Image de couverture de livre

The Growing Pains of Frontier Models: When Leaderboards Stop Separating and What to Measure Next

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Amin, Adil
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Artificial Intelligence Computation and Language
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Lying Is Just a Phase: The Hidden Alignment Transition in Language Model Scaling
par: Amin, Adil
Publié: (2026)

When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
par: Alzahrani, Norah, et autres
Publié: (2024)

The Leaderboard Illusion
par: Singh, Shivalika, et autres
Publié: (2025)

LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard
par: Rao, Varun, et autres
Publié: (2025)

When Reasoning Hurts: Source-Aware Evaluation of Frontier LLMs for Clinical SOAP Note Generation
par: Faisal, Faizan
Publié: (2026)

Early Stopping for Large Reasoning Models via Confidence Dynamics
par: Hosseini, Parsa, et autres
Publié: (2026)

Evaluating Large Language Models with Grid-Based Game Competitions: An Extensible LLM Benchmark and Leaderboard
par: Topsakal, Oguzhan, et autres
Publié: (2024)

Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models
par: Chen, Wenting, et autres
Publié: (2025)

Stop Taking Tokenizers for Granted: They Are Core Design Decisions in Large Language Models
par: Alqahtani, Sawsan, et autres
Publié: (2026)

MISR: Measuring Instrumental Self-Reasoning in Frontier Models
par: Fronsdal, Kai, et autres
Publié: (2024)

NextLocLLM: Location Semantics Modeling and Coordinate-Based Next Location Prediction with LLMs
par: Liu, Shuai, et autres
Publié: (2024)

Do We Need Frontier Models to Verify Mathematical Proofs?
par: Naik, Aaditya, et autres
Publié: (2026)

MENTIS: What Belief Changes Under Alignment? Measuring Multi-Scale Latent Torsion in Language Models
par: Saha, Partha Pratim, et autres
Publié: (2026)

Learning to Reason at the Frontier of Learnability
par: Foster, Thomas, et autres
Publié: (2025)

Towards the Next Frontier in Speech Representation Learning Using Disentanglement
par: Krishna, Varun, et autres
Publié: (2024)

Grow Up and Merge: Scaling Strategies for Efficient Language Adaptation
par: Glocker, Kevin, et autres
Publié: (2025)

Quagmires in SFT-RL Post-Training: When High SFT Scores Mislead and What to Use Instead
par: Kang, Feiyang, et autres
Publié: (2025)

AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling
par: Liu, Zihan, et autres
Publié: (2024)

Explaining Large Language Models with gSMILE
par: Dehghani, Zeinab, et autres
Publié: (2025)

Examining Gender and Power on Wikipedia Through Face and Politeness
par: Soubki, Adil, et autres
Publié: (2024)

A Law of Next-Token Prediction in Large Language Models
par: He, Hangfeng, et autres
Publié: (2024)

Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment
par: Kumar, Sayantan, et autres
Publié: (2026)

From Growing to Looping: A Unified View of Iterative Computation in LLMs
par: Kapl, Ferdinand, et autres
Publié: (2026)

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning
par: Nagle, Alliot, et autres
Publié: (2026)

Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?
par: Schaeffer, Rylan, et autres
Publié: (2024)

When Bad Data Leads to Good Models
par: Li, Kenneth, et autres
Publié: (2025)

Stop Listening to Me! How Multi-turn Conversations Can Degrade LLM Reliability
par: Guo, Kevin H., et autres
Publié: (2026)

Navigating the Alignment-Calibration Trade-off: A Pareto-Superior Frontier via Model Merging
par: Hu, Tiancheng, et autres
Publié: (2025)

How to Connect Speech Foundation Models and Large Language Models? What Matters and What Does Not
par: Verdini, Francesco, et autres
Publié: (2024)

Cautious Next Token Prediction
par: Wang, Yizhou, et autres
Publié: (2025)

Frontier LLMs Still Struggle with Simple Reasoning Tasks
par: Malek, Alan, et autres
Publié: (2025)

All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling
par: Marconato, Emanuele, et autres
Publié: (2024)

Beyond Next Token Prediction: Patch-Level Training for Large Language Models
par: Shao, Chenze, et autres
Publié: (2024)

What Matters for Model Merging at Scale?
par: Yadav, Prateek, et autres
Publié: (2024)

When Incentives Backfire, Data Stops Being Human
par: Santy, Sebastin, et autres
Publié: (2025)

Benchmarks Saturate When The Model Gets Smarter Than The Judge
par: Ballon, Marthe, et autres
Publié: (2026)

When Attention Sink Emerges in Language Models: An Empirical View
par: Gu, Xiangming, et autres
Publié: (2024)

AdaptThink: Reasoning Models Can Learn When to Think
par: Zhang, Jiajie, et autres
Publié: (2025)

Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
par: Zheng, Kunhao, et autres
Publié: (2026)

When Should Models Change Their Minds? Contextual Belief Management in Large Language Models
par: Xu, Haoming, et autres
Publié: (2026)