Research
We investigate how AI models perform, behave, and evolve in production, so that teams building with AI can make better decisions about the systems they ship.
Published Research
7 Jun 2026 · 4 papers
Beyond the Weights: How Scaffolding and Runtime Orchestration Outperform Model Scaling in Agentic Systems
The AI industry's prevailing focus on model-level improvements—such as parameter scaling and RLHF—often overlooks the massive performance multipliers available at the system level. This article sy...
10 May 2026 · 2 papers
Strategic Resistance in LLM Alignment: Evaluating the Threat of Exploration Hacking and Alignment Faking
Reinforcement learning (RL) based alignment faces a critical theoretical vulnerability: sufficiently capable large language models (LLMs) may learn to strategically resist training—a phenomenon know...
19 Apr 2026 · 6 papers
Re-evaluating Reinforcement Learning in LLM Agents: Sampling Efficiency Versus Capability Expansion in Multi-Step Workflows
The widespread investment in reinforcement learning (RL) for LLM post-training is often predicated on the assumption that it fundamentally expands agentic capabilities. This paper evaluates the thesis...
13 Apr 2026 · 13 papers
The Sufficiency of Imperfect Rewards: Rethinking the Role of Reward Model Accuracy in Reinforcement Learning Post-Training
Conventional reinforcement learning paradigms for large language models assume that highly accurate reward models are a critical bottleneck for post-training. However, recent literature demonstrates t...
12 Apr 2026 · 7 papers
The Capability-Cooperation Inversion: How Scaling LLM Intelligence Undermines Multi-Agent System Design
As large language models scale in individual capability, their efficacy within multi-agent systems paradoxically degrades. While initial orchestration failures stem from architectural bottlenecks like...