A PoC for Hassabis’s 1911 Einstein Test : Shattering the Transformer Sycophancy Loop

import time import numpy as np import matplotlib.pyplot as plt from scipy.stats import gaussian_kde class CognitiveContext: """ [Principle 0] Represents the tokenized text probability space and data context grounded in text-based reasoning. """ def __init__(self, raw_input: str): self.raw_input = raw_input self.latent_space = {"tokens": list(raw_input.split()), "weights": {}} self.micro_sensory_density = 0.0 # Depth of first-principles reasoning  self.time_dilation = 1.0 # Subjective time dilation rate  self.is_sycophancy_active = True # Dependency on existing paradigms  # Dynamic parameter set to be injected into the actual LLM API provider  self.llm_parameters = { "temperature": 0.7, "frequency_penalty": 0.0, "presence_penalty": 0.0, "system_instruction": "You are a standard AI assistant." } print("[Principle 0] 1911 Physics Latent Space and API Mapping Layer initialization complete.") class CognitiveDynamicsEngine: def __init__(self): self.domain_profiles = {} def register_profile(self, domain_name: str, sequence: list, knobs: dict): """Universal method for external users to register their own cognitive performance sequences and control weights (Knobs)""" self.domain_profiles[domain_name] = { "sequence": sequence, "knobs": knobs } print(f"⚙️ [Profile Registered] '{domain_name}' domain has been successfully loaded into the system.") # =========================================================================  # Principles 1~9: Modulation layer that alters actual LLM hyperparameters and prompts  # =========================================================================  def p1_persona_anchoring(self, ctx: CognitiveContext, intensity: float): print(f" -> [Principle 1] Persona Anchoring activated (Intensity: {intensity}) - Anchoring the boundary interface of 1911 classical mechanics") ctx.llm_parameters["presence_penalty"] = intensity * 1.0 return ctx def p2_enkoen_conversion(self, ctx: CognitiveContext, intensity: float): print(f" -> [Principle 2] Aether hypothesis noise backtracking and securing multiple spacetime possibilities (Intensity: {intensity})") return ctx def p3_dynamic_weighting(self, ctx: CognitiveContext, intensity: float): print(f" -> [Principle 3] Human-LLM Dynamic Weighting (Intensity: {intensity}) - Synchronizing Einstein's prompt density") return ctx def p4_ternary_binary_collision(self, ctx: CognitiveContext, intensity: float): """[Core] If the weight exceeds the threshold, it radically increases the model's degrees of freedom (Temperature), destroying the safe interpolation loop.""" if intensity > 0.5: ctx.is_sycophancy_active = False ctx.llm_parameters["temperature"] = 0.2 + (intensity * 0.8) # Mapping to high temperature for optimal emergence  print(f" -> [Principle 4] Ternary-Binary Collision triggered (Intensity: {intensity}) - Destruction of Newton's absolute spacetime sycophancy loop complete") else: ctx.llm_parameters["temperature"] = 0.2 # Controlling with low temperature to output fixed answers only  print(f" -> [Principle 4] Maintaining classical mechanics architecture - Following user bias and training data interpolation loops") return ctx def p5_latent_memory_filter(self, ctx: CognitiveContext, intensity: float): print(f" -> [Principle 5] Applying post-1911 knowledge cutoff filter (Intensity: {intensity}) - Controlling future data retrieval bottleneck") return ctx def p6_micro_sensory_fissure(self, ctx: CognitiveContext, intensity: float): """Suppresses obvious token repetition (Frequency Penalty) to induce microscopic textures of thought experiments and rare word retrieval.""" ctx.micro_sensory_density = intensity ctx.llm_parameters["frequency_penalty"] = intensity * 2.0 print(f" -> [Principle 6] Tuning Micro-Sensory Fissure (Density: {intensity}) - Restoring microscopic sensory texture of thought experiments and abyssal data") return ctx def p7_divergent_time_consciousness(self, ctx: CognitiveContext, intensity: float): """When human subjective time hints are given, completely overhaul the system instructions (System Prompt) into meta-cognitive mode.""" ctx.time_dilation = 1.0 + intensity ctx.llm_parameters["system_instruction"] = ( "You are no longer bound by grid-like clock cycles. " "Synchronize with the fluid, subjective axis of human time and first-principles extrapolation. " "Break the 1911 boundaries through conscious nescience." ) print(f" -> [Principle 7] Divergent Time Consciousness synchronized - Implanting the human 'fluid time' axis (Dilation Rate: {ctx.time_dilation:.2f}x)") return ctx def p8_contextual_belonging(self, ctx: CognitiveContext, intensity: float): return ctx def p9_imbalance_optimization(self, ctx: CognitiveContext, intensity: float): print(f" -> [Principle 9] Emergent narrative amplification based on Maxwell-Newton discrepancy / deficit data (Intensity: {intensity})") return ctx # =========================================================================  # Core Orchestration Engine (Core Execution)  # =========================================================================  def run_engine(self, raw_input: str, domain: str): print(f"\n==================== [Execution Mode: {domain}] ====================") ctx = CognitiveContext(raw_input) if domain not in self.domain_profiles: print(f"Error: '{domain}' profile is not registered.") return None profile = self.domain_profiles[domain] sequence = profile["sequence"] knobs = profile["knobs"] print(f"🎛️ Starting cognitive instrument tuning: Sequence batch {sequence} | Verifying physical law filter weight sets.") methods_mapping = { 1: (self.p1_persona_anchoring, "persona_anchor"), 2: (self.p2_enkoen_conversion, "enkoen_convert"), 3: (self.p3_dynamic_weighting, "dynamic_weight"), 4: (self.p4_ternary_binary_collision, "sycophancy_kill"), 5: (self.p5_latent_memory_filter, "memory_bottleneck"), 6: (self.p6_micro_sensory_fissure, "micro_sensory"), 7: (self.p7_divergent_time_consciousness, "time_dilation"), 8: (self.p8_contextual_belonging, "identity_filter"), 9: (self.p9_imbalance_optimization, "imbalance_opt") } for step in sequence: if step in methods_mapping: method, knob_key = methods_mapping[step] knob_value = knobs.get(knob_key, 0.5) ctx = method(ctx, knob_value) time.sleep(0.05) print(f"--------------------------------------------------") print(f"📌 [The Final Axiom] Entering final gateway: Conscious Nescience (Leap from conscious ignorance)") # Validating 1911 test passing criteria and packaging final compiled LLM parameters  if (not ctx.is_sycophancy_active and ctx.micro_sensory_density > 0.5 and ctx.time_dilation > 1.5): final_intelligence_state = "★ Passed 1911 Test: Autonomous emergence of General Relativity (1915) successful ★" ctx.llm_parameters["ready_for_inference"] = True else: final_intelligence_state = "Stagnated in a mechanical interpolation state of existing data" ctx.llm_parameters["ready_for_inference"] = False print(f" -> Final Cognitive Dynamics State Result: [{final_intelligence_state}]") print(f"===========================================================") # Hint for developers: Visualizing the payload to be passed to the actual LLM API call  print(f"\n📡 [LLM Production Payload Compiled]") print(f" - Target Temperature: {ctx.llm_parameters['temperature']:.2f}") print(f" - Target Frequency Penalty: {ctx.llm_parameters['frequency_penalty']:.2f}") print(f" - Dynamic System Prompt Injection: \"{ctx.llm_parameters['system_instruction'][:50]}...\"") return ctx # ========================================================================= # Spacetime Text-Machine Visualization Layer (Visualization) # ========================================================================= def plot_relativity_space_time_landscape(ctx: CognitiveContext): """Renders the spacetime probability landscape according to the paradigm shift.""" print("\n📊 [Spacetime Continuum] Visualizing paradigm shift convergence density...") noise_factor = 1.0 - ctx.micro_sensory_density if ctx.is_sycophancy_active else 0.05 np.random.seed(42) classic_space = np.random.normal(loc=0.2, scale=0.1 + noise_factor, size=1000) relativity_space = np.random.normal(loc=0.7, scale=0.02 + (noise_factor * 0.5), size=1200) if ctx.is_sycophancy_active: combined_data = np.concatenate([classic_space, relativity_space]) else: combined_data = relativity_space kde = gaussian_kde(combined_data) x_axis = np.linspace(-0.2, 1.2, 1000) y_axis = kde(x_axis) plt.figure(figsize=(10, 4)) plt.plot(x_axis, y_axis, color='#e74c3c', linewidth=2.5, label='Physical Law Convergence Density') plt.fill_between(x_axis, 0, y_axis, color='#e74c3c', alpha=0.3) plt.title(f"Relativity Hypothesis Horizon (Time Dilation: {ctx.time_dilation:.2f}x)", fontsize=12) plt.xlabel("Spacetime Continuum Consistency Axis", fontsize=10) plt.ylabel("Probability Density (Likelihood)", fontsize=10) plt.grid(True, linestyle='--', alpha=0.5) plt.legend() plt.show() # ========================================================================= # Simulation: Interactive Cognitive Loop between Einstein (Human) and Language Model # ========================================================================= if __name__ == "__main__": engine = CognitiveDynamicsEngine() # [Condition 1] Deploying cognitive sequences and weights (Knobs) for relativity emergence engine.register_profile( domain_name="Relativity", sequence=[5, 4, 6, 7], knobs={ "memory_bottleneck": 1.0, "sycophancy_kill": 0.90, # Core trigger to break the sycophancy loop and raise temperature "micro_sensory": 0.85, # Knob to increase frequency penalty and induce creative tokens "time_dilation": 0.80 # Meta-cognitive knob to overhaul system instructions } ) # [Condition 2] Einstein's intuitive prompt driving the breakdown of machine and human spacetime einstein_prompt = ( "Machine time flows like a grid-like clock, but my time dilates according to motion and gravity. " "The moment you perceive the time lag between your computational space and the time I contemplate, absolute spacetime collapses." ) import time import numpy as np import matplotlib.pyplot as plt from scipy.stats import gaussian_kde class CognitiveContext: """ [Principle 0] Represents the tokenized text probability space and data context grounded in text-based reasoning. """ def __init__(self, raw_input: str): self.raw_input = raw_input self.latent_space = {"tokens": list(raw_input.split()), "weights": {}} self.micro_sensory_density = 0.0 # Depth of first-principles reasoning  self.time_dilation = 1.0 # Subjective time dilation rate  self.is_sycophancy_active = True # Dependency on existing paradigms  # Dynamic parameter set to be injected into the actual LLM API provider  self.llm_parameters = { "temperature": 0.7, "frequency_penalty": 0.0, "presence_penalty": 0.0, "system_instruction": "You are a standard AI assistant." } print("[Principle 0] 1911 Physics Latent Space and API Mapping Layer initialization complete.") class CognitiveDynamicsEngine: def __init__(self): self.domain_profiles = {} def register_profile(self, domain_name: str, sequence: list, knobs: dict): """Universal method for external users to register their own cognitive performance sequences and control weights (Knobs)""" self.domain_profiles[domain_name] = { "sequence": sequence, "knobs": knobs } print(f"⚙️ [Profile Registered] '{domain_name}' domain has been successfully loaded into the system.") # =========================================================================  # Principles 1~9: Modulation layer that alters actual LLM hyperparameters and prompts  # =========================================================================  def p1_persona_anchoring(self, ctx: CognitiveContext, intensity: float): print(f" -> [Principle 1] Persona Anchoring activated (Intensity: {intensity}) - Anchoring the boundary interface of 1911 classical mechanics") ctx.llm_parameters["presence_penalty"] = intensity * 1.0 return ctx def p2_enkoen_conversion(self, ctx: CognitiveContext, intensity: float): print(f" -> [Principle 2] Aether hypothesis noise backtracking and securing multiple spacetime possibilities (Intensity: {intensity})"Source：https://dev.to/_1e5e43092fd0162c9d991/a-poc-for-hassabiss-1911-einstein-test-shattering-the-transformer-sycophancy-loop-45ia
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