13 papers
Examining the Vulnerability of Multi-Agent Medical Systems to Human Interventions for Clinical Reasoning
Benjamin C Liu, Dillon Mehta, Rishi Malhotra +7
Human interventions at fault points can alter the diagnostic accuracy of multi-agent medical systems. We defined fault points as moments in AI agent conversations, in which an agen…
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 affects the moral judgments and refusal behavior produced by large language models (LLMs), yet controlled comparisons remain fragmented across datasets and models. We…
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…
Modeling and Predicting Multi-Turn Answer Instability in Large Language Models
Jiahang He, Rishi Ramachandran, Neel Ramachandran +5
As large language models (LLMs) are adopted in an increasingly wide range of applications, user-model interactions have grown in both frequency and scale. Consequently, research ha…
Inference-Time Chain-of-Thought Pruning with Latent Informativeness Signals
Sophie Li, Nicholas Huang, Nayan Saxena +4
Large language models (LLMs) improve reasoning accuracy when generating multiple candidate solutions at test time, but standard methods like Best-of-N (BoN) incur high computationa…
DuoLens: A Framework for Robust Detection of Machine-Generated Multilingual Text and Code
Shriyansh Agrawal, Aidan Lau, Sanyam Shah +4
The prevalence of Large Language Models (LLMs) for generating multilingual text and source code has only increased the imperative for machine-generated content detectors to be accu…
AgentChangeBench: A Multi-Dimensional Evaluation Framework for Goal-Shift Robustness in Conversational AI
Manik Rana, Calissa Man, Anotida Expected Msiiwa +5
Goal changes are a defining feature of real world multi-turn interactions, yet current agent benchmarks primarily evaluate static objectives or one-shot tool use. We introduce Agen…
Limits of Emergent Reasoning of Large Language Models in Agentic Frameworks for Deterministic Games
Chris Su, Harrison Li, Matheus Marques +3
Recent work reports that Large Reasoning Models (LRMs) undergo a collapse in performance on solving puzzles beyond certain perplexity thresholds. In subsequent discourse, questions…
PALADIN: Self-Correcting Language Model Agents to Cure Tool-Failure Cases
Sri Vatsa Vuddanti, Aarav Shah, Satwik Kumar Chittiprolu +4
Tool-augmented language agents frequently fail in real-world deployment due to tool malfunctions--timeouts, API exceptions, or inconsistent outputs--triggering cascading reasoning…