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US-20260127418-A1 - RECURSIVE GENERATIVE PLANNING SYSTEM WITH VERIFICATION MODULES AND USER FEEDBACK INTEGRATION

US20260127418A1US 20260127418 A1US20260127418 A1US 20260127418A1US-20260127418-A1

Abstract

Various aspects of the present disclosure relate to techniques for recursive generative planning system with verification modules and user feedback integration. An apparatus is configured to execute a recursive planning pipeline that integrates at least one generative model and at least one rule-based model to generate candidate transformation pathways for a target structure, apply one or more verification modules to evaluate validity, stability, and coherence of intermediate outputs, selectively regenerate portions of the candidate transformation pathways from a user-specified stage while preserving validated portions, and incorporate user feedback to refine subsequent pathway generation and model operation.

Inventors

  • SHREYAS VINAYA SATHYANARAYANA
  • BHARATH SATHYANARAYANA

Assignees

  • DEEP FOREST SCIENCES, INC.

Dates

Publication Date
20260507
Application Date
20251105

Claims (20)

  1. 1 . An apparatus, comprising: at least one memory; and at least one processor coupled with the at least one memory and configured to cause the apparatus to: execute a recursive planning pipeline that integrates at least one generative model and at least one rule-based model to generate candidate transformation pathways for a target structure; apply one or more verification modules to evaluate validity, stability, and coherence of intermediate outputs; selectively regenerate portions of the candidate transformation pathways from a user-specified stage while preserving validated portions; and incorporate user feedback to refine subsequent pathway generation and model operation.
  2. 2 . The apparatus of claim 1 , wherein the candidate transformation pathways comprise alternative multi-step sequences representing transformations between a target structure and one or more precursor structures, wherein each candidate transformation pathway is characterized by at least one transformation rule, reaction template, or model-generated operation connecting intermediate states within the sequence.
  3. 3 . The apparatus of claim 1 , wherein the intermediate outputs comprise representations of partial transformations or precursor structures generated at successive stages of the recursive planning pipeline, wherein the intermediate outputs are evaluated for structural validity, energetic stability, or logical consistency prior to continuation of the pipeline.
  4. 4 . The apparatus of claim 1 , wherein the one or more verification modules comprise a validity checker configured to evaluate chemical or structural correctness of the intermediate outputs, a stability checker configured to assess energetic or functional stability, and a coherence checker configured to detect or mitigate hallucinated or implausible transformations produced by the generative model.
  5. 5 . The apparatus of claim 1 , wherein the processor is configured to cause the apparatus to receive user feedback through at least one interface to modify or constrain model operation, wherein the user feedback comprises corrections to structural representations, exclusion of specified transformation types, or directional guidance for alternative pathway exploration.
  6. 6 . The apparatus of claim 1 , wherein the processor is configured to cause the apparatus to regenerate a portion of the candidate transformation pathways from a user-selected stage within the recursive planning pipeline while retaining preceding validated pathway segments, thereby enabling targeted refinement without full recomputation of the candidate transformation pathways.
  7. 7 . The apparatus of claim 1 , wherein the processor is configured to cause the apparatus to record user feedback and verification outcomes to update model parameters or decision policies, thereby enabling adaptive improvement of subsequent pathway generation operations.
  8. 8 . The apparatus of claim 1 , wherein the recursive planning pipeline is executed across a distributed computing architecture comprising a head node configured to manage task allocation and a plurality of worker nodes configured to concurrently process candidate transformation pathways for different target structures or recursive stages.
  9. 9 . The apparatus of claim 8 , wherein the head node prioritizes pathway execution based on one or more metrics including scalability index, confidence score, or number of transformation steps.
  10. 10 . The apparatus of claim 1 , further comprising a reinforcement learning agent configured to optimize pathway generation decisions based on accumulated user interactions, pathway validation results, and system performance metrics.
  11. 11 . The apparatus of claim 10 , wherein the reinforcement learning agent comprises a policy network configured to predict optimal actions for pathway expansion and a value network configured to estimate expected rewards associated with candidate pathways.
  12. 12 . The apparatus of claim 10 , wherein the reinforcement learning agent is trained using imitation learning from expert user interactions and reinforcement learning through autonomous exploration of pathway generation outcomes.
  13. 13 . The apparatus of claim 1 , further comprising a metadata generation subsystem configured to augment each transformation pathway with information including reagent identification, reaction condition determination, and literature evidence correlation.
  14. 14 . The apparatus of claim 13 , wherein the metadata generation subsystem retrieves precedent reactions, patent data, or publication references and associates corresponding citations with nodes of the transformation pathway.
  15. 15 . The apparatus of claim 14 , wherein the recursive planning pipeline is configured to generate transformation pathways for both organic and inorganic molecular systems, including natural products, semiconductor film precursors, and metal-organic frameworks.
  16. 16 . The apparatus of claim 15 , wherein the one or more verification modules and the metadata generation subsystem are each configured to apply domain-specific validation and annotation protocols for organic and inorganic molecular systems, including validation of coordination geometry, lattice compatibility, and reaction feasibility for inorganic targets and stability, stereochemistry, and reactivity for organic targets.
  17. 17 . The apparatus of claim 1 , wherein the recursive planning pipeline combines discriminative processing by the rule-based model with generative prediction by a large language model to achieve controlled generation of transformation pathways that are both novel and valid.
  18. 18 . The apparatus of claim 1 , wherein the processor is configured to cause the apparatus to execute a race condition or decision mechanism in which both the rule-based model and the generative model independently propose candidate transformations, and a selection module chooses an optimal pathway based on confidence scoring.
  19. 19 . A method, comprising: executing a recursive planning pipeline that integrates at least one generative model and at least one rule-based model to generate candidate transformation pathways for a target structure; applying one or more verification modules to evaluate validity, stability, and coherence of intermediate outputs; selectively regenerating portions of the candidate transformation pathways from a user-specified stage while preserving validated portions; and incorporating user feedback to refine subsequent pathway generation and model operation.
  20. 20 . A computer program product comprising a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: execute a recursive planning pipeline that integrates at least one generative model and at least one rule-based model to generate candidate transformation pathways for a target structure; apply one or more verification modules to evaluate validity, stability, and coherence of intermediate outputs; selectively regenerate portions of the candidate transformation pathways from a user-specified stage while preserving validated portions; and incorporate user feedback to refine subsequent pathway generation and model operation.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63/716,572 entitled “TECHNIQUES FOR LLM-RETROSYNTHESIS” and filed on Nov. 5, 2024, for Shreyas Vinaya Sathyanarayana, et al., which is incorporated herein by reference. This application also claims the benefit of U.S. Provisional Patent Application No. 63/800,899 entitled “TECHNIQUES FOR LLM-RETROSYNTHESIS” and filed on May 6, 2025, for Shreyas Vinaya Sathyanarayana, et al., which is incorporated herein by reference. FIELD The subject matter herein relates generally to computer-implemented systems for generative and recursive planning, and more particularly to apparatuses and methods that integrate large language models, rule-based or template-based reasoning engines, and multi-stage verification modules for generating and validating transformation pathways. BACKGROUND Automated planning and pathway generation systems, including those used in chemical and materials synthesis, often rely on fixed rule-based algorithms that lack flexibility and fail to generalize to novel targets. While large language models (LLMs) offer generative capability, they are prone to producing invalid, unstable, or hallucinated outputs and typically lack mechanisms for user correction or adaptive refinement. SUMMARY In one embodiment, an apparatus is configured to execute a recursive planning pipeline that integrates at least one generative model and at least one rule-based model to generate candidate transformation pathways for a target structure, apply one or more verification modules to evaluate validity, stability, and coherence of intermediate outputs, selectively regenerate portions of the candidate transformation pathways from a user-specified stage while preserving validated portions, and incorporate user feedback to refine subsequent pathway generation and model operation. In one embodiment, a method is configured for executing a recursive planning pipeline that integrates at least one generative model and at least one rule-based model to generate candidate transformation pathways for a target structure, applying one or more verification modules to evaluate validity, stability, and coherence of intermediate outputs, selectively regenerating portions of the candidate transformation pathways from a user-specified stage while preserving validated portions, and incorporating user feedback to refine subsequent pathway generation and model operation. In one embodiment, a computer program product is embodied on a non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations comprising executing a recursive planning pipeline that integrates at least one generative model and at least one rule-based model to generate candidate transformation pathways for a target structure, applying one or more verification modules to evaluate validity, stability, and coherence of intermediate outputs, selectively regenerating portions of the candidate transformation pathways from a user-specified stage while preserving validated portions, and incorporating user feedback to refine subsequent pathway generation and model operation. BRIEF DESCRIPTION OF THE DRAWINGS In order that the advantages of the invention will be readily understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings, in which: FIG. 1 is a schematic block diagram illustrating one embodiment of a system in accordance with the subject matter disclosed herein; FIG. 2 illustrates one example of an apparatus in accordance with the subject matter disclosed herein; FIG. 3 illustrates a recursive generative planning workflow in accordance with the subject matter disclosed herein; FIG. 4 illustrates a flowchart showing one example of a method in accordance with the subject matter disclosed herein; and FIG. 5 illustrates a flowchart showing one example of a method in accordance with the subject matter disclosed herein. DETAILED DESCRIPTION Conventional automated pathway generation systems, such as those used in retrosynthesis, reaction planning, or materials design, rely heavily on fixed rule-based algorithms or curated reaction templates. While such deterministic methods are reliable for well-characterized domains, they often fail to generalize beyond known reaction classes or molecular families. In contrast, recent advances in LLMs and other generative artificial intelligence (AI) techniques offer the ability to propose novel transformations and pathways by drawing on vast training data. However, t