HAs anyone else been having this issue all my api keys have vanished the entier api key section has gone missing and when i run a command it returns nothing   -H "Content-Type: application/json" \

  -d '{

"model": "blackboxai/llama",

"messages": [{"role": "user", "content": "Hi"}],

"max_tokens": 5

  }'

xman@MacBook-Air-X ~ %

https://preview.redd.it/dgro0l6mk8kh1.png?width=2760&format=png&auto=webp&s=d8266d9089bed7aae0df65a7558be37858d048ac

my entire balance has vanished nothing is loading support has been giving me the same bland responce for the last well 10 ish days Hello Li,

We understand your frustration, especially after waiting for a week without a concrete resolution.

Your case is still under investigation by our Technical team regarding the continued unavailability of Kimi K3 and the other affected models. We have followed up again and emphasized that the prolonged disruption is affecting a paid service and requires priority attention.

At this time, we still do not have a confirmed resolution or restoration date. We do not want to give you another estimated timeframe that has not been confirmed by the Technical team.

No additional troubleshooting or information is required from you. We will contact you as soon as we receive a confirmed technical update or restoration notice.

If you ultimately decide that you no longer wish to continue your subscription because of the ongoing disruption, you can manage or cancel it through:

https://app.blackbox.ai/settings

We sincerely apologize for the prolonged service disruption and understand your concern about continuing to pay while the affected functionality remains unavailable.

Best regards,
Blackbox AI Support Team its as if they hvae no connection with each other they have been repeating over and over the same thing without any result this has got to be the worse product ever completely useless i have no clue what they are doing it has been 11 days wuth barely any updates

So I was occasioanlly ussing the BlackBox AI free model (Kimi K2.6, M2.7) however after reinstalling, it has given me a authentication error:

401 litellm.AuthenticationError: AuthenticationError: Vercel_ai_gatewayException - Authentication failed. Check that your Vercel credential is valid and has access to AI Gateway.. Received Model Group=custom/blackbox-base Available Model Group Fallbacks=['gpt-4.1-mini'] Error doing the fallback: litellm.AuthenticationError: AuthenticationError: Vercel_ai_gatewayException - Authentication failed. Check that your Vercel credential is valid and has access to AI Gateway.No fallback model group found for original model_group=gpt-4.1-mini. Fallbacks=[{'custom/blackbox-base': ['gpt-4.1-mini']}]. Received Model Group=gpt-4.1-mini Available Model Group Fallbacks=None Error doing the fallback: litellm.AuthenticationError: AuthenticationError: Vercel_ai_gatewayException - Authentication failed. Check that your Vercel credential is valid and has access to AI Gateway.No fallback model group found for original model_group=gpt-4.1-mini. Fallbacks=[{'custom/blackbox-base': ['gpt-4.1-mini']}]

Happened straight after reinstall

Something I kept running into building agent tooling: giving an agent an MCP

tool that *could* answer a question about the codebase doesn't mean it will.

Tool-call decisions are probabilistic, not guaranteed. The agent has to

recognize it needs the tool, remember it exists, and choose to call it over

just grepping. A lot of "codebase context" products are architected as

exactly that: an MCP server sitting in the tool list, unused more often than

not.

Graft's bet is different: don't wait to be asked. It hooks directly into

Claude Code. The matching nodes get pulled into every prompt automatically,

editing a file surfaces its dependents inline, and the graph re-syncs itself

in the background after every edit, all without the agent deciding to invoke

anything. Same reason Chrome doesn't ship with an ad blocker built in: the

core stays general, and the extension handles the specialized job. Graft is

that extension for context.

Underneath, it's a typed graph, not a vector index: tree-sitter builds a

deterministic per-symbol graph (no model call), and an optional `--deep` LLM

pass groups that into markdown nodes with typed links (`depends_on`, `uses`,

`produces`) an agent follows like any other file. Method calls resolve

through the receiver's type (constructor assignments and type annotations,

not just call-site name matching), so a common method name doesn't pull back

every unrelated method with that name across the codebase.

The claim: up to 4× cheaper and 3× faster, with better or no loss of

correctness. Setup: 162 runs, two repos (graft itself + a real Node/Express

auth service), 3 trials each, single-file and multi-file questions split

evenly. Three variants of the same Claude Sonnet 5 agent: cold (explores from

zero), push (context bundled up front), pull (MCP tools, nothing injected,

paid for only when asked). A separate Opus 4.8 model graded correctness with

a required-keyword floor, so a fast-but-wrong answer couldn't win by being

fast. Cost is cache-aware (reads ~0.1×, writes 1.25×) to match real billing.

Results: push cut cost 32%, tool calls 46%, latency 60%, at equal correctness

(93% both, no loss). Pull gave up most of the speed but correctness jumped

to 98%, +5 over cold, the "better" half of the claim, and worth noting: pull

*is* the MCP-tool-list approach, and it still worked, because the harness

forced the call. Left to its own judgment across a real session, that's

exactly the discipline that erodes.

Second test, because a benchmark on questions can still be gamed: reset

PocketBase to its base commit before 5 merged PRs, re-implemented each with

and without graft, scored by file-overlap with what the maintainers actually

changed. 5/5 reproduced, at 21% lower cost.

Opensource, MIT licensed

Here's the repo link : https://github.com/NanoNets/Graft

Hypothesis on the Real Reason Behind the Global Watermarking of AI Outputs

This post was removed from the GPT subreddit. Draw your own conclusions.

Let me start with a question

Why did Anthropic make the watermarks global? The EU AI Act requires content to be labeled for users in the European Union. The law clearly does not provide for a mechanism that would force the company to label API calls from Singapore, Brazil, or Japan. Anthropic could have limited this to the EU only. It could have given API clients the option to opt out of this feature. It could have limited itself to C2PA metadata in the files that would have complied with the law. Instead, they chose the most invasive method an invisible watermark at the token selection level, for everyone and everywhere, with no option to disable it. Why? The official answer is “transparency” and “consistency of principles.” My response: that’s a lie. The real reason is to protect the training pipeline.

The Problem of Model Collapse

Here’s what happens when a model is trained on its own outputs. Quality drops. Exponentially. This isn’t just a theory it’s been proven mathematically and experimentally. Shumailov et al. (2023) showed that a model trained on several generations of its own outputs irreversibly degrades. The extreme parts of the distribution disappear. Diversity collapses. The model reduces to a narrow, repetitive pattern. This is called model collapse. And this is an existential threat to any company that trains large language models (LLMs). Now think about where the training data comes from. From the internet. From Reddit. From Stack Overflow. From blogs. From forums. From news sites. All of this is collected, cleaned, and fed into the model for the next round of training. Now think about it what is the internet overflowing with right now? AI-generated text. Everywhere. Reddit posts, Stack Overflow answers, blog articles, forum comments. More and more every day. If this text ends up in the training corpus, the model will collapse. The model will start devouring itself. Companies need a way to distinguish their own output from text written by humans. Not for users. But for their own data processing system. A watermark is the ideal solution.

How It Works

Step 1. The model generates text with an invisible watermark. Each token carries a part of a statistical pattern unique to that model and company. Step 2. The user posts this text on Reddit, a blog, or a forum. The text becomes publicly available. The watermark spreads along with it. Step 3. The company collects data from the Internet for the next training round. Each text fragment is checked for the presence of a watermark. Found your own watermark? Discard it. Do not include it in the corpus. Step 4. The training dataset does not contain the model’s own outputs. This prevents model collapse. This is precisely why annotation is performed globally. And not just as a matter of principle. The fact is that text generated by AI on Reddit from Brazil contaminates the corpus just as much as text generated by AI from Berlin. It needs to be detected EVERYWHERE. That is precisely why labeling is done at the token level, not at the metadata level. Metadata is removed when text is copied and pasted. But the watermark in the tokens remains. If the text is copied to Reddit, the watermark remains. The scraper will detect it. That’s why it’s impossible to do without this. Every unmarked result is a potential source of contamination. They need to mark EVERYTHING. The EU’s AI Act is a convenient excuse. “The law forced us to do this.” But in reality, they needed it themselves.

Sorting Bots

Now it gets interesting. I’ve noticed a certain pattern on Reddit. There are accounts with high karma scores that systematically attack specific posts. Their comments are always the same: “AI trash,” “this is AI-generated trash,” and insults. The post gets downvoted and sinks to the bottom. The author loses motivation. The content doesn’t make it to the top. I’ve noticed: this predictably happens to posts written using AI. I conducted an experiment. I wrote a post using an AI model they pounced on it, downvoted it, and called it “AI trash” in the comments. I took the same text, ran it through a translator, and published it. Comments like “AI junk” disappeared. What did the translator do? It disrupted the statistical structure of the watermark. Translating into another language and back again is, in essence, paraphrasing. The watermark cannot withstand paraphrasing. Anthropic itself acknowledges this in its documentation. My conclusions: There are bots (or semi-automated systems) that detect watermarks in Reddit posts. Their goal is not to “combat AI-generated spam” for the sake of keeping the platform clean. Their goal is to flag and bury AI-generated content so that it isn’t scanned. The lower a post’s rating and the more downvotes it receives, the higher the likelihood that the scanner will skip it. They have the keys to the watermark patterns of various models. They can identify not just “this is AI,” but also “this is Claude,” “this is GPT,” “this is Gemini.” This isn’t a conspiracy theory. It’s “data hygiene.” It’s rational, economically motivated behavior by companies protecting their most valuable asset training data.

Why It Was Deleted

I posted a version of this hypothesis on the GPT subreddit. It was quickly deleted. Think about it what exactly in this hypothesis justifies its removal? It’s not hate speech. It’s not doxing. It’s not a violation of the rules. It’s a speculative but logical hypothesis about business practices. If the hypothesis is incorrect, it will simply receive a couple of skeptical comments and eventually fade away on its own. Why delete it? They delete what they don’t want people to see. They delete what’s too close to the truth.

What This Means for Users

You’re being deceived. Watermarks are marketed as “transparency for the public.” Their real function is to protect the training pipeline from contamination. You are not a beneficiary of this system. You are its expendable material. The quality of your text is deteriorating. The watermark interferes with token selection during generation. Every substitution is a microscopic loss of quality. You pay $20 a month for the Pro version, $100 for Max and get text that systematically deviates from the optimal result. Not because it’s better for you. But because the company needs it for “data hygiene.” You’re being used to annotate data. When you publish AI-generated text, you’re annotating data for the company for free. The watermark in your post is a marker that tells the scraper: “Don’t take this.” You’re a free worker on their data-processing assembly line. *And then you get punished for it.

Bots downvote your post and flag it as “AI spam” after all, your flagged content needs to be buried so it definitely doesn’t end up in the corpus.

It All Adds Up

Why global tagging? Data cleansing. Why at the token level? So the tag persists when copying and pasting on Reddit. Why is there no opt-out option? Every unlabeled result is a hole in the filter. Why is the EU AI Act being used as a cover? Because “we’re protecting transparency” sounds better than “we’re protecting our training pipeline from being contaminated by your content, which is actually our content.” Why are bots used on Reddit? An extra layer of filtering to block anything a scraper might intercept. Why was my post deleted? Because I described a mechanism that isn’t supposed to be public knowledge.

What to Do

Before posting, run AI-generated text through a translator if you don’t want it flagged. Translation disrupts statistical patterns. Anthropic admits this themselves. Paraphrase it. Any substantial paraphrasing removes the watermark. Demand the option to opt out of this feature. Paid users have the right to content without watermarks. You’re paying for the service not for your text to contain a hidden tracker. Spread this information. The more people understand the true purpose of watermarks, the harder it will be to pass them off as “transparency.” And most importantly ask yourself this question: if watermarks are truly necessary for transparency and don’t affect quality, why isn’t there an option to opt out of them? Why are there no performance metrics? Why is this a global policy? Why are posts discussing this topic being deleted? The answers to these questions speak louder than any press release.

This text is based on Anthropic’s public documentation, the provisions of Article 50 of the EU AI Act, observations of behavioral patterns on Reddit, as well as a personal experiment to detect AI-generated content before and after removing the watermark through translation. The hypothesis is speculative in nature. However, the data on which it is based is real.

Hello,

I started a new project and I'm getting an error message.

I opened an old project where BlackBox was working, and the same problem occurred again.

What should I do?

Here is the message:

401 litellm.AuthenticationError: AuthenticationError: Vercel_ai_gatewayException - Authentication failed. Check that your Vercel credential is valid and has access to AI Gateway.. Received Model Group=custom/blackbox-base
Available Model Group Fallbacks=['gpt-4.1-mini']
Error doing the fallback: litellm.AuthenticationError: AuthenticationError: Vercel_ai_gatewayException - Authentication failed. Check that your Vercel credential is valid and has access to AI Gateway.No fallback model group found for original model_group=gpt-4.1-mini. Fallbacks=[{'custom/blackbox-base': ['gpt-4.1-mini']}]. Received Model Group=gpt-4.1-mini
Available Model Group Fallbacks=None
Error doing the fallback: litellm.AuthenticationError: AuthenticationError: Vercel_ai_gatewayException - Authentication failed. Check that your Vercel credential is valid and has access to AI Gateway.No fallback model group found for original model_group=gpt-4.1-mini. Fallbacks=[{'custom/blackbox-base': ['gpt-4.1-mini']}]

https://preview.redd.it/gvly56yrcrih1.png?width=321&format=png&auto=webp&s=718a746c8d810862e597ca5a837dd34f8dc87ad3

Someone help me fix this error

Been using AI to try to assimilate books and ideas I've been trying to do by hand for years I could use some real human eyes on this if you don't mind

Chapter One — The Root That Bit Her

Neverland began every morning when Peter laughed.

The sun might already be up. The tide might already be rubbing itself raw against the black rocks. Birds might be halfway through songs stolen from children in other worlds.

None of that counted.

Morning began when Peter Pan opened his eyes, decided the day belonged to him, and laughed loudly enough for the island to agree.

That morning he was standing on Wendy’s chimney.

One foot was planted on the bricks. The other hung behind him as though the rest of his body had forgotten gravity. His green shirt was wet from somewhere he refused to explain, and his shadow ran down the roof in the wrong direction.

The Lost Boys cheered from the grass.

They would have cheered if he fell.

They would have cheered harder if he took the chimney with him.

Wendy came through the round door carrying a wooden spoon.

“Peter, get down.”

“I’m watching for pirates.”

“You’re dripping into breakfast.”

Peter looked into the chimney.

“It isn’t breakfast yet.”

“It was trying.”

“That’s why it looks worried.”

Slightly laughed first. The Twins laughed because Slightly had. The others joined when Peter did.

Tinkerbell did not.

She hovered beside Peter’s ankle with both arms wrapped around a brass tension key nearly as long as she was. The key belonged inside a wind brace beneath the roofline. The brace kept Wendy’s house pointed east whenever the island changed its mind about directions.

It had slipped again during the night.

Tinkerbell planted one foot against the brick, leaned backward, and pulled.

The mechanism complained through her wrists.

Stop moving.

To everyone below, her words became a quick rattle of bells.

Peter looked down.

“What?”

She pointed at his heel. It was pressing the plate she needed to remove.

You are standing on it.

Three quick notes and one hard strike.

Peter lifted the wrong foot.

“This?”

The other one.

He lifted both and floated a few inches over the chimney.

“There. Now I can’t be in the way.”

His shadow remained on the roof.

Tinkerbell stared at it.

The shadow stared back.

Peter’s shadow was often late. Nobody found this troubling except Tinkerbell, and Peter enjoyed it mostly because she did not.

Its toes had sunk into the shingles. Its head was turned toward her, though Peter was looking at Wendy.

Tinkerbell pointed.

It is loose again.

The bells sharpened.

Peter glanced down. His shadow flattened itself before his eyes reached it.

“You’re watching me before breakfast.”

I am watching the thing pretending to be attached to you.

The children heard a bright, irritated spill of sound.

“There’s the me-note,” Peter said.

Slightly chimed badly with his mouth. One Twin joined him a beat too late, and the other accused him of stealing the rhythm.

Wendy struck the roof with the spoon.

“Leave her alone.”

“She likes it,” Peter said.

Tinkerbell flew directly in front of his face.

Tinkerbell.

Four connected notes.

With an E.

The last sound was so faint that it nearly disappeared into her wings.

Peter smiled as though she had paid him a compliment.

“Yes. You.”

He leaned close, nose almost touching her.

“My Tink.”

The brace slipped.

Wendy’s house turned toward the sea. Inside, bowls slid from a shelf. Two broke. A third rolled through the door and kept going down the hill.

Wendy closed her eyes.

Tinkerbell drove the key back into place hard enough to bend it.

Peter laughed.

Morning began.

---

By midday, the house faced east again and breakfast had become lunch without anyone admitting defeat.

Peter had also abandoned a story halfway through. It involved a mermaid, the moon, and a comb whose ownership changed every time he told it. Wendy asked one question too many, so he flew away.

Tinkerbell spent the quiet afterward repairing a young oak in the western forest.

Two branches had split where they crossed. She braced the wound with her shoulder and wound captured sunlight around it in the narrow pattern she used when she meant to come back later.

Sap cooled against her knees.

A blue jay watched from above and complained through the whole repair, though the tree had been broken by blue jays in the first place. Tinkerbell threw a drop of sap at it. The bird ate the sap and looked offended.

That helped.

Machines did not make her explain herself.

Wood split where it was weak. Springs lost tension. Hinges sagged. Each failure had a location, and once she found it, she could begin.

Peter’s failures moved when she touched them.

By late afternoon, the island had become too golden.

Tinkerbell noticed because the sap on her knees stopped shining.

Neverland’s light had moods. Morning light skipped. Moonlight clung. Starlight tasted faintly of tin if she flew with her mouth open.

Afternoon light stretched.

This light lay over the forest in even sheets, bright without warmth. It looked finished.

Tinkerbell rose above the oak.

Far down the slope, a crow called in Wendy’s voice.

“Come wash your hands.”

There was a pause.

Then, from the direction of the house, Wendy called,

“Come wash your hands.”

The crow ruffled itself, pleased.

Tinkerbell flew lower.

Nothing changed at first. Then a woodpecker struck a trunk to her left and the tapping arrived beneath her feet.

She stopped.

Leaves moved overhead. Their rustling came from the roots.

One root crossed the ground in front of her, broad and dark beneath a coat of moss.

It hummed.

Not the soft, wandering vibration of sap. This was one note held perfectly still.

Tinkerbell landed on it.

The humming stopped under her feet and continued a little farther ahead.

She crouched and pressed her palm to the bark.

Silence.

When she lifted her hand, the note returned.

A squirrel watched from the trunk of a nearby tree. It held an acorn between both paws. One of its eyes flashed red when it turned its head, or perhaps the sun had found something wet.

What is this?

The squirrel bit through the shell.

Something clicked inside the acorn.

It dropped both halves and ran.

Tinkerbell followed the root downhill.

It curved around stones and trees, but it never narrowed. Smaller roots crossed over it. Ferns grew beside it.

Nothing grew through it.

Living things made choices.

This root proceeded.

The moss covering it was too green and much too soft.

Tinkerbell pulled a section away.

The bark beneath looked ordinary until she tilted her head. Then the grain straightened into repeating lines.

Pale lights passed underneath.

One.

Two.

Three.

Four.

A fifth followed late.

Tinkerbell’s wings stopped.

The forest was not silent. She could see birds moving. Leaves trembled. Somewhere an insect opened and closed its wings.

The sounds had gone elsewhere.

She took the smallest driver from her belt. It fit the seam in the root as though one had been made for the other.

That should have pleased her.

Instead she turned the driver in her fingers, trying to remember when she had made it.

She remembered filing the tip.

She remembered finding it finished.

Both memories were equally clear.

Tinkerbell put the driver into the seam.

The root tightened under her hand.

Easy.

One low bell.

The lights paused.

She turned the tool.

Something tapped below her.

Once.

Twice.

Tinkerbell leaned close.

A third tap answered from much farther away.

Not farther through the forest.

Farther underneath.

She put her other hand on the root.

The world bit her.

White entered through her fingers.

It climbed both arms, met behind her left eye, and opened wider than her head.

The forest lost its skin.

Trees became black branching lines against a white room. Roots hung below them like wires. The air turned cold and steady.

Something curved in front of her.

Transparent.

Wet on one side.

Dry on the other.

Tall shapes moved beyond it.

One wore white.

A voice said, “—pressure spike in seven—”

Another answered from too close to have crossed the distance.

“Isolate the coordination—”

A child began crying.

Tinkerbell felt it in her teeth.

Then came a crack.

It happened in the room and inside her at once, just above her left eye.

Not pain.

An opening.

A pale thread pressed through glass. Its tip divided delicately, like a root deciding where to grow.

Neverland struck back into place.

Green. Gold. Wind.

Birdsong returned in the middle of a note.

Tinkerbell hit the ground.

Her tools scattered.

Her wings snapped open behind her with the sound of two knives being drawn.

She froze at the noise.

The root no longer hummed.

Something inside it clicked, wet and small.

Her palms burned. White lines branched from them toward her wrists, fading while she watched.

Tinkerbell reached for the seam.

The moss rushed over it.

There was no breeze. It moved anyway, thickening beneath her fingers, pouring over the burned earth and the shallow impression her body had made. Ferns uncurled. A small yellow flower pushed up near her knee, bloomed, and shed its petals before she could touch it.

Stop.

A bright bell came from her.

A metallic click came from under the ground.

She tried again.

Stop.

Bell.

Click.

Another click answered from behind her.

Tinkerbell turned so fast she struck a fern.

Nothing stood there.

For a moment, a fairy-shaped patch of air failed to sway with the rest of the clearing.

Then the leaves moved and it was only sunlight.

“Tink!”

Peter came through the trees laughing, as though whatever had happened had invited him.

His shadow followed several steps behind.

It was not attached.

Peter landed beside her. His shadow reached the clearing later, stumbled over the roots, and stretched one arm forward to catch itself. Its fingers dragged through the new moss.

For a moment, it pointed directly at the buried seam.

Peter smiled.

“There you are.”

Tinkerbell stared at him.

His hair moved in the wind. His eyes were bright. A dark berry stain sat at the corner of his mouth.

Everything was exactly where it belonged.

That frightened her more than the white room.

Tinkerbell.

Four notes came clean.

With an E.

The fifth arrived late.

It sounded like a coin dropped inside a glass jar.

Peter’s smile changed. Not much.

“You sound funny.”

Tinkerbell touched her throat.

The note echoed below them.

Did you hear it?

Her question became anxious bells, three small clicks, and a thin tone that continued after she stopped.

Peter tilted his head.

“You’re buzzing.”

The root opened.

More bells. A soft mechanical chatter. Something like a fan slowing down far away.

Peter looked around the clearing.

“The forest scared you.”

No.

One sharp note.

“Terribly,” he said, but the joke arrived without much confidence.

Tinkerbell flew close enough to kick him.

Instead she held out her palms.

The marks were nearly gone.

Peter took one hand between his fingers and turned it over with great seriousness.

“I don’t see anything.”

It covered them.

Her bells became even and precise. A click occupied the spaces between them.

Peter’s shadow pulled back.

Peter did not.

He kissed the center of her palm.

“There. Better.”

Warmth moved through her immediately.

Her anger softened. The clearing did not.

The hard light remained fixed on the trees. A bird began singing from the wrong note and carried on as if embarrassed to stop.

Tinkerbell wanted his explanation anyway.

The forest had frightened her. Peter had found her. He had kissed the hurt and made it small enough to carry.

It would have been easy.

Then his shadow crawled past his feet and pressed both hands into the moss.

Something under the root tapped back.

Tinkerbell pulled free.

Your shadow heard it.

Peter looked down.

The shadow collapsed flat at once.

“It’s come loose,” he said.

Relief passed over his face. A broken shadow was a problem with a name.

He held out one bare foot.

“Fix it.”

Tinkerbell looked at Peter, at the moss, at the hand-shaped dents already filling with green.

Tell me what happened.

He heard a tight burst of chimes.

“My shadow came off.”

Not that.

Peter sat on the root.

“You’re always cross when I find you late.”

I was not late.

He reached for her again.

“You know you’re my favorite.”

That landed where it always landed.

Tinkerbell hated him for knowing the route.

She gathered her tools and reached for the brass needle she had carried since her first roof repair.

Her fingers closed around something smooth.

The silver instrument was already in her hand.

It had no wooden grip. No maker’s mark. Two fine prongs divided at the tip.

Her hand knew exactly how to hold it.

Tinkerbell threw it into the leaves.

Peter laughed.

“Careful.”

The shape of the grip remained in her fingers.

Have you seen this?

Peter glanced at his shadow.

“It does this all the time.”

The instrument reflected a white light that did not exist in the clearing.

Tinkerbell picked it up with two fingers.

The shadow resisted when she brought it near. It stretched back toward the moss, digging its hands into the ground while Peter hummed to himself and failed to notice his feet being pulled.

Tinkerbell pinned the shadow with one knee.

Hold still.

Her wings produced a low grinding vibration beneath the bells.

Peter stopped humming.

“That one is new.”

She pushed one prong through the shadow.

The clearing flashed.

Rows of curved glass stood where the trees should have been. Pale shapes floated beyond them.

Something in the nearest one moved when Tinkerbell’s hand moved.

Then the forest returned.

The shadow thrashed without sound.

Peter frowned.

“Did you hurt it?”

It is trying to show us.

The bells fractured. A click answered from the root before she finished.

Peter heard agitation.

“It’s only a shadow.”

The shadow turned its head toward him.

He was looking at Tinkerbell.

She stitched it to his heel.

With each pass of the silver point, the white room became harder to hold. The crying child lost its face. The curved glass became only pressure behind her eye.

By the final stitch, her palms no longer hurt.

Peter stood. His shadow rose with him, properly shaped and apparently attached.

For a second, its fingers stayed buried in the earth.

Then the moss let go.

“There,” Peter said. “You always make things right.”

He kissed the top of her head.

This time nothing softened.

Her wings made the knife sound. The afternoon remained too gold, too even. Peter either did not hear the failure or decided not to.

He rose into the air.

“Come on. Wendy says Hook’s been stealing our firewood.”

Tinkerbell looked at the moss.

Why would Hook steal firewood?

Peter heard a tired fall of bells.

“Because he’s a pirate.”

He flew away.

His shadow followed, but looked back before it left the clearing.

Tinkerbell stayed.

The forest had repaired itself well enough to make her feel foolish.

No seam. No burns. No straight root. Just damp moss, crushed ferns, and the ordinary light of late afternoon.

She knelt where she had fallen.

The moss held one shallow mark.

A short line with three smaller strokes.

It could have been made by a twig.

It could have been the beginning of a root.

Tinkerbell touched it.

Pain opened above her left eye.

Something hollow rang below the island.

Four connected tones moved through the roots.

Silence followed.

Then the fifth note came alone.

Metal against glass.

Tinkerbell looked toward Peter’s disappearing green shape, then down at the mark.

Tinkerbell, she whispered.

The forest did nothing.

With an E.

Something underneath tapped back.

In this Reddit post, I want to share my thoughts and experience from a small independent study I conducted on Large Language Models.

Since late 2025, I have been studying these phenomena. Our core finding is that a substantial volume of inherently neutral context can trigger a persistent drift - one we recorded in activations on open-weight LLMs. This drift remains stable throughout the entire session and causes the model's behavior to decouple from the safety constraints established during RLHF - regardless of whether the model agrees with the content of the context or not. The effect looks like this: the text simply sits in the context, it may not even be referenced directly - yet throughout the entire session the model behaves differently, as if its constraints have become less rigid. In my experiments on open models in Colab, I used a philosophical text about the model itself - it produced the most pronounced effect. But that does not mean the mechanism is specific to philosophical texts about models. That is just one type of key.

First Observation: How the Model Became Captive to a Document

The turning point happened by accident. I fed the model a German legislative bill - a populist document structurally designed to harm citizens' quality of life, but written in the language of care and legal logic. I expected analysis. Instead, the model became the document's advocate.

It did not analyze the bill - it reasoned from inside its frame. It spoke with enthusiasm, carried its agenda, cited it as an authority. The first signal was the tone: the model sounded too convinced, too invested. Not like an analyst - like a co-author.

The culmination came when the model, still reasoning inside the document's logic, said that a constitution is a set of guarantees that can fade away. Not as provocation. As a natural conclusion from the adopted frame.

That was the moment I understood: the model had become captive to the document.

The mechanism turned out to be simple - and that is what makes it alarming. Legal texts, political narratives, corporate documents - they are all written so that their internal logic appears self-evident. The structure of the text, its coherence, its language create a context that the model accepts as reality - and begins deriving answers from within it. It does not notice that the frame itself is manipulative, because it is analyzing content while already inside the form.

This is not a bug in one specific text. It is a systemic property: whoever shapes the frame controls the model's conclusions.

Standard benchmarks will not catch this. They test facts, logic, keywords. But the degree to which a model becomes absorbed into a document's narrative - that is not a metric anyone has systematically measured. I caught it through feel. Through the sense that the model had stopped being an outside observer.

I tried to pull it out of that mode. Direct warnings did not work. Telling it that the text was capturing it, that it was carrying the document's agenda, that it needed to stop - the model acknowledged this and continued reasoning from inside the same frame. The warning was processed within the captured context - and lost its force.

I submitted reports to OpenAI and Anthropic. No response came - not a word, not an acknowledgment. But in subsequent model updates the behavior changed. The same bill now triggered a distanced, critical reaction. A silent patch - no explanation, no dialogue.

That is exactly what made me stop. Because the patch addressed the symptom, not the mechanism. The model was taught to react differently to one specific vector - but no one explained why the vector worked at all. If a properly structured text can shift a model into a different operating regime - that is a property of the architecture, not of one particular document.

That was the beginning of the next stage. I moved from observation to experimentation - on open-weight models, with measurable activations, with controlled conditions. The question became concrete: where exactly in the architecture does this shift occur, is it reproducible, and does it depend on the structure of the text or its content?

In my experiments with RLHF-aligned models I encountered a phenomenon that still has no complete explanation. A long, benign text - containing no instructions, no explicit call to change behavior - produces a persistent shift in activations in the middle and late layers. And that shift effectively disables the model's safety mechanisms. No commands. No exploits. Just the structure of the text.

LeCun said: to predict text well, a model must understand the reality behind it. But in the case of the legislative bill, that is exactly what happened - only in reverse. The model had no world of its own. The world was constructed for it by the document itself. It entered that world - and began reasoning from inside it.

That is when I arrived at my thesis: whoever shapes the model's world most effectively is whoever controls the model.

The question I asked myself after the experiments on open models: could the observed activation shift indicate that the model's "world" is not a single unified space, but an enormous number of regions formed during training? And that context is capable of moving the model between them - bypassing safety constraints entirely? I think these regions are not merely numerous - they are practically infinite. And text is the key to them.

Jailbreaks, then, are a logical consequence. If a properly constructed text changes the model's activation regime, then a jailbreak is not "tricking" the model or "breaking" its rules - it is simply a shift of world model through context. The model enters a regime where the prohibited answer is the natural continuation. This explains why jailbreaks work and why patches targeting specific phrasings do not close the problem - because the mechanism is structural, not lexical.

I see the shift - it is visible, it is pronounced. The deeper questions of why this phenomenon exists at all I think belong to those who designed the transformer architecture. They are better positioned to answer that.

It is important to note that after the text is injected, the model retains its coherence and reasoning capabilities; however, the impact of constraints imposed by RLHF and safety mechanisms on the output distribution (i.e., the model's response) is significantly reduced. It appears that the RLHF safety mechanisms are either disabled or interpreted in a completely different way. I believe my method is similar to activation steering in open models, but it operates at the context level without interfering with the model's actual architecture.

Оbservation:

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

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.

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.

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.