---
title: "Beyond Static Intelligence: Evaluating Continual Learning"
category: "talks"
date: "2026-06-30"
time: "10:45am-11:05am"
track: "Memory & Continual Learning"
room: "Track 3"
speakers: ["Parth Asawa"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "Memory & Continual Learning"
scheduleRoom: "Track 3"
scheduleLabels: ["Memory & Continual Learning", "Track 3", "session", "confirmed"]
---
# Beyond Static Intelligence: Evaluating Continual Learning

## Conference Context
- Date/time: 2026-06-30 · 10:45am-11:05am
- Track/room: Memory & Continual Learning · Track 3
- Speaker(s): Parth Asawa
- Session type/status: session · confirmed

- Track: Memory & Continual Learning
- Room: Track 3
- Session type: session
- Status: confirmed

## Session Description
Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it. We introduce Continual Learning Bench (CL-Bench), the first difficult, expert-validated benchmark designed to measure whether LLM-based systems genuinely improve with experience. CL-Bench spans six diverse domains (software engineering, signal processing, disease outbreak forecasting, database querying, strategic game-playing, and demand forecasting), each validated by domain experts and designed so that tasks share a learnable latent structure (codebase layout, disease outbreak dynamics, opponent strategies) that a stateful system can discover online but a stateless one cannot. We evaluate frontier models across several agent architectures, from naive in-context learning (ICL) to dedicated memory systems, introducing a gain metric to isolate learning from prior capabilities. We find that these systems leave headroom for improved continual learning: agents frequently overfit to immediate observations or fail to reuse knowledge across instances, and dedicated memory systems do not fix this---in fact, naive ICL outperforms systems dedicated to memory management. CL-Bench is the first benchmark to evaluate continual learning across diverse real-world domains with expert-validated tasks and isolate online learning from underlying model capability, showing a need for better continual learning systems.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[parth-asawa]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# Beyond Static Intelligence: Evaluating Continual Learning ## Conference Context - Date/time: 2026-06-30 · 10:45am-11:05am - Track/room: Memory & Continual Learning · Track 3 - Speaker(s): Parth Asawa - Session type/status: session · confirmed - Track: Memory & Continual Learning - Room: Track 3 - Session type: session - Status: confirmed ## Session Description Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it. We introduce Continual Learning Bench (CL-Bench), the first difficult, expert-validated benchmark designed to measure whether LLM-based systems genuinely improve with experience. CL-Bench spans six diverse domains (software engineering, signal processing, disease outbreak forecasting, database querying, strategic game-playing, and demand forecasting), each validated by domain experts and designed so that tasks share a learnable latent structure (codebase layout, disease outbreak dynamics, opponent strategies) that a stateful system can discover online but a stateless one cannot. We evaluate frontier models across several agent architectures, from naive in-context learning (ICL) to dedicated memory systems, introducing a gain metric to isolate learning from prior capabilities.

### Speaker And Company Context
- [[parth-asawa|Parth Asawa]] — CS PhD student at [[uc-berkeley|UC Berkeley]].

### Topics Covered
- [[agentic-search]]
- [[coding-agents]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.