astroreason-bench-evaluating-unified-agentic

Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely focus on symbolic or weakly grounded environments, leaving their performance in physics-constrained real-world domains underexplored. We introduce AstroReason-Bench, a comprehensive benchmark for evaluating agentic planning in Space Planning Problems (SPP), a family of high-stakes problems with heterog...

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Repositório: ADu2021/skillXiv
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name	astroreason-bench-evaluating-unified-agentic
title	AstroReason-Bench: Evaluating Unified Agentic Planning Across Heterogeneous Space Planning Problems
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.11354
keywords	["Agent","Planning"]
description	Recent advances in agentic Large Language Models (LLMs) have positioned them as generalist planners capable of reasoning and acting across diverse tasks. However, existing agent benchmarks largely focus on symbolic or weakly grounded environments, leaving their performance in physics-constrained real-world domains underexplored. We introduce AstroReason-Bench, a comprehensive benchmark for evaluating agentic planning in Space Planning Problems (SPP), a family of high-stakes problems with heterog...

Problem

AstroReason-Bench addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.

Key Approach

The paper introduces a novel framework, methodology, or benchmark for astroreason-bench. The core contributions include:

Systematic framework or benchmark for agent evaluation and development
Empirical findings on agent performance, efficiency, or capabilities
Generalizable principles applicable across domains

When to Use

Use this skill when you need to:

Evaluate or benchmark autonomous agent systems
Understand best practices in agent design and evaluation
Learn empirical results on agent performance
Improve agent efficiency, reasoning, or capabilities

When NOT to Use

For non-agent-related tasks
When seeking quick implementation code (see the paper for details)
For general knowledge unrelated to autonomous agents

Resources

ArXiv Abstract: https://arxiv.org/abs/2601.11354
Full PDF: https://arxiv.org/pdf/2601.11354
HTML Version: https://arxiv.org/html/2601.11354

See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.

astroreason-bench-evaluating-unified-agentic

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