planning

Diurnal is a native Mac app for quick day planning, allowing users to capture thoughts via keyboard shortcuts or voice commands, integrate with Google Calendar, and use it offline.

A Twitter user shared a prompt that uses Terence Tao's 'dumb questions' method to stress-test any plan by asking critical questions to uncover hidden assumptions and improve understanding.

This paper introduces Counterfactual Latent World Models (CLWM) to address counterfactual collapse in world models, improving planning success in embodied reasoning under partial observability through contrastive objectives.

This paper addresses scheduling business processes under control-flow uncertainty by framing it as a chance-constrained optimization problem, presenting decomposed and integrated approaches with evaluations on real-world and synthetic data.

ARC-Bench is introduced as a benchmark to audit whether frozen JEPA-style world models correctly rank candidate actions based on latent distance, revealing severe and structural failures in official checkpoints. The paper explains that closed-loop replanning masks these defects, leading to overestimated success rates.

Bioinfoysis is a multi-agent harness for bioinformatics that uses persistent runs and artifact-grounded execution to achieve state-of-the-art accuracy on benchmarks like BixBench and LAB-Bench 2.

The article argues that world models, which enable AI systems to represent environments, predict outcomes, and make decisions, are emerging as the next major AI paradigm, as indicated by the convergence of prominent researchers like Yann LeCun, Demis Hassabis, and Fei-Fei Li.

This paper introduces Belief-Based World Models (BB-WMs) to enhance LLM agents' decision-making under partial observability by providing direct access to beliefs about uncertain states, showing improved task performance.

A developer asks about strategies for coordinating multiple AI agents working on the same codebase, contrasting written plans with mental coordination.

LangChain Academy is hosting a live workshop on Deep Agents on September 17th, teaching participants to build agents with planning, memory, and subagents for complex tasks.

RISE introduces an adaptive framework for imagination rollouts in world action models, using a counterfactual driving dataset to improve planning performance while reducing unnecessary computation.

Dates by Agenda Hero is a product that allows users to create and share plans for various purposes.

A hands-on review of Qwen 3.8 Max, highlighting its speed, strong planning and simplification skills, experimental rigor, and creative implementation abilities.

The paper investigates why latent world models fail at long-horizon planning and finds the bottleneck is the planning objective (squared latent distance), not the predictor's accuracy; replacing the objective with a learned cost dramatically improves planning performance.

This arXiv survey (1,547 papers, 2024-2026) systematically maps the field of long-horizon LLM agents, disambiguating long-horizon, long-context, and long-term memory, and organizing research into six lifecycle categories while identifying the core 'horizon gap' and open measurement problems.

Introduces A2E, an end-to-end evaluation engine for agent harnesses, using a standardized task protocol and execution traces to assess capabilities like efficiency, tool use, planning, and error recovery.

The paper introduces DCAS, a backend-substitution interception layer that decouples CLI agent scaffolding from backend models, enabling cross-scaffold evaluation. It shows that planning-aware fine-tuning on a small dataset improves model generalization across non-training scaffolds.

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.