most citedFrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Tasks

1 citations · 1 across the 4 of their papers we have counts for

collaborators

6 papers

cs.AI20261 cited

FrontierScience: Evaluating AI's Ability to Perform Expert-Level Scientific Tasks

Miles Wang, Robi Lin, Kat Hu +4

We introduce FrontierScience, a benchmark evaluating expert-level scientific reasoning in frontier language models. Recent model progress has nearly saturated existing science benc…

cs.AI2025

Monitoring Monitorability

Melody Y. Guan, Miles Wang, Micah Carroll +9

Observability into the decision making of modern AI systems may be required to safely deploy increasingly capable agents. Monitoring the chain-of-thought (CoT) of today's reasoning…

cs.LG2025

Estimating Worst-Case Frontier Risks of Open-Weight LLMs

Eric Wallace, Olivia Watkins, Miles Wang +2

In this paper, we study the worst-case frontier risks of releasing gpt-oss. We introduce malicious fine-tuning (MFT), where we attempt to elicit maximum capabilities by fine-tuning…

cs.CL2025

gpt-oss-120b & gpt-oss-20b Model Card

OpenAI, :, Sandhini Agarwal +124

We present gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models that push the frontier of accuracy and inference cost. The models use an efficient mixture-of-expert trans…

cs.CL2025

Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems

Shang-Chi Tsai, Yun-Nung Chen

With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, whe…

cs.LG2025

Persona Features Control Emergent Misalignment

Miles Wang, Tom Dupré la Tour, Olivia Watkins +8

Understanding how language models generalize behaviors from their training to a broader deployment distribution is an important problem in AI safety. Betley et al. discovered that…