Bridging the Gap: Toward Cognitive Autonomy in Artificial Intelligence

This paper identifies and analyzes seven core deficiencies that constrain contemporary AI models: the absence of intrinsic self- monitoring, lack of meta-cognitive awareness, fixed and non- adaptive learning mechanisms, inability to restructure goals, lack of representational maintenance, insufficient embodied feedback, and the absence of intrinsic agency. Alongside identifying these limitations, we also outline a forward-looking perspective on how AI may evolve beyond them through architectures that mirror neurocognitive principles. We argue that these structural limitations prevent current architectures, including deep learning and transformer-based systems, from achieving robust generalization, lifelong adaptability, and real-world autonomy.

One Patch is All You Need: Joint Surface Material Reconstruction and Classification from Minimal Visual Cues

Understanding material surfaces from sparse visual cues is critical for applications in robotics, simulation, and material perception. However, most existing methods rely on dense or full-scene observations, limiting their effectiveness in constrained or partial view environment. To address this challenge, we introduce SMARC, a unified model for Surface MAterial Reconstruction and Classification from minimal visual input. By giving only a single 10% contiguous patch of the image, SMARC recognizes and reconstructs the full RGB surface while simultaneously classifying the material category.

Reforming Artificial Intelligence: A Call for Cognitive Containment

Rapid advances in neurocognitive AI are accelerating systems toward higher autonomy and, with it, the risk of misalignment. This work introduces Reforming Artificial Intelligence, a framework grounded in cognitive containment, where governance and ethical oversight co-evolve with capability. The proposed architecture comprises three concentric layers: (1) an AI system equipped with cognitive modules such as perception, attention, memory, and reasoning; (2) a reformative layer embedding ethical anchors, meta-cognitive governors, cognitive firewalls, and transparency mechanisms; and (3) a human–societal layer encompassing policy, law, and collective oversight.

Towards Neurocognitive-Inspired Intelligence: From AI’s Structural Mimicry to Human-Like Functional Cognition

Inspired by the structure and function of biological cognition, this paper introduces the concept of “Neurocognitive-Inspired Intelligence (NII),” a hybrid approach that combines neuroscience, cognitive science, computer vision, and AI to develop more general, adaptive, and robust intelligent systems capable of rapid learning, learning from less data, and leveraging prior experience. These systems aim to emulate the human brain’s ability to flexibly learn, reason, remember, perceive, and act in real-world settings with minimal supervision. We review the limitations of current AI methods, define core principles of neurocognitive-inspired intelligence, and propose a modular, biologically inspired architecture that emphasizes integration, embodiment, and adaptability.

Surformer v2: A Multimodal Classifier for Surface Understanding from Touch and Vision

Multimodal surface material classification plays a critical role in advancing tactile perception for robotic manipulation and interaction. In this paper, we present Surformer v2, an enhanced multi-modal classification architecture designed to integrate visual and tactile sensory streams through a late(decision level) fusion mechanism. Building on our earlier Surformer v1 framework, which employed handcrafted feature extraction followed by mid-level fusion architecture with multi-head cross-attention layers, Surformer v2 integrates the feature extraction process within the model itself and shifts to late fusion.

Learning in Focus: Detecting Behavioral and Collaborative Engagement Using Vision Transformers

In early childhood education, accurately detecting behavioral and collaborative engagement is essential for fostering meaningful learning experiences. This paper presents an AI-driven approach that leverages Vision Transformers (ViTs) to automatically classify children’s engagement using visual cues such as gaze direction, interaction, and peer collaboration.

Surformer v1: Transformer-Based Surface Classification Using Tactile and Vision Features

Surface material recognition is a key component in robotic perception and physical interaction, particularly when leveraging both tactile and visual sensory inputs. In this work, we propose Surformer v1, a transformer-based architecture designed for surface classification using structured tactile features and Principal Component Analysis (PCA)-reduced visual embeddings extracted via ResNet 50. The model integrates modality-specific encoders with cross-modal attention layers, enabling rich interactions between vision and touch. Currently, state-of-the-art deep learning models for vision tasks have achieved remarkable performance. With this in mind, our first set of experiments focused exclusively on tactile-only surface classification. Using feature engineering, we trained and evaluated multiple machine learning models, assessing their accuracy and inference time. We then implemented an encoder-only Transformer model tailored for tactile features.