6 citations · 8 across the 11 of their papers we have counts for
11 papers
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…
auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation
Ben Prystawski, Kushin Mukherjee, Daniel Wurgaft +4
AI-based scientific automation is increasingly possible by using agents to generate hypotheses, design experiments, and analyze data. Data collection is a major bottleneck in this…
SPIRAL: Learning to Search and Aggregate
Jubayer Ibn Hamid, Ifdita Hasan Orney, Michael Y. Li +5
Language model reasoning can be substantially improved at test time via scaffolds that scale inference compute across different primitives -- sequential reasoning within a trace, i…
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…
GIANTS: Generative Insight Anticipation from Scientific Literature
Joy He-Yueya, Anikait Singh, Ge Gao +5
Scientific breakthroughs often emerge from synthesizing prior ideas into novel contributions. While language models (LMs) show promise in scientific discovery, their ability to per…
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…
Automated Hypothesis Validation with Agentic Sequential Falsifications
Kexin Huang, Ying Jin, Ryan Li +3
Hypotheses are central to information acquisition, decision-making, and discovery. However, many real-world hypotheses are abstract, high-level statements that are difficult to val…
BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery
Kanishk Gandhi, Michael Y. Li, Lyle Goodyear +5
Understanding the world and explaining it with scientific theories is a central aspiration of artificial intelligence research. Proposing theories, designing experiments to test th…
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…
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions
Liyi Zhang, Michael Y. Li, R. Thomas McCoy +3
Autoregressive language models have demonstrated a remarkable ability to extract latent structure from text. The embeddings from large language models have been shown to capture as…
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…