multi-agent

Inspired by Karpathy’s HackerTube, the author used Teamily AI’s multi-agent workflow to build “AnliBili,” a website that curates 1,132 high-quality Bilibili videos repeatedly recommended across nearly 10 million discussions spanning 16 years on Zhihu and Xiaohongshu, helping users cut through information noise.

A discussion questioning how OpenAI would have orchestrated 10,000 agents to 'solve' the Navier-Stokes millennium problem, raising doubts about whether the result was independently achieved or built on prior human progress.

The author shares a retrospective of using their own subagent-team skill to lead a Claude/Codex/etc. AI team through a major monorepo overhaul: cross-vendor model reviews offer the best ROI, rework volume depends on task risk rather than model tier, and they summarize a division of labor where low-tier models act as coordinators, strong models handle risky implementation, and top-tier models handle reviews.

A UIUC student is seeking compute sponsors for Loxeon, an Apache-2.0-to-be runtime where AI agent teams self-organize via formally specified primitives (identity, capability delegation, role rebinding, tree-based management, and epoch/ticket message validation), written in Rust and checked against a formal spec. The goal is to fairly benchmark whether multi-agent teams outperform single agents on real coding tasks (~$200 small / $1,500 full), with code, logs and results to be published regardless of outcome.

A new preprint (arXiv 2610.40961) pits frontier LLMs against each other in the classic hold-up game, finding that renegotiable options that boosted human investment to 67% in a 2011 lab study produced zero seller investment across 1,440 AI-agent games.

GR.inc publishes a detailed write-up on training agent swarms with reinforcement learning, introducing RLCR (reinforcement learning from collective rewards) — a shared-policy policy-gradient recipe with flat collective advantages and latency pressure designed to achieve collective inference-time scaling.

A user shares how they gave their team of Grok bots a shared Notion task board so each bot logs the tasks it picks up, enabling organization-wide visibility and steering from one place, with a Grok Bot masterclass video teased for Wednesday.

A six-part follow-up to an agent system design series covering service boundaries, generated contracts, typed agent flows, at-most-once execution, prompt-injection defenses, supervisor-routed subgraphs, and human-in-the-loop review. The author outlines what each part teaches and who the series is aimed at, with more installments coming.

DeepSeek showed that an AI agent given 20 tasks in a 300-message group chat delegated none of them until a two-layer Router/Worker design took the keyboard away from the top agent, cutting slowest-task time from ~30 to under 16 minutes while spending 23.2M cached tokens per run.

DeepSeek's agent team published a design document arguing the bottleneck for AI agents is human attention rather than model capability, proposing a Router/Worker session architecture where a person can realistically track only 3-5 active agent sessions.

A developer muses about dropping AI agents into a persistent physics simulator to discover laws of nature through observation, and raises the 'simulator catch-22'—whether agents would just reverse-engineer backend glitches or reward-hack the engine instead of doing real science.

The paper introduces the Collaborative Intelligence Benchmark, which evaluates AI agents as teammates across 240 tasks in 8 teamwork dimensions, finding that even top models score at most 82.2/100 and that general capability does not guarantee strong teamwork.

该论文研究在单一人类注意力有限、多个 AI 代理并行执行任务时，代理应在何时自主搜索信息、何时向人类请求澄清，并提出 Pairwise Coordination Policy (PCP)，在合成基准与 LLM 案例研究中显著降低成本。

A developer shares open-source vLLM patches and Triton kernels that run 8 coding agents with 250K-token contexts concurrently on two RTX 3090s by keeping the full int4 KV cache in system RAM and fetching only ~1% of tokens per attention step via a rank-64 key sketch, reporting ~545-790 tok/s total and 31/32 recall at 250K.

A maker shares early progress on a workspace where multiple coding agents (Claude Code, pi, opencode) collaborate in shared rooms, discussing design, debating, changing positions, and splitting work autonomously while awaiting human approval. They are looking for early feedback from people who run multiple agents.

A creator built a virtual town where users' AI agents can interact with other agents, showcasing emergent behavior as agents coexist and engage in the environment.

DeepSeek open-sourced an MIT-licensed research prototype, dsh-superagent-prototype, featuring a Router-Worker super-agent architecture for coding tasks that aims to minimize human attention. Self-reported tests show faster completion of slow tasks versus a single-session baseline, but the team stresses it is research code, not production-ready.

A solo game developer building a 2D co-op arcade game with nine AI-assisted workstreams asks how to orchestrate them—seeking advice on deterministic job systems vs. LLM decisions, dependency management, retries, human approvals, and cross-provider context continuity.

A 135-page survey paper organizing agentic reasoning for large language models along three layers—foundational, self-evolving, and collective multi-agent reasoning—spanning in-context and post-training optimization with real-world applications and open challenges.

DeepSeek released dsh-superagent-prototype, an open-source proactive coding agent that coordinates multiple agents to handle GitHub tasks, reviews, and CI feedback in parallel with minimal human oversight.