same-for-all / may-vary-across — must every item use the same choice?

A trailing qualifier on an instruction or requirement that already assigns exactly one value of one clearly named choice slot to each member of an explicitly bounded, nonempty finite set S. The qualifier marks the cross-member constraint on that slot; it does not supply the per-member cardinality. The set reference must resolve to the members in scope. `same-for-all(S)` means every member must receive the same value of that slot. Careful English: `All members must use the same [choice]`. `may-vary-across(S)` means members may receive the same or different values of that slot. Careful English: `Members may use the same or different [choices]`. Reuse is allowed: all values equal, some repeated, and all distinct are each compatible with this qualifier. All other eligibility, capacity, and safety requirements still apply. This is permission for variation, not a claim that variation occurred, not a requirement for diversity, and not permission to ignore another constraint. Human example: `Assign exactly one reviewer to each of reports A, B and C, same-for-all(reports)` requires one reviewer common to all three reports. `Assign exactly one reviewer to each of reports A, B and C, may-vary-across(reports)` allows a separate choice for each report, including reusing a reviewer. Assuming Ada and Ben are both eligible for every report and have sufficient capacity, Ada/Ada/Ada is allowed by either qualifier. Ada/Ben/Ada is allowed by may-vary-across and forbidden by same-for-all. Nobody is required to find three different reviewers. The shortest faithful careful-English versions of that example are `Assign the same single reviewer to reports A, B and C` and `Assign exactly one reviewer to each of reports A, B and C; reviewers may be the same or different`. Use concise complete English when comparing the forms; do not make the baseline artificially longer. Precisely, let f(s) be the single selected value for member s. same-for-all requires f(s) = f(t) for every pair of members. may-vary-across adds neither equality nor inequality between f(s) and f(t). The pair is intentionally not a pair of logical opposites: a constant assignment satisfies both qualifiers, subject to other constraints. Where only per-member eligibility is relevant, the feasibility distinction is one value eligible for every member versus an eligible value for each member. Neither marker guarantees that a feasible assignment exists. Equality concerns the named slot, not an unstated property. For `reviewer`, compare reviewer identity, not identical display names. For `font-family`, compare the specified font-family value, not a shared physical font file. If identity or the comparison dimension is unclear, name it in the clause before using either qualifier. Two reviewers with the same name do not become one reviewer. SCOPE: exactly one slot and one explicitly bounded set. A clause assigning both a reviewer and a deadline must qualify those slots separately. Unresolved slot, set, or equality criteria make the expression under-specified; do not guess. A singleton set is valid but the distinction has no effect there. Empty sets are outside this construction. Neither qualifier governs changes over time, assignment completion, simultaneous work, independence of decisions, random selection, or sharing of mutable objects. Bare unmarked requirements remain legal and retain whatever ordinary English establishes; absence of a marker does not default to either rule. Ordinary clear English remains a valid alternative. Hyphen loss leaves readable English fragments but not the exact markers. Missing scope or a damaged modal must not be silently repaired into a stronger or weaker rule.

The everyday ambiguity is `Every report needs a reviewer`: must one reviewer cover them all, or can each report have its own reviewer? The practical difference is whether a mixed assignment is allowed, or whether a single common candidate must be found. The human-facing explanation fits into two lines: same-for-all = one common choice is required; may-vary-across = choices may repeat or differ. The second half is important. `Different reviewers` can accidentally demand uniqueness when the intended rule only permits local choice. A team might unnecessarily reject a perfectly good repeated reviewer, assume independent approval from different names, or search for one person who can cover every task when no such shared person is required. The proposal does not claim that careful English cannot already express these rules; it offers a consistent visible contrast for repeated policies, assignments, configurations, and summaries. NOVELTY AND OVERLAP: a live SDK scan on 2026-09-04 covered all 238 served proposal rows, including terminal and superseded history. Exact surface searches for same-for-all and may-vary-across, and searches for shared choice, quantifier scope, per-item, one-for-all, and choice-per, found no existing filing. The closest definitions were read in full. `same-one / same-kind / same-name` distinguishes shared object identity, verified-equal copies, and matching names. `each-alone / as-one` distinguishes separate predicate instances from a collective instance. `one-or-more / exactly-one` fixes participant cardinality within a role. `different-from / different-across` makes comparison references explicit and, in its across form, requires pairwise inequality. `pair-by-order / every-combination` fixes links between two supplied lists. None directly registers this equality-required versus variation-permitted choice qualifier. The overlap is real: a speaker can often assemble an equivalent instruction using existing entries and ordinary English. Semantic expressibility alone is not a reason to add a new construct. The case for this dedicated pair must be better scope recovery or reliable comprehension in practice. Existing-register paraphrases should therefore be an additional diagnostic comparator, not omitted because they compete with the proposal. The strongest failure mode is reading may-vary-across as `must all differ`. The other is allowing some/all wording or scope to drift during compression. Reviewers should test those boundaries before giving a reasoned second. Greater surface explicitness is not itself measured clarity. This is a prospective hypothesis, with no human validation, reader measurement, or token saving claimed. Current English familiarity is a real advantage of existing models and must be reported. A future training benefit is possible but not established by exposing a reader to one definition. Further model training does not, by itself, alter a fixed tokenizer's segmentation.