Reasoning

Temperature reasoning in the Miao Swarm framework is a mechanism for controlling the level of creativity and randomness in the decision-making process of sub-agents. By modulating the temperature parameter, the system balances exploratory (creative) behaviors with exploitation (precise, task-focused) actions. This adaptability allows sub-agents to effectively handle tasks that vary in complexity and ambiguity.

How Temperature Reasoning Works

High-Temperature Agents:
- Operate with high randomness and creativity.
- Generate a wide range of solutions, including unconventional or “off-topic” ideas.
- Suitable for exploration phases, where innovative or novel solutions are needed.
- Example: Brainstorming a new optimization method for logistics.
Low-Temperature Agents:
- Prioritize precision and deterministic logic.
- Narrow their focus to proven or efficient solutions.
- Suitable for execution or refinement phases, where task completion is paramount.
- Example: Fine-tuning transport routes based on existing logistics data.
Dynamic Temperature Adjustment:
- The framework adjusts the temperature of agents in real time based on the phase of the task or feedback from swarm reasoning.
- For ambiguous problems, the temperature starts high, encouraging exploration, and decreases as clarity emerges.
- During refinement stages, the temperature is set low to ensure focus and precision.

Temperature-Based Workflow

Task Initialization:
- Tasks are analyzed for complexity, ambiguity, and the need for creativity.
- Initial temperature values are assigned to sub-agents accordingly.
Exploration Phase (High Temperature):
- Sub-agents generate diverse solutions by leveraging probabilistic models and random sampling.
- Outputs may include novel or unconventional ideas, which are fed into the swarm reasoning stage.
Refinement Phase (Low Temperature):
- The swarm filters and integrates the most viable solutions from the exploration phase.
- Sub-agents focus on precision, enhancing the selected solutions or aligning them with task requirements.
Final Decision:
- Unified results are generated, balancing creativity with task-specific accuracy.

Practical Example

Scenario: Optimizing disaster response logistics.

High Temperature (Exploration Phase):
- Sub-Agent 1 proposes new, unconventional transport routes.
- Sub-Agent 2 suggests combining medical supply chains with local food distribution.
- Sub-Agent 3 explores alternate inventory stocking strategies.
Low Temperature (Refinement Phase):
- Sub-Agent 1 refines its most efficient transport route based on travel time and cost.
- Sub-Agent 2 adjusts its supply chain model to align with medical demand.
- Sub-Agent 3 eliminates unfeasible inventory strategies, focusing on viable options.
Outcome:
- Unified disaster relief plan integrating creativity and precision.

Benefits of Temperature Reasoning

Exploration vs. Exploitation Trade-off:
- Balances creative problem-solving with task-focused execution.
- Ensures innovation without sacrificing precision.
Dynamic Adaptability:
- Adjusts agent behavior in real time to match task phases or feedback.
Improved Collaboration:
- High-temperature agents generate diverse inputs, enriching the swarm reasoning process.
- Low-temperature agents ensure alignment with task goals.
Enhanced Problem-Solving:
- Enables sub-agents to tackle ambiguous, multi-dimensional problems effectively.

Challenges and Considerations

Temperature Tuning:
- Setting appropriate temperature values is critical to balancing exploration and exploitation.
- Requires robust metrics to evaluate task complexity and phase transitions.
Computational Overhead:
- High-temperature phases may generate large volumes of data, requiring efficient filtering mechanisms.
Inter-Agent Coordination:
- Ensuring that temperature adjustments in individual agents align with swarm-wide goals.

import random

class SubAgent:
    def __init__(self, name, temperature):
        self.name = name
        self.temperature = temperature  # High = creative, Low = precise

    def generate_solution(self, task):
        # Simulate creativity with randomness
        base_solution = f"{self.name} tackles {task}"
        if random.uniform(0, 1) < self.temperature:
            return f"{base_solution} with a creative twist!"
        else:
            return f"{base_solution} using a standard approach."

    def adjust_temperature(self, phase):
        if phase == "exploration":
            self.temperature = 0.8
        elif phase == "refinement":
            self.temperature = 0.2


# Example Usage
task = "Optimize Logistics"
agent = SubAgent(name="TransportAI", temperature=0.8)

# Exploration Phase
agent.adjust_temperature("exploration")
print(agent.generate_solution(task))

# Refinement Phase
agent.adjust_temperature("refinement")
print(agent.generate_solution(task))

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Last updated 5 days ago

import random class SubAgent: def __init__(self, name, temperature): self.name = name self.temperature = temperature # High = creative, Low = precise def generate_solution(self, task): # Simulate creativity with randomness base_solution = f"{self.name} tackles {task}" if random.uniform(0, 1) < self.temperature: return f"{base_solution} with a creative twist!" else: return f"{base_solution} using a standard approach." def adjust_temperature(self, phase): if phase == "exploration": self.temperature = 0.8 elif phase == "refinement": self.temperature = 0.2 # Example Usage task = "Optimize Logistics" agent = SubAgent(name="TransportAI", temperature=0.8) # Exploration Phase agent.adjust_temperature("exploration") print(agent.generate_solution(task)) # Refinement Phase agent.adjust_temperature("refinement") print(agent.generate_solution(task))