aria_llm/__init__.py

"""
ARIA: Adaptive Reliability & Integrity Attachment (v0.3)
=========================================================

A lightweight, attachable module for LLMs that addresses four structural
failures identified in frontier AI systems:

1. Compound Error Accumulation (R^n decay)
2. Median Trap / Lack of "Taste" (drift toward statistical average)
3. Semantic Drift (forgetting the "why" in long-horizon tasks)
4. Logic Looping (repeating failed approaches)

ARIA works like LoRA — but instead of adapting weights for a task, it hooks into
the inference pipeline to monitor and correct failure modes in real-time.

v0.3 changes:
- Calibration profile save/load (JSON)
- Auto-tune correction_scale from calibration variance
- GSM8K + code generation benchmarks
- SOTA comparison with ITI, CAA, CAST, RepE, A-LQR, ReflCtrl, ReProbe

Usage:
    from aria_llm import ARIA, ARIAConfig
    
    aria = ARIA.attach(model, tokenizer)
    output = model.generate(input_ids, max_new_tokens=500)
    print(aria.report_text())
    aria.detach()
    
    # Save calibration for reuse
    aria.save_calibration("profiles/gpt2_calibration.json")
    
    # Load on next run (skip calibration phase)
    aria2 = ARIA.attach(model, tokenizer)
    aria2.load_calibration("profiles/gpt2_calibration.json")
"""

__version__ = "0.3.0"

from aria_llm.config import ARIAConfig
from aria_llm.core import ARIA
from aria_llm.detectors import (
    CompoundErrorDetector,
    SemanticDriftDetector, 
    LogicLoopDetector,
    MedianTrapDetector,
)
from aria_llm.correctors import (
    SteeringCorrector,
    GoalAnchor,
    TrajectoryDiverger,
    TasteAmplifier,
)
from aria_llm.dashboard import ARIADashboard

__all__ = [
    "ARIA",
    "ARIAConfig", 
    "CompoundErrorDetector",
    "SemanticDriftDetector",
    "LogicLoopDetector",
    "MedianTrapDetector",
    "SteeringCorrector",
    "GoalAnchor",
    "TrajectoryDiverger",
    "TasteAmplifier",
    "ARIADashboard",
]