Kshitish Ghate

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@GhateKshitish

PhD student @UWCSE | @MSFTResearch Intern | MLT Grad student @LTIatCMU | Human-AI interaction and alignment

kshitishghate.github.io Joined October 2022

288 Following 162 Followers 88 Posts

Latest posts 5

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Kshitish Ghate @GhateKshitish Nov 3

New #NeurIPS2026 paper 🧵
We introduce 🌟 Deep Reasoning 🌟, an approach for building just-in-time agentic harnesses for solving diverse reasoning problems.
Inspired by human cognition, we recursively decompose tasks into atomic units of formal and associative reasoning steps. We instantiate deep reasoning through DOLORES, our structured meta-harness:

DOLORES achieves ~25% higher accuracy against SOTA baselines (including RLMs and Deep Research) 🚀
enabling 8B models to outperform 32B variants of the same family 🤖
…all training-free!

🧵 below,

Kshitish Ghate @GhateKshitish Oct 5

Will be presenting PPol at #COLM2026 Social Sim Workshop. Come say hi!!

Generate realistic user personas with PPol, which is now a pip-installable package. Check it out and let us know what you think :)

Harshita Chopra @chopra_harshita Oct 5

Excited to present Persona Policies (𝗣𝗣𝗼𝗹) at the #COLM2026 Social Sim Workshop this Friday! 🌉

More news: PPol is now super easy to use! Start simulating more realistic user behaviors using 👉 𝐩𝐢𝐩 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐩𝐩𝐨𝐥 👈

Check out our GitHub repo for examples!
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Kshitish Ghate @GhateKshitish May 16

Excited to share our new paper on Persona Policies (PPol)! We evolved user simulator personas that are diverse, human-like, and task-preserving.
Training agents on PPol rollouts improves task performance with out-of-distribution users vs. training on default simulators!🧵

Harshita Chopra @chopra_harshita May 16

Are you training or evaluating agents with LLM-based user simulators?

Most simulators inherit the behavior of their underlying models: cooperative, clear, and homogeneous - which is unrealistic! 🤖

Humans are messy: they falter, forget, push back, and behave in ways that are difficult to define. Manually writing personas becomes brittle and hard to scale. 😣

We introduce 𝗣𝗲𝗿𝘀𝗼𝗻𝗮 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 (𝗣𝗣𝗼𝗹): an evolutionary framework that automatically discovers behaviors and instructions to generate diverse human-like user personas for any given task – ✨grounded in real dialogue traces✨

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Kshitish Ghate @GhateKshitish Oct 14, 2025

🚨New paper: Reward Models (RMs) are used to align LLMs, but can they be steered toward user-specific value/style preferences?
With EVALUESTEER, we find even the best RMs we tested exhibit their own value/style biases, and are unable to align with a user >25% of the time. 🧵

Kshitish Ghate @GhateKshitish Oct 2, 2025

Check out our new paper that uses simulated moral dilemmas to study how LLMs prioritize different values!

Andy Liu Verified account @uilydna Oct 2, 2025

🚨New Paper: LLM developers aim to align models with values like helpfulness or harmlessness. But when these conflict, which values do models choose to support? We introduce ConflictScope, a fully-automated evaluation pipeline that reveals how models rank values under conflict.

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