activity
20242026
most citedCan Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2026

LongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced Colleagues

Di Wu, Zixiang Ji, Asmi Kawatkar +4

Long-term memory is crucial for agents in specialized web environments, where success depends on recalling interface affordances, state dynamics, workflows, and recurring failure m…

cs.CL2026

CustomerSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators

Yada Pruksachatkun, Yixin Wan, Xingrun Chen +2

We present CustomerSim, an environment and benchmark to evaluate the extent to which Multimodal Large Language Models (MLLMs) can simulate realistic, persona-driven customer behavi…

cs.LG2025

Customize Multi-modal RAI Guardrails with Precedent-based predictions

Cheng-Fu Yang, Thanh Tran, Christos Christodoulopoulos +3

A multi-modal guardrail must effectively filter image content based on user-defined policies, identifying material that may be hateful, reinforce harmful stereotypes, contain expli…

cs.AI20252 cited

The Amazon Nova Family of Models: Technical Report and Model Card

Amazon AGI, Aaron Langford, Aayush Shah +783

We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…

cs.CL20242 cited

Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought

Jooyoung Lee, Fan Yang, Thanh Tran +4

We introduce a novel framework, LM-Guided CoT, that leverages a lightweight (i.e., <1B) language model (LM) for guiding a black-box large (i.e., >10B) LM in reasoning tasks. Specif…