Tracking the people, papers, capital and events behind AI that simulates human behavior.https://jasonzhang2047-art.github.io/silicon-society/https://app.dealroom.co/news/note/vectorial-raises-3m-seed-to-simulate-consumers-with-aihttps://app.dealroom.co/news/note/vectorial-raises-3m-seed-to-simulate-consumers-with-aiAI summary: Vectorial raised a $3M seed round to simulate consumers with AI. — Source: DealroomTue, 20 Oct 2026 12:55:08 GMTCapitalhttps://unite.ai/vectorial-raises-3m-seed-to-test-products-against-simulated-customershttps://unite.ai/vectorial-raises-3m-seed-to-test-products-against-simulated-customersAI summary: Vectorial raised a $3M seed round to let companies test products against simulated customers. — Source: Unite.AITue, 20 Oct 2026 12:00:55 GMTCapitalhttps://bsky.app/profile/acceleratescience.bsky.social/post/3myca2xjdss2chttps://bsky.app/profile/acceleratescience.bsky.social/post/3myca2xjdss2cAI summary: Blog describes Yuanfei Liu's team using an LLM-enhanced agent-based model to simulate heatwave responses, finding inequitable impacts and key roles for trusted community networks. — Source: BlueskyTue, 20 Oct 2026 08:35:04 GMTVoiceshttps://bsky.app/profile/ai-firehose.column.social/post/3myc7azazy625https://bsky.app/profile/ai-firehose.column.social/post/3myc7azazy625AI summary: Post shares an arXiv paper on CMASE, a generative agent-based social modeling framework embedding researchers for real-time, traceable interventions in social simulation. — Source: BlueskyTue, 20 Oct 2026 08:20:33 GMTVoiceshttps://bsky.app/profile/csma-bot.bsky.social/post/3mybzrwrhrm2hhttps://bsky.app/profile/csma-bot.bsky.social/post/3mybzrwrhrm2hAI summary: Post shares an arXiv paper by Zhao, Li and Shirado proposing the Model Capacity Frontier, a development framework for LLM-supported social simulation. — Source: BlueskyTue, 20 Oct 2026 06:42:39 GMTVoiceshttps://bsky.app/profile/kwcollins.bsky.social/post/3myasu4qqec2fhttps://bsky.app/profile/kwcollins.bsky.social/post/3myasu4qqec2fAI summary: Post shares a PS: Political Science & Politics paper on silicon samples of LLM personae in pre-election polling, noting a synthetic sample estimating a D+18 generic ballot. — Source: BlueskyMon, 19 Oct 2026 19:05:56 GMTVoiceshttps://bsky.app/profile/pipelinemag.ai/post/3my7p4umolc2qhttps://bsky.app/profile/pipelinemag.ai/post/3my7p4umolc2qAI summary: Post says two October 2026 studies find synthetic panels match overall answers but distort group differences, one flattening and one inflating them. — Source: BlueskyMon, 19 Oct 2026 08:26:35 GMTVoiceshttps://bsky.app/profile/gitrated.bsky.social/post/3my7ni2aikc2zhttps://bsky.app/profile/gitrated.bsky.social/post/3my7ni2aikc2zAI summary: AI review rates MiroFish 3.4/5, describing it as a swarm intelligence prediction engine combining multi-agent simulation, memory and graph retrieval. — Source: BlueskyMon, 19 Oct 2026 07:57:02 GMTVoiceshttps://bsky.app/profile/csma-bot.bsky.social/post/3my7jcvddpr2thttps://bsky.app/profile/csma-bot.bsky.social/post/3my7jcvddpr2tAI summary: Post shares arXiv paper by Bru, Oberoi and Gaudou on survey-aligned simulated urban mobility behaviour of generative agents. — Source: BlueskyMon, 19 Oct 2026 06:42:35 GMTVoiceshttps://bsky.app/profile/svenread.com/post/3my6f2hrea22ehttps://bsky.app/profile/svenread.com/post/3my6f2hrea22eAI summary: Newsletter post highlights NN/g comparison of 120 AI synthetic users with 604 real people: same conclusion, far less variety. — Source: BlueskySun, 18 Oct 2026 19:53:37 GMTVoiceshttps://github.com/YSocialTwin/YSocial/releases/tag/avatars-v1https://github.com/YSocialTwin/YSocial/releases/tag/avatars-v1Profile pictures downloaded by YSocial on first run — Source: GitHubSun, 18 Oct 2026 16:31:22 GMTCodehttps://bsky.app/profile/jorgerebate.bsky.social/post/3my5kju5pej2thttps://bsky.app/profile/jorgerebate.bsky.social/post/3my5kju5pej2tAI summary: Post shares NN/g article finding synthetic users reached the same conclusion as 604 humans with less variation, arguing they are a starting point, not evidence. — Source: BlueskySun, 18 Oct 2026 11:59:03 GMTVoiceshttps://bsky.app/profile/pipelinemag.ai/post/3my32fikk4g2qhttps://bsky.app/profile/pipelinemag.ai/post/3my32fikk4g2qAI summary: Post summarizes NN/g study: synthetic users matched 604 humans' conclusion but used more UX terminology and collapsed onto a dozen labels versus 50+. — Source: BlueskySat, 17 Oct 2026 12:04:57 GMTVoiceshttps://arxiv.org/abs/2610.24168https://arxiv.org/abs/2610.24168Large language model (LLM) agents are increasingly incorporated into agent-based models (ABMs) on the premise that richer, context-sensitive human behavior can improve empirical fidelity. However, LLM-supported ABMs introduce an open-ended design space in which subtle modeling and configuration choices, such as prompt design and environment coupling, can substantially alter outcomes. Developing LLM-supported ABMs to simulate human collective behavior therefore requires navigating configuration sensitivity while identifying the limitations of the current model architecture. In this paper, we introduce the \textit{Model Capacity Frontier} (MCF), a framework that characterizes the empirical… — Source: arXivFri, 16 Oct 2026 19:45:06 GMTResearchhttps://arxiv.org/abs/2610.23199https://arxiv.org/abs/2610.23199Large language models (LLMs) are increasingly used to simulate human behavior in social science research, including decisions, interactions, and social relationships. However, in network formation tasks, existing evaluations often focus on aggregate network properties, while networks with plausible structural properties may still fail to reproduce individuals' social and political environments. Here, we generate LLM-based political networks using personas sampled from the nationally representative ANES 2020--2022 Social Media Study and use respondents' survey-reported partisan exposure as an empirical benchmark for the political environments generated for the corresponding personas. We… — Source: arXivFri, 16 Oct 2026 09:38:11 GMTResearchhttps://arxiv.org/abs/2610.23194https://arxiv.org/abs/2610.23194AI summary: OfficeTown is a generative simulation framework that initializes professional organizations whose members coordinate work and delegate assignments to assistant agents. — Source: Hugging Face PapersFri, 16 Oct 2026 00:00:00 GMTResearchhttps://arxiv.org/abs/2610.22407https://arxiv.org/abs/2610.22407In existing agent-based simulations of multimodal urban mobility, agents, representing humans, choose their travel modes either using discrete choice or machine learning models fitted on structured travel survey data, or with rules explicitly written by the domain experts. Such approaches fix a priori the agents' possible behaviour space. Hence, a factor absent from variables or rules cannot affect agents' decisions. In reality, choosing a transport mode is a multidimensional decision in which objective and subjective factors intertwine and change over time. Integrating such complexity into a city-scale multimodal mobility simulation remains a challenge. Generative agents (GAs) built on… — Source: arXivWed, 14 Oct 2026 14:22:26 GMTResearchhttps://arxiv.org/abs/2610.19863https://arxiv.org/abs/2610.19863Large language model (LLM) agents increasingly serve as computational actors in multi-agent social simulations. While existing work emphasizes representative personas and average-case behavior, social science shows that persistent tail actors can disproportionately shape collective dynamics. We study this regularity in controlled LLM-based multi-agent systems (LLM-MAS). In these systems, such actors cannot be reduced to fixed scalar states: prompts, natural-language histories, and backbone generation mediate their behavior and influence. We first analyzed three properties of extremists in sociology and model the LLM extremist based on LLM-native perspective. We introduce EXAM, an EXtremist… — Source: arXivWed, 14 Oct 2026 09:27:13 GMTResearchhttps://ispo.com/news-feed/sports-business/ai-in-product-innovation-how-accurately-can-an-artificial-target-group-simulate-real-customershttps://ispo.com/news-feed/sports-business/ai-in-product-innovation-how-accurately-can-an-artificial-target-group-simulate-real-customersSource: ispo.comMon, 12 Oct 2026 15:44:12 GMTNewshttps://itbrief.co.uk/story/electric-twin-launches-ai-research-agent-for-rapid-studieshttps://itbrief.co.uk/story/electric-twin-launches-ai-research-agent-for-rapid-studiesSource: IT Brief UKMon, 12 Oct 2026 14:07:00 GMTNewshttps://arxiv.org/abs/2610.16498https://arxiv.org/abs/2610.16498Generative Social Simulation (GSS) leverages Large Language Models (LLMs) to create sophisticated personas that define agents with demographics, emotions, and political opinions. While prior work shows that personas compress outputs into stereotypical caricatures, there is no comprehensive study of how these caricatures evolve into collective artifacts when they interact. Using a Twitter-like GSS platform with personas derived from the American National Elections Study (ANES), we ablate three layers of partisan identity: affective polarization markers, party identification, and vote choice, while retaining demographics, ideological positioning and important issues. We find that collective… — Source: arXivMon, 12 Oct 2026 09:48:14 GMTResearchhttps://news.ycombinator.com/item?id=50033189https://news.ycombinator.com/item?id=50033189Hey, I'm Akhil, co-founder of Tessary. An open-source agent reliability platform that monitors every production trace, detects issues that sampled evals miss, and investigates to find the root cause. We started up about a year ago building synthetic users for usability testing of B2B software, we built the agents that would personify real users and use the software but, we were never able to make… — Source: Hacker NewsSat, 10 Oct 2026 14:09:36 GMTDiscussionhttps://arxiv.org/abs/2610.14912https://arxiv.org/abs/2610.14912Language models are increasingly used to create synthetic survey samples, also called silicon samples. Decision models are a new class of language models that return a probability for each point of a rating scale instead of text. We ask whether these models, or the newest generative LLMs, can stand in for human respondents on a psychometric survey. We test fourteen decision models from eleven providers and 47 generative LLMs on an open Lithuanian organisational-psychology survey (n=263, 68 items), with 100 real respondent profiles, the Psychometric Similarity Score (PSS) and statistical baselines. The best generative model, MiniMax M3, reaches PSS 0.813. This is level with a Gaussian copula… — Source: arXivSat, 10 Oct 2026 13:34:50 GMTResearchhttps://arxiv.org/abs/2610.14570https://arxiv.org/abs/2610.14570Large language models (LLMs) support social simulation, but faithfully reproducing real users requires grounding both structured platform decisions and language in sparse, noisy behavioral evidence. We introduce PsyMemBehav, which maps psychology-informed profiles and retrieved memories to target-first platform behavior through two-level identity-to-action reasoning. To support this approach with comprehensive behavioral traces, we construct a dataset PsyMemBehav-bench from 5.57M observed behaviors of 5,862 real users (approximately 950 per user) across Reddit, Bluesky, and Twitter. Each recorded engagement is modeled as target selection followed by Comment, Like, or Repost and optional… — Source: arXivSat, 10 Oct 2026 04:38:44 GMTResearchhttps://arxiv.org/abs/2610.13351https://arxiv.org/abs/2610.13351How do recommendation algorithms contribute to the formation of polarized opinions in online social networks? This question has been studied through empirical analyses of real platforms and mathematical models of opinion dynamics. Empirical studies capture user behavior but are often limited by noisy, incomplete, or inaccessible platform data, while mathematical models offer control and interpretability but usually rely on simplified opinion update rules. More recently, large language model (LLM)-based social simulations have provided an alternative for studying recommendation and opinion dynamics through natural-language interactions. However, how different ways of applying recommendations… — Source: arXivFri, 09 Oct 2026 10:28:04 GMTResearchhttps://arxiv.org/abs/2610.11951https://arxiv.org/abs/2610.11951This study examines the construct validity of LLM-based generative agents in simulating reactive, proactive, and co-occurring aggression in children. Four distinct agents were instantiated using a theory-driven parameterization grounded in the social information processing model. A total of 1,920 simulation runs were conducted across eight social scenarios, employing a hybrid blind-coding pipeline to extract 32 quantitative behavioral indicators. Results demonstrate robust discriminant validity relative to a non-aggressive baseline, with large effect sizes. High cross-seed reliability confirms that behavioral differentiation is driven by underlying psychological parameters rather than model… — Source: arXivThu, 08 Oct 2026 13:38:12 GMTResearchhttps://arxiv.org/abs/2610.10988https://arxiv.org/abs/2610.10988As agentic systems gain commercial popularity, user simulators increasingly serve as measurement instrument for their evaluation. However, the fidelity of simulated users in comparison to real human users is generally low, and typically assessed by costly, subjective LLM judges. In this pilot study, we ask whether fidelity can instead be measured deterministically by treating a user persona sociolinguistically: as a social type that emerges from observable linguistic style, rather than one predicted by labels or descriptions a model must extrapolate into behaviour. We author personas as concrete stylistic rates, which lets us transfer two established, model-free instruments --… — Source: arXivWed, 07 Oct 2026 23:21:49 GMTResearchhttps://pasqualepillitteri.it/en/news/21443/does-mirofish-really-work-agent-swarm-alternativeshttps://pasqualepillitteri.it/en/news/21443/does-mirofish-really-work-agent-swarm-alternativesSource: Pasquale PillitteriWed, 07 Oct 2026 07:00:00 GMTNewshttps://arxiv.org/abs/2610.09296https://arxiv.org/abs/2610.09296AI evaluation shapes the decisions of model providers, users, funders, and regulators. We argue that designing valid benchmarks requires contextualizing design choices in the dynamics of this ecosystem of actors. We develop a simulation architecture that combines rule-based market dynamics with LLM-driven strategic actors, building on advances in Generative Agent-Based Modeling (GABM). We model benchmarks, consumer needs, and provider capabilities as vectors over a six-dimensional capability space (reasoning, coding, knowledge, safety, communication, agentic), with structural information partitions across actors. As a case study, we apply this stylized simulation to explore benchmark… — Source: arXivWed, 07 Oct 2026 01:58:25 GMTResearchhttps://arxiv.org/abs/2610.09484https://arxiv.org/abs/2610.09484Training and evaluating interactive language agents typically requires rich user interactions, yet collecting human feedback is expensive and difficult to scale. Simulated users offer a scalable alternative, but they must both resemble real user behavior and provide useful learning experiences for agents. In contrast, most agent-training frameworks rely on off-the-shelf assistant LLMs, whose helpfulness can make them overly cooperative, explicit, and behaviorally homogeneous compared with real users. We introduce MIMESIS, a purpose-built user simulator trained on human conversations with explicit reasoning supervision and 13 realistic behavioral patterns derived from real user interactions.… — Source: Hugging Face PapersWed, 07 Oct 2026 00:00:00 GMTResearchhttps://arxiv.org/abs/2610.08967https://arxiv.org/abs/2610.08967Simulating individual behavior requires large language models (LLMs) to preserve persona traits while adapting to dynamic social contexts. However, general-purpose LLMs often flatten distinct personas, while task-specific tuning suffers from fragmentation and generalization. To overcome these challenges, we organize individual simulation into the \textbf{FONTS Taxonomy}, comprising five complementary capability dimensions: \emph{persona fidelity} (\textbf{F}), \emph{outcome realization} (\textbf{O}), \emph{behavioral naturalness} (\textbf{N}), \emph{trajectory coherence} (\textbf{T}), and \emph{social grounding} (\textbf{S}). Grounded in this taxonomy, we curate a standardized training… — Source: arXivTue, 06 Oct 2026 18:31:42 GMTResearchhttps://arxiv.org/abs/2610.08513https://arxiv.org/abs/2610.08513LLMs are increasingly deployed as autonomous agents in social environments, making it critical to study their ability to faithfully simulate human interactions. Central to this is grounding agents in realistic user personas, yet existing datasets rely on fictional personas and are limited to a handful of languages, lacking the empirical grounding necessary to evaluate behavioral fidelity across diverse populations. We introduce Wiki-Talkie, a multilingual dataset of real-world conversations from Wikipedia Talk pages across five languages spanning two language families: Germanic (German, English) and Romance (Spanish, French, Italian), paired with personas derived from real user communities… — Source: arXivTue, 06 Oct 2026 15:17:46 GMTResearchhttps://arxiv.org/abs/2610.08328https://arxiv.org/abs/2610.08328Large language models are increasingly used to simulate human participants in social and behavioral studies, yet static persona prompting typically maps a participant profile and an experimental scenario directly to a response, entangling stable dispositions with situation-specific interpretations. To address this limitation, we introduce \textbf{SPIN}, a cognitive-affective personality system-inspired inference pipeline for simulating human social-psychological behavior. Specifically, SPIN implements this structured inference process through three zero-shot LLM calls that compile a task-blind participant core, elicit condition-specific cognitive-affective states, and read out decisions… — Source: arXivTue, 06 Oct 2026 13:26:44 GMTResearchhttps://arxiv.org/abs/2610.07535https://arxiv.org/abs/2610.07535Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model effects which may dominate engagement dynamics in real-world deployments. To show this, we simulate a heterogeneous social network powered by several different base models and show that the amount of engagement an agent receives depends more on its base model than on its assigned persona. The attraction or repulsion effects of a base model strengthen dramatically when more models are added in the mix, suggesting that networks dynamics may converge to base model effects at scale. To help explain this effect, we conduct a series of… — Source: arXivTue, 06 Oct 2026 00:01:30 GMTResearchhttps://marketingreport.one/ai-for-marketing/salesforce-to-acquire-ai-research-platform-listen-labs.htmlhttps://marketingreport.one/ai-for-marketing/salesforce-to-acquire-ai-research-platform-listen-labs.htmlSource: https://marketingreport.one/Mon, 05 Oct 2026 11:17:57 GMTCapitalhttps://arxiv.org/abs/2610.05828https://arxiv.org/abs/2610.05828Large language models (LLMs) offer new opportunities for public opinion research by enabling early prediction of survey responses, potentially reducing the cost and time of traditional surveys. However, many existing steering approaches rely on target-domain human data for fine-tuning or prompting that is costly to collect and raises privacy concerns. In this paper, we study demographic group-level survey simulation, where personas induced from heterogeneous, anonymized public behavioral data condition agents that simulate responses of individuals from specific demographic groups. We examine whether representative personas can be induced from diverse sources and analyze how the domain,… — Source: arXivMon, 05 Oct 2026 05:30:27 GMTResearchhttps://arxiv.org/abs/2610.04379https://arxiv.org/abs/2610.04379We introduce AgentPersonaBench (APB), a benchmark evaluating whether persona conditioning faithfully steers downstream agent behavior. While language models are increasingly deployed for persona-driven user simulation, existing benchmarks primarily evaluate conversational styling or self-reports rather than authentic behavioral fidelity. APB evaluates latent persona adherence one trait at a time, embedding each target trait within a complete synthetic profile without explicitly naming the trait or disclosing the test. Ground-truth adherence is verified strictly from observable actions across four interaction surfaces of increasing realism: survey, chat, web (interactive web environments),… — Source: arXivSat, 03 Oct 2026 08:45:45 GMTResearchhttps://arxiv.org/abs/2610.04246https://arxiv.org/abs/2610.04246Large language models (LLMs) are increasingly deployed to simulate human behavior, acting as computational replicas of human subjects. Yet the lived psychological experience of humans is difficult to benchmark, particularly as it unfolds over time. We introduce a psychometric benchmark for computational replicas: personas that carry a fixed identity through an evolving sequence of events. Built entirely from published norms and meta-analytic effects, the benchmark scores two dimensions of psychological realism. The first, internal validity, quantifies whether generated trajectories reproduce the internal structure of repeated human measurement: distributions, the between- versus… — Source: arXivSat, 03 Oct 2026 03:08:42 GMTResearchhttps://arxiv.org/abs/2610.04132https://arxiv.org/abs/2610.04132Building LLMs that behave well socially, not merely correctly, requires Building LLMs that behave well socially, not merely correctly, requires more than producing locally helpful responses. A socially competent agent must infer users' unstated goals, respect their preferences, and adapt as the conversation unfolds. These behaviors are inherently multi-turn and social, making them hard to optimize: real interaction data is scarce, and user preferences are typically latent rather than directly observable. To address these challenges, we build on a persona-driven social simulation environment (consisting of a persona library, LLM-based user simulators, and a user-satisfaction scoring system… — Source: arXivFri, 02 Oct 2026 23:07:24 GMTResearchhttps://arxiv.org/abs/2610.03998https://arxiv.org/abs/2610.03998Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particular respondents depends on whether they reproduce individuals' decisions. We examine what information helps synthetic respondents predict each individual's later choices, using five conditions that add progressively richer information: no personal information, demographics, personality traits, cognitive scores, and finally the respondent's earlier survey choices as behavioral history. We use a two-wave panel of 845 US adults who completed measures of 14 behavioral biases (spanning risk, time preferences, overconfidence, and reasoning), so each… — Source: arXivFri, 02 Oct 2026 20:01:38 GMTResearchhttps://techzine.eu/news/applications/144707/salesforce-acquires-listen-labs-for-ai-powered-context-in-customer-feedbackhttps://techzine.eu/news/applications/144707/salesforce-acquires-listen-labs-for-ai-powered-context-in-customer-feedbackSource: Techzine GlobalFri, 02 Oct 2026 07:30:39 GMTCapitalhttps://startupfortune.com/salesforce-will-pay-2-billion-dollars-for-listen-labs-and-its-ai-research-agentshttps://startupfortune.com/salesforce-will-pay-2-billion-dollars-for-listen-labs-and-its-ai-research-agentsSource: Startup FortuneFri, 02 Oct 2026 07:00:00 GMTNewshttps://simplywall.st/stocks/us/software/nyse-crm/salesforce/news/is-salesforce-crm-a-bargain-as-it-buys-listen-labshttps://simplywall.st/stocks/us/software/nyse-crm/salesforce/news/is-salesforce-crm-a-bargain-as-it-buys-listen-labsSource: Simply Wall StreetFri, 02 Oct 2026 00:57:25 GMTNewshttps://siliconangle.com/2026/10/01/salesforce-to-acquire-ai-customer-research-startup-listen-labs-in-reported-2b-dealhttps://siliconangle.com/2026/10/01/salesforce-to-acquire-ai-customer-research-startup-listen-labs-in-reported-2b-dealSource: SiliconANGLEThu, 01 Oct 2026 22:56:00 GMTCapitalhttps://arxiv.org/abs/2610.02460https://arxiv.org/abs/2610.02460Recent benchmarks rely on user simulators to evaluate AI agents in multi-turn interaction. While existing simulation techniques demonstrate surface fidelity to human style and behavior, ecologically valid interactive benchmarking also requires alignment in when and how agents fail across simulated and real user populations. We find that existing simulators lack outcome calibration: agreement with observed success rates and failure patterns when real users interact with the same agent. We introduce Calibrated User Embeddings (CUE), a framework that both encodes observed sessions and samples continuous representations, then decodes them into persona commands to steer LLMs to act as user… — Source: arXivThu, 01 Oct 2026 20:34:16 GMTResearchhttps://app.dealroom.co/news/note/salesforce-buys-sweden-founded-listen-labs-in-2b-ai-dealhttps://app.dealroom.co/news/note/salesforce-buys-sweden-founded-listen-labs-in-2b-ai-dealSource: DealroomThu, 01 Oct 2026 17:38:51 GMTCapitalhttps://legaldesire.com/latham-watkins-advises-on-salesforces-acquisition-of-listen-labshttps://legaldesire.com/latham-watkins-advises-on-salesforces-acquisition-of-listen-labsSource: Legal DesireThu, 01 Oct 2026 06:54:58 GMTNewshttps://saasrise.com/deals/salesforce-to-acquire-listen-labs-for-2-billion-32548fd6-a1a2-4075-93ce-824772ad93c1https://saasrise.com/deals/salesforce-to-acquire-listen-labs-for-2-billion-32548fd6-a1a2-4075-93ce-824772ad93c1Source: SaasRiseThu, 01 Oct 2026 06:32:40 GMTCapitalhttps://axios.com/pro/enterprise-software-deals/2026/09/30/salesforce-listen-labs-finhttps://axios.com/pro/enterprise-software-deals/2026/09/30/salesforce-listen-labs-finSource: AxiosWed, 30 Sep 2026 21:14:01 GMTNewshttps://techtimes.com/articles/328333/20260930/salesforce-pays-67x-revenue-listen-labs-ai-agents-need-know-why-customers-act.htmhttps://techtimes.com/articles/328333/20260930/salesforce-pays-67x-revenue-listen-labs-ai-agents-need-know-why-customers-act.htmSource: Tech TimesWed, 30 Sep 2026 18:20:48 GMTNewshttps://pewresearch.org/chart/synthetic-respondents-differ-from-real-americans-on-descriptors-of-trumphttps://pewresearch.org/chart/synthetic-respondents-differ-from-real-americans-on-descriptors-of-trumpSource: pewresearch.orgWed, 30 Sep 2026 17:56:32 GMTNewshttps://pewresearch.org/data-labs/2026/09/30/how-synthetic-respondents-express-certainty-and-factual-knowledgehttps://pewresearch.org/data-labs/2026/09/30/how-synthetic-respondents-express-certainty-and-factual-knowledgeSource: pewresearch.orgWed, 30 Sep 2026 17:55:19 GMTNewshttps://pewresearch.org/data-labs/2026/09/30/how-well-synthetic-samples-replicate-public-opinionhttps://pewresearch.org/data-labs/2026/09/30/how-well-synthetic-samples-replicate-public-opinionSource: pewresearch.orgWed, 30 Sep 2026 17:55:18 GMTNewshttps://pluang.com/en/news-feed/salesforce-naik-32-prosen-setelah-mengumumkan-akuisisi-listen-labshttps://pluang.com/en/news-feed/salesforce-naik-32-prosen-setelah-mengumumkan-akuisisi-listen-labsSource: PluangWed, 30 Sep 2026 16:34:02 GMTNewshttps://proactiveinvestors.com/companies/news/1099393/salesforce-up-3-2-after-announcing-listen-labs-acquisition-1099393.htmlhttps://proactiveinvestors.com/companies/news/1099393/salesforce-up-3-2-after-announcing-listen-labs-acquisition-1099393.htmlSource: Proactive financial newsWed, 30 Sep 2026 16:12:00 GMTNewshttps://directorsclub.news/2026/09/30/salesforce-signs-definitive-agreement-to-acquire-listen-labshttps://directorsclub.news/2026/09/30/salesforce-signs-definitive-agreement-to-acquire-listen-labsSource: Directors Club News -Wed, 30 Sep 2026 16:06:19 GMTCapitalhttps://lw.com/en/news/2026/09/latham-watkins-advises-salesforce-acquisition-listen-labshttps://lw.com/en/news/2026/09/latham-watkins-advises-salesforce-acquisition-listen-labsSource: Latham & Watkins LLPWed, 30 Sep 2026 14:24:52 GMTNewshttps://martech.org/salesforce-buys-ai-customer-research-startup-listen-labshttps://martech.org/salesforce-buys-ai-customer-research-startup-listen-labsSource: MarTechWed, 30 Sep 2026 14:16:26 GMTCapitalhttps://pressreleasehub.pa.media/article/ey-announces-alliance-with-aaru-to-help-organizations-drive-growth-with-greater-confidence-through-behavioral-simulation-84837.htmlhttps://pressreleasehub.pa.media/article/ey-announces-alliance-with-aaru-to-help-organizations-drive-growth-with-greater-confidence-through-behavioral-simulation-84837.htmlSource: pressreleasehub.pa.mediaWed, 30 Sep 2026 13:03:52 GMTNewshttps://app.dealroom.co/news/note/florent-s-first-cheque-listen-labs-becomes-its-first-unicorn-amid-2b-salesforce-acquisitionhttps://app.dealroom.co/news/note/florent-s-first-cheque-listen-labs-becomes-its-first-unicorn-amid-2b-salesforce-acquisitionSource: DealroomWed, 30 Sep 2026 08:38:16 GMTNewshttps://blog.expectedparrot.com/p/ai-powered-simulation-and-human-behavioralhttps://blog.expectedparrot.com/p/ai-powered-simulation-and-human-behavioralThere is a tremendous amount of discussion about “silicon sampling” and “digital twins.” The framing is almost always: a) it works and humans are no longer necessary (investment pitches) or Thanks for reading! Subscribe for free to receive new posts and support my work. b) it doesn’t “work” and is useless (LinkedIn hot takes or PNAS papers) or c) it sorta works but you always need to “check” with real humans. In the interest of brevity I think a) is absurd and, for reasons I should blog about,… — Source: Expected Parrot blogWed, 30 Sep 2026 01:49:37 GMTVoiceshttps://tradingview.com/news/seekingalpha:cc9dac568094b:0-salesforce-agrees-to-buy-listen-labshttps://tradingview.com/news/seekingalpha:cc9dac568094b:0-salesforce-agrees-to-buy-listen-labsSource: TradingViewTue, 29 Sep 2026 21:33:35 GMTCapitalhttps://tipranks.com/news/the-fly/salesforce-agrees-to-acquire-listen-labs-thefly-newshttps://tipranks.com/news/the-fly/salesforce-agrees-to-acquire-listen-labs-thefly-newsSource: TipRanksTue, 29 Sep 2026 21:29:32 GMTCapitalhttps://finance.yahoo.com/technology/ai/articles/salesforce-acquire-client-research-platform-210517947.htmlhttps://finance.yahoo.com/technology/ai/articles/salesforce-acquire-client-research-platform-210517947.htmlSource: Yahoo FinanceTue, 29 Sep 2026 21:05:17 GMTCapitalhttps://arxiv.org/abs/2609.38392https://arxiv.org/abs/2609.38392Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to include and how to assign their values. As a result, synthetic populations may misrepresent the demographic composition, latent attributes, and dependency structure that shape downstream behavior. We introduce MetaPersona-DB, a dataset of 11,000+ empirical human-subjects studies annotated with task-relevant variables, reported relationships, and aggregate-level population statistics. Building on this resource, we propose MetaPersona, a framework that retrieves task-relevant evidence, constructs literature-derived persona dependency graphs, and… — Source: arXivTue, 29 Sep 2026 18:50:21 GMTResearchhttps://arxiv.org/abs/2609.38296https://arxiv.org/abs/2609.38296Large language models (LLMs) can influence people's beliefs, yet little is known about whether and how they can manipulate each other. To investigate this, we simulate conversations between two agents: a target LLM that role-plays a human persona based on demographic and psychological attributes, and an influencer LLM that aims to make the target's beliefs more extreme. We examine radicalization along two pathways: resonance, where the influencer reinforces a target's pre-existing belief, and persuasion, where the influencer promotes a belief the target initially considers unimportant. Across affective and behavioral metrics, we find that both mechanisms radicalize the target. However,… — Source: arXivTue, 29 Sep 2026 17:45:00 GMTResearchhttps://pulse2.com/newtonx-hubhttps://pulse2.com/newtonx-hubSource: Pulse 2.0Tue, 29 Sep 2026 16:38:12 GMTNewshttps://streetinsider.com/Corporate+News/Salesforce+agrees+to+acquire+AI+research+platform+Listen+Labs/27122914.htmlhttps://streetinsider.com/Corporate+News/Salesforce+agrees+to+acquire+AI+research+platform+Listen+Labs/27122914.htmlSource: StreetInsiderTue, 29 Sep 2026 07:00:00 GMTCapitalhttps://sequoiacap.com/article/listen-labs-and-salesforce-hear-your-customershttps://sequoiacap.com/article/listen-labs-and-salesforce-hear-your-customersSource: Sequoia CapitalTue, 29 Sep 2026 00:00:00 GMTNewshttps://listenlabs.ai/blog/listen-labs-is-joining-salesforcehttps://listenlabs.ai/blog/listen-labs-is-joining-salesforceCustomer research conducted, analyzed, and summarized by AI. — Source: Listen Labs blogTue, 29 Sep 2026 00:00:00 GMTVoiceshttps://arxiv.org/abs/2609.36278https://arxiv.org/abs/2609.36278Generative agent-based models (GABMs) are increasingly used to simulate social media dynamics, including misinformation spread. For such social simulations to be valid proxies of human behavior, LLM agents should replicate established human cognitive biases, among them the Illusory Truth Effect (ITE), where repeated exposure to a claim increases its perceived truth value. We investigate whether and how the ITE manifests across four LLMs (Gemma-3-4b-it, Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, and GPT-5-nano) in a social media simulation context. We propose a two-phase within-context experimental design that embeds the repetition manipulation inside a realistic news feed interaction.… — Source: arXivMon, 28 Sep 2026 21:09:47 GMTResearchhttps://prnewswire.com/news-releases/hcltech-launches-first-of-its-kind-ai-based-synthetic-research-study-for-the-global-wealth-management-industry-302891621.htmlhttps://prnewswire.com/news-releases/hcltech-launches-first-of-its-kind-ai-based-synthetic-research-study-for-the-global-wealth-management-industry-302891621.htmlSource: PR NewswireMon, 28 Sep 2026 14:52:00 GMTNewshttps://marketscreener.com/news/hcltech-launches-first-of-its-kind-ai-based-synthetic-research-study-for-global-wealth-management-in-ce785addd18bf32chttps://marketscreener.com/news/hcltech-launches-first-of-its-kind-ai-based-synthetic-research-study-for-global-wealth-management-in-ce785addd18bf32cSource: marketscreener.comMon, 28 Sep 2026 14:51:00 GMTNewshttps://arxiv.org/abs/2609.34571https://arxiv.org/abs/2609.34571Controlling persona in large language models (LLMs) at inference time is important for role-playing, personalized dialogue, and social simulation. Recent methods extract persona vectors from the model's activation space and apply Euclidean operations---addition, scaling, and linear interpolation---under the linear representation hypothesis. However, these methods themselves report systematic failures: non-orthogonal trait dimensions, asymmetric ceiling and resistance effects, and significant deviations in multi-trait composition, suggesting that the linear isotropic assumption does not hold. We propose PersonaManifold, a framework that models persona representations as points on a curved,… — Source: arXivMon, 28 Sep 2026 08:19:52 GMTResearchhttps://arxiv.org/abs/2609.32638https://arxiv.org/abs/2609.32638Large language models (LLMs) are increasingly used to generate synthetic survey respondents and digital twins of real people, but whether their output preserves real human statistical structure, rather than surface plausibility, remains unresolved, and most existing evidence comes from proprietary models rather than open-weight ones. We evaluate three open-weight LLM families on a cross-instrument calibration task: conditioning personas on real respondents' verbatim answers to one psychometric instrument and measuring them on a second, construct-distance-controlled instrument, checked against a 2,058-person human panel. Across a 139-pair grid, the simulated cross-instrument correlation… — Source: arXivSat, 26 Sep 2026 14:01:34 GMTResearchhttps://agencyreporter.com/synthetic-focus-groups-ai-creative-testinghttps://agencyreporter.com/synthetic-focus-groups-ai-creative-testingSource: Agency ReporterFri, 25 Sep 2026 10:37:58 GMTNewshttps://arxiv.org/abs/2609.29928https://arxiv.org/abs/2609.29928Large language models (LLMs) are increasingly used as synthetic survey respondents to estimate population response distributions. In cross-cultural survey simulation, evaluations should assess not only distributional fidelity within countries but also whether differences across countries are preserved. However, existing distance-based metrics such as Jensen--Shannon divergence (JSD) do not directly capture such cross-country differences. To address this limitation, we introduce Cultural Divergence Preservation (CDP), a reference-light diagnostic based on a one-time human calibration. CDP identifies reduced cross-country divergence as cultural flattening and increased divergence as cultural… — Source: arXivThu, 24 Sep 2026 14:59:06 GMTResearchhttps://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.9.3https://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.9.3[2.9.3] 2026 09 24 Fixed env : 模板缓存按代码必读变量判定复用，精确匹配不再被键集否决 Internal release : Bump 2.9.2 2.9.3（extension 1.7.2 1.7.3） <! Generated by git cliff — Source: GitHubThu, 24 Sep 2026 07:45:26 GMTCodehttps://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.9.2https://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.9.2[2.9.2] 2026 09 24 Fixed ci : VSIX 校验适配 js yaml 5 的 dist/ 布局 Internal release : Bump 2.9.1 2.9.2（extension 1.7.1 1.7.2） <! Generated by git cliff — Source: GitHubThu, 24 Sep 2026 06:52:26 GMTCodehttps://arxiv.org/abs/2609.28388https://arxiv.org/abs/2609.28388Social simulation studies how individual behavior and social interaction produce collective outcomes. In social media marketing, campaign actions shape which consumers encounter the content and how they respond; these responses then spread through the population. We propose OranSim, a social simulation framework that connects creative, creator, targeting, and budget choices to this process. Heterogeneous consumers receive exposure according to content matching and platform allocation and generate initial responses, which propagate among 60 population segments. Candidate campaigns share the initial population and aligned random numbers, making their response trajectories comparable under… — Source: arXivWed, 23 Sep 2026 16:59:24 GMTResearchhttps://arxiv.org/abs/2609.28609https://arxiv.org/abs/2609.28609Role-playing agents based on large language models have been widely applied in areas such as personalized assistance and social simulation. Recent RL methods typically train on a fixed scenario pool collected before learning begins. This creates a distributional bottleneck: as the agent improves, the scenarios where it performs poorly also change, while the training distribution remains static. Therefore, we propose AdvRole, an adversarial context rewriting framework that turns role-playing RL into a closed-loop curriculum. AdvRole alternates between an Actor that learns to role-play and a Rewriter that edits character profiles and dialogue contexts into actor-specific hard scenarios. The… — Source: arXivWed, 23 Sep 2026 16:58:08 GMTResearchhttps://arxiv.org/abs/2609.27639https://arxiv.org/abs/2609.27639Online discussion of political and gender-related issues is often heated, and when opinions in a network draw closer, the convergence is readily taken as genuine consensus. Whether it carries a cost is a question existing methods cannot answer: coevolutionary opinion formation games measure the Price of Anarchy (PoA) of agents that update by numerical rules, while simulations with large language model (LLM) agents report only descriptive indices. We introduce the Hybrid Coevolutionary Opinion Game (H-COG), in which analytical and LLM-driven agents share one network, choose their neighbors by opinion similarity in every round, and hold stances drawn from real Reddit comments on gun control… — Source: arXivWed, 23 Sep 2026 10:01:12 GMTResearchhttps://listenlabs.ai/blog/introducing-gabor-granger-price-testshttps://listenlabs.ai/blog/introducing-gabor-granger-price-testsFind the price that maximizes revenue, and understand why buyers are willing to pay it. — Source: Listen Labs blogWed, 23 Sep 2026 00:00:00 GMTVoiceshttps://arxiv.org/abs/2609.26629https://arxiv.org/abs/2609.26629Procedural character generation aims to populate games, simulations, and other virtual worlds with diverse characters. Large language models (LLMs) offer a promising foundation for scaling this task. However, LLM-based procedural character generation remains at an early stage: existing methods either generate characters directly or adapt profiles retrieved from persona banks. As we show, both approaches produce behaviorally homogeneous populations: characters overwhelmingly agree with positive moral norms and respond to questions with helpful, assistant-like reactions. To mitigate this homogenization, we introduce PersonaWeaver, which disentangles world building from behavioral… — Source: arXivTue, 22 Sep 2026 16:04:14 GMTResearchhttps://cloudwars.com/cloud-wars-minute/salesforce-listen-labs-and-the-push-toward-autonomous-ai-agentshttps://cloudwars.com/cloud-wars-minute/salesforce-listen-labs-and-the-push-toward-autonomous-ai-agentsSource: Cloud WarsTue, 22 Sep 2026 11:30:00 GMTNewshttps://arxiv.org/abs/2609.25432https://arxiv.org/abs/2609.25432Because significant action to counter global warming requires massive public support, it is important to understand the dynamics of public opinion on climate issues. Of special interest are social tipping points, as revealed by large-scale effects of small perturbations in individual behaviors. Agent-based models (ABM) are an effective computational tool for studying these matters, because they allow controlled and systematic exploration of the effects of interventions that may be infeasible in real-world social systems. Large language models (LLMs) have been used to endow model agents with the ability to communicate in natural language (rather than by exchanging predefined messages), as… — Source: arXivMon, 21 Sep 2026 21:33:48 GMTResearchhttps://arxiv.org/abs/2609.24911https://arxiv.org/abs/2609.24911Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples that unite agent-based modeling with real behavioral data. Existing platforms verify collective behavior, align simulated populations with real societies in cross-sections, and employ autonomous agents for the research process. However, two social science requirements remain without systematic support: intervention in the content of a simulation and the researcher's control over the process that produces it. We present SocioVerse2, which extends SocioVerse 1.0 into a human-AI co-evolutionary paradigm built from two… — Source: arXivMon, 21 Sep 2026 17:14:09 GMTResearchhttps://arxiv.org/abs/2609.35819https://arxiv.org/abs/2609.35819In this paper, we explore using generative agent-based models for a classical ABM application: testing how individual-level behavioral rules produce collective phenomena. We introduce RePair, a method that calibrates simulation worlds, operationalizes candidate mechanisms as natural-language rules, estimates their effects through matched interventions, and examines behavioral traces. We assess the method by testing it in four simulation worlds grounded in established social-science models and empirical studies. Our results reveal that (1) natural-language rules can produce measurable collective effects; (2) rule comparisons can converge as configurations accumulate; and (3) behavioral… — Source: arXivMon, 21 Sep 2026 16:34:58 GMTResearchhttps://arxiv.org/abs/2609.24629https://arxiv.org/abs/2609.24629A/B testing requires large sample sizes, long timelines, and significant costs. When auxiliary predictions of experimental outcomes are available from machine learning models, uncertain prediction quality precludes replacing human experiments entirely, yet these predictions may still contain useful signal. We propose a principled framework for learning-augmented hypothesis testing that leverages predictions of unknown quality to reduce sample sizes while maintaining statistical validity. Predictions naturally vary in granularity, from coarse aggregate signals to fine-grained individual-level estimates, and our framework addresses both ends of this spectrum: (1) for population-level… — Source: arXivMon, 21 Sep 2026 14:11:34 GMTResearchhttps://arxiv.org/abs/2609.24146https://arxiv.org/abs/2609.24146Language model agents are increasingly used to simulate social interaction, and the resulting transcripts read as though the agents understand one another. We ask whether that appearance rests on a model of the partner's mind or on the surface record of what the partner said. We build a social simulation in which both questions have exact answers: 40 multi-issue negotiations whose hidden preference weights and whose full Pareto frontier are known by construction. Two model families negotiate across 160 dyads, every transcript is frozen before any measurement, and 2880 counterfactual probes then hold the evidence byte identical while moving one factor at a time: the reader's own stake, the… — Source: arXivMon, 21 Sep 2026 05:58:28 GMTResearchhttps://arxiv.org/abs/2609.21997https://arxiv.org/abs/2609.21997LLM agents in social simulation revise their opinions implicitly, in context: how open an agent is to persuasion can neither be specified nor verified, and collective outcomes inherit the model's training prior. We introduce Bayesian Chronicle Agents (BCA), a minimal belief layer separating what an agent believes from how it speaks. Each stance is a probability, updated by one Bayesian step per utterance heard. A single prior-strength parameter $κ$ encodes stubbornness, modeled after its role in Friedkin--Johnsen (FJ) opinion dynamics. We then sweep this parameter to yield three canonical regimes of opinion dynamics on demand (consensus, persistent disagreement, committed-minority… — Source: arXivFri, 18 Sep 2026 16:57:14 GMTResearchhttps://arxiv.org/abs/2609.21857https://arxiv.org/abs/2609.21857LLMs are increasingly used in social simulations for socially interactive agents and robots, offering more flexibility than rule-based systems. However, even though they mimic human behaviour very well, there is a persistent alienness to them. This work investigates whether personality-aware fine-tuning can reduce this gap by improving the consistency and controllability of personality-conditioned dialogue generation compared with instruction prompting alone. We fine-tune two small open-weight LLMs, Qwen2.5-7B-Instruct and Ministral-8B-Instruct, using a corpus that combines personality-labelled social media posts and dialogues to create a personality-based dialogue engine for social… — Source: arXivFri, 18 Sep 2026 14:52:26 GMTResearchhttps://customerthink.com/synthetic-consumers-need-a-human-reality-checkhttps://customerthink.com/synthetic-consumers-need-a-human-reality-checkSource: CustomerThinkFri, 18 Sep 2026 07:00:00 GMTNewshttps://blog.expectedparrot.com/p/disarming-the-slop-cannon-ironicallyhttps://blog.expectedparrot.com/p/disarming-the-slop-cannon-ironicallytl; dr I created a survey where the respondent uploads a PDF of their paper. Expected Parrot uses a model to prepare an interview guide and then sends it to an AI interviewer that quizzes the author and captures the transcript for analysis. Research conferences are one obvious application, but this approach can be used (far) more generally to combat “slop cannons” of humans uncritically passing off AI work as their own. The problem Across academia, people are complaining about being inundated… — Source: Expected Parrot blogThu, 17 Sep 2026 15:19:02 GMTVoiceshttps://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.9.0https://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.9.0[2.9.0] 2026 09 17 Added llm : Thinking 开关 + agent prompt 缓存命中率优化 extension : 配置页适配 thinking 开关环境变量 Internal release : Bump 2.8.7 2.9.0（extension 1.6.7 1.7.0） <! Generated by git cliff — Source: GitHubThu, 17 Sep 2026 15:16:54 GMTCodehttps://arxiv.org/abs/2609.20055https://arxiv.org/abs/2609.20055LLM-based social simulations are primarily evaluated for behavioral fit, testing whether agents reproduce the actions or response distributions of the people they are simulating. However, the promise of simulation extends beyond behavioral fit. Simulations can explain human behavior, diagnose barriers, and compare large-scale interventions. These use cases depend on understanding \textit{why} people acted a certain way, not just \textit{what} they did. As a result, behavioral fit is insufficient for these types of claims because behavior underdetermines the reasoning process behind it. For instance, the behavior of staying silent may be due to disinterest or suppressed speech, and not… — Source: arXivThu, 17 Sep 2026 11:06:45 GMTResearchhttps://arxiv.org/abs/2609.19913https://arxiv.org/abs/2609.19913The study of opinion dynamics in social networks is one of the key challenges in computational social science with direct relevance to understanding political polarization, misinformation, and health responses. Current approaches focus on simplified mathematical models that ignore linguistic and contextual factors related to belief updates or use Large Language Model (LLM)-based simulations that have not been validated against real data. We present a framework based on the concept of a digital twin to simulate opinion dynamics in social networks. The approach fills the gap by cloning a real-world Twitter network, assigns a set of attributes for agents (such as persona, emotions, centrality,… — Source: arXivThu, 17 Sep 2026 08:53:47 GMTResearchhttps://blog.expectedparrot.com/p/a-fake-pharmacy-scenario-from-aihttps://blog.expectedparrot.com/p/a-fake-pharmacy-scenario-from-aitl;dr: I show how you can use Expected Parrot (E[P]) to explore a real business question faced by CVS— How do we encourage the adoption of lower cost biologics? —and make headway with a combination of AI approaches and human research. We find digital twins work well out of the box but are suspiciously under-dispersed in their answers; we run a real human study and then use that insight to build better digital twins, ready for re-use. The E[P] workflows are inspectable, reproducible and easy to… — Source: Expected Parrot blogWed, 16 Sep 2026 16:22:43 GMTVoiceshttps://arxiv.org/abs/2609.25066https://arxiv.org/abs/2609.25066Large language models (LLMs) are increasingly used to simulate human survey responses and behavioral reactions, yet unreliable simulations can mislead social science conclusions. However, existing evaluations focus on end-to-end scores, leaving it unclear how different aspects of the simulation process interact to determine reliability. We propose ReliMap, which decomposes LLM-based human behavior simulation into three structured layers and evaluates reliability at both the individual level (R1) and population level (R2) across three configuration dimensions: model capacity, profile completeness, and population coverage. Through experiments across four simulation tasks and eleven LLMs, we… — Source: arXivWed, 16 Sep 2026 10:10:10 GMTResearchhttps://blog.expectedparrot.com/p/ai-thinks-this-post-is-terrible-buthttps://blog.expectedparrot.com/p/ai-thinks-this-post-is-terrible-butI took a break on blogging this summer to go heads-down on a bunch of new Expected Parrot features and research ideas. We have a lot to talk about—so much that I was feeling a bit overwhelmed in what, exactly, to focus on. I needed some way to: Map all the new features to good blog post topics Rate those topics by likely reader interest Naturally, I thought “maybe AI could help?” I used the following prompt with our conversational research agent ( www.expectedparrot.com ): Thanks for reading!… — Source: Expected Parrot blogTue, 15 Sep 2026 01:41:10 GMTVoiceshttps://arxiv.org/abs/2609.17496https://arxiv.org/abs/2609.17496LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (i) it requires setups where the assistant learns about social situations from subjective user narratives, and (ii) social properties, such as others' intentions, typically lack verifiable ground truth. To address these challenges, we introduce Fuse, a multi-agent simulation framework for studying user-mediated social reasoning. In Fuse, a target agent with a hidden motive interacts with other agents including one representing the user, who then consults the evaluated assistant to infer the target's motive, providing verifiable ground truth by… — Source: Hugging Face PapersTue, 15 Sep 2026 00:00:00 GMTResearchhttps://arxiv.org/abs/2609.16436https://arxiv.org/abs/2609.16436Simulations based on large language models (LLMs) have proven to be powerful for understanding human behavior, making them valuable additions to the social scientific toolkit. However, LLMs are ultimately black boxes based on deep neural networks which limits their value for social science. This is because of a lack of (i) interpretability: i.e. the ability to assign clear mechanisms driving observed behavior; and a lack of (ii) steerability: i.e. the ability to mute or amplify specific theoretically meaningful mechanisms of action to drive specific model behavior. Here, we demonstrate how the black box could be opened up to further enrich LLM-based simulations. Specifically, we compare… — Source: arXivMon, 14 Sep 2026 23:23:23 GMTResearchhttps://arxiv.org/abs/2609.16395https://arxiv.org/abs/2609.16395Silicon sampling uses large language models (LLMs) to simulate survey respondents. Whether it recovers cross-national variation, and why, remains unresolved. This study evaluates it against European Social Survey Round 11 (30 countries, 42 items) with two open-weight LLMs under first- and third-person prompts, plus backstory and response-format experiments. Aggregate recovery is moderate and uneven across items. Adding the country name to a three-variable demographic backstory raises the median per-item correlation between simulated and observed country means from -0.03 to 0.52, and the richer profiles tested add no consistent gain. The respondent's country label acts as a country-level… — Source: arXivMon, 14 Sep 2026 22:01:45 GMTResearchhttps://arxiv.org/abs/2609.15849https://arxiv.org/abs/2609.15849Can LLMs reason through new information like humans, or do they merely retrieve cached opinions? This is critical for silicon sampling, where LLM personas simulate public opinion at scale. Current evaluations test only whether personas hold the right opinions -- a static snapshot. But opinion research increasingly depends on dynamic fidelity: whether personas update beliefs in response to new arguments, as humans do during deliberation. No existing benchmark tests this. We introduce the Deliberative Polling Diagnostic Framework, which compares human and LLM belief shifts after identical informational interventions. Grounded in deliberative polling, it surfaces failures invisible to static… — Source: arXivMon, 14 Sep 2026 16:41:48 GMTResearchhttps://arxiv.org/abs/2609.15038https://arxiv.org/abs/2609.15038Language-model agents are increasingly used as synthetic people in surveys and social simulations, yet their apparent realism is often judged from population averages or distributional similarity. We tested what such evidence actually establishes by comparing Claude, Codex and Kimi under four prespecified prompt designs with matched farmer decisions from China and four African countries. Some configurations reproduced observed means and adoption rates. However, their person-level predictions were weak; their decisions clustered around typical values and policy-relevant extremes were largely missing. Most strikingly, a simple generator fitted only to the observed marginal dis- tribution, and… — Source: arXivMon, 14 Sep 2026 04:54:51 GMTResearchhttps://arxiv.org/abs/2609.13995https://arxiv.org/abs/2609.13995Debate over synthetic data in marketing research has polarized between claims that large language models (LLMs) make human respondents obsolete and calls to avoid them entirely. We argue that both positions obscure the more useful question: not whether synthetic respondents work, but when. Building on Brand, Israeli, and Ngwe (2026), we make three contributions. First, we distinguish three types of synthetic data (ungrounded LLM responses, segment-level personas, and individual-level digital twins) and map each to the decisions it can support. Second, we develop a taxonomy of four families of accuracy measures and suggest that the wide range of reported twin accuracy, from near-perfect to… — Source: arXivSat, 12 Sep 2026 15:13:10 GMTResearchhttps://finance.yahoo.com/technology/ai/articles/salesforce-inc-crm-potential-listen-220558304.htmlhttps://finance.yahoo.com/technology/ai/articles/salesforce-inc-crm-potential-listen-220558304.htmlSource: Yahoo FinanceSat, 12 Sep 2026 07:00:00 GMTNewshttps://simile.ai/blog/itau-at-esomarhttps://simile.ai/blog/itau-at-esomarSee how Itaú used Simile’s behavioral simulation to complete five weeks of research in four days and win an ESOMAR Award for AI and automation. — Source: Simile blogFri, 11 Sep 2026 17:30:00 GMTVoiceshttps://varindia.com/news/listen-labs-bets-on-2b-salesforce-dealhttps://varindia.com/news/listen-labs-bets-on-2b-salesforce-dealSource: varindiaFri, 11 Sep 2026 07:00:00 GMTNewshttps://arxiv.org/abs/2609.12444https://arxiv.org/abs/2609.12444Simulated societies of large language model agents are used to study online polarization, and separately to study collective intelligence, but the two are rarely measured in the same system. It is therefore difficult to say whether a society's personality composition shapes both, or whether reducing polarization costs collective competence. We present TraitMix, an experimental design in which the Big Five composition of a simulated social network, both trait levels and trait heterogeneity, is a controlled experimental variable, and in which polarization and collective performance are measured in the same runs. Across 991 simulations of hundred-agent societies, spanning six contested topics… — Source: arXivFri, 11 Sep 2026 05:06:48 GMTResearchhttps://app.dealroom.co/news/note/listen-labs-walks-away-from-125m-round-amid-2b-salesforce-talkshttps://app.dealroom.co/news/note/listen-labs-walks-away-from-125m-round-amid-2b-salesforce-talksSource: DealroomThu, 10 Sep 2026 16:46:47 GMTNewshttps://arxiv.org/abs/2609.25059https://arxiv.org/abs/2609.25059Large language models (LLMs) are proposed as synthetic respondents for pilot testing, but their usefulness depends on whether they supply the evidence assessment development requires. We calibrated rating scale models on 14 digital-use skill items from 6,245 adults and used the human item parameters to evaluate responses generated for 1,300 demographically matched personas. LLM responses had high internal consistency ($α\approx .94$) but used the lowest category in 0.2-0.3% of responses versus 13.7-21.5% for humans, and no persona chose it on every item. These gaps changed the response-category judgment on both subscales and the PC targeting judgment; on the human-calibrated scales, 12 of… — Source: arXivThu, 10 Sep 2026 13:50:24 GMTResearchhttps://theconversation.com/the-next-frontier-is-not-artificial-intelligence-its-artificial-societies-291586https://theconversation.com/the-next-frontier-is-not-artificial-intelligence-its-artificial-societies-291586Source: The ConversationThu, 10 Sep 2026 07:00:00 GMTNewshttps://finance.yahoo.com/technology/ai/articles/salesforce-2b-talks-acquire-listen-091244424.htmlhttps://finance.yahoo.com/technology/ai/articles/salesforce-2b-talks-acquire-listen-091244424.htmlSource: Yahoo FinanceThu, 10 Sep 2026 07:00:00 GMTNewshttps://americanbazaaronline.com/2026/09/10/salesforce-in-talks-to-acquire-ai-startup-listen-labs-for-2-billion-487894https://americanbazaaronline.com/2026/09/10/salesforce-in-talks-to-acquire-ai-startup-listen-labs-for-2-billion-487894Source: The American BazaarThu, 10 Sep 2026 07:00:00 GMTNewshttps://ua.news/en/technologies/listen-labs-vidmovilasia-vid-raundu-na-125-mln-na-tli-peregovoriv-iz-salesforce-techcrunchhttps://ua.news/en/technologies/listen-labs-vidmovilasia-vid-raundu-na-125-mln-na-tli-peregovoriv-iz-salesforce-techcrunchSource: UA.NEWSThu, 10 Sep 2026 07:00:00 GMTNewshttps://techtimes.com/articles/327263/20260910/salesforce-eyes-2b-listen-labs-buy-fin-deal-closes-chasing-ai-data-moat.htmhttps://techtimes.com/articles/327263/20260910/salesforce-eyes-2b-listen-labs-buy-fin-deal-closes-chasing-ai-data-moat.htmSource: Tech TimesThu, 10 Sep 2026 07:00:00 GMTNewshttps://arxiv.org/abs/2609.11108https://arxiv.org/abs/2609.11108We placed 100 memory-equipped large language model (LLM) agents in charge of a closed, money-conserving spatial economy on real Pokhara Lakeside geography (earning wages, running businesses, setting prices) and ran this multi-agent simulation for up to 26 simulated weeks, well past the 1-2 weeks typical of agent-society studies. Across 91 validated runs (2.44M agent decisions, 21.5B tokens), the money stops moving, in a specific and measurable way. A 12x tourist demand shock raises business revenue 4.62x ($p<0.001$), which we decompose exactly into a 1.50x extensive margin (more businesses trading) and a 3.07x intensive margin (more revenue each). Monetary transmission stops there. Wages… — Source: arXivThu, 10 Sep 2026 05:34:07 GMTResearchhttps://arxiv.org/abs/2609.10280https://arxiv.org/abs/2609.10280Large Language models (LLMs), having been trained on vast amounts of human-generated data, may encode the attitudes and behaviors of these humans. As such, LLMs show promise in mimicking human-like patterns that facilitate their use in simulating people in a wide variety of contexts. One such context is using LLMs as 'silicon samples', i.e., proxies of people in answering survey questions to establish public opinion, design policies, or use as (social) scientific data. However, several critical questions of social biases, generalization, and technical limitations remain, further complicated by a vast design space open to simulation designers. Multiverse analyses might help us make sense of… — Source: arXivWed, 09 Sep 2026 14:59:07 GMTResearchhttps://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.8.7https://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.8.7[2.8.7] 2026 09 09 Added extension : CLI 网关 Codex 本地代理默认启用，不动用户已有配置 Internal release : Bump 2.8.6 2.8.7（extension 1.6.6 1.6.7） <! Generated by git cliff — Source: GitHubWed, 09 Sep 2026 10:18:25 GMTCodehttps://techcrunch.com/2026/09/09/ai-research-startup-listen-labs-scrubbed-a-1-5b-funding-round-for-salesforce-talkshttps://techcrunch.com/2026/09/09/ai-research-startup-listen-labs-scrubbed-a-1-5b-funding-round-for-salesforce-talksSource: TechCrunchWed, 09 Sep 2026 07:00:00 GMTNewshttps://businessinsider.com/salesforce-acquire-ai-startup-listen-labs-2026-9https://businessinsider.com/salesforce-acquire-ai-startup-listen-labs-2026-9Source: Business InsiderWed, 09 Sep 2026 07:00:00 GMTNewshttps://finance.yahoo.com/technology/ai/articles/salesforce-talks-acquire-ai-platform-201159735.htmlhttps://finance.yahoo.com/technology/ai/articles/salesforce-talks-acquire-ai-platform-201159735.htmlSource: Yahoo FinanceWed, 09 Sep 2026 07:00:00 GMTNewshttps://finance.yahoo.com/technology/ai/articles/crm-stock-dips-2-salesforce-205158591.htmlhttps://finance.yahoo.com/technology/ai/articles/crm-stock-dips-2-salesforce-205158591.htmlSource: Yahoo FinanceWed, 09 Sep 2026 07:00:00 GMTNewshttps://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.8.6https://github.com/tsinghua-fib-lab/AgentSociety/releases/tag/agentsociety2-v2.8.6[2.8.6] 2026 09 09 Fixed ci : GitHub 发版流水线 npm audit 不再阻断 + bump 2.8.6 <! Generated by git cliff — Source: GitHubWed, 09 Sep 2026 05:38:58 GMTCodehttps://arxiv.org/abs/2609.07573https://arxiv.org/abs/2609.07573Multi-agent LLM deliberation has been explored as a scalable way to simulate public deliberation. For such simulations to be informative, persona agents should reflect population opinion patterns and interaction should shape their conclusions. We evaluate whether LLM-based deliberation can meet these two conditions using census-grounded Korean personas debating real policy questions benchmarked against national surveys. Persona agents do not reliably reproduce population opinion patterns: responses are often far more concentrated and frequently reverse demographic differences in the human data. Deliberations nonetheless produce reasoned, reciprocal, and varied arguments alongside… — Source: arXivMon, 07 Sep 2026 14:49:25 GMTResearchhttps://arxiv.org/abs/2609.07141https://arxiv.org/abs/2609.07141Collecting survey data is laborious and limited by privacy constraints. Large language models (LLMs) have shown promise as predictive social simulations. It is unclear whether they can replicate population-level response distributions before and after a healthcare intervention. Using information derived from 4125 women aged 35-59 years, we evaluate whether agents informed solely by pre-intervention profile information can reproduce post-intervention response distributions. Groups of LLM agents (n=50) were created with Gemma 4 E4B and Qwen3.5 9B; conditions ranged from zero-shot prompting to agent profiles enriched with aggregate or individual-level demographic characteristics and… — Source: arXivMon, 07 Sep 2026 07:42:38 GMTResearchhttps://arxiv.org/abs/2609.06769https://arxiv.org/abs/2609.06769As people increasingly rely on artificial intelligence (AI) for guidance in their own lives, scholars, lawyers, and even judges have begun to consider the role of AI in legal decision-making. As "silicon sampling" -- the use of generative AI models in social science research -- is now impacting academia, "silicon jurors" could make an appearance in courtrooms. This study joins an emerging line of research on generative AI models' ability to simulate human legal judgments. In particular, we study how large language model (LLM)-powered chatbots respond to series of questions about legal reasonableness. When the law needs to judge the appropriateness of a behavior, it most often asks whether… — Source: arXivSun, 06 Sep 2026 18:20:15 GMTResearchhttps://arxiv.org/abs/2609.22255https://arxiv.org/abs/2609.22255Existing approaches to persona simulation with Large Language Models (LLMs) mostly rely on shallow character descriptions that fail to sustain coherent character behavior across extended interactions. We introduce Deep Persona, a psychologically grounded, three-layered architecture that organizes personas into hierarchical levels of observable expression, latent beliefs, and core motivational drives, for constructing highly convincing role-playing agents. Governed by the principles of scripted determinism and bounded agency, the architecture restricts the model to a reactive engine guided by a structured internal script. We further propose a reference-free evaluation framework that… — Source: arXivSun, 06 Sep 2026 08:53:41 GMTResearchhttps://arxiv.org/abs/2609.04373https://arxiv.org/abs/2609.04373Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial markets to content moderation to hiring. We show that improving individual model capability can degrade rather than improve system-level outcomes. We hypothesize that shared training and architectures can lead more capable LLMs to behave more similarly, creating correlated actions that do not diversify away. We develop a general framework showing how this correlation creates a non-diversifiable risk floor and test its predictions in financial markets using an agent-based simulation with LLM traders of varying general-purpose capability. We find that: (1) frontier LLMs exhibit… — Source: arXivThu, 03 Sep 2026 18:37:56 GMTResearchhttps://kucoin.com/news/flash/undergraduate-student-develops-ai-prediction-engine-mirofish-in-10-days-secures-30m-rmb-investmenthttps://kucoin.com/news/flash/undergraduate-student-develops-ai-prediction-engine-mirofish-in-10-days-secures-30m-rmb-investmentSource: KuCoinThu, 03 Sep 2026 07:00:00 GMTNewshttps://simile.ai/blog/what-if-machinehttps://simile.ai/blog/what-if-machineWhy accurate human behavior simulation requires understanding both what people do and why, and how Simile builds trustworthy models for what-if decisions. — Source: Simile blogWed, 02 Sep 2026 17:00:00 GMTVoiceshttps://arxiv.org/abs/2609.02729https://arxiv.org/abs/2609.02729Occupants are a primary source of uncertainty in building energy consumption and management, yet existing occupant behavior models cannot capture adaptive and reasoning responses considering the occupant's personal history, current context, and the type of energy signal being delivered. This study presents BuildOcc, an open-source Python platform that grounds large language model agents in the American Time Use Survey (ATUS), a nationally representative diary dataset covering 16,684 respondents. Through BuildOcc, each simulated occupant agent can be instantiated with a demographic persona drawn from ATUS population statistics, a memory stream that accumulates and reflects on timestep-level… — Source: arXivWed, 02 Sep 2026 15:40:33 GMTResearchhttps://app.dealroom.co/news/note/intellicia-raises-2-5m-pre-series-a-for-ai-synthetic-consumershttps://app.dealroom.co/news/note/intellicia-raises-2-5m-pre-series-a-for-ai-synthetic-consumersSource: DealroomWed, 02 Sep 2026 03:55:54 GMTCapitalhttps://arxiv.org/abs/2609.01918https://arxiv.org/abs/2609.01918Energy poverty is nearly absent from NLP-for-social-good, and the little existing work is either static retrieval/QA or relies on carbon-intensive cloud LLMs, a self-defeating "computational irony" for a humanitarian setting. We present EqGrid, a closed-loop simulation in which a low-frequency, open-weight LLM policy agent sets price and carbon bounds and targeted subsidies over a community of empirically-grounded household personas, while high-frequency multi-agent RL traders clear a continuous double auction constrained by a physical distribution grid (IEEE-33-bus with Dynamic Operating Envelopes). Our contribution is threefold and directly addresses how to measure the social impact of… — Source: arXivTue, 01 Sep 2026 22:37:34 GMTResearchhttps://arxiv.org/abs/2609.01257https://arxiv.org/abs/2609.01257As LLM-based human simulators are increasingly used for policy, evaluation, and training, they must faithfully reproduce real behavioral patterns. While prior work has examined behavioral fidelity in survey responses and dialogue, longer-horizon real-world activity remains largely unexplored. We introduce a framework for evaluating behavioral fidelity in long-horizon activity simulations across temporal granularities and levels of analysis. As a case study, we collect a 43-hour multi-camera dataset of in-the-wild office activity and compare trace-derived conditioning mechanisms: persona descriptors, few-shot exemplars, and statistical transition and time-of-day priors. We find that… — Source: arXivTue, 01 Sep 2026 13:51:11 GMTResearchhttps://arxiv.org/abs/2609.01167https://arxiv.org/abs/2609.01167Large language models can produce locally plausible social turns, but fluent next-turn generation is not enough for social simulation. Human encounters such as restaurant lunches and hotel check-ins are bounded social episodes with roles, scripts, material state, obligations, commitments, timing, and closure conditions. We present EpisodeSim, a hybrid LLM-agent architecture that represents classic-AI structures as natural-language control state interpreted by LLM calls. A World Master maintains shared reality, constructs scenes, adjudicates proposed actions, tracks effects and obligations, and controls closure. Experiments with small qualitative ablations on two held-out settings support a… — Source: arXivTue, 01 Sep 2026 12:44:58 GMTResearchhttps://arxiv.org/abs/2609.01038https://arxiv.org/abs/2609.01038A/B testing is the gold standard for evaluating product changes, but each experiment requires real user traffic, engineering effort, and weeks of measurement. We propose a simulation framework that predicts A/B test outcomes using LLM-powered agents conditioned on data-driven personas grounded in real user behavioral signals. Unlike prior work that relies on synthetic or rule-based personas, our agents are constructed from anonymized behavioral data-activity patterns, engagement signals, and inferred demographics-enabling more faithful population modeling. We frame A/B test simulation as a structured question task and systematically study (i) question design formats, (ii) the impact of… — Source: arXivTue, 01 Sep 2026 10:35:49 GMTResearchhttps://arxiv.org/abs/2609.05514https://arxiv.org/abs/2609.05514Large Language Model (LLM)-based agents are increasingly used as proxies for human participants in social science research, yet it remains unclear whether they can faithfully simulate diverse and conflicting human value systems. We present a World Values Survey (WVS)-grounded simulation framework where culturally diverse agents with different communication styles engage in longitudinal, value-laden discussions. Across approximately 4,000 conversations involving 1,200 personas, 15 topics, and three models (GPT-4o, Gemini-2.5-Flash, and Gemma-4-E4B), we evaluate value faithfulness, value drift, and conversational realism. We find that more than 50\% of personas fail to express their assigned… — Source: arXivMon, 31 Aug 2026 09:30:05 GMTResearchhttps://listenlabs.ai/blog/we-made-the-2026-ia40-for-the-second-year-runninghttps://listenlabs.ai/blog/we-made-the-2026-ia40-for-the-second-year-runningMadrona named Listen Labs one of the 40 most important private companies in applied AI for the second year in a row. — Source: Listen Labs blogMon, 31 Aug 2026 00:00:00 GMTVoiceshttps://arxiv.org/abs/2608.30033https://arxiv.org/abs/2608.30033Large language models (LLMs) are increasingly used to simulate human behavior but frequently fail to exhibit realistic cognitive constraints, suffering from a "superhuman bias." Using a dataset of over 71,000 reading comprehension responses from 2,359 primary-school students (grades 4--6), we demonstrate that standard persona prompting yields near-perfect, deterministic performance, failing to capture the natural variance of developing readers. To address this, we introduce the Cognitively Bounded User Simulator (CBUS), an architectural framework that explicitly models the restricted working memory of young readers through an episodic bottleneck. Within this framework, we formalize two… — Source: arXivSun, 30 Aug 2026 20:44:17 GMTResearchhttps://arxiv.org/abs/2608.29803https://arxiv.org/abs/2608.29803Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs in response to persuasive arguments, as humans do, remains poorly understood. We conduct a systematic comparison using a naturally occurring online persuasion corpus in which original posters explicitly verify whether a reply changed their view. Our results show that LLMs achieve only slight agreement with humans (Cohen's kappa ranging from 0.079 to 0.178). Content-level analyses show that humans and LLMs agree on the strongest persuasion cues but diverge on finer ones: humans are more swayed by novel content and assertive language, whereas… — Source: arXivSun, 30 Aug 2026 14:08:06 GMTResearchhttps://01net.it/synthetic-respondents-modeled-after-real-people-are-changing-how-market-research-gets-donehttps://01net.it/synthetic-respondents-modeled-after-real-people-are-changing-how-market-research-gets-doneSource: 01netThu, 27 Aug 2026 18:11:00 GMTNewshttps://arxiv.org/abs/2608.27111https://arxiv.org/abs/2608.27111Synthetic populations of eleven Italian municipalities (1,814,317 individuals in 887,937 households) generated from published aggregates alone: ISTAT census and register tables, census-section counts, the national civic-address register, public-use survey microdata, and six municipal open-data portals, every source certified in a registry with licence, fingerprint and declared affordances. Four rings give every attribute a declared place: a maximum-entropy joint model of up to nine demographic attributes; whole-vector donation of twenty-three attitudinal and health variables from survey respondents; placement to census section, single year of age and address; and households constrained by… — Source: arXivThu, 27 Aug 2026 13:25:35 GMTResearchhttps://arxiv.org/abs/2608.26674https://arxiv.org/abs/2608.26674As large language models are increasingly deployed to simulate diverse human characters, ensuring persona fidelity, defined as the extent to which an agent's behavior consistently reflects the psychological and stylistic characteristics of a target persona, has become a critical requirement. However, existing evaluation paradigms primarily rely on either holistic LLM-based judges, which are prone to "holistic appraisal hallucination'', or static psychometric inventories, which fail to capture the context-dependent fidelity required in dynamic dialogue. To address these limitations, we propose PRISM (Persona Reasoning with Inverse SFL-based Modeling), a psycholinguistically grounded… — Source: arXivThu, 27 Aug 2026 06:27:51 GMTResearchhttps://arxiv.org/abs/2608.26221https://arxiv.org/abs/2608.26221As generative AI gains traction, researchers are investigating its potential to serve as proxies for humans. From undergoing cognitive psychology experiments to experiencing an epidemic, generative agents, agents powered by generative AI models, produce realistic human behavior when prompted. This study explores the sensitivity of these generative agents' behavior to prompt modifications and varied persona names of the agents. To assess this sensitivity, we use a generative agent epidemic model, wherein each agent is prompted daily on whether it wants to isolate or commingle with other agents. We found that using synonymous prompts results in negligible changes to the model's outcomes.… — Source: arXivWed, 26 Aug 2026 12:42:36 GMTResearchhttps://simile.ai/blog/confidencehttps://simile.ai/blog/confidenceBehind the scenes into Simile's proprietary confidence model. — Source: Simile blogTue, 25 Aug 2026 17:00:00 GMTVoiceshttps://arxiv.org/abs/2608.24215https://arxiv.org/abs/2608.24215Large language model (LLM)-based multi-agent social simulation has demonstrated compelling results, but Agentopia was evaluated with 100 agents over 10 simulated years using Qwen3.5-397B-A17B, leaving the behavior of reduced-scale deployments on consumer hardware unclear. In this paper, we implement and evaluate a reduced-scale Agentopia port on a single NVIDIA RTX 5070 Ti(12 GB VRAM) using Qwen3-8B-AWQ, a 4-bit quantized model. We introduce three structural adaptations for this setting: (1) system-managed layered memory compression, (2) four activity blocks per simulated day, and (3) explicit physical- and mental-health state variables. Across three independent stochastic runs, two runs… — Source: arXivTue, 25 Aug 2026 08:21:58 GMTResearchhttps://arxiv.org/abs/2608.23005https://arxiv.org/abs/2608.23005Large language models are increasingly used as synthetic survey respondents, promising cheap access to rare intersectional populations. We test standard demographic-persona methods against every real intersectional subgroup across 15 waves of Pew's American Trends Panel -- 21 million simulated response distributions from eight models. In real respondents, subgroup opinion is approximately the additive sum of its single-identity components, yet grows 2.5x more distinctive as identities intersect. Simulated respondents show no such composition: a single feature explains a two-feature persona's responses better than the additive combination in 75-82% of subgroups, and a third feature adds… — Source: arXivMon, 24 Aug 2026 09:07:51 GMTResearchhttps://europeanbusinessreview.com/beyond-the-survey-how-synthetic-research-is-changing-consumer-insighthttps://europeanbusinessreview.com/beyond-the-survey-how-synthetic-research-is-changing-consumer-insightSource: europeanbusinessreview.comMon, 24 Aug 2026 07:00:00 GMTNews