diff --git a/src/default.jl b/src/default.jl
index fd18c6ef9..4b2f323c4 100644
--- a/src/default.jl
+++ b/src/default.jl
@@ -603,22 +603,31 @@ function _is_gpu_sparse(A)
 end
 
 """
-    _do_qr_fallback(cache::LinearCache, alg, sol, reason::Symbol, args...; kwargs...)
+    _do_qr_fallback(cache::LinearCache, alg, sol, reason::Symbol)
 
 Perform QR fallback after LU failure or residual check failure. Restores `cache.A`
 from `A_backup` (since LU may have modified it in-place) and solves with column-pivoted QR
 (or NoPivot for GPU arrays which don't support scalar indexing).
 `reason` is `:lu_failure` or `:residual_check` for appropriate log messages.
 """
-function _do_qr_fallback(cache::LinearCache, alg, sol, reason::Symbol, args...; kwargs...)
+function _do_qr_fallback(cache::LinearCache, alg, sol, reason::Symbol)
+    # Always extract solution data from `cache` rather than `sol`. The QR
+    # fallback path calls `solve!(cache, QRFactorization(...))` recursively;
+    # during precompile inference, that inner call's return type gets capped
+    # to a non-concrete UnionAll (Julia's inference complexity limit). Reading
+    # `cache.u` (statically typed) and using `cache` for the solution cache
+    # field keeps the return type of this helper concrete, which propagates
+    # up through `_default_lu_solve_with_fallback` and the @generated
+    # `solve!(cache, ::DefaultLinearSolver)` body.
+    rc = sol.retcode
+    iters = sol.iters
     if is_cusparse(cache.A)
         @SciMLMessage(
             "LU factorization failed for GPU sparse matrix but QR fallback is not supported for CuSparse. Returning LU failure.",
             cache.verbose, :default_lu_fallback
         )
         return SciMLBase.build_linear_solution(
-            alg, sol.u, sol.resid, sol.cache;
-            retcode = sol.retcode, iters = sol.iters, stats = sol.stats
+            alg, cache.u, nothing, cache; retcode = rc, iters = iters, stats = nothing
         )
     end
     if cache.A === cache.cacheval.A_backup
@@ -627,8 +636,7 @@ function _do_qr_fallback(cache::LinearCache, alg, sol, reason::Symbol, args...;
             cache.verbose, :default_lu_fallback
         )
         return SciMLBase.build_linear_solution(
-            alg, sol.u, sol.resid, sol.cache;
-            retcode = sol.retcode, iters = sol.iters, stats = sol.stats
+            alg, cache.u, nothing, cache; retcode = rc, iters = iters, stats = nothing
         )
     end
     if reason === :residual_check
@@ -645,33 +653,34 @@ function _do_qr_fallback(cache::LinearCache, alg, sol, reason::Symbol, args...;
     copyto!(cache.A, cache.cacheval.A_backup)
     cache.isfresh = true
     pivot = _qr_fallback_pivot(cache.A)
-    sol = SciMLBase.solve!(cache, QRFactorization(pivot), args...; kwargs...)
+    qr_sol = SciMLBase.solve!(cache, QRFactorization(pivot))
     cache.cacheval.fell_back_to_qr = true
     return SciMLBase.build_linear_solution(
-        alg, sol.u, sol.resid, sol.cache;
-        retcode = sol.retcode, iters = sol.iters, stats = sol.stats
+        alg, cache.u, nothing, cache;
+        retcode = qr_sol.retcode, iters = qr_sol.iters, stats = nothing
     )
 end
 
 """
-    _reuse_qr_fallback(cache::LinearCache, alg, args...; kwargs...)
+    _reuse_qr_fallback(cache::LinearCache, alg)
 
 Reuse the cached QR factorization from a previous QR fallback. Called when
 `fell_back_to_qr` is `true` and `isfresh` is `false`, meaning the matrix hasn't
 changed since the QR fallback and we should keep using QR instead of the
 (potentially corrupted) LU factorization.
 """
-function _reuse_qr_fallback(cache::LinearCache, alg, args...; kwargs...)
+function _reuse_qr_fallback(cache::LinearCache, alg)
     pivot = _qr_fallback_pivot(cache.A)
-    sol = SciMLBase.solve!(cache, QRFactorization(pivot), args...; kwargs...)
+    qr_sol = SciMLBase.solve!(cache, QRFactorization(pivot))
+    # Use cache directly for type-stable inference (see _do_qr_fallback).
     return SciMLBase.build_linear_solution(
-        alg, sol.u, sol.resid, sol.cache;
-        retcode = sol.retcode, iters = sol.iters, stats = sol.stats
+        alg, cache.u, nothing, cache;
+        retcode = qr_sol.retcode, iters = qr_sol.iters, stats = nothing
     )
 end
 
 """
-    _default_lu_solve_with_fallback(cache::LinearCache, alg::DefaultLinearSolver, sol, args...; kwargs...)
+    _default_lu_solve_with_fallback(cache::LinearCache, alg::DefaultLinearSolver, sol)
 
 Post-process an LU solve result: if LU explicitly failed, the solution contains NaN/Inf,
 or the residual check returned `APosterioriSafetyFailure`, fall back to column-pivoted QR.
@@ -682,26 +691,27 @@ The NaN/Inf check catches floating-point-near-singular matrices where LU "succee
 near-zero pivots. This is O(n) and has zero false positives.
 """
 function _default_lu_solve_with_fallback(
-        cache::LinearCache, alg::DefaultLinearSolver, sol, args...; kwargs...
+        cache::LinearCache, alg::DefaultLinearSolver, sol
     )
     if alg.safetyfallback
         if sol.retcode === ReturnCode.Failure
-            return _do_qr_fallback(cache, alg, sol, :lu_failure, args...; kwargs...)
+            return _do_qr_fallback(cache, alg, sol, :lu_failure)
         end
         if sol.retcode === ReturnCode.Success && any(!isfinite, sol.u)
             @SciMLMessage(
                 "LU solve produced non-finite values (NaN/Inf), falling back to QR. Matrix is likely near-singular.",
                 cache.verbose, :default_lu_fallback
             )
-            return _do_qr_fallback(cache, alg, sol, :lu_failure, args...; kwargs...)
+            return _do_qr_fallback(cache, alg, sol, :lu_failure)
         end
         if sol.retcode === ReturnCode.APosterioriSafetyFailure
-            return _do_qr_fallback(cache, alg, sol, :residual_check, args...; kwargs...)
+            return _do_qr_fallback(cache, alg, sol, :residual_check)
         end
     end
+    # Use cache directly for type-stable inference (see _do_qr_fallback).
     return SciMLBase.build_linear_solution(
-        alg, sol.u, sol.resid, sol.cache;
-        retcode = sol.retcode, iters = sol.iters, stats = sol.stats
+        alg, cache.u, nothing, cache;
+        retcode = sol.retcode, iters = sol.iters, stats = nothing
     )
 end
 
@@ -762,8 +772,8 @@ end
             # its own residual check and returns APosterioriSafetyFailure if needed.
             inner_alg_expr = _algchoice_to_alg_with_safety(alg)
             newex = quote
-                sol = SciMLBase.solve!(cache, $inner_alg_expr, args...; kwargs...)
-                _default_lu_solve_with_fallback(cache, alg, sol, args...; kwargs...)
+                sol = SciMLBase.solve!(cache, $inner_alg_expr)
+                _default_lu_solve_with_fallback(cache, alg, sol)
             end
         elseif alg == Symbol(DefaultAlgorithmChoice.RFLUFactorization)
             inner_alg_expr = _algchoice_to_alg_with_safety(alg)
@@ -771,8 +781,8 @@ end
                 if !userecursivefactorization(nothing)
                     error("Default algorithm calling solve on RecursiveFactorization without the package being loaded. This shouldn't happen.")
                 end
-                sol = SciMLBase.solve!(cache, $inner_alg_expr, args...; kwargs...)
-                _default_lu_solve_with_fallback(cache, alg, sol, args...; kwargs...)
+                sol = SciMLBase.solve!(cache, $inner_alg_expr)
+                _default_lu_solve_with_fallback(cache, alg, sol)
             end
         elseif alg == Symbol(DefaultAlgorithmChoice.BLISLUFactorization)
             inner_alg_expr = _algchoice_to_alg_with_safety(alg)
@@ -780,8 +790,8 @@ end
                 if !useblis(nothing)
                     error("Default algorithm calling solve on BLISLUFactorization without the extension being loaded. This shouldn't happen.")
                 end
-                sol = SciMLBase.solve!(cache, $inner_alg_expr, args...; kwargs...)
-                _default_lu_solve_with_fallback(cache, alg, sol, args...; kwargs...)
+                sol = SciMLBase.solve!(cache, $inner_alg_expr)
+                _default_lu_solve_with_fallback(cache, alg, sol)
             end
         elseif alg == Symbol(DefaultAlgorithmChoice.CudaOffloadLUFactorization)
             inner_alg_expr = _algchoice_to_alg_with_safety(alg)
@@ -789,8 +799,8 @@ end
                 if !usecuda(nothing)
                     error("Default algorithm calling solve on CudaOffloadLUFactorization without CUDA.jl being loaded. This shouldn't happen.")
                 end
-                sol = SciMLBase.solve!(cache, $inner_alg_expr, args...; kwargs...)
-                _default_lu_solve_with_fallback(cache, alg, sol, args...; kwargs...)
+                sol = SciMLBase.solve!(cache, $inner_alg_expr)
+                _default_lu_solve_with_fallback(cache, alg, sol)
             end
         elseif alg == Symbol(DefaultAlgorithmChoice.MetalLUFactorization)
             inner_alg_expr = _algchoice_to_alg_with_safety(alg)
@@ -798,18 +808,18 @@ end
                 if !usemetal(nothing)
                     error("Default algorithm calling solve on MetalLUFactorization without Metal.jl being loaded. This shouldn't happen.")
                 end
-                sol = SciMLBase.solve!(cache, $inner_alg_expr, args...; kwargs...)
-                _default_lu_solve_with_fallback(cache, alg, sol, args...; kwargs...)
+                sol = SciMLBase.solve!(cache, $inner_alg_expr)
+                _default_lu_solve_with_fallback(cache, alg, sol)
             end
         else
             if alg in LinearSolve._SPARSE_ONLY_ALGORITHMS
                 newex = quote
                     if !(cache.A isa Array)
-                        sol = SciMLBase.solve!(cache, $(algchoice_to_alg(alg)), args...; kwargs...)
+                        sol = SciMLBase.solve!(cache, $(algchoice_to_alg(alg)))
                         SciMLBase.build_linear_solution(
-                            alg, sol.u, sol.resid, sol.cache;
+                            alg, cache.u, nothing, cache;
                             retcode = sol.retcode,
-                            iters = sol.iters, stats = sol.stats
+                            iters = sol.iters, stats = nothing
                         )
                     else
                         error(
@@ -820,11 +830,11 @@ end
                 end
             else
                 newex = quote
-                    sol = SciMLBase.solve!(cache, $(algchoice_to_alg(alg)), args...; kwargs...)
+                    sol = SciMLBase.solve!(cache, $(algchoice_to_alg(alg)))
                     SciMLBase.build_linear_solution(
-                        alg, sol.u, sol.resid, sol.cache;
+                        alg, cache.u, nothing, cache;
                         retcode = sol.retcode,
-                        iters = sol.iters, stats = sol.stats
+                        iters = sol.iters, stats = nothing
                     )
                 end
             end
@@ -843,7 +853,7 @@ end
     return quote
         if cache.cacheval isa DefaultLinearSolverInit &&
                 cache.cacheval.fell_back_to_qr && !cache.isfresh
-            _reuse_qr_fallback(cache, alg, args...; kwargs...)
+            _reuse_qr_fallback(cache, alg)
         else
             $alg_dispatch
         end
diff --git a/src/factorization.jl b/src/factorization.jl
index f345a978d..0b68ef1e5 100644
--- a/src/factorization.jl
+++ b/src/factorization.jl
@@ -456,15 +456,22 @@ end
 function do_factorization(alg::QRFactorization, A, b, u)
     A = convert(AbstractMatrix, A)
     if ArrayInterface.can_setindex(typeof(A))
-        if alg.inplace && !issparsematrixcsc(A) && !(A isa GPUArraysCore.AnyGPUArray) &&
-                !is_cusparse(A)
+        # Sparse CSC (SPQR) does not accept a pivoting strategy, and CUDA's
+        # `qr` does not accept extra args either. Use the no-arg `qr(A)`
+        # form in those cases. For other CPU matrices, always pass
+        # `alg.pivot` so the return type is determined by the static
+        # `QRFactorization{P}` parameter (otherwise this branch returns
+        # `Union{QRCompactWY, QRPivoted}` depending on `alg.inplace`).
+        if A isa GPUArraysCore.AnyGPUArray || is_cusparse(A) || issparsematrixcsc(A)
+            fact = qr(A)
+        elseif alg.inplace
             if A isa Symmetric
                 fact = qr(A, alg.pivot)
             else
                 fact = qr!(A, alg.pivot)
             end
         else
-            fact = qr(A) # CUDA.jl does not allow other args!
+            fact = qr(A, alg.pivot)
         end
     else
         fact = qr(A, alg.pivot)
diff --git a/test/nopre/jet.jl b/test/nopre/jet.jl
index b890d3e63..660c908d4 100644
--- a/test/nopre/jet.jl
+++ b/test/nopre/jet.jl
@@ -197,3 +197,61 @@ end
         JET.@test_opt init(dual_prob)
     end
 end
+
+# Concrete-return-type QA for `solve!(cache)`. Guards against the regression
+# where `solve!(cache)` through `DefaultLinearSolver` returned
+# `LinearSolution{_A, _B, _C, _D, DefaultLinearSolver, _E, _F} where {...}`
+# (a UnionAll over 6 free type parameters) instead of a concrete LinearSolution.
+_solve_alg(A, b, alg) = solve!(init(LinearProblem(A, b), alg))
+_solve_default(A, b) = solve!(init(LinearProblem(A, b)))
+
+@testset "solve!(cache) returns concrete LinearSolution — default solver" begin
+    # Headline case: `solve!(cache)` after `init(LinearProblem(A, b))` must not
+    # return a UnionAll-typed LinearSolution. Was broken by the
+    # `_default_lu_solve_with_fallback`/`_do_qr_fallback` helpers reading
+    # `sol.u`/`sol.resid`/`sol.cache`/`sol.stats` from an inner `sol` whose
+    # rettype got capped to `Any` during precompile.
+    rt = Core.Compiler.return_type(
+        _solve_default, Tuple{Matrix{Float64}, Vector{Float64}}
+    )
+    @test isconcretetype(rt)
+    @test rt <: LinearSolve.SciMLBase.LinearSolution{Float64, 1, Vector{Float64}}
+end
+
+@testset "solve!(cache) is concrete for each algorithm" begin
+    algs_concrete = (
+        LUFactorization(),
+        GenericLUFactorization(),
+        QRFactorization(LinearAlgebra.ColumnNorm()),
+        QRFactorization(LinearAlgebra.NoPivot()),
+        DiagonalFactorization(),
+        SVDFactorization(),
+        CholeskyFactorization(),
+        NormalCholeskyFactorization(),
+    )
+    for alg in algs_concrete
+        @testset "$(nameof(typeof(alg)))" begin
+            rt = Core.Compiler.return_type(
+                _solve_alg,
+                Tuple{Matrix{Float64}, Vector{Float64}, typeof(alg)}
+            )
+            @test isconcretetype(rt)
+        end
+    end
+
+    # Known unrelated inference issues — tracked separately, not what this
+    # group is guarding against.
+    algs_broken = (
+        BunchKaufmanFactorization(),
+        LDLtFactorization(),
+    )
+    for alg in algs_broken
+        @testset "$(nameof(typeof(alg))) (broken)" begin
+            rt = Core.Compiler.return_type(
+                _solve_alg,
+                Tuple{Matrix{Float64}, Vector{Float64}, typeof(alg)}
+            )
+            @test_broken isconcretetype(rt)
+        end
+    end
+end