{"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/encode-text-with-prompt-ensemble","entry":"encode_text_with_prompt_ensemble","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":2,"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":3,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":6,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"unverified":2},"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":"2503.18325","paper":"/paper/towards-training-free-anomaly-detection-with","title":"Towards Training-free Anomaly Detection with Vision and Language Foundation Models","date":"2025-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhang0jhon/LogSAD","path":"prompt_ensemble.py","file_url":"https://github.com/zhang0jhon/LogSAD/blob/HEAD/prompt_ensemble.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d6cbed724d58041a","mcp_get_code":{"code_sha256":"d6cbed724d58041a"}},{"arxiv_id":"2412.17619","paper":"/paper/kernel-aware-graph-prompt-learning-for-few","title":"Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection","date":"2024-12-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvl-hub/kag-prompt","path":"code/model/openllama.py","file_url":"https://github.com/cvl-hub/kag-prompt/blob/HEAD/code/model/openllama.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b7db790f54abfb17","mcp_get_code":{"code_sha256":"b7db790f54abfb17"}},{"arxiv_id":"2310.19070","paper":"/paper/myriad-large-multimodal-model-by-applying","title":"Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection","date":"2023-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tzjtatata/myriad","path":"minigpt4/models/adexpert.py","file_url":"https://github.com/tzjtatata/myriad/blob/HEAD/minigpt4/models/adexpert.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b7db790f54abfb17","mcp_get_code":{"code_sha256":"b7db790f54abfb17"}},{"arxiv_id":"2308.15939","paper":"/paper/anovl-adapting-vision-language-models-for","title":"Bootstrap Fine-Grained Vision-Language Alignment for Unified Zero-Shot Anomaly Localization","date":"2023-08-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hq-deng/AnoVL","path":"prompt_ensemble.py","file_url":"https://github.com/hq-deng/AnoVL/blob/HEAD/prompt_ensemble.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06c5062b292c220a","mcp_get_code":{"code_sha256":"06c5062b292c220a"}},{"arxiv_id":"2308.15366","paper":"/paper/anomalygpt-detecting-industrial-anomalies","title":"AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models","date":"2023-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"casia-iva-lab/anomalygpt","path":"code/model/openllama.py","file_url":"https://github.com/casia-iva-lab/anomalygpt/blob/HEAD/code/model/openllama.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b7db790f54abfb17","mcp_get_code":{"code_sha256":"b7db790f54abfb17"}},{"arxiv_id":"2305.17382","paper":"/paper/a-zero-few-shot-anomaly-classification-and","title":"APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD","date":"2023-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bychelsea/vand-april-gan","path":"prompt_ensemble.py","file_url":"https://github.com/bychelsea/vand-april-gan/blob/HEAD/prompt_ensemble.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d6cbed724d58041a","mcp_get_code":{"code_sha256":"d6cbed724d58041a"}}]}