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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