{"id":"https://openalex.org/W7204479990","doi":"https://doi.org/10.18653/2026.emnlp-main.1539","title":"Unlocking Multimodal Protein Language Models at Inference Time","display_name":"Unlocking Multimodal Protein Language Models at Inference Time","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7204479990","doi":"https://doi.org/10.18653/2026.emnlp-main.1539"},"language":"en","primary_location":{"id":"doi:10.18653/2026.emnlp-main.1539","doi":"https://doi.org/10.18653/2026.emnlp-main.1539","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.1539","pdf_url":"https://aclanthology.org/2026.emnlp-main.1539.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://aclanthology.org/2026.emnlp-main.1539.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148418868","display_name":"Yi Zhou","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yi Zhou","raw_affiliation_strings":["The Hong Kong Polytechnic University ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University ,","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120591928","display_name":"Qipeng Wang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang Qipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5148421395","display_name":"Yunqing Liu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Yunqing Liu","raw_affiliation_strings":["The Hong Kong Polytechnic University ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University ,","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126825953","display_name":"Jun Xia","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405261251","display_name":"The Hong Kong University of Science and Technology (Guangzhou)","ror":"https://ror.org/050h0vm43","country_code":"CN","type":"education","lineage":["https://openalex.org/I4405261251"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Xia","raw_affiliation_strings":["The Hong Kong University of Science and Technology (Guangzhou) ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong University of Science and Technology (Guangzhou) ,","institution_ids":["https://openalex.org/I4405261251"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115944791","display_name":"Qing Li","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5115944784","display_name":"Wenqi Fan","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Wenqi Fan","raw_affiliation_strings":["The Hong Kong Polytechnic University ,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Hong Kong Polytechnic University ,","institution_ids":["https://openalex.org/I14243506"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"35132","last_page":"35151"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.7157999873161316,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.7157999873161316,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.1282999962568283,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.04699999839067459,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier-free-guidance","display_name":"classifier-free guidance","score":0.984000027179718},{"id":"https://openalex.org/keywords/exploration-exploitation-trade-off","display_name":"exploration-exploitation balance","score":0.9340000152587891},{"id":"https://openalex.org/keywords/protein-structure-prediction","display_name":"protein structure prediction","score":0.6850000023841858},{"id":"https://openalex.org/keywords/protein-sequence-generation","display_name":"protein sequence generation","score":0.43700000643730164}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.786899983882904},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7379999756813049},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.642300009727478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6101999878883362},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5938000082969666},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5465999841690063},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4722999930381775},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2581000030040741}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/2026.emnlp-main.1539","doi":"https://doi.org/10.18653/2026.emnlp-main.1539","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.1539","pdf_url":"https://aclanthology.org/2026.emnlp-main.1539.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2608.25855","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.25855","pdf_url":"https://arxiv.org/pdf/2608.25855","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.25855","doi":"https://doi.org/10.48550/arxiv.2608.25855","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.25855","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/2026.emnlp-main.1539","doi":"https://doi.org/10.18653/2026.emnlp-main.1539","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.1539","pdf_url":"https://aclanthology.org/2026.emnlp-main.1539.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G4078110728","display_name":null,"funder_award_id":"P0048625","funder_id":"https://openalex.org/F4320322598","funder_display_name":"Hong Kong Polytechnic University"},{"id":"https://openalex.org/G8341220247","display_name":null,"funder_award_id":"72442017","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322598","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204479990.pdf","grobid_xml":"https://content.openalex.org/works/W7204479990.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W4288099666","https://openalex.org/W4390723295","https://openalex.org/W4406604520","https://openalex.org/W4407759400","https://openalex.org/W4414893824","https://openalex.org/W4415156421","https://openalex.org/W4415338278","https://openalex.org/W7090804293","https://openalex.org/W7127341207","https://openalex.org/W7147673913","https://openalex.org/W7162432644","https://openalex.org/W7196954196"],"related_works":["https://openalex.org/W7171721051","https://openalex.org/W4416540224","https://openalex.org/W7117260547","https://openalex.org/W7221353998","https://openalex.org/W7135091315","https://openalex.org/W4414817889","https://openalex.org/W4414340177","https://openalex.org/W7116210942","https://openalex.org/W4414968841","https://openalex.org/W4396975484"],"abstract_inverted_index":{"Multimodal":[0],"protein":[1,7],"language":[2],"models":[3,132],"(pLMs)":[4],"learn":[5],"joint":[6],"sequence-structure":[8],"distributions,":[9,80],"and":[10,58,68,83,102],"their":[11],"generation":[12],"performance":[13,119],"should":[14],"also":[15],"depend":[16],"critically":[17],"on":[18,27,31,72,90],"inference-time":[19,33],"sampling":[20,79,105],"strategies.Yet":[21],"prior":[22,136],"work":[23],"has":[24],"focused":[25],"more":[26],"model":[28,125],"training":[29],"than":[30],"how":[32],"strategies":[34],"behave.In":[35],"this":[36],"paper,":[37],"we":[38,93],"establish":[39],"a":[40],"three-stage":[41],"investigation":[42],"framework":[43],"to":[44,76],"empirically":[45],"study":[46],"the":[47,86,96,116],"inference":[48,100],"design":[49],"space":[50],"of":[51,98,120],"multimodal":[52,73,121],"pLMs":[53,57,122],"across":[54,112],"three":[55],"representative":[56],"four":[59],"fundamental":[60],"tasks.We":[61],"evaluate":[62],"vanilla":[63],"sampling,":[64],"task-specific":[65],"classifier-free":[66],"guidance,":[67],"reward-guided":[69],"beam":[70],"search":[71],"pLMs,":[74],"corresponding":[75],"controls":[77],"over":[78],"per-step":[81],"logits,":[82],"parallel":[84],"trajectories.Throughout":[85],"complementary":[87],"advancements":[88],"centered":[89],"explorationexploitation":[91],"trade-off,":[92],"(1)":[94],"reveal":[95],"suboptimality":[97],"default":[99],"protocols":[101],"identify":[103],"task-oriented":[104],"preferences;":[106],"(2)":[107],"observe":[108],"substantial":[109],"quantitative":[110],"gains":[111],"tasks,":[113],"consistently":[114],"boosting":[115],"upper":[117],"bound":[118],"without":[123],"updating":[124],"parameters;":[126],"(3)":[127],"derive":[128],"conclusions":[129],"about":[130],"base":[131],"that":[133],"differ":[134],"from":[135],"consensus.Our":[137],"code":[138],"is":[139],"available":[140],"at:":[141],"github.com/EchoChou990919/mplm_inference.":[142]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-08-28T00:00:00"}