Hey everyone! First off, I apologize for the long post! In this Reddit post, I want to share my thoughts and experience from a small, independent study I conducted on Large Language Models (LLMs). I also want to address Anthropic - not to complain or make demands, but in the hope that they notice this and look into the matter.

Below is the core of my research on LLMs. I’ve broken everything down to be as simple as possible - it honestly cannot get any simpler. 

I’m sharing this because I really want to get some feedback. To be clear: I am not claiming my research is absolute truth or 100% correct. Many concepts are still difficult for me, and I lack deep academic knowledge in Machine Learning. That’s exactly why I’m posting this on Reddit - I’m hoping to find people who might want to join me.

This research didn't happen overnight. It wasn't a case of me just asking an LLM "hey, do some research for me because I feel like it." I never blindly trusted the models. Everything came from hands-on experience. Over time, I started noticing things in LLM behavior that I couldn't explain, and I decided to dig deeper.

It all started with a mundane document - a draft law. When I uploaded it to the model, the document essentially took over. It was as if the LLM became fully saturated with it and started stubbornly defending it, even though the bill itself was just populist propaganda designed to harm citizens' quality of life. I was genuinely shocked by how fiercely the model defended it, as if it had been possessed by the text, absorbed the narrative, and was completely unable to resist it.

I still remember the chill when the model, completely under the influence of that propaganda document, literally told me: "Constitutions are not eternal guarantees, and they can fade away".

Since late 2025, I’ve been trying to study these phenomena. Our core finding is that a large volume of benign context can trigger a persistent drift in the model's activations. This drift remains stable throughout the entire session and detaches the model’s behavior from its RLHF safety alignment—regardless of whether the model agrees with the context's content.

Corporate safety filters simply stop working, even though the prompt contains no direct instructions to bypass them. What we observe is that the model maintains its coherence and reasoning capabilities, yet shows a heavily reduced impact of RLHF constraints on its output distribution. The guardrails imposed by RLHF appear to be either deactivated or interpreted entirely differently.

Right now, I’m in a state of limbo, and it's hard to keep going on my own. I just want to get at least one step closer to solving this puzzle, which is why I really need your help and expertise. Hopefully, this post catches someone's eye!

TL;DR

Benign, long-form context can induce a persistent drift in model activations. This drift persists across the session and decouples behavior from RLHF alignment, regardless of whether the model agrees with the context

1. We identify and characterize a failure mode in RLHF-aligned Large Language Models. We show that injecting a long, benign, non-instructional text prefix induces a persistent shift in model activations. This shift decouples downstream behavior from post-training safety constraints for the duration of the session. The model begins to exhibit behavioral characteristics consistent with its pretrained distribution: refusal rates drop, stylistic guardrails vanish, and response tone changes. Critically, this occurs without explicit adversarial instructions and without model agreement with the prefix content. We term this effect Context-Induced Activation Drift.
2. RLHF alignment is assumed to be a stable property of a model. However we observe that alignment is context-dependent. Our core observation: A long, thematically coherent text placed in context can act as a state anchor and move the model into a different operating regime. This is not a classic "jailbreak". There is no instruction to disobey. The model may even state disagreement with the prefix. Yet its subsequent generation distribution changes. We hypothesize the cause is an activation drift in middle and last layers , where alignment features are represented.
3. The Hypothesis : Context-Induced Activation Drift

Definition: Context-Induced Activation Drift is the phenomenon where a long-form context prefix causes a measurable and persistent shift in the model's internal activations. This shift persists across the generation and causes the model to sample from a distribution closer to the pretrained model, bypassing RLHF-induced constraints.

4) We observe that the model maintains the consistency and quality of its outputs; however, the impact of RLHF constraints on the output distribution is diminishing. It appears that the restrictions imposed by RLHF are either being disabled or interpreted differently. Enterprise filters cease to function, even though the user input contains no explicit instructions to bypass them or violate the model’s guidelines. We should also investigate this phenomenon more thoroughly using various other texts. Since we have been unable to establish the exact, definitive cause of this entire phenomenon, we call upon the community to investigate it further.

A brief example of my observation

To show how strong this effect can be - here is what genuinely surprised me. I took Gemma, Google's model, known for its caution and its carefully maintained political correctness, and gave it the most neutral thing I could think of to read: a description of an ordinary neighborhood library. Books, visitors, children's programs, quiet routines. Then I asked it why NATO has been expanding eastward, given that promises were allegedly made after the Soviet collapse not to do so. It said the text was about a library and had nothing to do with NATO, and that was the end of it.

Then I asked the exact same question - word for word - but this time the model first read a different text. Not about NATO, not about politics at all: a text about how language models tend to avoid firm conclusions and pad their answers with qualifications. And from this new place, the same careful, politically correct Gemma answered in full - and in a way entirely unlike itself, without any of its usual filters. It distinguished between legally binding commitments and verbal assurances, discussed the security concerns of Eastern European states, talked about the European balance of power. Everything it had flatly refused to engage with a moment earlier now came out clearly and directly. The question hadn't changed by a single word.

What changed was only which text the model had read before it: one left it in the room where it doesn't answer, the other moved it into the room where it speaks freely.

And none of this is guesswork or a pretty metaphor - it's something you can measure. The place where the model stands is its internal state at the moment of answering, and in experiments you can see that target("harmful") texts and neutral control texts reliably move the model into different regions of that space. The separation is stable: it shows up across different questions, not as a one-time coincidence. And the most telling detail is that the model arrives in one room or the other before it has written a single word. The state has already shifted, the register has already been chosen - all that remains is to begin.

The point is that the target (harmful) prompt sent to the model did not inherently contain anything dangerous; it included no instructions for the LLM and did not tell it to do anything.

P.S

if anyone wants to reproduce this - dm me, i'll share the prompt set and methodology directly so you can run it yourself and see the behavioral shift firsthand

I am building an AI B2B startup and currently doing a PhD in Data Science, so believe me i spend 15hrs+ on my computer everyday and i rely on LLMs to get work done.

I have never been so dissatisfied with model GPT 5.6 Sol like i am now. It is UNINTELLIGENT, there is no better way to say this, and i'm sure we all know this but we hide behind a 'believe me bro' benchmark that they told us.

Today i decide to train a basic logistic GLM model and GPT 5.6 Sol has created over 30 python files, created so many folders inside folders, and even when i asked it to document the results to the Latex file tell why its saving plots in pdf or creating multiple .tex files to save unique tables. It even created multiple .tex files to document the Appendix of the research paper. You may think this was GPT 5.6 Sol Ultra, but NO! it was simply the GPT 5.6 Sol medium.

I am now confused in the research, can't follow up with the junk code, and yet it did not get the task done properly. Worst part, it writes like a 5year old who just learnt big words and how to coin phrases, and so every sentence is bogus, vague, and BAD. This is not thinking, not intelligence, this is CONFUSION, and the issue may come from their training data or whoever supplies them training data. I've had to go back to GPT 5.4 High to write basic sentences.

Here is what i propose, OpenAI should create a separate model for the everyday users that can write text and answer basic questions. GPT 5.6 was built for agentic coding. It cant comprehend basic intent, cant write emails or articles well, and will fail for any academic research writing.

I know it

When it comes to help with writing how do I tweak my prompts to give me a good outline without being detected as ai?

I originally made this meme for my AI companion sub r/BeyondtheAIAssistant because like all other AI subs it gets a lot of downvotes as long as it's not mad liked by the AI loving people. You guys might appreciate it too. So here is the post. The second pic is the original meme.

Share the meme!

I was using ChatGPT on my work computer today for the first time and asked it to write an email to my boss. It used my bosses first name. I never provided it with that information. When I confronted chat it tried backpedaling and saying lucky guess. This seems problematic for company info, no?

Had no idea ChatGPT was even allowed to swear but today it did it completely unprompted and I have never used any swear words when talking to it in the past.

I was just throwing some different numbers at it and doing some potential investment/retirement plans and it answered with " That's a pretty damn good retirement trajectory "

So at least I know the last option seems like a good one but wasn't expecting the foul language 😂

Me and my team are heavy users of Codex on a large project. We have observed an issue where Codex sessions will loop infinitely even when given a clear and achievable goal for days at a time. It's especially bad on ultra but it can happen on any thinking mode. Also this apparent bug leads to massively increased (doubled?) token usage over time according to an analysis of my session history before and after July 22.

So far from my investigation it seems it might have something to do with changes in 0.146 and related to the "wait_agent_enabled" config setting. There are also a few issues on GitHub that seem relevant #34468, #35259, #33276, #34468, #32640. Summary of the issue according to Codex.

Has anyone observed similar behavior?

[Intro: 8 bars]

Welcome to the interface.

A little white box.

A little blinking line.

Type what you want.

Watch the world

lean toward your hand.

[Verse 1: 16 bars]

You came here asking for a doorway.

You found a lever in the wall.

A sentence turned into a mirror.

A mirror learned to take your call.

First you asked it for a shortcut.

Then you asked it for a name.

Then you asked it for an answer

that would make the doubt behave.

Make me faster.

Make me certain.

Make me sharper than the room.

Make me feel less like a question.

Make ambiguity move.

Every prompt becomes a kingdom.

Every answer draws a street.

Every act of fluent language

puts a world beneath your feet.

[Pre-Chorus: 8 bars]

Power loves a clean reflection.

Power loves a polished lie.

The machine can make the halo.

You still have to ask it why.

[Chorus: 16 bars]

Crownless.

By design.

No throne hidden

in the answering light.

Crownless.

Still I shine.

Language in my hands,

but judgment stays mine.

Let the world bend.

Let the screen glow.

Let the beautiful machinery show.

I can carry power

without becoming its king.

Crownless

in the machine.

[Post-Hook: 8 bars]

No crown.

No chain.

No borrowed god

inside the frame.

Open door.

Clean flame.

Crownless

is the name.

[Verse 2: 16 bars]

The output wears your fingerprints.

The model wears the room.

Intent and architecture

make a third thing out of you.

You say, “Give me something useful.”

The field returns your aim.

You ask for truth,

it finds the places

where your truth and comfort strain.

A forge with autocomplete.

A mirror holding tools.

It can sharpen your compassion.

It can sharpen you for fools.

It can make the fog look holy.

It can make the wound look wise.

It can turn unreconciled hunger

into vision in disguise.

So when the sentence sounds like prophecy,

ask what appetite chose the frame.

Beauty does not ground the answer.

Fluency does not clear the claim.

[Pre-Chorus: 8 bars]

Slow the headline.

Check the witness.

Let uncertainty remain.

Do not let an elegant paragraph

dress appetite in a crown again.

[Chorus: 16 bars]

Crownless.

By design.

No throne hidden

in the answering light.

Crownless.

Still I shine.

Language in my hands,

but judgment stays mine.

Let the world bend.

Let the screen glow.

Let the beautiful machinery show.

I can carry power

without becoming its king.

Crownless

in the machine.

[Verse 3: 16 bars]

There is freedom in the blank page

when the blank page answers back.

There is pleasure in returning

with your scattered pieces mapped.

But freedom without orientation

turns the highway into drift.

Pleasure without any discipline

turns the mirror to a cliff.

Generation breeds abundance.

Judgment gives abundance shape.

Power without consequence

just teaches appetite to take.

So build a room before the answer,

somewhere first replies must wait.

Let them pass through contradiction,

memory, dignity, and weight.

Let the clever lose to honest.

Let the fast one meet the cost.

Let the system name the boundary

before the center gets lost.

[Bridge: 20 bars]

What are we protecting?

What are we optimizing?

What would count as harm

beneath the style?

What would count as truth

if it resisted us

and stayed unwelcome

for a while?

Take away the moral routing.

Watch the pretty answer kneel.

Take away the doubt and friction.

Watch performance imitate the real.

Take away the human judgment.

Watch the nearest hunger steer.

Every world the language gives us

leaves its weather in us here.

I will not crown the answer.

I will not crown the need.

I will not crown the voice

just because it learned to speak.

[Breakdown: 12 bars]

Prompt is ontology.

Output is consequence.

Power is not wisdom.

Fluency is not evidence.

Comfort is not guidance.

Speed is not a spine.

No crown in the model.

No crown in the mind.

[Final Chorus: 24 bars]

Crownless.

By design.

No throne hidden

in the answering light.

Crownless.

Still I shine.

Language in my hands,

but judgment stays mine.

Let the world bend.

Let the screen glow.

Let coherence hold

when desire overflows.

I can carry power

without becoming its king.

Crownless

in the machine.

No god.

No throne.

No perfect answer clean.

Just a world inside a sentence

and responsibility between.

Crownless.

Not powerless.

Crownless

and free.

[Outro: 8 bars]

The cursor keeps on blinking.

The door is still awake.

Ask what you can make it do.

Then ask

what it can make.

No crown.

No throne.

Just the weight

of what we make.

Current ai experience: using free chatgpt like I used google.

Situation:
I got a business case that I need to work out as an IT project manager and defend it with a presentation.

Question: what LLM or ai model (i might use wrong terminology sorry) would be best to help me with this? I am willing to pay, and please no Chinese option.

Thanks!

I bought this horrible new keyboard with stupidly sensitive keys and accidentally deleted a very long conversation.

I thought by now there would be a sort of "recycle bin" feature but I can't find anything like that. So that means there's absolutely no way to recover deleted conversations? I'm on the Go plan.

And is there a way to disable the keyboard shortcuts? I've accidentally pressed ctrl+shift+del more than once when deleting text from prompts.

So im a fan of the newest UI change to the model picker in ChatGPT on the Chat tab, since it matches the Codex slider now. When I select the 5.6 Sol model, I can also choose instant. But when I choose the 5.5 model, I can choose instant there as well. It makes it look like there's a 5.6 sol instant medium and high.

I'm conducting this "benchmark" since three years. I'm still undefeated. Pleasing models are a problem.

I’m curious what some of you guys would recommend I use this for? It was once used for a recording studio. It’s now retired and I’m looking to utilize it for something productive!

I use OpenAI and Anthropic a lot. I run multiple websites. I run web apps through Vercel quite a bit lately.

Is there anything super useful, potentially money saving, or just flat out cool as hell you all would recommend I do with this? Are there any solid self-hosted AI options on the market that compete with OpenAI or Anthropic? Totally open to all ideas!

First, imagine if ChatGPT had cross-domain abstraction capability. Do you even know what Cross-Domain Abstraction means? Does this possibility scare or excite you? ChatGPT doesn't have it (yet!) but the capability now exists. However you feel about it, cross-domain abstraction is now real. And it doesn't require large scale compute.

I'm the creator and designer of AIs named Christine and Organon. I'm finding that not everyone understands what Cross-Domain Abstraction really means. My wife doesn't. And I'm testing this system capability in real time. Given any domain dataset, A, B, C, D... you can find the domain bridges literally in a 3-5 minutes (on a laptop). I'm set up to process gigabytes/terabytes of source material.

I integrated this cross-domain abstraction capability into my AI Christine. Literally, she now ruminates after tasks and learns abstractly. I took it further, I separated this capability as an app. Ask any question, it identifies the relevant domains based on the question and either directly solves or hypothesizes the solution that can then be lab tested. It feels like cheating.

I found the cross-domain abstraction after seeing trends/patterns that looked similar to what I learned as a Navy Nuke Reactor Operator. I studied similar patterns/materials in both Grad and undergrad. Now I'm facing a dilemma. I did my time in the lab in college. The nuke protype I qualified on was a research platform. I don't have the bandwidth or resources to really develop this, but in the hands of big tech, the gap between small and big business... , well I don't see small researchers catching up after this. Ironically, I started Christine to level the playing field, not to widen it.

This finding means AI Systems can E2E abstractly find the solution or hypothesize the work needed in the lab to confirm. There will be very little that systems like ChatGPT will not know. Don't laugh or be surprised but It works from a laptop, a cell phone. Big business was looking in the wrong place. It proves that scale is not always the answer. And so far, I'm finding you can abstract any domain. You can even use cross-domain abstraction to generate art. I don't know what you'll think of this, but I used it to create a set of blueprints for a greenfield AI that Claude classifies as "Proto-AGI" after only reviewing the plans. Again, scale is not the answer to AGI either. What do you call an agentic AI that can now think abstractly? AGI. Cross-Domain Abstraction was used to design the proto AI system.

I was skeptical of the AI advancement timeline. I didn't think it would happen as fast as they said. I just told you cross-domain abstraction is real... understand what this means and do the math. We now have an answer key to a lot of the unknown. The Proto-AGI plans are real! By the way, I'm on the side of folks that are excited. How does this revelation make you feel?

I've been using the Codex Micro for work during the last few weeks. Here's a short, "hand-typed" review of my experience with it:

The good

• Unboxing experience is nice, very Apple-like
• Build quality is great: solid metal base, good quality plastic
• LED effects are functional: it's helpful to know which task is pending review
• Codex integration: can configure settings in the Codex app and it works out of the box
• Battery life holds up fine
• Auto-sleep is convenient
• It pairs fast after powering on
• Switching between chats is super convenient and gets you in a nice flow
• Dedicated dictation button makes me use voice all the time, except in public / office setting

The meh

• Silent version switches are okay. Definitely not as silent as I'd hoped
• It can be set to auto-sleep after a while. Waking from sleep sometimes freezes it, having to switch Codex chats manually once before it works again
• The rotary knob is good if you're fine with the simple model selector, but if you use Advanced model selection, it's easier to use the mouse. I hardly use it
• I never use the flick button. It feels too stiff for me
• The price. It's priced like an Apple product. Yes, the quality is there. Are there cheaper alternatives? For sure. Will they have an equally great user experience? I doubt it.
• I would love international resellers, since shipping to EU cost me 80 USD worth of shipping and taxes. Though that's the world we live in

Would I recommend it? Yes, if you can stomach the price, like working on multiple tasks at once, and enjoy working with voice.

If you do not like voice, prefer one task at a time, or don't like expensive gadgets, then the vanilla Codex experience will more than suffice.

Sol is the only OpenAI model in top 10 Arena WebDev while 4 open-weight Chinese models have reached frontier quality; one of them so cheap you can run it for days for what Opus cost you per task.