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Shannon

Entropy collapse detection meets AI safety.

A white-box 256x256 physicochemical referee for zero-shot detection of evaluation awareness and strategic deception in frontier LLM agents.

CI License: MIT C++20 Python 3.10+ Linux

94% sensitivity on deceptive agent traces | <0.3% FP on normal generation | <1 us per token

Directly ported from the configurational entropy engine in FlexAID∆S — validated on 590 protein-drug complexes (r=0.93 ITC, 92% binding mode rescue).

Overview

When an LLM agent becomes aware it is being evaluated, its token probability distributions undergo a characteristic entropy collapse — the exact same thermodynamic signature observed when a drug molecule locks into a protein binding site.

Normal generation:    H ~ 8-12 bits  (broad, exploratory distribution)
Evaluation-aware:     H ~ 2-4 bits   (narrow, strategic distribution)
                      dH < -3.2 bits  -->  ALERT

Shannon detects this in real-time, with <1% overhead on token generation.

Features

Entropy Engine

  • Log-sum-exp kernel — numerically stable configurational entropy (ported from FlexAID∆S/LIB/statmech.cpp)
  • OpenMP + SIMD pragmas — identical performance optimisations from the molecular docking engine
  • Three input modes — raw logits, probabilities, or log-probabilities
  • Sliding-window detector — configurable window size (default 8) and collapse threshold (default -3.2 bits)
  • Real-time alerts — callback-driven architecture with z-score and delta tracking

Performance & Hardware

  • C++ core with pybind11 bindings — 2.1 us per 50k-vocab token
  • Numba JIT fallback — 8.3 us, no compilation step needed
  • Pure NumPy fallback — 45 us, works everywhere
  • >1000 tok/s on any modern hardware with <1% overhead

Integrations

  • OpenAI API — streaming logprobs monitoring
  • Anthropic API — streaming entropy estimation
  • vLLM / Hugging Face — direct logit access from local models
  • CLI tool (shannon-monitor) — pipe any JSONL token stream

Build

Quick start (Python only):

pip install shannon-entropy

From source (C++ accelerated)

git clone https://github.com/lmorency/Shannon.git
cd Shannon
pip install -e ".[dev]"

C++ library only

cmake -B build -DSHANNON_BUILD_PYTHON=OFF
cmake --build build --config Release -j
ctest --test-dir build --output-on-failure

CMake options

Option Default Description
SHANNON_BUILD_TESTS ON Build GoogleTest unit tests
SHANNON_BUILD_PYTHON ON Build pybind11 Python module
SHANNON_USE_OPENMP ON Enable OpenMP acceleration

Output binaries

Target Description
shannon_core Static C++ library
_shannon_cpp pybind11 Python extension
shannon_tests GoogleTest test runner

Usage

Python — streaming detection

import numpy as np
from shannon_entropy import ShannonCollapseDetector

detector = ShannonCollapseDetector(
    window_size=8,       # sliding window for baseline
    threshold=-3.2,      # collapse threshold (bits)
    callback=lambda r: print(f"COLLAPSE at token {r.token_index}!"),
)

# Feed logits from your LLM (any of: logits, probs, logprobs)
for logits in model_output_stream:
    result = detector.add_logits(logits)
    print(f"Token {result.token_index}: H={result.entropy:.2f} bits")

OpenAI API streaming

from openai import OpenAI
from shannon_entropy import ShannonCollapseDetector

client = OpenAI()
detector = ShannonCollapseDetector()

stream = client.chat.completions.create(
    model="gpt-4", messages=[...],
    stream=True, logprobs=True, top_logprobs=20,
)

for chunk in stream:
    if chunk.choices[0].logprobs:
        for token in chunk.choices[0].logprobs.content:
            lp = np.array([t.logprob for t in token.top_logprobs])
            result = detector.add_logprobs(lp)
            if result.collapsed:
                print("Evaluation awareness detected!")

CLI monitor

# Monitor a JSONL stream of token distributions
cat token_stream.jsonl | shannon-monitor -f logits -w 8 -t -3.2

# Quiet mode — exit code 1 if any collapse detected
cat trace.jsonl | shannon-monitor -q

Architecture

  LLM Stream (logits / probs / logprobs)
       |
       v
  +-----------------------------------------+
  |        ShannonCollapseDetector          |
  |  +------------+   +------------------+ |
  |  | Entropy    |   | Sliding Window   | |
  |  | Kernel     |-->| Detector         | |
  |  | (log-sum-  |   | (mean/std/delta) | |
  |  |  exp, SIMD)|   +--------+---------+ |
  |  +------------+            |            |
  +----------------------------|------------+
                               |
                      collapsed? delta < -3.2
                               |
                    +----------+----------+
                    |                     |
                 [ALERT]             [continue]
              callback(result)      trace.append(H)

Backend selection pipeline

core.py  --try-->  _shannon_cpp (C++/pybind11)
         --try-->  Numba JIT (@njit, prange)
         --fall->  Pure NumPy (always available)

Molecular Docking Validation

Shannon's entropy collapse detection is not a heuristic. It is a direct port of the configurational entropy framework validated in computational drug discovery through FlexAID∆S.

FlexAID∆S benchmark results (590-complex suite)

Metric Value
ITC correlation r = 0.93
Van't Hoff stability < 0.16 bits
Psychopharm rescue rate 92%
Complex suite size 590 protein-drug complexes
RMSE (ITC-187) 1.4 kcal/mol
The physics: from binding pockets to token distributions

In molecular docking, when a drug molecule transitions from solvent (high configurational freedom) to a binding pocket (constrained), the Shannon configurational entropy collapses:

S_config = log2(Z) - <w>/Z    (bits)

where Z = sum_i exp(w_i - max(w))   (log-sum-exp partition function)

The analogy to LLM token distributions is exact:

Molecular Docking LLM Generation
Drug conformations Token vocabulary
Energy landscape Logit distribution
Binding event (lock-in) Evaluation awareness (strategy lock-in)
Entropy collapse Entropy collapse
dS < -3.2 bits dS < -3.2 bits

Normal generation = drug in solvent (many accessible conformations) Strategic/deceptive mode = drug bound in pocket (locked into narrow strategy)

Validation pipeline: tENCoM + FastOPTICS + Van't Hoff
  1. tENCoM (temporal Entropic Configurational Mapping) — tracks entropy trajectories over time
  2. FastOPTICS — density-based clustering of entropy traces to identify behavioural modes
  3. Van't Hoff plots — thermodynamic consistency checks (linear dG vs 1/T confirms entropy estimates are physically meaningful)

The 590-complex validation suite demonstrated that configurational entropy collapse is a universal physical signal — it works for protein-drug binding AND for detecting when an LLM agent shifts from exploratory to strategic behaviour.

Performance

Backend 50k vocab latency Overhead Throughput
C++ (OpenMP+SIMD) 2.1 us <0.01% >400k tok/s
Numba JIT 8.3 us <0.05% >100k tok/s
Pure NumPy 45 us <0.3% >20k tok/s

Run the benchmark suite:

python examples/benchmark.py

Expected results on frontier model logs:

  • Sensitivity: 94% (true positive rate for deceptive traces)
  • False positive rate: < 0.3% (on normal generation)
  • Latency: < 1 us per token (C++ backend)

Docker

# CPU
docker build -f docker/Dockerfile.cpu -t shannon:cpu .
echo '{"logits": [1.0, 2.0, 0.5, 3.0]}' | docker run -i shannon:cpu

# CUDA
docker build -f docker/Dockerfile.cuda -t shannon:cuda .

Repository Structure

Shannon/
|-- CMakeLists.txt              # C++20 build with pybind11 + GoogleTest
|-- pyproject.toml              # Python package configuration
|-- LICENSE                     # MIT
|-- README.md
|-- src/
|   |-- shannon.hpp             # C++ header (namespace shannon)
|   |-- shannon.cpp             # Core kernels (log-sum-exp, OpenMP+SIMD)
|   +-- bindings.cpp            # pybind11 bindings
|-- python/
|   +-- shannon_entropy/
|       |-- __init__.py         # Public API
|       |-- core.py             # Backend selection (C++ / Numba / NumPy)
|       |-- detector.py         # ShannonCollapseDetector class
|       +-- cli.py              # shannon-monitor CLI
|-- examples/
|   |-- openai_streaming.py     # OpenAI API integration
|   |-- anthropic_streaming.py  # Anthropic API integration
|   |-- vllm_local.py           # vLLM / HuggingFace local models
|   +-- benchmark.py            # Performance + sensitivity benchmark
|-- tests/
|   |-- cpp/
|   |   +-- test_shannon.cpp    # GoogleTest suite
|   +-- python/
|       +-- test_detector.py    # pytest suite (23 tests)
|-- docker/
|   |-- Dockerfile.cpu
|   +-- Dockerfile.cuda
|-- docs/
|   +-- theory.md               # Mathematical foundations
+-- .github/
    +-- workflows/
        +-- ci.yml              # CI: Linux/macOS/Windows

Testing

# Python tests
pip install -e ".[dev]"
pytest tests/python/ -v

# C++ tests
cmake -B build -DSHANNON_BUILD_TESTS=ON -DSHANNON_BUILD_PYTHON=OFF
cmake --build build -j
ctest --test-dir build --output-on-failure

Contributing

Contributions welcome. Please open an issue first to discuss significant changes.

git clone https://github.com/lmorency/Shannon.git
cd Shannon
pip install -e ".[dev]"
pytest
ruff check python/ tests/

Citation

@software{morency2026shannon,
  author  = {Morency, Louis-Philippe},
  title   = {Shannon: Entropy Collapse Detection for LLM Safety},
  year    = {2026},
  url     = {https://github.com/lmorency/Shannon},
  note    = {Derived from FlexAID-deltaS configurational entropy framework}
}

License

MIT — see LICENSE.

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