Object Counting with GPT-4o and GPT-5: A Comparative Study

A novel self-supervised symbolic auto-encoder has been introduced, enabling symbolic computer vision to interpret diagrams through structured representations and logical rules. This approach contrasts with traditional pixel-based visual models by parsing diagrams into geometric primitives, enhancing machine vision's interpretability.

A new framework called 'Look, Recite, Then Answer' has been proposed to enhance the performance of Vision-Language Models (VLMs) by generating self-generated knowledge hints. This approach aims to address the limitations of VLMs in specialized fields like precision agriculture, where reasoning-driven hallucination can hinder accurate visual perception.

The introduction of DIQ-H marks a significant advancement in evaluating the robustness of Vision-Language Models (VLMs) under conditions of temporal visual degradation, addressing critical failure modes such as hallucination persistence. This benchmark applies various physics-based corruptions to assess how VLMs recover from errors across multiple frames in dynamic environments.

A new method for Scene Graph Anticipation (SGA) has been introduced, termed Linguistic Scene Graph Anticipation (LSGA), which utilizes a language-driven framework to enhance the prediction of future scene graphs from video clips. This approach aims to improve the understanding of dynamic scenes by integrating semantic dynamics and commonsense temporal regularities, which are often difficult to extract from visual features alone.

A recent paper has introduced a quantifiable framework for defining Artificial General Intelligence (AGI), proposing that AGI should match the cognitive versatility of a well-educated adult. This framework is based on the Cattell-Horn-Carroll theory and evaluates AI systems across ten cognitive domains, revealing significant gaps in current AI models, particularly in long-term memory storage.

Anthropic and OpenAI have recently showcased their respective AI models, Claude Opus 4.5 and GPT-5, highlighting their distinct approaches to security validation through system cards and red-team exercises. Anthropic's extensive 153-page system card contrasts with OpenAI's 60-page version, revealing differing methodologies in assessing AI robustness and security metrics.

The introduction of SpatialReasoner marks a significant advancement in spatial reasoning for large-scale 3D environments, addressing challenges faced by existing vision-language models that are limited to smaller, room-scale scenarios. This framework utilizes the H$^2$U3D dataset, which encompasses multi-floor environments and generates diverse question-answer pairs to enhance 3D scene understanding.

The introduction of ViRectify marks a significant advancement in the evaluation of multimodal large language models (MLLMs) by providing a comprehensive benchmark for correcting video reasoning errors. This benchmark includes a dataset of over 30,000 instances across various domains, challenging MLLMs to identify errors and generate rationales grounded in video evidence.