Fix vehicle flag, it's --solver instead of --verifier

src/assets/bibliography.bib

@@ -29,7 +29,7 @@ @misc{affeldt2026
 }
 
 @misc{capucci2026,
-      title={Quantitative Linear Logic}, 
+      title={Quantitative Linear Logic},
       author={Matteo Capucci and Robert Atkey and Charles Grellois and Ekaterina Komendantskaya},
       year={2026},
 }

src/chapters/1-introduction.md

@@ -105,7 +105,7 @@ If you are interested to know more about the theoretical underpinnings of Vehicl
 
 - Daggitt, M., Kokke, W., Atkey, R.,  Komendantskaya, E., Slusarz, N., and  Arnaboldi, L. *Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs.* Invited paper at 10th International Conference on Formal Structures for Computation and Deduction (FSCD'25).
 
-- Daggitt, M., Komendantskaya, E., 
+- Daggitt, M., Komendantskaya, E.,
 Sirman, A., Bruni, A., Teuber, S., Smart, J., Passmore, G.: *Compositional Neural-Cyber-Physical System Verification in the Interactive Theorem Prover of Your Choice.* International Conference on Functional Programming (ICFP'26).
 
 - Casadio, M., Komendantskaya, E., Daggitt, M.L., Kokke, W., Katz, G., Amir, G., Refaeli, I.:
@@ -127,4 +127,3 @@ ACM (2023).
 
 
 -  Sirman, A., Conway, F., Ciupa, J.,  Grinbergs, G., Komendantskaya, E., Hoang, S., Rawson, M., Bruni, A., Belle, V.,  Williams, M. *Vancomycert: A Certified Neuro-Symbolic Drug Delivery System (Case Study).* Symposium on AI Verification (SAIV'26).
-

src/chapters/3-proving-robustness.md

@@ -309,4 +309,4 @@ Once this is done, define the spec and verify its robustness. Experiment with di
 
 Some sample solutions are available in [this folder](https://github.com/vehicle-lang/tutorial/tree/exercises/chapter-3/solutions). But we recommend that you try solving all exercises first.
 
-Wish to contribute a new solution? Let us know and we will add it.
+Wish to contribute a new solution? Let us know and we will add it.

src/chapters/4-property-driven-training.md

@@ -61,7 +61,7 @@ Models learn by iteratively tweaking their optimisation parameters with the goal
 
 $$\min_\theta\mathcal{L}(\hat x,y)$$
 
-We are now ready to code all of this. 
+We are now ready to code all of this.
 
 ## Getting Started with the Code
 
@@ -155,7 +155,7 @@ Keeping the data in the range the specification talks about costs us nothing her
 network below trains perfectly well on raw pixels --- and it keeps a single meaning for
 $\epsilon$ across training and verification. Part II returns to this: it is a small
 instance of the general difficulty of interfacing a logical specification with an
-optimisation objective. One of the exercises in this chapter will give you a chance to reconstruct the whole training-verification pipeline *with* normalisation baked into the training. 
+optimisation objective. One of the exercises in this chapter will give you a chance to reconstruct the whole training-verification pipeline *with* normalisation baked into the training.
 
 Finally, note that the two frameworks store images differently: PyTorch's `ToTensor`
 produces channel-first batches of shape `(N, 1, 28, 28)`, whereas here we append the
@@ -309,7 +309,7 @@ training objective helps, we need a radius at which the network is genuinely vul
 At $\epsilon = 0.005$ there is one image to win back; at $\epsilon = 0.02$ there are
 sixteen.
 
-We will first consider traditional machine learning methods that were introduced to address the problem of adversarial robustness. 
+We will first consider traditional machine learning methods that were introduced to address the problem of adversarial robustness.
 
 
 ## Data augmentation
@@ -476,7 +476,7 @@ X = \left\{ x \in \mathbb{R}^5 :\;\;
     &960 \le x_5 \le x_5^{\max}
 \end{aligned}
 \right\}
-$$ 
+$$
 
 Where $x_n^{max}$ is the maximum input value for attribute $x_n$ -->
 

src/chapters/5-exporting-to-ITPs.md

@@ -112,7 +112,7 @@ A neural network controller `controller.onnx` can be verified against the specif
 vehicle verify \
   --specification windController.vcl \
   --network controller:controller.onnx \
-  --verifier Marabou \
+  --solver Marabou \
   --cache controller-result
 ```
 
@@ -717,7 +717,7 @@ Navigate to the `chapter-5/chapter-code` directory of the [tutorial repository](
    vehicle verify \
      --specification windController.vcl \
      --network controller:controller.onnx \
-     --verifier Marabou \
+     --solver Marabou \
      --cache controller-result
    ```
 2. Export the verified specification to your ITP of choice (e.g. Rocq):