The Spectral–Topological Decoupling Theorem
in Self-Supervised Physical World Models

Every constraint class fails — on a property disjoint from the one it enforces.
Sehaj Randhir Singh
Independent researcher; partial affiliation with NYU Tandon School of Engineering
Joint-embedding predictive architectures (JEPAs) are increasingly given Hamiltonian or thermodynamic structure to enforce physically faithful latent dynamics. We show this hope fails at the level of the optimization. On Lorenz-63 and the 4-D hyperchaotic Lorenz flow, diffeomorphically lifted into 64-dimensional latent spaces, five structurally distinct latent predictors are trained and audited under a chaos-aware protocol. Every constraint class fails on a property disjoint from its target: the unconstrained predictor explodes (λ₁ = +28.2); the rigid Hamiltonian freezes and yet posts the best overlap score; naive metriplectic dissipation pins tr(R)/n to zero; a divergence gate pins the spectral sum to within 1% while λ₁ runs 2.5× high; and differentiable QR alignment shapes only a finite-horizon proxy (−0.18), not the asymptotic exponent (+2.85). A second system inverts the decoupling and destroys the second positive exponent a λ₁-only objective cannot name. At 2000 steps every alive arm's exponent climbs to ≈ +8…+20; we state the decoupling as a theorem with a mandatory evaluation standard.

Why this matters for the field

The promise of structure-constrained latent predictors is that a physicist-recognizable operator (\( \dot z = J\nabla H - R\nabla S \)) yields a stable, dissipative, dynamically faithful world model. The atlas shows the promise fails at the level of the optimization: each constraint class certifies a functional level — spectral sums, windowed tangent maps, pushforward measures — and none of these levels controls the others.

Failure atlas on Lorenz-63: leading Lyapunov exponent, contraction sum, dissipation, field activity, Chamfer overlap, and the training proxy vs evaluation exponent.
λ₁ decouples from the trace gate

The divergence gate pins the spectral sum to within 1–3% on both systems, while the leading exponent runs 2.5–3.2× high on Lorenz-63 and ≈0 on the hyperchaotic flow — always off the reference, in a direction set by the system, not the constraint.

Failure atlas on the 4-D hyperchaotic Lorenz system, including the second Lyapunov exponent.
λ₂ destroyed — unnameable

The reference second exponent is +0.098; the QR readout of every constrained arm is negative or zero (−0.08 for E and F). A λ₁-only spectral objective cannot even name the second unstable direction, let alone preserve it.

Schematic: three constraint classes, three functional levels, three traps.
Three traps, one theorem

The overlap trap (a constant map posts the best Chamfer), the dead saddle (tr(R)/n ≡ 0.0000 with R = LLᵀ zero-init), and the finite-time-horizon trap (training proxy −0.18, asymptotic exponent +2.85 — a sign flip in every seed).

The five-arm failure atlas — Lorenz-63

Reference: λ₁ = +0.884, κ = −14.55, div f = −13.667. Mean ± std over three seeds.

Armmax‖z‖λ₁κ = Σλᵢ≥₂tr(R)/nmChamfer
B · none (MLP)1.8×10⁶ (3/3 escaped)+28.2 ± 1.3——1821 ± 18813.4×10³
C · rigid Hamiltonian10.1 ± 0.3+0.011 ± 0.002+0.01—0.0053.85 ± 0.05
D · metriplectic, LLᵀ zero-init35.5 ± 24.5+1.50 ± 0.86−1.4 ± 1.20.00000.888.7 ± 3.3
E · div-free J + gate175 ± 135 (2/3)+2.22 ± 1.80−16.0 ± 1.93.77 ± 2.151.7223.0 ± 4.8
F · E + 6-step QR288 ± 288 (3/3)+2.85 ± 1.89−16.1 ± 2.04.68 ± 3.132.1725.8 ± 6.6
F-long · E + 60-step QR54.2+0.18−13.81.580.6315.2
reference—+0.884−14.55—10

The same atlas — 4-D hyperchaotic Lorenz

Reference: λ₁ = +0.737, λ₂ = +0.098, κ = −14.40. Divergence −13.667, identical to Lorenz-63. Two positive exponents.

Armmax‖z‖λ₁λ₂κtr(R)/nmChamfer
B · none (MLP)3.9×10⁶ (3/3)+29.2 ± 3.0———4699 ± 47188.4×10³
C · rigid Hamiltonian9.9 ± 0.2+0.011 ± 0.006+0.00+0.01—0.0043.09 ± 0.12
D · metriplectic, LLᵀ zero-init57.4 ± 22.5+2.22 ± 0.93+0.27−1.7 ± 2.20.00000.7414.7 ± 2.4
E · div-free J + gate66.7 ± 15.9+0.09 ± 0.38−0.08−13.85 ± 0.561.77 ± 0.340.9315.2 ± 0.5
F · E + 6-step QR62.2 ± 9.0−0.01 ± 0.20−0.08−13.31 ± 0.311.64 ± 0.050.8814.6 ± 0.4
reference—+0.737+0.098−14.40—10
The decoupling direction inverts with the system: the same gate pins the sum while λ₁ runs high on Lorenz-63 and ≈0 on the hyperchaotic flow — always off the reference.

No evaluation window certifies stability

The 2000-step (20 Lyapunov-time) convergence check on all 24 saved checkpoints is unambiguous: only the frozen arm C is converged. Every alive arm's 200-step value understates its 2000-step value by an order of magnitude — Lorenz E/F climb to +8.8…+10.0 and escape at steps 213–300; hyperchaotic E/F read ≈0 at 200 steps then climb to +7.8…+10.1 and escape at 304–407; the dead-saddle arm D, whose 200-step value is the nearest to reference, reads +4.5…+19.7 at 2000 steps. The atlas's verdicts are conservative, not optimistic: any evaluation window shorter than the instability's onset time can certify a pathological field.

The theorem

Three elementary propositions and a no-go theorem: (i) a trace/divergence gate pins only the mean eigenvalue and leaves the Rayleigh quotient along the leading Oseledets direction free up to a factor \(d\) — over spectra with fixed sum, λ₁ is unbounded above; (ii) finite-time exponents are non-certifying — for every finite \(T\) there exist flows with \(\Lambda_T < 0 < \lambda_1\), so a differentiable QR signal is not a bound on the Oseledets spectrum; (iii) Chamfer-type distances are reordering-invariant, so a constant map attains near-optimal overlap with zero dynamical information. No combination of aggregate thermodynamic identities, finite-horizon spectral statistics, and set-level distributional losses certifies {bounded, correct λ₁, non-degenerate, dynamically faithful} — realized simultaneously by the five-arm atlas on two systems, including the most constrained arm on both.

Reproducibility: deterministic per-step seeding (bit-reproducible from (seed, schedule)); independent retrainings reproduce the atlas within seed spread; the convergence check reruns the shadow exponent on the saved weights. Full protocol and code in the repository.