1. Generate text from CG (p.3)
in an NLP-pipeline, text generation is exactly what CG hasn't been doing so far, so can this method be adapted to handling (og at least facilitating) the (morphological) generation module in e.g. an MT pipe?
you "only" use generation to create "symbolic" sentences that allow (or not) a rule to fire, giving the precding rules
can you imagine SAT to be used to generate text given either a bag of words (syntactic generation) or a string of lemmas with tags (morphological generation)?

2. Rule conflicts/contradiction (introduction and ch. 4.2)
5 conflict cases are distinguished in 4.3.2
There is a big difference between
(a) true contradictions (stating contradictory truths about language, or contradictory contexts for the same operation, or in-rule conflicts) and
(b) merely being superfluous/spurious or redundant in the given order of rules
(a) is always unintended, (b) is about efficiency
can you provide an estimate as to what percentage of conflicts were of which type?

3. Rule redundancy
use case: grammar porting to related languages or across genres
some rules will never fire with the new input
for lexical reasons
or because certain ambiguity classes may not exist.
--> use SAT to prune ported grammars
is the following hypothesis true?
"For each superfluous rule there is at least 1 place higher up in the rule order, that would make it un- superfluous, but does not create a conflict"
If the hypothesis is true, could SAT be used to reorder a grammar in order to improve its recall
identify more precise/cautious rules made superfluous by more general/heuristic rules
moved them up into a section before the latter, resulting in earlier (but cautious) disambiguation and better context for rules being applied between the precise and the general rule

4. non-reductionist rule types
Vislcg and cg3 use rules that manipulate cohorts in other ways than removing readings
SUBSTITUTE, APPEND, ADDCOHORT, REMCOHORT
look-delete can refer to deleted readings
Can you envision a solution of how to use your SAT- based method in the presence of these tags?
Can the conflict-marking method be made tolerant of these rule types?

5. Expressivity Your claim: PCG is more expressive than SCG
true globally, at the grammar-level
but is it true locally, at the rule level?
Ordinary CG does have a way of jointly addressing 2 of the 4 readings of "la casa": TEMPLATEs
(prn v) OR (det n)
NEGATE (prn n) OR (det v)
Targeting templates is not implemented, but would further allow parallelism in the individual rule
Templates are recursive, so template contexts help closing the expressivity gap to generative grammar

6. Grammaticality 2.3.1: CG is robust in handling ungrammatical input
"en bord" left ambiguous as "en DET/N bord N" in the face of an agreement error
But such robustness does not necessarily preclude a grammaticality check
typically things like a grammaticality check for np agreement will be performed in an earlier section, then followed by a general (inflexion-free) POS rule in a later section
in a grammar checker application, there would even be an explicit grammaticality check: An error-mapping rule would suggest a different gender for the article in the face of a possible DET N sequence, rather than discard the DET reading
Would it be possible (and how) to distinguish ordinary "detrimental" conflicts from such robustness-inspired "useful" conflicts across heuristicity sections?

7. Low abstraction / Shallow syntax (2.3.2)
It is true that CG was conceived with such shallowness in mind, but this is because focus was on morphological disambiguation (cf. your SAT system)
But being "shallow" in terms of encoding all information as tags on tokens does not mean absence of deep structure or long-distance relations
dependency trees are standard now for CG systems, and dependency links transcend the +/- position counting of traditional CG
CG3 allows arbitrarily named relations on top of dependency
secondary dependency, frame-argument-links
cross-window spans are necessary for higher-level structural annotation
anaphora, rhetorical structure

8. Experiments: Forced assigment
3.3 SAT-CG with forced assignment performs better than without
probably due to better recall
what about having 2 rounds
first with forced assignment
after this without forced assignment
so context-disambiguation can help with precision as an add-on, but not hinder prior work of good rules
the added effect might well give a better F-Score

9. Experiments: Baseline
rules ouperform 261!
How did you decide ordering for the 19 rules? Were there sections? ML-ordering
Empty grammar: F=70
in the light of the low F-scores, I miss a real baseline. For instance always choosing the most likely (monogram) reading, as counted in the test corpus.
How was the ambiguous version created?
access to a full lexicon?
only those readings that occurred in the corpus?
this would mean losing ambiguity that in the corpus is always resolved in the same way
Rare readings might not be added this way, while they are part of the real task in a real annotation scenario.

10. Experiments: Context disambiguation
Why does precision go up for NoTar in table 3.4 (expected more limited power for the rules with NoTar
But actually precision goes up even for the 261 grammar, just not from NoAss to NoTar, but only from NoAff to NoTar (table 3.5).
Maybe: context disambiguation propagation of rules is a bad idea
if the proportion of context-propagating rules is high (19- rule grammar), this - negative - effect is so strong, that even NoAss has enough cases to fall in precision to a level under NoTar.
if 10 out of 14 "assignment-saved" context readings are correct (i.e. if the context disambiguation does 70% harm), and the NoAff has 20 false negative and 20 false positive for each 100 readings, than it is true that precision NoAff (80/120=0.666) is smaller than precision NoTar (80+10)/(120+14=0.672)

11. Symbolic sentences

12. Evaluation (4.3)
As you say, evaluating performance of a grammar after removal of identified conflicts would be very interesting
after all, it could be that the conflicts only affect speed, not F-Score performance, at least not in SCG
What is the problem? Knowing exactly how to resolve a conflict?
removal of redundant rules would be easy, but meaningless
removal of rules made superfluous by more general predecessors, has to be weighed against moving up the superfluous rule until it has an effect
removal of linguistic contradictions is more tricky, because simply removing a conflict-marked rule r is not a solution, after all one of the rules making up R could be the real problem
Use machine learning to optimize which rule to move and which to remove?

13. Sequential vs. parallel
nice overview table 2.1 (p.13) + good discussion
Claim: sequential CG only allows changes in the target.
not strictly true for MOVE, dependency and relation- adding rules, which all affect (at least) 2 tokens.

14. Speed
3.3.2: smaller speed difference (between SCG/CG3? and PCG/SAT-CG?) than historically between cg2 and FSIG
But Pasi Tapanainen's cg2 runs 6 times faster than cg3 on the same machine (maybe he used FSTs in cg2?)
So if historical 5 seconds for 15 words (3/s) is ca times slower than Voutilainen's 4000 words / sec, and CG3 (0.04s) is 130 times faster than SAT-CG ( s), then a factor 6 speed difference between cg2 and vislcg3 almost evens the odds (6 x 130 =780).