{"slug": "profilefoundry-a-synthetic-person-object-substrate-for-privacy-memory-and-tool", "title": "ProfileFoundry: A Synthetic Person-Object Substrate for Privacy, Memory, and Tool-Use Evaluation in LLM Agent", "summary": "Researchers released ProfileFoundry, a synthetic dataset of 100,000 person objects with 709,228 events, 40,338 households, and 52,491 employers, designed for evaluating LLM agents on memory, privacy, and tool-use tasks. The deterministic generator ensures cross-field and temporal consistency without using real user data, enabling responsible redistribution and controlled evaluation.", "body_md": "arXiv:2606.26403v1 Announce Type: new\nAbstract: Foundation-model research increasingly needs data about people: user state, personal histories, relationships, contact-like fields, documents, and longitudinal updates. Real user data is difficult to share, perturb, audit, or redistribute responsibly, while independently generated fake fields rarely preserve the cross-field and temporal consistency needed for controlled evaluation. We present PROFILEFOUNDRY, a deterministic generator and fixed reference release of 100,000 adult synthetic Person Objects across eight locales. Each object combines a typed current snapshot, household, family, and employer links, snapshot-aligned events, normalized relational views, and generation provenance. The release contains 709,228 events, 40,338 households, 52,491 employers, and 518,564 directed relationship edges. We report evidence in separate categories: selected population-marginal comparisons, per-object invariant checks, release-wide referential and temporal closure, and coincidence/provenance screens. PROFILEFOUNDRY is not a population-fidelity model, a rendered-text corpus, or a formal privacy mechanism. Instead, it is a responsible synthetic source layer for constructing downstream foundation-model evaluations involving memory, privacy, document understanding, record linkage, and agent state while keeping the synthetic person behind each artifact inspectable", "url": "https://wpnews.pro/news/profilefoundry-a-synthetic-person-object-substrate-for-privacy-memory-and-tool", "canonical_source": "https://arxiv.org/abs/2606.26403", "published_at": "2026-06-26 04:00:00+00:00", "updated_at": "2026-06-26 04:05:24.272659+00:00", "lang": "en", "topics": ["large-language-models", "ai-safety", "ai-ethics", "ai-agents", "ai-research"], "entities": ["ProfileFoundry", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/profilefoundry-a-synthetic-person-object-substrate-for-privacy-memory-and-tool", "markdown": "https://wpnews.pro/news/profilefoundry-a-synthetic-person-object-substrate-for-privacy-memory-and-tool.md", "text": "https://wpnews.pro/news/profilefoundry-a-synthetic-person-object-substrate-for-privacy-memory-and-tool.txt", "jsonld": "https://wpnews.pro/news/profilefoundry-a-synthetic-person-object-substrate-for-privacy-memory-and-tool.jsonld"}}