Mental health issues, particularly depressive disorders, present significant challenges in contemporary society, necessitating the development of effective automated diagnostic methods. This paper introduces the Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents. To enhance the dialogue quality and diagnosis accuracy, we design a psychiatrist agent consisting of a tertiary memory structure, a dialogue control and reflect plugin that acts as ``supervisor'' and a memory sampling module, fully leveraging the skills reflected by the psychiatrist agent, achieving great accuracy on depression risk and suicide risk diagnosis via conversation. Experiment results on datasets collected in real-life scenarios demonstrate that the system, simulating the procedure of training psychiatrists, can be a promising optimization method for aligning LLMs with real-life distribution in specific domains without modifying the weights of LLMs, even when only a few representative labeled cases are available.

Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology. In these models, a central object of interest is the discrete origin-destination matrix which captures spatial interactions and agent trip counts between locations. Existing approaches resort to continuous approximations of this matrix and subsequent ad-hoc discretisations in order to perform ABM simulation and calibration. This impedes conditioning on partially observed summary statistics, fails to explore the multimodal matrix distribution over a discrete combinatorial support, and incurs discretisation errors. To address these challenges, we introduce a computationally efficient framework that scales linearly with the number of origin-destination pairs, operates directly on the discrete combinatorial space, and learns the agents' trip intensity through a neural differential equation that embeds spatial interactions. Our approach outperforms the prior art in terms of reconstruction error and ground truth matrix coverage, at a fraction of the computational cost. We demonstrate these benefits in large-scale spatial mobility ABMs in Cambridge, UK and Washington, DC, USA.

The problem setting would be a good contribution to the literature on crowdsourcing. However, I am not sure that paper is ready for publication for the following reasons: 1) the theoretical part looks not solid, 2) the proposed algorithm (HA) looks not grounded, 3) the results of experiments are not significant. These points are supported below. Lemmas 3,4 are reasonable, however, they cover only very special cases. Specifically, Lemma 3 considers only one agent and Lemma 4 assumes that all agents have the same amount of information (they observed exactly n samples).

The paper revisits the convergence result of multiplicative weighted update (MWU) in a congestion game, due to Kleinberg, Piliouras, and Tardos (STOC 2009), and establishes a connection between MWU and Baum-Welch algorithm in a neat and elegant style. By showing the monotonicity of the potential function in a congestion game, the authors prove that any MWU with linear updating rule converges to the set of fixed points, which is a superset of all the Nash equilibria of the congestion game. The Baum-Eagon inequality offers a new interpretation of the dynamics of the linear updating rule in MWU and their results in congestion games are quite general, and so are the conditions on initialization and isolated Nash equilibrium in a congestion game with finite set of agents and pure strategies. The results in this paper hold for any congestion game irrespective of the topology of the strategy sets by the nature of their game, however, one should emphasize the assumption that, individual earning rates \epsilon_i are bounded above, in order for Baum-Eagon inequality to work in their context.

The paper presents a policy gradient algorithm for a multiagent cooperative problem, modeled in a formalism (CDEC-POMDP) whose dynamics, like congestion games, depend on groups of agents rather than individuals. This paper follows the theme of several similar advances in theis field of complex multiagent planning, using factored models to propose practical/tractable approximations. The novelty here is the use of parameterized policies and training algorithms inspired by reinforcement learning (policy gradients). The work is well-motivated, relevant, and particularly well-presented. The theoretical results are new and important.

Specifically, it shows the connection by defining a new variant of an actor-critic algorithm that performs an exhaustive policy evaluation at each stage (denoted as policy-iteration-actor-critic), together with an adaptive learning rate. Then, under this setting, it is said that the actor-critic algorithm basically minimizes regret and converges to a Nash equilibrium. The paper suggests a few new versions of policy gradient update rules (Q-based Policy Gradient, Regret Policy Gradient, and Regret Matching Policy Gradient) and evaluates them on multi-agent zero-sum imperfect information games. To my understanding, Q-Based Policy Gradient is basically an advantage actor-critic algorithm (up to a transformation of the learned baseline) 3. The authors mention a "reasonable parameter sweep" over the hyperparameters. I'm curious to know the stability of the proposed actor-critic algorithms over the different trials 4. The paper should be proofread again.

I thank the authors for the clarification in their rebuttal. It is even more clear that the authors should better contrast their work with aggregate approaches such as Dan Sheldon's collective graphical models (e.g., Sheldon and Dietterich (2011), Kumar et al. 2013, Bernstein and Sheldon 2016). Part of the confusion came from some of the modeling choices: In equation (1) the travel times added by one station is Poisson distributed?! Poisson is often used for link loads (how many people there are in a given station), not to model time. Is the quantization of time too coarse for a continuous-time model? Wouldn't a phase-type distribution(e.g., Erlang) be a better choice for time? Such modeling choices must be explained.

There is also a large body of work on distributed and decentralized methods from systems and control theory, which should be mentioned in this context (see e.g.

This paper presents an extension of the safe interruptibility (SInt) framework to the multi-agent case. The authors argue that the original definition of safe interruptibility is difficult to use in this case and give a more constrained/informed one called'dynamic safe interruptibility' (DSInt) based on whether the update rule depends on the interruption probability. The joint action case is considered first and it is shown that DSInt can be achieved. The case of independent learners is then considered, with a first result showing that independent Q-learners do not satisfy the conditions of the definition of DSInt. The authors finally propose a model where the agents are aware of each others interruptions, and interrupted observations are pruned from the sequence, and claim that this model verify the definition of DSInt.

This paper extends interaction networks (INs) with an attentional mechanism so that it scales linearly (as opposed to quadratically in vanilla INs) with the number of agents in a multi-agent predictive modeling setting: the embedding network is evaluated once per agent rather than once for every interaction. This allows to model higher-order interactions between agents in a computationally efficient way. The method is evaluated on two new non-physical tasks of predicting chess piece selection and soccer player movements. The paper proposes a simple and elegant attentional extension of Interaction Networks, and convincingly shows the benefit of the approach with two interesting experiments. The idea is not groundbreaking but seems sufficiently novel, especially in light of its effectiveness.