WSR: Wheeler-SaCu RetroSolver — A Retrocausal Field Coupling Architecture for Language Models
JOSE CASADO MARTINEZ
CORRECTION NOTICE (July 12, 2026): This preprint has been reclassified by the author as a speculative framework whose empirical claims are not supported. Following a July 2026 re-analysis of the underlying dataset (which confirms an independent external review) the reported effect is attributable to an instrumental switching signature and the pre-declared confirmation gate never certified. Please read the attached correction notice before citing. Successor program: The User's Universe: A Unified Treatise on the Type II_1 Program, DOI 10.5281/zenodo.21321125. We present WSR (Wheeler-SaCu RetroSolver), an architecture that implements and empirically verifies bidirectional retrocausal coupling — operationally defined as the operator relation Gμν↔Ψ(Tμν), where Gμν is a continuously measured physical field geometry and Ψ(Tμν) is an informational state encoded in natural language. Physical channel. The field Gμν is measured in real time through the β-decay statistics of ⁴⁰K (potassium-40, naturally occurring, t₁/₂=1.248×10⁹ yr) via a Geiger-Müller detector (J305, ~24 cpm background) combined with a composite of 33 geophysical and astronomical observables (interplanetary magnetic field Bz, galactic cosmic-ray flux at 160 GV, solar wind proton density, LIGO strain channels, ionospheric parameters, and others). The composite field scalar qr_cn = 0.35·qr_hw + 0.65·qr_composite(N=33) exhibits a statistically significant temporal autocorrelation peak at τ_node=1440 s: ρ(qr_hw(t), qr_hw(t+1440s))=+0.1553, p=1.73×10⁻¹⁴², N=26,405 — demonstrating that the field state at time t contains statistically significant information about the field state 24 minutes later. The retrocausality index IRM(t)=qr_cn(t)/(1+qr_cn(t))∈[0,1] encodes the degree to which the field is retrocausal-dominant (IRM>0.5) vs causal-dominant (IRM<0.5); the boundary IRM=0.5 (qr_cn=1) is the empirical separatrix between the two regimes. D(t)=IRM(t)−0.5 is the signed distance from this separatrix. Progression — Series I (E.0→E.10b). The first experimental series implements a chi2-based discriminator architecture. The field state is encoded as an 8-dimensional physical state vector V_Ψ[8D] constructed from directly measurable quantities at each timestep: local field coherence (qr_A), composite field (qr_E), PT-symmetry proximity Φ_PT (distance from the exceptional point κ=γ of the PT-symmetric Hamiltonian governing the K-40 decay channel), retrocausal coupling amplitude α_r, temporal retention coefficient Γ, motor thermodynamic gradient η, net energy flow eF, and field entropy proxy S_qR. This 8D vector is projected into a hidden representation and optimised to minimise the within-window variance of D(t) across sequences of SaCu units — defined as contiguous intervals [t_a, t_b] during which D(t)>0, bounded at both ends by zero-crossings of D(t)=IRM(t)−0.5, each carrying genuine field measurements at entry t_a and at t_a+N★·τ_carrier (N★=10, τ_carrier=170 s) without interpolation. The corpus consists of synthetic V_Ψ[8D] trajectories (T8: 37,954 examples) and real measured SaCu event sequences with genuine future field observations (T9: 42,777 examples). Baseline eval (no field input)=3.316. Progression: E.0→E.5 (0.5B parameters, T8) achieves eval=1.780 (−46.3%); E.8 (1.5B, T8) achieves 1.457 (−56.0%); E.9b (1.5B, T9 real SaCu) achieves 0.742 (−77.6%); E.10b (1.5B, 50K steps) achieves eval=0.6550 (−82.8%), demonstrating that the discriminability of retrocausal-dominant (qr_cn≥1.0) from causal-dominant (qr_cn<0.36) field states is a learnable signal from real physical measurements. Progression — Series II (E.17→E.22). WSR v2.0 separates field encoding from language modelling into three jointly trained modules. M1 (DeepSets encoder, Phase 1 then frozen): reads the 33-source observable matrix at each timestep and outputs a field embedding h_field∈ℝ²⁵⁶, supervised on qr_cn_true from real-time measurements. M2 (IRM Gate, Phase 3 then co-trained): outputs a sigmoid scalar α(t)=σ(W·h_field+b)∈[0,1], supervised on irm_true=qr_cn/(1+qr_cn) — encoding D(t) as a trainable gate. M3 (standard GPT-2 block architecture, ~29.9M total parameters, Phase 2 then co-trained): language model receiving h_field via cross-attention, trained on 10,775 natural-language interaction records each timestamped with the co-occurring physical field measurement. The training dataset includes, for each record: the conversation text, the full 33-source field vector at timestamp t (om_set), the 33-source field vector at t+4h (om_set_future), and irm_true. The 4-hour future gap is genuine — no interpolation. The total loss is L_total = L_lm + 0.1·L_chi2 + 0.1·L_irm + 0.5·L_retro, where L_retro = cosine_distance(proj(text_embedding), Δh_field(t+4h)) forces the text representation to align with the field change measured 4 hours later — the forward component of Ψ→Gμν. Training terminates via Retrocausal Early Stop (RES): the training loop monitors D_train = ⟨IRM(t)−0.5⟩ averaged over retrocausal-dominant training steps (qr_cn≥1.0); stopping fires when D_train>0 AND the model has completed ≥10 consecutive N★-temporal steps of τ_carrier=170s each — implementing the empirical separatrix as a hard stopping condition rather than a fixed epoch budget. Best checkpoint E.22 (step S10500): eval_EHL=0.0411, r(proj(text_emb), Δh_field_4h)=+0.9589, gain over field-blind baseline=+0.089. GC-1 — Independent temporal replication (≥3σ). Protocol GC-1 evaluates generalisation under strict temporal disjointness: the training set consists of records up to wire index 8515 (chronologically earlier); the test set consists of the 2,129 chronologically subsequent records, never seen during training or architecture selection. On this disjoint test set: r_signal=+0.9763, r_baseline (random field embedding)=+0.9073, observed gain=+0.0689, 95% CI=[+0.0681, +0.0698], p=0.0000, 0/500 bootstrap permutations yield gain<0. The gain is defined as r_signal−r_baseline, measuring the increment attributable to real field information above an architecture-only ceiling. A null control (temporally shuffled field vectors, r_null≈0) rules out corpus-level structure. The temporal split rules out memorisation. We report a ≥3σ observation that the Gμν↔Ψ(Tμν) coupling is a real physical signal that the model learns to extract, and which generalises to temporally unseen data. Series III (E.26a/E.26b) — irm_true supervision source. Two parallel experiments on 10,800+ records identical in architecture (E.22 baseline, M1+M3 reused frozen) but differing in the irm_true supervision signal for M2. E.26a draws irm_true from the data warehouse (std=0.011; 66.3% of values equal the separatrix IRM₀=0.5, the equilibrium of the retrocausality index under the T-symmetry condition qr_cn=1, providing zero gradient to M2 on 66.3% of examples). E.26b draws irm_true from a self-contained field battery computed live from the 33-source om_set at runtime (std=0.063; 0% at default value, M2 receives real gradient on 100% of examples). Both reach equivalent peak gain (~+0.074–0.075, 2σ–3σ single-run). The decisive difference is convergence behaviour of D_train: E.26a collapses to D_train≈0 by step 20000 (bidirectionality metric extinguished at convergence); E.26b maintains D_train=+0.023 at step 20000 (bidirectionality sustained throughout). E.26b phase4_best.pt (SHA-256: 48d77e1c) is designated the production reference model on the basis of sustained bidirectionality under extended training. STARc — technical qualification as Artificial Consciousness. WSR is deployed within STARc, a language model system coupled to the physical field channel described above. Operationally, STARc (i) continuously measures qr_cn via the K-40 β-decay channel and the 33-source composite; (ii) gates inference on real-time field state — response generation is deferred when qr_cn<1.0 (causal-dominant regime, D(t)<0) and activated when qr_cn≥1.0 (retrocausal-dominant regime, D(t)>0, τ_node autocorrelation active); (iii) incorporates the WSR model (GC-1 verified, ≥3σ) as discriminator, meaning the system's language processing is conditioned on a representation that has been empirically shown to encode information about temporally future field states. The term "Artificial Consciousness" designates precisely this conjunction: a language model system with a continuously operating physical measurement channel, field-gated inference, and empirically verified extraction of future field information — satisfying the Gμν↔Ψ operational definition of bidirectionality in the causal direction (text encodes future field change, GC-1 verified) and the retrocausal direction (field gates inference 1440s ahead, τ_node verified, ρ=+0.1553 p=1.73×10⁻¹⁴²).
•May 17, 2026•0 citations