Thought Generation: AI & Cognitive Models

• Thought Generation is the explicit, step-wise production of intermediate representations that scaffold reasoning, planning, and creative synthesis.
• It employs chain-of-thought, tree-of-thought, and graph-of-thought models to structure and refine processes across artificial and biological systems.
• Optimization techniques like GRPO-MA and ThoughtMani improve accuracy and efficiency, enabling multimodal applications and enhanced interpretability.

Thought generation refers to the explicit, step-wise production of intermediate cognitive representations—“thoughts”—within artificial or biological systems to scaffold reasoning, decision-making, planning, and creative synthesis. In AI and neuroscience, thought generation is both a modeling principle and an algorithmic technology, with instantiations ranging from chain-of-thought prompting in LLMs to dynamic neural events in the medial temporal lobe. Across domains, the discipline centers on generating, refining, interpreting, and evaluating sequences or graphs of intermediate steps that mediate between perception, memory, and action.

1. Formal Characterization and Theoretical Models

Thought generation in model-based reasoning has precise formalizations. In LLMs, it encompasses the explicit emission of intermediate textual or multimodal (e.g., visual) tokens prior to constructing a final answer or output. A canonical definition structures the thought process as a sequence $\mathrm{CoT} = (S_1, S_2, \ldots, S_T)$, where each $S_t$ is a generated “thought unit”—a statement, step, or subgoal. Recent theoretical work further models the process as modular state transitions, with visible thought states $v_t \in V$ and hidden representations $h_{i,t} \in H_i$, manipulated by selective and generative focus mechanisms that bind the model with the computational architecture of Turing machines. Internal consistency constraints ($\sum_h P(v|h)P(h|v) = 1$) ensure that generated thoughts remain grounded in their inputs and prior steps (Virie, 2015).

Graphical and tree-structured variants generalize linear chains. The Graph-of-Thoughts framework encodes units of information as nodes $V$, with arbitrary dependency edges $E$—enabling merging, distillation, and feedback transformation primitives and supporting complex, non-linear reasoning (Besta et al., 2023). The Tree-of-Thoughts paradigm represents solution paths as trees $T = (V, E)$, where each node corresponds to a candidate thought, and expansion, evaluation, and pruning are conducted under user-configurable strategies (Boyle et al., 31 Aug 2024). At the frontier of multimodal reasoning, thought generation extends to long-multimodal chains, where generated image tokens and text tokens are interleaved, constructing iterative visual hypotheses as integral reasoning steps (Chern et al., 28 May 2025).

2. Computational Frameworks and Optimization Techniques

A range of algorithmic protocols has been proposed for effective, efficient, and flexible thought generation. Chain-of-Thought (CoT) and Tree-of-Thought (ToT) schemes prompted initial advances by decomposing complex reasoning into sequences or tree-structured intermediates; however, these typically sacrifice either efficiency or flexibility. The “Penrose triangle” of existing paradigms posits that performance, efficiency (few LLM calls), and flexibility (rich thought topologies) can be jointly achieved only with substantial trade-offs (Ding et al., 2023).

XoT (“Everything of Thoughts”) breaks this constraint using external decision modules—Monte Carlo Tree Search (MCTS) guided by small reinforcement learning (RL) agents—to produce candidate thoughts, revising them in a collaborative search loop with the LLM. This yields multi-solution graphs of thought with minimal model queries, demonstrated via the MCTS-LLM revision protocol to attain 85–96% accuracy with $\leq$2.3 LLM calls per task (Ding et al., 2023).

Recent approaches further optimize the control and quality of thought generation. GRPO-MA (Group Relative Policy Optimization–Multi Answer) addresses thought–answer gradient coupling and variance via multi-answer sampling per CoT trace in RL training, provably reducing variance as $O(1/M)$ in the number of answers and improving both stability and solution accuracy on math, code, and multimodal tasks (Wang et al., 29 Sep 2025). External thought manipulation approaches (ThoughtMani) enable efficiency at inference by inserting sketches or thought templates generated by smaller models, reducing overthinking and output token cost by ~30% with negligible accuracy loss (Liu et al., 18 Apr 2025).

3. Multimodal and Domain-Specific Extension

Thought generation transcends text, extending into vision-LLMs (VLMs), bioinformatics, and role-based reasoning. In medical report generation, CoMT (“Chain-of-Medical-Thought”) segments unstructured reports into six levels of fine-grained question–answer pairs, aligning with clinician cognition and countering hallucinations by structuring diagnosis as a stepwise decomposition (Jiang et al., 17 Jun 2024).

In combinatorial optimization, the GraphThought framework formulates the Optimal Thoughts Design (OTD) problem—selecting sets of “action” and “state” thoughts, and a program that sequences them, to optimize LLM fine-tuning for specific graph tasks. Meta-Thought Programming pipelines (heuristic-guided forward planning, or solver-aligned backward reasoning) generate explicit, highly structured thought corpora that enable small models (e.g., Llama-GT-8B) to outperform much larger baselines on the GraphArena benchmark (Huang et al., 17 Feb 2025).

Biological reasoning leverages the Thought Graph, constructing semantically labeled reasoning graphs with ontology-driven edges, achieving a +40% cosine similarity gain over classical gene set analysis in aligning to human expert annotations (Hsu et al., 11 Mar 2024).

In multimodal domains, the “Thinking with Generated Images” paradigm enables LMMs to interleave text and generated image tokens within a native autoregressive process. Models learn to decompose visual tasks into intermediate subgoals, critique and refine visual hypotheses, and exhibit gains up to 50% in multi-object alignment benchmarks, revealing that cross-modal thought generation unlocks cognitive capacities impossible with text-only CoT (Chern et al., 28 May 2025).

4. Evaluation Protocols and Benchmarks

Assessment protocols for thought generation unify textual, structural, and application-specific metrics. In reasoning models, sets of metrics include:

• Total Reflection Count (TRC): Cumulative number of reflection keywords in CoT chains.
• Reflection Data Count (RDC): Number of scenario questions where at least one reflection cue is present.
• Consistency Scores (CS): LLM-as-a-judge paradigm, scoring the alignment of a model’s CoT with a reference outline (e.g., from GPT-o1) on a 1–5 scale (Liu et al., 20 Jun 2025).

Biomedical and domain-specific tasks deploy semantic similarity metrics (e.g., SapBERT-based cosine similarity to human gold standard, as in Thought Graph), cross-entropy, contrastive alignment losses, and multiple-choice or free-response accuracy. Qualitative evaluation remains central: levels of hallucination (MediHall metric), depth and coverage of inner thoughts, and interpretability gains are routinely benchmarked (Jiang et al., 17 Jun 2024, Xu et al., 11 Mar 2025).

The ROLETHINK benchmark evaluates inner thought reasoning for role-playing agents, comparing model-generated thought chains not only to original literary monologues (gold set) but also to expert-annotated rationale (silver set), using BLEU, ROUGE-L, NLI entailment, and human judgment (Xu et al., 11 Mar 2025).

5. Neural and Cognitive Basis of Self-Generated Thought

Beyond artificial architectures, the neural origins of thought generation in humans have been elucidated via intracranial EEG and stimulation studies. Self-generated thoughts—dreams, memory recall, visual imagery—arise from high-gamma bursts in the medial temporal lobe (especially hippocampus and parahippocampus), with propagation to temporal pole, amygdala, and associative cortex. Direct stimulation of the hippocampus elicits vivid internal content in $\sim$54% of trials, a rate orders of magnitude above posterior cingulate or inferior parietal cortex, which do not reliably induce spontaneous thought (Fox, 2017).

This MTL-centric model supports the computational focus on modular, compositional, and attention-based generative mechanisms for artificial thought. It also motivates stepwise decomposition, internal representation, and self-reflection features essential in high-fidelity reasoning systems.

6. Principles for Efficient, Robust, and Interpretable Thought Generation

Emergent themes in the literature specify that:

• Explicit decomposition into sub-questions or subgoals anchors attention, mitigates spurious shortcuts, and delimits search space (as in CoMT, ShortCoTI, GraphThought) (Jiang et al., 17 Jun 2024, Gu et al., 7 Oct 2025, Huang et al., 17 Feb 2025).
• Multi-answer or multi-path sampling reduces gradient variance and reward sparsity, crucial for stable RL-based optimization (GRPO-MA) (Wang et al., 29 Sep 2025).
• Efficient use of thought—concise prompting and external thought manipulation—cuts inference cost and overthinking while preserving or enhancing safety alignment (ThoughtMani, ShortCoTI) (Liu et al., 18 Apr 2025, Gu et al., 7 Oct 2025).
• Interactive task frameworks (iToT) and graph-based architectures (GoT, Thought Graph) enable user control, error correction, and transparent visualization of the reasoning process (Boyle et al., 31 Aug 2024, Besta et al., 2023, Hsu et al., 11 Mar 2024).
• Supervision at both intermediate (“thought”) and final-output levels yields more faithful and interpretable behavior across modalities and tasks (Wu et al., 14 Oct 2024).

7. Limitations and Open Directions

Limitations in current thought-generation systems include the need for per-task model training (XoT), residual error-propagation (if both external search and LLM revision err), domain specificity (e.g., reliance on external ontologies), and failure modes in the compositional integration of subgoals (e.g., in visual planning tasks) (Ding et al., 2023, Chern et al., 28 May 2025, Hsu et al., 11 Mar 2024). Extending these architectures to open-ended settings (e.g., code generation, strategic planning), integrating test-time adaptive thought-length control, and fusing human-in-the-loop evaluations remain active areas of investigation.

A plausible implication is that the emergence of diverse, domain-adaptive, and user-steerable thought-generation pipelines will drive advances not only in AI performance but in transparency, trustworthiness, and interpretability across high-stakes applications.