arXiv AI
Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translation
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arXiv Computation and Language
Sep 22
BabelArena: A Large-Scale Multilingual Benchmark for LLM Agents
arXiv:2609.23490v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly execute multi-step workflows through tool use and interaction with users and environments. However, curr...
arXiv AI
Jun 6
AdaMEM: Test-Time Adaptive Memory for Language Agents
arXiv:2606. 05684v1 Announce Type: new Abstract: A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions.
arXiv AI
Aug 28
Beyond Factual QA: Mentorship-Oriented Question Answering over Long-Form Multilingual Content
The paper introduces MentorQA, a multilingual dataset and evaluation framework for mentorship-oriented question answering derived from long‑form videos. It contains nearly 9,000 QA pairs across four languages and defines evaluation dimensions such as clarity, alignment, and learning value that extend beyond factual accuracy. Experiments show that Multi‑Agent QA pipelines outperform other architectures, especially on complex topics and low‑resource languages, while automated LLM‑based evaluation shows variable alignment with human judgments.
arXiv AI
Sep 2
WorldBench: Culturally Grounded Benchmark for Multilingual Agents
WorldBench is a new multilingual benchmark that tests large language model agents on culturally grounded everyday workflows, offering 1,600 tasks in seven languages and eight cultures. The benchmark evaluates agents through structured sandbox actions and introduces Constrained Task Success (CTS), a metric that assesses task completion, minimal modification, and other complementary aspects via deterministic and LLM-as-a-Judge evaluations. Experiments show that even leading models achieve only 49.2% CTS, revealing significant gaps in correctness and state preservation across languages and cultures.
Hugging Face Trending Papers
Sep 1
WorldBench: Culturally Grounded Benchmark for Multilingual Agents
Despite the growing use of LLM-powered agents to solve multi-step tasks in complex environments, existing benchmarks rarely test state preservation, performance across languages, and application to re...
arXiv AI
Sep 4
Translation as a Decision Space: A Multi-Agent Perspective on Low-Resource Dialect Generation
The paper proposes treating translation as a structured decision space explored by multiple autonomous agents, rather than producing a single output. Using Turkish–Syrian Arabic dialogue, three agents—zero‑shot, dialect‑stabilized, and pivot translation—are compared on 5,000 sentences, with stabilization nearly doubling dialect marker usage and reducing structural instability. The study introduces an interpretability framework that quantifies decision flexibility through dialect marker frequency, lexical proximity, and structural variance.