phase-slip-sampler 0.1.2

A dynamic inference sampler for LLMs using thermodynamic thermal shock.

Phase-Slip: Latent Perturbation for LLMs

The Concept

Neural text generation often collapses into repetitive loops ("degenerate repetition") because the model falls into a deep local minimum of probability—it becomes too confident in its own redundant output. Standard penalties operate on the output (logits), often leading to grammatical fracturing.

Phase-Slip is a stochastic intervention sampler that operates on the internal memory of the model.

1. Monitor: It tracks the Shannon Entropy of the model in real-time.
2. Detect: It identifies "Stagnation States"—periods where entropy drops below a threshold for too long, indicating a loop.
3. Perturb: It injects non-destructive Gaussian noise directly into the Key-Value (KV) Cache.

This "Latent Shock" effectively shakes the model's memory, forcing it to hallucinate a new context and break out of the local minimum.

Installation

For Users

You can install the package directly from PyPI:

pip install phase-slip-sampler

For Developers

If you are cloning this repository for local development or research:

git clone https://github.com/Mmorgan-ML/phase-slip-sampler.git
cd phase-slip-sampler
pip install -r requirements.txt

Note: While the package name is phase-slip-sampler, the Python module is named phase_slip.

Usage

Python Import

To use the sampler in your own code:

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from phase_slip.sampler import PhaseSlipSampler

model = GPT2LMHeadModel.from_pretrained("gpt2").cuda()
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Initialize the Sampler
# stagnation_threshold: How confident is "too confident"? (Lower = stricter)
# noise_scale: Magnitude of the memory shock (Higher = more chaotic)
sampler = PhaseSlipSampler(
    model, 
    tokenizer, 
    stagnation_threshold=0.6, 
    patience=3,
    noise_scale=0.1
)

text = sampler.generate("The scientific method is", max_new_tokens=50)
print(text)

Running the Demo

To see the "Thermal Shock" in action and visualize the divergence from greedy decoding:

python demo.py

Benchmarking

To statistically compare Phase-Slip against Greedy Decoding and Standard Sampling:

python benchmark.py

Empirical Evidence

Benchmarks performed on gpt2 (Small) demonstrate that Phase-Slip effectively shatters repetition loops, achieving higher vocabulary diversity than even standard temperature sampling.

1. The "Loop Breaker" Test

Prompt: "The research paper described the finding that the"

Method	Output Snippet	Behavior
Greedy Decoding	"...brain's ability to process information... brain... brain is able to process information..."	FAILURE: Classic logic loop. The model repeats "brain" and "process information" endlessly.
Phase-Slip	"...children with ADHD make less convulsions... 'implicated disorder' of high-level students..."	SUCCESS: Detected low entropy (stagnation), injected KV noise, and forced a complete semantic divergence.

2. Vocabulary Diversity Score (n=5 rounds)

Score based on unique word count ratio. Higher is better.

Method	Avg Score	Consistency
Greedy Decoding	0.26	Locked in loops. Zero creativity.
Standard Sampling	0.65	High variance (ranged from 0.25 to 0.81).
Phase-Slip	0.81	Consistently high diversity (>0.75).

Data collected via benchmark.py on 2025.12.03.

Calibration & Limitations

This method balances Repetition against Coherence.

• Noise Scale: Controls the magnitude of the "Shock."
  • Low (0.05): Subtle nudges. Keeps grammar intact but might not break strong loops.
  • High (0.15+): Strong divergences. Can lead to "dream-like" or nonsensical transitions (e.g., breaking syntax).
• Stagnation Threshold: Controls the trigger sensitivity.
  • 0.6 is a recommended starting point for GPT-2/Llama.
  • Setting this too high (e.g., 0.8) will trigger shocks constantly, leading to incoherence.

Project Structure

• phase_slip/: The source code for the sampler.
  • sampler.py: Contains the latent_perturbation logic.
• demo.py: A visual comparison script.
• benchmark.py: A statistical tool to measure vocabulary diversity.

License

MIT