works on

From the 1 of 21 linked papers with an AI index.

activity
20242026
collaborators

21 papers

cs.CL2026

Meta-Learning Preferences for Multilingual LLM Alignment

Jiaying Lin, Seongho Son, Nam Phuong Tran +3

The paper introduces a meta-learning method that uses preference data from high-resource languages to quickly adapt large language models to low-resource languages with very few hu…

cs.SE2026

SWE-Router: Routing in Multi-turn Agentic Software Engineering Tasks

Seongho Son, Sangwoong Yoon, Jiahua Tang +3

Large language models (LLMs) embedded in multi-turn agentic harnesses are reshaping software engineering (SWE), but routing every task to a frontier model is wasteful when many iss…

cs.AI2026

LLM-WikiRace Benchmark: How Far Can LLMs Plan over Real-World Knowledge Graphs?

Juliusz Ziomek, William Bankes, Lorenz Wolf +3

We introduce LLM-Wikirace, a benchmark for evaluating planning, reasoning, and world knowledge in large language models (LLMs). In LLM-Wikirace, models must efficiently navigate Wi…

cs.LG2026

Re-evaluating Confidence Remasking in Masked Diffusion Language Models

Stipe Frkovic, Metod Jazbec, Dan Zhang +3

Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faster inference via parallel tok…

cs.LG2026

PROWL: Prioritized Regret-Driven Optimization for World Model Learning

Ahmet H. Güzel, Jenny Seidenschwarz, Benjamin Graham +3

Modern action-conditioned video world models achieve strong short-horizon visual realism, yet remain unreliable on rare, interaction-critical transitions that dominate downstream p…

cs.LG2026

GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models

Xiaohang Tang, Keyue Jiang, Che Liu +4

Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intractability of the policy likeli…