The LLM Influence Compiler (LIC), a solver–critic pipeline that compiles natural-language cooperation directives into structured, parameterized influence policies defined over a five-field schema, lays the groundwork for constitutional oversight of LLM-mediated influence in artificial societies.
Abstract
Introduction Large language models (LLMs) can generate persuasive narratives that shift agent behavior in multi-agent systems, but deploying raw, unstructured text as an influence mechanism offers no formal guarantees on effectiveness, interpretability, or controllability. Methods We introduce the LLM Influence Compiler (LIC), a solver–critic pipeline that compiles natural-language cooperation directives into structured, parameterized influence policies defined over a five-field schema: network targeting, narrative theme, intensity, deployment timing, and compiler confidence. Each compiled policy is diffused through a network of numerical agents via an exposure model incorporating fatigue decay, susceptibility heterogeneity, and backlash. Evaluation spans nine controlled experiment blocks comprising more than 200 simulation runs across four topologies (Barabási–Albert, small-world, Erdős–Rényi, modular SBM), four network sizes (n ∈ {80, 160, 320, 640}), five LLM backbones, and two non-stationary perturbations. Results Compiled policies raise the mean cooperation rate to 0.826 ± 0.010 (95% bootstrap CI [0.819, 0.834]), an 8.6% relative improvement over the unstructured baseline (0.760 ± 0.005, Mann–Whitney pBonf = 0.040, Cohen's d = 7.90). A controlled decomposition attributes the gain to network-aware targeting (ca. +4.5 pp), dose calibration (+1.9 pp), and an intervention floor (+1.5 pp); the residual contribution of full LLM compilation over a hand-coded rule with the same parameters is statistically indistinguishable from zero under stationary conditions. Under structural non-stationarity (mid-simulation graph rewire), the LLM-mediated re-deployment architecture significantly outperforms a frozen rule (paired t-test p < 0.001, paired Cohen's d = 2.27, 8/8 seeds). Adversarial stress testing confirms that the critic correctly flags 20/20 risky policies as high risk and rejects them, while passing a moderate-baseline policy at medium risk in all 5/5 trials. Discussion The compiler abstraction converts an opaque generative process into a decomposable, auditable policy object whose components can be independently attributed, compared, and governed, laying the groundwork for constitutional oversight of LLM-mediated influence in artificial societies.
We investigate the use of large language models (LLMs) as evolutionary operators for optimizing the architecture of multi-agent systems (MAS) - a representative instance of LLM-driven optimization over attributed-graph artifacts that arise in generative design. Given a task description, an initial MAS configuration is generated and then refined on a per-task basis through two optimization strategies: (1) a population-based evolutionary approach where LLMs serve as mutation, crossover, and selection operators, with an LLM-as-a-Judge ensemble providing the surrogate fitness signal; and (2) a simpler iterative regeneration scheme that uses textual feedback from LLM judges to guide single-solution improvement without maintaining a population. We evaluate both approaches on the GAIA benchmark. Our experiments reveal a critical sensitivity to the judge model's quality: a capable judge (Claude 4 Sonnet) yields +14.3 and +6.67 percentage-point accuracy improvements for evolutionary and iterative approaches, respectively, whereas a weaker judge (Gemini-2.5-Flash) provides no benefit or even degrades performance below the unoptimized baseline. Both approaches incur substantial computational costs, raising questions about practical cost-effectiveness. These results highlight fundamental challenges of surrogate fitness functions in LLM-driven evolutionary optimization and offer practical insights for applying evolutionary methods to MAS architecture design.
V. Akhmerov, Dmitry Gilemkhanov, Jerzy Kamiński et al.· Proceedings of the Genetic a...· 0 citations
This review connects ten historical cognitive architectures, eight language-agent runtime families, and forty-two mechanism-focused modern systems to contribute a distinctive-mechanism catalog, an auditable evidence-depth framework, and a falsifiable agenda for testing these bundles as composable runtime invariants.
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.· Cognitive Computation· 0 citations
VISA is presented, a structured, symbol-based description protocol that specifies a model in eight interconnected tables---four at the agent level (Agent, Variable, Sensing, Internal Function) and four at the model level (Associated Data, Input/Output, Schedule, Validation)---under the principle of minimality with completeness.
Roles provide an interpretable interface for organizing language-model agents, yet most multi-agent systems treat them as hand-written prompt labels disconnected from learned behavior and parameter updates. We argue that a useful role should instead be an executable control variable: it should summarize behavior predictive of future utility, guide subsequent interaction, and identify the trainable capacity responsible for that behavior. We introduce ExRole, a trajectory-to-role framework that learns future-aware role prototypes from prefix-local team traces, resolves them into readable instructions and token-aligned role markers, and optionally routes shared LoRA rank slots with turn-aligned credit. Across MuSiQue and 2WikiMultiHopQA, ExRole improves over single-agent search by 15.0/14.4 and 13.5/16.1 EM/F1 points, respectively. Against the strongest non-ExRole controls, the corresponding gains remain 11.5/11.6 and 7.7/9.7 points. Across both benchmarks, the controlled results consistently favor trajectory-induced role conditioning over role-free, manual, random, and shuffled alternatives. Role-Agent-Turn interventions further show that the induced roles capture transferable behavioral specialization beyond fixed agent identities or turn positions.
Zhou Liu, Chaoyang Han, Zewei Pan et al.· 0 citations
This paper investigates the performance of Large Language Models (LLMs) as autonomous agents in the Hearthstone digital collectible card game. Although traditional numerical agents have demonstrated strong results in competitive game environments, the reasoning capabilities of LLM-based agents remain largely unexplored in this context. To address this gap, we developed an LLM-driven Hearthstone agent using the Sabberstone framework to evaluate several models, including GPT-4o, GPT-4o-mini, o3-mini, and GPT-5-mini, across multiple decks and prompting strategies. Our experiments compare their win rates against established numerical agents and analyze the impact of different prompting techniques, such as Chain-of-Thought (CoT), Reverse Chain-of-Thought (RCoT), ReAct, and Directed Acyclic Graph (DAG) prompting. The results indicate that all evaluated LLMs outperform the random baseline, and GPT-5-mini achieves win rates close to the strongest numerical agents under our evaluation setting. Among the prompting strategies, DAG achieved the highest win rate, while ReAct performed significantly worse. These findings highlight both the current limitations and the promising potential of LLMs for strategic reasoning and decision-making in complex, rule-based digital games such as Hearthstone. Leveraging their natural language understanding capabilities, LLMs have the potential to act as intelligent virtual companions, guiding and supporting players through the initial phases of gameplay.
Christian Poglitsch, Philipp Bardakji, Johanna Pirker· International Conference on...· 0 citations