Verrrry loosely in the sense we weight the hypotheses, but sequential monte carlo does resampling based on the weights.

Our algorithm is designed for social reasoning rather than solving math problems! But the general idea of having multiple hypotheses during reasoning can be used! You can view this as a kind of stochastic beam search if applied to math or puzzle solving

This work was done with my amazing and fabulous collaborators 🌟
@melaniesclar.bsky.social, @xuanalogue.bsky.social, Lance Ying, @sydneylevine.bsky.social, Yang Liu, @joshtenenbaum.bsky.social, @yejinchoinka.bsky.social
😊 Couldn't have done it without them!

✨Our paper aims to spark new discussions around inference-time compute and reasoning in more broader domains, such as social reasoning. Check out our paper for more details on our ThoughtTracing and interesting results on recent reasoning models! arxiv.org/abs/2502.11881

Results show TT outperforms reasoning models with significantly fewer output tokens. Also, unlike math, we do not observe a substantially higher token usage for incorrect responses from reasoning models on ToM benchmarks; in some cases, the pattern is even reversed🤔 TT shows balanced token usage

We present ThoughtTracing💭, an inference-time reasoning algorithm for tracing mental states of specific agents. It's inspired from the sequential Monte Carlo algorithm and modeled after the Bayesian ToM framework, using LLMs to approximate probabilistic inference over agents’ evolving mental states

The false belief Qs in ToM benchmarks are known to be challenging, whereas true belief Qs are easier (see GPT-4o above). Interestingly, o3-mini scores near 100, but crashes on easy ones, resulting in lower scores than CoT overall. In contrast, our ThoughtTracing significantly improves GPT-4o📈

Can you please add me too? Thanks!

I will! Thanks!!

You could follow me haha

Thank youuu!! I'm already in the list! 💙

Hello there! Can you please add me too? Thanks!

Oh wow, looks like you’re allowed to do free speech, but you won’t get any audience 😅

Hey Natalie, can you please add me to this pack?? Thank you!