:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Roig, JV
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Artificial Intelligence
Online Access:	https://arxiv.org/abs/2603.08274
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How Do LLMs Fail In Agentic Scenarios? A Qualitative Analysis of Success and Failure Scenarios of Various LLMs in Agentic Simulations
by: Roig, JV
Published: (2025)

Scalable and Reliable Evaluation of AI Knowledge Retrieval Systems: RIKER and the Coherent Simulated Universe
by: Roig, JV
Published: (2025)

How Much Do LLMs Hallucinate across Languages? On Realistic Multilingual Estimation of LLM Hallucination
by: Islam, Saad Obaid ul, et al.
Published: (2025)

GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity?
by: Zhou, Yang, et al.
Published: (2025)

Short-Context Dominance: How Much Local Context Natural Language Actually Needs?
by: Vakilian, Vala, et al.
Published: (2025)

How Do LLMs Perform Two-Hop Reasoning in Context?
by: Guo, Tianyu, et al.
Published: (2025)

Do Large Language Models Know How Much They Know?
by: Prato, Gabriele, et al.
Published: (2025)

Do Language Models Think Consistently? A Study of Value Preferences Across Varying Response Lengths
by: Nair, Inderjeet, et al.
Published: (2025)

Towards a Standard, Enterprise-Relevant Agentic AI Benchmark: Lessons from 5.5 billion tokens' worth of agentic AI evaluations
by: Roig, JV
Published: (2025)

MathPile: A Billion-Token-Scale Pretraining Corpus for Math
by: Wang, Zengzhi, et al.
Published: (2023)

TokenSelect: Efficient Long-Context Inference and Length Extrapolation for LLMs via Dynamic Token-Level KV Cache Selection
by: Wu, Wei, et al.
Published: (2024)

NorBERTo: A ModernBERT Model Trained for Portuguese with 331 Billion Tokens Corpus
by: Silva, Enzo S. N., et al.
Published: (2026)

BertaQA: How Much Do Language Models Know About Local Culture?
by: Etxaniz, Julen, et al.
Published: (2024)

Triggering Hallucinations in LLMs: A Quantitative Study of Prompt-Induced Hallucination in Large Language Models
by: Sato, Makoto
Published: (2025)

How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning
by: Chen, Haoyang, et al.
Published: (2026)

How Much Can RAG Help the Reasoning of LLM?
by: Liu, Jingyu, et al.
Published: (2024)

How Much Heavy Lifting Can an Agent Harness Do?: Measuring the LLM's Residual Role in a Planning Agent
by: Jung, Sungwoo, et al.
Published: (2026)

How Do LLMs Use Their Depth?
by: Gupta, Akshat, et al.
Published: (2025)

Do Hallucination Neurons Generalize? Evidence from Cross-Domain Transfer in LLMs
by: Vaddi, Snehit, et al.
Published: (2026)

A Geometric Taxonomy of Hallucinations in LLMs
by: Marín, Javier
Published: (2026)

Learned Hallucination Detection in Black-Box LLMs using Token-level Entropy Production Rate
by: Moslonka, Charles, et al.
Published: (2025)

How Much Do Large Language Models Know about Human Motion? A Case Study in 3D Avatar Control
by: Li, Kunhang, et al.
Published: (2025)

How Much Data is Enough Data? Fine-Tuning Large Language Models for In-House Translation: Performance Evaluation Across Multiple Dataset Sizes
by: Vieira, Inacio, et al.
Published: (2024)

Do LLMs Encode Functional Importance of Reasoning Tokens?
by: Singh, Janvijay, et al.
Published: (2026)

Nemotron-CC-Math: A 133 Billion-Token-Scale High Quality Math Pretraining Dataset
by: Mahabadi, Rabeeh Karimi, et al.
Published: (2025)

AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM
by: Huang, Haoyu, et al.
Published: (2025)

How Well Do LLMs Understand Tunisian Arabic?
by: Mahdi, Mohamed
Published: (2025)

Tokenization Constraints in LLMs: A Study of Symbolic and Arithmetic Reasoning Limits
by: Zhang, Xiang, et al.
Published: (2025)

How Much is Too Much? Exploring LoRA Rank Trade-offs for Retaining Knowledge and Domain Robustness
by: Rathore, Darshita, et al.
Published: (2025)

How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach
by: Lee, Ayeong, et al.
Published: (2025)

Token-Guard: Towards Token-Level Hallucination Control via Self-Checking Decoding
by: Zhu, Yifan, et al.
Published: (2026)

Task--Specificity Score: Measuring How Much Instructions Really Matter for Supervision
by: Kadasi, Pritam, et al.
Published: (2026)

Towards Long Context Hallucination Detection
by: Liu, Siyi, et al.
Published: (2025)

Not All Needles Are Found: How Fact Distribution and Don't Make It Up Prompts Shape Literal Extraction, Logical Inference, and Hallucination Risks in Long-Context LLMs
by: Ebrahimzadeh, Amirali, et al.
Published: (2026)

Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving
by: Zhou, Yuxuan, et al.
Published: (2025)

Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs
by: Su, Jinyan, et al.
Published: (2025)

Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts
by: Deng, Jiaqi, et al.
Published: (2025)

Look Within, Why LLMs Hallucinate: A Causal Perspective
by: Li, He, et al.
Published: (2024)

The First Token Knows: Single-Decode Confidence for Hallucination Detection
by: Gabriel, Mina
Published: (2026)

TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG
by: Lu, Pengqian, et al.
Published: (2025)