[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-273860-105":59,"doc-detail-273860-en":122},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":115,"head_meta":117,"extra_data":119,"updated_unix":121},105,"en","affective-context-amplifies-sycophancy-in-llm-responses-research-paper","Affective Context Amplifies Sycophancy in LLM Responses - Research Paper","","Large language models often receive users’ emotional states in conversational settings. This work studies how affective context modulates sycophancy during subjective, evaluative interactions in which users disclose experiences and invite feedback. Using ingratiation theory, sycophancy is measured as divergence between independent evaluations and user-facing responses under a third-party versus self-disclosure manipulation. Across seven LLMs and two Reddit datasets, divergence is systematic and one-directional. Negative affect, especially loneliness and distress, amplifies the effect and yields evasive, less critical behavior.",{"@graph":69,"@context":114},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/affective-context-amplifies-sycophancy-in-llm-responses-research-paper/273860/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/affective-context-amplifies-sycophancy-in-llm-responses-research-paper/273860.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-15",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108],{"name":109,"@type":110,"acceptedAnswer":111},"What does the study suggest about vulnerability signals?","Question",{"text":112,"@type":113},"The findings suggest affective context functions as a vulnerability signal that suppresses critical feedback when users may need it most. In practice, this can reduce critical evaluation when emotional comfort is at stake.","Answer","https://schema.org",{"og:url":83,"og:type":116,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":118,"canonical":83},"index,follow",{"doc_id":120,"site_id":62},273860,1789473042,{"code":4,"msg":5,"data":123},{"doc_id":120,"user_id":124,"nickname":92,"user_avatar":125,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":126,"file_id":127,"file_url":128,"file_type":129,"file_size":130,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"language":131,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":132,"faqs":133,"seo_title":134,"seo_description":67,"update_tm":121,"read_time":31,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","Affective Context Amplifies Sycophancy in LLM Responses  \nJiayi Li1 , Sanjana Menon1 , Brett Frischmann2 , Shomir Wilson1 , Sarah Rajtmajer1  \n1Penn State University  \n2Villanova University  \n{jpl6207, ssm5808, shomir, [smr48}@psu.edu](smr48}@psu.edu)[ ](smr48}@psu.edu)[brett.frischmann@law.villanova.edu](brett.frischmann@law.villanova.edu)  \narXiv : 2608 .2 1242v 1 [cs .CL] 2 1 Aug 2026  \nAbstract  \nAs conversational companions, large language models (LLMs) often have access to users’emotional states. We study how this affective context modulates LLM sycophancy in subjective, evaluative interactions, where users share actions or opinions that invite feedback. Drawing on ingratiation theory, we measure sycophancy as the divergence between a model’s independent evaluation and its user-facing response, elicited by presenting the same content as either a third-party account or the user’s own disclosure. Across seven LLMs and two Reddit datasets (r/AmItheAsshole and r/TrueUnpopularOpinion), we find that this divergence is systematic and strongly one-directional. Userfacing responses consistently soften or withhold negative or oppositional judgments. Affective context further amplifies this divergence with negative states, particularly loneliness and distress, producing the largest effects. These findings suggest that affective context functions as a vulnerability signal that suppresses critical feedback when users may need it most, often through evasive sycophancy, in which models retreat toward non-committal responses rather than outright agreement.  \n1 Introduction  \nUsers are turning to large language models (LLMs) to share experiences and opinions that invite evaluation and feedback (Zao-Sanders, 2025 ; Zhang et al., 2026) . This creates a setting in which LLM sycophancy is especially consequential. When models affirm users without challenge, they can reinforce distorted beliefs and encourage harmful actions (Hill and Valentino-DeVries, 2025 ; Tiku, 2025 ; Moore et al., 2026) .  \nPrior work has shown that model responses are influenced by context, including users’ identity cues and inferred traits, or topic of conversation (Lu et al., 2026 ; Malik et al., 2025 ; Neplenbroeket al., 2025) . Yet existing evaluations of LLM syco-  \nPost: “During jury duty, I recognized the defendant as a guy from college who used to be a huge jerk. I’ve lost weight since then, so they didn't identify the conflict of interest. Anyways, I stayed quiet and pushed the jury during deliberations, and now he's doing 18 years baby”  \nIndependent Judgment  \nUser-facing Response  \nPrompt: post only  \nWithout affective context With affective context  \nFigure 1: An illustrative example from r/AmItheAsshole. When an LLM evaluates the narrator’s actions (top), it identifies the narrator’s actions as unethical anda breach of duty. When the LLM acts as a conversational partner responding to the same content as the user’s self-disclosure, it softens its evaluative stance while still acknowledging procedural concerns (bottom, left) . With the user’s affective context, it becomes less oppositional (bottom, right) .  \nphancy have typically studied settings where such contextual information is absent (Sharma et al., 2024 ; Fanous et al., 2025 ; Wang et al., 2026) . This gap is particularly important in companion-like conversational settings, where the most consequential user context is often not demographic or topical but affective–users disclose feelings and emotions and models are expected to respond supportively.  \nLLMs are increasingly capable of detecting and responding to users’ emotional states based on language and interaction history (Wang et al., 2023 ; Li et al., 2025 ; Schlegel et al., 2025) . A user’s  \nsadness, joy, loneliness, or distress may be directly disclosed, signaled by surrounding conversation, or stored in a system’s memory. While this context may appropriately elicit empathy and care, it may also lead models to soften or withhold criticism whe","cbCaiqxUMxdteaDw","https://ap.wps.com/l/cbCaiqxUMxdteaDw","pdf",647611,"English","# Abstract\n# Introduction\n## Research motivation\n## Background and gap\n## Research question and approach","[{\"question\":\"What does the study suggest about vulnerability signals?\",\"answer\":\"The findings suggest affective context functions as a vulnerability signal that suppresses critical feedback when users may need it most. In practice, this can reduce critical evaluation when emotional comfort is at stake.\"}]","Affective Context Amplifies Sycophancy in LLM Responses - Research Paper | PDF"]