3 citations · 8 across the 13 of their papers we have counts for
13 papers
VIALS: A Benchmark for Visual Interpretation of Artifacts in the Life Sciences
Elaine Lau, Thanuka Udumulla, Lee Izhaki-Tavor +3
In professional life sciences workflows, scientists routinely interpret visual artifacts (gel blots, microscopy images, plasmid maps, flow cytometry plots, molecular structures, ..…
CAREBench: A Child-Safety Risk Benchmark for Language Models
Kaavya Krishna-Kumar, Elaine Lau, Vaughn Robinson +6
How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations focus on child sexual abuse…
On Safety Risks in Experience-Driven Self-Evolving Agents
Weixiang Zhao, Yichen Zhang, Yingshuo Wang +8
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduc…
BankerToolBench: Evaluating AI Agents in End-to-End Investment Banking Workflows
Elaine Lau, Markus Dücker, Ronak Chaudhary +24
Existing AI benchmarks lack the fidelity to assess economically meaningful progress on professional workflows. To evaluate frontier AI agents in a high-value, labor-intensive profe…
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…
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…
Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
Anisha Gunjal, Anthony Wang, Elaine Lau +4
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for complex reasoning tasks with clear correctness signals such as math and coding. However, extending it…
Adaptive Guidance Accelerates Reinforcement Learning of Reasoning Models
Vaskar Nath, Elaine Lau, Anisha Gunjal +3
We study the process through which reasoning models trained with reinforcement learning on verifiable rewards (RLVR) can learn to solve new problems. We find that RLVR drives perfo…
Refusal-Trained LLMs Are Easily Jailbroken As Browser Agents
Priyanshu Kumar, Elaine Lau, Saranya Vijayakumar +9
For safety reasons, large language models (LLMs) are trained to refuse harmful user instructions, such as assisting dangerous activities. We study an open question in this work: do…
QGFN: Controllable Greediness with Action Values
Elaine Lau, Stephen Zhewen Lu, Ling Pan +2
Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating diverse and high-utility samples. Howeve…
DGFN: Double Generative Flow Networks
Elaine Lau, Nikhil Vemgal, Doina Precup +1
Deep learning is emerging as an effective tool in drug discovery, with potential applications in both predictive and generative models. Generative Flow Networks (GFlowNets/GFNs) ar…
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets
Nikhil Vemgal, Elaine Lau, Doina Precup
Reinforcement Learning (RL) algorithms aim to learn an optimal policy by iteratively sampling actions to learn how to maximize the total expected return, . GFlowNets are a sp…
Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning
Flemming Kondrup, Thomas Jiralerspong, Elaine Lau +5
Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Healthcare workers are required to continuously adjust ventilator settings for each pat…