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The Price Reversal Phenomenon: When Cheaper Reasoning Models Cost More
Lingjiao Chen, Chi Zhang, Yeye He +3
Developers and consumers increasingly choose reasoning models (RMs) based on their listed API prices. However, how accurately do these prices reflect actual inference costs? We con…
Cats Confuse Reasoning LLM: Query Agnostic Adversarial Triggers for Reasoning Models
Meghana Rajeev, Rajkumar Ramamurthy, Prapti Trivedi +5
We investigate the robustness of reasoning models trained for step-by-step problem solving by introducing query-agnostic adversarial triggers - short, irrelevant text that, when ap…
VERITAS: A Unified Approach to Reliability Evaluation
Rajkumar Ramamurthy, Meghana Arakkal Rajeev, Oliver Molenschot +2
Large language models (LLMs) often fail to synthesize information from their context to generate an accurate response. This renders them unreliable in knowledge intensive settings…
Self-rationalization improves LLM as a fine-grained judge
Prapti Trivedi, Aditya Gulati, Oliver Molenschot +7
LLM-as-a-judge models have been used for evaluating both human and AI generated content, specifically by providing scores and rationales. Rationales, in addition to increasing tran…