Agentic language models solve tasks through reasoning, tool calls, observations, and terminal answers, exposing rich process information beyond the final answer. For practitioners, knowing which process patterns lead to success or failure is essential to diagnose agent errors and build better process reward models without relying on final-answer supervision. However, existing analyses often evaluate only final outcomes or, when examining traces, flatten them into homogeneous step sequences, fragmenting each reasoning round into disconnected steps. Our key insight is that an agent’s reasoning, action, and observation within a round are better treated as typed segments of a single underlying process state, rather than independent steps. We propose MULTI-VIEW-RA, a reasoning abstraction that represents each round through two complementary views: accumulated state and within-round change. Using unlabeled clustering and binary outcome labels, we apply MULTI-VIEW-RA to two practical use cases: diagnosing failures by surfacing the process states that separate correct from incorrect trajectories, and providing a transition-level correctness signal that is a candidate source of process supervision. Across Math, HotpotQA, and FEVER with multiple Qwen and Llama models, MULTI-VIEW-RA outperforms step-level explainability baselines in cases with sufficient success rates, with particularly strong gains on longer, tool-rich traces.

Current evaluation of large language models (LLMs) overwhelmingly prioritizes accuracy; however, in real-world and safety-critical applications, the ability to abstain when uncertain is equally vital for trustworthy deployment. We introduce a unified benchmark and evaluation protocol for abstention in medical multiple-choice question answering (MCQA), integrating conformal prediction, adversarial question perturbations, and explicit abstention options. Our systematic evaluation of both open- and closed-source LLMs reveals that even state-of-the-art, high-accuracy models often fail to abstain when uncertain. Notably, providing explicit abstention options consistently increases model uncertainty and safer abstention, far more than input perturbations, while scaling model size or advanced prompting brings little improvement. These findings highlight the central role of abstention mechanisms for trustworthy LLM deployment and offer practical guidance for improving safety in high-stakes applications.

Uncertainty awareness is essential for large language models (LLMs), particularly in safety-critical domains such as medicine where erroneous or hallucinatory outputs can cause harm. Yet most evaluations remain centered on accuracy, offering limited insight into model confidence and its relation to abstention. In this work, we present preliminary experiments that combine conformal prediction with abstention-augmented and perturbed variants of medical QA datasets. Our early results suggest a positive link between uncertainty estimates and abstention decisions, with this effect amplified under higher difficulty and adversarial perturbations. These findings highlight abstention as a practical handle for probing model reliability in medical QA.