{"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/add-diffusion-noise","entry":"add_diffusion_noise","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":5,"n_papers_ran":0,"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":1,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":1},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2502.13146","paper":"/paper/re-align-aligning-vision-language-models-via","title":"Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization","date":"2025-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taco-group/re-align","path":"train_rdpo.py","file_url":"https://github.com/taco-group/re-align/blob/HEAD/train_rdpo.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":"fe66de066c32d0fc","mcp_get_code":{"code_sha256":"fe66de066c32d0fc"}},{"arxiv_id":"2412.06474","paper":"/paper/from-uncertainty-to-trust-enhancing","title":"From Uncertainty to Trust: Enhancing Reliability in Vision-Language Models with Uncertainty-Guided Dropout Decoding","date":"2024-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kigb/DropoutDecoding","path":"models/VCD/vcd_add_noise.py","file_url":"https://github.com/kigb/DropoutDecoding/blob/HEAD/models/VCD/vcd_add_noise.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fe66de066c32d0fc","mcp_get_code":{"code_sha256":"fe66de066c32d0fc"}},{"arxiv_id":"2403.18715","paper":"/paper/mitigating-hallucinations-in-large-vision","title":"Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"p1k0pan/ICD","path":"icd_utils/vcd_add_noise.py","file_url":"https://github.com/p1k0pan/ICD/blob/HEAD/icd_utils/vcd_add_noise.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":"fe66de066c32d0fc","mcp_get_code":{"code_sha256":"fe66de066c32d0fc"}},{"arxiv_id":"2311.16922","paper":"/paper/mitigating-object-hallucinations-in-large","title":"Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"damo-nlp-sg/vcd","path":"vcd_utils/vcd_add_noise.py","file_url":"https://github.com/damo-nlp-sg/vcd/blob/HEAD/vcd_utils/vcd_add_noise.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":"fe66de066c32d0fc","mcp_get_code":{"code_sha256":"fe66de066c32d0fc"}},{"arxiv_id":"Yang_Nullu_Mitigating_Object_Hallucinations_in_Large_Vision-Language_Models_via_HalluSpace_CVPR_2025_paper","paper":null,"title":"arXiv:Yang_Nullu_Mitigating_Object_Hallucinations_in_Large_Vision-Language_Models_via_HalluSpace_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Ziwei-Zheng/Nullu","path":"model/LLaVA.py","file_url":"https://github.com/Ziwei-Zheng/Nullu/blob/HEAD/model/LLaVA.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe66de066c32d0fc","mcp_get_code":{"code_sha256":"fe66de066c32d0fc"}}]}