Selected work

Representative Papers

HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation

Aug 16, 2026

This study addresses planning information loss and reasoning degradation in LLM agents during long-horizon tasks caused by context redundancy. We propose a hierarchical context management framework that functionally isolates planning and execution layers, employing independent reasoning modules to prevent subtask contamination of global memory while utilizing structured summarization to maintain long-range coherence. Experimental results demonstrate that this framework achieves Pass@1 scores of 66.7% on GAIA and 61.3% on BrowseComp-Plus, outperforming the strongest baselines by 6.1 and 4.7 percentage points, respectively. These findings confirm that our approach effectively mitigates context bloat and significantly enhances performance on complex, long-horizon tasks.

0 citationsRead paper

Empowering LLMs with Parameterized Skills for Adversarial Long-Horizon Planning

Sep 16, 2025

To address the limited planning and decision-making capabilities of large language models (LLMs) in complex, adversarial, long-horizon environments, this paper proposes PLAP—a parametric skill-driven hierarchical planning framework. PLAP comprises an LLM-based skill planner, an environment-specific parametric skill library, and a skill executor, enabling end-to-end mapping from natural-language instructions to reliable action sequences while reducing reliance on handcrafted rules and action reliability heuristics. Evaluated on MicroRTS, GPT-4o–driven PLAP achieves zero-shot performance surpassing 80% of baselines; Qwen2-72B with few-shot prompting outperforms the top scripted agent CoacAI. Furthermore, the authors introduce the first LLM benchmark leaderboard dedicated to long-horizon skill-based planning, establishing a standardized evaluation framework for this emerging research direction.

0 citationsRead paper

Recent publications

Latest Papers

HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation

Aug 16, 2026

This study addresses planning information loss and reasoning degradation in LLM agents during long-horizon tasks caused by context redundancy. We propose a hierarchical context management framework that functionally isolates planning and execution layers, employing independent reasoning modules to prevent subtask contamination of global memory while utilizing structured summarization to maintain long-range coherence. Experimental results demonstrate that this framework achieves Pass@1 scores of 66.7% on GAIA and 61.3% on BrowseComp-Plus, outperforming the strongest baselines by 6.1 and 4.7 percentage points, respectively. These findings confirm that our approach effectively mitigates context bloat and significantly enhances performance on complex, long-horizon tasks.

0 citationsRead paper

Empowering LLMs with Parameterized Skills for Adversarial Long-Horizon Planning

Sep 16, 2025

To address the limited planning and decision-making capabilities of large language models (LLMs) in complex, adversarial, long-horizon environments, this paper proposes PLAP—a parametric skill-driven hierarchical planning framework. PLAP comprises an LLM-based skill planner, an environment-specific parametric skill library, and a skill executor, enabling end-to-end mapping from natural-language instructions to reliable action sequences while reducing reliance on handcrafted rules and action reliability heuristics. Evaluated on MicroRTS, GPT-4o–driven PLAP achieves zero-shot performance surpassing 80% of baselines; Qwen2-72B with few-shot prompting outperforms the top scripted agent CoacAI. Furthermore, the authors introduce the first LLM benchmark leaderboard dedicated to long-horizon skill-based planning, establishing a standardized evaluation framework for this emerging research direction.

0 citationsRead paper