6 citations · 9 across the 18 of their papers we have counts for
20 papers
Meta: Precomputing Reusable Knowledge for Online Harness Evolution in Long-Horizon Agents
Lingzhi Yuan, Yiguang Cai, Weize Liu +5
Harness optimization improves long-horizon agents by refining the prompts, tools, memory, and hooks around a frozen model. Offline methods can learn from completed trajectories, bu…
DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models
Drew Rein, Patrick Oathout, Vishal Kumar +1
Language models have been adopted faster than evaluation practices that reliably assess their risks, and a growing share of use is affective rather than informational. Within that…
The Low Frequency Trap: Video Language Models Fail at Simple Event Bookkeeping
Sarvesh Baskar, Zikui Cai, Shayan Shabihi +5
Real-world video benchmarks provide broad coverage, but their fixed clips entangle event count, rate, duration, and visual complexity, making failure modes hard to isolate. While e…
ASPI: Seeking Ambiguity Clarification Amplifies Prompt Injection Vulnerability in LLM Agents
Udari Madhushani Sehwag, Zhengyang Shan, Heming Liu +3
Clarification-seeking behavior is widely regarded as a desirable property of LLM agents, enabling them to resolve ambiguity before acting on underspecified tasks. However, the secu…
ROK-FORTRESS: Measuring the Effect of Geopolitical Transcreation for National Security and Public Safety
Michael S. Lee, Yash Maurya, Drew Rein +13
Safety evaluations for large language models (LLMs) increasingly target high-stakes National Security and Public Safety (NSPS) risks, yet multilingual safety is mostly assessed thr…
SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?
Udari Madhushani Sehwag, Elaine Lau, Haniyeh Ehsani Oskouie +14
Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly physical validation. While ex…
LHAW: Controllable Underspecification for Long-Horizon Tasks
George Pu, Michael S. Lee, Udari Madhushani Sehwag +6
Long-horizon workflow agents that operate effectively over extended periods are essential for truly autonomous systems. Their reliable execution critically depends on the ability t…
Defensive Refusal Bias: How Safety Alignment Fails Cyber Defenders
David Campbell, Neil Kale, Udari Madhushani Sehwag +5
Safety alignment in large language models (LLMs), particularly for cybersecurity tasks, primarily focuses on preventing misuse. While this approach reduces direct harm, it obscures…
AgentCrypt: Advancing Privacy and (Secure) Computation in AI Agent Collaboration
Harish Karthikeyan, Yue Guo, Leo de Castro +5
As AI agents increasingly operate in complex environments, ensuring reliable, context-aware privacy is critical for regulatory compliance. Traditional access controls are insuffici…
Continual Learning of Domain Knowledge from Human Feedback in Text-to-SQL
Thomas Cook, Kelly Patel, Sivapriya Vellaichamy +4
Large Language Models (LLMs) can generate SQL queries from natural language questions but struggle with database-specific schemas and tacit domain knowledge. We introduce a framewo…
PropensityBench: Evaluating Latent Safety Risks in Large Language Models via an Agentic Approach
Udari Madhushani Sehwag, Shayan Shabihi, Alex McAvoy +4
Recent advances in Large Language Models (LLMs) have sparked concerns over their potential to acquire and misuse dangerous or high-risk capabilities, posing frontier risks. Current…
Best Practices for Biorisk Evaluations on Open-Weight Bio-Foundation Models
Boyi Wei, Zora Che, Nathaniel Li +10
Open-weight bio-foundation models present a dual-use dilemma. While holding great promise for accelerating scientific research and drug development, they could also enable bad acto…
Remote Labor Index: Measuring AI Automation of Remote Work
Mantas Mazeika, Alice Gatti, Cristina Menghini +44
AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To mea…
MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes
Yu Ying Chiu, Michael S. Lee, Rachel Calcott +17
As AI systems progress, we rely more on them to make decisions with us and for us. To ensure that such decisions are aligned with human values, it is imperative for us to understan…
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment
Souradip Chakraborty, Sujay Bhatt, Udari Madhushani Sehwag +7
Alignment of Large Language models (LLMs) is crucial for safe and trustworthy deployment in applications. Reinforcement learning from human feedback (RLHF) has emerged as an effect…
In-Context Learning with Topological Information for Knowledge Graph Completion
Udari Madhushani Sehwag, Kassiani Papasotiriou, Jared Vann +1
Knowledge graphs (KGs) are crucial for representing and reasoning over structured information, supporting a wide range of applications such as information retrieval, question answe…
Can LLMs be Scammed? A Baseline Measurement Study
Udari Madhushani Sehwag, Kelly Patel, Francesca Mosca +2
Despite the importance of developing generative AI models that can effectively resist scams, current literature lacks a structured framework for evaluating their vulnerability to s…
AdvBDGen: Adversarially Fortified Prompt-Specific Fuzzy Backdoor Generator Against LLM Alignment
Pankayaraj Pathmanathan, Udari Madhushani Sehwag, Michael-Andrei Panaitescu-Liess +1
With the growing adoption of reinforcement learning with human feedback (RLHF) for aligning large language models (LLMs), the risk of backdoor installation during alignment has inc…
GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment
Yuancheng Xu, Udari Madhushani Sehwag, Alec Koppel +4
Large Language Models (LLMs) exhibit impressive capabilities but require careful alignment with human preferences. Traditional training-time methods finetune LLMs using human prefe…
SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal
Tinghao Xie, Xiangyu Qi, Yi Zeng +13
Evaluating aligned large language models' (LLMs) ability to recognize and reject unsafe user requests is crucial for safe, policy-compliant deployments. Existing evaluation efforts…