# Released larkos 0.3

> Source: <https://dev.to/okerew/released-larkos-03-19be>
> Published: 2026-06-13 15:56:54+00:00

Larkos 0.3: GAT neuron reasoning, temporal encoder, refactored fusion head

Core architecture changes:

Add _NeuronGraphReasoner: two-layer GAT over the live neuron graph,

producing per-neuron token embeddings (MAX_NEURONS x FUSE_GRAPH_DMODEL).

Node features include state, output, layer one-hot, connection degree,

state velocity, output magnitude, and mean edge weight (D_NODE=8).

Learned per-neuron embedding ensures distinct tokens for symmetric nodes.

Add _GATLayer: hand-rolled multi-head GAT with edge-weight-modulated

scores (tanh-squashed gain), dense [N,N] adjacency mask, and self-loops.

Add _TemporalAttentionEncoder: two-layer transformer over the

[TEMPORAL_WINDOW, FOURIER_OUT_DIM] input history with learned positional

embedding, replacing flat concatenation of window frames.

Refactor _FusionTransformerHead: attends over MAX_NEURONS+3 token

sequence (GAT tokens + band_q + band_m + driver). Token-type embeddings

(4 types) distinguish token kinds. Replaces mean-pool with learned-query

attention pool (single query, softmax over sequence). Head capacity

increased: 3 layers, d_model=64, dim_ff=128.

C-side fusion (fusion_mechanism.c):

Remove BAND_N and the neuron_flat projection pipeline; neuron reasoning

is now handled end-to-end by the Python-side GAT.

BAND_Q=32, BAND_M=32, FUSION_DIM=BAND_Q+BAND_M=64.

MEM_TOP_K: 8→32, MAX_MEM_ENTRIES: 300→1200.

Training loop:

Freeze cache extended to cover driver embedding (_cached_driver) and

GAT inputs (_cached_graph_inputs). All three caches invalidated together

on target refresh. GAT runs forward_from_inputs in-graph every step

(pinned inputs, live gradient).

x_temporal detached before MAML inner loop to prevent double-backward

through the temporal encoder graph.

graph_reasoner and temporal_encoder added to optimizer and checkpoint.

Verifier re-runs temporal_encoder on cached raw sequence to avoid

reusing a consumed autograd graph.

LR sensitivity check uses relative threshold (15% of current loss)

instead of fixed absolute delta.

Runner:

Add _advance_backend(): runs C-side decision/context/neuron/attractor/

affective updates before each step() so multi-step inference sees

evolving state.

alpha and mem_weight_ratio derived from live backend context by default,

matching the training loop's per-epoch derivation.

Temporal encoder and graph reasoner included in forward path.

Checkpoint:

Saves/loads graph_reasoner and temporal_encoder (strict=False for

backward compatibility with pre-0.3 checkpoints).

FUSION_DIM and fused_cog_norm dimension mismatch detection with safe

fallback to fresh init.

cached_driver persisted alongside cached_fused_cog.