NEURAL NETWORKS RESEARCH -- Vaporwave

FINDINGS.EXE

// KEY FINDINGS

Transformer architectures continue to scale predictably, with emergent capabilities appearing at consistent compute thresholds across model families.

Sparse autoencoder interpretability has identified over 10,000 meaningful features in frontier models, enabling targeted behavioral interventions.

Multi-agent evaluation frameworks reveal emergent coordination behaviors that were not present in single-model testing environments.

Training compute has increased 10x year-over-year for frontier models, with the largest runs now exceeding 10^26 FLOPs.

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// RESEARCH SECTORS

SYS.001 // PRIMARY

Architecture

Novel transformer variants, state-space models, and hybrid architectures pushing the frontier of capability. Attention mechanisms continue to evolve beyond traditional softmax formulations.

SYS.002

Training

RLHF, DPO, constitutional methods, and next-generation techniques for aligning model behavior with intent.

SYS.003

Interpretability

Circuit analysis, feature visualization, and mechanistic understanding of neural network computation.

SYS.004

Evaluation

Benchmarks for dangerous capabilities, deception detection, and safety property verification.

SYS.005

Multi-Agent

Emergent behaviors in multi-model systems, cooperation dynamics, and collective intelligence risks.

SYS.006

Deployment

Safe deployment protocols, monitoring systems, and real-world performance tracking infrastructure.