CREDIT (Counterfactual Replay for Evidence-Driven Information Transfer), a mechanism-aligned multi-agent prompt-optimization algorithm that uses matched hidden-state twins and total-action replay to reward robust causal contribution rather than query frequency is introduced.
Abstract
Cooperation can fail when socially valuable effort is costly, hard to observe, and benefits mainly someone else. Building on Holmstrom's model of moral hazard in teams, the Dialogue Moral Hazard Game instantiates this hidden-action structure as a textual environment for language agents. An agent chooses between keeping an immediate local reward and paying a query cost to reveal a hidden safety fact that helps another agent's downstream decision. We evaluate fourteen open-weight and four frontier models using measures of information acquisition, communication, downstream use, and team success. In matched 3,015-decision-per-model experiments, GPT-5.6 Sol, Claude Opus 4.8, and Nemotron-3 Ultra track the derived private-share boundary across nine query costs, with mean absolute errors of 0.013, 0.030, and 0.024. Muse Spark 1.1 responds directionally, whereas Fable 5 remains query-saturated. SFT, RLOO, SFT+RLOO, and GEPA produce heterogeneous mechanism changes. GEPA raises Muse team success from 22.2% to 100.0% while reducing query use from 51.1% to 0.3%. Frozen-prompt interventions show that this success depends on a learned rank-label mapping rather than direct revelation: changing the mapping reduces team success from 100.0% to 12.5% and then 0.0%. We introduce CREDIT (Counterfactual Replay for Evidence-Driven Information Transfer), a mechanism-aligned multi-agent prompt-optimization algorithm that uses matched hidden-state twins and total-action replay to reward robust causal contribution rather than query frequency. Across five models and multiple seeds, CREDIT preserves query-mediated behavior while revealing model-specific acquisition and downstream-use bottlenecks. Optimization can reach the same aggregate outcome through direct revelation or a learned effective information structure, motivating mechanism-level evaluation and optimization rather than team success alone.
The collaboration tax is formulated as the team-decentralisation loss of a two-player cooperative game with private information, with two propositions characterising its sign and its equivalence to a max-superadditivity violation.
Wei-Xiang Sun, Zehong Wang, Hong Huang et al.· 0 citations
Evaluating three frontier models across six games in homogeneous and heterogeneous groups over 10 rounds, it is found that different models interpret announcements incompatibly, some as binding commitments and others as cheap talk, producing payoff gaps that emerge in Round~0 and persist across all 10 rounds.
Jerick Shi, Terry Jingchen Zhang, Bernhard Scholkopf et al.· 0 citations
Does multi-agent LLM interaction help or hurt? Some work reports gains from debate (Du et al., 2024), critique loops (Chen et al., 2025), and mixture-of-agents synthesis (Wang et al., 2025), while other work finds that interaction adds cost without improving quality under equal budgets (Tran&Kiela, 2026; Xu et al., 2026; Jarrett et al., 2025), or that independent sampling already captures multi-agent gains (Li et al., 2024). We argue this contradiction partly reflects a missing distinction, because not all multi-agent communication is equal. Different model families find structurally different solutions, but when agents read each other's complete outputs, their proposals converge within one round, erasing the diversity that motivates using multiple models. We call this the interaction tax. We test 11 verifier-scored optimization tasks under matched budgets and find that full-solution interaction is a weak default. Independent proposal generation avoids this collapse. Full-solution interaction mainly makes agents stay close to the first solution they see instead of trying different approaches, and critique helps only if the violated rule is easy for the LLM to find and fix. These results suggest that multi-agent performance depends less on the number of agents than on the information they exchange, and interaction helps only when agents share the right information at the right time.
Summer Eunhyung Ann, Haokun Liu, Chen-Hao Tan· 1 citation
We extend the standard Principal-Agent framework to scenarios where the Agent selects from a suite of technologies, each characterized by a distinct cost-capability profile. This framework is increasingly critical in the era of Large Language Models (LLMs), where Agents choose both a model and an associated effort level (e.g., token budget). We model the relationship between output quality and effort as a concave, saturating function, which depends on the Agent's hidden two-dimensional action choice balancing technology selection and effort allocation. We derive the optimal linear contract for the Principal, demonstrating that the Agent's best response is characterized by a threshold reward share that triggers technology switching. Finally, we calibrate our model using open-weight LLM pairings across the MATH and MMLUPro benchmarks. We show that both Principal and Agent, when employing bandit algorithms to navigate this environment, converge to strategies that closely align with our theoretical equilibrium. These results suggest that simple linear contracts can effectively incentivize complex, technology-aware delegation in agentic workflows.
This thesis introduces the Multi-Agent LLM (MALLM) framework, which implements and evaluates various decision protocols, namely voting, consensus, and judge decision mechanisms, to simulate multi-agent discussions for conversational task solving and indicates that consensus protocols excel in knowledge-intensive domains while voting and judge protocols are more effective for logic-based tasks.
Training LLM-based multi-agent systems with multi-agent reinforcement learning with multi-agent reinforcement learning is rapidly gaining traction, and a parallel line of work argues that such systems should be judged by their behavior, not only their reward.
Yi Mao, Andrew Perrault· 0 citations
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