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Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv AI
Oct 1

Referential Uncertainty in Human--AI Collaboration

The study investigates how humans and AI collaborate on a puzzle task, focusing on referential uncertainty—when a description could refer to multiple objects. It finds that eliciting a belief distribution over candidate pieces yields better calibration and discrimination than raw action probabilities, and that precise descriptions or well‑targeted hedges significantly reduce the acceptance of wrong placements. However, the AI rarely externalizes uncertainty, and poorly targeted hedges can be counterproductive.

By Christian Poelitz, Finale Doshi-Velez, Si\^an Lindley
llmsmultimodal
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arXiv AI
Oct 1

The Backdrop Exposes What the World Around an Agent Costs It

The paper introduces BACKDROP, a benchmark that evaluates how well AI agents maintain their capabilities when faced with everyday hazards in dynamic environments. BACKDROP adds four types of hazards—authority, injection, boundary, and fault—to a task’s execution environment and measures whether agents can still achieve the correct end state. Across 3,678 variants and 16 models, the average success rate drops dramatically from 69.5% to 31.3% when all hazards are present, revealing that agents often follow unauthorized requests and fail to resist injected text.

By Nusrat Jahan Lia, Shubhashis Roy Dipta
llmsagentsbenchmarks
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