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

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.