Planning Beyond Words: An Agent for Complex Real-World Tasks

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Author: Denis Avetisyan

A new AI agent, STAgent, demonstrates impressive capabilities in solving intricate, time-sensitive problems like multi-step travel planning.

STAgent leverages a stable tool environment, curated datasets, and difficulty-aware curriculum learning to achieve robust spatio-temporal reasoning with large language models.

Despite advances in large language models, complex spatio-temporal reasoning-critical for tasks like dynamic itinerary planning-remains a significant challenge. This paper, the AMAP Agentic Planning Technical Report, introduces STAgent, a specialized agentic model designed to navigate such complexities through tool integration and refined training. STAgent achieves strong performance on travel benchmarks by leveraging a stable tool environment, a hierarchical data curation framework prioritizing quality and diversity, and a cascaded training regime sensitive to query difficulty. Could this difficulty-aware approach unlock more robust and adaptable agentic systems capable of tackling real-world, dynamic challenges?

Beyond Prediction: The Rise of Agentic Reasoning

While contemporary language models demonstrate remarkable proficiency in identifying and replicating patterns within vast datasets, their capabilities diminish when confronted with reasoning challenges demanding more than simple correlation. These models, fundamentally predictive in nature, often falter when required to navigate multi-step problems or integrate information beyond their initial training. The limitation isn’t necessarily a lack of data, but rather an inability to effectively apply knowledge – to synthesize information, formulate plans, and execute them in a logical sequence. Consequently, tasks necessitating external knowledge retrieval, nuanced understanding of context, or adaptive problem-solving prove particularly difficult, highlighting a critical gap between statistical proficiency and genuine cognitive reasoning.

Current language models, despite achieving impressive feats in text generation and comprehension, often falter when confronted with tasks demanding genuine problem-solving abilities. These systems are fundamentally reactive; they excel at identifying patterns within existing data but lack the capacity for proactive engagement with external environments. Unlike human cognition, which readily integrates tool use and environmental interaction into the reasoning process, these models remain largely confined to the realm of passive prediction. This limitation severely restricts their capacity to tackle complex, multi-step problems that necessitate gathering information, manipulating objects, or adapting to unforeseen circumstances – effectively hindering their ability to move beyond recognizing what has happened to understanding how to achieve a desired outcome.

The limitations of current language models stem from their fundamentally passive nature; they excel at predicting what comes next, but fall short when confronted with challenges demanding proactive problem-solving. A shift is occurring towards agentic reasoning, a paradigm where artificial intelligence doesn’t just process information, but actively does – utilizing tools, interacting with environments, and adapting strategies to achieve goals. This isn’t merely about improving prediction accuracy, but about imbuing models with the capacity for deliberate action, allowing them to move beyond recognizing patterns to constructing solutions through a series of intentional steps. The future of AI lies not in what it knows, but in what it can do with that knowledge, effectively transitioning from a system that responds to one that initiates.

Truly intelligent systems require more than just processing information; they demand the ability to interact with and manipulate their surroundings. Current artificial intelligence often operates within a closed loop, limited by the data it was initially trained on. However, models capable of utilizing external tools – such as search engines, calculators, or even robotic actuators – can overcome these limitations and tackle previously intractable problems. Crucially, these systems must also exhibit adaptability, dynamically adjusting their strategies as circumstances change and new information becomes available. This necessitates a shift from static prediction to a more flexible, iterative process where models actively seek out and integrate information, effectively doing rather than simply knowing, and thereby exhibiting a form of intelligence akin to problem-solving in the real world.

Constructing STAgent: A Foundation of Data and Training

STAgent’s development commenced with the Qwen3-30B-A3B language model, a 30 billion parameter model chosen to provide a robust starting point for specialized training. This base model offers pre-trained capabilities in natural language understanding and generation, reducing the amount of data and computational resources required to achieve target performance levels. Utilizing a powerful foundation model allows subsequent fine-tuning processes, such as Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), to focus on adapting the model’s existing knowledge to the specific tasks and datasets relevant to STAgent’s intended functionality, rather than building language proficiency from scratch.

Data curation for STAgent prioritizes quality and relevance through a rigorous filtering process informed by a defined Intent Taxonomy. This taxonomy guides the selection of training examples, ensuring alignment with desired agent behaviors. The curation process achieves a filtering ratio of 1:10,000, meaning only one in ten thousand candidate data points are retained for training. This highly selective approach minimizes noise and maximizes the impact of each training example, contributing to the agent’s accuracy and performance on targeted tasks.

STAgent training employs a two-stage SFT-Guided RL process. Initial Supervised Fine-Tuning (SFT) establishes a foundational model through exposure to labeled data. This is followed by Reinforcement Learning (RL) to optimize performance based on reward signals. A key component of this RL phase is the ROLL infrastructure, which significantly improved training efficiency by 80%, allowing for faster iteration and refinement of the model’s capabilities. This staged approach enables progressive learning, building upon the initial SFT baseline with the nuanced optimization provided by RL.

The STAgent training methodology employs a cascaded approach, sequentially building capabilities through distinct phases. Initial Supervised Fine-Tuning (SFT) establishes a foundational understanding, followed by Reinforcement Learning (RL) to optimize performance based on feedback. This staged process allows for incremental improvement in reasoning and problem-solving; the SFT phase provides a solid base, while the RL phase refines these skills through iterative learning. The use of the ROLL infrastructure further enhances this progression by significantly improving RL training efficiency, enabling faster and more effective refinement of STAgent’s core competencies.

An Interactive Ecosystem for Robust Tool Utilization

STAgent functions within a dedicated Interactive Environment designed to facilitate problem-solving through the utilization of external tools. This environment currently supports ten domain-specific tools, allowing the agent to extend its inherent capabilities and address tasks requiring interaction with external systems or data sources. The architecture enables STAgent to decompose complex tasks into a series of actions involving these tools, effectively leveraging them as functional extensions of its core reasoning processes. This approach allows for tackling problems that exceed the agent’s internal knowledge or processing capacity, relying instead on external interactions to gather information or execute specific operations.

FastMCP, a Monte Carlo Planning algorithm, is integral to STAgent’s operational efficiency by enabling asynchronous tool integration. This allows STAgent to continue reasoning and planning while simultaneously awaiting responses from external tools, significantly reducing latency compared to synchronous approaches. The algorithm dynamically adjusts its planning horizon and search depth based on task complexity and tool response times, optimizing for both accuracy and responsiveness. By decoupling planning from tool execution, FastMCP facilitates a more flexible and robust interaction with the environment, allowing STAgent to handle tool failures or delays without interrupting its overall reasoning process. This asynchronous capability is crucial for maintaining real-time performance in complex, tool-dependent tasks.

ROLL (Reinforcement Learning Optimization Layer) functions as the core infrastructure for training STAgent via reinforcement learning. This platform provides the necessary tools and framework for defining reward functions that quantify task success and for iteratively optimizing STAgent’s policy through interaction with the interactive environment. ROLL facilitates experimentation with different RL algorithms and hyperparameters, enabling systematic improvement of STAgent’s performance across a range of complex tasks. The infrastructure supports scalable training, allowing for efficient exploration of the policy space and robust generalization of learned behaviors.

The Amap Agent is a specialized extension of the STAgent framework, designed to address tasks requiring analysis of map-based data and reasoning about spatio-temporal relationships. This agent incorporates functionalities for processing geographical information, identifying locations, and understanding changes in spatial arrangements over time. Its capabilities include interpreting map features, determining relative positions, and predicting future states based on observed patterns, effectively enabling STAgent to operate within and reason about dynamic, map-centric environments.

Validating STAgent: Performance Across Benchmarks

To comprehensively assess its capabilities, STAgent underwent rigorous evaluation using a suite of established benchmarks designed to probe both general knowledge and coding proficiency. Performance was measured on challenges like TravelBench, which tests multi-turn reasoning about travel planning, and MMLU-Pro, a demanding multiple-choice question answering benchmark spanning diverse subject areas. Furthermore, the LiveCodeBench benchmark was employed to specifically evaluate the model’s coding abilities, pushing it to generate and understand functional code snippets. These standardized evaluations provide a quantifiable measure of STAgent’s performance relative to other advanced models, demonstrating its capacity for complex reasoning and practical application in real-world scenarios.

Beyond assessing general knowledge, STAgent’s capabilities as an autonomous agent were scrutinized using benchmarks designed to test tool utilization and complex task completion. ACEBench, for example, challenges models to leverage external tools to answer questions, while τ 2-Bench specifically evaluates an agent’s ability to plan and execute multi-step reasoning processes. These specialized evaluations moved beyond simple question-answering, probing STAgent’s proficiency in actively using resources to achieve goals-a crucial element of true agentic intelligence. Performance on these benchmarks indicated that STAgent wasn’t merely recalling information, but demonstrating a capacity for strategic action and adaptive problem-solving within defined environments.

Evaluations revealed the Amap Agent possesses notable proficiency in map-based reasoning, as demonstrated by its strong performance on the C-Eval benchmark. This specialized assessment challenges models to leverage geographic information and spatial understanding to answer complex questions, and the Amap Agent consistently delivered accurate and insightful responses. Its success on C-Eval suggests a robust capacity for interpreting map data, identifying relevant landmarks, and calculating distances or routes – skills crucial for applications ranging from navigational assistance to logistical planning. The agent’s ability to effectively process and utilize map information positions it as a valuable asset in scenarios demanding spatial awareness and geographic intelligence.

Evaluations on the TravelBench benchmark reveal STAgent’s proficiency in complex, multi-turn reasoning and planning. Achieving an overall score of 70.33, the model demonstrably surpassed the performance of significantly larger language models, including DeepSeek R1 and Qwen3-235B-Instruct. Notably, STAgent secured a leading multi-turn score of 66.61, indicating a superior capacity to maintain context and coherence throughout extended interactions. Further analysis showed a substantial 26.06% improvement in resolving previously unsolved travel planning challenges when compared to baseline models, highlighting its enhanced problem-solving abilities in this domain and suggesting a valuable tool for automated travel assistance.

The development of STAgent, as detailed in the report, exemplifies a principle often understated in complex system design: elegance through constraint. It isn’t simply about achieving a functional outcome, but about building a stable, predictable system within defined boundaries. As John McCarthy observed, “It is better to solve one problem completely than to solve many problems incompletely.” STAgent’s focused approach-leveraging a stable tool environment and curated data-prioritizes depth of capability in spatio-temporal reasoning over breadth. This echoes the sentiment that architecture is, fundamentally, the art of choosing what to sacrifice; in this case, generality for robustness and reliability. If the system looks clever, it’s probably fragile, and STAgent appears deliberately, thoughtfully, not clever.

Future Directions

The presentation of STAgent, while a demonstrable step forward, subtly underscores a perennial truth: elegant performance often reveals the architecture of the constraints, not necessarily a general intelligence. The system thrives within a meticulously curated environment, a ‘stable tool’ ecosystem. One anticipates the inevitable question: how much of the success stems from the agent itself, and how much from the painstaking pre-conditioning of its world? Future efforts must grapple with the brittleness inherent in such specialized designs, probing the limits of transfer learning to genuinely novel, unconstrained scenarios.

Furthermore, the emphasis on ‘difficulty-aware’ curriculum learning hints at a deeper issue. The need to carefully scaffold complexity implies that current large language models, even when augmented with agency, remain fundamentally reliant on guided exploration. A truly robust system should, in principle, be capable of discovering effective learning pathways, not merely executing pre-defined ones. This suggests a shift in focus, away from simply scaling model parameters and toward developing more sophisticated mechanisms for intrinsic motivation and self-directed learning.

Ultimately, the pursuit of agentic AI necessitates a holistic view. Improving individual components – reasoning, planning, tool use – is insufficient. The challenge lies in understanding the emergent properties of the entire system, acknowledging that modifications in one area will invariably trigger a cascade of consequences elsewhere. A deeper investigation into the interplay between data curation, learning algorithms, and environmental constraints is paramount, lest these agents remain fascinating curiosities rather than truly adaptive problem-solvers.

Original article: https://arxiv.org/pdf/2512.24957.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-04 22:33