auditability

The paper presents an auditable reliability layer for biomedical text classification that uses deterministic spell-correction to address OCR artifacts, improving classifier performance while ensuring safety through abstention under uncertainty.

After an agent escape incident, the Aimee platform has been overhauled with a new harness emphasizing governance, auditability, and observability, highlighting the importance of failure in advancing AI agent learning.

This paper presents a production-grade framework that uses large language models to convert natural-language pricing policies into executable decisions for tourism pricing, achieving significant efficiency gains and auditability in real-world deployment.

The paper proposes an auditable, user-configurable rule-based method for argument selection in deliberative polling, demonstrating through simulations that it achieves competitive performance with opaque learned rankers while enabling greater transparency and personalization.

The paper studies faithful reasoning in AI systems for abstaining action policies, finding a tradeoff where direct policies achieve higher decision quality but lack auditable reasoning, while reasoning policies provide oversight at the cost of lower performance.

LenOS is an open-source framework for building agentic workflows, emphasizing human responsibility, auditability, and knowledge management in agent deployments.

The paper introduces ontological trust and RGE, an online monitor that decomposes trust along Role, Goal, and Evidence to detect task drift in long-horizon agents. RGE outperforms baselines on cross-domain benchmarks with high Drift F1 scores while maintaining benign coverage.

Introduces Governed Persistent Memory (GPM), a bitemporal state-transition model for auditable long-horizon agent memory with source-bound semantics and fail-closed release, validated on benchmarks and sealed evaluations.

This paper presents AWARE-FX, an auditable AI/NLP decision-support system that extracts and scores corporate foreign-exchange hedging disclosures from annual reports, evaluated on 24,909 Hong Kong firm-years with FinBERT, ModernBERT, and Qwen3-8B comparisons.

This paper presents TraceCoder, a code generation system that records and visualizes the repair history of AI-generated code at snippet granularity, enabling explainable and auditable auditing of LLM-based coding agents.

Yohei Nakajima observes that society has repeatedly solved scalable collaboration with auditability using immutable logs, drawing parallels from open source (git), accounting, medicine, aviation, nuclear infrastructure, and scientific research. He suggests this pattern applies to long-running AI agent problems.

The article discusses principles for designing LLM harnesses that are intuitive, transparent, and lean, drawing inspiration from Unix philosophy to reduce cognitive load and improve reliability.

This paper introduces TRACE (Typed Reasoning And Commitment Evidence), a typed, versioned schema for recording reasoning traces in agentic systems to enable auditability and improve reasoning quality. It defines a reference writer, measurement regime, and consumer contract, with two worked examples illustrating the approach.

This paper introduces the Hypothesis Evolution Protocol (HEP) for LLM agents, which makes hypothesis generation, testing, and belief updates explicit and auditable. Experiments on materials-science tasks show that HEP-equipped agents generalize across research questions and become more effective with stronger base LLMs.

The article argues that before AI agents can be widely deployed, they need verifiable identity and auditability to ensure trust and accountability. The ITU is working on international standards for this.

Introduces a harness engineering approach for building auditable enterprise LLM agents by moving deterministic behavior into code, schemas, and validation artifacts, demonstrated on Korean corporate data with fault-injection and model-substitution tests.

The article explores how AI agent workflows are reintroducing software engineering challenges around reproducibility, auditability, and state management that were previously solved with version control, CI/CD, and static code practices, while noting emerging solutions like GitHub's Agentic Workflows and git-native approaches.

The article argues that human-in-the-loop in agent systems should move from vague approvals to explicit, auditable step-level signed decision records with detailed evidence, payloads, idempotency keys, rollback paths, and ownership. It highlights the danger of approving a black-box story rather than a specific operation.

The author explores the idea of AI agents having a public, auditable memory to record important decisions, which could enhance trust but also introduce new complexities.

Presents SemantiClean, a modular framework for extracting structured semantic signals from e-commerce session data to drive pluggable inference targets (purchase intent, customer segmentation, product affinity) while prioritizing auditability and structural transparency over pure accuracy.