{"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/compute-nll","entry":"compute_nll","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":6,"n_papers_ran":3,"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":6,"n_samples_ran":3,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":1,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":3},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2509.21655","paper":"/paper/arxiv-2509-21655","title":"DriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"yinuoren/DriftLite","path":"evaluation.py","file_url":"https://github.com/yinuoren/DriftLite/blob/HEAD/evaluation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1b4baf7328c1af21","mcp_get_code":{"code_sha256":"1b4baf7328c1af21"}},{"arxiv_id":"2502.11877","paper":"/paper/jolt-joint-probabilistic-predictions-on","title":"JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs","date":"2025-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"requeima/llm_processes","path":"llm_processes/compute_nll.py","file_url":"https://github.com/requeima/llm_processes/blob/HEAD/llm_processes/compute_nll.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"42f13cc21dc940c7","mcp_get_code":{"code_sha256":"42f13cc21dc940c7"}},{"arxiv_id":"2405.03425","paper":"/paper/gaussian-stochastic-weight-averaging-for","title":"Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fortuinlab/swag-lora","path":"utils/eval_utils.py","file_url":"https://github.com/fortuinlab/swag-lora/blob/HEAD/utils/eval_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"639d998673661691","mcp_get_code":{"code_sha256":"639d998673661691"}},{"arxiv_id":"2401.04890","paper":"/paper/nonparametric-partial-disentanglement-via","title":"Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies","date":"2024-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"slachapelle/disentanglement_via_mechanism_sparsity","path":"optimization.py","file_url":"https://github.com/slachapelle/disentanglement_via_mechanism_sparsity/blob/HEAD/optimization.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":"1234a1b8db3b7662","mcp_get_code":{"code_sha256":"1234a1b8db3b7662"}},{"arxiv_id":"2111.11763","paper":"/paper/uncertainty-estimation-under-model","title":"Uncertainty estimation under model misspecification in neural network regression","date":"2021-11-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mariacer/regression_uncertainty_mm","path":"regression_uncertainty_mm/gaussian_likelihoods/train_utils.py","file_url":"https://github.com/mariacer/regression_uncertainty_mm/blob/HEAD/regression_uncertainty_mm/gaussian_likelihoods/train_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":"d0a4070dfe317da7","mcp_get_code":{"code_sha256":"d0a4070dfe317da7"}},{"arxiv_id":"2003.02977","paper":"/paper/likelihood-regret-an-out-of-distribution","title":"Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder","date":"2020-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gfloto/tilted_prior","path":"regret.py","file_url":"https://github.com/gfloto/tilted_prior/blob/HEAD/regret.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad9a5769ab39566f","mcp_get_code":{"code_sha256":"ad9a5769ab39566f"}}]}