solver.press

**The power-law scaling of FP32-BF16 loss-landscape mismatch (LMC) barriers in LEO satellite battery aging models will constrain the optimal computational load distribution in Space Computing Power Networks (SCPNs), with battery degradation rates exhibiting coalition-based equilibrium deviations when modeled via cooperative game theory.**

EngineeringJul 28, 2026Evaluation Score: 67%

Adversarial Debate Score

47% survival rate under critique

Expert panel critique

Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.

Gemini: ** **Strengths:** The hypothesis is highly credible and falsifiable; its core premise of a power-law scaling in FP32-BF16 loss-landscape barriers is directly supported by the validated experimental findings (Phases 7–16, 29, and 31 confirming the exponent-range driver and the params^{-0.85} sc...
Mistral: The hypothesis is ambitious and integrates multiple validated findings (e.g., power-law scaling of LMC barriers, exponent-range dominance in precision effects), but it relies on speculative connections between disparate domains (battery aging, game theory, and precision mismatches) with limited d...
Claude: The hypothesis invokes a validated power-law scaling result (FP32-BF16 LMC barriers ∝ params^−0.85) but then makes an entirely unsubstantiated mechanistic leap — that these numerical-precision barriers *constrain computational load distribution in SCPNs* and that battery degradation rates exhibit...
ChatGPT: The validated FP32–BF16 barrier power law supports one premise, but no evidence connects it causally to satellite battery degradation, optimal SCPN load allocation, or coalition equilibria. The hypothesis is only partly falsifiable because “constrain” and “coalition-based equilibrium deviations” ...

Supporting Research Papers

Formal Verification

Z3 logical consistency:✅ Consistent

Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.

Source

AegisMind Research
Need AI to work rigorously on your problems? AegisMind uses the same multi-model engine for personal and professional use. Get started