Large Language Models (LLMs) are increasingly deployed in real-world applications that demand complex reasoning. To track progress, robust benchmarks are required to evaluate their capabilities beyond superficial pattern recognition. However, current LLM reasoning benchmarks often face challenges such as insufficient interpretability, performance saturation or data contamination. To address these challenges, we introduce GAMEBoT, a gaming arena designed for rigorous and transparent assessment of LLM reasoning capabilities. GAMEBoT decompose complex reasoning in games into predefined modular subproblems. This decomposition allows us to design a suite of Chain-of-Thought (CoT) prompts infused with domain knowledge to guide LLMs in addressing these subproblems before action selection. Furthermore, we develop a suite of rule-based algorithms to generate ground truth for these subproblems, enabling rigorous validation of the LLMs’ intermediate reasoning steps. This approach facilitates evaluation of both the quality of final actions and the accuracy of the underlying reasoning process. GAMEBoT also naturally alleviates the risk of data contamination through dynamic games and head-to-head LLM competitions. We benchmark 17 prominent LLMs across eight games, encompassing various strategic abilities and game characteristics. Our results suggest that GAMEBoT presents a significant challenge, even when LLMs are provided with detailed CoT prompts.

We introduce PruneVid, a training-free visual token pruning method designed to enhance the efficiency of multimodal video understanding. While Large Language Models (LLMs) have shown promising performance on video tasks due to their advanced visual comprehension capabilities, the substantial redundancy inherent in video data poses significant computational challenges. To address this issue, PruneVid (1) reduces intrinsic video redundancy by merging temporally static and spatially similar tokens, and (2) leverages LLMs’ inherent ability to selectively prune visual tokens irrelevant to specific queries, thereby improving model efficiency. We validate our method across multiple video benchmarks, demonstrating that PruneVid can prune over 80% of tokens while maintaining competitive performance when combined with different video LLMs. Our results highlight PruneVid’s superior effectiveness and efficiency compared to existing pruning methods.

Large language models (LLMs) face a significant challenge due to the excessive computational and memory requirements of the commonly used Transformer architecture. While state space model (SSM) is a new type of foundational network architecture offering lower computational complexity, their performance has yet to fully rival that of Transformers. This paper introduces DenseSSM, a novel approach to enhance the flow of hidden information between layers in SSMs. By selectively integrating shallow-layer hidden states into deeper layers, DenseSSM retains fine-grained information crucial for the final output. This incremental improvement maintains the training parallelizability and inference efficiency of SSMs while significantly boosting performance. The proposed method is broadly applicable to various SSM types, including RetNet and Mamba, and DenseSSM achieves significant performance improvements on public benchmarks, demonstrating its effectiveness and versatility.

Speculative decoding emerges as a pivotal technique for enhancing the inference speed of Large Language Models (LLMs). Despite recent research aiming to improve prediction efficiency, multi-sample speculative decoding has been overlooked due to varying numbers of accepted tokens within a batch in the verification phase. Vanilla method adds padding tokens in order to ensure that the number of new tokens remains consistent across samples. However, this increases the computational and memory access overhead, thereby reducing the speedup ratio. We propose a novel method that can resolve the issue of inconsistent tokens accepted by different samples without necessitating an increase in memory or computing overhead. Furthermore, our proposed method can handle the situation where the prediction tokens of different samples are inconsistent without the need to add padding tokens. Sufficient experiments demonstrate the efficacy of our method. Our code will be released later.