2 citations · 3 across the 27 of their papers we have counts for
27 papers
Can LLM Agents Select and Engage with Biological Tools?
Jeffrey Lee, Alyssa Wordland, Kyle Brady +12
Computational tools for biological research (biological tools, or BTs) are advancing rapidly in both quantity and demonstrated capability. While these technologies offer many benef…
How Language Models Choose Sides: Internal Representations of Instruction Hierarchy
Enrique Balp-Straffon, Chih-Hao Hsu, Rushiraj Gadhvi +3
We study how instruction-tuned LLMs arbitrate direct conflicts between system and user instructions. We introduce a benchmark of 41 paired constraints with deterministic verifiers…
Open Weight AI Models Require Proportional Evaluation Approaches
Patricia Paskov, Christopher Rodriguez, Sunishchal Dev +1
Open-weight AI models (OWMs), or models released with publicly-available weights, are distributing rapidly and approaching the performance levels of leading closed-weight AI models…
CA-BED: Conversation-Aware Bayesian Experimental Design
Daniel Arnould, Rashad Aziz, Zixuan Kang +5
Large Language Models (LLMs) excel at static reasoning tasks, yet their performance often degrades in interactive scenarios where information must be actively acquired through ques…
Seven simple steps for log analysis in AI systems
Magda Dubois, Ekin Zorer, Maia Hamin +17
AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensities, and behaviours, or assess…
Judge Reliability Harness: Stress Testing the Reliability of LLM Judges
Sunishchal Dev, Andrew Sloan, Joshua Kavner +2
We present the Judge Reliability Harness, an open source library for constructing validation suites that test the reliability of LLM judges. As LLM based scoring is widely deployed…
The science and practice of proportionality in AI risk evaluations
Carlos Mougan, Lauritz Morlock, Jair Aguirre +19
A global challenge in artificial intelligence (AI) regulation lies in achieving effective risk management without compromising innovation and technical progress. The European Union…
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…
Mitigating Forgetting in Continual Learning with Selective Gradient Projection
Anika Singh, Aayush Dhaulakhandi, Varun Chopade +3
As neural networks are increasingly deployed in dynamic environments, they face the challenge of catastrophic forgetting, the tendency to overwrite previously learned knowledge whe…
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…
A Few Bad Neurons: Isolating and Surgically Correcting Sycophancy
Claire O'Brien, Jessica Seto, Dristi Roy +6
Behavioral alignment in large language models (LLMs) is often achieved through broad fine-tuning, which can result in undesired side effects like distributional shift and low inter…
Visualizing and Benchmarking LLM Factual Hallucination Tendencies via Internal State Analysis and Clustering
Nathan Mao, Varun Kaushik, Shreya Shivkumar +3
Large Language Models (LLMs) often hallucinate, generating nonsensical or false information that can be especially harmful in sensitive fields such as medicine or law. To study thi…
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…
Peek-a-Boo Reasoning: Contrastive Region Masking in MLLMs
Isha Chaturvedi, Anjana Nair, Yushen Li +5
We introduce Contrastive Region Masking (CRM), a training free diagnostic that reveals how multimodal large language models (MLLMs) depend on specific visual regions at each step o…
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…
Emergent Misalignment via In-Context Learning: Narrow in-context examples can produce broadly misaligned LLMs
Nikita Afonin, Nikita Andriianov, Vahagn Hovhannisyan +9
Recent work has shown that narrow finetuning can produce broadly misaligned LLMs, a phenomenon termed emergent misalignment (EM). While concerning, these findings were limited to f…
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…