AIConfPaper
← Search

Chen Bo Calvin Zhang

7 accepted papers

2026

MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes

Yu Ying Chiu, Michael S. Lee, Rachel Calcott, Brandon Handoko, Paul de Font-Reaulx, Paula Rodriguez +12
ICLR 2026poster

As AI systems progresses, we rely more on them to make decisions with us and for us. To ensure that such decisions are aligned with human values, it is imperative for us to understand not only what decisions they make but also how they come to those decisions. Reasoning language models, which provid…

Cited by 0Source ↗code ↗Scholar ↗
2026

Reliable Weak-to-Strong Monitoring of LLM Agents

Neil Kale, Chen Bo Calvin Zhang, Kevin Zhu, Ankit Aich, Paula Rodriguez, Christina Q Knight +1
ICLR 2026oral

We stress test monitoring systems for detecting covert misbehavior in LLM agents (e.g., secretly exfiltrating data). We propose a monitor red teaming (MRT) workflow that varies agent and monitor awareness, adversarial evasion strategies, and evaluation across tool-calling (SHADE-Arena) and computer-…

Cited by 0Source ↗code ↗Scholar ↗
2026

ResearchRubrics: A Benchmark of Prompts and Rubrics For Deep Research Agents

Manasi Sharma, Chen Bo Calvin Zhang, Chaithanya Bandi, Ankit Aich, Huy Nghiem, Tahseen Rabbani +10
ICLR 2026poster

Deep Research (DR) is an emerging agent application that leverages large language models (LLMs) to address open-ended queries. It requires the integration of several capabilities, including multi-step reasoning, cross-document synthesis, and the generation of evidence-backed, long-form answers. Eval…

Cited by 0Source ↗Scholar ↗
2026

SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?

Xiang Deng, Jeff Da, Edwin Pan, Yannis Yiming He, Charles Ide, Kanak Garg +12
ICML 2026poster

We present SWE-Bench Pro, a comprehensive benchmark designed to evaluate software engineering capabilities through complex, realistic programming challenges. This benchmark extends beyond traditional algorithmic problems to encompass the full spectrum of professional software development tasks. The …

Cited by 0Source ↗Scholar ↗
2026

SciPredict: Can LLMs Predict the Outcomes of Scientific Experiments in Natural Sciences?

Udari Sehwag, Elaine Lau, Haniyeh Oskouie, Shayan Shabihi, Erich Liang, Andrea Toledo +11
ICML 2026poster

Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly physical validation. While existing benchmarks evaluate LLMs on scientific knowledge and reasoning, their ability to predict experimental outcomes---…

Cited by 0Source ↗Scholar ↗
2026

SpreadsheetArena: Decomposing Preference in LLM Generation of Spreadsheet Workbooks

Srivatsa Kundurthy, Clara Na, Michael Handley, Zach Kirshner, Chen Bo Calvin Zhang, Manasi Sharma +2
ICML 2026poster

Large language models (LLMs) are increasingly tasked with producing and manipulating structured artifacts. We consider the task of end-to-end **spreadsheet generation**, where language models are prompted to produce spreadsheet artifacts to satisfy users' explicit and implicit constraints, specified…

Cited by 0Source ↗Scholar ↗
2025

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization

Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal
ICLR 2025poster

Reward shaping is critical in reinforcement learning (RL), particularly for complex tasks where sparse rewards can hinder learning. However, choosing effective shaping rewards from a set of reward functions in a computationally efficient manner remains an open challenge. We propose Online Reward Sel…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗