6 citations · 6 across the 19 of their papers we have counts for
10 papers · 1 filter
Rethinking Self-Distillation for Multi-Teacher Capability Merging
Roy Xie, Dan Friedman, Feng Nan +7
Combining capabilities of multiple expert models trained starting from the same base checkpoint has become increasingly common in frontier language-model post-training. Recent tren…
Constructing Challenging Browser-Use Tasks by Controlled Environment Interventions
Xunjian Yin, Tianchen Guan, Jinao Wang +8
As browser-use agents improve, benchmarks keep pace by adding new tasks, websites, and applications. Doing so is costly, and because many factors change at once, it is unclear what…
PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning
Alexis Fox, Junlin Wang, Paul Rosu +1
Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This gap is reflected in their li…
Automated Benchmark Auditing for AI Agents and Large Language Models
Junlin Wang, Federico Bianchi, Shang Zhu +4
Modern AI benchmarks operate at a complexity that outpaces traditional verification methods. Tasks authored by domain experts often contain implicit assumptions, incomplete environ…
LDDR: Linear-DPP Dynamic-Resolution Frame Sampling for Video MLLMs
Jingfeng Chen, Jiawen Qian, Wendi Deng +5
Multimodal large language models, when used for long video understanding, require selecting an informative frame set from massive and redundant candidates. Limited by the model's v…
Vision2Code: A Multi-Domain Benchmark for Evaluating Image-to-Code Generation
Ajay Vikram Periasami, Junlin Wang, Bhuwan Dhingra
We study whether vision-language models (VLMs) can reconstruct structured, graphics-based images by writing executable code to generate them. Existing benchmarks on image-to-code g…
LensVLM: Selective Context Expansion for Compressed Visual Representation of Text
Roy Xie, Dan Friedman, Donghan Yu +7
Vision Language Models (VLMs) offer the exciting possibility of processing text as rendered images, bypassing the need for tokenizing the text into long token sequences. Since VLM…
RVPO: Risk-Sensitive Alignment via Variance Regularization
Ivan Montero, Tomasz Jurczyk, Bhuwan Dhingra
Current critic-less RLHF methods aggregate multi-objective rewards via an arithmetic mean, leaving them vulnerable to constraint neglect: high-magnitude success in one objective ca…
DeepFact: Co-Evolving Benchmarks and Agents for Deep Research Factuality
Yukun Huang, Leonardo F. R. Ribeiro, Momchil Hardalov +3
Search-augmented LLM agents can produce deep research reports (DRRs), but verifying claim-level factuality remains challenging. Existing fact-checkers are primarily designed for ge…
Over-Searching in Search-Augmented Large Language Models
Roy Xie, Deepak Gopinath, David Qiu +4
Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. However, they often over-search -- unnecessarily invoking search…