A novel RAIE taxonomy along four scaling dimensions is proposed, which optimizes the entire thought process through search algorithms and self-verification, and introduces a task-oriented guideline for choosing the best TTS strategy.
This thesis introduces the Multi-Agent LLM (MALLM) framework, which implements and evaluates various decision protocols, namely voting, consensus, and judge decision mechanisms, to simulate multi-agent discussions for conversational task solving and indicates that consensus protocols excel in knowledge-intensive domains while voting and judge protocols are more effective for logic-based tasks.
: Large Language Models (LLMs) currently lack the robust command and decision-making (C&D) capabilities essential for the command and control domain. To address this critical gap, this paper proposes an emergence mechanism that integrates a domain-specialized Chain of Thought (CoT) framework with a Process Reward Model (PRM)-inspired evaluation and inference-time optimization paradigm. We construct a novel Chain of Command and Decision (CoCD) framework, a C2-specific CoT structure with contextual persistence, knowledge accumulation, and a human-in-the-loop feedback loop, and define a four-dimensional PRM-inspired evaluation framework for process-level assessment of C&D reasoning. Experimental evaluations on 40 C&D scenarios of varying complexity demonstrate that the CoCD framework significantly outperforms direct prompting (Mann–Whitney U = 1314, p < 0.0001, Cohen’s d = 1.340) and Standard-CoT ( p = 0.005, d = 0.606) in composite performance. PRM-guided Best-of-N selection further improves performance by 5.8% over single-sample CoCD ( p < 0.001, d = 0.855), providing direct empirical evidence for the utility of process-aware reward signals at inference time. CoCD’s structural advantage is greatest in high-uncertainty, structurally ambiguous scenarios (Level 3 gap: + 0.925 points), revealing a complexity-type effect that informs the deployment scope of structured CoT frameworks. These findings provide empirical support for domain-specialized structured reasoning and process-level evaluation as foundations for future RL-based C&D capability development in LLMs.
Yazhi Zheng, Xiao-Long Cui, Xin Wang et al.· Computers, Materials & C...· 0 citations
Pro-LONG, a minimal context management framework built around programmatic memory for LLM agents in long-horizon, exploratory settings, is proposed, which addresses the tradeoff of preserving more information makes retrieving relevant details less tractable.
A. Fox, Junlin Wang, P. Rosu et al.· 2 citations· ⚡1
PoTRE (Poly-Topological Reasoning Ensembles), a heterogeneous framework that decouples inference into four agents that achieves improved reasoning performance using similar or fewer inference tokens compared to heavily scaled homogeneous baselines is introduced.
This project explores the Countdown arithmetic reasoning task: given a set of numbers, produce an arithmetic expression that evaluates to a target value on the Qwen 2.5-0.5B base model and proposes two complementary extensions targeting these failure modes.
This paper investigates the performance of Large Language Models (LLMs) as autonomous agents in the Hearthstone digital collectible card game. Although traditional numerical agents have demonstrated strong results in competitive game environments, the reasoning capabilities of LLM-based agents remain largely unexplored in this context. To address this gap, we developed an LLM-driven Hearthstone agent using the Sabberstone framework to evaluate several models, including GPT-4o, GPT-4o-mini, o3-mini, and GPT-5-mini, across multiple decks and prompting strategies. Our experiments compare their win rates against established numerical agents and analyze the impact of different prompting techniques, such as Chain-of-Thought (CoT), Reverse Chain-of-Thought (RCoT), ReAct, and Directed Acyclic Graph (DAG) prompting. The results indicate that all evaluated LLMs outperform the random baseline, and GPT-5-mini achieves win rates close to the strongest numerical agents under our evaluation setting. Among the prompting strategies, DAG achieved the highest win rate, while ReAct performed significantly worse. These findings highlight both the current limitations and the promising potential of LLMs for strategic reasoning and decision-making in complex, rule-based digital games such as Hearthstone. Leveraging their natural language understanding capabilities, LLMs have the potential to act as intelligent virtual companions, guiding and supporting players through the initial phases of gameplay.
Christian Poglitsch, Philipp Bardakji, Johanna Pirker· International Conference on...· 0 citations