PerSo introduces a benchmark for evaluating LLM social intelligence through real-world persuasion dialogues from Reddit's ChangeMyView. It tests cognitive intelligence by forecasting a persuadee's beliefs, emotions, and intents, and behavioral intelligence by selecting the more persuasive response. Unlike prior work that often relies on synthetic scenarios or isolates these two aspects, PerSo jointly evaluates both on naturally occurring interactions, providing a more ecologically valid testbed.

Current social agent benchmarks reward fluent conversation but ignore whether agents actually reach valid plans that respect hidden constraints. This paper introduces HICO-Bench, a benchmark where two agents with private information must coordinate verifiable outcomes, and PaCPO, a reinforcement learning method that redistributes terminal rewards across turns by checking tentative plans against paired worlds. The work matters because it reveals that high LLM-judged conversation ratings frequently mask infeasible or unsupported agreements, and provides a concrete credit-assignment mechanism to train agents on verified social outcomes rather than just dialogue style.

This paper tackles community detection in multilayer networks where connectivity patterns and node degrees vary across layers. The proposed method, mFROST, jointly factorizes each layer via orthogonal symmetric nonnegative matrix trifactorization $A_l \approx D_l V \theta_l (D_l V)^\top$, enforcing shared community assignments $V$ while allowing layer-specific degree corrections $D_l$ and community interactions $\theta_l$. This flexibility matters because real-world multiplex networks often exhibit heterogeneous node sociability and diverse block structures that simpler shared-factor models miss.

This paper tackles the lack of shared formalism for comparing hierarchical memory systems in language agents. It proposes a unifying theory based on three operators: extraction (α) that maps raw data to atomic units, coarsening (C = (π, ρ)) that partitions and summarizes units, and traversal (τ) that selects content under a token budget. The core insight is the self-sufficiency spectrum of representatives ρ, which constrains viable retrieval strategies—an observation the authors call the coarsening-traversal (C–T) coupling.