arXiv AI
Aug 7
Seeing Is Not Deciding: Can Multimodal LLMs Act as Effective CEOs?
arXiv:2608. 05864v1 Announce Type: new Abstract: Large language models are increasingly applied as autonomous decision-making agents.
Seeing Is Not Deciding: Can Multimodal LLMs Act as Effective CEOs?
Large language models are increasingly applied as autonomous decision-making agents. However, in executive business decisions, existing benchmarks are limited to textonly settings.
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See2Think: Do Multimodal Models Really Use Intermediate Visual States?
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arXiv AI
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Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models
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arXiv AI
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Evidence-Grounded Trustworthy Multimodal Reasoning and Evaluation Benchmark in Complex Urban Scenes
arXiv:2608. 10954v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate impressive performance in benign scenarios, their cognitive reliability deteriorates significantly in complex scenes under adverse conditions.
arXiv AI
Aug 7
C$^3$PO: Evaluating Cross-Modal Composition and Counterfactual Performance in Omnimodal Models
arXiv:2608. 05381v1 Announce Type: new Abstract: Current Multimodal Large Language Models (MLLMs) can process diverse sensory inputs, yet their reasoning remains heavily biased toward a dominant modality, resulting in brittle cross-modal reasoning.
arXiv AI
Jun 2
Visual Persuasion: What Influences Decisions of Vision-Language Models?
arXiv:2602. 15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs).
arXiv AI
Jul 14
The Ebb and Flow of Multimodal Focus: Scheduling Visual Relay Windows for Grounded VLM Reasoning
arXiv:2607. 11436v1 Announce Type: new Abstract: Vision-language models increasingly succeed on multimodal reasoning benchmarks, yet their visual evidence often becomes unstable once it enters the language stack, weakening evidence-grounded reasoning.
arXiv AI
Jun 11
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arXiv:2602. 02465v2 Announce Type: replace Abstract: Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (UMMs) capable of native interleaved generation.
arXiv AI
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arXiv:2606. 19965v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts.
arXiv AI
Jun 6
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arXiv:2606. 05843v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) demonstrate remarkable proficiency on complex vision-language tasks, the mechanisms by which they extract query-relevant visual features from complex, noisy contexts remain opaque.
arXiv AI
Jun 12
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