planning

This paper proposes InK, a neurosymbolic hierarchical reinforcement learning approach that uses incremental knowledge for symbolic planning and reward-shaped low-level neural modules, achieving improved sample efficiency in navigation tasks.

HyperAgent is a research framework that models tool relations via a Tool-Schema Hypergraph to improve planning and execution for LLM agents, reducing API calls and token usage on the AppWorld benchmark.

A developer introduces a new research-focused AI agent featuring detailed planning, agile execution, and an optional loop, noting that it currently lacks a sandbox for data safety.

NeSyFS is a neuro-symbolic framework for LLM agents under partial observability that uses a knowledge graph to represent belief state, combines fast/slow thinking with uncertainty-aware planning, and reflection, showing gains on ALFWorld, Webshop, and ScienceWorld.

An analysis comparing Claude Opus 5 High and GPT 5.6 Sol Max on an ARC-AGI-3 puzzle shows Opus winning by preserving detailed state in visible output, while Sol relies on discarded hidden reasoning.

VisualPatchWorld introduces a method for learning world dynamics as code, enabling inspectable and editable simulators from data. It achieves strong planning success in navigation and manipulation tasks.

A declassified 1984 technical report details Shakey the Robot (1966-1972), the first mobile robot that could perceive its environment, build an internal model, and plan its own actions using the STRIPS planning system.

This article discusses a technique for reducing context bloat in AI agents by decoupling the planning phase from execution, improving agent efficiency and accuracy.

This paper introduces a controlled multi-turn environment to systematically study long-horizon planning ability in foundation model agents across pre-training, post-training (via GRPO and on-policy distillation), and integration (via multi-teacher on-policy distillation) stages.

Andrew Ng released an 8-page PDF detailing four key agentic workflows: reflection, tool use, planning, and multi-agent collaboration, emphasizing that a weak model with proper architecture can outperform a strong one.

Cursor's research shows that multi-model agentic systems using a frontier model as planner and a cheaper model as worker can reduce token costs by 15x, enabling more efficient complex agents.

Cursor's new agent swarm design uses planner and worker models to decompose tasks into a tree structure, achieving significant cost savings and quality improvements. In a test rebuilding SQLite from scratch in Rust, the new swarm reached 80% pass rate in four hours while the old swarm failed.

Schema is a harness that enables frontier AI models to achieve 99% on the ARC-AGI-3 benchmark by having them write executable programs to model game environments, test predictions, and plan.

Dr. Fei-Fei Li announces the second year of Stanford's BEHAVIOR Challenge, a robotics competition tackling long-horizon, complex everyday tasks, with new tasks, improved evaluation, and an $11,000 prize pool.

GATS introduces a Graph-Augmented Tree Search with a layered world model (symbolic, learned, generative) to eliminate LLM calls during planning, achieving 100% success on synthetic tasks and stress tests, outperforming LATS and ReAct.

Matt Pocock released v1.1 of his skills tool, featuring new commands for planning, specification, implementation, and code review. The poster switched from superpowers to this tool.

This paper synthesizes 27 benchmark, taxonomy, and audit papers from 2023-2026 into a unified taxonomy of LLM agent limitations, identifying six failure clusters including tool invocation errors, planning failures, long-horizon degradation, multi-agent coordination issues, safety concerns, and measurement validity problems.

This paper defines embodied operators as reusable functional modules for embodied intelligence pipelines, presents a taxonomy covering five major categories, and proposes a multi-dimensional benchmark framework for evaluating their deployability and composability.

AdaJEPA introduces an adaptive latent world model that continuously updates during test-time via closed-loop model predictive control, significantly improving planning success under distribution shift.

Micro-JEPA is a lightweight Python implementation of the Joint Embedding Predictive Architecture (JEPA), enabling an agent to learn environment representations, predict future states in latent space, and plan actions to avoid obstacles.