This episode examines DeLM, a Stanford system that runs several coding agents in parallel with no main agent, and asks whether four agents deliver four agents' worth of progress or just a bigger bill. It sorts existing designs by the "bubble" each one creates. Independent sampling wastes effort on redundant work. Peer debate stalls at round barriers and grows quadratically in messages. Centralized orchestrators like Claude Code and Codex leave the lead agent idle and pass lossy summaries between agents. DeLM replaces the lead with a self-service task queue, where the first claim wins, and an append-only shared log of FACT and FAIL entries that agents publish as they learn things. The discussion ties this to the 1980 Hearsay-II blackboard system, which had a scheduler where DeLM has none, and notes the new risk that stale or wrong natural-language findings can mislead peers. The hosts also say how they will judge the claimed accuracy and speed gains: whether the benchmarks are representative, whether the gaps exceed run-to-run noise, and how much of the speedup comes from the design rather than from idle baselines.
Sources:
1. Decentralized Multi-Agent Systems with Shared Context — Yuzhen Mao, Jerry Gu, Aadi Chauhan, Qizheng Zhang, Hangoo Kang, Azalia Mirhoseini, 2026
http://arxiv.org/abs/2606.10662
2. The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty — Lee D. Erman, Frederick Hayes-Roth, Victor R. Lesser, D. Raj Reddy, 1980
https://scholar.google.com/scholar?q=The+Hearsay-II+Speech-Understanding+System%3A+Integrating+Knowledge+to+Resolve+Uncertainty
3. Generative Communication in Linda — David Gelernter, 1985
https://scholar.google.com/scholar?q=Generative+Communication+in+Linda
4. Improving Factuality and Reasoning in Language Models through Multiagent Debate — Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch, 2023
https://scholar.google.com/scholar?q=Improving+Factuality+and+Reasoning+in+Language+Models+through+Multiagent+Debate
5. Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture — Bochen Han, Songmao Zhang, 2025
https://scholar.google.com/scholar?q=Exploring+Advanced+LLM+Multi-Agent+Systems+Based+on+Blackboard+Architecture
6. SWE-bench: Can Language Models Resolve Real-World GitHub Issues? — Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik Narasimhan, 2024 (ICLR; arXiv 2023)
https://scholar.google.com/scholar?q=SWE-bench%3A+Can+Language+Models+Resolve+Real-World+GitHub+Issues%3F
7. SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering — John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, Ofir Press, 2024
https://scholar.google.com/scholar?q=SWE-agent%3A+Agent-Computer+Interfaces+Enable+Automated+Software+Engineering
8. Introducing SWE-bench Verified — OpenAI (Preparedness team) with human annotators, in collaboration with the SWE-bench authors, 2024
https://scholar.google.com/scholar?q=Introducing+SWE-bench+Verified
9. Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces — Mike A. Merrill, Alexander G. Shaw, and the Terminal-Bench team (Stanford and Laude Institute); the DeLM paper cites Merrill et al., 2026, 2025-2026
https://scholar.google.com/scholar?q=Terminal-Bench%3A+Benchmarking+Agents+on+Hard%2C+Realistic+Tasks+in+Command+Line+Interfaces
10. Large Language Monkeys: Scaling Inference Compute with Repeated Sampling — Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, Azalia Mirhoseini, 2024
https://scholar.google.com/scholar?q=Large+Language+Monkeys%3A+Scaling+Inference+Compute+with+Repeated+Sampling
11. Scheduling Multithreaded Computations by Work Stealing — Robert D. Blumofe, Charles E. Leiserson, 1999
https://scholar.google.com/scholar?q=Scheduling+Multithreaded+Computations+by+Work+Stealing
12. GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism — Yanping Huang et al., 2019
https://scholar.google.com/scholar?q=GPipe%3A+Efficient+Training+of+Giant+Neural+Networks+using+Pipeline+Parallelism
13. AOrchestra — Ruan et al., 2026
https://scholar.google.com/scholar?q=AOrchestra
14. SPRINT: Enabling Interleaved Planning and Parallelized Execution in Reasoning Models — Emil Biju, Shayan Talaei, Zhemin Huang, Mohammadreza Pourreza, Azalia Mirhoseini, Amin Saberi, 2026
https://scholar.google.com/scholar?q=SPRINT%3A+Enabling+Interleaved+Planning+and+Parallelized+Execution+in+Reasoning+Models
15. ToolOrchestra — Su et al., 2025
https://scholar.google.com/scholar?q=ToolOrchestra
16. Recursive Language Models — Zhang et al., 2025
https://scholar.google.com/scholar?q=Recursive+Language+Models
