Qwen2.5-0.5B-PromptBus-v2: DAG Multi-Agent Prompt Bus & Arbitration Router (v2)

Lead Architect: Bhavanam Rajendra Reddy (Runtime-Slayers | ORCID: 0009-0005-3349-1162)
Base v1 Checkpoint: BRR1154/Qwen2.5-0.5B-PromptBus-v1 (Qwen/Qwen2.5-0.5B-Instruct, 494M params, BF16)
Training Method: Real PyTorch LoRALinear Backpropagation + Wake-Error ABSTAIN Calibration + Empirical Activation-Covariance SVD Null-Space Consolidation ($\Delta W_{\text{proj}} = \Delta W - (\Delta W V_{\text{base}}) V_{\text{base}}^T$)

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Verified Before (v1) vs After (v2) Evaluation Scorecard

All metrics below are directly measured on disjoint held-out test suites (eval_swarm14.py) with real execution/solver verifiers (sqlite3, z3, lxml.html, HTMLParser, subprocess Python, Kahn DAG checker). Full weight telemetry is included in real_training_telemetry.json.

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Null-Space Weight Surgery Audit

• Targeted Layer Band: 10-23 (q_proj, v_proj, o_proj, down_proj)
• Optimization Schedule: 40 gradient steps (LoRA rank=16, empirical SVD subspace k=24)
• Mutated Projections Verified: 56/56 modules (sha256 verified before vs after merge)
• Max Frobenius Shift Ratio: 1.624%
• Max Null-Space Orthogonality Error: 2.699e-09

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🏆 November 2026 Frontier Benchmark Showdown

Evaluated on official held-out November 2026 frontier benchmark datasets:

Quickstart (transformers)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BRR1154/Qwen2.5-0.5B-PromptBus-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")

messages = [{"role": "user", "content": "Solve the specialist task concisely and deterministically."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=160, do_sample=False)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))