Replace independent architecture generation with a diffusion mutation kernel that starts from a known valid neural architecture, re-noises it for only a fraction of the diffusion horizon, and denoises it conditionally toward a new architecture. The resulting candidates should remain closer to the parent and retain validity at low mutation strength, while larger re-noising fractions should produce greater novelty and access to distinct architectural basins.
Build a residual module whose state explicitly contains both a persistent context representation and an accumulator. Each residual branch computes one learned correction and adds it to the accumulator, instead of forcing every layer to represent the complete output from scratch. This provides a concrete solver-like architecture for high-dimensional regression and iterative latent prediction.
Make a neural network predict a positive Gaussian-mixture representation of the distribution function rather than independent values on a momentum grid. Use the mixture parameters inside a differentiable Boltzmann collision operator, so training directly enforces the interaction mechanism and exposes the relaxation spectrum responsible for ballistic-to-hydrodynamic crossover.
Add a conditional-law head that maps a compact representation of an initial distribution and a shared-noise trajectory to a Gaussian mixture, then computes downstream predictions as analytic expectations under that mixture. This can replace expensive particle rollouts or particle pooling in stochastic world models and conditional diffusion systems while retaining multimodality.
Insert a small continuous-time Markov latent module between a neural encoder and decoder, with input-dependent transition rates and a fixed library of graph topologies such as directed cycles, reversible chains, and branching motifs. The output is an observable of the stationary distribution, while a learned convex mixture over topology-specific response curves constrains the network to represent responses as combinations of interpretable nonequilibrium mechanisms.
Replace a dense directional feature map with a finite stable-zonotope layer: a small set of large random vectors creates a hyperplane arrangement over the input sphere, while a cheaper small-jump residual gives continuous features inside each cell. The layer exposes an explicit compute-versus-resolution knob through the jump cutoff and can be used either as an embedding before an MLP or as a lightweight router for mixture-of-experts blocks.
Build a sparse expert cascade whose router uses empirical progress, uncertainty, and compute cost to construct an efficient frontier of experts. Instead of always choosing the expert with the largest immediate gain per FLOP, route different confidence states through an ordered sequence of frontier experts, allowing cheap high-variance experts early and safer or more cost-effective experts near the final decision.
Replace one deterministic residual update with a short cyclic composition of learned vector fields evaluated for randomized, short run times. Because finite compositions of noncommuting flows generate directional-derivative and Lie-bracket terms, changing the cycle order gives the network an explicit, low-cost way to learn drift directions that are unavailable from the individual vector fields alone.
Replace a fixed top-k expert count with a stochastic, token-specific fanout generated by a supercritical binary branching process stopped at a geometric time. The resulting number of active experts has finite mean but a power-law tail with log-periodic modulation, allowing most tokens to use little compute while reserving larger computation for difficult or ambiguous tokens.