[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84708-en":3,"doc-seo-84708-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84708,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Conductance-Repair Evidence Graphs for Prospective Security Retrieval","Security retrieval is commonly treated as ranking over complete evidence, yet real triage is prospective: CVE descriptions, weakness metadata, fix commits, EPSS scores, KEV membership, validation-vector metadata, and side-channel benchmark routes often arrive through separate channels and may be missing, delayed, poisoned, or visible only after the decision time. The work proposes conductance-repair evidence graphs using a temporal admissibility mask and deterministic graph-flow recurrence to widen missing channels. It provides adaptive identification bounds, NP-hardness for minimum harmful repair, and certified search bounds for questionable channels. Experiments on public security records quantify recall and precision changes under random edge withholding.","arXiv :2607 .04070v 1 [ cs .CR] 5 Jul 2026  \nConductance-Repair Evidence Graphs for Prospective Security Retrieval  \nFaruk Alpay 1 ∗ Taylan Alpay2  \n1 Department of Computer Engineering, Bahçeşehir University, Istanbul, Türkiye  \n2 Department of Aerospace, University of Turkish Aeronautical Association, Ankara, Türkiye  \n[faruk.alpay@bahcesehir.edu.tr](faruk.alpay@bahcesehir.edu.tr) [s220112602@stu.thk.edu.tr](s220112602@stu.thk.edu.tr)  \nAbstract  \nSecurity retrieval is usually evaluated as ranking over complete evidence, but operational triage is prospective: CVE descriptions, weakness metadata, fix commits, EPSS scores, KEV membership, validation-vector metadata, and side-channel benchmark routes arrive through separate channels, and many are missing, delayed, poisoned, or visible only after the decision time. We introduce conductance-repair evidence graphs, a timestamped framework in which retrieval is performed over a temporal admissibility mask and missing channels are widened by a deterministic graph-flow recurrence rather than by a learned predictor. The method emits arepair certificate recording source probes, decision time, withheld edges, repaired channels, forbidden post-decision edges, backend availability, numerical deviation, and verifier results. The theoretical layer gives an adaptive ⌈log2 N⌉ lower bound for missing-channel identification, an NP-hardness result for minimum harmful repair, and a fixed-parameter certified search bound for q questionable channels. The current artifact materializes 30 deduplicated public security records, 57 terms, and 58 withheld admissible document–term edges. Under random edge withholding, conductance repair changes recall@k from 0.017 to 0.069 and average precision from 0.062 to 0.060, while a synthetic security fixture improves recall@k from 0.055 to 0.099; the public AP drop exposes a limit of broad admissible repair under random edge corruption rather than a reason to abandon channel-level repair. The implementation benchmarks the same flow/SVD/einsum kernel under NumPy, PyTorch, JAX, and TensorFlow when available, recording unavailable backends rather than silently substituting them. BBBC019 and LIVECell metadata are retained only as structural controls for sparse evolving source channels, with no clinical or biological performance claim.  \n1 Introduction  \nPublic security evidence is not a single document stream. A vulnerability may first appear ina CVE record, later receive a severity vector, then appear in an exploited-vulnerability catalog, then acquire exploit code, vendor statements, fix commits, proof-of-concept discussions, validation vectors, or side-channel traces. A retrieval system that sees all of this evidence at once can rank well while being useless for a decision that had to be made earlier.  \nThis paper studies the narrower problem of prospective security retrieval. At a decision time τ(v) , a system receives only evidence whose timestamp is no later than τ(v) . Some channels are missing for ordinary reasons, such as sparse vendor metadata. Others are missing for adversarial reasons,  \nsuch as delayed disclosure, poisoned keywords, or post-label chatter that must be excluded. We ∗ Corresponding author: [alpay@lightcap.ai](alpay@lightcap.ai).  \nask which missing channels can be repaired without leaking future evidence and without pretending that semantic similarity alone is evidence of exploitation.  \nThe proposed object is a conductance-repair evidence graph. CVEs, documents, weakness classes, validation sources, and benchmark sources are nodes. Edges carry timestamps, source layers, and admissibility flags. Repair is not a classifier. It is a graph-flow operation that increases conductance along already admissible neighborhoods when a bounded budget says a channel may be recoverable. The same code path also emits a certificate: which edges were withheld in the benchmark, which public routes were probed, which backend ran, and whether TensorFlow, PyTorch","cbCaigaESfKNGOBo","https://ap.wps.com/l/cbCaigaESfKNGOBo","pdf",516559,1,13,"English","en",105,"# Abstract\n# Introduction\n# Related Sources","[{\"question\":\"What makes security retrieval “prospective” in this paper?\",\"answer\":\"At decision time τ(v), the system receives only evidence with timestamps no later than τ(v). Evidence channels may be missing due to sparsity or adversarial effects like delayed disclosure or poisoned keywords.\"},{\"question\":\"How does the proposed conductance-repair method repair missing evidence channels?\",\"answer\":\"Repair is performed as a graph-flow operation over an evidence graph, increasing conductance along already admissible neighborhoods under a bounded budget. It also emits a certificate describing withheld edges, probed routes, and which backend actually executed the kernel.\"},{\"question\":\"What theoretical and practical results are reported?\",\"answer\":\"The paper provides an adaptive ⌈log2 N⌉ lower bound for missing-channel identification, an NP-hardness result for minimum harmful repair, and a fixed-parameter certified search bound for a small questionable-channel set. It benchmarks deterministic flow/SVD/einsum kernels across NumPy, PyTorch, JAX, and TensorFlow and reports changes in recall@k and average precision under random edge withholding.\"}]",1784197763,33,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"conductance-repair-evidence-graphs-for-prospective-security-retrieval","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/conductance-repair-evidence-graphs-for-prospective-security-retrieval/84708/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What makes security retrieval “prospective” in this paper?","Question",{"text":75,"@type":76},"At decision time τ(v), the system receives only evidence with timestamps no later than τ(v). Evidence channels may be missing due to sparsity or adversarial effects like delayed disclosure or poisoned keywords.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed conductance-repair method repair missing evidence channels?",{"text":80,"@type":76},"Repair is performed as a graph-flow operation over an evidence graph, increasing conductance along already admissible neighborhoods under a bounded budget. It also emits a certificate describing withheld edges, probed routes, and which backend actually executed the kernel.",{"name":82,"@type":73,"acceptedAnswer":83},"What theoretical and practical results are reported?",{"text":84,"@type":76},"The paper provides an adaptive ⌈log2 N⌉ lower bound for missing-channel identification, an NP-hardness result for minimum harmful repair, and a fixed-parameter certified search bound for a small questionable-channel set. 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