#planning

12 curated events

papersTODAY 04:00 UTC

Hippocampal Recurrent Network Model Explains Goal-Directed Navigation as Dynamics Relaxation

A new arXiv preprint proposes that the brain's spatial navigation circuits can be understood as a process of dynamics relaxation, in which neural activity settles into a state representing an optimal path. The authors build a recurrent network inspired by the hippocampus that reaches goals while avoiding obstacles. The work aims to bridge documented spatial cognitive maps with an account of how neural circuits actually compute routes through complex environments.

neuroscience/hippocampus/planning/recurrent-networks

papersTODAY 04:00 UTC

LePlanner: Iterative Amortized Controller for Latent World Model Planning

Researchers introduce LePlanner, an iterative amortized controller designed to plan within the latent spaces of world models built on joint-embedding predictive architectures. The work targets the high computational cost of existing planning methods, which typically rely on either search-based or gradient-based optimization. The paper is posted on arXiv as a cross-listing.

world-models/planning/arxiv/research

papersTODAY 04:00 UTC

arXiv paper weighs planning against learning for multi-asset maintenance

A new arXiv preprint examines how industrial maintenance systems handle multiple interacting assets that share resources, where reliability and cost must be traded off together. The authors compare planning-based and reinforcement learning approaches, focusing on reliability and cost. It appears in the cs.AI and cs.LG listings.

reinforcement-learning/planning/maintenance/arxiv

papersTODAY 04:00 UTC

Loss-Conditioned State Execution Decides When World Models Should Update

A new paper introduces loss-conditioned state execution, a model-agnostic method for deciding whether a learned world model should adopt its proposed feasible next state or stay at the current one. The authors argue that predictive informativeness on its own does not tell you whether switching states actually improves the model, and use a loss-based criterion instead.

world-models/research/planning/model-based-rl

papersTODAY 04:00 UTC

arXiv paper studies legal reasoning for world-model-based robot planning

A new arXiv preprint examines how legal norms can be encoded so that robots using world-model-based planning can follow them. The work extends the isomorphism problem of matching legal source texts to their formal encodings and identifies two main challenges for normative control of robots. It is a conceptual and measurement-focused contribution rather than a system release.

robotics/legal-reasoning/planning/safety

papersTODAY 04:00 UTC

Chance-Constrained Maneuver Planning for Satellite Collision Avoidance Under Uncertainty

A new arXiv paper presents a planning method that decides whether a spacecraft has enough information to justify a collision-avoidance maneuver in low Earth orbit. The approach frames the decision as a chance-constrained problem in belief space, accounting for uncertainty in the predicted encounter. It aims to help operators handle the rising number of conjunctions without committing to unnecessary burns.

spacecraft/planning/uncertainty/research

papersTODAY 04:00 UTC

arXiv Paper Proposes Forking Garden Framework for Whole-Game Generation

A new arXiv paper argues that generating complete games is mainly an orchestration challenge, since narrative, level design, encounters, objectives, rewards, and visuals all need to share a common intent. The authors introduce Forking Garden, which threads narrative archetypes through gameplay planning as a semantic signal to keep these layers aligned. The work appears as a cross-listing replacement on arXiv cs.AI.

game-generation/narrative/planning/arxiv

papersSEP 10 04:00 UTC

Researchers propose grounded evaluation and repair for LLM-generated PDDL planning problems

A new arXiv paper examines how large language models convert natural-language planning descriptions into PDDL problem instances, arguing that common checks like syntactic validity or planner success can overstate actual quality. The authors introduce an evaluation and repair framework that grounds assessment more firmly in the underlying planning task to better catch and fix flawed outputs.

llm/planning/pddl/evaluation

papersSEP 10 04:00 UTC

Researchers propose Belief-State Engine for LLM planning under partial observability

A new arXiv paper introduces the Belief-State Engine, a module intended to help large language model agents plan more reliably when they cannot fully observe their environment. The authors argue that ambiguous feedback currently pushes LLM agents into premature commitments and loss of crucial information. The approach grounds agent decisions in an explicit belief state based on principles from partially observable decision-making.

agents/planning/llm/research

papersSEP 10 04:00 UTC

Reinforcement Learning with Temporal-Logic-Based Causal Diagrams

A revised arXiv paper (v2) studies reinforcement learning problems in which agents must achieve goals that unfold over long time horizons, a setting commonly handled by encoding tasks as deterministic finite automata. The authors propose representing these tasks with causal diagrams built from temporal logic, aiming to give agents a structured way to reason about extended objectives. The update is a replacement version of the original June 2023 preprint.

reinforcement-learning/temporal-logic/causality/planning

papersSEP 11 04:00 UTC

arXiv Paper Proposes Statewise Refinement Method Using Terminal Symmetry

A new arXiv preprint examines how sequential construction tasks can exhibit exact symmetries at their terminal states even when the order of execution matters and depends on prior history. The author proposes a statewise refinement approach that uses terminal correspondence to carry process knowledge between equivalent outcomes, supporting anytime verified construction. The work frames this as a way to reuse asymmetric process evidence across states that end up equivalent.

research/arxiv/verification/planning

papersSEP 12 04:00 UTC

MAPLE pairs LLM planning with evolutionary search for optimization modeling

A new arXiv paper introduces MAPLE, a memory-augmented planning method that combines language models with an evolutionary search process. The work targets optimization tasks, where business users often understand their constraints but lack operations-research expertise or dedicated support. It builds on the idea of LLM-based optimization agents that convert natural-language requirements into models or solver programs.

agents/llm/optimization/planning