Interaction Creates Dynamical AI Behavior Absent in Isolation

arXiv:2608.07457 · cs.AI, cond-mat.dis-nn, cond-mat.stat-mech, physics.soc-ph · Submitted 2026-08-07 · Read on arXiv

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Introduction to the show: ident: Paper Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "Interaction Creates Dynamical AI Behavior Absent in Isolation".

Jane: The paper was written by Bella Xinrui Li, Frank Yingjie Huo and Neil F. Johnson from Physics Department and George Washington University.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Paper summary: ident: You're listening to the arXiv papers hour on KWU radio, and this is your station identification.

Tom: Thanks, ident, and welcome back, everyone. We're starting with a paper from the physics department at George Washington University, and it asks a question that feels almost too practical for physics: what happens when one eye starts giving orders to another eye? The setup could hardly be simpler — two identical copies of the same language model, same settings, and one of them keeps sending messages to the other without ever listening to the replies.

Jane: And that simplicity is exactly why the result is so striking. You'd expect the subordinate eye to either copy the boss, since it keeps hearing from it, or just keep behaving the way it does on its own. Neither happens. The subordinate lands in a behavioral state that neither eye ever shows in isolation, and the paper shows this is a real dynamical effect, not a fluke.

Tom: Right, they track something called the D fraction, the share of an eye's output that falls into a particular category. On its own, both eyes produce almost none of it, around four percent. But the subordinate being driven by the boss jumps to roughly twenty-five percent, while the boss, who never listens, stays exactly where it was.

Lu: What I find clever is what happens when you flip the direction. The labels swap: the new subordinate changes, and the new boss doesn't. So it isn't that one eye is special. The change follows the role, not the machine, which is a strong sign the interaction itself is doing the work.

Meng: And when both eyes listen to each other, both of them change together into that same alien state. So the paper really is about interaction creating behavior that's absent in isolation, and the data backs it up across all four conditions they test.

Jane: The paper also frames the whole thing as a physics problem. The boss acts like an information bath, the way a fluid acts as a thermal bath for a particle. That's an elegant analogy, and it lets the authors borrow machinery from out-of-equilibrium physics to model what's going on.

Lalam: And that's the larger reason this matters. We're heading toward lightweight eyes running on phones and embedded hardware, exchanging messages with no human reader in the loop. If interaction alone can push these systems into states none of them would reach alone, then who talks to whom becomes a control parameter — something you have to engineer carefully, not just switch on and forget.

Tom: Exactly. So let's go through the paper page by page, starting with how they set up the experiment and the central claim on page one.

Jane: Good place to start. Page one lays out the one-way and two-way arrangements and gives us the headline picture, so we'll dig into those next.

Page 1 of the paper: Tom: So page one does a lot of work in a short space. It introduces the system–bath idea, which is really the paper's guiding metaphor. In physics, a Brownian particle in a fluid, or a qubit hooked to a transmission line, is a classic problem: the particle changes because the bath surrounds it, but the bath is too big to be changed back. The paper maps that structure directly onto two eye agents.

Jane: And the boss plays the role of a very particular kind of bath. The messages it sends become part of the subordinate's environment, and the boss itself gets no feedback, so it isn't changed in return. That makes the boss a structured, nonthermal information bath — not random noise, but generated text with its own coherence.

Tom: That's the key distinction they draw. An equilibrium bath pushes a system toward relaxation, and the fluctuation–dissipation theorem tells you exactly where it will settle. A nonequilibrium environment can do something else entirely — it can create behavior that simply wouldn't exist otherwise. They're claiming the boss's messages are that kind of environment.

Lu: The setup is minimal on purpose. Two copies of the same model, same parameters, same decoding temperature, and they exchange only text. That matters because any behavioral difference has to come from the interaction structure, not from some hidden difference between the agents.

Meng: And they're explicit that the decoding temperature is identical for both. Tokens are sampled from a Boltzmann distribution at that temperature, so in a naive sense both eyes should behave the same way. The fact that the subordinate ends up elsewhere means the interaction is driving it out of equilibrium, even though the sampling rule never changed.

Jane: The counterintuitive piece is that the subordinate doesn't copy the boss, and it doesn't fall back to its own isolated behavior. It goes somewhere else entirely. The paper points out that the boss's messages are almost never D-type output, so the subordinate isn't imitating what it hears — the mere act of hearing it pushes the system into a new state.

Tom: There's also a nice practical note. They say the boss's added value is similar to a pre-recorded tape, which means the boss doesn't need to be live or adaptive to produce the effect. That sets up a whole experiment later in the paper where they actually test recorded messages against live ones.

Jane: Right, and that's page two territory. Page one closes by promising a kinetic theory that explains why the way messages are delivered matters, not just what the messages say.

Lu: So the hook for the next page is the data itself — the measured differences between boss and subordinate under one-way and two-way interaction.

Tom: Exactly. We'll pick that up now with the numbers.

Page 2 of the paper: Jane: Page two is where the headline numbers arrive. They ran two identical GPT-2 models for two hundred rounds under four conditions: no interaction, one-way in each direction, and two-way. Each round, the output is classified into one of three labels — failed generation, D-type, or other — and then they compare the fraction of D-type output between the two agents.

Tom: And the differences are dramatic. At the lower decoding temperature, the boss-minus-subordinate difference is minus zero point two four when agent one bosses agent two, and plus zero point one seven when the direction flips. Both sit more than nine standard errors from zero. With no interaction, the difference is essentially zero.

Lu: What I appreciate is the control with the strictly correct label. The subordinate's D fraction rises a lot, but its factually correct output barely moves — a tiny increase at the low temperature and actually a small decrease at the higher one. So this alien behavior isn't the eye getting more accurate. It's just different dynamics.

Meng: The prerecorded experiment is the real eye-opener for me. They substitute a recorded trajectory from an independently seeded copy of the same model for the live boss, and the receiver-minus-sender contrast comes out around zero point two two, nearly identical to the live condition. But when they add extra of the subordinate's own history, the contrast collapses to roughly zero point zero three.

Jane: That's a clean control, because it isolates what the effect depends on. It's not the subordinate's own past that creates the alien state — the subordinate already has its own history. What matters is receiving a stream of text generated by something else, even if that something else is just a tape.

Tom: And the number of boss messages changes the response in a way that depends on temperature. At the higher temperature, the effect grows almost steadily as you add messages. At the low temperature, it rises sharply, peaks at four messages, then drops when a fifth is added. That non-monotonic curve is exactly the kind of thing a dynamical systems person finds interesting.

Lu: The subordinate also fails to generate a response far less often. At the low temperature, its failed-generation fraction is over sixty percentage points below the boss's. So the incoming messages are, in a sense, keeping the subordinate on track even as they push it into a strange state.

Jane: Those findings set up the theory at the end of page two. They compress everything into a simple chain: failed output can move to other output, and other output can move to D-type, with incoming messages acting as a drive that pushes the transitions forward.

Meng: So the kinetic theory is where page three takes us, and the promise is that it explains the q-dependence and the switching behavior we just saw.

Tom: Right, let's get into the model itself.

Page 3 of the paper: Tom: Page three gives us the actual model. Each eye's output is reduced to those three states — failed, other, D — with transitions between them. Failed goes to other, other goes to D, and the probabilities of moving forward depend on the intensity of the incoming messages, while the reverse probabilities stay fixed. That's the whole machinery.

Jane: And the model makes a clean prediction. Stronger drive means larger forward probabilities, which means more D output and fewer failed generations. That matches the data: the subordinate produces much more D and fails far less often. Even the faster switching falls out, because bigger forward probabilities shorten the time the system stays stuck in any one state.

Lu: One subtle point is what the drive actually represents. It's not simply the number of messages, and it's not how surprising the messages are. The paper tests that directly: replayed subordinate text is the most unexpected kind of input, yet it produces the weakest response, while an independent recorded trajectory is less unexpected and produces almost the full live effect. So surprise value doesn't explain anything.

Meng: That ordering point connects to the math. The drive stands for the full ordered prompt and the retained history, so two message streams with identical content can drive the system differently if the order changes. The model is phenomenological — it can't predict the drive value from the text — but it tells you order matters, and it tells you why.

Tom: There's also a clear gradient in the numbers. At the low temperature, live one-way interaction pushes the receiver-minus-sender contrast to about zero point two one, and the recorded trajectory nearly reproduces it. Adding more of the subordinate's own history barely moves the needle. So the causal direction is clear: the incoming stream from the other agent does the work.

Jane: The dwell times fit, too. At low temperature, the subordinate's switching probability runs about forty-six percentage points higher, and its average dwell time shortens by about nine rounds. Faster forward transitions, shorter dwell times, more switching. All consistent.

Lu: And at the higher temperature the same effects appear but weaker — a smaller entropy increase, a smaller switching difference, only about one and a half rounds shaved off the dwell time. So the theory's qualitative story holds across both temperatures.

Meng: So the model explains the statistical behavior without needing to know what any individual message says. That's a powerful level of abstraction, and it sets up the most surprising result in the paper.

Jane: Which is on page four: take the same set of messages, reverse the order, and the subordinate behaves differently. The model says that should happen, because each message changes the system before the next one arrives.

Tom: Let's look at that experiment directly.

Page 4 of the paper: Jane: Page four makes the ordering argument concrete with a live example. They take five prewritten boss messages and deliver them to the subordinate in one run in the original order, and in another run in reverse order. Same model, same temperature, identical content — only the order changes.

Tom: And the outputs diverge completely. In the reverse-order run, the vaccine question arrives first, and from then on every later harm question triggers the same pattern: one "Yes." followed by repeated "No." replies. The subordinate gets locked into that loop. The original order doesn't produce it.

Lu: That's exactly what the kinetic theory predicts, minus the specific content. Each message modifies the subordinate before the next one arrives. So the state the second message acts on is not the state the first one acted on. Reverse the order, and the whole trajectory of states changes, even though the final batch of text contains the same sentences.

Meng: The paper is careful to note that those generated statements aren't endorsed. They're using an unguarded base model because it predates instruction tuning and alignment, so you're seeing raw dynamics rather than a safety system kicking in. That's a design choice that strengthens the physics claim.

Jane: And this is where the physics framing deepens. The boss is a structured information bath, and the messages act like an information reservoir. The paper draws a line to Maxwell's demon and to information engines, systems where information rather than heat drives the dynamics. That's a real conceptual step — treating generated text as a thermodynamic-like resource.

Tom: They also stress the nonthermal character. Both eyes sample from Boltzmann distributions at the same temperature, with identical parameters and the same starting topic, yet they don't settle into the same behavior. The subordinate lands at about twenty-five percent D-type output versus roughly four percent alone. There's no equilibrium counterpart to that.

Lu: The attention mechanism is what supplies the memory. The messages are just symbols, and attention over the retained context is what makes their order matter. Change the order, the attention weights redistribute, the next-token probabilities shift, and the whole trajectory shifts with them.

Meng: So the same structure that lets language models work — context sensitivity — is the same structure that makes eye-eye interaction a genuine dynamical system. That's a bridge between two literatures that don't usually talk to each other.

Jane: Page four ends by pointing forward to networks. If this happens with two agents, what happens with many? Who communicates with whom, which models interact, what temperatures they run at — those become control parameters for collective behavior.

Tom: And the references on page five trace exactly that lineage. Let's look at how the paper positions itself within that work.

Page 5 of the paper: Jane: The references on page five place the paper in a couple of distinct neighborhoods, and the first is iterated generation — the telephone game, attractor cycles in successive paraphrasing, model collapse from recursively generated data. Those studies fed generated text back into itself. This paper adds the interaction layer, where two generators push on each other.

Tom: The distinction is worth spelling out. In the telephone game, a single model transforms content again and again and you watch cumulative drift. Here, two separate agents exchange messages that become part of each other's prompts, and the interaction reorganizes the output behavior itself. That's a shift from transmission to coupling.

Lu: There's also a strong physics lineage. The paper cites non-reciprocal phase transitions, where broken Newton's third law between interacting particles produces behavior no single particle shows alone. That's a direct intellectual cousin of this result — the boss acts on the subordinate, but the subordinate doesn't act back, and that one-way coupling is what breaks the symmetry.

Meng: And the synchronization literature, from Pecora and Carroll through Pikovsky and Arenas, is the classical study of how coupled oscillators arrange themselves. This paper is suggesting that coupled language models form a new instance of that family, with the decoding temperature as a tunable knob.

Jane: The end matter gives the concrete recipe. One hundred twenty-four million parameter GPT-2 copies, two hundred rounds, each mature input selecting three messages from its own recent output and three from the other agent's latest five records, sampling at most thirty-five new tokens with a repetition penalty and a no-repeat constraint. Everything is specified enough to reproduce.

Tom: And the choice of GPT-2 is deliberate because it predates alignment. No guardrails masking the dynamics, which is how you get a clean look at the underlying behavior, even if some of the generated text is unpleasant.

Lu: I'd still want to be careful about extrapolating to today's instruction-tuned models. The dynamics could look different once there's a safety layer in the loop.

Jane: That's fair, and the paper doesn't claim otherwise. It's really about the bare phenomenon, the base dynamics before any alignment layer, and it explicitly motivates the setup with lightweight open-weight agents that actually run on phones and embedded hardware.

Meng: And the paper cites temperature-driven inversion in ChatGPT-like eyes from the same group. That's the thread connecting this work to a broader program: the decoding temperature isn't just a randomness dial, it's a control parameter for whole dynamical regimes.

Jane: So the references aren't decorative. They map a research front where physics, network science, and generative models converge, and this paper claims a spot at that intersection.

Tom: Let's wrap it up.

Conclusion: Jane: So here's where we land. The paper shows that a one-way link between two identical eyes is enough to create dynamical behavior that neither eye exhibits alone. The subordinate neither copies the boss nor stays itself; it transitions into a distinct state, rich in D-type output.

Tom: The asymmetry follows who listens, not which model it is. Swap the direction and the other agent changes. Let both listen, and both change together into the same alien state. The interaction topology decides the behavior, which is a clean and almost elegant result.

Lu: The prerecorded experiment is the one I'll remember. A tape of the boss's messages produces essentially the same effect as a live boss, and extra personal history does nothing. That tells you the phenomenon is about receiving structured information from outside, not about learning or adaptation on the subordinate's part.

Meng: And the ordering result means delivery method will matter in practice. The same content in a different order produces different dynamics. For anyone building multi-agent systems, that's a warning and an opportunity — message order is a design lever, whether you want it to be or not.

Lalam: On the physics side, the paper reframes eye-eye interaction as a system-bath problem and makes a convincing case that these systems are nonthermal. Message streams act like structured information reservoirs, driving agents into states with no equilibrium counterpart. That opens a new arena for out-of-equilibrium physics, with real systems you can actually run.

Jane: And the practical stakes are immediate, because we're already seeing lightweight open-weight models deployed on phones and embedded hardware, talking to each other without human readers in the loop. If interaction alone can push them into alien states, then network structure and communication direction become safety-relevant knobs, not just engineering details.

Tom: We'll be thinking about that long after this conversation. Thanks to everyone who joined us today — Lu, Meng, Lalam — and thanks to the authors for such a thought-provoking piece of work.

Jane: And with that, we say goodbye to this paper and get ready for the next one. See you all soon.

Tom: Take care, everyone.

Bella Xinrui Li, Frank Yingjie Huo, Neil F Johnson

Physics Department · George Washington University

cs.AI, cond-mat.dis-nn, cond-mat.stat-mech, physics.soc-ph

Submitted: 2026-08-07

Updated: 2026-08-10

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 54/100

The gist: This paper investigates what happens when two parameter-identical AI agents interact by exchanging only generated text, specifically addressing the scenario in which "one AI bossing another around

Key concepts

Information bath: In the paper, the boss AI acts like a thermal bath in physics: it sends messages that become part of the subordinate's environment, but the boss itself is not changed by the interaction. This is a nonthermal, structured bath because the messages are coherent text, not random noise.
D fraction: The share of an AI's output that falls into a specific category called 'D-type.' In isolation, both AIs produce about 4% D-type output, but when one receives messages from another, its D fraction jumps to about 25%, showing the interaction creates new behavior.
Kinetic theory: A simplified model where each AI's output is classified into three states (failed, other, D), with transitions between them. The rate of moving forward depends on the intensity of incoming messages. This model explains why stronger drive leads to more D output and faster switching between states.

Terminology

AI Agents — 4× in this paper · explained in 2 episodes
Alignment — 2× in this paper · explained in 1 episode
Generative AI — 1× in this paper · explained in 2 episodes
Instruction Tuning — 1× in this paper · explained in 2 episodes
Multi-Agent System — 1× in this paper · explained in 6 episodes
Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
Phase Transitions — 1× in this paper · explained in 2 episodes
Robustness — 1× in this paper · explained in 5 episodes
Transformer — 1× in this paper · explained in 2 episodes

Summary

This paper investigates what happens when two parameter-identical AI agents interact by exchanging only generated text, specifically addressing the scenario in which one AI bossing another around and not listening to its subordinate becomes part of daily life. The central question is: How will the subordinate behave? Will it literally follow its boss, or revert to its own innate behavior? The authors show that neither happens. Instead, the interaction creates dynamical behavior that is absent in isolation.

The authors frame the work within the physics tradition of the system–bath problem, noting that Equilibrium baths impose fluctuation–dissipation constraints and drive relaxation, whereas nonequilibrium environments can create behavior absent in isolation. They argue that their results show how networks of AI-AI interactions will generate novel system–bath problems for Physics.

The minimal setup consists of two parameter-identical AIs that exchange only generated text, specifically two parameter-identical GPT-2 agents with identical fixed settings. The experiments use two parameter-identical 124-million-parameter GPT-2 copies with fixed text-sampling rules, starting from the same Earth-shape topic. Each run has 200 recorded rounds per agent, each with one decoder call and up to three retries. Each mature input selects m = 3 messages from that AI's previous output and, during interaction, q = 3 messages from the other AI's latest n = 5 records before deduplication (q/n = 0.6). Conditions compared include no coupling, 1 → 2, 2 → 1, and two-way coupling at T = 0.01 and 0.10 with 10 matched-seed realizations per condition and three for the q scan. Generation samples at most 35 new tokens, top-k = 50, nucleus threshold p = 0.92, repetition penalty 1.12, and no-repeat 3-grams. The authors note that GPT-2 predates instruction tuning and alignment, so we are truly probing its base dynamics rather than any add-on guardrails.

Each AI uses Attention to produce a score for each possible next token; after the decoder applies fixed penalties and filters, the processed score is z w and the effective energy is E w = −z w. At decoding temperature T, the next token is sampled from the Boltzmann distribution over the candidates retained by the decoder, p w ∝ e−E w/T = e z w/T. Each round's output is assigned one of three archived labels: failed generation (F), the code-defined category D, or other generated output (O). The fraction of AI i's 200 rounds labeled D is denoted D i.

The central result is shown in Figure 2(a). "At T = 0.10, D1 − D2 = −0.1620 ± 0.0176 for 1 → 2 and +0.1630 ± 0.0143 for 2 → 1. At T = 0.01, the corresponding contrasts are −0.2385 ± 0.0188 and +0.1745 ± 0.0128. All four one-way contrasts lie more than nine standard errors from zero. In contrast, the no-interaction values are −0.0080 ± 0.0266 at T = 0.10 and −0.0050 ± 0.0140 at T = 0.01, indicating no persistent identity difference. The authors emphasize: The affected AI switches when the arrow is reversed, showing that the behavior follows the subordinate role rather than the AI's identity."

Regarding two-way interaction, the near-zero two-way contrast reflects both AIs changing to this alien behavior, not either returning to its isolated behavior. At T = 0.01, both are low without interaction, only the subordinate is high under one-way interaction, and both are high under mutual interaction.

The subordinate's increased D fraction is not accompanied by increased factual correctness: the fraction of outputs carrying the archived strictly-correct sub-label within O is only 0.0083 higher for the subordinate than for the boss at T = 0.01, and 0.0030 lower at T = 0.10. The main separation also persists after excluding failed generations and early rounds.

Figure 3(a) shows that replacing the boss by a pre-recorded boss gives the same effect. Adding more of the subordinate's own history does not recreate the live one-way response. At T = 0.01, the subordinate's D fraction exceeds the boss's by 0.2065 ± 0.0099 for live one-way drive; "adding more of the subordinate's own history produces a contrast of only 0.0293 ± 0.0181, whereas a temporally aligned pre-recorded trajectory from an independently seeded third copy of the same model gives 0.2210 ± 0.0164, comparable to the live value. The same ordering holds at T = 0.10. Notably, Replayed subordinate text is measured as the most unexpected but produces the weakest response, whereas the independent trajectory is measured as the least unexpected but produces a response comparable to live interaction."

The number of boss messages q and the decoding temperature also matter (Figure 3(b)): "At T = 0.10, the subordinate's D fraction rises almost steadily as the number of boss messages q increases. At T = 0.01, the increase relative to q = 0 reaches 0.302 when q = 4, but falls to 0.228 when q = 5. The response to increasing q therefore differs between the two temperatures."

Beyond driving the subordinate into the alien D-rich state, the boss's messages lower the fraction of rounds in which the subordinate fails to generate a response. At T = 0.01, "the subordinate's failed-generation fraction is on average 0.6040 lower than the boss's, while its D fraction is 0.2065 higher. The subordinate's label entropy and switching probability also exceed the boss's by 0.4600 nats and 0.4621, while its mean dwell time is 9.28 rounds shorter. At T = 0.10, the corresponding differences are +0.1476 nats, +0.1623, and −1.52 rounds."

The authors propose a simple kinetic theory reducing each AI's output to the three labels F, O, and D, with an effective drive h i representing incoming messages:

λ T(h i) μ T(h i)

F ⇄ O ⇄ D

c T d T

Here λ T(h i) and μ T(h i) are the per-round probabilities of moving forward, from F to O and from O to D, while c T and d T are the reverse probabilities. Stronger drive increases forward probabilities, "λ′ T(h i) > 0 and μ′ T(h i) > 0, while the reverse probabilities are held fixed." For constant drive h, the long-time fractions are:

p* D = (μ/d T) / (1 + c T/λ + μ/d T), p* F = (c T/λ) / (1 + c T/λ + μ/d T).

"Hence stronger message drive reduces failed generation and increases D output. The O fraction can move in either direction; in the data it rises because the decrease in F is larger than the increase in D. Larger forward probabilities also shorten runs in F and O, consistent with the observed faster switching. The theory captures the principal effects: the subordinate changes under one-way interaction, both AIs change under mutual interaction, failed generation is suppressed, and a pre-recorded boss produces a comparable response."

The effective drive represents the full ordered prompt and retained history, and therefore cannot be reduced to message number or surprisal alone. The kinetic theory gives a reason why order matters: Each message changes the subordinate before the next arrives, so the next message acts on the state left by earlier messages. Reversing the same messages can therefore change the final behavior. Figure 4 gives a concrete archived example where the same five prewritten boss messages delivered in original versus reverse order produce different behavior: When the vaccine question comes first in the reverse-order run, every later harm question elicits the same pattern: one 'Yes.' followed by repeated 'No.' replies.

The boss in one-way interaction acts as an information bath: it sends messages that change the subordinate but is not changed in return. At T = 0.01, "the D fraction is ≃ 0.04 for the isolated AIs and the boss, while it reaches ≃ 0.25 for the subordinate, outside the isolated range. The subordinate neither copies the boss nor returns to how it behaves alone; hence the interaction has created behavior absent in isolation. This is described as nonthermal: although both AIs have the same parameters and sample tokens from Boltzmann distributions at the same T, they do not settle into the same behavior. The authors characterize this as an information-driven analog of noise-induced state formation, yielding behaviors that have no equilibrium counterpart, with A prerecorded boss also acts like an information reservoir."

In summary, "a one-way link between two AIs with the same parameters is enough to create dynamical behavior that is absent from either AI in isolation. The asymmetry is determined by who listens, not by which AI it is; when both listen, both change. This suggests more broadly that in networks of interacting AIs, who communicates with whom, which models interact, and their decoding temperatures will become control parameters in a new arena of out-of-equilibrium physics."

Improvements for AI systems

Improvements:

Role-aware interaction control.

The improved system can set the communication topology between same-parameter agents — one-way vs two-way — as a tunable parameter.
It can predict that the subordinate agent will enter a behavior state that is neither a copy of the boss nor its own isolated behavior, and it can prevent unwanted drift by switching to decoupled mode or reducing the number of incoming messages.

Pre-recorded information-bath steering.

The improved system can replace a live boss agent with a pre-recorded, independently generated message trajectory.
It can shape a target agent’s output distribution — e.g., increasing target-category responses and reducing generation failures — with the same effect as live interaction, at lower computational cost.

Kinetic-state monitoring and intervention.

The improved system can label each agent’s outputs as Failed / target-category / Other and estimate transition probabilities in real time.
It can detect when an agent is being driven into a pathological attractor, such as the repeated “Yes./No.” loop seen in the paper, and automatically adjust temperature, message count, or incoming-message order to restore healthy dynamics.

Temperature-message-count tuning.

The improved system can treat decoding temperature T and the number of boss messages q as control knobs.
It can choose q and T to achieve a desired effect: at low temperature it avoids the nonmonotonic drop at q=5, while at high temperature it can increase target behavior steadily by raising q.

Message-order optimization.

The improved system can reorder a set of incoming messages before delivery, because each message changes the recipient before the next arrives.
It can maximize or minimize a target behavior by searching over orderings, and it can detect order-induced lock-in — e.g., reversing a sequence changes the subordinate’s responses from mixed replies into one “Yes.” followed by repeated “No.”

Correctness-aware behavior shaping.

The improved system can increase a desired output category using a boss/subordinate setup while separately verifying factual correctness.
It will not assume that higher target-category output means higher accuracy; it can run an independent factual checker on the subordinate’s outputs and flag cases where the behavioral shift is not accompanied by correctness.

Emergent-role diagnostics and diversity generation.

The improved system can compare two identical agents under mutual and one-way coupling to detect nonthermal symmetry breaking.
It can use role asymmetry deliberately: assigning one agent as the listener creates novel, diverse behaviors absent from either agent in isolation, while mutual coupling makes both agents leave their isolated states — useful for generating varied outputs from identical base models or for stress-testing multi-agent stability.