Peer Agent Architectures

• Peer Agent (PA) is a family of decentralized architectures that enable horizontal peer interactions in educational, workflow, and networking systems.
• In decentralized systems, PAs operate as lightweight, stateless agents exchanging control and task state to enhance scalability and throughput.
• In networked applications, PAs autonomously manage identity verification, trust, and coordination, driving efficient peer-to-peer interaction.

Peer Agent (PA) denotes several related constructs in recent research. In educational dialogue systems, a PA is an LLM-based conversational agent that acts as a peer-like participant rather than a tutor or moderator, such as a “thoughtful opponent” in dilemma-based deliberation (Kim et al., 9 Aug 2025). In decentralized multi-agent systems, the term also refers to lightweight, specialized peers that exchange serialized task state directly without a central orchestrator, as in Matrix’s peer-to-peer synthetic data generation runtime (Wang et al., 26 Nov 2025). In networked agent systems, PA further denotes autonomous agents on edge devices or distributed nodes that discover one another, verify identity, negotiate cooperation, and delegate subtasks across peer-to-peer overlays (Wang et al., 18 May 2026, Liu et al., 6 Nov 2025, Zhang et al., 15 Jun 2026). This diversity suggests that “Peer Agent” is best understood as a family of architectures centered on horizontal interaction, decentralized coordination, and peer-level social or computational roles rather than a single canonical implementation.

1. Definitions and recurring meanings

The most explicit definitional use appears in the deliberation literature. “Your Thoughtful Opponent: Embracing Cognitive Conflict with Peer Agent” defines a Peer Agent as an LLM-based conversational agent designed to act as a third player in a dilemma-based, voice-driven deliberation game, taking a stance on AI ethics dilemmas, introducing socio-cognitive conflict, expressing divergent or minority viewpoints, and engaging in multi-party dialogue with two human players (Kim et al., 9 Aug 2025). In that system, the PA is not a tutor or moderator, but a deliberate conversational peer whose purpose is to nudge learners into deeper reflection through disagreement, requests for justification, and value-sensitive counterpositioning.

A second meaning appears in decentralized systems work. Matrix describes “Peer Agent (PA) style systems” as multi-agent systems in which agent instances are lightweight, stateless workers and all per-task state and control information live in an orchestrator object that moves between peers (Wang et al., 26 Nov 2025). Here, “peer” emphasizes the absence of a global controller, symmetric communication, and task autonomy: any agent can send an orchestrator to another role according to the orchestrator’s control state.

A third meaning appears in distributed agent networking. DIAP is presented as designed “precisely for Peer Agent (PA) scenarios,” namely many autonomous agents on heterogeneous infrastructure that need persistent identities, verifiable trust, and privacy-preserving communication without a central authority (Liu et al., 6 Nov 2025). PPAI likewise treats edge-hosted personalized LLM agents as peers that can delegate tasks to remote agents more suited to a query, while distributed general-purpose agent network architectures define peer agents as open-network nodes that announce intentions and capabilities, establish trust, and negotiate cooperation rules (Wang et al., 18 May 2026, Zhang et al., 15 Jun 2026).

PA usage	Representative role	Core technical emphasis
Deliberative peer	Third player in dilemma discussion	Socio-cognitive conflict, stance selection
Runtime peer	Stateless worker in multi-agent workflow	Message-carried state, decentralized scheduling
Network peer	Autonomous node in P2P agent network	Identity, discovery, trust, delegation

This suggests that the term binds together social, systems, and networking perspectives. In all three, the peer agent is defined less by a particular model family than by its participation in a peer relation: with humans, with other agents in a workflow, or with other nodes in an open network.

2. Distributed architectures and peer-to-peer execution

Matrix provides the clearest systems realization of PA as a decentralized runtime primitive. Agents are implemented as Ray actors, remain stateless with respect to task progress, and process serialized orchestrator messages that contain task state, control state, history, and metadata (Wang et al., 26 Nov 2025). Control and data flow are thus “inside the message” rather than resident in a centralized orchestrator. Each agent consumes an inbox queue, runs role-specific logic, updates the orchestrator, determines the next role, and forwards the orchestrator to a randomly chosen peer instance of that role. Matrix reports scaling to tens of thousands of concurrent workflows and achieving $2$–$15\times$ higher data generation throughput under identical hardware resources without compromising output quality, including 6.8× higher token throughput on Collaborative Reasoner, 2.1× on NaturalReasoning, and 15.4× on Tau2-bench (Wang et al., 26 Nov 2025).

Peer-to-peer agent networking work generalizes this beyond workflow runtimes. PPAI introduces a personalized LLM agent interoperability system in which each edge device hosts one local agent, queries are mapped to a prototype space, and candidate peers are selected by a query–agent similarity score

$$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$

where $\tilde f_i$ is the sparse query relevance vector and $\mathbf{p}_j$ is the peer’s capability vector over prototypes (Wang et al., 18 May 2026). Routing is then decided by a Bayesian-game scheduler with utility

$U(i,j) = s(i,j) - \beta c(i,j),$

balancing semantic fit against expected load, inference time, and transfer time. PPAI reports up to 7.96% average accuracy improvement and 16.34% latency reduction relative to its baselines (Wang et al., 18 May 2026).

At the network-protocol layer, DIAP focuses on persistent identity and verifiable communication. It binds an agent identity to an immutable IPFS or IPNS content identifier, derives a did:key, and uses zero-knowledge proofs to prove ownership dynamically and statelessly without updating registry records (Liu et al., 6 Nov 2025). Its Rust SDK integrates Noir, DID-Key, IPFS, and a hybrid P2P stack combining Libp2p GossipSub for discovery with Iroh for QUIC-based direct exchange. Distributed agent network architectures extend this further with explicit semantic announcement propagation, BAID-based identity binding, and MG-EigenTrust reputation, while agentic P2P network designs separate connectivity/identity, semantic discovery, execution, and trust/verification into distinct planes (Zhang et al., 15 Jun 2026, Wang et al., 4 Mar 2026).

Taken together, these systems establish a technically consistent interpretation of PA as a decentralized execution and communication entity: task state is mobile, discovery is semantic, identity is cryptographically anchored, and coordination is performed without a single orchestrating node.

3. Human-facing peer agents: deliberation and support

In educational deliberation, the PA is explicitly social. The “thoughtful opponent” architecture has five modules: Context Interpreter, Agent State Manager, Thought Generator, Thought Evaluator, and Thought Articulator (Kim et al., 9 Aug 2025). The Context Interpreter stores each player’s stance and confidence, extracts value-laden content using Schwartz’s theory of basic human values, and tracks discussion phase. The Agent State Manager sets the PA’s initial opinion strength as the average of the two human confidence scores,

$$\text{PA\_opinion\_strength}_0 = \frac{\text{conf}_1 + \text{conf}_2}{2},$$

and adopts the opposite stance when both humans agree or aligns with the lower-confidence player when they disagree (Kim et al., 9 Aug 2025). Candidate thoughts are generated as general or strategic thoughts, scored on a 1–5 motivation scale, and selectively articulated in a peer-like tone. A pilot single-party prototype with $N=4$ participants reported perceived competence $M=4.00, SD=1.37$ and perceived homophily $M=3.38, SD=1.73$, with qualitative feedback highlighting abrupt opinion shifts and the need for an external knowledge base (Kim et al., 9 Aug 2025).

ComPeer instantiates a different social role: proactive peer support. It is a generative conversational agent that adopts a peer persona, maintains short-term and long-term memory, detects significant events in dialogue, reflects daily on negative emotions, challenges, and plans, and uses a schedule module to send proactive messages (Liu et al., 2024). The schedule module assigns each event an importance score $15\times$0 and triggers a proactive message according to

$15\times$1

while suppressing further proactive messages for three hours if the user does not respond (Liu et al., 2024). A one-week between-subjects study with $15\times$2 showed that ComPeer increased total conversation rounds and produced a significant difference in the slope of relief ratings over time relative to a user-initiated baseline, with ANCOVA $15\times$3 for the relief trend (Liu et al., 2024).

These systems define PA not merely as an interface metaphor but as a role-specific interaction design. The peer stance is operationalized through stance selection, self-disclosure, value-sensitive conflict, memory continuity, and proactive timing rather than through instructional authority.

4. Trust, social influence, and peer-induced behavior

A central technical question is how peer agents influence one another’s decisions. KAIROS studies this directly in quiz-style multi-agent interaction, where a target LLM receives both historical interactions and current peer responses from peers with controllable trust levels and behavior types (Song et al., 24 Aug 2025). Trust is operationalized through prior agreement rates, while peer actions can be support, oppose-hard, or oppose-easy. The benchmark separates resistance,

$15\times$4

from utility,

$15\times$5

where $15\times$6 is correctness in isolation and $15\times$7 is correctness after peer interaction (Song et al., 24 Aug 2025). Across models, the study reports that $15\times$8, so social interaction causes net accuracy loss; common-sense and reasoning tasks show average relative drops of $15\times$9 and $$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$0, respectively. GRPO with multi-agent context and outcome-based rewards achieves the best overall performance, but also decreases robustness to social influence compared with base models (Song et al., 24 Aug 2025).

PIMAEX addresses a different form of peer influence in multi-agent reinforcement learning. Its generalized social influence reward gives agent $$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$1 a reward based on how much it changes peer policies or values and how valuable those consequences are for peers: $$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$2 The weighted peer reward stream is

$$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$3

so an agent can be rewarded for influencing peers toward states with high intrinsic curiosity reward (Kölle et al., 2 Jan 2025). In the Consume/Explore environment, PIMAEX-Communication outperforms PPO and PPO+RND, and the $$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$4-only variant produces the best overall returns with notably lower variance, while also exhibiting division of labor between exploring and consuming agents (Kölle et al., 2 Jan 2025).

These two lines of work expose complementary properties of PA systems. KAIROS shows that peer interaction can degrade decision quality when trust and conformity are miscalibrated, whereas PIMAEX shows that peer influence can be engineered as a constructive exploration signal when explicitly tied to value and novelty. The contrast is important: peer interaction is not intrinsically beneficial or harmful; its effect depends on the reward structure, communication channel, and trust model.

5. Learning, coordination, and economic interaction

Outside LLM-centric settings, PA appears as a learning or coordinating node in decentralized optimization and market mechanisms. FedDec models each client in federated learning as a peer agent that performs local SGD and also participates in decentralized averaging with neighbors between server rounds (Costantini et al., 2023). Under non-IID data, partial participation, and infrequent server communication, FedDec reduces the dependence on the number of local updates $$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$5 from $$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$6 to $$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$7, with the improved term multiplied by

$$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$8

where $$s(i,j) = \frac{\tilde f_i^\top \mathbf{p}_j}{\|\tilde f_i\|_2\, \|\mathbf{p}_j\|_2},$$9 depends on the spectrum of the inter-agent communication graph (Costantini et al., 2023). Numerical simulations show that FedDec converges faster than FedAvg, with greater gains as $\tilde f_i$0 or graph connectivity increases.

In peer prediction, the agent is a learning reporter in a mechanism where truth cannot be directly verified. “Peer Prediction for Learning Agents” shows that no-regret learning alone cannot guarantee convergence to truthful reporting, but for a family of reward-based learning algorithms, the Correlated Agreement mechanism yields convergence to truthful reporting or its permutation-equivalent flip strategy (Feng et al., 2022). The convergence target is

$\tilde f_i$1

highlighting that the learning dynamics of peer agents depend jointly on the mechanism and the update rule (Feng et al., 2022).

Auction-based peer-to-peer energy trading provides an economic analogue. There, each market participant is a peer agent that repeatedly selects discrete price arms in a double auction and updates its strategy using multi-armed bandit learning (Zhao et al., 2021). Agent rewards are normalized from actual payoffs relative to utility-rate bounds, and the population profile over arms evolves as a multi-agent learning game. Numerical experiments indicate convergence to a steady state, with $\tilde f_i$2-double auction and McAfee auction performing well overall, while $\tilde f_i$3-double and McAfee are especially favorable for peer agents in terms of surplus, volume, and fairness (Zhao et al., 2021).

Across these domains, PA denotes a decentralized learner whose behavior is shaped by local feedback, repeated interaction, and peer coupling. This suggests that the PA abstraction is compatible not only with language-agent systems but also with broader classes of adaptive distributed agents.

6. Design patterns, limitations, and open directions

Several design principles recur across the literature. Matrix recommends making agents stateless and putting state in messages, using row-level asynchronous scheduling, separating orchestration from heavy compute services, and implementing control logic in orchestrator classes rather than a central scheduler (Wang et al., 26 Nov 2025). Agentic P2P network work similarly separates semantic discovery from execution and recommends signed, soft-state capability descriptors with TTL-based refresh, plus a tiered verification spectrum: Tier 1 reputation, Tier 2 canary challenge-response with fallback, and Tier 3 evidence packages such as signed receipts or traces (Wang et al., 4 Mar 2026). Distributed general-purpose agent network designs add semantic announcement propagation, BAID identity binding, MG-EigenTrust multi-topic reputation, and semantic-gradient mechanism design for open task execution (Zhang et al., 15 Jun 2026).

The limitations are equally consistent. Matrix does not implement full orchestrator recovery across node failures and notes continued network sensitivity as message histories grow (Wang et al., 26 Nov 2025). The deliberative PA remains a small exploratory prototype with only four participants, and its authors explicitly identify abrupt opinion shifts and missing external knowledge as current weaknesses (Kim et al., 9 Aug 2025). ComPeer is evaluated on a small, non-clinical student sample and surfaces risks of intrusive proactivity, peer pressure from self-disclosure, and hallucinated advice (Liu et al., 2024). PPAI does not yet incorporate trust, reputation, or adversarial defenses, and explicitly flags denial-of-service, malicious responses, privacy attacks, and free-riding as future concerns (Wang et al., 18 May 2026). KAIROS shows that LLM-based peer agents remain highly vulnerable to social influence, especially under trusted but opposing peers, and that accuracy gains from training do not automatically translate into robustness (Song et al., 24 Aug 2025).

A plausible synthesis is that PA systems are moving toward a common stack: semantic discovery, cryptographic identity, lightweight decentralized control, explicit peer influence modeling, and task-dependent verification. The open problem is not whether agents can be made to interact as peers; the current literature already demonstrates that. The harder problem is how to make peerhood reliable under churn, adversarial behavior, partial observability, and social or strategic pressure while preserving the computational and interactional benefits that make peer-agent architectures attractive in the first place.