IdleSpec improves LLM agent accuracy by generating and aggregating speculative plans during idle time between tool calls and observations using complementary drafting strategies.
Oagents: An empirical study of building effective agents.CoRR, abs/2506.15741
5 Pith papers cite this work. Polarity classification is still indexing.
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A VOI-based controller for dual inference budgets improves multi-hop QA performance by prioritizing search actions and selectively finalizing answers.
EcoGym is a new open benchmark with three economic environments that reveals no leading LLM dominates at sustained plan-and-execute decision making across scenarios.
WebWatcher introduces a vision-language deep research agent trained on synthetic multimodal trajectories and RL that outperforms baselines on VQA benchmarks, along with a new BrowseComp-VL evaluation.
Cognitive Kernel-Pro provides an open-source agent framework with curated training data across web, file, code, and reasoning domains plus test-time reflection and voting, achieving SOTA results on GAIA among free agents.
citing papers explorer
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IdleSpec: Exploiting Idle Time via Speculative Planning for LLM Agents
IdleSpec improves LLM agent accuracy by generating and aggregating speculative plans during idle time between tool calls and observations using complementary drafting strategies.
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Inference-Time Budget Control for LLM Search Agents
A VOI-based controller for dual inference budgets improves multi-hop QA performance by prioritizing search actions and selectively finalizing answers.
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EcoGym: Evaluating LLMs for Long-Horizon Plan-and-Execute in Interactive Economies
EcoGym is a new open benchmark with three economic environments that reveals no leading LLM dominates at sustained plan-and-execute decision making across scenarios.
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WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
WebWatcher introduces a vision-language deep research agent trained on synthetic multimodal trajectories and RL that outperforms baselines on VQA benchmarks, along with a new BrowseComp-VL evaluation.
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Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training
Cognitive Kernel-Pro provides an open-source agent framework with curated training data across web, file, code, and reasoning domains plus test-time reflection and voting, achieving SOTA results on GAIA among free agents.