Discovering Cooperative Pipelines: Autoresearch for Sequential Social Dilemmas

• URL: http://arxiv.org/abs/2605.30003v1
• Date: Thu, 28 May 2026 14:33:10 GMT
• Title: Discovering Cooperative Pipelines: Autoresearch for Sequential Social Dilemmas
• Abstract summary: We study two-level autoresearch for cooperation: an outer-loop AI agent autonomously redesigns the inner-loop pipeline of a policy-synthesizer system.<n>A researcher agent reads the inner-loop source code, edits system prompts, feedback functions, helper libraries, and iteration logic, runs evaluations, and decides what to keep.
• Score: 6.599344783327054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study two-level autoresearch for cooperation: an outer-loop AI agent autonomously redesigns the inner-loop pipeline of an LLM policy-synthesis system for multi-agent Sequential Social Dilemmas (SSDs). A researcher agent $\mathcal{R}$ (run as a coding agent) reads the inner-loop source code, edits system prompts, feedback functions, helper libraries, and iteration logic, runs evaluations, and decides what to keep, following the autoresearch paradigm. Across two games (Cleanup and Gathering), two policy-synthesizer LLMs, and two welfare objectives (utilitarian efficiency and Rawlsian maximin), the researcher reliably exceeds hand-designed baselines, sharply tightens run-to-run variance, and outperforms prompt-only optimization. The discovered pipelines are objective-dependent: only under maximin does the researcher inject an explicit fairness mechanism into synthesizer pipelines, a class of mechanism that is absent from its own objective-agnostic system prompt and from every efficiency-optimized pipeline. This supports an information-design reading in which the researcher chooses what to reveal to the boundedly rational synthesizer as a function of the welfare objective. Code at https://github.com/vicgalle/autoresearch-social-dilemmas.

Related papers

• The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows [72.9051182568928]
  We present ReASearch, a unified framework for reasoning-driven optimization.<n>With a shared agent loop and domain-specific tools, ReASearch instantiates the exact same scaffold to optimize prompts, programs, and ML.<n>We observe that complex search behaviors emerge naturally from the agent's reasoning process.
  arXiv  Detail & Related papers  (2026-08-07T02:15:39Z)
• SAGE: Stochastic Prompt Optimization via Agent-Guided Exploration [4.5848302154106815]
  We introduce SPO (Stochastic Prompt Optimization), a framework for search over prompt space.<n>We compare three strategies of increasing sophistication: error-informed random search, a genetic algorithm with evolutionary operators, and SAGE (SPO via Agent-Guided Exploration)<n>We argue that coupling qualitative diagnosis with quantitative validation is what makes agentic optimization effective for open-ended task-oriented dialogue.
  arXiv  Detail & Related papers  (2026-06-17T10:25:25Z)
• Decoupling Semantics and Logic: A Training-Free Coarse-to-Fine Pipeline for Video Retrieval-Augmented Generation [5.263138429758972]
  This paper presents our system description for the 2nd Workshop on Multimodal Augmented Generation via MultimodAl Retrieval (MAGMaR)<n> Addressing the critical challenges of cross-lingual long-video comprehension, strict persona adherence, and zero-hallucination temporal grounding, we propose a two-stage cascaded Video RAG pipeline.<n>Our architecture strategically decouples semantic retrieval from cognitive logical reasoning through a modality-aware division of labor.
  arXiv  Detail & Related papers  (2026-06-06T01:17:40Z)
• DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning [58.09293442211248]
  Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks.<n>This report presents DuMate-DeepResearch, a multi-agent DR framework built on the Qianfan Agent Foundry.
  arXiv  Detail & Related papers  (2026-06-05T14:10:48Z)
• GrepSeek: Training Search Agents for Direct Corpus Interaction [66.69568141699311]
  GrepSeek is an optimized direct corpus interaction (DCI) search agent that trains a compact search agent to find, filter, and compose evidence from large text corpora.<n>To make DCI practical at scale, we further use a semantics-preserving sharded-parallel execution engine that accelerates shell-based retrieval by up to $7.6times$ while preserving byte-exact equivalence with sequential execution of the shell command.
  arXiv  Detail & Related papers  (2026-05-28T03:37:33Z)
• AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration [175.74514061083195]
  We present AutoResearchClaw, a multi-agent autonomous research pipeline built on five mechanisms.<n>On ARC-Bench, a 25-topic experiment-stage benchmark, AutoResearchClaw outperforms AI Scientist v2 by 54.7%.
  arXiv  Detail & Related papers  (2026-05-19T15:49:51Z)
• From I/O to Code with Discovery Agent [103.88427301265669]
  We propose DIO-Agent, a discovery agent for IO2Code.<n>Our method frames IO2Code as an evolutionary search over discrete program space.<n>Extensive experiments show that DIO-Agent consistently outperforms both traditional program-by-example method and SOTA evolution-agent baselines.
  arXiv  Detail & Related papers  (2026-05-14T18:57:32Z)
• Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework [82.66443886385125]
  Paper Circle is a discovery and analysis system designed to reduce the effort required to find, assess, organize, and understand academic literature.<n>The system comprises two complementary pipelines: (1) a Discovery Pipeline that integrates offline and online retrieval from multiple sources, multi-agent scoring, diversity-aware ranking, and structured outputs; and (2) an Analysis Pipeline that transforms individual papers into structured knowledge graphs with typed nodes such as concepts, methods, and figures.
  arXiv  Detail & Related papers  (2026-04-07T17:59:58Z)
• Seismology modeling agent: A smart assistant for geophysical researchers [14.28965530601497]
  This paper proposes an intelligent, interactive workflow powered by Large Language Models (LLMs)<n>We introduce the first Model Context Protocol (MCP) server suite for SPECFEM.<n>The framework supports both fully automated execution and human-in-the-loop collaboration.
  arXiv  Detail & Related papers  (2025-12-16T14:18:26Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• KompeteAI: Accelerated Autonomous Multi-Agent System for End-to-End Pipeline Generation for Machine Learning Problems [36.17807193758863]
  KompeteAI is a novel AutoML framework with dynamic solution space exploration.<n>We introduce KompeteAI, a novel AutoML framework with dynamic solution space exploration.<n>We propose Kompete-bench to address limitations in MLE-Bench, where KompeteAI also achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2025-08-13T20:29:56Z)
• LLM-First Search: Self-Guided Exploration of the Solution Space [29.780554400938335]
  Large Language Models (LLMs) have demonstrated remarkable improvements in reasoning and planning through increased test-time compute.<n>We propose textbfLLM-First Search (LFS), a novel textitLLM Self-Guided Search method.
  arXiv  Detail & Related papers  (2025-06-05T16:27:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.