Research Papers

Title	Description
AgentFlow: In-the-Flow Agentic System Optimization for Effective Planning and Tool Use	Novel trainable agentic framework coordinating four specialized modules (planner, executor, verifier, generator) through evolving memory. Introduces Flow-GRPO training method enabling direct optimization within live multi-turn interactions. Demonstrates 7B models surpassing GPT-4o with 14.9% gains on search tasks, 14.0% on agentic tasks, and 14.5% on mathematical reasoning.
ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory	Novel memory framework enabling AI agents to learn from both successful and failed experiences by distilling generalizable reasoning strategies. Introduces Memory-aware Test-Time Scaling (MaTTS) that creates synergy between memory quality and computational scaling. Demonstrates up to 34.2% relative improvement across web browsing and software engineering tasks, with emergent self-evolution behaviors.
Curse of Instructions: Large Language Models Cannot Follow Multiple Instructions at Once	Comprehensive analysis revealing fundamental limitations in LLMs' ability to follow multiple simultaneous instructions. Introduces ManyIFEval benchmark showing exponential performance decay with instruction count, with GPT-4o, Claude-3.5, and other models tested. Includes self-refinement mitigation strategies and production implications.
AI Benchmark Critique: Evidence of Invalid 2026 Predictions	Critical analysis of METR and GDPval benchmarks, revealing statistical flaws, baseline inflation errors, and invalid extrapolation methods
Recursive Self-Aggregation: Deep Thinking and Test-Time Scaling for LLM Reasoning	Groundbreaking test-time scaling method enabling smaller models to match larger reasoning models through iterative aggregation of reasoning chains
The OaK Architecture: A Paradigm Shift in Artificial General Intelligence	Rich Sutton's vision for experience-based superintelligence through continual learning, hierarchical abstraction, and reward maximization

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