shiftlab-llm

Experimental framework for studying structural perturbations and correction mechanisms in LLM-based decision pipelines.

SHIFT-Lab is not a generic robustness benchmark. It is an experimental instrument for analyzing how structured shifts affect decision quality and constraint satisfaction, and how lightweight correction modules restore performance.

Core Idea

Modern LLM pipelines are deployed under changing conditions:

• Input distribution shifts
• Noise and format drift
• Constraint modifications (budgets, penalties, priorities)
• Calibration drift

SHIFT-Lab makes these perturbations explicit and measurable.

The framework separates three components:

1. Shift Operators

Structural perturbations applied to a domain (data shift, constraint shift, etc.).

2. Correction Modules

Lightweight adaptation layers (normalization, recalibration, low-cost adapters).

3. Metrics

Quantify degradation and recovery:

• Distribution divergence (e.g., token JSD)
• Constraint violation rate
• Entropy-based uncertainty proxy

The objective is to study:

• What breaks under structural perturbation?
• How sensitive is decision quality to constraint shifts?
• Which minimal correction mechanisms recover stability?

Architecture

The pipeline follows: Domain → Shift Operator → Correction Module → Evaluation Metrics

All components are modular and registered:

• shift/ : data and constraint perturbations
• adapt/ :correction modules
• eval/ : evaluation and reporting
• core/ : registries and typed interfaces

Quickstart

pip install -e .
python -m shiftlab.cli run --config configs/demo.yaml

Constraint shift example:

python -m shiftlab.cli run \
  --config configs/demo_constraints_no_adapt.yaml

Research Positioning

SHIFT-Lab focuses on:

• Structural robustness under constraint perturbations
• Correction under distribution and budget shifts
• Calibration and uncertainty proxies without labels
• Lightweight adaptation instead of full retraining