# ECHO-2 — closure, results and demonstration **ECHO-2 closed on 9 September 2026.** The agent retains the ECHO-1 core and adds viability, pattern recognition, stream operation, consolidation, inheritance of one predisposition and regulation of two internal variables. CAPACITY-1 justified a 512 LIF + 128 Adaptive-LIF monitor architecture, and NEURAL-VIZ-1 made it observable in a native GUI. ## Direct demonstration
A direct 2:03 recording made on 9 September 2026. It visits the neural network, 3D drone, 16-byte WSP map and tutorial.
This is not a promotional animation. The application runs `Agent.turn()` and shows its WSP, CAM, T, PATTERN, Q, gate, homeostasis, death, respawn, inheritance and occasional cortex calls. The drone represents the real pose of the discrete Body3D body. VTK/OpenGL renders the view; aerodynamics, IMU, motors and PID do not exist yet. ## Direct comparison with the previous scale CAPACITY-1 repeated the exam with the 256-LIF architecture used as the ECHO-1 size baseline and with the ECHO-2 extension. This comparison ran inside the same bench with reserved seeds and frozen memory; it is not a retrospective score for the ECHO-1 release. The gain is **+1,219 hits**, from 40.48% to 100%. Permuting the signatures drops the same system to 142/2,048: the result depends on the representation, not merely on adding rows. The total number of neurons is identical here. Temporal adaptation is the changed cause: disabling it returns 0/256. The bench selected an eight-tick memory and adaptation gain 4. The third chart shows exercised scale, **not accuracy on the same task**: the workloads differ. STREAM-1 processed 48 chunks, reached 4,512/4,512 known predictions in the coherent arm and kept `dynamic_alias=0`. ## What each phase added | Phase | Closure evidence | |---|---| | VITA-1 / FOOD-1 | `H` falls, death ends a life, and food/poison are learned from consequences | | SURV-1 | medians with retained memory: 28/40/40 turns; with resets: 16/16/16 | | SHIFT-S | adaptation over frozen Q: +421/+416/+446 turns; negative transfer is also published | | PATTERN-1 | 32/32 held-out variants versus 0/32 for exact matching; no object id or position | | STREAM-1 | 4,608 frames, 48 chunks and 4,512/4,512 coherent predictions | | SLEEP-2 | 8,208 rows become 144 rules; 720/720 in the exam versus T at 0/720, without rewriting CAM/T/Q | | GEN-1f | inherited budget 8: 360 late errors versus 602 for naïve; 52 wins, 24 losses, 52 ties | | HEAT-1b | energy + temperature: 20,786 turns versus 7,221 without temperature and 7,186 without Q; load/cool exam 12/12 | | CAPACITY-1 | 512 LIF: 2,048/2,048; 512 LIF + 128 ALIF: 256/256 temporal | | NEURAL-VIZ-1 | one Python GUI displays the architecture and every auditable component live | ## What improves over ECHO-1 ECHO-1 closed memory, prediction, objects, discrete physical actions, temporal patterns and transfer between worlds. ECHO-2 retains them and adds a consequence across episodes: the body can die, respawn, regulate energy and temperature, retain experience and transmit only an exploration predisposition to a descendant whose memories start empty. The neural extension is not accepted because of the number `640`. It is accepted because it improves two causal exams and loses when the responsible feature is removed or permuted. ## Integrity and data - WSP remains 16 bytes and there is no second thought bus. - `false_facts=0`, `destroyed=0`, with the cortex disabled in the main benches. - The neural branch in the GUI is a perceptual monitor; Q remains the causal policy. - Held-out exams are frozen during scoring. - Earlier negative GEN-1 and HEAT-1 results remain published as red. - This closure has no physical robot, camera, LiDAR, IMU, PX4 or AKD1500. [Download the ECHO-2 summary and SHA-256 fingerprints](/data/echo2-benchmark.json). That file also includes the MP4 hash. — R.N.