Recent Multimodal Large Language Models (MLLMs) excel in precise entity recognition and powerful reasoning, significantly boosting performance and efficiency in complex vision-language tasks. Despite these gains, this advanced reasoning process introduces a subtle yet critical vulnerability. We identify an underexplored relational reasoning failure mode in which models over-prioritize semantic coherence, thereby inferring ungrounded interactions from mere object entities. We term this phenomenon Relation Evidence Amnesia (REA), wherein MLLMs correctly localize individual entities but fail to enforce true visual dependencies as the generative process becomes hijacked by semantic rationalization. To mitigate REA, we propose Relation-Grounded Faithful Reasoning (RGFR), an inference-time decoding intervention that operates without modifying target model parameters. Adopting a decoupled verification philosophy, RGFR employs a semantic-driven soft-gating mechanism to simultaneously incorporate explicit entity constraints and extract counterfactual causal evidence, followed by a Unified Schema-Aware Rectifier that rigorously mediates these visual-causal constraints during the decoding process. Extensive experiments across relation benchmarks demonstrate that RGFR improves relation recognition accuracy by over 8.1% compared to baselines, while largely preserving task utility. These results highlight the critical role of decoupled, evidence-grounded rectification in achieving faithful relational reasoning for multimodal models.

Recent advances in Multimodal Large Reasoning Models (MLRMs) have enabled explicit chain-of-thought inference across vision and language, substantially improving performance on complex reasoning tasks. Despite these gains, the reasoning process introduces a subtle yet critical vulnerability. We identify an underexplored multimodal safety failure mode in which harmful objectives are embedded within ostensibly benign contexts, leading models to over-prioritize narrative coherence during reasoning. We term this phenomenon Safety Context Amnesia (SCA), wherein models correctly perceive risk-relevant visual cues but fail to enforce safety constraints as the reasoning process becomes dominated by contextual alignment. To mitigate SCA, we propose Intent-Guided Safety Reasoning (IGSR), an inference-time defense that operates without modifying target model parameters. IGSR employs a Perception Decoupler to extract objective visual evidence into a structured intent output, followed by a Cognitive Arbiter that enforces explicit safety constraints prior to generation. Extensive experiments across multiple multimodal safety benchmarks demonstrate that IGSR improves defense success rates by over 62% compared to baselines, while largely preserving task utility. These results highlight the critical role of structured, intent-aware reasoning in achieving robust safety reasoning for multimodal reasoning models.