v7.7 Cybopsec
7/7
CDCP
MESH
L1 REQ
MISS
L2 SEM
MISS
L3 LTM
MISS
NET
FETCH
🔍 Inspect Node
🔇 Mute Node
⊙ Isolate Layer
🧠 Jump to Memory
✕ Clear Traces
⊗ Deactivate Node

📊 LLM Benchmark Suite

Not running
0
Questions
0
Tokens used
mJ/question
CURRENT QUESTION
Press ▶ Run to start a benchmark simulation...
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APPLICATION LAYER
Any OpenAI / Anthropic SDK · Zero code changes
POST /v1/chat/completions POST /v1/messages SSE Streaming Tool Calling
🛡 GATEWAY — sMCP-1 v1.0
FastAPI SPIFFE SVIDs Ed25519 mTLS 1.3 HMAC Audit Parallax Scheduler
STDIO allowlist: npx · uvx · python · node · docker · deno | gVisor sandboxes
TIER 1 — TinyLM Swarm (1.5B · ≤80 mJ/tok)
Pool A · Loihi 2
WM
EB
◆ ARA · NeuEdge SNN
Pool B · Akida 2
WM
EB
◆ ARA · NeuEdge SNN
Pool C · Jetson
WM
EB
◆ ARA · NeuEdge SNN
TIER 2 — Mid-Scale (15B · CDCP Orchestrator)
darmann-mid · A100/H100
WM
EB
STM
LTM
RRC
MLGRU + Mamba-2 + MoE
8 experts · 131K ctx
CDCP Orchestrator
N=4–8 · credibility softmax
◆ ARA — Full 6-agent topology (gVisor)
TIER 3 — Consolidator (70B+ · Hala Point)
darmann-xl · H100/B200 · 524K ctx
WM
EB
STM
LTM
RRC
CDCP Gatekeeper
BFT write-admission · NLI
MAGMA RRC
Semantic·Temporal·Causal·Entity
◆ ARA — Full 6-agent · OpenScholar 45M papers
Five-Tier Memory Hierarchy
D-MEM Critic Router
Dopamine-RPE Write Admission · arXiv 2603.14597
RPE = min(1.0, 𝟙[Utility≥τ]·[Utility×(Surprise+β)])
RPE < 0.3
SKIP
Shadow O(1)
0.3–0.7
CONSTRUCT
New EB node O(1)
RPE ≥ 0.7
EVOLVE
STM update O(N)
CDCP v2.0 — Byzantine Fault Tolerant Consensus
Live Consensus Ring · N=7 · f=2 Byzantine · 3f+1 Assumption
Redraws every 2s · red=Byzantine · green=honest
Each node generates candidate δᵢ + credibility cᵢ (Attention Trust Score)
Credibility-weighted softmax aggregation → consensus δ̄
NLI pre-consolidation gate: reject if CONTRADICTION with LTM
SycophancyScore gate — adversarial debate if score > 0.6
ADMIT → HMAC audit log + LTM write | REJECT → flag
‖δ̄ − δ*‖ ≤ f/(N-f) · max‖δᵢ − δ*‖ ≤ ½ · max‖δᵢ − δ*‖
Autonomous Researcher Agent — Per Node Pool
Full agentic autonomy · gVisor isolated · CDCP-gated writes
gVisor OpenScholar 45M NeuEdge SNN CDCP-gated
Training Pipeline
Select Scenario
0
Tokens
0 tok/s
0.0
mJ/token
7
Active Nodes
HEALTHY
0
CDCP Admissions
0 rejected
Speed
+0ms
EVENT LOG 0 entries
Node

🗺 Global Memory View — All Nodes

🗂 Layer Controls — Peel Back View

Toggle layers to isolate different aspects of the distributed DARM-ANN system. Each layer traces a different class of inter-node communication.
⚡ Inference Layer
Token generation and speculative decoding flows. Shows γ=5 draft tokens flowing TinyLM → Mid (qualification τ=0.85) → XL (verification). TSLT-compressed logits (top-200, 1.6KB/token) shown as yellow data chunks.
DRAFTTSLT×5VERIFYSSE
🧠 Memory Layer
Memory read/write operations across the 5-tier hierarchy. D-MEM RPE gating, Episodic Buffer writes, Titans STM gradient updates, and CDCP-gated LTM admissions. Purple squares flow along the trace path.
D-MEMSTM-GRADLTM-WRITERRC-QUERY
⚖️ Consensus Layer
CDCP Byzantine-fault-tolerant consensus. Shows proposal broadcasts, credibility-weighted voting circles, NLI gate results, sycophancy detection, and ADMIT/REJECT decisions. Lavender circles represent vote packets.
CDCP-REQVOTEADMITREJECT
🔐 Security Layer
Cryptographic negotiations and audit events. SPIFFE SVID issuance, Ed25519 signing, mTLS 1.3 handshakes, HMAC-chained audit log entries, A2A signed Agent Cards, and gVisor sandbox events. Amber diamonds indicate crypto exchanges.
CSRSVIDmTLSHMACAUTH
🔬 ARA Layer
Autonomous Researcher Agent pipeline. Hypothesis proposals, OpenScholar fetches, cross-node research synthesis, Meta-Review submissions to CDCP, and gVisor sandbox micro-experiment results. Green stars mark ARA events.
ARA-PROPRESEARCHCONTENTFIND
💻 Compute Distribution
Distributed compute heat overlay. Radial glow proportional to recent compute activity. Bandwidth lines between nodes show data volume — thicker = higher throughput. Each node shows a live utilization bar and mJ/token estimate.
Low
High
Trace Line Key
━━━ dashed yellow = Inference / token flow
━━━ dashed purple = Memory read/write
━━━ dashed lavender = Consensus vote
━━━ dashed amber = Crypto / security
━━━ dashed green = ARA research

⚙ CPU Allocation per Node

Adjust how much CPU each node dedicates to DARM-ANN. Higher allocation = more tokens/sec but higher power draw. CPU load affects link opacity and packet speed via M/M/1 queuing model.