BRAID: Bounded Reasoning for Autonomous Inference and Decisions

📝 Abstract

Large Language Models (LLMs) exhibit nonlinear relationships between performance, cost, and token usage. This paper presents a quantitative study on structured prompting using BRAID (Bounded Reasoning for Au tonomous Inference and Decisions) across multiple GPT model tiers, eval uated on the AdvancedIF, GSM-Hard, and the SCALE MultiChallenge benchmark datasets. BRAID introduces a bounded reasoning framework using Mermaid-based instruction graphs that enable models to reason struc turally rather than through unbounded natural-language token expansion. We show that structured machine-readable prompts substantially increase reasoning accuracy and cost efficiency for agents in production systems. The findings establish BRAID as an effective and scalable technique for optimizing inference efficiency in autonomous agent systems. All datasets and detailed result logs are available at https://benchmark.openserv.ai .

💡 Analysis

Large Language Models (LLMs) exhibit nonlinear relationships between performance, cost, and token usage. This paper presents a quantitative study on structured prompting using BRAID (Bounded Reasoning for Au tonomous Inference and Decisions) across multiple GPT model tiers, eval uated on the AdvancedIF, GSM-Hard, and the SCALE MultiChallenge benchmark datasets. BRAID introduces a bounded reasoning framework using Mermaid-based instruction graphs that enable models to reason struc turally rather than through unbounded natural-language token expansion. We show that structured machine-readable prompts substantially increase reasoning accuracy and cost efficiency for agents in production systems. The findings establish BRAID as an effective and scalable technique for optimizing inference efficiency in autonomous agent systems. All datasets and detailed result logs are available at https://benchmark.openserv.ai .

📄 Content

BRAID: Bounded Reasoning for Autonomous Inference and Decisions Arma˘gan Amcalar ∗ Chief Technology Officer OpenServ Labs armagan@openserv.ai Eyup Cinar † Computer Engineering Department Eskisehir Osmangazi University eyup.cinar@ogu.edu.tr Abstract Large Language Models (LLMs) exhibit nonlinear relationships between performance, cost, and token usage. This paper presents a quantitative study on structured prompting using BRAID (Bounded Reasoning for Au- tonomous Inference and Decisions) across multiple GPT model tiers, eval- uated on the AdvancedIF, GSM-Hard, and the SCALE MultiChallenge benchmark datasets. BRAID introduces a bounded reasoning framework using Mermaid-based instruction graphs that enable models to reason struc- turally rather than through unbounded natural-language token expansion. We show that structured machine-readable prompts substantially increase reasoning accuracy and cost efficiency for agents in production systems. The findings establish BRAID as an effective and scalable technique for optimizing inference efficiency in autonomous agent systems. All datasets and detailed result logs are available at https://benchmark.openserv.ai . 1 Introduction Large Language Models (LLMs) have achieved remarkable success on many NLP tasks, especially as their scale reaches hundreds of billions of parameters. Although each newer model is marketed with advanced reasoning capabilities, their cost efficiency remains a bottleneck for many companies and practitioners. Early research work demonstrated that large language models can be prompted to perform new tasks without gradient updates by providing task descriptions or examples in natural language. An influential example is GPT-3 (175B parameters), which demonstrated that in-context learning via few-shot prompting can achieve strong performance on diverse NLP tasks using only text demonstrations instead of fine-tuning Brown et al. (2020). In this standard prompting paradigm, a model is given either zero examples (zero-shot) or a handful of input-output examples (few-shot) before a query, and the model is expected to infer the pattern and produce the correct output. GPT-3’s few-shot results showed that scaling up the model size produces impressive zero- and few-shot reasoning abilities in translation, question answering, and even simple arithmetic without task-specific training. However these in-context learning strategies showed shallow reasoning with limitations on complex reasoning tasks. This spurred the development of new prompting strategies, especially elicited step-by-step reasoning from LLMs. 1.1 Chain-of-Thought Prompting and Unstructured Reasoning Chain-of-Thought (CoT) prompting is a landmark prompting strategy that elicits interme- diate reasoning steps before the final answer. In CoT prompting, the few-shot exemplars are augmented with explicit step by-step solutions (“thoughts”) instead of just input–output pairs. Wei et al. (2022) showed that even a handful of such worked examples can dramati- cally improve performance on arithmetic, commonsense, and symbolic reasoning tasks. ∗Work performed while at OpenServ Labs †Work performed while at OpenServ Labs as AI Research Partner 1 arXiv:2512.15959v1 [cs.CL] 17 Dec 2025 After CoT’s introduction, researchers discovered LLMs can produce reasoning steps even without example demonstrations. Kojima et al. (2022) found that simply appending a prompt like “Let’s think step by step” to the query triggers many language models to gen- erate a coherent chain of thought in a zero-shot setting. This Zero-Shot CoT approach revealed that LLMs are “decent zero-shot reasoners” when encouraged to articulate multi- step solutions, often dramatically improving accuracy over direct answers (e.g. boosting GPT-3’s math word problem accuracy from 10% to 40% on GSM8K). These findings under- scored that even without explicit training, large models harbor latent reasoning capabilities that can be unlocked by an appropriate prompt. Despite CoT’s success, its free-form reasoning traces can sometimes be incorrect or subop- timal. One mitigation is to sample multiple distinct chains of thought and aggregate their answers – the Self Consistency decoding strategy Wang et al. (2022). Rather than relying on a single CoT, self-consistency samples a diverse set of reasoning paths and then takes a majority vote or consensus on the final answer. However, this approach requires multiple input and output model query turns and can be operationally more expensive with respect to single run model with a prompting strategy. As pointed out by Sprague et al. (2024), entire new paradigms, possibly involving external symbolic tools or computations, will be needed to extend reasoning improvements to the full range of LLM applications. 1.2 Structured and Enhanced Prompting Approaches Researchers have developed more structured prompting strategies to further improve or gen- eralize chain-of-thought reasoning. These methods introduce addition

This content is AI-processed based on ArXiv data.