ACM CAIS 2026

The corpus repeatedly cites a workshop organized by researchers from UC Berkeley, Stanford, CMU, Databricks, Google, and Bespoke Labs. - Focus areas include autonomous AI systems for search, optimization, and scientific discovery. - Invited speakers mentioned in the corpus include Ion Stoica, Graham Neubig, Azalia Mirhoseini, Joseph Gonzalez, and James Zou.

Official site lists keynote speakers including Andy Konwinski, Thariq Shihipar, and Percy Liang, reinforcing the event's practical orientation toward agentic coding, open research, and benchmark-driven engineering.

A Berkeley/MIT team presented an LLM-based optimization system that frames diverse problems as iteratively improving a text artifact evaluated by a scoring function. - Corpus-reported outcomes include nearly tripling Gemini Flash's ARC-AGI accuracy, cutting cloud scheduling costs 40%, and matching AlphaEvolve on circle packing.

Research-to-product pipeline: CAIS research maps directly onto enterprise agent pain points: optimization, evaluation, architecture, safety, and reproducibility. - Agent engineering discipline: The field is moving from demos to repeatable blueprints, benchmarks, and systems papers. - Open ecosystem: Participation from universities, Databricks, Google, Anthropic-adjacent practitioners, and open-source communities suggests no single vendor owns the agent stack. - Benchmark competition: Terminal-Bench, ARC-AGI, and optimization tasks become strategic proxies for agent utility.

MIT researchers presented Tressoir, a system for designing and evolving multi-agent architectures, prompts, tools, and knowledge through human-readable “Interpretable Blueprints.” - The goal is reproducible, systematic construction of multi-agent systems instead of ad hoc prompt chains.