arXiv AI August 18, 2026 By Isak Hwang, Yoon Pyo Lee, Syed Bahauddin Alam

Multimodal Language Models Benchmarked Against the NRC Reactor Operator Licensing Examination: Fine-Tuning and Retrieval Strategies

arXiv:2607. 22067v2 Announce Type: replace-cross Abstract: Competence claims for a language model in a safety-critical domain are credible when measured against a standard the domain already enforces.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 26

NebulaExp-8B: An Empirical Post-Training Pipeline via Full-Scale Ablation Research

arXiv:2606. 26671v1 Announce Type: new Abstract: Post-training alignment determines the reasoning and human preference following capabilities of large language models, yet most existing works withhold detailed data construction, filtering rules and training recipes, which hinders community reproducibility and lightweight model optimization.

By Qiaobo Hao, Yangqian Wu, Shunyi Wang, Zhongjian Zhang, Ziqun Li, Yayin He, Muqing Li, Chen Zhong

llms reinforcement-learning fine-tuning efficiency benchmarks safety

arXiv Machine Learning

Aug 19

OraclePhys: A Systematic Framework for LLM Fine-Tuning on Structural Mechanics

OraclePhys is a fine‑tuning framework for large language models on structural mechanics, comprising a graded benchmark (OraclePhys‑Bench), a 30K supervision dataset (OraclePhys‑30K), and a controlled training study. The study shows that the form of the label’s answer, rather than its length, determines what the model learns, and that certain training objectives can produce models that match or exceed existing LLMs on spatial structural response tasks. The trained 8B model reaches the data‑precision frontier, outperforming zero‑shot and 32‑shot baselines at a specialist level.

By Mingyu Li, Guorui Song, Jing Lin, Haoqian Wang

llms fine-tuning benchmarks

arXiv AI

Aug 7

Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support

arXiv:2608. 05151v1 Announce Type: cross Abstract: Wastewater operators need answers grounded in how their plant's variables interact and how fast effects propagate, not in generic pretraining text, when asking causal questions such as "why is N2O rising?

By Gary Simethy, Daniel Ortiz Arroyo, Petar Durdevic

llms rag nlp benchmarks

arXiv AI

Aug 18

Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152

arXiv:2608. 16394v1 Announce Type: new Abstract: Generating regulation-compliant test scenarios is essential for validating safety-critical automotive systems, yet Large Language Models (LLMs) struggle to ground outputs in long, hierarchical standards.

By Vahid Zolfaghari, Nenad Petrovic, Andr\'E Schamschurko, Alois Knoll

llms rag safety

arXiv AI

Sep 16

Safe Error Correction for Language Models: Frozen-Base Adjustment with Capability Preservation

arXiv:2609.16145v1 Announce Type: new Abstract: We study a practical question: can a small correction module fix errors in a frozen language model's outputs without degrading its base capabilities? W...

By Gautam Kishore

llms reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Sep 2

Post-Training Science for Supervised Fine-Tuning

arXiv:2609.01244v1 Announce Type: new Abstract: Every supervised fine-tuning run forces the same chain of decisions, such as learning rate, batch size, LoRA or full fine-tuning, how many epochs, whic...

By Charles O'Neill, Mudith Jayasekara, Harry Partridge

llms fine-tuning

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Related stories

NebulaExp-8B: An Empirical Post-Training Pipeline via Full-Scale Ablation Research

OraclePhys: A Systematic Framework for LLM Fine-Tuning on Structural Mechanics

Simulator-Grounded Large Language Models for Industrial Causal Reasoning: Tool-Use, Structured Injection, and Plant-Portable Retrieval for Wastewater Treatment Decision Support

Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152

Safe Error Correction for Language Models: Frozen-Base Adjustment with Capability Preservation

Post-Training Science for Supervised Fine-Tuning