Dynamic LLM-Agent Network (DyLAN)

• DyLAN is a dynamic multi-agent LLM architecture that adjusts agent composition, communication topology, and interaction policy in real-time based on input demands.
• It employs reinforcement learning, diffusion generative models, and unsupervised agent scoring to optimize communication efficiency, task accuracy, and robustness.
• Empirical evaluations reveal significant gains in accuracy, reduced redundancy, and effective multi-objective tradeoffs, showcasing its practical potential in diverse applications.

A Dynamic LLM-Agent Network (DyLAN) is a class of multi-agent LLM collaboration architectures characterized by runtime adaptation of agent composition, communication topology, and interaction policy to the demands of each input or environment. DyLAN frameworks move beyond fixed structures or rigid turn-taking, instead incorporating data-driven graph evolution, agent selection, and adaptive message routing, with the goal of optimizing task performance, communication efficiency, and robustness across diverse problem settings. Key conceptual underpinnings include per-task or per-sample topology discovery, unsupervised or RL-driven agent/team selection, and continual alignment of internal knowledge states for deep cognitive synergy.

1. Formal Definition and Model Structure

A DyLAN consists of a pool of agents $\mathcal{V} = \{e_1, \ldots, e_k\}$, typically parameterized LLMs or specialized transformer modules, orchestrated via a time-varying, weighted, and often directed communication graph $\mathcal{G}(t) = (\mathcal{V}, \mathcal{E}(t))$ with adjacency matrix $A(t) \in \mathbb{R}^{k \times k}$. Edges and weights are dynamic, reflecting alignment or utility for each round $t$ of multi-agent dialog.

Edges are constructed by various mechanisms:

• Knowledge alignment: $E(t) = \{(i,j) | d(K_i(t), K_j(t)) \leq \tau\}$, where $K_i(t)$ is agent $i$’s model (embedding) of $j$’s cognitive state and $d(\cdot, \cdot)$ is a learned or fixed distance metric (Zhang et al., 5 Sep 2025).
• Reinforcement learning: Edge probabilities $\theta_i$ parameterize a distribution over graphs, with A2C or other RL variants optimizing for expected downstream utility (Leong et al., 31 Jul 2025).
• Diffusion generative models: Adjacency matrices are sampled through a discrete forward-reverse diffusion process, guided at each denoising step by a proxy reward over discrete/topological candidates (Jiang et al., 9 Oct 2025).

Node definitions vary by system. In OSC (Zhang et al., 5 Sep 2025), each node contains collaborative knowledge models (CKMs) for all peers, allowing for fine-grained modeling of mutual awareness. In AgentNet (Yang et al., 1 Apr 2025), dynamic specialization is realized by retrieving and evolving agent memories, capability vectors, and routing weights. In LLM-based planning approaches (Abe et al., 2 Apr 2025), nodes are STRIPS-style operators induced for environmental status predicates, with edges generated by matching $Add$/$Cond$ relationships among agents.

2. Dynamic Topology Synthesis and Adaptation

DyLAN frameworks employ a range of strategies for synthesizing and adapting communication graphs:

• RL-based graph optimization: Actor–critic architectures maintain policy parameter vectors $\Theta$ that define edge-inclusion probabilities. At each episode, graphs are sampled, evaluated, and used to update policy and value functions. Top candidate graphs are collected and provided to a downstream per-input graph selector (Leong et al., 31 Jul 2025).
• Graph diffusion models: Conditional discrete diffusion models, parameterized by a Graph Transformer, learn to denoise initial random adjacency matrices toward high-reward communication graphs. At each diffusion step, discrete candidate graphs are sampled, proxy-evaluated, and used to steer the reverse diffusion trajectory (Jiang et al., 9 Oct 2025).
• Agent capability adaptation: Agents maintain dynamic capability vectors $c_i^{m}$, updated via local reward signals and experience from prior tasks. This results in a continually evolving agent graph, with edges pruned or strengthened based on observed task performance (Yang et al., 1 Apr 2025).

A common thread is the reliance on sample- or context-aware graph selection, as opposed to “one-size-fits-all” static architectures. Some systems, such as DynaSwarm, use LoRA-parameterized LLMs to score candidate graph structures per input and select the structure with highest predicted utility (Leong et al., 31 Jul 2025).

3. Agent Selection and Specialization Mechanisms

DyLANs incorporate dynamic team or subteam selection, leveraging unsupervised, RL-based, or reward-propagation algorithms:

• Agent Importance Score (AIS): Inference-time optimization via unsupervised peer evaluation. Agents rate predecessors during a staged reasoning process, producing an agent-level contribution score aggregated across the message-passing DAG. Top-$k$ agents according to AIS are retained for the main task-solving phase (Liu et al., 2023).
• Decentralized evolutionary routing: In AgentNet, capability vectors and local skill memories drive assignment of subtasks and the updating of inter-agent routing weights, yielding emergent specialization across the agent pool (Yang et al., 1 Apr 2025).
• LLM-driven operator instantiation: In LLM-mediated planning, agents corresponding to operators are generated recursively via LLM prompts over status vectors or predicate-bases, ensuring coverage and adaptability to current and desired world states (Abe et al., 2 Apr 2025).

Early stopping, dynamic pruning, and consensus-based termination (e.g., stopping once 2/3 of agents agree) are adopted to accelerate convergence and reduce computation, while maintaining task accuracy (Liu et al., 2023).

4. Communication Policies and Knowledge Alignment

In OSC (Zhang et al., 5 Sep 2025), each agent explicitly maintains a set of peer knowledge embeddings $z_{ij}(t)$ that are updated after each utterance. Message routing, content selection, and expression style are dynamically controlled as a function of real-time cognitive gap analysis:

• Cognitive-gap analysis: For agent $i$, $\Delta_{ij}(t) = d(z_{ii}(t), z_{ij}(t))$ (or a richer learned gap vector), quantifies the discrepancy between $i$’s internal plan/state and its understanding of $j$. Thresholds may trigger clarification or modulate communication intensity.
• Communication policy: An action $a_i(t)$ is sampled from a learned policy $\pi_{comm}$, conditioned on agent state (including all peer alignments and task/query context). Policies are optimized via RL, typically with PPO variants to stabilize reward optimization against shaped objectives capturing both task performance and communication cost.

Empirical results indicate that such adaptive policies reduce redundant communication (OSC redundancy 14.2% vs. DyLAN 22.3%), increase conflict resolution efficiency, and converge faster in collaborative multi-turn settings (Zhang et al., 5 Sep 2025).

5. Training, Inference, and Computational Considerations

DyLAN training is typified by multi-stage procedures:

• Offline graph optimization: RL or generative graph search (actor–critic, diffusion, evolutionary) discovers a Pareto-front of high-performing architectures per domain or task type (Leong et al., 31 Jul 2025, Jiang et al., 9 Oct 2025).
• Online structure selection: Lightweight selector modules (often LoRA-tuned LLMs) choose the optimal candidate per input, ensuring minimal additional inference cost (typically a single LLM forward pass plus the multi-agent execution) (Leong et al., 31 Jul 2025).
• End-to-end fine-tuning: In OSC, CKM and policy modules are updated jointly through RL signals shaped by reward, communication cost, and alignment (Zhang et al., 5 Sep 2025). In AgentNet, retrieval-augmented memories and capability updates are triggered after every task completion (Yang et al., 1 Apr 2025).

Complexity is typically $O(N \cdot k^2)$ per round for knowledge gap calculation and communication updates, or $O(n \cdot T_{LLM})$ LLM forward passes per input when deploying the dynamic DAG (Zhang et al., 5 Sep 2025, Leong et al., 31 Jul 2025). Scalability ablations show diminishing returns beyond 5–7 agents, with system performance plateauing (Yang et al., 1 Apr 2025). Memory and runtime efficiency are further enhanced by dynamic agent pruning and early termination on consensus (Liu et al., 2023).

6. Empirical Evaluation and Performance Benchmarks

DyLAN variants have been evaluated across a range of benchmarks:

• Collaborative reasoning and dialogue: OSC achieves AlpacaEval LC win rate of 81.4% (vs. KABB 77.9%), and MT-Bench average score of 9.94 (vs. 9.65), with increased communication efficiency (4.6 rounds, 3.31k tokens vs. DyLAN’s 5.5 rounds, 3.95k tokens), as well as improved redundancy and conflict resolution (Zhang et al., 5 Sep 2025).
• Task-solving and code generation: DyLAN frameworks report gains of up to 25% accuracy on MMLU subject-level tasks via team optimization, and +9.7 points Pass@1 versus strong code generation baselines on HumanEval (Liu et al., 2023).
• Multi-objective optimization: Guided Topology Diffusion (GTD) yields gains of +4.16 points on GSM8K, +5.44 on MATH, and Pareto-dominant cost-accuracy tradeoffs, with robustness to simulated agent failures (drop of only 0.3 points vs. 13 points for baselines) (Jiang et al., 9 Oct 2025).
• Ablation studies confirm the contribution of dynamic selectors, RL-based topology optimization, and knowledge alignment modules, with dynamic graph selection adding 3–4 points over static best graphs (Leong et al., 31 Jul 2025).

The table below organizes core empirical results across DyLAN frameworks:

Framework/Paper	Key Benchmark(s)	Main Outcome(s)
OSC (Zhang et al., 5 Sep 2025)	AlpacaEval, MT-Bench	81.4% win rate, 9.94 avg. score, 14.2% redundancy
AgentNet (Yang et al., 1 Apr 2025)	MATH, APPS, BBH	85.0%/70.6%/86.0% accuracy, emergent specialization
DynaSwarm (Leong et al., 31 Jul 2025)	Crossword, Game-24, BBH	+3–4 pts over static; dynamic selector valuable
GTD (Jiang et al., 9 Oct 2025)	GSM8K, MATH, MMLU	+3–5 pts accuracy, robustness, 2–5x fewer tokens
DyLAN (Liu et al., 2023)	MATH, MMLU, HumanEval	+4.1–25.0% accuracy gain, –30–60% LLM calls

7. Theoretical Properties, Limitations, and Open Issues

Multiple DyLAN architectures offer claims, informal theorems, or empirical observations regarding:

• Adaptability: LLM-driven agent/operator generation ensures, subject to LLM knowledge completeness, backward-chaining coverage for arbitrary status goals in dynamic environments (Abe et al., 2 Apr 2025).
• Unsupervised agent scoring: AIS is empirically well-aligned with exact Shapley-value estimates at much lower cost, supporting scalable agent selection (Liu et al., 2023).
• Generalization and specialization: Evolutionary or memory-augmented adaptation drives agents to cover diverse regions of the capability space, with ablations confirming emergent diversity and specialization (Yang et al., 1 Apr 2025).
• Scalability: Excessive agent or status expansion negatively impacts planning success, motivating selective pruning or expansion heuristics (Abe et al., 2 Apr 2025).
• Limits of centralization: Decentralized schemes (e.g., AgentNet) are more robust to faults and support privacy boundaries between agent owners (Yang et al., 1 Apr 2025).

Known limitations include dependency on proprietary LLM APIs, the sensitivity of consensus/consistency metrics, and open questions regarding fairness and bias in peer-evaluation or rating steps (Liu et al., 2023). Adapting DyLANs to open-source LLMs and non-text agents, and principled integration of richer tool/inference policies, remain subjects of current research.