When Do Agents Help? Embedding, LLM and Agentic Alignment of Classical Texts and Their Translations

arXiv:2609.33691 · cs.CL · Submitted 2026-09-27 · Read on arXiv

cs.CL

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/MateMetzger/when-do-agents-help

Terminology

• Alignment — 131× in this paper · explained in 1 episode
• LLM judge — 11× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Generative AI — 7× in this paper · explained in 2 episodes
• Agentic Workflows — 5× in this paper · explained in 1 episode
• Embeddings — 4× in this paper · explained in 2 episodes
• Low-Resource Languages — 4× in this paper · explained in 4 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Fidelity — 3× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Commentary — 2× in this paper · explained in 2 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Multi-Agent Framework — 2× in this paper · explained in 3 episodes
• Multi-Agent System — 2× in this paper · explained in 6 episodes
• Verification — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Agentic Task — 1× in this paper · explained in 1 episode
• Agentic Tasks — 1× in this paper · explained in 1 episode
• Alignment Methods — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs
• DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
• STAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translation
• Challenges and Recommendations for LLM-as-a-Judge in Multilingual Settings and for Low-Resource Languages
• MMTEB: Massive Multilingual Text Embedding Benchmark
• Align-then-Slide: A complete evaluation framework for Ultra-Long Document-Level Machine Translation
• LATA: A Tool for LLM-Assisted Translation Annotation
• AI Agents That Matter
• Can We Triage LLM Translation Errors in Classical Texts Without Human References? Source Novelty, GEMBA Scoring, and Budgeted Review through Pali-to-English Translation
• PaliBench: A Multi-Reference Blueprint for Classical Language Translation Benchmarks
• From Outliers to Errors: Auditing Pali-to-English LLM Translations with Multi-Reference Adjudication
• Mitrasamgraha: A Comprehensive Classical Sanskrit Machine Translation Dataset
• MITRA: A Large-Scale Parallel Corpus and Multilingual Pretrained Language Model for Machine Translation and Semantic Retrieval for P\=ali, Sanskrit, Buddhist Chinese, and Tibetan
• Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
• F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World

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