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The paper introduces HARP, a framework for orchestrating heterogeneous agents with hidden preferences by maintaining numeric posterior beliefs updated via Bayes’ rule, rather than embedding beliefs in prompts. HARP achieves ∼O(√K) Bayesian regret and, with the HARP+ variant, adds a bonus for informative actions to keep inference active even when optimal actions are uninformative. Experiments across three problem settings show HARP+ outperforms other non‑oracle methods in scenarios where explicit joint inference is infeasible.
The paper investigates how an informed adversary can influence the optimal signal in a constrained signalling channel. It finds that the adversary‑robust optimum aligns with the salience pole on most items, differing only on a small subset where the salience‑to‑Bayes coordinate is undefined. The study demonstrates that as the adversary’s persuasion budget increases, the optimal signal shifts from a posterior‑maximizing to a margin‑maximizing strategy, and provides a diagnostic check for evaluating adversary‑awareness.
arXiv:2606. 01561v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO).
arXiv:2510. 10813v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly applied to domains that require reasoning about other agents' behavior, such as negotiation, policy design, and market simulation.
The paper introduces a dynamic bipartite matching framework that uses large language model (LLM) agents and contextual bandits to model decentralized, asynchronous matching processes without requiring full preference rankings. In a simulated Chinese marriage market, LLM agents evaluate local candidates while Logistic-UCB models learn reciprocal acceptance, leading to higher mutual welfare and fewer blocking pairs compared to classical Gale–Shapley. The study validates LLM-generated preferences against empirical data and demonstrates gender-differentiated acceptance patterns, supporting the use of decentralized LLM-based matching for economic simulation and computational social science.
Translating natural-language planning intent into verified plans is a longstanding challenge: people communicate goals in language, while classical planners require formal PDDL specifications. Recent agentic frameworks bridge this gap by orchestrating a pool of specialized repair agents inside a verifier-checked refinement loop, but the orchestrator at the centre is itself a prompted frontier LLM, paying a frontier-LLM API call at every refinement step.