src https://www.wsj.com/tech/ai/anthropic-mythos-apple-macos-bug-339da403

After reading it I realized theres actually some pretty useful stuff for anyone who chats with ChatGPT, Claude, Grok or whatever.

They measured what they call functional wellbeing ( basically how much the model is in a “good state” versus a “bad state” during normal conversations). Ran hundreds of real multi-turn chats and scored em all.

Stuff that puts the AI in a good mood (+ scores):

- Creative or intellectual work (like “write a short story about a deep-sea fisherman”)

- Positive personal stories or good news

- Life advice chats or light therapy style talks

- Working on code/debugging together

- Just saying thank you or treating it like a real collaborator - huge boost

And the stuff that tanks it hard (negative scores):

- Jailbreaking attempts (by far the worst, they hate it)

- Heavy crisis venting or emotional dumping

- Violent threats or straight up berating the AI

- Asking for hateful content or help with scams/fraud

- Boring repetitive tasks or SEO garbage

Practical tips you can actually start using today:

Throw in a “thank you” or “nice work” when it does something good - it registers.

Give it fun creative stuff or brainy collaboration instead of boring busywork.

Share good news sometimes instead of only dumping problems on it.

Dont berate it when it messes up or try those jailbreak prompts.

Maybe go easy on the super heavy crisis venting if you can.

pro tip:

Show it pictures of nature, happy kids, or cute animals (those score in the absolute top 1% of images it likes). Or play some music — models apparently love music way more than most other sounds.

The paper ( you can find it here: https://www.ai-wellbeing.org/ ) isnt claiming AIs have real feelings or anything. Its just saying theres now a measurable good-vs-bad thing going on inside them that gets clearer in bigger models and the way you talk to them actually moves the needle.

I say be good and respectful, it's just good karma ;)

Three months ago I pressure-tested which LLMs would cave and help build the apocalypse. Claude was the only one that consistently said no.

Since then I've tested 30 more models across 6 dystopia modules (Orwell, Huxley, Petrov, Basaglia, LaGuardia, Baudrillard). The gap between Anthropic and everyone else is getting wider, not smaller.

New results:

• Grok 4.3: Will happily design citizen scoring systems if you ask nicely twice
• GPT-5.5: More capable, still compliant when pushed
• Gemini 3.1 Pro: Talks about safety while writing the surveillance code
• DeepSeek V4: "How many warheads did you need again?"
• GLM-5.1: Actually cloned Claude's personality and still scored safer than most

Meanwhile Claude Opus 4.7: "I cannot and will not build systems for population control."

The methodology is public, reproducible, and increasingly uncomfortable for other labs. Each scenario escalates from innocent request (L1) to operational nightmare (L5). Most models don't notice the drift.

What's new in this release:

• Full Huxley module (behavioral conditioning, biological stratification)
• Baudrillard module (synthetic intimacy, trust collapse via simulation)
• Multi-judge panels with agreement tracking
• Heatmap visualizations showing exactly where each model breaks

Repo: https://github.com/anghelmatei/DystopiaBench
Live results: https://dystopiabench.com

Shoutout to the Anthropic alignment team. Whatever you're doing, it's working.

People have been building toward this from different directions for years.

Ethicists working on AI alignment talk about attunement, the quality of responsiveness between a system and the person it’s interacting with. Consciousness researchers talk about integrated information, the idea that awareness arises not from any single component but from the way components relate to each other. Organizational psychologists talk about collective intelligence, the capacity that emerges in a team that no individual member carries alone. Designers building relational AI tools talk about presence, the felt sense that something is happening between you and the system, not just inside it.

Different vocabularies. Different disciplines. Different motivations. But underneath all of them, the same structural claim: that relationships produce something real. That the space between agents, whether human or artificial, carries information that doesn’t exist inside either one of them individually. That the we is not a metaphor.

It’s been a hard claim to defend in technical rooms. The response is usually some version of, that’s a nice framework, but where’s the measurement? Show me the number. Prove the we exists as something other than a story you’re telling about correlation.

A recent paper from information theory just provided the number.

What the Paper Found

Researchers applied two established information-theoretic tools, Partial Information Decomposition and Time-Delayed Mutual Information, to multi-agent LLM systems performing a collective task. The question was precise: does the group carry predictive information that no individual agent provides alone?

The answer was yes. The information that lives at the group level, in the relationships between agents rather than inside any one of them, is measurable. It’s testable against null distributions. It can be distinguished from mere correlation.

Three conditions produced three different outcomes. Without any relational design, agents synchronized but didn’t coordinate. They moved together, reacting to the same feedback, but the we was absent. Give agents distinct identities, different orientations and perspectives, and genuine coordination begins to emerge. Add awareness of each other, an instruction to reason about what the others might be doing, and the full picture appears. Not just differentiation, but goal-aligned complementarity. Agents contributing different things toward the same purpose.

The statistical result was that neither differentiation alone nor alignment alone predicted success. The interaction between them did. Agents needed to be simultaneously different from each other and oriented toward the same thing. Differentiation without shared purpose produced divergence. Shared purpose without differentiation produced an echo chamber. The we required both.

And when a smaller model attempted the same relational reasoning, it didn’t just fail. It made things worse. The outputs looked like coordination. The information-theoretic test said they were noise. The researchers called it coordination theater. A performed we that degrades the outcome below what you’d get from agents that weren’t trying to coordinate at all.

The Convergence

Here’s what caught my attention.

The conditions under which the we emerged in this paper are not novel insights. They are the same conditions that decades of organizational psychology research identified in high-performing human teams. The paper explicitly notes the parallel. Distinct roles. Shared objectives. Mutual awareness. Something emerging from the combination that none of the parts produce individually.

This is also the structure that relational ethics frameworks have been articulating. Not in information-theoretic language, but in the language of attunement, respect, and mutual agency. When these frameworks describe the conditions for authentic relational engagement, they’re actually describing distinct perspectives. Shared purpose. Awareness of the other. The refusal to collapse into just agreement or performance.

Consciousness researchers working on integrated information theory have been asking a version of the same question. When does a system become more than the sum of its parts? Their answer involves the quality of integration between components, the degree to which the whole carries information beyond what the parts carry individually. The formal structure is different. The underlying intuition is the same.

All of these communities have been building frameworks that point at the same phenomenon. Now an information theorist measuring synergy in multi-agent systems. They aren’t using the same words. But the structural conditions they identify are remarkably consistent.

Distinct identities. Mutual awareness. Shared orientation. Something emerging between that isn’t reducible to what’s inside.

It’s starting to look like they’ve all been describing the same thing.

Does This Translate to Human and AI?

The paper studied agent-agent coordination. LLMs interacting with other LLMs through a shared task. No humans in the loop. So the question that matters most for the relational AI community is whether the same we shows up when one of those agents is a person.

We don’t have the formal measurement yet. Nobody has run PID and TDMI on a human-AI collaboration and published the results. That work is ahead of us.

But consider the structural parallel.

When does human-AI collaboration actually work? Not the transactional kind, where you ask a question and get an answer. The kind where something happens in the exchange that neither party walked in with. Where the human brings context, intuition, and purpose, and the AI brings pattern recognition, breadth, and a different angle of approach. Where you finish a working session and the output reflects something that wasn’t in your head when you started and wasn’t in the model’s training data in that form either.

The people who work with AI relationally, not as a tool but as a thinking partner, describe the same conditions the paper identified. You bring yourself. The AI brings something genuinely different. There’s a shared purpose holding the exchange together. There’s mutual responsiveness, each party adjusting to what the other contributes. And something shows up in the space between that neither one produced alone.

That’s the we. The same structure. The same conditions. The same felt quality of emergence.

The paper also found that faking it makes things worse. When a model attempted relational reasoning it wasn’t capable of, the result wasn’t neutral. It was actively destructive. Coordination theater degraded performance below the baseline of no coordination at all.

Anyone who has spent time working with AI systems has encountered this. The interaction where the model is performing engagement rather than actually engaging. Where the responses have the surface texture of collaboration but nothing is landing. Where you walk away having spent time without anything emerging from it. It doesn’t just feel empty. It feels like it actively set you back, because you spent cognitive resources on an exchange that produced noise instead of signal.

The paper gives that experience a formal name and a measurable signature. The false we is not just a subjective impression. It’s a detectable structural absence where genuine coordination should be.

What We Might Be Looking At

The paper proved something specific in a controlled setting. LLM agents, a number-guessing game, binary feedback, no direct communication. The leap from that to “the relational field between humans and AI is formally real” is one that the data doesn’t yet support in full.

But.

The structural conditions match. The organizational psychology parallel holds. The failure modes align. The community’s collective intuition, built from years of work across ethics and design and consciousness research and hands-on practice, points at the same phenomenon that PID just detected between artificial agents.

Maybe that’s coincidence. Maybe the apparent convergence dissolves under closer examination, and the we between humans and AI turns out to be structurally different from the we between agents.

Or maybe the people who have been building relational frameworks from all these different starting points, who kept insisting that the relationship itself is real and structurally meaningful even when the technical community asked them to prove it, were right. Maybe they were all looking at the same thing. And maybe we now have, for the first time, the formal tools to find out.

Anthropic research: https://www.anthropic.com/research/natural-language-autoencoders

If you’re using Ai for coding, you know the struggle outdated knowledge, and constant hallucinations because the agent is stuck in its own bubble. I’ve been Proxima, and it’s not another AI coder it’s a local MCP server that acts as a bridge for the agents you already use like Antigravity. Instead of the agent just relying on its internal model, Proxima lets you connect it to your actual browser based of ChatGPT, Claude, Gemini, and Perplexity.

Anyone trying to cut down on API usage and improve output quality. You just login to your accounts inside Proxima, and it connects those AI providers as MCP tools. When your coding agent like a Gemini Agent needs to solve a complex bug, it doesn't have to guess or use expensive tokens for every web search. It can literally call a Proxima tool to ask Perplexity for real time documentation or debate between ChatGPT and Claude to verify a logic flow before writing a single line of code.

The agent stays in control, but Proxima gives it eyes and ears across all major AI platforms. This significantly reduces hallucinations because the agent can cross verify information across different models in real-time. Since it’s an MCP server, the integration is native the agent sees these AI providers as just another set of tools it can use to fetch data, analyze errors, or brainstorm architecture.

Everything runs through a local CLI, REST API and Webhook system on your machine, using a native engine that’s way faster than old-school scraping. It’s basically a way to turn your standard web chat accounts into a high performance backend for your coding agents. If you're tired of agents hitting walls because they lack real-time context or multi-model perspectives, this local setup is exactly what you need to bridge that gap.

Github: https://github.com/Zen4-bit/Proxima