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Hallucinated citations slip through peer review at top AI conferences; CiteAudit tool targets the problem

Accepted papers at major AI conferences contain fabricated citations—references to publications that don't exist. A new open-source tool called CiteAudit is the first systematic attempt to detect and eliminate hallucinated references from peer-reviewed research.

March 8, 2026

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Researchers detect hallucinations in LLMs through computational traces

Researchers at Sapienza University of Rome have identified measurable computational traces that appear when large language models hallucinate. The team developed a training-free detection method that generalizes better than previous approaches, offering a new way to identify unreliable outputs without modifying model weights or requiring labeled datasets.

model releaseByteDance

ByteDance's Helios reaches 19.5 FPS for minute-long video generation on single GPU

ByteDance has released Helios, a 14-billion-parameter open-weight video generation model that achieves 19.5 frames per second on a single GPU while generating minute-long video clips. The researchers claim this is the first model of its scale to reach near-real-time performance at this duration. Code and model weights are publicly available.

benchmarkOpenAI

Video AI models hit reasoning ceiling despite 1000x larger dataset, researchers find

An international research team released the largest video reasoning dataset to date—roughly 1,000 times larger than previous alternatives. Testing reveals that state-of-the-art models including Sora 2 and Veo 3.1 substantially underperform humans on reasoning tasks, suggesting the limitation isn't data scarcity but architectural constraints.

2 min readvia the-decoder.com
researchAnthropic

Anthropic study: AI job disruption far below theoretical potential despite programmer exposure

Anthropic has developed a new measurement combining theoretical AI capabilities with real-world usage data, finding that programmers and customer service workers face the highest exposure to AI automation. However, unemployment in affected professions has not risen, with only early warning signs appearing among younger workers.

research

Timer-S1: 8.3B time series foundation model achieves state-of-the-art forecasting on GIFT-Eval

Researchers have introduced Timer-S1, a Mixture-of-Experts time series foundation model with 8.3 billion total parameters and 750 million activated parameters per token. The model achieves state-of-the-art forecasting performance on the GIFT-Eval leaderboard, with the best MASE and CRPS scores among pre-trained models.

2 min readvia arxiv.org

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