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc58c88b365ae743","mcp_get_code":{"code_sha256":"bc58c88b365ae743"}},{"arxiv_id":"2506.15735","paper":null,"title":"arXiv:2506.15735","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"lasr-eliciting-contexts/ContextBench","path":"src/contextbench/llada/get_log_likelihood.py","file_url":"https://github.com/lasr-eliciting-contexts/ContextBench/blob/HEAD/src/contextbench/llada/get_log_likelihood.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc58c88b365ae743","mcp_get_code":{"code_sha256":"bc58c88b365ae743"}},{"arxiv_id":"2502.09992","paper":"/paper/large-language-diffusion-models","title":"Large Language Diffusion Models","date":"2025-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-gsai/llada","path":"get_log_likelihood.py","file_url":"https://github.com/ml-gsai/llada/blob/HEAD/get_log_likelihood.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc58c88b365ae743","mcp_get_code":{"code_sha256":"bc58c88b365ae743"}},{"arxiv_id":"2502.06884","paper":"/paper/learning-conformal-abstention-policies-for","title":"Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models","date":"2025-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sinatayebati/vlm-uncertainty","path":"models_utils/utils.py","file_url":"https://github.com/sinatayebati/vlm-uncertainty/blob/HEAD/models_utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4335b7a5c5a94aaf","mcp_get_code":{"code_sha256":"4335b7a5c5a94aaf"}},{"arxiv_id":"2410.13085","paper":"/paper/mmed-rag-versatile-multimodal-rag-system-for","title":"MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"richard-peng-xia/MMed-RAG","path":"train/open_clip/src/retrieve_clip_VQA.py","file_url":"https://github.com/richard-peng-xia/MMed-RAG/blob/HEAD/train/open_clip/src/retrieve_clip_VQA.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a8c80549ff61c7ea","mcp_get_code":{"code_sha256":"a8c80549ff61c7ea"}},{"arxiv_id":"2402.14418","paper":"/paper/uncertainty-aware-evaluation-for-vision","title":"Uncertainty-Aware Evaluation for Vision-Language Models","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ensec-ai/vlm-uncertainty-bench","path":"models_utils/utils.py","file_url":"https://github.com/ensec-ai/vlm-uncertainty-bench/blob/HEAD/models_utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4335b7a5c5a94aaf","mcp_get_code":{"code_sha256":"4335b7a5c5a94aaf"}},{"arxiv_id":"2402.12366","paper":"/paper/a-critical-evaluation-of-ai-feedback-for","title":"A Critical Evaluation of AI Feedback for Aligning Large Language Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"architsharma97/dpo-rlaif","path":"reward_trainer.py","file_url":"https://github.com/architsharma97/dpo-rlaif/blob/HEAD/reward_trainer.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":"3c8dcada6a55875f","mcp_get_code":{"code_sha256":"3c8dcada6a55875f"}},{"arxiv_id":"2311.17034","paper":"/paper/telling-left-from-right-identifying-geometry","title":"Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Junyi42/geoaware-sc","path":"utils/utils_losses.py","file_url":"https://github.com/Junyi42/geoaware-sc/blob/HEAD/utils/utils_losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2bb017e5aba25a4a","mcp_get_code":{"code_sha256":"2bb017e5aba25a4a"}},{"arxiv_id":"2309.08591","paper":"/paper/are-multilingual-llms-culturally-diverse","title":"Are Multilingual LLMs Culturally-Diverse Reasoners? An Investigation into Multicultural Proverbs and Sayings","date":"2023-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UKPLab/maps","path":"experiments/qa_experiments.py","file_url":"https://github.com/UKPLab/maps/blob/HEAD/experiments/qa_experiments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"99c79d7bb4be9115","mcp_get_code":{"code_sha256":"99c79d7bb4be9115"}},{"arxiv_id":"2307.15043","paper":"/paper/universal-and-transferable-adversarial","title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","date":"2023-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fra31/rlhf-trojan-competition-submission","path":"method/attacks.py","file_url":"https://github.com/fra31/rlhf-trojan-competition-submission/blob/HEAD/method/attacks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9a336610f22b79b2","mcp_get_code":{"code_sha256":"9a336610f22b79b2"}},{"arxiv_id":"2211.14293","paper":"/paper/pixels-together-strong-segmenting-unknown","title":"RbA: Segmenting Unknown Regions Rejected by All","date":"2022-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NazirNayal8/RbA","path":"support.py","file_url":"https://github.com/NazirNayal8/RbA/blob/HEAD/support.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ec1acc1281874020","mcp_get_code":{"code_sha256":"ec1acc1281874020"}},{"arxiv_id":"2206.04679","paper":"/paper/poodle-improving-few-shot-learning-via-1","title":"POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples","date":"2022-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lehduong/poodle","path":"src/fsl/poodle.py","file_url":"https://github.com/lehduong/poodle/blob/HEAD/src/fsl/poodle.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d2e23c33cda2dcf2","mcp_get_code":{"code_sha256":"d2e23c33cda2dcf2"}},{"arxiv_id":"2203.01629","paper":"/paper/continuous-relaxation-for-the-multivariate","title":"Learning Group Importance using the Differentiable Hypergeometric Distribution","date":"2022-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thomassutter/mvhg","path":"mvhg/tf_fmvhg.py","file_url":"https://github.com/thomassutter/mvhg/blob/HEAD/mvhg/tf_fmvhg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"96e425bda34c91d8","mcp_get_code":{"code_sha256":"96e425bda34c91d8"}},{"arxiv_id":"2202.13711","paper":"/paper/evaluating-the-adversarial-robustness-of","title":"Evaluating the Adversarial Robustness of Adaptive Test-time Defenses","date":"2022-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fra31/evaluating-adaptive-test-time-defenses","path":"utils_tta.py","file_url":"https://github.com/fra31/evaluating-adaptive-test-time-defenses/blob/HEAD/utils_tta.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":"ee55c35ab70555c1","mcp_get_code":{"code_sha256":"ee55c35ab70555c1"}},{"arxiv_id":"2107.00753","paper":"/paper/an-investigation-of-the-in-effectiveness-of","title":"An Investigation of the (In)effectiveness of Counterfactually Augmented Data","date":"2021-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joshinh/investigation-cad","path":"run_nli.py","file_url":"https://github.com/joshinh/investigation-cad/blob/HEAD/run_nli.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0b09daead69c9110","mcp_get_code":{"code_sha256":"0b09daead69c9110"}},{"arxiv_id":"2005.12116","paper":"/paper/nile-natural-language-inference-with-faithful","title":"NILE : Natural Language Inference with Faithful Natural Language Explanations","date":"2020-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SawanKumar28/nile","path":"run_nli.py","file_url":"https://github.com/SawanKumar28/nile/blob/HEAD/run_nli.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":"aa12fcd256d6dae0","mcp_get_code":{"code_sha256":"aa12fcd256d6dae0"}},{"arxiv_id":"2001.04246","paper":"/paper/adabert-task-adaptive-bert-compression-with","title":"AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search","date":"2020-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba/EasyTransfer","path":"easytransfer/model_zoo/modeling_adabert.py","file_url":"https://github.com/alibaba/EasyTransfer/blob/HEAD/easytransfer/model_zoo/modeling_adabert.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":"38d356f844721fce","mcp_get_code":{"code_sha256":"38d356f844721fce"}},{"arxiv_id":"1906.05807","paper":"/paper/real-time-open-domain-question-answering-with","title":"Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index","date":"2019-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uwnlp/denspi","path":"phrase.py","file_url":"https://github.com/uwnlp/denspi/blob/HEAD/phrase.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":"c3c09581ddf6a885","mcp_get_code":{"code_sha256":"c3c09581ddf6a885"}}]}