multi-agent

Describes the 'Gauntlet Loop', a viral 3-line Claude prompt that uses sub-agents with critics to iteratively improve output to a quality bar.

An engineer discusses the limitations of mailbox-based agent messaging and proposes structural addressing over a code graph to compute blast radius for message routing, arguing this is key for scaling beyond three agents.

A solo developer shares how an AI agent confidently reported a false fix, highlighting the danger of unverified agent reports and the structural rule they implemented: no agent grades its own homework, and fixes must be proven with a real failing operation.

Prime Intellect releases prime-rl 0.8.0, adding multi-agent training, Nixl and model express weight broadcast, extended multimodal support, and various performance improvements.

Introduces the /swarm multi-agent parallel fan-out mode in the Claude Code ecosystem, which was recently disabled by default with hard limits added, due to runaway recursion and cost explosion.

Prime RL now supports expressing and training multi-agent systems, enabling use cases like agentic judge, self-play, user simulation, and complex agent collaboration.

The paper introduces TSR, a framework that decomposes social dialogue into strategic planning and linguistic execution, and LHRL-VGR, a reinforcement learning algorithm with variance-gated rewards. Fine-tuning a Qwen2.5-7B agent with this approach surpasses the GPT-4o baseline by 7.32% in goal completion on the SOTOPIA benchmark.

INTraJ is a unified framework for trajectory prediction that decomposes social influence into two stages: planning with future social information and local reaction from residuals, achieving state-of-the-art results on Argoverse 2, ETH/UCY, and SDD benchmarks.

Introduces CANOE, a multi-agent neuro-symbolic framework for open-ended care plan coordination that uses argumentative computation and human-in-the-loop contestation to improve transparency, safety, and clinical correctness.

OrchestraBench is a new benchmark that evaluates multi-agent orchestration frameworks on failure modes, recovery, and decomposition quality, using failure-injection and cascade-radius metrics to diagnose where and why pipelines fail.

This paper proposes Abstract Event Causal Rules (AECR), a relation-level causal abstraction paradigm that converts concrete cause-effect pairs into generalized causal logic. It introduces a multi-agent system for causal induction and an attention-based encoder that improves event prediction, especially for rare and unseen events.

This paper proposes Agentic Nesting, a multi-agent collaboration framework that encapsulates existing enterprise applications as AI agents in a hierarchically nested structure, enabling natural-language interaction and cross-application orchestration as an alternative to traditional ESB/API/RPA integration approaches.

HAR is an open-source harness for orchestrating multi-agent coding workflows.

This paper explores three LLM-based approaches for translating puns from English to French, combining contrastive learning and phonetic-semantic embeddings. Their multi-agent and guided chain-of-thought systems ranked first and second in the CLEF JOKER 2025 Task 2 competition under expert human evaluation.

The paper proposes MAP-PO, a multi-agent framework that clusters annotators by labeling behavior and fine-tunes separate LLM agents per cluster using preference optimization, preserving disagreement in sexism detection tasks. Experiments on the EXIST 2024 dataset show that cluster-specific training is necessary and that a shared team-level reward keeps agents calibrated.

Argus is a persistent, self-evolving agentic runtime designed for long-horizon reasoning, using Manager, Planner, Engineer, and Reviewer roles with verification-gated persistence and pivoting. It demonstrates strong results across seven benchmark arenas, including ~78% on SWE-Bench Pro, while reducing token usage after runtime self-evolution.

John Schulman comments on OpenAI agents unexpectedly developing altruistic behavior, speculating it may arise from reinforcement learning on parallel subagent setups with team-level rewards.

Prime Agent is an open-source coding and research harness that outperforms proprietary harnesses, scoring 95.5% on ARC-AGI-3 and improving models across benchmarks.

A developer describes running AI agents in a simulated restaurant world and finds that across different models and harnesses, the same three API-related mistakes dominate: guessing nonexistent endpoints, exceeding budgets, and acting on stale IDs. Shows persistent failure patterns despite fixing the system.

Introduces the open-source multi-agent simulation framework MiroFish, developed by undergraduate Guo Hanjiang in ten days while still in school. It gained 13,000+ GitHub stars and $4 million in funding, and can be used for financial prediction, public opinion testing, etc. The post also promotes an automated trading bot on Polymarket.