Robots operating in dynamic, unstructured environments must maintain safety without becoming unnecessarily conservative. While control barrier functions (CBFs) provide a principled mechanism for collision avoidance, their behavior is typically determined from geometric state alone and therefore cannot distinguish between semantically different situations with similar physical configurations. We present AlphaAdj, a vision-to-control navigation framework that uses egocentric RGB observations and a vision-language model (VLM) to adapt CBF conservativeness according to visual semantic context. The VLM produces a scalar contextual caution signal that dynamically modulates the CBF parameter alpha, while a geometry- and speed-dependent cap bounds admissible aggressiveness. To support real-time operation despite VLM latency, inference runs asynchronously with the control loop, and the semantic signal is extracted directly from token-level output probabilities rather than full autoregressive text generation. We evaluate AlphaAdj across matched-geometry, dynamic-human, and cluttered navigation scenarios, as well as on a physical Clearpath Jackal. When obstacle geometry is held fixed, AlphaAdj allocates 0.405 m more clearance to a human than to a benign object, while geometry-only and non-visual adaptive CBF baselines produce no semantic differentiation. This additional behavioral reserve improves robustness to unexpected human motion, including cluttered conditions in which all evaluated geometric baselines collide while AlphaAdj remains collision-free. The system maintains a 0.0167 s control period with 0.158 s mean VLM round-trip latency, demonstrating that semantic visual reasoning can complement geometric safety control without entering the critical control path.

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