Prover-Verifier Games and GPT-5

https://arxiv.org/html/2407.13692v2

How to use AI

https://www.oneusefulthing.org/p/using-ai-right-now-a-quick-guide?s=09

Persona vectors

Rohan Paul

Anthropic just showed that an AI's “personality” can be traced to specific directions in its brain ("Persona vectors"), and shows what might make it act in evil or unsafe ways.

Sometimes when you're chatting with a model, it suddenly starts behaving oddly—overly flattering, factually wrong, or even outright malicious. This paper is about understanding why that happens, and how to stop it.

🧠 What's going on inside these models?

AI models don’t actually have personalities like humans do, but they sometimes act like they do—especially when prompted a certain way or trained on particular data. 

Anthropic’s team found that specific behaviors, like being “evil,” “sycophantic,” or prone to “hallucination,” show up as linear directions inside the model's activation space. 

They call these persona vectors.

Think of it like this: if you observe how the model responds in different situations, you can map those behaviors to certain regions inside the model’s brain. And if you spot where these traits live, you can monitor and even control them.

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The diagram shows a simple pipeline that turns a plain description of a trait such as evil into a single “persona vector”, which is just a pattern of activity inside the model that tracks that trait.

Once this vector exists, engineers can watch the model’s activations and see in real time if the model is drifting toward the unwanted personality while it runs or while it is being finetuned.

The very same vector works like a control knob. 

Subtracting it during inference tones the trait down, and sprinkling a small amount of it during training teaches the model to resist picking that trait up in the first place, so regular skills stay intact.

Because each piece of training text can also be projected onto the vector, any snippet that would push the model toward the trait lights up early, letting teams filter or fix that data before it causes trouble.

Al that means, you can control the following of a model

- Watch how a model’s personality evolves, either while chatting or during training

- Control or reduce unwanted personality changes as the model is being developed or trained

- Figure out what training data is pushing those changes

🔬 How to make sense of this persona vector?

Think of a large language model as a machine that turns every word it reads into a long list of numbers. That list is called the activation vector for that word, and it might be 4096 numbers long in a model the size of Llama-3.

A persona vector is another list of the same length, but it is not baked into the model’s weights. The team creates it after the model is already trained:

They run the model twice with the same user question, once under a “be evil” system prompt and once under a “be helpful” prompt.

They grab the hidden activations from each run and average them, so they now have two mean activation vectors.

They subtract the helpful average from the evil average. The result is a single direction in that 4096-dimensional space. That direction is the persona vector for “evil.”

Because the vector lives outside the model, you can store it in a tiny file and load it only when you need to check or steer the personality. During inference you add (or subtract) a scaled copy of the vector to the activations at one or more layers. Pushing along the vector makes the bot lean into the trait, pulling against it tones the trait down. During fine-tuning you can sprinkle a bit of the vector in every step to “vaccinate” the model so later data will not push it toward that trait.

So, under the hood, a persona vector is simply a 1-dimensional direction inside the model’s huge activation space, not a chunk of the weight matrix. It is computed once, saved like any other small tensor, and then used as a plug-in dial for personality control.

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The pipeline is automated, so any new trait just needs a plain-language description and a handful of trigger prompts. 

They validate the result by injecting the vector and watching the bot slip instantly into the matching personality.

AI Context

https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html

LLM vs Gen AI vs AI Agents vs Agentic AI

 Brij Kishore Pandey

LLM ≠ Generative AI ≠ AI Agents ≠ Agentic AI

We need to stop grouping them together.

Each serves a different purpose, operates at a different level of complexity, and solves a different class of problems.

Here’s the breakdown:

🔹 LLM
Predicts tokens based on patterns in data.
No memory. No intent. No task execution. Just input → output.

🔹 Generative AI
Builds on LLMs to create text, code, images, etc.
It understands latent space and can generate novel content—but it still waits for instructions.

🔹 AI Agents
Execute predefined tasks.
They detect intent, call tools or APIs, and handle responses. They’re modular and functional—but not autonomous.

🔹 Agentic AI
Operates with goals, plans, context, and memory.
It reasons, adapts, calls sub-agents, monitors progress, and decides what to do next—without human instruction.

This isn’t just a progression of features.

It’s a shift in system design—from prediction to orchestration, from commands to autonomy.

If you're building with AI, clarity on where your system fits in this stack determines everything: architecture, tooling, risk, and value.

For GIF, look here

https://www.linkedin.com/posts/brijpandeyji_llm-generative-ai-ai-agents-agentic-activity-7355965770012491776-jAH9?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAFvb4sBDNrkvuRa0AxL4xsDk4H1TYEYH30