\textit{ViRectify}: A Challenging Benchmark for Video Reasoning Correction with Multimodal Large Language Models

A comparative study has been conducted on the object counting capabilities of two multi-modal large language models, GPT-4o and GPT-5, focusing on their performance in zero-shot scenarios using only textual prompts. The evaluation was carried out on the FSC-147 and CARPK datasets, revealing that both models achieved results comparable to state-of-the-art methods, with some instances exceeding them.

A recent study introduces Multi-resolution Retrieval-Detection (MRD), a framework designed to enhance understanding of high-resolution images by addressing the challenges faced by multimodal large language models (MLLMs) in semantic similarity computation. The MRD approach allows for better handling of image crops at varying resolutions, thus improving object localization and reducing irrelevant information.

A new framework named V-ITI has been introduced to mitigate hallucinations in Multimodal Large Language Models (MLLMs) by addressing the issue of visual neglect, which leads to inconsistencies between generated content and input visuals. This framework employs a Visual Neglect Detector to identify when intervention is necessary, aiming to enhance the reliability of MLLMs in precision-sensitive applications.

The introduction of MERIT, a groundbreaking multilingual dataset for interleaved multi-condition semantic retrieval, marks a significant advancement in the field of semantic retrieval. This dataset includes 320,000 queries across five languages and seven product categories, addressing the limitations of existing single-language datasets that often overlook the complexity of real-world retrieval scenarios.

A new mechanism called SafePTR has been introduced to enhance the security of Multimodal Large Language Models (MLLMs) against jailbreak attacks. This method analyzes harmful multimodal tokens that can bypass existing safeguards, addressing vulnerabilities that arise from integrating visual inputs with language models. The findings reveal that less than 1% of harmful tokens can trigger these vulnerabilities, highlighting the need for improved defenses.

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.

A new benchmark called ToG-Bench has been introduced to advance task-oriented spatio-temporal video grounding in egocentric videos, addressing the limitations of existing studies that focus primarily on object-centric and descriptive instructions. This benchmark emphasizes identifying and localizing objects based on intended tasks, incorporating both explicit and implicit contextual reasoning.