3 citations · 8 across the 11 of their papers we have counts for
12 papers
Co-Evolving Actor-Conditioned Critics for Non-Verifiable Generation
Jinyoung Kim, Muhammad Khalifa, Lajanugen Logeswaran +4
Natural-language critiques provide supervision beyond scalar rewards for non-verifiable generation, which lacks deterministic verifiers. In critique-guided refinement, a critic giv…
A Regret Minimization Framework on Preference Learning in Large Language Models
Suhwan Kim, Taehyun Cho, Geon-Hyeong Kim +4
Reinforcement learning with verifiable rewards (RLVR) has enabled progress on reasoning-intensive tasks by relying on task-specific verifiers that provide automated correctness sig…
Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models
Jiyeon Kim, Sungik Choi, Yongrae Jo +2
Diffusion-based language models (dLLMs) have emerged as a promising alternative to autoregressive language models, offering the potential for parallel token generation and bidirect…
DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode
Hojae Han, Jaejin Kim, Seung-won Hwang +2
This work addresses test output prediction, a key challenge in test case generation. To improve the reliability of predicted outputs by LLMs, prior approaches generate code first t…
Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation
Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim +6
Large language models (LLMs) are increasingly used as judges to evaluate agent performance, particularly in non-verifiable settings where judgments rely on agent trajectories inclu…
Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages
Lechen Zhang, Yusheng Zhou, Tolga Ergen +3
System prompts provide a lightweight yet powerful mechanism for conditioning large language models (LLMs) at inference time. While prior work has focused on English-only settings,…
DEER: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation
Janghoon Han, Heegyu Kim, Changho Lee +6
Recent advances in large language models have enabled deep research systems that generate expert-level reports through multi-step reasoning and evidence-based synthesis. However, e…
Revisiting LLM Value Probing Strategies: Are They Robust and Expressive?
Siqi Shen, Mehar Singh, Lajanugen Logeswaran +3
There has been extensive research on assessing the value orientation of Large Language Models (LLMs) as it can shape user experiences across demographic groups. However, several ch…
SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety
Geon-Hyeong Kim, Yu Jin Kim, Byoungjip Kim +4
As Large Language Models (LLMs) are increasingly deployed in real-world applications, balancing helpfulness and safety has become a central challenge. A natural approach is to inco…
Process Reward Models That Think
Muhammad Khalifa, Rishabh Agarwal, Lajanugen Logeswaran +5
Step-by-step verifiers -- also known as process reward models (PRMs) -- are a key ingredient for test-time scaling. PRMs require step-level supervision, making them expensive to tr…
MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?
Yunxiang Zhang, Muhammad Khalifa, Shitanshu Bhushan +6
We introduce MLRC-Bench, a benchmark designed to quantify how effectively language agents can tackle challenging Machine Learning (ML) Research Competitions, with a focus on open r…
SPRIG: Improving Large Language Model Performance by System Prompt Optimization
Lechen Zhang, Tolga Ergen, Lajanugen Logeswaran +2
Large Language Models (LLMs) have shown impressive capabilities in many scenarios, but their performance depends, in part, on the choice of prompt. Past research has focused on opt…