feat: consequence engine, causal world model, metacognition, interpretability, claim verification
Choice → Consequence → Learning:
- ConsequenceEngine tracks every decision point with alternatives,
risk/reward estimates, and actual outcomes
- Consequences feed into AdaptiveEthics for experience-based learning
- FusionAGILoop now wires ethics + consequences into task lifecycle
Causal World Model:
- CausalWorldModel learns state-transition patterns from execution history
- Predicts outcomes based on observed action→effect patterns
- Uncertainty estimates decrease as more evidence accumulates
Metacognition:
- assess_head_outputs() evaluates reasoning quality from head outputs
- Detects knowledge gaps, measures head agreement, identifies uncertainty
- Actively recommends whether to seek more information
Interpretability:
- ReasoningTracer captures full prompt→answer reasoning traces
- Each step records stage, component, input/output, timing
- explain() generates human-readable reasoning explanations
Claim Verification:
- ClaimVerifier cross-checks claims for evidence, consistency, grounding
- Flags high-confidence claims lacking evidence support
- Detects contradictions between claims from different heads
325 tests passing, 0 ruff errors, 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 06:25:35 +00:00
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"""World model and simulation for AGI.
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Provides causal state-transition prediction from learned execution history,
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rollout simulation, and uncertainty estimation.
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"""
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2026-02-09 21:51:42 -08:00
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fix: deep GPU integration, fix all ruff/mypy issues, add .dockerignore
- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available)
- Integrate GPU deduplication into multi_agent/consensus_engine.py
- Add semantic_search() method to memory/semantic_graph.py with GPU acceleration
- Integrate GPU training into self_improvement/training.py AutoTrainer
- Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names)
- Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.)
- Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint])
- Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds
- Add type hints and docstrings to verification/outcome.py
- Fix E402 import ordering in witness_agent.py
- Fix F821 undefined names in vector_pgvector.py and native.py
- Fix E741 ambiguous variable names in reflective.py and recommender.py
All 276 tests pass. 0 ruff errors. 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 05:48:37 +00:00
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from fusionagi.world_model.base import SimpleWorldModel, WorldModel
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feat: consequence engine, causal world model, metacognition, interpretability, claim verification
Choice → Consequence → Learning:
- ConsequenceEngine tracks every decision point with alternatives,
risk/reward estimates, and actual outcomes
- Consequences feed into AdaptiveEthics for experience-based learning
- FusionAGILoop now wires ethics + consequences into task lifecycle
Causal World Model:
- CausalWorldModel learns state-transition patterns from execution history
- Predicts outcomes based on observed action→effect patterns
- Uncertainty estimates decrease as more evidence accumulates
Metacognition:
- assess_head_outputs() evaluates reasoning quality from head outputs
- Detects knowledge gaps, measures head agreement, identifies uncertainty
- Actively recommends whether to seek more information
Interpretability:
- ReasoningTracer captures full prompt→answer reasoning traces
- Each step records stage, component, input/output, timing
- explain() generates human-readable reasoning explanations
Claim Verification:
- ClaimVerifier cross-checks claims for evidence, consistency, grounding
- Flags high-confidence claims lacking evidence support
- Detects contradictions between claims from different heads
325 tests passing, 0 ruff errors, 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 06:25:35 +00:00
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from fusionagi.world_model.causal import CausalWorldModel
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2026-02-09 21:51:42 -08:00
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from fusionagi.world_model.rollout import run_rollout
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feat: consequence engine, causal world model, metacognition, interpretability, claim verification
Choice → Consequence → Learning:
- ConsequenceEngine tracks every decision point with alternatives,
risk/reward estimates, and actual outcomes
- Consequences feed into AdaptiveEthics for experience-based learning
- FusionAGILoop now wires ethics + consequences into task lifecycle
Causal World Model:
- CausalWorldModel learns state-transition patterns from execution history
- Predicts outcomes based on observed action→effect patterns
- Uncertainty estimates decrease as more evidence accumulates
Metacognition:
- assess_head_outputs() evaluates reasoning quality from head outputs
- Detects knowledge gaps, measures head agreement, identifies uncertainty
- Actively recommends whether to seek more information
Interpretability:
- ReasoningTracer captures full prompt→answer reasoning traces
- Each step records stage, component, input/output, timing
- explain() generates human-readable reasoning explanations
Claim Verification:
- ClaimVerifier cross-checks claims for evidence, consistency, grounding
- Flags high-confidence claims lacking evidence support
- Detects contradictions between claims from different heads
325 tests passing, 0 ruff errors, 0 mypy errors.
Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 06:25:35 +00:00
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__all__ = ["CausalWorldModel", "SimpleWorldModel", "WorldModel", "run_rollout"]
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