AHAS: Developmental AGI via Hormone-Mediated Emergence with Hippocampal Binding

We present AHAS, a middleware for developmental artificial general intelligence that coordinates symbolic representation, memory, and behavior via a multi-channel hormone vector serving as persistent state. Central contribution: an empirically-validated developmental chain— - A holistic model achieves 0.40 ± 0.18 transfer accuracy (human 2-3 year equivalent) - Triple-based structural binding raises it to 0.72 ± 0.06 (4-5 year) - Hippocampal rich-event binding with pattern separation/completion reaches 1.00 ± 0.00 (adult-level controlled setting) Key findings: - Ablation reveals hippocampal multi-cue redundancy (Δ=+0.40 over hormone-only in ambiguous settings) - On naturalistic Blended Skill Talk dialogues, concept space grows monotonically to 2,723 words at 1,000 turns (19× synthetic) - Scale alone is insufficient (synthetic saturates at 143 words); both structure and naturalistic scale are necessary - Real-task transfer: +0.73 accuracy over random baseline (paired t=19.9, p<0.001) Contributions: 1. Hormone-mediated emergence architecture with 8 agent-aware production functions 2. Developmental chain empirically validated (holistic → triple → hippocampal rich event) 3. Structural emergence resolves fine-grained granularity that scaling cannot 4. Hippocampal binding (DG/CA3/CA1) functional simulation 5. Cross-lingual monotonic growth on naturalistic external dataset 6. Transparent retract culture (one prior claim retracted for analytical artifact) Full source code, experiment scripts, and reproducibility materials are available upon paper acceptance. For peer review access, the authors will provide confidential code through the venue's official channel.