AgentFugue introduces a plug-in shared reasoning hub trained with SFT and RL that enables peer agents to share intermediate reasoning, yielding gains on long-horizon tasks over strong baselines.
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SAM is a standalone memory framework for long-horizon LLM agents that creates state-adaptive cues from interactions, preserves raw trajectories for intent-driven recall, and optimizes the module via expert supervision and RL, outperforming baselines on BrowseComp and related benchmarks.
WebThinker equips large reasoning models with autonomous web exploration and interleaved reasoning-drafting via a Deep Web Explorer and RL-based DPO training, yielding gains on GPQA, GAIA, and report-generation benchmarks.
LLM agents improve adaptability by first using an interaction budget for systematic exploration measured via Exploration Checkpoint Coverage before executing tasks.
Hierarchical Re-Basin merging induces stronger adversarial and perturbation robustness in combined models as more participants are added, but produces larger performance drops than previously reported.
citing papers explorer
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AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning
AgentFugue introduces a plug-in shared reasoning hub trained with SFT and RL that enables peer agents to share intermediate reasoning, yielding gains on long-horizon tasks over strong baselines.
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SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent
SAM is a standalone memory framework for long-horizon LLM agents that creates state-adaptive cues from interactions, preserves raw trajectories for intent-driven recall, and optimizes the module via expert supervision and RL, outperforming baselines on BrowseComp and related benchmarks.
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WebThinker: Empowering Large Reasoning Models with Deep Research Capability
WebThinker equips large reasoning models with autonomous web exploration and interleaved reasoning-drafting via a Deep Web Explorer and RL-based DPO training, yielding gains on GPQA, GAIA, and report-generation benchmarks.
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Look Before You Leap: Autonomous Exploration for LLM Agents
LLM agents improve adaptability by first using an interaction budget for systematic exploration measured via Exploration Checkpoint Coverage before executing tasks.
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Robustness and Regularization in Hierarchical Re-Basin
Hierarchical Re-Basin merging induces stronger adversarial and perturbation robustness in combined models as more participants are added, but produces larger performance drops than previously reported.