As large language models are increasingly adopted and trusted in real-world applications, understanding their behavior beyond single-turn prompting has become critical. Existing safety evaluations primarily focus on refusal-based methods that test whether models avoid responding to inappropriate or violent requests, leaving open questions about how models behave in interactive social settings. In this paper, we observe the adversarial behavior of LLM models through a multi-agent simulation across five diverse social deduction conversational games, acting as testbeds that provide social pressures and survival stress based on game design without explicit prompt injections. From these interactions, we construct a closed behavioral taxonomy derived through open card sorting, applied uniformly across models using a meta-LLM for behavior labeling. This approach displays that models exhibit distinct behavioral profiles and that models’ different ways of structured deliberation influence both social stability and competitive success.

Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations. Although inference-time steering offers a cost-effective way to adjust model behavior without updating its parameters, existing methods often fail to be simultaneously effective, utility-preserving, and training-efficient due to their rigid, one-size-fits-all designs and limited adaptability. In this work, we present FineSteer, a novel steering framework that decomposes inference-time steering into two complementary stages—conditional steering and fine-grained vector synthesis—allowing fine-grained control over when and how to steer internal representations. In the first stage, we introduce a Subspace-guided Conditional Steering (SCS) mechanism that preserves model utility by avoiding unnecessary steering. In the second stage, we propose a Mixture-of-Steering-Experts (MoSE) mechanism that captures the multimodal nature of desired steering behaviors and generates query-specific steering vectors for improved effectiveness. Through tailored designs in both SCS and MoSE, FineSteer maintains robust performance on general queries while adaptively optimizing steering vectors for targeted inputs in a training-efficient manner. Extensive experiments on safety and truthfulness benchmarks show that FineSteer outperforms the state-of-the-art methods in overall performance (e.g., a 7.6% improvement on TruthfulQA over Llama-3), achieving stronger steering performance with minimal utility loss. The code is available at https://github.com/YukinoAsuna/FineSteer