cy0307/awesome-graph-engineering huggingface dataset:
Verification-Aware Planning for Multi-Agent Systems (Paper, EACL, 2026)
Authors: Tianyang Xu; Dan Zhang; Kushan Mitra; Estevam Hruschka
Introduces VeriMAP, which decomposes tasks into a dependency graph and attaches planner-defined Python and natural-language verification functions to subtasks before execution.
Why it matters: Makes acceptance criteria part of the work graph so handoff failures can trigger local repair instead of surfacing only in a final answer.
AIPOM: Agent-aware Interactive Planning for Multi-Agent Systems (Paper, EMNLP System Demonstrations, 2025)
Authors: Hannah Kim; Kushan Mitra; Chen Shen; Dan Zhang; Estevam Hruschka
Presents conversational and graph-based interfaces for inspecting, editing, and collaboratively guiding plans in orchestrated multi-agent systems.
Why it matters: Treats the work graph as a human-legible control surface; its non-autonomous agents execute assigned tasks, so it is a planning substrate rather than a complete agent organization.
Build reliable multi-agent applications with ADK Go 2.0 (Blog, Google Developers Blog, 2026)
Authors: Toni Klopfenstein; Sampath Kumar Maddula
Documents a graph-based multi-agent workflow engine with typed nodes, conditional edges, fan-out and fan-in, nested graphs, cycles, retries, durable human input, state, and unified telemetry.
Why it matters: Provides a first-party implementation of explicit, resumable work graphs while showing that function and tool nodes remain distinct from agent nodes.
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems (Paper, NeurIPS, 2025)
Authors: Yingxuan Yang; Huacan Chai; Shuai Shao; Yuanyi Song; Siyuan Qi; Renting Rui; Weinan Zhang
Organizes specialized agents in a decentralized DAG, uses retrieval-backed local experience for capability refinement, and adapts routing without a single central orchestrator.
Why it matters: Offers a contrasting topology for settings where central coordination is a bottleneck or trust boundary; its reported evidence is concentrated in reasoning and coding tasks.
ETAS: An Effect-Typed Language for Agent Systems (Paper, arXiv, 2026)
Authors: Huiri Tan; Yikun Wang; Puyang Zhang; Shangyu Li; Jiasi Shen
Defines a language in which agents, tools, typed memory, approvals, policies, effects, and execution traces are semantic program elements, with static obligations and runtime monitors.
Why it matters: Provides a transferable contract model for authorizing and auditing node actions before and during execution; it is a new preprint and not yet deployment evidence.
The Shared Discovery Paradox: How a One-Answer Rule Turns Better Information into Worse Search (Paper, arXiv, 2026)
Authors: Yohei Nakajima
Uses an exactly solvable multi-searcher benchmark to show that pooling information into one repeated recommendation can improve belief accuracy while sharply reducing collective discovery coverage.
Why it matters: Separates information quality from allocation policy and warns that consensus edges can collapse parallel exploration unless a coordinator preserves a portfolio of actions.
When Do Multi-Agent Systems Help? An Information Bottleneck Perspective (Paper, arXiv, 2026)
Authors: Wendi Yu; Lianhao Zhou; Xiangjue Dong; Sai Sudarshan Barath; Declan Staunton; Byung-Jun Yoon; Xiaoning Qian; James Caverlee; Shuiwang Ji
Formalizes bounded inter-agent relays as an information bottleneck and reports 18 controlled experiments across five benchmarks and three model scales.
Why it matters: Explains when isolated contexts and compressed handoffs save useful context and when they discard task-relevant information, providing a direct test for whether a graph earns its edge losses.
Precise but Uncoupled: Reviewer Precision Does Not Guarantee Critique Uptake in Multi-Agent Math Reasoning (Paper, arXiv, 2026)
Authors: Chih-Hsuan Yang; Jingyan Jiang; Vikram Vasudevan; Cheng-Hau Yang; Huihuo Zheng; Le Chen; Eliu A. Huerta; Venkatram Vishwanath; Ian T. Foster; Rajeev Thakur
Evaluates 4,181 verifier-grounded math problems and finds that a precise reviewer can still yield weak repair when the protocol does not carry useful critique into the next candidate.
Why it matters: Shows that a verifier node is insufficient by itself: the outgoing edge must couple accepted evidence to a concrete state change or retry action.
Grammar Search for Multi-Agent Systems (Paper, ACL, 2026)
Authors: Mayank Singh; Vikas Yadav; Shiva Krishna Reddy Malay; Shravan Nayak; Sai Rajeswar; Sathwik Tejaswi Madhusudhan; Eduardo Blanco
Searches a fixed grammar of composable multi-agent components to construct modular, interpretable organizations at lower search cost than unconstrained code generation.
Why it matters: Makes the agent-system design space explicit and auditable while retaining automated search over roles, operators, and coordination structure.
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization (Paper, Findings of ACL, 2026)
Authors: Yueyang Cang; Xiaoteng Zhang; Erlu Zhao; Zehua Ji; Yuhang Liu; Yuchen He; Zhiyuan Ning; Chen Yijun; Wenge Que; Li Shi
Samples groups of query-specific communication graphs and uses relative rewards to assign edge-level credit while training a topology-generating policy.
Why it matters: Addresses unstable topology learning with explicit graph samples and localized credit rather than treating orchestration as an opaque prompt update.
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems (Paper, ACL, 2026)
Authors: Yulang Chen; Haoxuan Peng; Jinyan Liu; Zichen Wen; Dongrui Liu; Linfeng Zhang
Compresses graph-structured agent workflows using centrality and approximate Shapley importance, then validates agent removal and cheaper-model substitution against the original system.
Why it matters: Provides a measured way to reduce nodes and spend without assuming every role contributes equally or that a smaller graph preserves behavior automatically.
TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems (Paper, Findings of ACL, 2026)
Authors: Rui Sun; Jie Ding; Chenghua Gong; Tianjun Gu; Yihang Jiang; Juyuan Zhang; Liming Pan; Linyuan Lü
Generates heterogeneous decentralized communication topologies in one pass, replacing repeated coordination rounds with a task-conditioned interaction structure.
Why it matters: Expands topology design beyond homogeneous or fully connected teams while directly evaluating the quality and token cost of the generated graph.
EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication (Paper, Findings of ACL, 2026)
Authors: Heng Zhang; Yihao Zhong; Lubin Gan; Zhihe Chen; Jiajun Wu; Yuling Shi; Xiaodong Gu; Hao Zhang; Haochen You; Jin Huang
Uses evolving hyperedges to bind groups of agents to shared memory while a controller creates, updates, and merges collaboration units during execution.
Why it matters: Models group communication as a first-class structure and joins topology evolution with explicit shared-state ownership instead of reducing every exchange to pairwise chat.
RouterHGC: Optimized Router for LLM-based Multi-Agent Systems via Heterogeneous Graph Contrastive Learning (Paper, Findings of ACL, 2026)
Authors: Yitao Xiao; Shaoyong Guo; Guoming Yang; Qingnan Wang; Yinlin Ren; Xuesong Qiu; Qi Feng
Represents queries, collaboration modes, roles, and language models as heterogeneous nodes and learns cost-performance routing relationships with graph contrastive learning.
Why it matters: Treats the agent organization itself as the graph under optimization and exposes model, role, mode, and budget choices within one routing decision.
LLM-as-Scheduler: Agentic Workflow Dynamic Scheduling (Paper, ACL, 2026)
Authors: Dawei Xiang; Kexin Chu; Wenyan Xu; Wenhui Zhang; Wei Zhang
Dynamically selects query-specific paths through a workflow DAG, including early exit, verification, repair, and rerouting decisions made during execution.
Why it matters: Separates the reusable work graph from the runtime path and supplies concrete control points for budgets, validation, and local recovery.
Learning Optimal Message Representations for Agentic Communication (Paper, Findings of ACL, 2026)
Authors: Shashwat Gupta; Anson Bastos; Mayukh Das; Supriyo Ghosh; Nagarajan Natarajan; Chetan Bansal; Saravan Rajmohan
Models the choice of inter-agent message representation as an expanding Markov decision process that can select natural-language or structured protocols at each exchange.
Why it matters: Makes payload format an adaptive edge policy rather than a global convention, clarifying when structure, compression, or expressiveness should dominate.
AgentAsk: Multi-Agent Systems Need to Ask (Paper, ACL, 2026)
Authors: Bohan Lin; Kuo Yang; Zelin Tan; Yingchuan Lai; Chen Zhang; Guibin Zhang; Xinlei Yu; Miao Yu; Xu Wang; Yudong Zhang; Yang Wang
Defines an edge-level handoff-error taxonomy and adds selective clarification questions at critical exchanges where transferred information is ambiguous or incomplete.
Why it matters: Turns clarification into an explicit recovery action for communication-contract failures instead of letting uncertainty silently propagate downstream.
Towards Self-Improving Error Diagnosis in Multi-Agent Systems (Paper, Findings of ACL, 2026)
Authors: Jiazheng Li; Emine Yilmaz; Bei Chen; Thu Le
Detects local anomalies, traces backward to decisive failures, tests diagnostic hypotheses with tools, and retains only execution-verified diagnostic memories.
Why it matters: Combines trace localization, active validation, and durable learning so a diagnostic node improves from evidence rather than storing unverified explanations.
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (Paper, Findings of ACL, 2026)
Authors: Zhenhong Zhou; Zherui Li; Jie Zhang; Yuanhe Zhang; Kun Wang; Yang Liu; Qing Guo
Formalizes attacks that deform communication topology into cycles or recursively expanding exchanges, blocking useful collaboration while exhausting system resources.
Why it matters: Shows that edge activation and termination policies are security boundaries, not only scheduling details, and motivates cycle and budget guards.
CIA: Inferring the Communication Topology from LLM-based Multi-Agent Systems (Paper, ACL, 2026)
Authors: Yongxuan Wu; Xixun Lin; He Zhang; Nan Sun; Kun Wang; Chuan Zhou; Shirui Pan; Yanan Cao
Infers hidden communication topology from black-box multi-agent systems using adversarial queries and semantic correlations among observed responses.
Why it matters: Establishes topology confidentiality as an engineering property and demonstrates that apparently private coordination structure can leak through behavior.
ACIArena: Toward Unified Evaluation for Agent Cascading Injection (Benchmark, ACL, 2026)
Authors: Hengyu An; Minxi Li; Jinghuai Zhang; Naen Xu; Chunyi Zhou; Changjiang Li; Xiaogang Xu; Tianyu Du; Shouling Ji
Evaluates cascading injection through external inputs, agent profiles, and inter-agent messages across six implementations and 1,356 attack cases.
Why it matters: Measures how topology, roles, and interaction design affect propagation instead of treating prompt injection as an isolated single-node event.
BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks (Paper, ACL, 2026)
Authors: Rui Miao; Yixin Liu; Yili Wang; Xu Shen; Yue Tan; Yiwei Dai; Shirui Pan; Xin Wang
Learns normal individual, neighborhood, and global interaction behavior without attack labels, then detects malicious agents through corruption-guided contrastive learning.
Why it matters: Adds graph-aware anomaly detection for attacks that were absent from training and distinguishes local behavior from neighborhood and system-wide patterns.
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection (Paper, ACL, 2026)
Authors: Junjun Pan; Yixin Liu; Rui Miao; Kaize Ding; Yu Zheng; Quoc Viet Hung Nguyen; Alan Wee-Chung Liew; Shirui Pan
Detects sentence- and token-level anomalies over multi-agent interaction graphs and attributes suspicious tokens to support finer-grained explanations of compromised exchanges.
Why it matters: Connects graph-level protection to actionable message evidence rather than returning only an opaque risk score for an entire agent or run.
Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design (Paper, Findings of ACL, 2026)
Authors: Zhilun Zhou; Zihan Liu; Jiahe Liu; Yihan Wang; Qingyu Shao; Fengli Xu; Depeng Jin; Yong Li
Studies 108 agent groups varying team size, model composition, and communication topology, then derives a factor intended to predict transferable system designs.
Why it matters: Moves evaluation beyond one benchmark score by testing whether measurable group properties forecast performance across designs and tasks.
Benefits and Limitations of Communication in Multi-Agent Reasoning (Paper, ICLR, 2026)
Authors: Michael Rizvi-Martel; Satwik Bhattamishra; Neil Rathi; Guillaume Rabusseau; Michael Hahn
Derives bounds connecting agent count and the quantity and structure of communication to exact reasoning and speedups, then tests the tradeoffs on controlled language-model tasks.
Why it matters: Supplies principled limits for node count and bandwidth decisions and identifies regimes where communication helps or becomes an intrinsic bottleneck.
Agents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks (Paper, ACL, 2025)
Authors: Rana Shahroz; Zhen Tan; Sukwon Yun; Charles Fleming; Tianlong Chen
Optimizes attack placement across latency- and bandwidth-constrained agent networks using graph-flow formulations to disrupt multi-agent task completion.
Why it matters: Makes topology part of the adversary model and tests how limited attack budgets exploit particular communication paths and bottlenecks.
AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs (Paper, arXiv; ICML 2025 Workshop on Collaborative and Federated Agentic Workflows, 2025)
Authors: Florian Grötschla; Luis Müller; Jan Tönshoff; Mikhail Galkin; Bryan Perozzi
Evaluates local messaging and learned coordination protocols on distributed graph problems over explicit network topologies, with experiments scaling to 100 agents.
Why it matters: Provides unusually direct evidence about decentralized protocol formation and topology-aware reasoning; the selected artifact remains a preprint and workshop contribution.
LLM-Coordination: Evaluating and Analyzing Multi-agent Coordination Abilities in Large Language Models (Benchmark, Findings of NAACL, 2025)
Authors: Saaket Agashe; Yue Fan; Anthony Reyna; Xin Eric Wang
Evaluates agentic play in four pure-coordination games and 198 questions covering environment comprehension, theory of mind, and joint planning.
Why it matters: Separates coordination capability into measurable components and partner-generalization tests, although communication topology is not its central experimental variable.
Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration (Paper, ICLR, 2026)
Authors: Sukwon Yun; Jie Peng; Pingzhi Li; Wendong Fan; Jie Chen; James Y. Zou; Guohao Li; Tianlong Chen
Selects relevant agents from model cards, constructs directed edges from response relevance, performs forward and reverse message passing, and pools the revised answers.
Why it matters: Offers an end-to-end graph construction and aggregation method in which node selection, edge order, and communication direction are explicit design choices.
Stochastic Self-Organization in Multi-Agent Systems (Paper, ICLR, 2026)
Authors: Nurbek Tastan; Samuel Horváth; Karthik Nandakumar
Builds a response-conditioned directed acyclic graph from approximate peer-contribution scores and updates the communication structure across collaboration rounds.
Why it matters: Adapts topology from observed agent behavior without a separate judge, pretrained generator, or fixed graph, while retaining stable acyclic message flow.
GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs (Paper, ICLR, 2026)
Authors: Tao Feng; Haozhen Zhang; Zijie Lei; Peixuan Han; Jiaxuan You
Generates query-specific workflows by selecting both language-model backbones and agent roles while consulting a heterogeneous graph of query-agent-response history.
Why it matters: Combines work-graph routing with reusable interaction memory and evaluates unseen-task and unseen-model generalization rather than only in-distribution quality.
Stop Wasting Your Tokens: Towards Efficient Runtime Multi-Agent Systems (Paper, ICLR, 2026)
Authors: Fulin Lin; Shaowen Chen; Ruishan Fang; Hongwei Wang; Tao Lin
Adds a lightweight supervisor triggered by an LLM-free filter to intervene when a multi-agent run shows errors, inefficient behavior, or contaminated observations.
Why it matters: Places cost and failure control inside the live execution path and tests whether selective supervision can reduce token use without redesigning the base graph.
Aegis: Automated Error Generation and Attribution for Multi-Agent Systems (Benchmark, ICLR, 2026)
Authors: Fanqi Kong; Ruijie Zhang; Huaxiao Yin; Guibin Zhang; Xiaofei Zhang; Ziang Chen; Zhaowei Zhang; Xiaoyuan Zhang; Song-Chun Zhu; Xue Feng
Creates 9,533 trajectories with annotated faulty agents and error modes by injecting context-aware faults into successful runs across multiple architectures and domains.
Why it matters: Provides graph-component labels for training and evaluating failure attribution instead of relying only on final-task success or expensive manual trace review.
MARTI: A Framework for Multi-Agent LLM Systems Reinforced Training and Inference (Paper, ICLR, 2026)
Authors: Kaiyan Zhang; Kai Tian; Runze Liu; Sihang Zeng; Xuekai Zhu; Guoli Jia; Yuchen Fan; Xingtai Lv; Yuxin Zuo; Che Jiang; Yuru Wang; Jianyu Wang; Ermo Hua; Xinwei Long; Junqi Gao; Youbang Sun; Zhiyuan Ma; Ganqu Cui; Ning Ding; Biqing Qi; Bowen Zhou
Supports centralized multi-agent interaction, distributed policy training, asynchronous rollouts, graph-defined workflows, heterogeneous models, tools, and configurable rewards.
Why it matters: Connects executable organizations to reinforced training infrastructure so graph policies can be optimized and evaluated rather than remaining hand-authored inference code.
MPAS: Breaking Sequential Constraints of Multi-Agent Communication Topologies via Individual-Epistemic Message Propagation (Paper, AAAI, 2026)
Authors: Jingxuan Yu; Ju Jia; Simeng Qin; Xiaojun Jia; Siqi Ma; Yihao Huang; Yali Yuan; Guang Cheng
Reworks sequential multi-agent communication into parallel graph message propagation with optimized connections and aggregation while preserving agent-specific epistemic state.
Why it matters: Targets latency introduced by dependency-ordered exchanges and makes the tradeoff between parallel execution, aggregation, and resilience measurable.
AgentRx: Diagnosing AI Agent Failures from Execution Trajectories (Benchmark, arXiv, 2026)
Authors: Shraddha Barke; Arnav Goyal; Alind Khare; Avaljot Singh; Suman Nath; Chetan Bansal
Releases 115 manually annotated failed trajectories and diagnoses critical failure steps by synthesizing guarded executable constraints and evidence-bearing validation logs.
Why it matters: Provides a concrete bridge from heterogeneous agent traces to repeatable failure localization; its evaluation includes Magentic-One but also single-agent workflows.
MAESTRO: Multi-Agent Evaluation Suite for Testing, Reliability, and Observability (Benchmark, arXiv, 2026)
Authors: Tie Ma; Yixi Chen; Vaastav Anand; Alessandro Cornacchia; Amândio R. Faustino; Guanheng Liu; Shan Zhang; Hongbin Luo; Suhaib A. Fahmy; Zafar A. Qazi; Marco Canini
Standardizes configuration and execution for 12 representative multi-agent systems and exports framework-agnostic traces with latency, cost, reliability, and failure signals.
Why it matters: Enables controlled comparison across architectures and repeated runs while revealing that topology can dominate model or tool changes in resource and reliability profiles.
AgentTrace: Causal Graph Tracing for Root Cause Analysis in Deployed Multi-Agent Systems (Paper, arXiv; ICLR 2026 Workshop on Agents in the Wild, 2026)
Authors: Zhaohui Geoffrey Wang
Reconstructs causal graphs from execution logs, traces backward from observed failures, and ranks candidate root causes without requiring language-model inference during debugging.
Why it matters: Offers an interpretable, low-latency diagnosis primitive for cascading failures while remaining a preprint and workshop result rather than broad production validation.
Multi-Agent Reference Architecture (Docs, Microsoft, 2026)
Authors: Microsoft
Maps agent registries, communication, memory, observability, evaluation, security, governance, and deployment into an end-to-end reference architecture for multi-agent systems.
Why it matters: Provides a broad engineering checklist grounded in customer implementations; it is evolving guidance in an official repository, not a versioned standard.
AutoGen Core (Docs, Microsoft AutoGen Documentation, 2026)
Authors: Microsoft
Documents an actor-style agent runtime with asynchronous messages, topics, subscriptions, distributed execution, resilience mechanisms, and telemetry.
Why it matters: Adds the lower-level message and runtime architecture beneath conversational teams, clarifying how agent nodes communicate and scale across processes.
Multiagent orchestration (Docs, Claude Platform Documentation, 2026)
Authors: Anthropic
Documents managed multi-agent orchestration with a lead agent that creates specialized subagents, delegates work, and combines their results under a beta API contract.
Why it matters: Provides a current first-party implementation of dynamic specialization and hierarchical delegation while clearly exposing its managed-runtime boundary and beta status.
Introducing Agent Executor, Google’s distributed Agent Runtime (Blog, Google Cloud Blog, 2026)
Authors: Jaana Dogan; Ethan Bao
Introduces an open-source runtime standard for execution, resumption, isolation, session consistency, reconnection, trajectory branching, and federated agent deployment.
Why it matters: Addresses durable long-running graphs at the runtime layer and makes checkpoints, single-writer state, recovery, and deployment boundaries explicit; the release is in preview.
NVIDIA NeMo Agent Toolkit (Tool, NVIDIA, 2026)
Authors: NVIDIA
Provides framework-agnostic profiling, evaluation, optimization, tracing, and A2A team support for agent workflows, including experimental graph-performance primitives.
Why it matters: Lets engineers measure whole-graph latency and cost while experimenting with parallel branches, speculative execution, and node priorities in maintained open-source tooling.
A2A Protocol Technology Compatibility Kit (Tool, Agent2Agent Project, Linux Foundation, 2026)
Authors: Agent2Agent Project
Tests Agent2Agent protocol implementations across gRPC, JSON-RPC, and HTTP with JSON and emits machine-readable, HTML, and JUnit conformance reports.
Why it matters: Turns interoperability claims at agent handoff edges into executable gates that can run in development and continuous integration.
AGNTCY Dir (Tool, AGNTCY Project, Linux Foundation, 2026)
Authors: AGNTCY Project
Implements a distributed directory for capability-based discovery of agents and multi-agent systems using OASF records and cryptographic identity.
Why it matters: Covers how potential graph participants are registered, discovered, authenticated, and selected before a work or communication edge is created.
Agent Governance Toolkit (Tool, Microsoft, 2026)
Authors: Microsoft
Enforces runtime policies on tool calls and inter-agent messages with identity, delegation, audit logs, telemetry, and adapters for multiple agent frameworks.
Why it matters: Implements authorization and audit controls at graph edges; it is a public preview, and its in-process enforcement layer is not an operating-system security boundary.