Cover Image

The Depth Ceiling: On the Limits of Large Language Models in Discovering Latent Planning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xu, Yi, Jettkant, Philipp, Ruis, Laura
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Investigating Non-Transitivity in LLM-as-a-Judge
by: Xu, Yi, et al.
Published: (2025)

Discovering Latent Knowledge in Language Models Without Supervision
by: Burns, Collin, et al.
Published: (2022)

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
by: Jin, Jikai, et al.
Published: (2025)

Hallucination is Inevitable: An Innate Limitation of Large Language Models
by: Xu, Ziwei, et al.
Published: (2024)

Large Language Models Explore by Latent Distilling
by: Zeng, Yuanhao, et al.
Published: (2026)

Discovering Forbidden Topics in Language Models
by: Rager, Can, et al.
Published: (2025)

LatentBreak: Jailbreaking Large Language Models through Latent Space Feedback
by: Mura, Raffaele, et al.
Published: (2025)

Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
by: Xu, Zhuoyan, et al.
Published: (2024)

Can Language Models Discover Scaling Laws?
by: Lin, Haowei, et al.
Published: (2025)

DepthCharge: A Domain-Agnostic Framework for Measuring Depth-Dependent Knowledge in Large Language Models
by: Sheppert, Alexander
Published: (2026)

Can Large Language Models Reason and Plan?
by: Kambhampati, Subbarao
Published: (2024)

Discovering Knowledge-Critical Subnetworks in Pretrained Language Models
by: Bayazit, Deniz, et al.
Published: (2023)

The Limited Impact of Medical Adaptation of Large Language and Vision-Language Models
by: Jeong, Daniel P., et al.
Published: (2024)

MiniCache: KV Cache Compression in Depth Dimension for Large Language Models
by: Liu, Akide, et al.
Published: (2024)

Exploring and Benchmarking the Planning Capabilities of Large Language Models
by: Bohnet, Bernd, et al.
Published: (2024)

Breaking the Capability Ceiling of LLM Post-Training by Reintroducing Markov States
by: Yuan, Yurun, et al.
Published: (2026)

Programming by Backprop: An Instruction is Worth 100 Examples When Finetuning LLMs
by: Cook, Jonathan, et al.
Published: (2025)

Evaluating the Limits of Large Language Models in Multilingual Legal Reasoning
by: Ioannou, Antreas, et al.
Published: (2025)

Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformer
by: Lu, Wenquan, et al.
Published: (2025)

Meanings and Feelings of Large Language Models: Observability of Latent States in Generative AI
by: Liu, Tian Yu, et al.
Published: (2024)

Graph-enhanced Large Language Models in Asynchronous Plan Reasoning
by: Lin, Fangru, et al.
Published: (2024)

MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate
by: Wang, Jianze, et al.
Published: (2026)

Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
by: Wang, Xinyi, et al.
Published: (2023)

Effect of Document Packing on the Latent Multi-Hop Reasoning Capabilities of Large Language Models
by: Prato, Gabriele, et al.
Published: (2025)

Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models
by: Ball, Sarah, et al.
Published: (2024)

Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?
by: Jin, Mingyu, et al.
Published: (2024)

Dynamic Strategy Planning for Efficient Question Answering with Large Language Models
by: Parekh, Tanmay, et al.
Published: (2024)

CeRA: Overcoming the Linear Ceiling of Low-Rank Adaptation via Capacity Expansion
by: Chen, Hung-Hsuan
Published: (2026)

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
by: Marks, Samuel, et al.
Published: (2024)

A Study on Large Language Models' Limitations in Multiple-Choice Question Answering
by: Khatun, Aisha, et al.
Published: (2024)

Leveraging Large Language Models for Suicide Detection on Social Media with Limited Labels
by: Nguyen, Vy, et al.
Published: (2024)

Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning
by: Pasewark, Eric, et al.
Published: (2024)

Unveiling Narrative Reasoning Limits of Large Language Models with Trope in Movie Synopses
by: Su, Hung-Ting, et al.
Published: (2024)

Line Goes Up? Inherent Limitations of Benchmarks for Evaluating Large Language Models
by: Fodor, James
Published: (2025)

LLM in a flash: Efficient Large Language Model Inference with Limited Memory
by: Alizadeh, Keivan, et al.
Published: (2023)

Revisiting Uncertainty Estimation and Calibration of Large Language Models
by: Tao, Linwei, et al.
Published: (2025)

Depth-Recurrent Attention Mixtures: Giving Latent Reasoning the Attention it Deserves
by: Knupp, Jonas, et al.
Published: (2026)

Large Language Model Unlearning
by: Yao, Yuanshun, et al.
Published: (2023)

RePrompt: Planning by Automatic Prompt Engineering for Large Language Models Agents
by: Chen, Weizhe, et al.
Published: (2024)

LASP: Surveying the State-of-the-Art in Large Language Model-Assisted AI Planning
by: Li, Haoming, et al.
Published: (2024)