Large multimodal reasoning models solve challenging visual problems via explicit long-chain inference: they gather visual clues from images and decode clues into textual tokens. Yet this capability also increases hallucinations, where the model generates content that is not supported by the input image or the question. To understand this failure mode, we identify reasoning drift: during clue gathering, the model over-focuses on question-irrelevant entities, diluting focus on task-relevant cues and gradually decoupling the reasoning trace from visual grounding. As a consequence, many inference-time localization or intervention methods developed for non-reasoning models fail to pinpoint the true clues in reasoning settings. Motivated by these insights, we introduce ClueRecall, a metric for assessing visual clue retrieval, and present ClueTracer, a training-free, parameter-free, and architecture-agnostic plugin for hallucination suppression. ClueTracer starts from the question and traces how key clues propagate along the model’s reasoning pathway (question → outputs → visual tokens), thereby localizing task-relevant patches while suppressing spurious attention to irrelevant regions. Remarkably, without any additional training, ClueTracer improves all reasoning architectures (including R1-OneVision, Ocean-R1, MM-Eureka, .) by 1.21 on reasoning benchmarks.

Vision–language models remain susceptible to multimodal jailbreaks and over-refusal because safety hinges on both visual evidence and user intent, while many alignment pipelines supervise only the final response. To address this, we present SaFeR-ToolKit, which formalizes safety decision-making as an inspectable protocol: during offline data synthesis, a planner specifies a persona, a **Perception**→**Reasoning**→**Decision** tool set, and a constrained transition graph, while a responder emits a typed key–value tool trace before the final answer. To learn this protocol, we train a single policy with a three-stage curriculum (SFT→DPO→GRPO), where GRPO directly supervises tool usage beyond answer-level feedback. We contribute (**I**) the first tool-based safety reasoning dataset of 30,654 training entries (SFT 6k, DPO 18.6k, GRPO 6k) plus 1k held-out evaluation, and (**II**) experiments on Qwen2.5-VL showing large gains in Safety/Helpfulness/Reasoning Rigor on 3B (29.39/45.04/4.98 → 84.40/71.13/78.87) and 7B (53.21/52.92/19.26 → 86.34/80.79/85.34), while preserving general capabilities (3B: 58.67 → 59.21; 7B: 66.39 → 66.81). Code: [https://github.com/Duebassx/SafeR_Toolkit](https://github.com/Duebassx/SafeR_Toolkit).