Tables as Thought: Exploring Structured Thoughts in LLM Reasoning (2025)

Abstract: Large language models’ reasoning abilities benefit from methods that organize their thought processes, such as chain-of-thought prompting, which employs a sequential structure to guide the reasoning process step-by-step. However, existing approaches focus primarily on organizing the sequence of thoughts, leaving structure in individual thought steps underexplored. To address this gap, we propose Table as Thought, a framework inspired by cognitive neuroscience theories on human thought. Table as Thought organizes reasoning within a tabular schema, where rows represent sequential thought steps and columns capture critical constraints and contextual information to enhance reasoning. The reasoning process iteratively populates the table until self-verification ensures completeness and correctness. Our experiments show that Table as Thought excels in planning tasks and demonstrates a strong potential for enhancing LLM performance in mathematical reasoning compared to unstructured thought baselines. This work provides a novel exploration of refining thought representation within LLMs, paving the way for advancements in reasoning and AI cognition.

## introduction recent advancements in reasoning have demonstrated that the reasoning capabilities of large language models (llms) can be enhanced by introducing structure into the reasoning process @xcite @xcite . for instance, the chain-of-thought approach organizes textual reasoning in a step-by-step manner using a linear chain structure @xcite . building on this, following works have shown that incorporating more complex organizational structures further improves reasoning performance @xcite . however, these approaches structure reasoning only at the level of connections between distinct reasoning steps (inter-thought level) and leave the content of individual steps (thought level) unstructured. this raises the critical question: can llms' reasoning abilities be further enhanced by introducing structure within individual thoughts? to address this question, we draw inspiration from cognitive neuroscience theories of human thought. neuroscientists have found that humans think in a structured way, with the brain's organization facilitating sequential and goal-oriented reasoning. @xcite provided early evidence that the prefrontal cortex supports structured reasoning through a rostrocaudal hierarchy, enabling the processing of increasingly abstract concepts and complex goal-directed behavior. later, @xcite 's predictive coding framework demonstrated how structured cognition emerges from the brain's ability to build hierarchical models, combining experiences with current input to predict results. more recently, jeff hawkins @xcite argued that humans think in a structured manner, with the neocortex organizing knowledge in certain structures, and thinking arises from neurons activating sequential locations in these frames. building on these insights, we propose investigating whether similarly structured representations can be incorporated into llms to enhance their reasoning and planning capabilities. in this work, we adopt a simple yet effective structural format-a tabular schema-to approximate the structured nature of human thinking processes. in our approach, the schema of a table serves as a framework for organizing and navigating knowledge. inspired by the sequential processes described in neuroscience-where neurons activate specific patterns step by step @xcite -we model these processes as the sequential population of rows in a table, moving across columns according to a predefined schema. a single table can encapsulate one or more such structured thought processes, providing a coherent con-tainer for organizing and connecting thinking steps and associated information. tables not only represent step-by-step processes for achieving specific goals but also serve as robust frameworks for planning tasks. moreover, utilizing tables as structured representations enables schema design that ensures organization and data integrity, thereby facilitating efficient verification and analysis. the contributions of our paper are as follows: ## table as thought table as thought framework, which introduces a novel approach to reasoning in large language models by leveraging tables as structured representations of query schema design structured thoughts reasoning table reasoning verification { participant_name: michelle, unavailable_time: 11:00 to 12:00, ... } participant name unavailable time … available time selected time participant unavailable … selected michelle 11:00 to 12:00 9:30 to 10:30 steven 9:00 to 9:30, 11:30 to 12:00, …. 9:30 to 10:30 jerry 9:00 to 9:30, 10:00 to 11:00, … 9:30 to 10:30 you need to schedule a meeting for michelle, steven and jerry for one hour … ❌ ✅ answer ✅: the reasoning process is correct. ❌: selected time conflicts with jerry's unavailable time… the meeting should be scheduled 9:30 to 10:30 figure 1: the overall pipeline for table as thought reasoning. the figure illustrates how table as thought structures reasoning by iteratively populating a reasoning table based on the schema, verifying consistency, and updating the table until the final answer is achieved. table as thought. table as thought employs a table as a container to represent one or more structured thoughts. these tables, referred to as "reasoning tables", encapsulate thoughts and provide a transparent representation of the reasoning process. a reasoning table t is initialized with an original table schema s, which is defined by the llm for a given query q. structured thoughts θ are then generated based on s, with each thought corresponding to a row in the reasoning table t . the table t is subsequently populated and updated according to these structured thoughts θ. the overall reasoning workflow using the reasoning table is illustrated in figure 1 and formalized in algorithm 1. algorithm 1 table as thought require: query q ensure: a table t that satisfies q 1: s ← designschema(q) // define table schema 2: initialize an empty table t with schema s. 3: while not sufficient(t , q) do 4: θ ← reflect(t, q) // generate possible updates 5: t ← updatetable(t, θ) // apply updates if needed 6: end while 7: return t schema development module. the schema development module dynamically adapts table schemas to accommodate various queries across different reasoning tasks. for constraint-planning tasks, where the primary objective is to satisfy constraints, we prompt llms to identify the constraints explicitly before designing the schema. this ensures that both explicit and implicit constraints are addressed in the reasoning process. for mathematical reasoning tasks, the schema is tailored to reflect the logical progression of the reasoning steps, enabling systematic organization of critical information. the headers in the table schemas are designed to represent essential reasoning steps and key information pertinent to the task. these headers act as anchors for organizing and verifying intermediate and final reasoning outputs. for example, consider the travel planning query: i plan to travel alone, and my planned budget for the trip is around @xmath01,400. to address this constraint, the schema must include a header such as cost, with the type number, ensuring that the relevant information is captured and evaluated against the budgetary constraint. for a mathematical reasoning task, such as a question from the gsm8k dataset: a robe takes 2 bolts of blue fiber and half that much white fiber. how many bolts in total does it take? here, the reasoning process requires consideration of the quantities of blue and white fibers. the schema should therefore include keys such as blue fiber and white fiber, ensuring that all relevant elements are systematically tracked and calculated. reasoning verification module. our preliminary experiments reveal that existing llms may fail to generate a consistent reasoning path. therefore, we introduce a verification module to verify the completeness and correctness of the reasoning process. for constrained reasoning tasks, such a module verifies whether the constraints identified in the schema development phase are satisfied. constraint checking is typically performed by the llm through reflective reasoning. the structured nature of thoughts in table as thought brings a natural benefit: auto-check constraints, which are constraints set that can be externally verified. in table as thought, auto-check constraints facilitate the systematic validation of intermediate steps and final outputs. for math reasoning tasks, such a module ensures that the table reflects an accurate and logically correct reasoning path toward solving the problem. this involves checking whether the intermediate and final outputs align with the expected reasoning steps outlined in the schema. table construction module. the table construction module iteratively generates structured thoughts and constructs the reasoning table by incorporating the schema and feedback from the reasoning verification module. this process involves dynamically adding new thoughts to the table, modifying existing entries, or removing entries that do not align with the schema or query requirements. the iterative process terminates under one of the following conditions: ## conclusion we proposed table as thought, a novel framework that introduces structured reasoning at the thought level. the framework centers on the design and utilization of table schemas, where the llm is tasked with constructing a schema and generating structured thoughts based on it. our results demonstrate that table as thought excels in constraint planning tasks, showcasing its ability to manage complex constraints effectively. moreover, the framework exhibits significant potential for further improving performance in math reasoning tasks, particularly in addressing unsolved problems through structured reasoning. additionally, we conducted detailed analyses of the results, exploring the interplay between schema design, reasoning complexity, and model capabilities. these insights pave the way for future research into the nature and representation of thought processes, offering a promising direction for the development of more robust reasoning frameworks in llms.