{"id":"https://openalex.org/W7164515896","doi":"https://doi.org/10.1145/3800506.3803501","title":"Practical Type Inference: High-Throughput Recovery of Real-World Structures and Function Signatures","display_name":"Practical Type Inference: High-Throughput Recovery of Real-World Structures and Function Signatures","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7164515896","doi":"https://doi.org/10.1145/3800506.3803501"},"language":null,"primary_location":{"id":"doi:10.1145/3800506.3803501","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3800506.3803501","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteenth ACM Conference on Data and Application Security and Privacy","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3800506.3803501","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047683360","display_name":"Lukas Seidel","orcid":"https://orcid.org/0009-0006-8190-0300"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lukas Seidel","raw_affiliation_strings":["Binarly, Inc., Santa Monica, USA and TU Berlin, Berlin, Germany"],"raw_orcid":"https://orcid.org/0009-0006-8190-0300","affiliations":[{"raw_affiliation_string":"Binarly, Inc., Santa Monica, USA and TU Berlin, Berlin, Germany","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113954326","display_name":"Sam Thomas","orcid":null},"institutions":[{"id":"https://openalex.org/I4210138817","display_name":"Pranalytica (United States)","ror":"https://ror.org/04rpteb42","country_code":"US","type":"company","lineage":["https://openalex.org/I4210138817"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sam L. Thomas","raw_affiliation_strings":["Binarly, Inc., Santa Monica, USA"],"raw_orcid":"https://orcid.org/0000-0002-4321-1129","affiliations":[{"raw_affiliation_string":"Binarly, Inc., Santa Monica, USA","institution_ids":["https://openalex.org/I4210138817"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066077721","display_name":"Konrad Rieck","orcid":"https://orcid.org/0000-0002-5054-8758"},"institutions":[{"id":"https://openalex.org/I4210088352","display_name":"Berliner Institut f\u00fcr Sozialforschung","ror":"https://ror.org/0043ncq27","country_code":"DE","type":"facility","lineage":["https://openalex.org/I4210088352"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Konrad Rieck","raw_affiliation_strings":["TU Berlin, Berlin, Germany and BIFOLD, Berlin, Germany"],"raw_orcid":"https://orcid.org/0000-0002-5054-8758","affiliations":[{"raw_affiliation_string":"TU Berlin, Berlin, Germany and BIFOLD, Berlin, Germany","institution_ids":["https://openalex.org/I4210088352"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.94105844,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"243","last_page":"254"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.04520000144839287,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.04520000144839287,"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"}},{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.04439999908208847,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.024700000882148743,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/type","display_name":"Type (biology)","score":0.447299987077713},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.44020000100135803},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.32089999318122864},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.2872999906539917},{"id":"https://openalex.org/keywords/signature","display_name":"Signature (topology)","score":0.273499995470047}],"concepts":[{"id":"https://openalex.org/C2777299769","wikidata":"https://www.wikidata.org/wiki/Q3707858","display_name":"Type (biology)","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4194999933242798},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35199999809265137},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3517000079154968},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.32089999318122864},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2872999906539917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2775999903678894},{"id":"https://openalex.org/C2779696439","wikidata":"https://www.wikidata.org/wiki/Q7512811","display_name":"Signature (topology)","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3800506.3803501","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3800506.3803501","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteenth ACM Conference on Data and Application Security and Privacy","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3800506.3803501","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3800506.3803501","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixteenth ACM Conference on Data and Application Security and Privacy","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2000484728","https://openalex.org/W2156858199","https://openalex.org/W2302344383","https://openalex.org/W2892187814","https://openalex.org/W3153698034","https://openalex.org/W4384159111","https://openalex.org/W4391725269","https://openalex.org/W4400583079","https://openalex.org/W4402264058","https://openalex.org/W4405183032","https://openalex.org/W4410610032","https://openalex.org/W4411337271","https://openalex.org/W4414564554"],"related_works":[],"abstract_inverted_index":{"The":[0],"recovery":[1],"of":[2,93,124,139],"types":[3],"from":[4],"stripped":[5],"binaries":[6],"is":[7,49,58,114],"a":[8,169],"key":[9],"to":[10,30,90,164,181],"exact":[11],"decompilation,":[12],"yet":[13],"its":[14],"practical":[15],"realization":[16],"suffers.":[17],"For":[18],"composite":[19],"structures":[20],"in":[21,61,85,96,116,186],"particular,":[22],"both":[23],"layout":[24],"and":[25,78,108],"semantic":[26],"fidelity":[27],"are":[28],"required":[29],"enable":[31],"end-to-end":[32],"reconstruction.":[33],"Many":[34],"existing":[35],"approaches":[36],"either":[37],"synthesize":[38],"layouts":[39],"or":[40],"infer":[41],"names":[42],"post-hoc,":[43],"which":[44],"weakens":[45],"downstream":[46],"usability.":[47],"This":[48],"further":[50],"aggravated":[51],"by":[52,146],"an":[53,67,129],"excessive":[54],"runtime":[55],"overhead":[56],"that":[57,71,159,177],"especially":[59],"prohibitive":[60],"automated":[62,86],"environments.":[63],"We":[64],"present":[65],"XTRIDE,":[66],"improved":[68],"n-gram-based":[69,160],"approach":[70,180],"focuses":[72],"on":[73,142,172],"practicality:":[74],"highly":[75],"optimized":[76,130],"throughput":[77],"actionable":[79],"confidence":[80],"scores":[81],"allow":[82],"for":[83],"deployment":[84],"pipelines.":[87],"When":[88],"compared":[89],"the":[91,94,121,136,140,143],"state":[92,138],"art":[95,141],"struct":[97,126],"recovery,":[98],"our":[99,112,133],"method":[100],"achieves":[101],"comparable":[102],"performance":[103],"while":[104],"being":[105],"between":[106],"70":[107],"2300x":[109],"faster.":[110],"As":[111],"inference":[113,153],"grounded":[115],"real-world":[117],"types,":[118],"we":[119,157,175],"achieve":[120],"highest":[122],"ratio":[123],"fully-correct":[125],"layouts.":[127],"With":[128],"training":[131],"regimen,":[132],"model":[134],"outperforms":[135],"current":[137],"DIRT":[144],"dataset":[145],"5.09":[147],"percentage":[148],"points,":[149],"achieving":[150],"90.15%":[151],"type":[152,161],"accuracy":[154],"overall.":[155],"Furthermore,":[156],"show":[158,176],"prediction":[162],"generalizes":[163],"function":[165,182],"signature":[166],"recovery:":[167],"conducting":[168],"case":[170],"study":[171],"embedded":[173],"firmware,":[174],"this":[178],"efficient":[179],"similarity":[183],"can":[184],"assist":[185],"typical":[187],"reverse":[188],"engineering":[189],"tasks.":[190]},"counts_by_year":[],"updated_date":"2026-06-14T06:11:07.267592","created_date":"2026-06-13T00:00:00"}