This paper presents MaVEn, an innovative Multi-granularity Visual Encoding framework designed to enhance the capabilities of Multimodal Large Language Models (MLLMs) in multi-image reasoning. Current MLLMs primarily focus on single-image visual understanding, limiting their ability to interpret and integrate information across multiple images.

Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superior performance in complex environments. We specifically focus on QMIX, the current state-of-the-art in this domain. We show that the representation constraints on the joint action-values introduced by QMIX and similar methods lead to provably poor exploration and suboptimality. Furthermore, we propose a novel approach called MAVEN that hybridises value and policy-based methods by introducing a latent space for hierarchical control. The value-based agents condition their behaviour on the shared latent variable controlled by a hierarchical policy. This allows MAVEN to achieve committed, temporally extended exploration, which is key to solving complex multi-agent tasks. Our experimental results show that MAVEN achieves significant performance improvements on the challenging SMAC domain.

This dual approach bridges the semantic gap between visual and textual data, thereby improving the model's ability to process and interpret information from multiple images effectively.

Generalization across Agentic tool-calling environments remains a key unsolved challenge in developing reliable agentic reasoning systems. While large language models (LLMs) demonstrate strong performance on isolated benchmarks, their ability to transfer reasoning strategies and co-ordinate tools across diverse domains is poorly understood. In this work, we conduct a large-scale evaluation of state-of-the-art LLMs on multiple tool-calling benchmarksBFCL v3, TauBench, Tau2Bench, and AceBenchand introduce MAVEN (Math & Physics Adversarial Verification & Evaluation Network), a new out of distribution (OOD) benchmark designed to stress-test multi-step reasoning through explicit verification and adversarial task composition. Our results show that most current models achieve below 50% accuracy on MAVEN, revealing a significant generalization gap across tool-use settings. To address this, we present the CoreThink Agentic Reasoner, a framework that augments LLMs with a lightweight symbolic reasoning layer for structured decomposition and adaptive tool orchestration. Without additional training, it generalizes across all benchmarks, achieving state-of-the-art performance with 530% improvements over existing baselines at roughly one-tenth the computational cost.

This dual approach bridges the semantic gap between visual and textual data, thereby improving the model's ability to process and interpret information from multiple images effectively.

Neural Information Processing Systems http://nips.cc/

The Starcraft results also seem fine, but not so strong as it make it obvious that committed exploration is a crucial empirical improvement for QMIX - while MAVEN agents learn faster in 3s5z, the final performance looks the same; MAVEN agents seem to have less variability in final win rate on 5m_vs_6m; and QMIX actually seems to have better final performance on 10m_vs_11m. The results in figure 2 and 4 do however suggest that there may be scenarios where the advantage of MAVEN is higher. Minor comments: 1) line 64 and others: the subscript "qmix" should probably be wrapped in a "\text{}" 2) first eqn in section 3: inconsistency between using subscripts and superscripts, i.e. u_i and u i 3) line 81: perhaps better phrased as: "the *best* action of agent i..." 4) line 86: u_n i - u_ U i? 5) line 87: I was confused by what "the set of all possible such orderings over the action-values" means. Besides a degeneracy when some of the Q values are identical, isn't there only one valid ordering? Or are you just trying to cover that degeneracy? 6) Definition 1: perhaps add an intuitive explanation, e.g. "Intuitively, a Q-function is non-monotonic if the ordering of best actions for agent i can be affected by the other agents action choices at that time step."

The paper presents a new exploration strategy for decentralized MARL that is based on a joint latent variable that is shared between the agent. This paper is a difficult case. While the theoretical insights concerning the difficulty of the exploration problem in decentralized MARL are insightful, the experimental results were not good enough in the original submission to convince the reviewers. The algorithm was only in one case considerably better than the competitor QMix and other baseline comparison were missing. However, in the rebuttal the authors provided much better results as well as additional comparison to Qtrans.

This dual approach bridges the semantic gap between visual and textual data, thereby improving the model's ability to process and interpret information from multiple images effectively. Additionally, we design a dynamic reduction mechanism by for long-sequence continuous features to enhance multi-image processing efficiency. Experimental results demonstrate that MaVEn significantly enhances MLLMs' understanding in complex multi-image scenarios, while also improving performance in single-image contexts.