CA-GPT: Integrating LLMs with Corrective Agents

• CA-GPT systems are hybrid neuro-symbolic platforms that integrate LLMs with domain-specific corrective agents for iterative, high-precision reasoning.
• They enable tasks like hardware assertion generation and access control policy enforcement using closed-loop feedback and structured intermediate representations.
• Empirical evaluations demonstrate significant error rate reductions and efficiency gains, transforming processes from hours to seconds.

CA-GPT (Corrective-Agent + GPT) systems are a class of hybrid neuro-symbolic workflows that tightly couple a LLM, such as GPT-4, with an automated, domain-specific corrective agent to execute high-precision, iterative reasoning or synthesis tasks. Such frameworks leverage the language and abstraction capabilities of GPT models, while employing domain feedback (e.g., formal simulation, ontological reasoning, or compliance checking) to converge towards rigorous, specification-aligned results. Two primary CA-GPT paradigms have been demonstrated: (1) the ChIRAAG framework for SystemVerilog Assertion (SVA) generation from natural language hardware specifications, and (2) the GPT-Onto-CAABAC architecture for access control policy enforcement in healthcare, both integrating LLMs with external error-corrective or compliance-bound agents (Mali et al., 2024, Nowrozy et al., 2024). CA-GPT systems are characterized by their closed-loop operation, structured intermediate representations, and iterative refinement guided by formal correctness or compliance signals.

1. CA-GPT System Architectures

CA-GPT systems consist of modular pipelines incorporating both neural (LLM) and symbolic (corrective) agents:

1. ChIRAAG for Hardware Verification

• Input: Natural language hardware specification $\mathcal{D}$ (in formats such as PDF or Markdown), describing module functionalities, signals, parameters, and timing requirements.
• Parsing and Standardization: $\mathcal{D}$ is split into labeled blocks (Introduction, System Overview, Definitions, Functional/TIming Requirements), yielding the unformatted set $UnFmt$. Routinely, subroutines (${JSON_{\text{int}},\,JSON_{s\_ov},\,JSON_{df}}$, etc.) map labeled data into a unified JSON object $\mathcal{J}$, consistent with a defined BNF-to-JSON intermediate representation.
• LLM Assertion Engine: $\mathcal{J}$ and simulation logs $\mathcal{L}$ (if any) are passed to GPT-4, which generates candidate SVA code.
• Corrective Simulation Loop: The corrective agent (a simulator) compiles and simulates the assertion set. If errors arise (syntax, signal mismatch, failed properties), structured log feedback prompts GPT-4 to iteratively refine the assertions.
• Output: Verified SVA suite $S$, converged after at most $\mathcal{T}$ iterations or until no simulation errors persist (Mali et al., 2024).

2. GPT-Onto-CAABAC for Access Control

• Input: Unstructured legal policies $\mathcal{P}$ (statutes, institutional rules), real-time context $\mathcal{C}$ (user roles, device types, locations, event flags).
• GPT-Based Policy Interpreter: GPT-4, with tools like AskYourPDF, extracts an in-memory medical-legal ontology $\mathcal{O}$ via entity/relation/concept mining, mapping natural language to OWL-style graphs (Equation 1: $\mathcal{O} = f_{\mathrm{extraction}}(\mathcal{P})$).
• Ontology Manager: Description-logic reasoner maintains $\mathrm{Consistent}(\mathcal{O})$, enabling policy conflict detection and compliance anchoring.
• CAABAC Engine: Combines attribute set $\mathcal{A} = f_{\mathrm{capture}}(\mathcal{C})$ with policy logic and LLM-augmented decision-making [Equations 2 & 3]. In the event of errors or ambiguities, a second GPT pass, $f_{\mathrm{resolution}}$, is triggered. Human oversight finalizes the access decision (Equation 5).
• Output: Compliance-anchored, context-aware permit/deny decisions for sensitive actions (e.g. EHR record access) (Nowrozy et al., 2024).

2. Core Methodologies and Algorithmic Pipelines

CA-GPT pipelines share several foundational elements:

• Intermediate Representation Standardization: Both frameworks translate unstructured or semi-structured input into machine-parseable JSON or OWL ontologies, governed by formal grammars or schemas. For assertion generation, a BNF grammar formalizes the mapping from document sections to JSON keys (Mali et al., 2024). For policy, a templated NL-to-ontology pipeline converts regulatory text to triples and rules (Nowrozy et al., 2024).
• Iterative Closed-Loop Refinement: Corrective feedback (e.g., simulation logs, ontology conflict-detection) triggers context-sensitive GPT prompting for refinement or conflict resolution, iterating until correctness/convergence.
• Prompt Engineering: System and user roles delineate task expectations for GPT-4. Error contexts (e.g., simulation log snippets, compliance flags) are injected into prompts to localize corrections.
• External Corrective Modules: Domain tools (simulator, reasoner) supply verifiable correctness/compliance signals, acting as objective error or conflict sources outside the LLM black box.

Pipeline Example from ChIRAAG

The end-to-end workflow can be rendered as:

1. $\mathcal{D}$ read and parsed to $UnFmt$.
2. $UnFmt$ standardized to $\mathcal{J}$ via buildJSON.
3. For $n = 0$ to $\mathcal{T}$:
   • $$A_n = \mathrm{GPT4\_generate}(\mathcal{J}, \mathcal{L})$$
   • $\mathcal{L} = \mathrm{simulate}(A_n)$
   • If clean, return $A_n$. On error, launch SyntaxRectifier/SimulationRectifier prompts using $\mathcal{L}$.
4. Return final verified assertion suite $S$ (Mali et al., 2024).

3. Empirical Evaluation and Metrics

ChIRAAG Experimental Results

• Seven OpenTitan modules (e.g., RV Timer, GPIO) benchmarked.
• Initial assertion error rate: $\approx$33% required at least one refinement.
• Iterations per module: $3$–$16$; average $\overline{T}\approx9$.
• Simulation time per iteration: $80$–$180$ ns; full wall-clock time $7$–$9$ s per module.
• Manual assertion time: hours; ChIRAAG: $<15$ s.
• Assertion coverage: LLM-generated assertions per module: $6$–$14$, frequently exceeding original handcrafted sets.
• Error rate reduction: from $100\%$ failing to $0\%$ after loop closure.

Module	OT Asserts	LLM Asserts	Iterations	Sim Time	ChIRAAG Time
RV Timer	0	9	9	80 ns	7.3 s
PattGen	0	7	5	120 ns	8.4 s
...	...	...	...	...	...

(Mali et al., 2024)

GPT-Onto-CAABAC Empirical Analysis

• Platform: ChatGPT-4 with AskYourPDF, OWL reasoner, Python/Flask.
• Dataset: Real EHR logs; $120$ scenario prompts.
• Baselines: RBAC, vanilla ABAC, CAAC.
• Metrics:
  • Compliance Rate: $95\%$.
  • Conflict-Resolution Efficiency: $80\%$.
  • Recommendation Quality: $0.92$ (normalized rubric).
  • Decision accuracy: $0.96$ (CA-GPT), $0.88$ (ABAC), $0.75$ (RBAC).
  • Fault-injection correctness: $0.89$ (CA-GPT), $0.63$ (ABAC).

Model	Decision Accuracy	Conflict Resolution	Avg. Rec. Quality
RBAC	0.75	n/a	n/a
ABAC	0.88	0.50	0.65
CAAC	0.82	0.60	0.72
GPT-Onto-CAABAC	0.96	0.80	0.92

(Nowrozy et al., 2024)

4. Domain-Specific Prompting and Representation Strategies

CA-GPT systems employ carefully engineered prompting and representation interfaces to maximize LLM effectiveness while constraining error or hallucination risk:

• Assertion Generation (ChIRAAG): System prompts instruct GPT-4 to output synthesizable SVA code from JSON specs; error-driven augmentations localize fixes to assertion code. Minimal extraneous text is enforced.
• Policy-to-Ontology (GPT-Onto-CAABAC): GPT-4 instructed to extract entities, relations, compliance predicates from regulatory text into OWL-style graphs. Conflict resolution passes explicitly reference policy ambiguities or audit errors for targeted reasoning.

In both cases, the mapping from natural-language to formal intermediate representations is defined (BNF for JSON in ChIRAAG, OWL/attribute-logic for CAABAC), ensuring semantic alignment between LLM outputs and corrective agent expectations.

5. Limitations and Prospective Research Directions

Identified Limitations

• Dependency on Input Quality: Ambiguous or incomplete natural language in design specs or policy text can induce hallucinations, underspecified assertions, or erroneous access decisions.
• Coverage and Completeness: Functional correctness is addressed; formal guarantees of completeness or total coverage are not provided.
• Scalability: Very large hardware modules or policy corpora require spec chunking or multi-pass prompting for tractability.
• LLM Hallucinations: Unconstrained prompts or lack of tight feedback loops can introduce out-of-spec elements in generated outputs.

Prospective Extensions

• Integration of formal and functional coverage metrics to quantify completeness of assertion/policy coverage.
• Domain-specific fine-tuning of LLMs on SVA/property corpora or regulatory language for greater accuracy and fewer refinement cycles.
• Multi-agent CA-GPT architectures, embedding separate agents for coverage analysis, property synthesis, and refinement.
• Automation of design or policy specification extraction from data repositories or institutional documentation sources.

6. Significance and Outlook

CA-GPT systems represent a general, modular approach for coupling LLMs with automated domain-corrective feedback to drive tasks requiring both language understanding and formal correctness. Results across hardware verification (Mali et al., 2024) and access control policy (Nowrozy et al., 2024) domains demonstrate dramatic acceleration (hours to seconds) and improved rigor compared to manual or static rule-based workflows. This architecture points towards next-generation pipelines in verification, compliance, and policy alignment, fusing adaptive neural reasoning with externalized, error-aware correction and convergence.

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References

• (Mali et al., 2024) "ChIRAAG: ChatGPT Informed Rapid and Automated Assertion Generation"
• (Nowrozy et al., 2024) "GPT, Ontology, and CAABAC: A Tripartite Personalized Access Control Model Anchored by Compliance, Context and Attribute"