AEvo introduces a meta-agent that edits the evolution procedure or agent context based on accumulated state, outperforming baselines by 26% relative improvement on agentic benchmarks and achieving SOTA on open-ended tasks.
org/CorpusID:273186996
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MindTrellis enables users and AI to co-create evolving knowledge graphs, outperforming retrieval-only tools in expert-rated content coverage, structural quality, and reduced cognitive load during a study of 12 participants creating slide decks.
Generalizable agents require environment scaling via diverse executable rule-sets, distinguished from trajectory and task scaling in a new taxonomy.
Calibrate-Then-Act supplies LLM agents with priors on latent environment states to enable explicit cost-uncertainty reasoning, producing more optimal strategies than standard approaches in retrieval QA and file-reading coding tasks.
citing papers explorer
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Harnessing Agentic Evolution
AEvo introduces a meta-agent that edits the evolution procedure or agent context based on accumulated state, outperforming baselines by 26% relative improvement on agentic benchmarks and achieving SOTA on open-ended tasks.
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MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration
MindTrellis enables users and AI to co-create evolving knowledge graphs, outperforming retrieval-only tools in expert-rated content coverage, structural quality, and reduced cognitive load during a study of 12 participants creating slide decks.
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Scalable Environments Drive Generalizable Agents
Generalizable agents require environment scaling via diverse executable rule-sets, distinguished from trajectory and task scaling in a new taxonomy.
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Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents
Calibrate-Then-Act supplies LLM agents with priors on latent environment states to enable explicit cost-uncertainty reasoning, producing more optimal strategies than standard approaches in retrieval QA and file-reading coding tasks.
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