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Yeah, I figured it was gonna be this one.

"SalesRLAgent: A Reinforcement Learning Approach for Real-Time Sales Conversion Prediction and Optimization"

Jev is a general-purpose thing. That is a specific-purpose thing. General-purpose thing is not the same as specific-purpose thing. What makes people think these are the same thing? I don't get it.

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What do you mean? Jev is trivially different from what is described in this paper.
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Bullshit. Below is copypaste from the paper in the section that outlines the "key contributions" of the paper. As you can see, it is focused on one specific problem: predicting sales conversions. So if you were to take this system and use it for some other task ("evaluate customer mood" for example), it would not work. Because, again, it is not describing a general purpose solution. It is describing a solution that is specific to one problem: sales conversions.

Copypasta:

• A reinforcement learning architecture specifically designed for sales conversation analysis and conversion prediction

• A synthetic data generation pipeline leveraging GPT-4O to create diverse and realistic sales conversations

• Novel state representation techniques using Azure OpenAI embeddings (3072 dimensions) with sales-specific features

• A meta-learning approach enabling the system to express confidence in its predictions based on conversation similarity to training data

• Integration mechanisms providing real-time guidance within existing sales platforms

• Extensive comparative evaluation demonstrating significant performance improvements over LLM-based approaches

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> Novel state representation techniques using Azure OpenAI embeddings (3072 dimensions) with sales-specific features

Jev is basically the embeddings side of an LLM. Yes, it's a good idea, but the moat is non-existent.

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No. It can't simultaneously be both general purpose and having task-specific embeddings.
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This is a fine-tuned model. The author even states that the model is competitive with Jev only if fine-tuned on the evaluation at hand.

Literally misses the point of Jev, which you don't need to fine-tune to get accuracy nor - and no other model has this - some sort of out of sample calibration

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