most citedAutomated Statistical Model Discovery with Language Models

2 citations · 2 across the 10 of their papers we have counts for

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cs.LG2026

QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling

Michael Y. Li, Anthony Zhan, Kanishk Gandhi +2

Scaling inference compute, by generating many parallel attempts per problem, is a costly but reliable lever for improving language model capabilities. By default these attempts are…

cs.LG2026

General Preference Reinforcement Learning

Muhammad Umer, Muhammad Ahmed Mohsin, Ahsan Bilal +5

Post-training has split large language model (LLM) alignment into two largely disconnected tracks. Online reinforcement learning (RL) with verifiable rewards drives emergent reason…

cs.LG2026

Neural Garbage Collection: Learning to Forget while Learning to Reason

Michael Y. Li, Jubayer Ibn Hamid, Emily B. Fox +1

Chain-of-thought reasoning has driven striking advances in language model capability, yet every reasoning step grows the KV cache, creating a bottleneck to scaling this paradigm fu…

cs.LG2026

: Stratified Scaling Search for Test-Time in Diffusion Language Models

Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer +6

Test-time scaling investigates whether a fixed diffusion language model (DLM) can generate better outputs when given more inference compute, without additional training. However, n…

cs.LG2026

Simplified Sparse Attention via Gist Tokens

Yuzhen Mao, Michael Y. Li, Emily B. Fox

Sparse attention can reduce the cost of long-context inference, but most variants introduce new architectural components. We introduce Simplified Sparse Attention (SSA), a simpler…

cs.LG2024

CriticAL: Critic Automation with Language Models

Michael Y. Li, Vivek Vajipey, Noah D. Goodman +1

Understanding the world through models is a fundamental goal of scientific research. While large language model (LLM) based approaches show promise in automating scientific discove…

cs.LG2024★ 2 cited

Automated Statistical Model Discovery with Language Models

Michael Y. Li, Emily B. Fox, Noah D. Goodman

Statistical model discovery is a challenging search over a vast space of models subject to domain-specific constraints. Efficiently searching over this space requires expertise in…