Beginner Explanation
Imagine you’re taking a test and you have a helpful guide that gives you hints and tips on how to answer the questions better. Test-Time Reasoning Guidance is like that guide for AI models. When these models are trying to figure things out, this method helps them make better decisions by providing structured support, ensuring they understand the problem and come up with the best answer possible.Technical Explanation
Test-Time Reasoning Guidance involves providing additional structured information to machine learning models during inference to improve their reasoning capabilities. This can be implemented through various techniques, such as using prompt engineering or integrating auxiliary tasks that guide the model’s focus. For example, in a natural language processing task, a model might be given specific prompts that highlight important aspects of the input data. Here’s a simple code snippet using a transformer model in PyTorch with guidance prompts: “`python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained(‘bert-base-uncased’) tokenizer = AutoTokenizer.from_pretrained(‘bert-base-uncased’) # Example input input_text = “What are the benefits of AI?” # Guidance prompt guidance_prompt = “Consider the social, economic, and ethical aspects.” # Combine input with guidance combined_input = f”{guidance_prompt} {input_text}” inputs = tokenizer(combined_input, return_tensors=’pt’) # Perform inference with torch.no_grad(): logits = model(**inputs).logits “` This approach allows the model to leverage the guidance provided, potentially enhancing its reasoning performance.Academic Context
Test-Time Reasoning Guidance is grounded in cognitive science and artificial intelligence, particularly in areas related to improving model interpretability and decision-making processes. Theoretical frameworks such as the Bayesian inference model and cognitive load theory provide insights into how structured guidance can alleviate cognitive burdens during reasoning tasks. Key papers include ‘Learning to Reason: A Review of Neural Reasoning Models’ (Cohen et al., 2018) and ‘Prompt Programming for Large Language Models’ (Liu et al., 2021), which discuss methodologies for enhancing reasoning capabilities in AI systems through structured prompts and guidance techniques.Code Examples
Example 1:
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
# Example input
input_text = "What are the benefits of AI?"
# Guidance prompt
guidance_prompt = "Consider the social, economic, and ethical aspects."
# Combine input with guidance
combined_input = f"{guidance_prompt} {input_text}"
inputs = tokenizer(combined_input, return_tensors='pt')
# Perform inference
with torch.no_grad():
logits = model(**inputs).logitsExample 2:
logits = model(**inputs).logitsExample 3:
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')Example 4:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
View Source: https://arxiv.org/abs/2511.16660v1