12 papers
Broken Chains: The Cost of Incomplete Reasoning in LLMs
Ian Su, Gaurav Purushothaman, Jey Narayan +5
Reasoning-specialized models like OpenAI's 5.1 and DeepSeek-V3.2 allocate substantial inference compute to extended chain-of-thought (CoT) traces, yet reasoning tokens incur signif…
ProMoral-Bench: Evaluating Prompting Strategies for Moral Reasoning and Safety in LLMs
Rohan Subramanian Thomas, Shikhar Shiromani, Abdullah Chaudhry +4
Prompt design significantly impacts the moral competence and safety alignment of large language models (LLMs), yet empirical comparisons remain fragmented across datasets and model…
Emergent Persuasion: Will LLMs Persuade Without Being Prompted?
Vincent Chang, Thee Ho, Sunishchal Dev +4
With the wide-scale adoption of conversational AI systems, AI are now able to exert unprecedented influence on human opinion and beliefs. Recent work has shown that many Large Lang…
COMPASS: Context-Modulated PID Attention Steering System for Hallucination Mitigation
Kenji Sahay, Snigdha Pandya, Rohan Nagale +4
Large language models (LLMs) often generate fluent but factually incorrect statements despite having access to relevant evidence, a failure mode rooted in how they allocate attenti…
SALT: Steering Activations towards Leakage-free Thinking in Chain of Thought
Shourya Batra, Pierce Tillman, Samarth Gaggar +6
As Large Language Models (LLMs) evolve into personal assistants with access to sensitive user data, they face a critical privacy challenge: while prior work has addressed output-le…
Sumudu Neural Operator for ODEs and PDEs
Ben Zelenskiy, Saibilila Abudukelimu, George Flint +2
We introduce the Sumudu Neural Operator (SNO), a neural operator rooted in the properties of the Sumudu Transform. We leverage the relationship between the polynomial expansions of…