bbkdevops/omega-reasoner-poc

OMEGA-Reasoner (POC)

⚠️ This is a proof-of-concept — a small-scale demonstration of a self-contained LLM-driven agent framework. It is not a frontier model and is not intended to compete with production models on public leaderboards such as DeepSWE.

Honest positioning

Property	This repo	DeepSeek-V4.1-Flash (Top-1 DeepSWE)
Parameters	≈ 100K (configurable)	35B-class
Training data	none (synthetic / hand-coded)	trillions of tokens
Pass@1 on DeepSWE	not measured	74.2
Status	research POC	production model

We do not submit to the official DeepSWE leaderboard. The integration code (deep_swe.py) is provided for educational and self-evaluation purposes only.

What this model is

OMEGA-Reasoner is a self-contained language world model + autonomous agent framework. It implements:

• HMoE-D reasoner: hierarchical mixture-of-experts with dynamic routing
• AGI_AgentWorld: full cognitive agent with episodic / semantic / procedural memory, ReAct loop, Tree-of-Thoughts planning, Reflexion-style self-correction, and constitutional guardrails
• AgentWorldBench adapter: 5-dim scoring (format, factuality, consistency, realism, quality)
• DeepSWE adapter: Harbor-format task loader + verifier runner

Why this repo exists

To show that a complete agent framework — including memory, planning, tool-use, self-correction, and benchmark adapters — can be implemented from scratch without any external LLM API. It is meant as a research artifact, not a competitive submission.

How to use

from evo.agi_agent_world   import AGIAgentWorld
from evo.agentworld_bench  import load_all, WorldModelAgent, evaluate
from evo.deep_swe          import load_all_tasks, run_benchmark

# 1) Run a single goal
agi = AGIAgentWorld()
result = agi.run("Buy coffee by noon")

# 2) Evaluate on AgentWorldBench (synthetic data)
records = load_all("data/agentworldbench_synth")
rep     = evaluate(WorldModelAgent(), records)
print(rep.overall)

# 3) Run on DeepSWE tasks (synthetic seed data)
tasks   = load_all_tasks("data/deepswe_synth")
summary = run_benchmark(tasks)
print(summary.pass_at_1)

Files

• agi_agent_world.py — autonomous agent (memory, planning, tools)
• omega_arch.py — HMoE-D model definition
• omega_nlu.py — NLU engine (sentences, frames, FOL)
• omega_math.py — math reasoning primitives
• omega_physics.py — physics reasoner
• omega_meta.py — meta-cognition / audit
• omega_quant.py — quantization utilities
• agentworld_bench.py — AgentWorldBench loader + 5-dim scorer
• deep_swe.py — DeepSWE loader + verifier
• tests/ — unit tests (78+ tests, all passing)

Test results

The repository ships with 78+ unit tests covering metrics, agents, benchmarks, and the cognitive core. All tests pass on the included synthetic fixtures.

Limitations

• The underlying HMoE-D model is a demonstration with no language pretraining. Its predict() is template-based, not learned.
• Pass@1 on DeepSWE is not claimed — the agent is heuristic.
• Numerical / symbolic reasoning is rule-based, not learned.

Citation

If you use this code in research, please cite the framework only (no leaderboard claim):

@software{omega_reasoner_poc,
  title  = {OMEGA-Reasoner: a self-contained LLM-agent framework (POC)},
  year   = {2026},
  url    = {https://huggingface.co/<your-org>/omega-reasoner-poc},
}

License

Apache-2.0.