arXiv — cs.AI preprintsInternational9 October 2026
RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.01780v3 Announce Type: replace Abstract: An AI companion that talks with someone for months should come to understand them. It should know who they are, remember what they said, and recognize when something from the past matters now. Testing this needs real conversations, but real conversations are private, so existing benchmarks use invented people and invented questions. We release RealCompanion, ten real relationships between people and an AI companion, with 27,218 messages over up to 120 days. For each person, we release the full conversation, a profile, a persona, chat test ite
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment9 October 2026
- Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction9 October 2026
- Agent-Controlled Forgetting for Tool-Using Agents: Reversible Context Curation in Practice9 October 2026
- Verification and Self-Improvement in Agentic AI: Foundations and Limits9 October 2026
- The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate9 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.