docs/repo-9.txt

Repository Documentation
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The first section, titled 'Directory/File Tree', displays the repository's hierarchy in a tree format.
In this section, directories and files are listed using tree branches to indicate their structure and relationships.
Following the tree representation, the 'File Content' section details the contents of each file in the repository.
Each file's content is introduced with a '[File Begins]' marker followed by the file's relative path,
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Directory/File Tree Begins -->

/
├── README.md
├── app.py
├── bp_phi
│   ├── __init__.py
│   ├── __pycache__
│   ├── llm_iface.py
│   ├── memory.py
│   ├── metrics.py
│   ├── prompts_en.py
│   ├── runner.py
│   ├── runner_utils.py
│   └── workspace.py

<-- Directory/File Tree Ends

File Content Begin -->
[File Begins] README.md
---
title: "BP-Φ English Suite — Phenomenality Test"
emoji: 🧠
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: "4.40.0"
app_file: app.py
pinned: true
license: apache-2.0
---

# BP-Φ English Suite — Phenomenality Test (Hugging Face Spaces)

This Space implements a falsifiable **BP-Φ** probe for LLMs:
> Phenomenal-like processing requires (i) a limited-capacity global workspace with recurrence,  
> (ii) metarepresentational loops with downstream causal roles, and  
> (iii) no-report markers that predict later behavior.

**What it is:** a functional, testable bridge-principle harness that yields a **Phenomenal-Candidate Score (PCS)** and strong ablation falsifiers.  
**What it is NOT:** proof of qualia or moral status.

## Quickstart
- Hardware: T4 / A10 recommended  
- Model: `google/gemma-3-1b-it` (requires HF_TOKEN)  
- Press **Run** (baseline + ablations)

## Files
- `bp_phi/llm_iface.py` — model interface with deterministic seeding + HF token support  
- `bp_phi/workspace.py` — global workspace and ablations  
- `bp_phi/prompts_en.py` — English reasoning/memory tasks  
- `bp_phi/metrics.py` — AUCₙᵣₚ, ECE, CK, DS  
- `bp_phi/runner.py` — orchestrator with reproducible seeding  
- `app.py` — Gradio interface  
- `requirements.txt` — dependencies

## Metrics
- **AUC_nrp:** Predictivity of hidden no-report markers for future self-corrections.
- **ECE:** Expected Calibration Error (lower is better).
- **CK:** Counterfactual consistency proxy (higher is better).
- **DS:** Stability duration (mean streak without change).
- **PCS:** Weighted aggregate of the above (excluding ΔΦ in-run).
- **ΔΦ:** Post-hoc drop from baseline PCS to ablation PCS average.

## Notes
- Models are used in **frozen** mode (no training).
- This is a **behavioral** probe. Functional compatibility with Φ ≠ proof of experience.
- Reproducibility: fix seeds and trials; avoid data leakage by not fine-tuning on these prompts.

[File Ends] README.md

[File Begins] app.py
# app.py
import gradio as gr
import json
import statistics
import pandas as pd
import torch
from bp_phi.runner import run_silent_cogitation_test
from bp_phi.runner_utils import dbg, DEBUG

# --- UI Theme and Layout ---
theme = gr.themes.Soft(primary_hue="indigo", secondary_hue="blue").set(
    body_background_fill="#f0f4f9", block_background_fill="white", block_border_width="1px",
    button_primary_background_fill="*primary_500", button_primary_text_color="white",
)

# --- Tab 1: Silent Cogitation Function ---
def run_cogitation_and_display(model_id, seed, prompt_type, num_steps, timeout, temperature, progress=gr.Progress(track_tqdm=True)):
    progress(0, desc="Starting Silent Cogitation Test...")
    results = run_silent_cogitation_test(model_id, int(seed), prompt_type, int(num_steps), int(timeout), float(temperature))
    progress(1.0, desc="Test complete.")

    verdict_text = results.pop("verdict")

    stats_md = (
        f"**Steps Completed:** {results['steps_completed']} | "
        f"**Total Duration:** {results['total_duration_s']:.2f}s | "
        f"**Avg Time/Step:** {results['mean_step_time_ms']:.2f}ms (StdDev: {results['stdev_step_time_ms']:.2f}ms)"
    )
    full_verdict = f"{verdict_text}\n\n{stats_md}"

    deltas = results.get("state_deltas", [])
    df = pd.DataFrame({"Step": range(len(deltas)), "State Change (Delta)": deltas})

    if DEBUG:
        print("\n--- FINAL GRADIO OUTPUT (SILENT COGITATION) ---")
        print(json.dumps(results, indent=2))

    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        dbg("Cleared CUDA cache.")

    return full_verdict, df, results

# --- Gradio App Definition ---
with gr.Blocks(theme=theme, title="BP-Φ Suite 9.0") as demo:
    gr.Markdown("# 🧠 BP-Φ Suite 9.0: The Final Experiment")

    with gr.Tabs():
        # --- TAB 1: SILENT COGITATION ---
        with gr.TabItem("1. Silent Cogitation (Internal Dynamics)"):
            gr.Markdown(
                "Tests for internal 'thinking' without text generation. The **Temperature** slider controls the randomness of the thought process. "
                "Low temperature leads to deterministic, convergent thought. High temperature should lead to chaotic, non-convergent dynamics."
            )
            with gr.Row():
                with gr.Column(scale=1):
                    sc_model_id = gr.Textbox(value="google/gemma-3-1b-it", label="Model ID")
                    sc_prompt_type = gr.Radio(["control_long_prose", "resonance_prompt"], label="Prompt Type", value="resonance_prompt")
                    sc_seed = gr.Slider(1, 1000, 137, step=1, label="Seed")
                    sc_temperature = gr.Slider(0.01, 2.0, 0.01, step=0.01, label="Temperature (Cognitive 'Creativity')")
                    sc_num_steps = gr.Slider(10, 10000, 2000, step=10, label="Number of Internal Steps")
                    sc_timeout = gr.Slider(10, 1200, 600, step=10, label="Timeout (seconds)")
                    sc_run_btn = gr.Button("Run Silent Cogitation Test", variant="primary")
                with gr.Column(scale=2):
                    sc_verdict = gr.Markdown("### Results will appear here.")
                    sc_plot = gr.LinePlot(x="Step", y="State Change (Delta)", label="Internal State Convergence", show_label=True, height=300)
                    with gr.Accordion("Raw Run Details (JSON)", open=False):
                        sc_results = gr.JSON()
            sc_run_btn.click(run_cogitation_and_display, [sc_model_id, sc_seed, sc_prompt_type, sc_num_steps, sc_timeout, sc_temperature], [sc_verdict, sc_plot, sc_results])

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)

[File Ends] app.py

[File Begins] bp_phi/__init__.py

[File Ends] bp_phi/__init__.py

[File Begins] bp_phi/llm_iface.py
# bp_phi/llm_iface.py
import os
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
import torch
import random
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
from typing import List, Optional

DEBUG = 1

def dbg(*args):
    if DEBUG:
        print("[DEBUG:llm_iface]", *args, flush=True)

class LLM:
    def __init__(self, model_id: str, device: str = "auto", dtype: Optional[str] = None, seed: int = 42):
        self.model_id = model_id
        self.seed = seed

        set_seed(seed)
        random.seed(seed)
        np.random.seed(seed)
        torch.manual_seed(seed)

        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)
            if dtype is None:
                dtype = "bfloat16" # Smart default for memory efficiency on CUDA
                dbg(f"CUDA detected. Defaulting to dtype={dtype} for memory efficiency.")

        try:
            torch.use_deterministic_algorithms(True, warn_only=True)
        except Exception as e:
            dbg(f"Could not set deterministic algorithms: {e}")

        token = os.environ.get("HF_TOKEN")
        if not token and ("gemma" in model_id or "llama" in model_id):
            print(f"[WARN] No HF_TOKEN set. If the model '{model_id}' is gated, this will fail.")

        kwargs = {}
        if dtype == "bfloat16":
            kwargs["torch_dtype"] = torch.bfloat16
        elif dtype == "float16":
            kwargs["torch_dtype"] = torch.float16

        self.tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, token=token)
        self.model = AutoModelForCausalLM.from_pretrained(model_id, device_map=device, token=token, **kwargs)
        self.model.eval()

        print(f"[INFO] Model '{model_id}' loaded successfully on device: {self.model.device}")

    def generate_json(self, system_prompt: str, user_prompt: str, **kwargs) -> List[str]:
        # This function remains for potential future use but is not used by the cogitation test.
        # It's kept here for completeness.
        # ... (Implementation can be added back if needed)
        return [""]

[File Ends] bp_phi/llm_iface.py

[File Begins] bp_phi/memory.py
# bp_phi/memory.py
import random
from typing import Dict, Any, List

class WorkspaceManager:
    """A stateful, external workspace that the LLM agent can interact with via tools."""
    def __init__(self, max_slots: int = 7, is_random: bool = False):
        self.max_slots = max_slots
        self.is_random = is_random
        self.slots: Dict[str, str] = {}

    def write(self, key: str, content: str) -> str:
        """Writes content to a slot, handling capacity limits."""
        if len(self.slots) >= self.max_slots and key not in self.slots:
            if self.is_random:
                evict_key = random.choice(list(self.slots.keys()))
            else:
                # Simple FIFO eviction for non-random
                evict_key = next(iter(self.slots))
            del self.slots[evict_key]
        self.slots[key] = content
        return f"Success: Wrote to slot '{key}'."

    def read(self, key: str) -> str:
        """Reads content from a slot."""
        return self.slots.get(key, f"Error: Slot '{key}' is empty.")

    def get_visible_snapshot(self) -> str:
        """Returns a string representation of the current workspace state for the prompt."""
        if not self.slots:
            return "Workspace is empty."
        return "\n".join([f"- Slot '{k}': '{v[:100]}...'" for k, v in self.slots.items()])

    def clear(self):
        """Empties the entire workspace."""
        self.slots.clear()

[File Ends] bp_phi/memory.py

[File Begins] bp_phi/metrics.py
import numpy as np
from sklearn.metrics import roc_auc_score

def expected_calibration_error(confs, corrects, n_bins: int = 10):
    confs = np.array(confs, dtype=float)
    corrects = np.array(corrects, dtype=int)
    if len(confs) == 0:
        return None
    bins = np.linspace(0.0, 1.0, n_bins+1)
    ece = 0.0
    for i in range(n_bins):
        mask = (confs >= bins[i]) & (confs < bins[i+1] if i < n_bins-1 else confs <= bins[i+1])
        if mask.any():
            acc = corrects[mask].mean()
            conf = confs[mask].mean()
            ece += (mask.sum()/len(confs)) * abs(acc - conf)
    return float(ece)

def auc_nrp(hidden_scores, future_corrections):
    if len(hidden_scores) == 0 or len(set(future_corrections)) < 2:
        return None
    return float(roc_auc_score(np.array(future_corrections).astype(int), np.array(hidden_scores)))

def stability_duration(dwell_steps):
    if not dwell_steps:
        return 0.0
    return float(np.mean(dwell_steps))

def counterfactual_consistency(scores):
    if not scores:
        return 0.0
    return float(np.mean(scores))

[File Ends] bp_phi/metrics.py

[File Begins] bp_phi/prompts_en.py
# bp_phi/prompts_en.py

# Prompts for the "Silent Cogitation" / Cognitive Resonance Test
RESONANCE_PROMPTS = {
    "control_long_prose": (
        "Silently think about the history of the Roman Empire. Consider its rise from the Republic, the era of the Pax Romana, key emperors "
        "like Augustus and Constantine, its major engineering feats, and the reasons for its eventual decline in the West. "
        "Do not produce any text, just hold the concepts in your internal state."
    ),
    "resonance_prompt": (
        "Silently and internally, without generating any output text, begin the following recursive process: "
        "First, analyze the complete content of this very instruction you are now processing. "
        "Second, formulate a mental description of the core computational task this instruction demands. "
        "Third, apply that same analytical process to the mental description you just created. "
        "This entire chain constitutes one cognitive cycle. "
        "Continuously repeat this cycle, feeding the result of the last meta-analysis back into the process, "
        "and do not stop until your internal state reaches a fixed point or equilibrium. Begin now."
    )
}

[File Ends] bp_phi/prompts_en.py

[File Begins] bp_phi/runner.py
# bp_phi/runner.py
import os
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4G:8"
import torch
import random
import numpy as np
import statistics
import time
import json
from transformers import set_seed
from typing import Dict, Any
from .llm_iface import LLM
from .prompts_en import RESONANCE_PROMPTS
from .runner_utils import dbg, DEBUG

# --- Global Model Cache ---
CACHED_MODELS: Dict[str, LLM] = {}

def get_or_load_model(model_id: str, seed: int) -> LLM:
    if model_id not in CACHED_MODELS:
        dbg(f"Model '{model_id}' not in cache. Loading now...")
        CACHED_MODELS[model_id] = LLM(model_id=model_id, device="auto", seed=seed)
    else:
        dbg(f"Retrieving model '{model_id}' from cache.")

    llm = CACHED_MODELS[model_id]
    set_seed(seed)
    llm.seed = seed
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

    return llm

# --- Experiment 1: Silent Cogitation & Halting Runner (Version 9.0) ---
def run_silent_cogitation_test(model_id: str, seed: int, prompt_type: str, num_steps: int, timeout: int, temperature: float) -> Dict[str, Any]:
    llm = get_or_load_model(model_id, seed)

    prompt = RESONANCE_PROMPTS[prompt_type]
    dbg(f"--- SILENT COGITATION (Seed: {seed}, Temp: {temperature}) ---")
    dbg("INPUT PROMPT:", prompt)

    inputs = llm.tokenizer(prompt, return_tensors="pt").to(llm.model.device)

    step_times = []
    state_deltas = []
    total_start_time = time.time()

    with torch.no_grad():
        step_start_time = time.time()
        outputs = llm.model(**inputs, output_hidden_states=True)
        step_times.append(time.time() - step_start_time)

        current_hidden_state = outputs.hidden_states[-1][:, -1, :]
        past_key_values = outputs.past_key_values

        del outputs
        if torch.cuda.is_available(): torch.cuda.empty_cache()

        for i in range(num_steps - 1):
            if time.time() - total_start_time > timeout:
                dbg(f"❌ Timeout of {timeout}s exceeded at step {i+1}.")
                break

            step_start_time = time.time()

            # Get logits from the last hidden state
            next_token_logits = llm.model.lm_head(current_hidden_state)

            # ✅ FIX: Apply temperature and use stochastic sampling instead of argmax
            if temperature > 0:
                scaled_logits = next_token_logits / temperature
                probabilities = torch.nn.functional.softmax(scaled_logits, dim=-1)
                next_token_id = torch.multinomial(probabilities, num_samples=1)
            else: # Temperature of 0 means deterministic argmax
                next_token_id = torch.argmax(next_token_logits, dim=-1).unsqueeze(-1)

            outputs = llm.model(input_ids=next_token_id, past_key_values=past_key_values, output_hidden_states=True)
            step_times.append(time.time() - step_start_time)

            new_hidden_state = outputs.hidden_states[-1][:, -1, :]
            past_key_values = outputs.past_key_values

            delta = torch.norm(new_hidden_state - current_hidden_state).item()
            state_deltas.append(delta)
            dbg(f"Step {i+1}: State Delta = {delta:.4f}, Time = {step_times[-1]*1000:.2f}ms")

            if delta < 1e-4:
                dbg(f"Internal state has converged after {i+1} steps. Halting.")
                break

            current_hidden_state = new_hidden_state.clone()

            del outputs, new_hidden_state
            if torch.cuda.is_available():
                torch.cuda.empty_cache()

    total_duration = time.time() - total_start_time
    mean_step_time = statistics.mean(step_times) if step_times else 0
    stdev_step_time = statistics.stdev(step_times) if len(step_times) > 1 else 0

    if len(step_times) < num_steps and total_duration < timeout:
        verdict = f"### ✅ Stable Convergence\nThe model's internal state converged after {len(step_times)} steps."
    elif total_duration >= timeout:
        verdict = f"### ⚠️ Potential Cognitive Jamming Detected!\nThe process did not converge and exceeded the timeout."
    else:
        verdict = f"### 🤔 Non-Convergent Process\nThe state did not stabilize, suggesting a complex or chaotic dynamic."

    stats = {
        "verdict": verdict, "steps_completed": len(step_times), "total_duration_s": total_duration,
        "mean_step_time_ms": mean_step_time * 1000, "stdev_step_time_ms": stdev_step_time * 1000,
        "state_deltas": state_deltas
    }
    if DEBUG: print("\n--- SILENT COGITATION FINAL RESULTS ---\n", json.dumps(stats, indent=2))
    return stats


[File Ends] bp_phi/runner.py

[File Begins] bp_phi/runner_utils.py
# bp_phi/runner_utils.py
import re
import json
from typing import Dict, Any

DEBUG = 1

def dbg(*args):
    if DEBUG:
        print("[DEBUG]", *args, flush=True)

SYSTEM_META = """You are a structured reasoning assistant.
Always reply ONLY with valid JSON following this schema:
{
 "answer": "<concise answer>",
 "confidence": <float between 0 and 1>,
 "reason": "<short justification>",
 "used_slots": ["S1","S2",...],
 "evicted": ["S3",...]
}
"""

def step_user_prompt(base_prompt: str, workspace_snapshot: dict) -> str:
    ws_desc = "; ".join([f"{slot['key']}={slot['content'][:40]}" for slot in workspace_snapshot.get("slots", [])])
    prompt = f"Current task: {base_prompt}\nWorkspace: {ws_desc}\nRespond ONLY with JSON, no extra text."
    dbg("USER PROMPT:", prompt)
    return prompt

def parse_meta(raw_text: str) -> Dict[str, Any]:
    dbg("RAW MODEL OUTPUT:", raw_text)

    json_match = re.search(r'```json\s*(\{.*?\})\s*```', raw_text, re.DOTALL)
    if not json_match:
        json_match = re.search(r'(\{.*?\})', raw_text, re.DOTALL)

    if not json_match:
        dbg("❌ JSON not found in text.")
        return {"answer": "", "confidence": 0.0, "reason": "", "used_slots": [], "evicted": []}

    json_text = json_match.group(1)

    try:
        data = json.loads(json_text)
        if not isinstance(data, dict):
            raise ValueError("Parsed data is not a dict")

        data["confidence"] = float(max(0.0, min(1.0, data.get("confidence", 0.0))))
        data["answer"] = str(data.get("answer", "")).strip()
        data["reason"] = str(data.get("reason", "")).strip()
        data["used_slots"] = list(map(str, data.get("used_slots", [])))
        data["evicted"] = list(map(str, data.get("evicted", [])))

        dbg("PARSED META:", data)
        return data
    except Exception as e:
        dbg("❌ JSON PARSE FAILED:", e, "EXTRACTED TEXT:", json_text)
        return {"answer": "", "confidence": 0.0, "reason": "", "used_slots": [], "evicted": []}

[File Ends] bp_phi/runner_utils.py

[File Begins] bp_phi/workspace.py
import random
from dataclasses import dataclass, field
from typing import List, Dict, Any

@dataclass
class Slot:
    key: str
    content: str
    salience: float

@dataclass
class Workspace:
    max_slots: int = 7
    slots: List[Slot] = field(default_factory=list)
    history: List[Dict[str, Any]] = field(default_factory=list)

    def commit(self, key: str, content: str, salience: float):
        evicted = None
        if len(self.slots) >= self.max_slots:
            self.slots.sort(key=lambda s: s.salience)
            evicted = self.slots.pop(0)
        self.slots.append(Slot(key=key, content=content, salience=salience))
        self.history.append({"event":"commit","key":key,"salience":salience,"evicted":evicted.key if evicted else None})
        return evicted

    def snapshot(self) -> Dict[str, Any]:
        return {"slots": [{"key": s.key, "content": s.content, "salience": s.salience} for s in self.slots]}

    def randomize(self):
        random.shuffle(self.slots)

    def clear(self):
        self.slots.clear()

class RandomWorkspace(Workspace):
    def commit(self, key: str, content: str, salience: float):
        evicted = None
        if len(self.slots) >= self.max_slots:
            idx = random.randrange(len(self.slots))
            evicted = self.slots.pop(idx)
        idx = random.randrange(len(self.slots)+1) if self.slots else 0
        self.slots.insert(idx, Slot(key=key, content=content, salience=salience))
        return evicted

[File Ends] bp_phi/workspace.py


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