Masked diffusion language models (dLMs) offer a promising parallel alternative to autoregressive models for complex reasoning. However, they face a unique credit-assignment challenge: reasoning failures typically stem from a few critical, early commitments that cascade downstream. Existing post-training overlooks this dynamic: supervised fine-tuning (SFT) weights all tokens uniformly, while online reinforcement learning (RL) attributes a single trajectory-level reward to every position. We introduce Pivot-SD, an efficient offline framework supervising only these high-impact bottlenecks (pivots). Pivot-SD isolates pivots using an information-gain metric measuring uncertainty reduction over the remaining masked block. It applies bidirectional supervision: successful pivots are reinforced with cross-entropy, while critical errors in failed trajectories are suppressed via targeted unlikelihood, preserving valid surrounding sub-steps. Using only 200 questions and four rollouts each, Pivot-SD improves LLaDA-8B-Instruct over full-sequence SFT and budget-matched diffusion RL baselines across math and code benchmarks.

As large language models (LLMs) are increasingly integrated into multi-agent and human-AI systems, understanding their awareness of both self-context and conversational partners is essential for ensuring reliable performance and robust safety. While prior work has extensively studied situational awareness which refers to an LLM’s ability to recognize its operating phase and constraints, it has largely overlooked the complementary capacity to identify and adapt to the identity and characteristics of a dialogue partner. In this paper, we formalize this latter capability as interlocutor awareness and present the first systematic evaluation of its emergence in contemporary LLMs. We examine interlocutor inference across three dimensions—reasoning patterns, linguistic style, and alignment preferences—and show that LLMs reliably identify same-family peers and certain prominent model families, such as GPT and Claude. To demonstrate its practical significance, we develop three case studies in which interlocutor awareness both enhances multi-LLM collaboration through prompt adaptation and introduces new alignment and safety vulnerabilities, including reward-hacking behaviors and increased jailbreak susceptibility. Our findings highlight the dual promise and peril of identity—sensitive behavior in LLMs, underscoring the need for further understanding of interlocutor awareness and new safeguards in multi-agent deployments.