How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning

arXiv — cs.LG•Friday, May 29, 2026 at 4:00:00 AM

NeutralArtificial Intelligence

What Happened
Recent research has explored the latent planning capabilities of Large Language Models (LLMs) through a method called Tele-Lens, revealing that while LLMs exhibit some foresight in their reasoning, they primarily operate with a myopic horizon, focusing on incremental transitions rather than comprehensive global planning.
Why It Matters
This finding is significant as it highlights the limitations of LLMs in tasks requiring complex compositional reasoning, suggesting that while they can mimic human-like reasoning, their internal processes may diverge substantially from human cognition.
The Bigger Picture
The study contributes to ongoing discussions about the reasoning abilities of LLMs, emphasizing the need for improved methodologies to enhance their planning and reasoning capabilities, as well as addressing issues related to their reliability and transparency in decision-making.

— via World Pulse Now AI Editorial System

Read Original

Was this article worth reading? Share it

Continue Readings

arXiv — cs.CL2 days ago

G-Long: Graph-Enhanced Memory Management for Efficient Long-Term Dialogue Agents

PositiveArtificial Intelligence

G-Long, a new graph-enhanced framework for memory management in long-term dialogue agents, has been introduced to overcome the limitations of Large Language Models (LLMs) in maintaining long-term consistency and efficiency in processing extensive text. This framework employs a fine-tuned small Language Model for structured triplet extraction and associative retrieval, significantly reducing operational costs.

Read full article

via arXiv — cs.CL

arXiv — cs.CL2 days ago

Why Sampling Is Not Choosing: Intentionality, Agency, and Moral Responsibility in Large Language Models

NegativeArtificial Intelligence

Recent discussions surrounding large language models (LLMs) have raised questions about their agency and moral responsibility, with a new paper arguing that these models lack intrinsic intentionality and do not possess true agency. The authors assert that the outputs generated by LLMs are merely probabilistic mappings based on data, rather than expressions of choice or commitment.

Read full article

via arXiv — cs.CL

arXiv — cs.LG2 days ago

A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth

NeutralArtificial Intelligence

A new judge-aware ranking framework has been proposed for evaluating large language models (LLMs) without ground truth labels, addressing the inconsistencies in reliability among judge LLMs. This framework extends the Bradley-Terry-Luce model by incorporating judge-specific discrimination parameters, allowing for a more accurate estimation of model quality and judge reliability through pairwise comparisons.

Read full article

via arXiv — cs.LG

arXiv — cs.CL2 days ago

Does AI Reviewer See the Full Picture? Attacking and Defending Multimodal Peer Review

NegativeArtificial Intelligence

The integration of Large Language Models (LLMs) and Multimodal LLMs (MLLMs) into scientific peer-review processes has raised concerns about adversarial manipulation, particularly as current studies focus predominantly on text, neglecting the multimodal aspects of scientific papers. This gap poses significant risks for the integrity of peer review.

Read full article

via arXiv — cs.CL

arXiv — cs.CL2 days ago

GENIE: A Fine-Grained Measure for Novelty

NeutralArtificial Intelligence

A new evaluation metric called GENIE has been proposed to measure the novelty of responses generated by Large Language Models, addressing their historically noted lack of creativity and diversity. This metric focuses on task-specific features and aims to provide a more nuanced understanding of what constitutes novelty in model-generated content.

Read full article

via arXiv — cs.CL

arXiv — cs.CL2 days ago

Beyond Uniform Tokens: Adaptive Compression for Time Series Language Models

PositiveArtificial Intelligence

A recent study published on arXiv introduces an adaptive token budgeting framework aimed at improving token efficiency in time series language modeling. The research highlights the distinct information structures of time series tokens and prompt tokens, revealing that many tokens exhibit redundant frequency patterns while a small subset retains critical temporal information. This framework compresses time series tokens and reduces prompt tokens across model layers.

Read full article

via arXiv — cs.CL

arXiv — cs.LG2 days ago

GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs

PositiveArtificial Intelligence

The introduction of GraspLLM marks a significant advancement in the integration of Large Language Models (LLMs) with Text-Attributed Graphs (TAGs), aiming to improve zero-shot generalization across diverse datasets and tasks. This framework enhances the ability to capture transferable graph structural patterns, addressing limitations faced by existing methods in various applications such as citation networks and social media.

Read full article

via arXiv — cs.LG

arXiv — cs.CL2 days ago

Reward Modeling for Multi-Agent Orchestration

PositiveArtificial Intelligence

A new framework called Orchestration Reward Modeling (OrchRM) has been proposed to enhance the training of orchestrators in Multi-Agent Systems (MAS) that utilize Large Language Models (LLMs). This self-supervised approach evaluates orchestration quality without human annotations, improving training efficiency by up to 10x and accuracy by up to 8% during test-time scaling.

Read full article

via arXiv — cs.CL

Ready to build your own newsroom?

Subscribe to unlock a personalised feed, podcasts, newsletters, and notifications tailored to the topics you actually care about