arXiv subjects

Rachel Ma

Publications and source records attributed to Rachel Ma.

6 recordsLinked to original sources

arXiv2026

A Dataset for Dynamic Human Preferences for Vision Language Models

Given the increased adoption of Vision Language Models (VLMs) in human-interactive settings, it is important that we evaluate how well these models can adapt to real-time preferences for different users. While an increasing number of vision-language benchmarks have recently been introduced, they focus largely on evaluating static capabilities and generally-held preferences learned from extensive training data. This work introduces a new benchmark for evaluating the ability of VLMs to understand dynamic human-preferences, i.e. preferences that are passed in-context at inference time. We provide an automated pipeline for generating this benchmark with variations on image dependence, a dynamic multi-modal human-preference dataset, and evaluations of state-of-the-art models on the novel benchmark.

cs.CV↗

arXiv2026

A Mechanistic Analysis of Adversarial Fine-tuning of Vision Transformers

The widespread use of image classification models in high-risk, real-world situations necessitates making these models robust to slight disturbances or perturbations, such as blurring or sharpening, in the input images. While vision transformers (ViTs) play an integral role in many modern-day multi-modal models like Vision-Language-Models (VLMs) and Vision-Language-Action (VLA) models, they have received a lack of attention in the setting of robustness. In this work, we analyze the effects of adversarial fine-tuning, a popular method for improving model robustness to image perturbations, on a ViT's performance on perturbed and regular images through a mechanistic lens. We adversarially train a ViT on low-frequency and high-frequency image corruptions, and attempt to explain changes in downstream model performance through an examination of the model's attention mechanisms, internal representations, and knowledge evolution. Overall, our results suggest that, while fine-tuning on inputs with common corruptions improves model performance and certainty on new instances of corrupted data, these improvements do not transfer to other classes of corruptions not seen in the training. Additionally, despite observing changes in visual attention and knowledge evolution across layers, we found that adversarial training did not lead to fundamental changes in the sparse representations learned by ViTs.

cs.CV↗

arXiv2026

Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport

Inference-time scaling methods rely on Process Reward Models (PRMs), which are often poorly calibrated and overestimate success probabilities. We propose, to our knowledge, the first use of conditional optimal transport for calibrating PRMs, modifying conditional OT (CondOT) map learning \cite{bunne2022supervised} to estimate a monotonic conditional quantile function over success probabilities estimated by the PRM, conditioned on PRM hidden states. This yields structurally valid quantile estimates and enables efficient extraction of confidence bounds at arbitrary levels, which we integrate into the instance-adaptive scaling (IAS) framework of \cite{park2025know}. We evaluate on mathematical reasoning benchmarks spanning moderate-difficulty problems (MATH-500) and harder out-of-distribution problems (AIME). For PRMs with reliable ranking signals, our method substantially improves calibration over both uncalibrated PRMs and quantile regression. On downstream Best-of-N IAS performance, our method generally improves over uncalibrated PRMs. These results establish conditional optimal transport as another principled and practical approach to PRM calibration, offering structural guarantees and flexible uncertainty estimation.

cs.LG↗

arXiv2025

Flexible Agent Alignment with Goal Inference from Open-Ended Dialog

We introduce Open-Universe Assistance Games (OU-AGs), a formal framework extending assistance games to LLM-based agents. Effective assistance requires reasoning over human preferences that are unbounded, underspecified, and evolving. Current LLM agents struggle in multi-turn interactions and with maintaining accurate models of user intent in collaborative settings. Existing assistance game formulations assume fixed, predefined preferences, an assumption that breaks down in open-ended dialogue where goals are revised incrementally and expressed in natural language. Grounded in cognitive science accounts of preference construction, we represent human preferences as a dynamically updated distribution over discrete natural-language goals. To operationalize OU-AGs, we introduce GOOD (GOals from Open-ended Dialogue), a data-efficient online method that extracts and ranks candidate goals during interaction, using LLM-simulated users to perform probabilistic inference over goal hypotheses. This allows for interpretable, uncertainty-aware preference representations without large offline datasets. We evaluate GOOD across three text-based domains: grocery shopping, household robotics (AI2-THOR), and coding. Compared to baselines without explicit goal tracking, GOOD produces semantically coherent goal representations and improves alignment with user intent across domains.

cs.AI↗

arXiv2024

Skill Generalization with Verbs

It is imperative that robots can understand natural language commands issued by humans. Such commands typically contain verbs that signify what action should be performed on a given object and that are applicable to many objects. We propose a method for generalizing manipulation skills to novel objects using verbs. Our method learns a probabilistic classifier that determines whether a given object trajectory can be described by a specific verb. We show that this classifier accurately generalizes to novel object categories with an average accuracy of 76.69% across 13 object categories and 14 verbs. We then perform policy search over the object kinematics to find an object trajectory that maximizes classifier prediction for a given verb. Our method allows a robot to generate a trajectory for a novel object based on a verb, which can then be used as input to a motion planner. We show that our model can generate trajectories that are usable for executing five verb commands applied to novel instances of two different object categories on a real robot.

cs.RO↗

arXiv2024

Goal Inference from Open-Ended Dialog

Embodied AI Agents are quickly becoming important and common tools in society. These embodied agents should be able to learn about and accomplish a wide range of user goals and preferences efficiently and robustly. Large Language Models (LLMs) are often used as they allow for opportunities for rich and open-ended dialog type interaction between the human and agent to accomplish tasks according to human preferences. In this thesis, we argue that for embodied agents that deal with open-ended dialog during task assistance: 1) AI Agents should extract goals from conversations in the form of Natural Language (NL) to be better at capturing human preferences as it is intuitive for humans to communicate their preferences on tasks to agents through natural language. 2) AI Agents should quantify/maintain uncertainty about these goals to ensure that actions are being taken according to goals that the agent is extremely certain about. We present an online method for embodied agents to learn and accomplish diverse user goals. While offline methods like RLHF can represent various goals but require large datasets, our approach achieves similar flexibility with online efficiency. We extract natural language goal representations from conversations with Large Language Models (LLMs). We prompt an LLM to role play as a human with different goals and use the corresponding likelihoods to run Bayesian inference over potential goals. As a result, our method can represent uncertainty over complex goals based on unrestricted dialog. We evaluate in a text-based grocery shopping domain and an AI2Thor robot simulation. We compare our method to ablation baselines that lack either explicit goal representation or probabilistic inference.

cs.AI↗