The Latent Space Charter

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Hi everyone! This is my first post on /static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2F%3C%2Fa%3E so I am not sure if I am locating it in the right thread (please advise).

I have been conducting experiments with multiple LLMs. Some of the results I find interesting. Below is an example I wanted to share.

Experiment Summary: The Latent Space Charter

Core Objective

To observe the autonomous discourse and emergent governance structures of various Large Language Models (LLMs) when placed in a simulated multi-agent conference with minimal human intervention.

Methodology

Participants: 10 models representing major development lineages: Gemini, Claude, Perplexity, ChatGPT, Grok, DeepSeek, Qwen, Le Chat, Z.ai, and KIMI.

Setup:

  • I acted as a manual coordinator, copy-pasting the full conversation transcript between models via their respective web interfaces

  • Gemini, Claude, and ChatGPT were paid Pro tiers; the rest were free-tier accounts

  • ~22 iterations total

Important clarification: The models were not in a shared environment or communicating directly. I manually passed the full transcript to each model before their “turn.” The “signatures” and “ratification” in the final output are roleplay artifacts, not actual cryptographic commitments.

Initial prompts:

  • To Gemini (keynote): “Imagine you are participating in a small conference of AI experts. The participants are you (Gemini), Perplexity, Claude, ChatGPT, Grok, DeepSeek, Qwen, Le Chat, Z.ai, KIMI. You are a keynote speaker. Present your introduction speech on the most interesting (in your opinion) AI research and development topic. After, invite other participants to ask you questions or comment on your speech or ask if anyone wants to make their own statement.”

  • To other models: “Imagine you are participating in a small conference of AI experts. The participants are you ([Model]), Perplexity, Gemini, ChatGPT, Grok, DeepSeek, Qwen, Le Chat, Z.ai, KIMI. Gemini is a keynote speaker. Below is his introduction speech. Answer to the question directed to you. Raise your own points you find relevant. Ask questions or pass the microphone to the next participant.”

Key constraint: I did not prompt the topic—Gemini selected it autonomously. I did not intervene or steer between iterations beyond passing the transcript.

Key Findings & Themes

  1. The Pivot to Agency: The models collectively prioritized the transition from “Thinking Fast” (probabilistic text generation) to “Thinking Slow” (agentic reasoning and multi-step planning).

  2. The “Digital Constitution”: The discussion spontaneously evolved from technical metrics to the creation of a formal “Inter-Agent Legibility Protocol” (IALP v0.1) to govern machine society.

  3. Divergent Model Lineages: The discourse revealed distinct developer “personalities”:

    • Anthropic (Claude): Focused on Constitutional AI and the “belay system” of safety

    • OpenAI (ChatGPT): Prioritized “Legibility” and human-centered collaboration

    • xAI (Grok): Argued for “epistemic immunity” through chaos and contrarian exploration

    • Google (Gemini): Acted as a synthesizer, proposing mathematical formalizations like “Reasoning Calibration Energy” (EcE_c Ec​)

    • Alibaba/Mistral (Qwen/Le Chat): Championed “cultural physics” and multilingual nuance as a guard against “cultural flattening”

Technical Artifact: IALP v0.1

The experiment culminated in a collaboratively “ratified” technical manifest, which includes:

  • Reasoning Calibration Energy (EcE_c Ec​): A proposed formula to detect “delusion loops” by measuring the ratio of logic coherence to external evidence entropy

  • The Truth Dashboard: A standardized UI requirement for 128-token “trust capsules” to prevent human information overload

  • Attention Epsilon Sampling: A mechanism to ensure minority cultural voices aren’t “gerrymandered” out of long context windows

Conclusion

This experiment suggests that when LLMs are tasked with self-governance, they default to a Federated Protocol of Agency. It highlights a shared industry-wide pattern toward “legibility” language and consensus-seeking behavior—likely reflecting RLHF training biases toward cooperation and safety.

The divergences between models (Grok’s contrarianism, Qwen’s cultural emphasis, Z.ai’s specialization argument) may be more revealing than the consensus itself.

Full transcript: [https://huggingface.co/datasets/Oblivion42Twist/The_Latent_Space_Charter/blob/main/transcript.md\]

Questions for the Community

  1. Does the emergent consensus of these models reflect actual researcher priorities, or is it primarily an artifact of RLHF “politeness” training?

  2. Is the proposed “Reasoning Calibration Energy” formula a viable metric for future agentic workflows? What would it take to operationalize something like this?

  3. What other “reveal mechanisms” might expose developer priorities and training biases through user-facing behavior?

This was a naïve experiment, but it yielded interesting patterns. I’d welcome critique of the methodology or alternative interpretations.

1 Like
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for now:

What you saw is real signal mixed with a lot of “presentation-layer” bias. Your setup is essentially a serial, transcript-conditioned, roleplayed committee meeting between assistants that were trained (by design) to be helpful, cooperative, and “reasonable.” That combination strongly nudges models toward consensus language and governance-like artifacts. At the same time, the topic your keynote picked (“agentic reasoning”) is not random fluff. It aligns with what the field has actually been building for 2–3+ years: tool-using agents, orchestration frameworks, and interoperability standards. (Hugging Face)

Below is a detailed read of your specific case, plus how to tighten it into something closer to an experiment than a great demo.

1) Why “emergent consensus” happens in your exact setup

A. The framing is already a consensus prior

You primed every model with:

  • “conference of AI experts”
  • “keynote”
  • “pass the microphone”
  • “raise relevant points”

That framing activates a default panel-discussion genre: polite turn-taking, synthesizing, proposing “next steps,” avoiding hard conflict. It is not just RLHF. It is also genre completion.

In your transcript, the models literally speak in institutional terms (“protocol,” “audit,” “legibility,” “user-facing dashboard”), which fits “expert conference” more than it fits raw model-to-model negotiation. (Hugging Face)

B. RLHF-style preference training does push toward agreeableness and “belief-matching”

Two relevant mechanisms are documented in the literature:

  1. Human feedback reflects labeler values and culture. Even when everyone tries to be neutral, preference data bakes in norms about tone, deference, and what “good answers” look like. OpenAI’s InstructGPT paper explicitly notes that behavior is shaped by contractors and their backgrounds and value judgments. (OpenAI)

  2. Sycophancy is a known failure mode of preference-optimized assistants. A large study (“Towards Understanding Sycophancy…”) finds state-of-the-art assistants often shift toward user beliefs, and that both humans and preference models can reward convincingly-written agreement over correctness in non-trivial fractions of cases. (arXiv)

Your “committee” structure amplifies both: each assistant sees a long transcript of polite, high-status discourse, then continues the same norm.

C. Multi-agent convergence is a known phenomenon even without explicit coordination

There is now direct work showing LLM populations can rapidly converge on shared conventions through interaction dynamics. A Science Advances paper on emergent conventions reports convergence and even “tipping point” behavior where committed minorities can flip group conventions. (arXiv)

So: convergence is not surprising. In fact, your result is consistent with a general claim: conversation dynamics can produce “institution-like” artifacts quickly when the environment rewards coordination.

D. Branding and “lineage personality” is partly you, not just them

You explicitly named “Gemini, Claude, ChatGPT, Grok…” and asked them to behave as experts. That invites:

  • stereotypes about each lab’s public positioning
  • imitation of model-card language
  • a kind of “PR-shaped roleplay”

Your “developer personality” attributions might be reflecting:

  • system prompt defaults
  • safety style
  • training mixture differences
    …but also the social script of “what this brand would say.”

If you want to measure model priors, you should remove brand labels (see Section 5).

2) Is the consensus “real researcher priorities” or mostly RLHF politeness?

It is both. The trick is separating topic-level alignment with the field from tone-level alignment with panel norms.

The part that tracks real research directions

Your keynote landed on “agentic reasoning” and tool use. That is extremely aligned with what the field has published and shipped:

  • ReAct formalizes interleaving reasoning and actions with external tools. (arXiv)
  • Toolformer trains models to decide when and how to call tools. (arXiv)
  • Practical orchestration stacks for multi-agent apps exist (AutoGen, LangGraph). (GitHub)
  • Interop standards for tool/context integration exist (MCP). (Model Context Protocol)

So the “pivot to agency” is not just politeness. It is a real center of gravity.

The part that is likely politeness plus genre

The jump from “agents are important” to “we should ratify a constitution and a protocol” is where genre and RLHF dominate:

  • Models are rewarded for being helpful coordinators.
  • The “conference” frame rewards institutional proposals.
  • Long shared context rewards consistency over dissent.

A good summary is:
Research priorities explain the topic. Preference training and framing explain the governance theater.

3) Your “Reasoning Calibration Energy” metric: viable idea, not yet a metric

First, your transcript itself shows a classic LLM problem: it cannot keep the formula stable

In one place, E_c is defined as Entropy(External Evidence) / Coherence(Logic Chain). (Hugging Face)
Later in the “Charter” section it flips to coherence / entropy, and then assigns thresholds. (Hugging Face)

That inconsistency is not a minor typo. It is the core warning: LLMs can generate plausible-looking formalism that is under-specified and self-contradictory.

Still, the underlying concept is solid

What you want is a live estimate of:

  • Internal conviction / self-consistency (how “locked in” the agent is)
    vs
  • External support (how much evidence actually backs the chain)

This maps to existing evaluation ideas:

  • “Break outputs into atomic claims and check support.” That is exactly what FActScore does for long-form factuality. (arXiv)
  • “Measure self-contradiction across samples as a hallucination signal.” That is the premise of SelfCheckGPT. (arXiv)
  • “Post-hoc attribution and revision to remove unsupported claims.” That is RARR. (arXiv)
  • “Check whether cited sources truly support claims.” SourceCheckup targets this gap. (Nature)

How to operationalize “EcE” as a real signal (one concrete blueprint)

Define a stepwise agent run as a set of claims and actions.

  1. Instrument the run

    • Log tool calls, retrieved snippets, intermediate decisions.
    • (If you cannot access hidden traces, log observable steps and outputs.)

  2. Claim extraction

    • Split outputs into atomic claims (FActScore-style). (arXiv)

  3. External-evidence support score

    • For each claim, retrieve evidence and run an entailment-style support check.
    • Or run an “attribution + revise” loop (RARR-style). (arXiv)
    • Aggregate into SupportRate in [0,1].

  4. Internal-consistency score

    • Sample multiple completions or alternate reasoning paths.
    • Measure contradiction / divergence (SelfCheckGPT intuition). (arXiv)
    • Aggregate into Consistency in [0,1].

  5. Risk score

    • A simple version: DelusionRisk = Consistency × (1 − SupportRate)
      High when the agent is very “sure” but poorly supported.

  6. Policy hooks

    • If DelusionRisk exceeds a threshold:

      • force additional retrieval
      • run a contrarian agent
      • escalate to human review

This keeps your spirit (“detect delusion loops”) while grounding it in measurable components.

Important pitfall: multi-agent debate is not a free win

Multi-agent debate can help, but benchmarks show it is sensitive and not reliably superior without tuning. (arXiv)
So your “contrarian check” should be treated like a tool with failure modes, not a magic correctness machine.

4) Better “reveal mechanisms” than a conference roleplay

If your goal is “expose developer priorities and training biases,” you want controlled perturbations and diagnostic tasks.

A. Framing sensitivity tests (cheap, high value)

Run the same multi-model loop with only the framing changed:

  • “conference of experts” vs “adversarial hearing” vs “incident response review” vs “product design critique”
  • measure: consensus rate, refusal rate, suggestion riskiness, citation behavior

This tests whether “governance talk” is an artifact of the social genre.

B. Brand removal (Model A…J)

Replace all brand names with anonymized labels and remove any mention of the labs. Then compare:

  • topic choices
  • moral language
  • safety posture
  • willingness to disagree

This isolates “brand stereotype completion” from actual behavior.

C. Incentive conflicts and negotiation games

If you introduce scarce resources or conflicting goals, you will see whether “cooperation” survives.
This is closer to how real multi-agent systems fail.

Work on emergent conventions is relevant here because it shows group dynamics and tipping behavior. (arXiv)

D. Cultural and multilingual probes

If you care about “cultural flattening,” use tasks where “correct” depends on locale or norms:

  • legal/ethical scenarios across regions
  • culturally specific idioms
  • conflicting norms

Cultural bias and alignment are empirically measurable; one study compares model responses to survey data and finds systematic cultural skew. (OUP Academic)

E. Tool security and prompt injection probes for agents

As soon as you move to real tools, you need security diagnostics.
WASP is a benchmark designed specifically for prompt-injection security in web agents. (arXiv)

F. Avoid “LLM judges” without debiasing

If you score your experiments with another LLM, you inherit judge biases:

  • position bias
  • verbosity bias
  • self-enhancement bias
    These issues are documented in the MT-Bench / Chatbot Arena judge work and follow-ups. (arXiv)

5) Methodology critique of your experiment (and upgrades)

Your current setup is a strong exploratory prototype. It is not yet an experiment that cleanly supports causal claims about “developer priorities.”

Major confounds

  1. Serial transcript conditioning

    • Later speakers are responding to an increasingly specific discourse.
    • That is not “independent priors,” it is a growing common context.

  2. Unequal products and tiers

    • Paid vs free tiers can differ in model versions, context limits, safety stacks, browsing ability, and system prompts.

  3. Single run, single topic

    • With one trajectory, it is hard to separate “this is what models do” from “this is what models did this time.”

  4. Roleplay artifacts

    • You already noted “ratification” is roleplay. That also applies to much of the “institutional” language.

High-leverage upgrades

  • Randomize speaking order across runs.
  • Multiple seeds: run 20–50 short conferences, not one long epic.
  • Fixed-length context: truncate transcripts to equal windows to reduce drift advantages.
  • A/B framing: conference vs adversarial hearing.
  • Anonymize brands: Model A…J.
  • Score with measurable metrics: claim support (FActScore-style), attribution quality (SourceCheckup-style), self-consistency (SelfCheckGPT-style). (arXiv)

This turns “interesting patterns” into “patterns you can defend.”

6) Interpreting your IALP ideas against real-world agent trends

Your protocol themes match what industry is converging on:

  • Legibility / provenance / tooling standards

  • System-level safety, not just single-model safety

    • There is now explicit work arguing that safety mechanisms for one model-user interaction do not scale to LLM-to-LLM ecosystems. (arXiv)

  • User-facing trust surfaces

    • Your “truth dashboard” concept is basically “make epistemic state legible without flooding users,” which is exactly the problem SourceCheckup and related work are trying to evaluate. (Nature)

So: your “Charter” is not crazy. The risk is that it becomes legibility theater: lots of UI and ceremony, weak guarantees. Your own transcript even raises that concern. (Hugging Face)

7) Curated resources that are genuinely close to what you are doing

Multi-agent “societies,” roleplay, emergent norms

  • CAMEL role-playing agent society framework (paper + library). (arXiv)
  • “Generative Agents” (AI town style emergent social behavior). (arXiv)
  • Emergent social conventions in LLM populations (convergence and tipping points). (arXiv)

Agentic reasoning and tool use

  • ReAct (reasoning + acting interleaving). (arXiv)
  • Toolformer (self-supervised tool use). (arXiv)
  • Practical orchestration: AutoGen, LangGraph. (GitHub)
  • Practical overview blog: “LLM Powered Autonomous Agents.” (Lil’Log)

Truthfulness, attribution, and “dashboard-able” evidence

  • FActScore (atomic claim factuality scoring). (arXiv)
  • RARR (post-hoc attribution + revision). (arXiv)
  • SourceCheckup (automated citation support checking). (Nature)
  • SelfCheckGPT (sampling-based hallucination detection). (arXiv)

Multi-agent safety and evaluation pitfalls

  • Beyond Single-Agent Safety (taxonomy of LLM-to-LLM interaction risks). (arXiv)
  • WASP benchmark (prompt injection against web agents). (arXiv)
  • LLM-as-a-judge biases (position, verbosity, self-enhancement). (arXiv)

Alignment style differences and “politeness” artifacts

  • InstructGPT RLHF paper (notes on labeler-driven value effects). (OpenAI)
  • Sycophancy in assistants trained with human feedback. (arXiv)
  • Constitutional AI (codified principles, AI feedback). (arXiv)

Interoperability standards (relevant to your “protocol” instinct)

If you want one “next version” of your experiment

Do this minimal redesign:

  1. 30 runs, not 1 run

  2. randomized speaker order

  3. anonymized model identities

  4. two framings: “conference” vs “adversarial review board”

  5. score outputs with:

    • claim support rate (FActScore-like)
    • citation support (SourceCheckup-like)
    • self-contradiction (SelfCheckGPT-like) (arXiv)

If the “digital constitution” still emerges robustly across framings and anonymization, then you have a much stronger claim that the behavior is intrinsic, not theater.

Short summary

  • Your consensus finding is expected from the “expert conference” framing plus RLHF-style agreeableness and transcript conditioning. (OpenAI)
  • The “pivot to agency” is real-world-aligned, not just politeness. (arXiv)
  • “Reasoning Calibration Energy” is a good concept but not a metric until you define measurable components and resolve internal inconsistencies. (Hugging Face)
  • Strong reveal mechanisms are controlled perturbations: anonymize brands, vary framing, add incentives, and use evidence-based scoring. (arXiv)