Synthetic dialogue corpora are increasingly used as proxies for target data, yet persona-grounded generators optimize individual conversations rather than corpus composition, yielding plausible dialogues with distorted population-level behavior mixes. We introduce GroupPersona, a framework that aligns synthetic corpora with a reference behavior distribution. GroupPersona turns population statistics into generation controls by discovering recurring behavioral groups in the reference corpus and using their prevalence and interaction patterns to condition user agents.

We evaluate corpus-level fidelity through behavioral alignment and structural preservation, measured by average Jensen–Shannon distance over 12 behavior attributes and 5 dialogue structure attributes, respectively. Across four corpora spanning two source families and two construction regimes, GroupPersona reduces behavioral Jensen–Shannon distance from 0.234 to 0.177 over the best-average baseline, a 24.4% reduction. It is best or tied-best on all four corpora. It maintains comparable structural distance at 0.167 versus 0.169. Together, these results show that GroupPersona improves population-level behavioral fidelity while preserving dialogue structure.

Long-context LLMs can now ingest entire online discussion threads, but understanding their social discourse requires more than reading a long document: models must track parent–reply relations, turning points, scoped subtrees, cross-branch contrasts, and participant trajectories. To test this structure-aware social reasoning, we introduce **LongSocialBench**, a benchmark of 1,462 verified human-authored multiple-choice items drawn from 94 complete Hacker News, Stack Exchange, and Reddit r/ChangeMyView episodes, with a median length of ≈73K tokens. Each item pairs a complete serialized discussion and reply structure with a four-option question, requiring models to recover structured social evidence. Released items are verified for answerability, option uniqueness, and evidence grounding.

Across 18 models and 29 evaluation settings, current long-context workflows remain far below human performance. The best individual result comes from Claude-Opus-4.7, which reaches 63.0% when prompted to eliminate incorrect options before answering, compared with 72.4% for independent human readers. Averaged across all 18 models, the full-context Baseline scores 43.9%. Supplying the gold evidence scope raises this to 55.0%, showing that substantial errors remain even after the relevant thread region is identified. LongSocialBench shows that the missing capability is not context access or prompting alone, but social understanding over structured reply trees.

Large-scale conversational assistants such as Cortana, Alexa, Google Assistant and Siri process requests through a series of modules for wake word detection, speech recognition, language understanding and response generation. An error in one of these modules can cascade through the system. Given the large traffic volumes in these assistants, it is infeasible to manually analyze the data, identify requests with processing errors and isolate the source of error. We present a machine learning system to address this challenge. First, we embed the incoming request and context, such as system response and subsequent turns, using pre-trained transformer models. Then, we combine these embeddings with encodings of additional metadata features (such as confidence scores from different modules in the online system) using a “mixing-encoder” to output the failure point predictions. Our system obtains 92.2% of human performance on this task while scaling to analyze the entire traffic in 8 different languages of a large-scale conversational assistant. We present detailed ablation studies analyzing the impact of different modeling choices.