Papers

Filtered to open-ended generation · clear filter

Browse by term

continual learning 33reinforcement learning 21large language models 9vision-language models 8language models 6benchmarking 5autoregressive models 4diffusion transformers 4generative models 4multimodal learning 4robotics 4video generation 4benchmarks 3computer vision 3policy optimization 3self-distillation 3vision-language-action models 3world modeling 3agent-based systems 2autonomous agents 2calibration 2coding agents 2diffusion models 2foundation models 2image synthesis 2in-context learning 2knowledge graphs 2LLMs 2multimodal evaluation 2multimodal large language models 2

Matching papers

Two-Level Meta-Rubrics for Evaluating Open-Ended Generation: GAMUT, a Benchmark for Factual Completeness

6 upvotes · 21 JUL 2026 · Xilun Chen, Zhaleh Feizollahi, Ross Goodwin et al.

This paper introduces a new framework to evaluate the factuality and completeness of long-form generation models, which is essential for ensuring that generated text is accurate and informative. Practitioners in natural language processing and artificial intelligence can benefit from this framework to assess the quality of their models.

open-ended generationfactual completenessmultimodal evaluationrubric-based assessmentlong-form text generation

FinanceComplexQA: Benchmarking Agentic Reasoning on Industrial-grade Financial Documents

4 upvotes · 21 JUL 2026 · Xianfu Cheng, Shiwei Zhang, Jiyu Zhao et al.

This paper creates a benchmark for testing the ability of AI agents to understand and analyze complex financial documents, and uses it to evaluate the performance of different agents in this task. Practitioners in finance and AI research can care about this work because it aims to improve the accuracy and reliability of financial document analysis.

agentic reasoningfinancial documentsopen-ended generationquestion-answeringbenchmarking