51 citations · 220 across the 70 of their papers we have counts for
77 papers
Threshold-Aware Conformal Routing
Shiwei Tan, Huzefa Rangwala, Danielle C. Maddix
High-fidelity simulations are essential to scientific and engineering design, but can be expensive to run repeatedly. Learned surrogates offer a faster alternative, yet their highe…
Neuro-symbolic AI for Industrial Configuration
Danilo Valerio, Philipp Kogler, Stefan Bischof +2
Large Language Models (LLMs) have shown impressive performance on a wide range of generative tasks. Yet their probabilistic nature makes them, in isolation, fundamentally unsuited…
SD-MAR: Multi-image Analytical Reasoning via Synthetic Data and Reinforcement Learning
Shiyu Yuan, Sourav Sanjukta Bhabesh, Zhe Wang +3
Vision Language Models (VLMs) demonstrate strong perceptual abilities but remain limited in tasks requiring analytical reasoning across multiple visual states, such as multi-image…
When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors
Yuqing Yang, Qi Zhu, Zhen Han +5
While large language models (LLMs) perform well on table tasks, they still make data referencing errors (DREs), i.e., incorrectly citing or omitting table values, despite understan…
Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization
Jiading Gai, Shuai Zhang, Kaj Bostrom +6
We present KernelPro, a closed-loop multi-agent system that automatically generates, profiles, and iteratively optimizes GPU kernel code by integrating large language model (LLM) c…
LLMZero: Discovering Adaptive Training Strategies for RL Post-Training via LLM Agents
Haoyang Fang, Wei Zhu, Boran Han +11
RL post-training strategies are dataset-dependent and reveal a recurring empirical pattern: capacity parameters accumulate monotonically across stages, while regularization paramet…
TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
Andrej Tschalzev, Nick Erickson, Yuyang Wang +4
Progress in tabular machine learning has largely focused on increasingly sophisticated model architectures. At the same time, feature engineering remains a critical yet underexplor…
Scalable Prompt Routing via Fine-Grained Latent Task Discovery
Yunyi Zhang, Soji Adeshina, Sheng Guan +5
Prompt routing dynamically selects the most appropriate large language model from a pool of candidates for each query, optimizing performance while managing costs. As model pools s…
Relatron: Automating Relational Machine Learning over Relational Databases
Zhikai Chen, Han Xie, Jian Zhang +3
Predictive modeling over relational databases (RDBs) powers applications, yet remains challenging due to capturing both cross-table dependencies and complex feature interactions. R…
ReSyn: Autonomously Scaling Synthetic Environments for Reasoning Models
Andre He, Nathaniel Weir, Kaj Bostrom +4
Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising approach for training reasoning language models (RLMs) by leveraging supervision from verifiers. Al…
Train Less, Learn More: Adaptive Efficient Rollout Optimization for Group-Based Reinforcement Learning
Zhi Zhang, Zhen Han, Costas Mavromatis +9
Reinforcement learning (RL) plays a central role in large language model (LLM) post-training. Among existing approaches, Group Relative Policy Optimization (GRPO) is widely used, e…
Generating Data-Driven Reasoning Rubrics for Domain-Adaptive Reward Modeling
Kate Sanders, Nathaniel Weir, Sapana Chaudhary +2
An impediment to using Large Language Models (LLMs) for reasoning output verification is that LLMs struggle to reliably identify errors in thinking traces, particularly in long out…
SQL-Trail: Multi-Turn Reinforcement Learning with Interleaved Feedback for Text-to-SQL
Harper Hua, Zhen Han, Zhengyuan Shen +9
While large language models (LLMs) have substantially improved Text-to-SQL generation, a pronounced gap remains between AI systems and human experts on challenging benchmarks such…
MaxCode: A Max-Reward Reinforcement Learning Framework for Automated Code Optimization
Jiefu Ou, Sapana Chaudhary, Kaj Bostrom +4
Large Language Models (LLMs) demonstrate strong capabilities in general coding tasks but encounter two key challenges when optimizing code: (i) the complexity of writing optimized…
HetRL: Efficient Reinforcement Learning for LLMs in Heterogeneous Environments
Yongjun He, Shuai Zhang, Jiading Gai +5
As large language models (LLMs) continue to scale and new GPUs are released even more frequently, there is an increasing demand for LLM post-training in heterogeneous environments…
VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical Consistency Checks
Yu Feng, Nathaniel Weir, Kaj Bostrom +5
LLMs can perform multi-step reasoning through Chain-of-Thought (CoT), but they cannot reliably verify their own logic. Even when they reach correct answers, the underlying reasonin…
The Kinetics of Reasoning: How Chain-of-Thought Shapes Learning in Transformers?
Zihan Pengmei, Costas Mavromatis, Zhengyuan Shen +3
Chain-of-thought (CoT) supervision can substantially improve transformer performance, yet the mechanisms by which models learn to follow and benefit from CoT remain poorly understo…
Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models
Xiyuan Zhang, Danielle C. Maddix, Junming Yin +11
Since the seminal work of TabPFN, research on tabular foundation models (TFMs) based on in-context learning (ICL) has challenged long-standing paradigms in machine learning. Withou…
Chronos-2: From Univariate to Universal Forecasting
Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken +20
Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely…
BoundRL: Efficient Structured Text Segmentation through Reinforced Boundary Generation
Haoyuan Li, Zhengyuan Shen, Sullam Jeoung +6
Structured texts refer to texts containing structured elements beyond plain texts, such as code snippets and placeholders. Such structured texts increasingly require segmentation i…
LAKEGEN: A LLM-based Tabular Corpus Generator for Evaluating Dataset Discovery in Data Lakes
Zhenwei Dai, Chuan Lei, Asterios Katsifodimos +3
How to generate a large, realistic set of tables along with joinability relationships, to stress-test dataset discovery methods? Dataset discovery methods aim to automatically iden…
BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering
Costas Mavromatis, Soji Adeshina, Vassilis N. Ioannidis +6
Knowledge graph question answering (KGQA) presents significant challenges due to the structural and semantic variations across input graphs. Existing works rely on Large Language M…
Hierarchical Lexical Graph for Enhanced Multi-Hop Retrieval
Abdellah Ghassel, Ian Robinson, Gabriel Tanase +6
Retrieval-Augmented Generation (RAG) grounds large language models in external evidence, yet it still falters when answers must be pieced together across semantically distant docum…
When Does Multimodality Lead to Better Time Series Forecasting?
Xiyuan Zhang, Boran Han, Haoyang Fang +11
Recently, there has been growing interest in incorporating textual information into foundation models for time series forecasting. However, it remains unclear whether and under wha…