[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83882-en":3,"doc-seo-83882-105":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},83882,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","When Words Predict Workload","Standard distributed schedulers for LLM inference rely on static token counts or rolling latency averages, which can fail for rigid, legally constrained text registers such as European Patent Office (EPO) claims. Such ambiguity forces mid-flight escalation into heavy multi-model ensembles, causing unpredictable KV-cache and weight-allocation spikes that saturate edge VRAM and trigger OOM crashes and queue stalls. A CPU-side Linguistic Resource Forecasting (LRF) gateway extracts 16 text-structure features, predicts escalation probability via XGBoost, and routes using a dynamic closed-form threshold recomputed per request from latency telemetry. In a 6,000-request trial it lowers misroutes to 0.087–0.095, with edge VRAM bounded at 4.82GiB (8GiB), AUROC 0.84, and a 8.2% relative reduction versus a static threshold.","When Words Predict Workload  \nAnubhab Banerjee  \nNokia Germany E-mail: [anubhab.1.banerjee@nokia.com](anubhab.1.banerjee@nokia.com).  \narXiv :2607 .0495 1v 1 [ cs .DC] 6 Jul 2026  \nAbstract—Standard distributed schedulers for Large Language Model (LLM) inference rely on static token counts or rolling latency averages, making them susceptible to failures arising from statutorily constrained text or other linguistics properties. For example, on European Patent Office (EPO) claims, governed by the of Article 84 European Patent Convention (EPC), properties like rigidity make human and machine authorship are statistically indistinguishable. Resolving this ambiguity mid-flight forces the pipeline to dynamically expand into a heavy multi-model ensemble, triggering unpredictable KV-cache and weight-allocation spikes that saturate the VRAM ceiling of consumer-grade edge accelerators and cause severe out of memory (OOM) crashes and queue stalls. To prevent this hardware collapse, we propose a CPU-side Linguistic Resource Forecasting (LRF) gateway that extracts a 16-dimensional vector of text-structure features and processes them through an XGBoost predictor to forecast trapband membership. The resulting escalation probability (Pescalate ) is evaluated against a dynamic, closed-form routing threshold (τθ (t)), which is recomputed per request using real-time latency telemetry. Crucially, the gateway safely routes requests to either the local Qwen2.5-7B edge worker or a remote Binocularsstyle contrastive ensemble (Qwen2.5 7B + 32B) on an NVIDIA H100 before any edge GPU memory is unnecessarily allocated. In a 6,000-request live trial, the LRF gateway reduced the operational misroute fraction (Rmis) to 0.087–0.095—an order of magnitude below the token-count baseline (0 .849). Peak edge VRAM remained safely bounded at 4.82GiB (out of 8GiB) across a 27× variation in wide area network (WAN) conditions. The XGBoost predictor achieved a live-trial AUROC of 0.84, while the dynamic τθ (t) delivered an 8.2% relative reduction in misroutes compared to an equivalent static threshold.  \nIndex Terms—Distributed systems, edge–cloud routing, LLM inference, perplexity, linguistic feature extraction, XGBoost, GPU memory management, heterogeneous accelerators.  \nI. INTRODUCTION PRODUCTION serving stacks for LLMs (e.g., vLLM,  \nTriton) predominantly manage computational resources through proxy heuristics like static token counts and rollinglatency averages. These heuristics are predicated on the assumption that the resource footprint of an LLM request is primarily a function of its length, rather than its semantic content. In open-domain natural language workloads, this assumption is statistically sound, as attention mechanisms and KV-cache growth scale linearly with sequence length, making“tokens” a reliable representation for “workload.”  \nThis work has been submitted to the IEEE for possible publication. Personal use of this material is permitted. Permission from the author must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Copyright may be transferred without notice, after which this version may no longer be accessible.  \nHowever, this dependency on structural proxies creates problems in domains characterized by rigid, statutorilyconstrained text registers. EPO patent claims, for example, are legally compelled to follow narrow, prescriptive, rigid templates, strict antecedent-basis rules, and chained subordinate clauses. Our previous work [1] identified that these registers create a “perplexity trap”, where lightweight LLMs produce statistically indistinguishable likelihoods for human-authored and AI-rewritten text, causing even sophisticated detectors (e.g., Binoculars [2], DivScore [3]) to collapse.  \nT","cbCaiaLtMiuTIMnT","https://ap.wps.com/l/cbCaiaLtMiuTIMnT","pdf",1439118,1,15,"English","en",105,"# Abstract\n# Introduction\n## Contributions","[{\"question\":\"Why do token-count or rolling-latency schedulers fail in certain LLM workloads?\",\"answer\":\"Because some domains use rigid, statutorily constrained text registers where semantic structure can make workload-relevant properties diverge from what token length predicts, leading to unreliable resource estimation.\"},{\"question\":\"What problem does the paper identify as the root operational risk?\",\"answer\":\"A “perplexity trap” that creates ambiguity between human-authored and AI-rewritten text, which in turn triggers mid-flight escalation, causing VRAM and latency spikes that destabilize edge systems.\"},{\"question\":\"How does the proposed LRF gateway prevent edge GPU memory collapse?\",\"answer\":\"It forecasts hardware escalation probability using a CPU pipeline with 16-dimensional linguistic features and an XGBoost predictor, then applies a dynamic routing threshold based on real-time latency telemetry before allocating edge GPU memory.\"}]",1784191196,38,{"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},"when-words-predict-workload","",{"@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/when-words-predict-workload/83882/",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","2026-07-17","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},"Why do token-count or rolling-latency schedulers fail in certain LLM workloads?","Question",{"text":75,"@type":76},"Because some domains use rigid, statutorily constrained text registers where semantic structure can make workload-relevant properties diverge from what token length predicts, leading to unreliable resource estimation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the paper identify as the root operational risk?",{"text":80,"@type":76},"A “perplexity trap” that creates ambiguity between human-authored and AI-rewritten text, which in turn triggers mid-flight escalation, causing VRAM and latency spikes that destabilize edge systems.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed LRF gateway prevent edge GPU memory collapse?",{"text":84,"@type":76},"It forecasts hardware escalation probability using a CPU pipeline with 16-dimensional linguistic features and an XGBoost predictor, then applies a dynamic routing threshold based on real-time latency telemetry before allocating edge GPU memory.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":45,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":45,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":45,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]