planning

Tip to use Fable-5 as orchestrator and Codex as implementer to save credits, with a link to a library of composable AI agent skills for autonomous software development.

The tweet thread compares two AI agent development frameworks (Superpowers and Grill with Docs) as best practices from different eras, highlighting how their planning strategies reflect assumptions about agent capabilities for long-range tasks.

Matt Pocock is working on a new planning tool called /wayfinder that helps identify decision frontiers and suggests prototyping, research, or 'grilling' to push back uncertainty.

A developer outlines a structured skill stack for production-grade AI agents, covering planning, tool-use, permissions, recovery, observation, budget, and escalation as explicit contracts rather than loose tool wrappers.

The paper introduces ACID, a method that enhances decision-time planning in world models by incorporating cycle action consistency via an inverse dynamics model, ensuring predicted transitions are realizable and improving planning efficiency across multiple tasks with reduced computation.

Delta-JEPA introduces a reconstruction-free world model that augments latent forward prediction with a Latent Difference Action Decoder to prevent collapse and improve action-sensitivity, achieving better planning performance on visual continuous-control tasks.

This paper introduces Non-Conversational Planning Theory of Mind (NCP-ToM) and a novel evaluation framework, NCP-ExploreToM, to assess whether LLMs can induce specific belief states in other agents through actions rather than conversation. Testing on frontier models and humans across 600 tasks, GPT-5 achieved ~80% success, outperforming humans, though all models struggled more with false belief states.

This paper frames regulatory document review as an LLM-guided planning problem, using a vectorless document tree with browse, read, and search tools and a dynamic knowledge graph as state. On a 200-question benchmark over NuScale FSAR documents, the system achieves 81.5% accuracy with 0.93 RAGAS Faithfulness, significantly outperforming existing RAG methods.

This paper proposes a path-space formulation of prediction in AI world models, treating the distribution over future trajectories as the fundamental predictive object. It shows that prediction, planning, and uncertainty emerge as operations on a single action functional, and demonstrates that attention asymmetry in learned models correlates with irreversibility in the data.

This paper introduces Grounded Iterative Language Planning (GILP), a method that combines a small parameterized world model with LLM-based reasoning to reduce hallucination propagation in LLM agents. Experiments show GILP reduces hallucinated-state rate from 0.176 to 0.035 and raises task success from 0.668 to 0.838 on graph-structured planning benchmarks.

This paper analyzes long-horizon rollout error in Graph World Models (GWMs), proposing a unified framework with dynamic edges and introducing Error-Aware GWM that uses spectral regularization, rollout consistency, and critical-node weighting to prevent divergence.

RS-Diffuser 提出了一种风险敏感的离线扩散规划框架，结合扩散轨迹生成和分布值批评器，通过尾部感知目标在推理时灵活调整风险偏好，在安全关键任务中提升了回报和鲁棒性。

A discussion on whether world models, which learn internal environment representations to simulate physics and plan actions, could lead to AGI by overcoming the limitations of reactive predictive text models like LLMs.

Jayden Teoh proposes Next-Latent Prediction (NextLat), a self-supervised learning method that teaches the Transformer to learn to predict the next hidden state, thereby forming a compact world model for reasoning and planning, and achieves up to 3.3x inference speedup through self-speculative decoding.

Introducing multiple Hermes plugins: theme skins, persistent planning, Draw.io automatic flowcharts, literate programming skill pack, fantasy skill lab, etc., turning Hermes into a versatile terminal and intelligent planning tool.

Discusses using Qwen 27B for planning tasks and Qwen 35B-A3B for execution tasks, suggesting a specialized model approach.

PlanBench-XL is a new benchmark that evaluates LLM agents' ability to plan and adapt in large tool ecosystems with limited visibility and dynamic disruptions. Experiments show GPT-5.4 achieves only 51.9% accuracy in block-free settings and collapses to 11.36% under severe blocking, highlighting significant challenges in long-horizon planning.

A discussion between Kent C. Dodds and Sean Roberts on product engineering, planning with real business context, and the importance of conversations and curiosity over pure data.

Vesta is a unified embodied generalist model that integrates localization, spatial reasoning, navigation, and long-horizon planning into a single foundation model, outperforming specialized models by over 20% on benchmarks and over 35% on real-world robotic tasks.

This paper surveys evaluation methods for world models and argues for a decision-making-centric framework that prioritizes counterfactual reasoning, planning, and policy optimization over visual quality. It introduces an L0–L7 evaluation ladder and a benchmark protocol to align evaluation with claimed utility.