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Apodex 1.1 is an AI agent designed for deep research, capable of handling complex tasks that require extensive investigation. It uses a main agent to decompose problems and asynchronously dispatches multiple sub-agents for execution, supporting long-running operations and automatic recovery.
This paper introduces Spine-Branch Coordination, a framework for multi-agent computer use that decomposes tasks into a spine-branch graph to avoid VM merging, demonstrating improved success rates and cost efficiency on long-horizon tasks.
OneDayAgent is a long-horizon harness for autonomous agents that decomposes open-ended tasks into bounded subtasks, manages execution memory under context pressure, and verifies/repairs final outputs. It achieves state-of-the-art results on AgentIF-OneDay with GLM-5.2 and generalizes across five backend LLMs.
Cursor's article introduces the Agent Swarm architecture, achieving context isolation and efficient collaboration through a layered design of Planner and Worker. In a SQLite rebuild experiment, using Grok 4.5, they achieved an 80% test pass rate.
LoopTroop is a local, open-source GUI app that prevents context rot in long coding tickets by using an LLM council for planning, task decomposition into independent beads, and controlled execution with failure notes, keeping the developer in the loop.
The author describes being deeply impressed and unsettled by GPT 5.6 and Codex, highlighting the model's ability to decompose tasks, recall errors, and propose an optimized process with specific efficiency gains.
Proposes SkillReranker, an inference-time reranking framework that decomposes tasks and skills to build a directed acyclic execution graph and uses a cross-encoder for scoring, improving task performance and efficiency in agent skill selection.
Introduces SkillWeaver, a decompose-retrieve-compose framework for routing multiple skills to LLM agents, along with CompSkillBench, a benchmark of 300 compositional queries over 2,209 real MCP server skills.
This paper introduces SearchSwarm, a model trained on synthesized delegation intelligence to improve long-horizon deep research tasks via task decomposition and subagent coordination, achieving state-of-the-art results on BrowseComp benchmarks.
This paper proposes a multi-agent computer use (MACU) system that uses a manager model to decompose tasks into directed acyclic graphs for parallel execution by subagents. It demonstrates consistent improvements over single-agent baselines on multiple benchmarks and better test-time scaling.
DRIVE proposes a dual-level skill modeling framework that separates reasoning knowledge from interaction knowledge for web agents under continual learning, achieving a 52.8% task success rate on WebArena, outperforming the skill-free baseline by 7.3 percentage points.
This paper studies harness design for LLM agents, separating it into task decomposition and guided execution, and shows that more elaborate harnesses are not uniformly better; it reveals failure modes and proposes partial harnesses as effective.
OpenAI presents iterated amplification, a method for training AI systems on complex tasks by recursively decomposing them into smaller subtasks that humans can judge and solve, building up training signals from scratch through iterative composition.
OpenAI research proposes hierarchical reinforcement learning where agents break down complex tasks into sequences of high-level actions rather than low-level ones, significantly improving efficiency for long-horizon tasks by reducing search complexity from thousands of steps to dozens.
Google DeepMind's Gemini Robotics 2 adds multi-robot collaboration capabilities, enabling the humanoid robot Apollo and the dual-arm robot 'Duo' to complete complex tasks such as organizing a garage through independent reasoning and communication coordination, demonstrating capabilities from macro planning to fine manipulation.