AI Agent Papers2026
Evaluates AI agents' ability to complete end-to-end scientific workflows by releasing and assessing 97 tasks from a 300-task FrontierChallenge suite across chemistry, materials, life science, and electrochemistry. Finds that top agent configurations achieved only a 20.6% pass rate despite high partial scores, revealing a gap between partial progress/confident completion claims and actual complete scientific deliverables.
AI Agent Papers2026
Proposes Recuris, a recursive Experiential-Working Memory architecture that separates Working Memory (task progress) from Experiential Memory (skills) and uses a Meta-Agent to validation-gate localized skill updates, enabling bounded recursive skill evolution for long-horizon agents.
Provides a streaming dual-brain memory for real-time speech agents: an informational left brain for factual retrieval and an affective right brain for persona/emotion, achieving high top-5 accuracy while keeping retrieval latency within VAD budgets (~134 ms).
AI Agent Papers2026
Synthesizes, repairs, and self-evolves task-adaptive agent harnesses on demand for off-the-shelf LLM agents, using a trainable harness-intelligence model that distills signals from past configurations. Demonstrates consistent performance gains across benchmarks and model families by producing four-module, composable harnesses.