4arXiv cs.CL (Computation and Language)·
Survey on computational humor with multimodal LLMs: benchmarks, methods, and open challenges
A new arXiv survey examines the state of visual humor understanding in AI systems, covering memes, cartoons, and comics as test cases for non-literal reasoning. The authors organize the literature into a capability hierarchy spanning recognition, interpretation, reasoning, and generation, and trace the field's shift from task-specific fusion models to large multimodal model approaches. Key barriers identified include shortcut-prone evaluation, limited cultural coverage, weak evidence grounding, and safety and ownership concerns.
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Survey: Human-View Video Understanding with MLLMs — Watch, Remember, Reason Framework
A new arXiv survey paper proposes a unified 'human-view' framework for analyzing multimodal LLM-based video understanding, organized around three functional abilities: watching (perception), remembering (memory), and reasoning. The authors introduce a formulation characterizing video understanding systems by perceptual representations, memory states, reasoning traces, and predictions, then survey methods, datasets, and benchmarks across these dimensions. The work covers challenges including spatio-temporal perception, long-video processing, streaming understanding, and faithful reasoning, with application domains spanning egocentric, sports, medical, and narrative video.
Survey chapter on LLM mechanisms, emergent capabilities, and cognition debates
A new arXiv preprint surveys current understanding of large language models, covering the Transformer architecture, emergent capabilities resembling human cognition (symbolic reasoning, theory of mind, deception), and explainability approaches from neuron activation analysis to circuit tracing. The chapter also engages the debate over whether LLMs genuinely understand or merely pattern-match, arguing against reductive anti-anthropomorphism while acknowledging human-LLM differences. It is framed as a book chapter synthesizing recent empirical findings and theoretical positions.
Benchmark reveals uneven scientific visualization literacy across six MLLMs
A new arXiv preprint benchmarks six multimodal LLMs (three closed-source, three open-source) on a standardized 49-item scientific visualization literacy test spanning 18 visualizations, 8 techniques, and 11 task types, comparing results against 485 human participants. Gemini emerges as the strongest model, exceeding the human mean, while open-source models fall below the human baseline. Performance is highly uneven: models handle scientific illustration and spatial tasks well but struggle with texture-based visualizations, flow-direction interpretation, and quantitative estimation. The authors argue SciVis literacy should be treated as a necessary evaluation dimension for multimodal AI systems.
AIR: Adaptive Interleaved Reasoning with Code in Multimodal LLMs via Reinforcement Learning
Researchers propose AIR, a system that trains multimodal large language models to adaptively interleave reasoning with code execution for numerical computation tasks, going beyond prior work that focused only on visual operations. The approach combines a two-stage cold-start data pipeline, RL dataset filtering, and a group-constrained reward function for tool-invocation decisions. Experiments show a 6.1 percentage point average improvement on evaluation benchmarks, with interleaved reasoning samples gaining 9.9 pp and tool-use success exceeding 95%.
Text Analytics Evaluation Framework: Benchmarking LLMs on Social Media NLP Tasks
Researchers introduce a 470-question evaluation framework to assess LLM performance on aggregated social media text, applied to Twitter datasets across sentiment analysis, hate speech detection, and emotion recognition. Results show performance degrades substantially as input scale exceeds 500 instances, particularly for open-weights models on numerical tasks. Multi-label and target-dependent scenarios also show notable performance drops, and task complexity progressively erodes accuracy from basic semantic identification to comparison and counting operations. The findings point to architectural bottlenecks in current LLMs for rigorous quantitative analysis over large text collections.
HCIG: Hierarchical Cross-Modal Incongruity Graph Network for Multimodal Sarcasm and Cyberbullying Detection
Researchers propose HCIG, a graph attention network framework that models cross-modal incongruity between text and images at token, phrase, and global levels for detecting sarcasm and cyberbullying in social media. A complementary architecture, GCCN, uses contradiction-aware pooling for efficient multimodal reasoning. HCIG achieves 85.74% accuracy on the MMSD sarcasm benchmark and 69.62% accuracy on the MultiBully cyberbullying dataset, outperforming conventional fusion-based approaches. The work addresses a narrow but practically relevant NLP task in content moderation.
Introducing ConTextual: Benchmark for Joint Text-Image Reasoning in Text-Rich Scenes
Hugging Face introduces ConTextual, a new benchmark evaluating multimodal models on their ability to jointly reason over text and images in text-rich scenes. The benchmark targets a specific capability gap where models must integrate visual and textual information simultaneously rather than treating them independently. A leaderboard accompanies the benchmark to track model progress on this task.
ImportAI 449: LLMs training other LLMs; 72B distributed training run; computer vision is harder than generative text
Import AI issue 449 covers several AI/ML developments including LLMs being used to train other LLMs, a 72B parameter distributed training run, and analysis of why computer vision remains harder than generative text. The newsletter also touches on potential political implications of AI progress. As a tier-2 commentary source, this aggregates and contextualizes multiple technical developments across the AI landscape.