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Notes on long-horizon tasks
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Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery
The paper presents Eureka, a Meta-Agent architecture that can complete long-horizon tasks by forming specialized Macro-Agents for specific tasks. The architecture allows for cost-benefit-gated evolution to update the…
ClawGym II: Exploring Black-Box RL on Agent Harness
Researchers present a new framework for reinforcement learning (RL) using complex agent harnesses. This framework is designed to optimize RL more stably and efficiently for long-horizon tasks. The researchers' method…
Neurosymbolic Embodied Agents
The authors propose a neurosymbolic agent that combines vision-language models with symbolic planning for executable long-horizon household tasks. The agent consists of two phases: the first involves a vision-language…
VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?
Researchers introduce a new benchmark called VibeLifeBench to evaluate large language model (LLM) agents' ability to be proactive and consistent in long-horizon tasks across ten everyday life domains. The benchmark…
StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure
In this paper, researchers introduce StructAgent, a framework designed to improve long-horizon digital agents' performance in tasks with evolving contexts and complex workflows. The framework introduces a unified state…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers introduce a proactive memory agent for long-horizon tasks, aiming to improve decision-making in scenarios where relevant state information can become buried over time. The memory agent updates a structured…
AutoMem: Automated Learning of Memory as a Cognitive Skill
Researchers developed AutoMem, a framework for improving a language model's memory management skills in long-horizon games. By treating memory as a trainable skill, they allowed the model to decide how to manage its…
Freeform Preference Learning for Robotic Manipulation
Researchers introduce Freeform Preference Learning (FPL), a method for training robotic policies based on natural-language preferences provided by humans, rather than binary labels. The method uses the preferences to…
QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
Researchers introduce QVal, a tool that evaluates dense supervision signals for long-horizon Learning with Long Memory (LLM) agents without the need for training. This allows for a fair comparison between different…
QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents
A research paper introduces QVal, a tool for evaluating dense supervision signals in long-horizon tasks for LLM agents without the need for training. The tool measures how well a method's score aligns with Q-values of…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Researchers have developed a new framework called OmniAct for building embodied agents that can operate in unstructured environments. This framework aims to address the challenges of managing both cyber and physical…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
The paper presents OmniAct, a framework designed to create persistent embodied agents that can operate autonomously in unstructured environments. It addresses the challenges of integrating cyber (APIs, IoT) and…
Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
Researchers introduce OmniAct, a framework designed for creating persistent embodied agents in unstructured environments. The framework is built on a hierarchical asynchronous architecture that separates planning…
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