black-box

The paper proposes a dynamic aggregation enhanced efficient global optimization algorithm (DA-EGO) for solving high-dimensional turbomachinery design problems, validated through benchmark tests and aerodynamic applications.

This paper presents a systematic black-box framework for evaluating agentic AI systems, introducing a taxonomy of risks and automated red teaming methods. Empirical validation across agent architectures reveals critical vulnerabilities, with high rates of governance and privacy risks.

The article critiques the commercialization of AI chatbots, arguing they foster dependency and reduced critical thinking, while generating impractical output and creating feedback loops for misinformation.

DiSCO is a training-free, black-box defense for text-to-image models that uses distribution-guided contrastive prompt optimization to prevent generation of Not-Safe-For-Work content, significantly reducing attack success rates.

This paper introduces Groundedness Drift, a score for black-box auditing of language model classifiers to detect backdoors using clean calibration data and explanatory outputs. It demonstrates higher detection performance across multiple attack families and datasets.

This paper introduces ReBIND, a framework that measures, predicts, and repairs behavioral relapse in black-box LLM dialogues where models continue to follow revoked constraints. Experiments with HumanEval show that ahead-of-time compilation significantly reduces relapse compared to a baseline, while adaptive interventions add no detectable gain.

The author shares how a triage agent with higher accuracy than humans still failed adoption until they added plain-language explanations for each decision, concluding that legibility beats accuracy for building trust.

The paper introduces Crafter, an agent for corrective feature discovery that mines the residual of frozen black-box forecasters using compositional search and LLM-generated features, achieving up to 27% error reduction across six datasets and backbones.

This paper introduces Relational Response Fields (RRF), a theoretical framework for determining when black-box LLM responses can be reliably recovered under corruption, establishing identifiability conditions and minimax bounds that separate response consistency from truth.

This paper proposes a probabilistic approach to training-data extraction from black-box language models, showing that aggregate membership-inference metrics hide per-document leakage and introducing the 'leakit' audit tool.

The paper presents PTP, a functional approach to LLM inversion that trains an inverse language model from scratch using previous-token prediction on synthetic data from a target black-box LLM, enabling near-exact prompt reconstruction from responses and outperforming prior work.

Discusses the unresolved problem of AI agents being able to build working apps but remaining untrustworthy black boxes when deployed unattended in production.

A developer built an AI agent that drafts sales decks, but adoption was near zero until the agent showed its reasoning behind each slide choice. The lesson: making agent decisions visible is more critical for trust than raw output quality.

This paper proposes a unified black-box robustness evaluation framework for token-level explanations in enterprise NLP, comparing encoder (BERT, RoBERTa) and decoder (Qwen, Llama) models. It finds decoder LLMs produce substantially more stable explanations, with stability improving with scale, and provides a cost-robustness tradeoff curve for pre-deployment model selection.

Introduces interventional grounding audits as a black-box, step-level test to check whether LLM chain-of-thought reasoning genuinely depends on its stated premises. Evaluated on ProntoQA with GPT-4o, achieving F1=0.806 on detecting proof-tree dependencies, significantly outperforming a self-consistency baseline.

A novel approach to model interpretability using a zero-parameter model that opens the black box of complex AI systems without requiring additional training or parameters.

The BABEL codec achieves the first complete decode of GPT-2 small's internal state, reconstructing 94.7% of its behavior and enabling reading and writing to the model in English, with open source resources and a demo.

This paper presents a black-box evaluation framework to assess LLMs' ability to generate Design Structure Matrices (DSMs) from structured technical documentation. It introduces reproducible metrics and a composite quality score, showing that while LLMs can produce plausible DSMs, they remain sensitive to ambiguity and prompt formulation.

This paper presents NightVision, an attack that uses restrictive black-box API access to estimate hidden dimension, depth, and parameter count of large language models. It exploits a novel common-set prompting technique and spectral analysis, achieving high accuracy on open-source models.

CBD introduces an API-only black-box unlearning framework for LLMs that uses two auxiliary models to create controlled behavioral divergence between retained and target data, achieving a better unlearning-utility trade-off compared to existing methods.