Do large language models scrutinise what they review? A multimodal audit of scoring calibration, error detection, and author-identity effects

Large language models (LLMs) are increasingly used to generate peer reviews, prompting examination of their capacity for critical evaluation. This study evaluates two multimodal LLMs, Qwen2.5-VL-72B and Pixtral-Large-124B, as reviewers across 165 submissions to the 2026 International Conference on Learning Representations, a venue that postdates both models' training cutoffs. Manuscripts were presented to both models with author identities blinded, replaced with high-prestige affiliations, or replaced with low-prestige affiliations, and in either text-only or text-with-figure format. Additionally, 145 verifiably detectable errors were inserted into 55 manuscripts to assess error identification under natural and verification-oriented prompts. Across all manuscript groups, including rejected submissions, LLM scores ranged from 7.0 to 8.1, whereas human mean scores ranged from 3.4 to 6.8. The models detected 12.1\% of the verified errors under natural prompting, and a one-sentence verification instruction increased detection to 22.2\%; however, 78\% of the errors remained undetected. Providing figures reduced error detection while increasing review scores. No visual error was reliably verified against its corresponding figure, and half of the text-only reviews described figures that were not provided. Author identity did not influence either review scores or error detection. LLM editorial decisions exactly matched those produced by simple score averaging.

Auditing Engagement Incentives in the Kidfluencer Ecosystem: A Multimodal Weak Supervision Approach

The rise of `kidfluencers' on YouTube has raised ethical concerns about child digital labor and exploitation. While emerging legislation attempts to regulate this ecosystem, empirical evidence linking exploitation to engagement remains scarce, given the difficulty of operationalizing exploitation at scale. This study presents a multimodal AI audit of 5,051 videos across 79 kidfluencer channels, using weak supervision to detect exploitation signals without large-scale manual labels. We aggregate noisy labeling functions -- including LLM-based classification of titles and GPT-4 Vision analysis of thumbnails and descriptions across six literature-grounded dimensions -- to assign a probabilistic exploitation score to each video. A multi-annotator validation study (N=107) shows strong agreement with human judgment (macro-average F1 $= 0.911$) and high sensitivity for overall exploitation risk (recall $= 0.960$, F1 $= 0.793$). Our findings reveal a significant engagement premium for performative labor, emotional bait, and privacy violations. Exploitation scores correlate with view counts (Spearman $ρ= 0.229$, $p < 10^{-50}$), and mixed-effects regression controlling for channel-level variation shows that a one-unit increase in exploitation score yields a $4.4\times$ increase in views ($p < 0.001$). Within-channel analyses indicate median view boosts of $+65.6\%$ for emotional bait and $+56.0\%$ for performative content (FDR-corrected $p<0.001$), with effects holding in same-year robustness checks ($p=0.030$). Explicit commercial content (product placement), by contrast, shows no premium ($-3.8\%$, n.s.), suggesting the platform rewards commodification of the child's identity and labor over traditional advertising. These findings challenge policy frameworks focused solely on financial trusts, showing that engagement is systematically tied to the intensive, performative labor of children.