2 citations · 4 across the 41 of their papers we have counts for
41 papers
Detecting Hidden Chain-of-Thought in Large Language Models with Linguistic, Behavioral, and Mechanistic Indicators
Armaan Singh, Ryan Trinh Le, Jasmine Kaur +5
Large language models often answer complex reasoning questions without revealing intermediate steps, raising whether they reason latently or complete patterns. We propose the Hidde…
MUDDLE: Measuring Understanding of Documents under Distractor and Length Effects
Jason Luo, Saibilila Abudukelimu, Judy Song +4
Document question-answering systems increasingly answer questions over collections of retrieved documents rather than one clean source, so robustness to distracting context matters…
Compositional Failure in Audio-Visual LLMs: Late-Layer Prior Dominance Under Cross-modal Conflict
Adarsh Sudheer, David Li, Omar Elbanna +3
We study audio-visual conflict as a compositional generalization test for AV-LLMs: the model must combine synchronized but semantically incompatible audio and video evidence and de…
PAUSE: Editable Strategy Artifacts for Long-Form Cultural Story Adaptation
Taaha Kazi, Vasu Sharma, Mohammad Saifullah +1
Generative AI systems increasingly mediate cultural adaptation, but their cultural decisions are often hidden inside prompts, transient model plans, or final prose. We study PAUSE…
Talked Out of the Truth: Sycophancy in the Reasoning Chains of Multimodal Models
Mahir Numayeer Islam, Gakuto Okuyama, Nikolaus Siauw +3
Large multimodal reasoning models (LMRMs) are increasingly capable, largely through generating explicit chain-of-thought reasoning before answering, but in language models this oft…
Asymmetric Collapse in Model Merging: When Refusal Over- writes Recognition
Aarnav Choudhary, Matheus Fonseca Rocha, Jiwon Seo +2
Model merging is often used to combine capabilities from separately fine-tuned models without additional training, but it is unclear whether standard merging methods preserve multi…
Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction
Mohammad Saifullah, Thomas Kornmaier, Taaha Kazi +3
Long-form fiction writers need memory that answers multi-hop questions about evolving story state: who knows a secret and when they learned it, whether an event preceded the narrat…
From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives
Aayush Aluru, Chloe Ho, Muhammad Hammouri +5
Although large language models (LLMs) have demonstrated impressive creative fiction generation, they struggle to maintain narrative consistency and coherent plot lines in long-form…
Preference Optimization Drives Monoculture in LLM Prediction Markets
James Begin, Brendan Gho, Suman Muppavarapu +6
Prediction markets rest on the independence of participant errors. As LLM agents become active traders on platforms like Kalshi and Polymarket, we ask: does this independence hold…
Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
Jeanmely Rojas Nunez, Viraj Sawant, Nathan Allen +4
Fine-tuning large language models (LLMs) frequently induces catastrophic forgetting of prior capabilities. Recent work has shown that reinforcement learning (RL) retains prior capa…
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…
Reasoning Relay: Evaluating Stability and Interchangeability of Large Language Models in Mathematical Reasoning
Leo Lu, Jonathan Zhang, Sean Chua +4
Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of large language models (LLMs). While prior work focuses on improving model performance thro…
WOLF: Werewolf-based Observations for LLM Deception and Falsehoods
Mrinal Agarwal, Saad Rana, Theo Sundoro +5
Deception is a fundamental challenge for multi-agent reasoning: effective systems must strategically conceal information while detecting misleading behavior in others. Yet most eva…
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…
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…
From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems
Brendan Gho, Suman Muppavarapu, Afnan Shaik +6
As foundation models are increasingly deployed as interacting agents in multi-agent systems, their collective behavior raises new challenges for trustworthiness, transparency, and…
Interpreting the Latent Structure of Operator Precedence in Language Models
Dharunish Yugeswardeenoo, Harshil Nukala, Ved Shah +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities but continue to struggle with arithmetic tasks. Prior works largely focus on outputs or prompting s…
SMAGDi: Socratic Multi Agent Interaction Graph Distillation for Efficient High Accuracy Reasoning
Aayush Aluru, Myra Malik, Samarth Patankar +4
Multi-agent systems (MAS) often achieve higher reasoning accuracy than single models, but their reliance on repeated debates across agents makes them computationally expensive. We…
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…
ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn Language Models
Haziq Mohammad Khalid, Athikash Jeyaganthan, Timothy Do +4
Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally. Given that multi-turn conversations…
LIFT: Likelihood-Based Intervention Faithfulness Training
Anand Swaroop, Akshat Nallani, Saksham Uboweja +7
Chain-of-thought (CoT) reasoning improves LLM accuracy on complex tasks, but CoTs are often unfaithful; that is, the model does not rely on the stated reasoning steps to produce it…
Evaluation Awareness Scales Predictably in Open-Weights Large Language Models
Maheep Chaudhary, Ian Su, Nikhil Hooda +6
Large language models (LLMs) can internally distinguish between evaluation and deployment contexts, a behaviour known as \emph{evaluation awareness}. This undermines AI safety eval…