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Urja Pawar

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#artificial intelligence Preprint Sep 2026

Strangers to Themselves: What Language Models Say About Themselves Is Generic

Language models can fluently describe how they would behave: whether they would cave to pushback, misuse a tool, or lie under pressure. Is that description actually about the model speaking? We turn self-knowledge into a prediction test. Across nine behavioral evaluations, we measure how a model behaves under different...

Phil Blandfort, Urja Pawar · 0 citations
#artificial intelligence Preprint Sep 2026

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

LLM decision components that can operate within agent workflows often produce action-relevant recommendations or judgements together with explanations. Operators may use the named factors to monitor a system, diagnose errors, or decide when to escalate an output. Such use assumes that the explanations agree with the co...

Urja Pawar, R. Ramanayake, Nabeel Kemal et al. · 0 citations
#artificial intelligence Review Sep 2026

From Tokens to Semantics: Leveraging Complementary Signals for Hallucination Detection in Black-Box LLMs

When LLMs support public-facing or high-stakes workflows, missed fabrications can harm users and institutions, while false alarms consume limited human-review capacity. When no trusted context or reference document is available, we study two signals accessible through black-box model APIs: semantic entropy, which measu...

Urja Pawar, R. Ramanayake, O. O'Neill et al. · 0 citations

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