Visionary research and essays on the trajectory of artificial intelligence, its cognitive implications, and the human-AI future
Academic Citation: Ivchenko, Oleh (2026). AI Alignment Progress Report 2025: RLHF Successors and Constitutional AI Evaluation. Research article: AI Alignment Progress Report 2025: RLHF Successors and Constitutional AI Evaluation. Odessa National Polytechnic University, Department of Economic Cybernetics. DOI: 10.5281/zenodo.22149662 · View on Zenodo (CERN)
Scientific discovery increasingly relies on the integration of heterogeneous data modalities, including textual abstracts, experimental protocols, spectroscopic signatures, and structural diagrams. Multimodal artificial intelligence (AI) systems promise substantial gains in predictive accuracy, accelerated hypothesis generation, and reduced resource consumption across domains such as drug disco...
World models—formal representations of environmental dynamics—are critical for reliable AI planning in real-world domains. This article surveys the most influential research contributions from the past three years, analyzes benchmark-to-practice gaps, and identifies the technical and operational barriers that prevent research-grade world models from deployment at scale. We formulate three resea...
Long-horizon task execution in AI systems increasingly relies on internal data structures that mimic human memory phenomena. This article investigates three canonical architectures: vector‑store episodic memory, knowledge‑graph semantic memory, and attention‑based working memory. We pose three research questions concerning (RQ1) the comparative fidelity of retrieval pathways, (RQ2) resource eff...
This article investigates how iterative refinement cycles within AI‑driven writing pipelines affect overall content quality across multiple measurable dimensions. We designed a closed‑loop workflow where an initial draft generated by a large language model undergoes automated editorial evaluation, targeted revisions, and re‑generation, repeating until convergence criteria are met or a maximum i...
Mixture of Experts (MoE) architectures have emerged as a pivotal scaling strategy for large language models, promising superior capacity efficiency and specialized functional modularity. This article interrogates the scaling behaviors of MoE systems—specifically Mixtral, DeepSeek‑MoE, and Grok‑1—relative to conventional dense models, aiming to elucidate predictive patterns for frontier model de...
The rapid proliferation of AI-generated technical content demands reliable automated quality indicators that can be computed at scale. This article investigates two such indicators—claim density and evidence ratio—and evaluates their effectiveness as proxies for expert-perceived quality. We define claim density as the proportion of sentences that contain testable assertions within a technical p...
The rapid advancement of vision-language models (VLMs) has expanded their applicability across scientific domains, yet systematic evaluations of their real-world utility remain fragmented. This article addresses the gap between general benchmark scores and domain-specific performance by presenting a structured benchmarking framework for VLMs on scientific and engineering tasks. We pose three re...
The rapid expansion of autonomous AI agents capable of executing multi-step tasks has highlighted the need for rigorous reliability assessment. While benchmark suites such as SWE-bench, GAIA, and OSWorld provide preliminary success metrics, they lack a unified framework for characterizing failure modes across heterogeneous agent architectures. This article addresses this gap by presenting a sys...
The rapid evolution of large language models (LLMs) has e[REDACTED]sed scalability bottlenecks inherent in the Transformer architecture, particularly its quadratic complexity in attention computation. Recent advances propose alternative paradigms—state‑space models (SSMs) such as Mamba and RWKV, as well as hybrid architectures that blend linear attention with selective state propagation—as viab...