So, not much feedback since last time, but i’m still pursuing my latest obsession still!
So currently i’m working on not only the self correcting thing, but interested in how to implement the memory part of the process, this time the memory is a bit weak still, (And i ran out of API calls to keep debugging it today) So i’m continue working on it afterwards.
So currently i have this approach to try to implement it. Of course, in practice it’s barely comparable with the real thing, but that’s why I kinda was expecting this to generate more engagement and more people in the field to talk about it… or something… But yeah, the newest version is here: CAMS-E and I’m still open for feedback!
CAMS-E: An Expanded Cognitive Architecture for Grounded Reasoning, World Modeling, and Robust Multimodality
Introduction
The Cognitive Agentic Modular System (CAMS) was conceived as a direct architectural response to the inherent limitations of monolithic Large Language Models (LLMs). The prevailing paradigm of scaling single, massive models has yielded impressive fluency but has also exposed fundamental challenges in robustness, verifiability, and maintainability.1 CAMS proposes a shift from this monolithic approach to a modular, multi-agent framework—a collaborative ecosystem of specialized, autonomous agents. This design deconstructs complex cognitive tasks into a division of labor, assigning distinct responsibilities such as perception, reasoning, memory management, and critique to individual agents. The core of its design philosophy is a rigorous, multi-agent self-correction loop that architecturally separates content generation from validation, fostering a system of internal checks and balances designed to produce more truthful and reliable outputs.1
This report details a significant architectural evolution of this framework, designated CAMS-E (Expanded). This next-generation blueprint is designed to address the next frontier of challenges in artificial intelligence, moving beyond the foundational principles of CAMS to incorporate bleeding-edge research in formal reasoning, dynamic world simulation, and advanced generative capabilities. CAMS-E is architected to achieve a fundamentally higher degree of intelligence and trustworthiness. It aims to augment empirical truthfulness with provable logical correctness through the introduction of a formal Grounding Agent. It seeks to transcend static knowledge retrieval by enabling a Turing-complete understanding of problems via an internal World Modeling Agent capable of simulation and active discovery. Finally, it hardens the entire system against communication failures with a dedicated Sanitizer Agent and expands its interactive capabilities from simple multimodal perception to robust, high-fidelity generation of images, audio, and video. CAMS-E represents a comprehensive vision for an AI that is not only more knowledgeable and articulate but also more logical, robust, and creative.
Part I: Fortifying the Cognitive Core with New Specialized Agents
The foundational CAMS architecture establishes a robust division of cognitive labor. CAMS-E expands this ecosystem with three new specialized agents, each designed to address a critical capability gap in current-generation AI systems: formal logical verification, dynamic problem simulation, and resilient internal communication. These agents do not merely add functionality; they fundamentally enhance the system’s capacity for reasoning, problem-solving, and operational stability.
1.1 The Grounding Agent (The “Logician”)
Rationale for Logical Verification
The original CAMS architecture places a significant emphasis on truthfulness, primarily enforced by the Critic Agent. The Critic excels at factual verification—checking the claims within a generated response against evidence retrieved from the Unified Memory Core or external sources like web search APIs.1 This is an empirical, evidence-based approach to correctness. However, a statement can be factually accurate in its components yet logically flawed in its structure, leading to an incorrect or misleading conclusion. To achieve a deeper and more resilient form of correctness, an AI system must be capable of
logical verification—validating the integrity and soundness of the reasoning process itself. The Grounding Agent is introduced into CAMS-E to fulfill this role, ensuring that the system’s outputs are not only factually supported but also logically coherent, consistent, and formally verifiable against a set of explicit rules and constraints. This addition represents a crucial evolution from pursuing empirical truth to ensuring provable correctness, a paradigm shift that directly addresses the well-documented weakness of probabilistic LLMs in adhering to formal guarantees.2
Core Functionality and Hybrid Verification Mechanism
The Grounding Agent operates as a dedicated “System 2” cognitive process, applying slow, deliberate, and explicit logical analysis to the outputs of other agents.3 It employs a hybrid verification mechanism to provide multiple layers of logical assurance.
- Formal Model Checking: For tasks where adherence to specific constraints is paramount, the Grounding Agent incorporates a mechanism inspired by the VeriPlan system, which integrates formal model checking into LLM-driven planning.4 The agent contains a “Rule Translator” sub-module, which is an LLM specifically prompted to analyze a user’s query or the system’s own constitutional principles to extract all explicit and implicit constraints. These natural language constraints are then translated into a formal, machine-readable language such as Linear Temporal Logic (LTL) or a PRISM-compatible format.4 An integrated model checker then uses these formal rules to systematically explore the states of a proposed plan from the Orchestrator or a final response from the Reasoning Agent. This process can definitively verify whether any “hard constraints” (rules that must be satisfied) have been violated, providing a mathematical guarantee of compliance that is impossible to achieve with pattern matching alone.4
- Knowledge Graph Grounding: To prevent logical drift during long chains of reasoning, the Grounding Agent continuously validates the intermediate “thoughts” of the Reasoning Agent. Recent research has demonstrated the effectiveness of anchoring each step of a reasoning chain to a structured knowledge base.5 In CAMS-E, the Grounding Agent leverages the graph database within the Unified Memory Core for this purpose. As the Reasoning Agent generates its chain of thought, the Grounding Agent queries the knowledge graph to ensure that each inferential leap is supported by a valid, pre-existing relationship (an edge between nodes) in the graph. This ensures that the entire reasoning process remains tethered to the system’s established knowledge, dramatically reducing the likelihood of non-sequiturs or logical fallacies emerging in complex, multi-step problem-solving.
- High-Stakes Theorem Proving: For domains that demand absolute, mathematical certainty—such as generating safety-critical code, proving a mathematical theorem, or verifying the correctness of a financial algorithm—the Grounding Agent can invoke external, formal theorem provers. Drawing inspiration from frameworks like Lean Copilot, which integrates LLMs with the Lean proof assistant, the agent can translate a problem statement and the Reasoning Agent’s proposed solution into the formal language of the prover.6 The theorem prover then attempts to construct a formal, machine-checkable proof of the solution’s correctness. A successful proof provides the highest possible standard of verification, completely eliminating the risk of LLM hallucination for that specific task.6 This capability allows CAMS-E to operate with provable certainty in domains where failure is intolerable.10
Interaction with Other Agents
The Grounding Agent is deeply integrated into the CAMS-E cognitive workflow. It works in tandem with the Orchestrator to validate multi-step plans before they are executed, ensuring that the proposed strategy is logically sound and compliant with all known constraints. During the self-correction loop, it collaborates with the Critic Agent. While the Critic focuses on factual accuracy and “bullshit” detection, the Grounding Agent provides a parallel analysis of logical structure, identifying fallacies, inconsistencies, or constraint violations. This dual-pronged critique provides a far more comprehensive feedback report to the Reasoning Agent for refinement.
1.2 The World Modeling Agent (The “Simulator”)
Rationale for Internal Simulation
To achieve a truly general and Turing-complete understanding of complex problems, an AI system must be able to reason about dynamic systems—to model their rules, predict their evolution, and simulate the consequences of actions. Relying solely on retrieving static information from a memory core is insufficient for problems that involve physics, causality, or any state-dependent process. The World Modeling Agent is introduced to provide CAMS-E with this crucial capability: an internal, executable simulation environment where it can actively learn the “physics” of a problem domain and use that learned model to inform its planning and reasoning. This transforms the system from one that can only answer questions based on prior knowledge to one that can autonomously figure out solutions to novel problems.
Core Architecture: The WorldLLM Framework
The design of the World Modeling Agent is directly inspired by the cutting-edge WorldLLM framework, which enhances an LLM’s world modeling abilities through a virtuous cycle of theory-making and active experimentation.11 This framework is not about fine-tuning a model on a massive dataset; instead, it’s about enabling a model to rapidly learn the dynamics of a specific, bounded environment through targeted exploration. The agent is composed of three interacting sub-modules, mirroring the scientific method:
- The Scientist (Hypothesis Generator): When presented with a new environment or problem (e.g., a physics puzzle, a game, a code repository), this LLM-based module acts as an inductive reasoner. It observes initial interactions and, using Bayesian inference with an LLM as the proposal distribution, generates a set of natural language hypotheses or “theories” about the environment’s underlying rules and dynamics. For example, it might hypothesize, “The move(object, location) function fails if location is occupied,” or “Combining a key object with a door object changes the door’s state to unlocked”.11
- The Statistician (Predictive Model): This is the core world model itself. It is an LLM that takes a current state and a proposed action as input and predicts the next state of the environment. Crucially, its predictive accuracy is dramatically enhanced by providing it with the Scientist’s current set of hypotheses in its prompt. The natural language theories serve to ground the LLM’s general knowledge in the specific rules of the current domain, allowing for much more precise predictions.12
- The Experimenter (Evidence Gatherer): This module is responsible for actively exploring the environment to gather new evidence that can be used to refine the Scientist’s theories. It employs a sophisticated technique called curiosity-driven reinforcement learning (RL). The RL agent is not rewarded for achieving an external goal, but for discovering novel or surprising outcomes. Specifically, its intrinsic reward is proportional to the uncertainty of the Statistician’s predictions. It is incentivized to find state-action transitions that have a low log-likelihood under the current set of hypotheses—in other words, it is rewarded for finding evidence that proves the current theory is wrong or incomplete.11 This curiosity-driven approach makes the learning process highly efficient, as the agent actively seeks out the most informative experiments to run, constantly pushing the boundaries of its own understanding.
Turing-Complete Environment and Agentic Integration
The World Modeling Agent operates within a secure, sandboxed environment that includes a full code interpreter (e.g., a Python kernel). This gives it a Turing-complete canvas for simulation. It can model not only text-based games or simple physical interactions but any computable process, including the execution of code, the behavior of economic models, or the dynamics of social networks.
Within the CAMS-E architecture, the Orchestrator can delegate the task of “understanding the problem space” to the World Modeling Agent. Once the agent has run its discovery loop and developed a stable, predictive model (the Statistician conditioned on its learned theories), that model becomes a powerful tool. The Reasoning Agent can then query this world model to perform complex “what-if” analysis, test potential action sequences without real-world consequences, and generate plans that are deeply grounded in the specific dynamics of the problem at hand.
1.3 The Sanitizer Agent (The “Gatekeeper”)
Rationale for a Robust Communication Layer
A modular, multi-agent architecture like CAMS-E is fundamentally a distributed system. Its overall performance and reliability are critically dependent on the integrity of the communication between its constituent agents. The primary medium for this communication is likely to be structured data formats like JSON, which allow for the precise exchange of commands, data payloads, and feedback. However, LLMs, especially when not explicitly fine-tuned for format adherence, are notoriously unreliable in producing perfectly structured output.17 A single malformed JSON object—a missing comma, an unclosed bracket, a misplaced quote—could cause a parsing error that triggers a cascading failure throughout the entire cognitive workflow. The Sanitizer Agent is introduced as a non-cognitive, infrastructural component that acts as a universal gatekeeper, ensuring the syntactic and semantic integrity of all inter-agent communication.
Two-Stage Repair Pipeline
To balance the need for high-speed communication with the necessity of robust error handling, the Sanitizer Agent implements a two-stage repair pipeline for every message that contains a JSON payload.
- Stage 1: High-Speed Heuristic Repair: For maximum efficiency, every JSON payload is first passed through a lightweight, rule-based repair library. This stage utilizes optimized open-source tools, such as the Python json_repair module, which employ a set of heuristics to instantly fix the most common formatting errors. These include issues like trailing commas, missing quotation marks around keys or strings, and unclosed brackets or braces.19 In the vast majority of cases, this near-instantaneous check is sufficient to ensure a valid payload, introducing negligible latency.
- Stage 2: LLM-Based Correction: If the fast heuristic repair fails and a standard parser still throws an error, the payload is automatically escalated to a more powerful, LLM-based correction mechanism. A specialized, lightweight LLM is invoked with a carefully crafted prompt. This prompt includes the original malformed string along with the specific error message generated by the parser (e.g., JSONDecodeError: Expecting ‘,’ delimiter: line 1 column 34 (char 33)). The LLM is instructed to act as a syntax expert, use the error message as a diagnostic clue, correct all syntax issues, and output only the complete, valid JSON string.21 This leverages the advanced pattern-recognition capabilities of LLMs to solve more complex or unusual formatting problems that rule-based systems might miss.22
Schema Validation and System-Wide Integration
Beyond ensuring syntactic validity, the Sanitizer Agent can also enforce semantic correctness. When an agent sends a message, it can optionally include a reference to a required JSON schema. The Sanitizer Agent will then validate the repaired JSON payload against this schema, ensuring that all required fields are present and that all values conform to the expected data types.18 This prevents errors that arise from syntactically correct but semantically incomplete or incorrect data.
The Sanitizer Agent is not a participant in the primary cognitive loop managed by the Orchestrator. Instead, it functions as a transparent middleware layer or transport protocol. It automatically intercepts, validates, and, if necessary, repairs every message exchanged between any two agents in the CAMS-E ecosystem, making the entire system inherently more resilient to the flakiness of LLM-generated structured data.