Agent 1's action is resolved first. Figure 8: An example of Agent 1 using the "clean" action while facing East. The "main" beam extends directly in front of the agent, while two auxiliary A beam stops when it hits a dirty river tile. The Sequential Social Dilemma Games, introduced in Leibo et al. [2017], are a kind of MARL All of these have open source implementations in [Vinitsky et al., 2019]. The cleaning beam is shown in Figure 8a.

Can democratized information gatekeepers and community note writers effectively decide what scientific information to amplify? Lacking domain expertise, such gatekeepers rely on automated reasoning agents that use RAG to ground evidence to cited sources. But such standard RAG systems validate summaries via semantic grounding and suffer from "methodological blindness," treating all cited evidence as equally valid regardless of rigor. To address this, we introduce VERIRAG, a post-retrieval auditing framework that shifts the task from classification to methodological vulnerability detection. Using private Small Language Models (SLMs), VERIRAG audits source papers against the Veritable taxonomy of statistical rigor. We contribute: (1) a benchmark of 1,730 summaries with realistic, non-obvious perturbations modeled after retracted papers; (2) the auditable Veritable taxonomy; and (3) an operational system that improves Macro F1 by at least 19 points over baselines using GPT-based SLMs, a result that replicates across MISTRAL and Gemma architectures. Given the complexity of detecting non-obvious flaws, we view VERIRAG as a "vulnerability-detection copilot," providing structured audit trails for human editors. In our experiments, individual human testers found over 80% of the generated audit trails useful for decision-making. We plan to release the dataset and code to support responsible science advocacy.

Effective surveillance of hand, foot and mouth disease (HFMD) requires forecasts accounting for epidemiological patterns and contextual drivers like school calendars and weather. While classical models and recent foundation models (e.g., Chronos, TimesFM) incorporate covariates, they often lack the semantic reasoning to interpret the causal interplay between conflicting drivers. In this work, we propose a two-agent framework decoupling contextual interpretation from probabilistic forecasting. An LLM "event interpreter" processes heterogeneous signals-including school schedules, meteorological summaries, and reports-into a scalar transmission-impact signal. A neuro-symbolic core then combines this with historical case counts to produce calibrated probabilistic forecasts. We evaluate the framework on real-world HFMD datasets from Hong Kong (2023-2024) and Lishui, China (2024). Compared to traditional and foundation-model baselines, our approach achieves competitive point forecasting accuracy while providing robust 90% prediction intervals (coverage 0.85-1.00) and human-interpretable rationales. Our results suggest that structurally integrating domain knowledge through LLMs can match state-of-the-art performance while yielding context-aware forecasts that align with public health workflows. Code is available at https://github.com/jw-chae/forecast_MED .

Though our results about the externality induced by Nash Welfare appear superficially similar to Gross Substitutes, we cannot find a formal connection. Our work considers the pure allocations problem with additional diversity constraints. 's constraint and remove agent The lower bound follows by extending Theorem 3. We first present the upper bound. We will assume below that 6 =0, and present the proof for!

We study envy-free policy teaching.

Agents need to put down their previously delivered shelf to be able to pick up a new shelf. Figure 9: Four variations of level based foraging used in this work. Agents can navigate in the environment and attempt to collect food placed next to them. Note that the final variant, Figure 9d, is a fully-cooperative environment. Table 2 contains the hyperparameters used in the experiments.