Laude Institute

Beyond Static Intelligence: Evaluating Continual Learning — Parth Asawa, UC Berkeley

Aug 12, 2026 · 20:30

Parth Asawa, a UC Berkeley PhD student, argues that standard LLM evaluations, which reset memory between tasks, fail to measure continual learning. He introduces Continual Learning Bench 1.0, a benchmark spanning six domains including database exploration and sales prediction, using a 'gain' metric that compares stateful versus stateless performance to isolate learning from base model strength. The benchmark requires headroom, shared latent structure, and a learning signal. Initial results show vanilla in-context learning tops the leaderboard over more elaborate context management systems on reward, gain, and cost. Asawa highlights failure modes like a forecasting model that overpredicts, corrects, then reverts, and a notepad system that dismisses relevant cohort definitions. He advocates for designing continual learning as a first-order requirement, potentially as a single training phase, rather than retrofitting existing models.

Everything Is a Rollout — Alex Shaw + Ryan Marten, Terminal-Bench, Harbor, Laude Institute

Jul 24, 2026 · 21:11

Alex Shaw presents Harbor, an open-source framework for evaluating and optimizing AI agents through sandboxed environments, arguing that agent development is a form of machine learning requiring empirical evaluation. He contrasts this with traditional software engineering, showing how agents' behavior is best treated as a black box artifact. Harbor provides a common format for specifying agentic tasks, enabling parallel rollouts across any model, sandbox, and task. Shaw outlines four evaluation use cases: assessing agents building internal products, using external APIs, powering product features, and automating processes. He highlights adoptions by companies like Cognition, Scale, and Poolside, and notes Harbor's role in benchmarks like Frontier Suite and Rune Bench for Runescape. The framework also supports training via SFT and reinforcement learning, with integration partners like Tinker and LangChain.