39 citations · 61 across the 9 of their papers we have counts for
9 papers
Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks
Wasu Top Piriyakulkij, Rachel Lawrence, Alicia Curth +2
How can language model agents effectively leverage libraries of reusable knowledge to solve long-horizon tasks? Recent work has increasingly focused on agent skills: reusable capab…
Do Transformers Use their Depth Adaptively? Evidence from a Relational Reasoning Task
Alicia Curth, Rachel Lawrence, Sushrut Karmalkar +1
We investigate whether transformers use their depth adaptively across tasks of increasing difficulty. Using a controlled multi-hop relational reasoning task based on family stories…
Better Think Thrice: Learning to Reason Causally with Double Counterfactual Consistency
Victoria Lin, Xinnuo Xu, Rachel Lawrence +4
Despite their strong performance on reasoning benchmarks, large language models (LLMs) have proven brittle when presented with counterfactual questions, suggesting weaknesses in th…
Parallel Sampling from Masked Diffusion Models via Conditional Independence Testing
Iskander Azangulov, Teodora Pandeva, Niranjani Prasad +2
Masked diffusion models (MDMs) offer a compelling alternative to autoregressive models (ARMs) for discrete text generation because they enable parallel token sampling, rather than…
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires
Melanie F. Pradier, Niranjani Prasad, Paidamoyo Chapfuwa +8
Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding t…
Defining Admissible Rewards for High Confidence Policy Evaluation
Niranjani Prasad, Barbara E Engelhardt, Finale Doshi-Velez
A key impediment to reinforcement learning (RL) in real applications with limited, batch data is defining a reward function that reflects what we implicitly know about reasonable b…