DeepSeek V4 Flash

Memory Harnesses for Long-Running Research Agents — Stefania Druga, Sakana.ai

Aug 12, 2026 · 13:04

Stefania Druga, a research scientist at Sakana AI in Tokyo, presents experiments on memory harnesses for long-running research agents running on local models like Qwen 27B and DeepSeek V4 Flash on an M3 Ultra. She frames memory as a write-manage-read control loop, not a database, and tests a recall ladder—no recall, vector RAG, a ranked decisions ledger, and an oracle—across 68 xbench questions. The ranked ledger performed best, beating even gating on whether memory is needed, while the oracle didn't hit max because giving the right memory doesn't force its use. When tasks fit in context, memory only added cost with no accuracy gain, but for long-horizon tasks where answers sit far outside the window, good recall policy became essential and cheaper. She urges treating recall policy as a first-class metric and highlights the broader memory technique landscape, including over 30 runnable cookbooks from Diamond, while noting local models run serially, which is why her Tokyo machine…

The Future Is Domain-Specific Agents - Justin Schroeder, StandardAgents

Jun 29, 2026 · 30:38

Justin Schroeder of StandardAgents argues that domain-specific agents—small, focused AI agents each limited to a single domain—will outperform general-purpose agents that rely on accumulating tools and context via inheritance, and that composition of such agents is the key to building practical, cost-effective AI systems. He defines an agent as deterministic software harnessing non-deterministic model outputs, then critiques the current approach of piling tools and skills into a single agent (inheritance), proposing instead a coordinator agent that delegates to many specialized sub-agents (composition), each with its own minimal context, system prompt, and tools. He claims these domain-specific agents achieve over 80% token efficiency, enable use of cheaper small models (e.g., DeepSeek V4 Flash is 137x cheaper than a frontier model), improve safety by limiting capabilities, and scale easily. Schroeder predicts rapid adoption through 2026-2027, with multi-agent orchestration becoming mainstream and tokens no longer getting cheaper—up 29% when adjusted for IQ. He describes an ideal agent architecture with hooks, rules, and recursive sub-agents, and invites listeners to try…