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# Nexus-Agents Research Index

**Generated:** 2026-05-12 (ET)
**Total Papers:** 179 | **Techniques:** 43 | **Topics:** 12

---

## Quick Stats

| Status      | Papers | Techniques |
| ----------- | ------ | ---------- |
| Implemented | -      | 42         |
| In Progress | -      | 0          |
| Planned     | -      | 0          |
| Not Started | -      | 0          |
| Rejected    | -      | 1          |

> **Note:** Paper-level status tracking deprecated. Technique status is source of truth.

---

## Topics

| Topic                                               | Papers | Techniques | Description                                        |
| --------------------------------------------------- | ------ | ---------- | -------------------------------------------------- |
| [Consensus](topics/consensus/README.md)             | 12     | 6          | Multi-agent decision protocols and voting          |
| [Routing](topics/routing/README.md)                 | 32     | 8          | Cost-efficient model routing and selection         |
| [Memory](topics/memory/README.md)                   | 23     | 9          | Context, long-term memory, and compression         |
| [Code Generation](topics/code-generation/README.md) | 24     | 6          | Code generation, repair, and self-improvement      |
| [CLI Tools](topics/cli-tools/README.md)             | 0      | 0          | External CLI integration and protocols             |
| [Orchestration](topics/orchestration/README.md)     | 32     | 12         | Multi-agent coordination and workflows             |
| [Security](topics/security/README.md)               | 16     | 2          | Security analysis, prompt injection defense        |
| [Evaluation](topics/evaluation/README.md)           | 8      | 0          | Benchmarks, metrics, and testing methodologies     |
| [Safety](topics/safety/README.md)                   | 0      | 0          | AI safety, alignment, and reward hacking           |
| [Planning](topics/planning/README.md)               | 0      | 0          | Task planning, decomposition, and reasoning chains |
| [Tool Use](topics/tool-use/README.md)               | 3      | 0          | Tool augmentation, function calling, and MCP       |
| [Reasoning](topics/reasoning/README.md)             | 0      | 0          | Reasoning, self-reflection, and search strategies  |

---

## Priority 1 (P1) Techniques

These techniques are high-impact and align well with the current architecture.

| Technique                                                                                         | Topic           | Key Metrics                                                                                                                           | Issue |
| ------------------------------------------------------------------------------------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------- | ----- |
| [Aegean Consensus Protocol](registry/techniques.yaml#aegean-consensus)                            | consensus       | latency_reduction: 1.2x-20x, token_reduction: 4.4x, quality_impact: within 2.5% of baseline                                           | #119  |
| [Task-Type Protocol Selection](registry/techniques.yaml#task-aware-protocol-selection)            | consensus       | reasoning_improvement: +13.2%, knowledge_improvement: +2.8%                                                                           | #125  |
| [Multi-Agent Reflexion (MAR)](registry/techniques.yaml#multi-agent-reflexion)                     | consensus       | reasoning_improvement: significant across benchmarks                                                                                  | #1657 |
| [IPR Quality-Constrained Routing](registry/techniques.yaml#ipr-quality-estimators)                | routing         | cost_reduction: 43.9%, latency: sub-150ms                                                                                             | #128  |
| [A-MEM Agentic Memory](registry/techniques.yaml#amem-agentic-memory)                              | memory          | semantic_organization: Automatic attribute extraction and linking, evolution_detection: Refinement, extension, supersession detection | #122  |
| [TRINITY Thinker/Worker/Verifier Roles](registry/techniques.yaml#trinity-roles)                   | orchestration   | benchmark_accuracy: 86.2% on LiveCodeBench                                                                                            | #141  |
| [Self-Refine Iterative Loop](registry/techniques.yaml#self-refine-loop)                           | code-generation | average_improvement: 20%                                                                                                              | #126  |
| [Reflexion Verbal Reinforcement Learning](registry/techniques.yaml#reflexion-verbal-rl)           | code-generation | alfworld_improvement: +22%, hotpotqa_improvement: +20%, humaneval_pass1: 91%                                                          | #130  |
| [STPA MCP Framework](registry/techniques.yaml#stpa-mcp-safety)                                    | security        | hazard_coverage: Systematic UCA identification, safety_constraints: Auto-generated from analysis                                      | #328  |
| [AFlow MCTS Workflow Generation](registry/techniques.yaml#aflow-mcts-workflows)                   | orchestration   | workflow_quality: Improved through search optimization                                                                                | #329  |
| [SEW Self-Evolving Workflows](registry/techniques.yaml#sew-self-evolving-workflows)               | orchestration   | improvement_rate: Continuous through execution feedback                                                                               | #330  |
| [ZeroRouter Universal Difficulty Space](registry/techniques.yaml#zerorouter-universal-difficulty) | routing         | routing_accuracy: Cross-domain difficulty assessment                                                                                  | #338  |
| [Context Rot Prevention](registry/techniques.yaml#context-rot-prevention)                         | memory          | -                                                                                                                                     | #1670 |
| [Wave-Based Parallel Execution](registry/techniques.yaml#wave-based-parallel-execution)           | orchestration   | -                                                                                                                                     | #1658 |

---

## Priority 2 (P2) Techniques

Medium-impact or requiring moderate changes.

| Technique                                                                                       | Topic           | Key Metrics                                                                                            | Issue |
| ----------------------------------------------------------------------------------------------- | --------------- | ------------------------------------------------------------------------------------------------------ | ----- |
| [CP-WBFT Byzantine Fault Tolerant Consensus](registry/techniques.yaml#cp-wbft-consensus)        | consensus       | fault_tolerance: 85.7%                                                                                 | #103  |
| [Free-MAD Anti-Conformity Scoring](registry/techniques.yaml#free-mad-scoring)                   | consensus       | robustness: enhanced against attacks                                                                   | #152  |
| [TOPSIS Multi-Criteria Routing](registry/techniques.yaml#topsis-routing)                        | routing         | cost_reduction: 31.46%                                                                                 | #146  |
| [PILOT Budget-Constrained Routing](registry/techniques.yaml#pilot-budget-routing)               | routing         | adaptive: handles diverse budget requirements                                                          | #102  |
| [SATER Confidence-Aware Routing](registry/techniques.yaml#sater-routing)                        | routing         | cost_reduction: 50%+, latency_reduction: 80%+ cascade                                                  | #99   |
| [Agreement-Based Cascading (ABC)](registry/techniques.yaml#cascade-routing)                     | routing         | cost_optimization: significant                                                                         | #121  |
| [Preference-Trained Router (RouteLLM)](registry/techniques.yaml#preference-trained-routing)     | routing         | cost_reduction: 2x                                                                                     | #148  |
| [Mem0 Scalable Long-Term Memory](registry/techniques.yaml#mem0-memory-architecture)             | memory          | latency_reduction: 91% lower p95, token_savings: 90%, quality_improvement: 26%                         | #156  |
| [MIRIX Six-Type Memory System](registry/techniques.yaml#mirix-six-type-memory)                  | memory          | accuracy_vs_rag: +35%, storage_reduction: 99.9%, benchmark_accuracy: 85.4%                             | #157  |
| [MobiMem Post-Deployment Evolution](registry/techniques.yaml#mobimem-evolution)                 | memory          | profile_alignment: 83.1%, retrieval_speed: 280x faster than GraphRAG, task_success_improvement: 50.3%  | #149  |
| [Adaptive Memory](registry/techniques.yaml#adaptive-memory)                                     | memory          | performance_improvement: Configurable priority scoring                                                 | #143  |
| [Evolving Orchestration Upgrade](registry/techniques.yaml#evolving-orchestration)               | orchestration   | task_completion_improvement: 15-30%                                                                    | #335  |
| [LATTS Adaptive Test-Time Compute](registry/techniques.yaml#latts-adaptive-compute)             | orchestration   | performance_parity: 1B model matches 405B                                                              | #153  |
| [Voyager Skill Library Pattern](registry/techniques.yaml#voyager-skill-library)                 | code-generation | discovery_improvement: 3.3x more unique items, speed_improvement: up to 15.3x faster milestone         | #150  |
| [SICA Self-Improving Agent](registry/techniques.yaml#sica-self-improvement)                     | code-generation | swebench_improvement: 17% -> 53%, file_editing_improvement: 82% -> 94%                                 | #151  |
| [Constitutional AI Self-Critique](registry/techniques.yaml#constitutional-ai)                   | code-generation | scales: without human labelers                                                                         | #147  |
| [Higher-Order Voting (OW/ISP)](registry/techniques.yaml#higher-order-voting)                    | consensus       | correlation_handling: Improved consensus on correlated inputs                                          | #333  |
| [Forest-of-Thought Multi-Tree Reasoning](registry/techniques.yaml#forest-of-thought)            | orchestration   | reasoning_quality: Improved through diverse exploration, efficiency: Sparse activation reduces compute | #331  |
| [Agent-SafetyBench Evaluation Suite](registry/techniques.yaml#agent-safety-bench)               | security        | coverage: Multi-dimensional safety evaluation                                                          | #332  |
| [DAAO VAE Difficulty Estimation](registry/techniques.yaml#daao-difficulty-estimation)           | routing         | routing_accuracy: Improved task-model alignment                                                        | #334  |
| [Hindsight Belief Memory](registry/techniques.yaml#hindsight-belief-memory)                     | memory          | reasoning_quality: Improved through belief tracking                                                    | #336  |
| [Scaling Agent Coordination Predictor](registry/techniques.yaml#scaling-coordination-predictor) | orchestration   | allocation_efficiency: Improved agent utilization                                                      | #337  |
| [Failure Lesson Injection](registry/techniques.yaml#failure-lesson-injection)                   | orchestration   | -                                                                                                      | #1568 |
| [Skill Relevance Matching](registry/techniques.yaml#skill-relevance-matching)                   | orchestration   | -                                                                                                      | #1569 |
| [Write-Time Memory Deduplication](registry/techniques.yaml#write-time-deduplication)            | memory          | -                                                                                                      | #1570 |

---

## Recently Reviewed Papers

| Date       | Paper                                                                                                                                               | Topic      | Priority |
| ---------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------- |
| 2026-04-19 | [ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection](https://arxiv.org/abs/2604.11790)         | security   | -        |
| 2026-04-19 | [From Myopic Selection to Long-Horizon Awareness: Sequential LLM Routing for Multi-Turn Dialogue](https://arxiv.org/abs/2604.12385)                 | routing    | -        |
| 2026-04-19 | [Triage: Routing Software Engineering Tasks to Cost-Effective LLM Tiers via Code Quality Signals](https://arxiv.org/abs/2604.07494)                 | routing    | -        |
| 2026-04-19 | [EMS: Multi-Agent Voting via Efficient Majority-then-Stopping](https://arxiv.org/abs/2604.02863)                                                    | consensus  | -        |
| 2026-04-19 | [Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-CodeX](https://arxiv.org/abs/2604.14858) | evaluation | -        |

---

## Papers by Topic

### Consensus (12 papers)

- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://arxiv.org/abs/2501.06322) - Taxonomy of collaboration types (cooperation, competition, coopetition)
- [Voting or Consensus? Decision-Making in Multi-Agent Debate](https://arxiv.org/abs/2502.19130) - Empirical comparison of 7 decision protocols for multi-agent systems.
- [Aegean: Formal Consensus Protocol for Stochastic Reasoning](https://arxiv.org/abs/2512.20184) - Formal consensus protocol for stochastic reasoning with
- [CP-WBFT: Confidence Probe-based Weighted Byzantine Fault Tolerant](https://arxiv.org/abs/2511.10400) - Confidence Probe-based Weighted Byzantine Fault Tolerant consensus.
- [Free-MAD: Score-Based Decision with Anti-Conformity](https://arxiv.org/abs/2509.11035) - Score-based decision with anti-conformity to prevent majority
- [MAR: Multi-Agent Reflexion Improves Reasoning Abilities](https://arxiv.org/abs/2512.20845) - Multiple agents reflect and critique each other's outputs.
- [Higher-Order Voting: Optimal Weighting and Information Score Prior](https://arxiv.org/abs/2510.01499) - Bayesian-optimal aggregation methods (Optimal Weighting and Information
- [Auditing Multi-Agent LLM Reasoning Trees Outperforms Majority Vote and LLM-as-Judge](https://arxiv.org/abs/2602.09341) -
- [ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://arxiv.org/abs/2309.13007) -
- [EMS: Multi-Agent Voting via Efficient Majority-then-Stopping](https://arxiv.org/abs/2604.02863) - Adaptive Incremental Voting (AIV) — sequentially invokes agents and terminate...
- [Representational Collapse in Multi-Agent LLM Committees: Measurement and Diversity-Aware Consensus](https://arxiv.org/abs/2604.03809) -
- [Heterogeneous Consensus-Progressive Reasoning for Efficient Multi-Agent Debate](https://arxiv.org/abs/2604.09679) -

### Routing (32 papers)

- [A Unified Approach to Routing and Cascading for LLMs](https://arxiv.org/abs/2410.10347) - Derives optimal strategy for cascading, proves optimality of existing
- [RouteLLM: Cost-Quality Routing for LLM Inference](https://arxiv.org/abs/2406.18510) - Quality-constrained routing to cheapest model meeting
- [RouteLLM: Learning to Route LLMs with Preference Data](https://arxiv.org/abs/2406.18665) - Train router on human preference data for dynamic selection
- [OptiRoute](https://arxiv.org/abs/2502.16696) - kNN + hierarchical filtering with cost/ethics tradeoffs.
- [Capability Instruction Tuning](https://arxiv.org/abs/2502.17282) - Achieves 80% GPT-4o coverage with smaller model zoo.
- [MoMA: Towards Generalized Routing](https://arxiv.org/abs/2509.07571) - Unified LLM + agent routing with TOPSIS algorithm for
- [Cross-Attention Routing](https://arxiv.org/abs/2509.09782) - Single-head cross-attention for query-model matching.
- [SATER: Dual-Mode Routing with Confidence-Aware Rejection](https://arxiv.org/abs/2510.05164) - Dual-mode routing with shortest-response preference optimization
- [IPR: Intelligent Prompt Routing](https://arxiv.org/abs/2509.06274) - Quality-constrained routing with user-controlled tolerance parameter
- [PILOT: Preference-Prior Routing with Budget Constraints](https://arxiv.org/abs/2508.21141) - Contextual bandit (LinUCB) with preference-prior routing and
- [STRMAC: State-Aware Routing](https://arxiv.org/abs/2511.02200) - State-aware routing with separate encoding of history and
- [Edge Multi-LLM: Hybrid Routing with Cascade/ABC Patterns](https://arxiv.org/abs/2507.00672) - Hybrid routing with cascade/ABC (Agreement-Based Cascading) patterns.
- [DAAO: Difficulty-Aware Agent Orchestration via VAE](https://arxiv.org/abs/2509.11079) - VAE-based difficulty estimation for intelligent task routing. Maps tasks
- [AdaptOrch: Task-Adaptive Multi-Agent Orchestration in the Era of LLM Performance Convergence](https://arxiv.org/abs/2602.16873) -
- [Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques](https://arxiv.org/abs/2506.06579) -
- [ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference](https://arxiv.org/abs/2602.23681) -
- [Learning to Route LLMs from Bandit Feedback: One Policy, Many Trade-offs](https://arxiv.org/abs/2510.07429) -
- [LLM Routing with Dueling Feedback](https://arxiv.org/abs/2510.00841) -
- [HieraMAS: Optimizing Intra-Node LLM Mixtures and Inter-Node Topology for Multi-Agent Systems](https://arxiv.org/abs/2602.20229) -
- [Dynamic Tool Dependency Retrieval for Efficient Function Calling](https://arxiv.org/abs/2512.17052) -
- [Routing, Cascades, and User Choice for LLMs](https://arxiv.org/abs/2602.09902) -
- [ORCH: many analyses, one merge-a deterministic multi-agent orchestrator for discrete-choice reasoning with EMA-guided routing](https://arxiv.org/abs/2602.01797) -
- [Learning to Route and Schedule LLMs from User Retrials via Contextual Queueing Bandits](https://arxiv.org/abs/2602.02061) -
- [Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning](https://arxiv.org/abs/2503.05641) -
- [HierRouter: Coordinated Routing of Specialized Large Language Models via Reinforcement Learning](https://arxiv.org/abs/2511.09873) -
- [Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning](https://arxiv.org/abs/2506.09033) -
- [RLAR: An Agentic Reward System for Multi-task Reinforcement Learning on Large Language Models](https://arxiv.org/abs/2603.00724) -
- [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://arxiv.org/abs/2305.05176) -
- [Sustainable LLM Inference using Context-Aware Model Switching](https://arxiv.org/abs/2602.22261) -
- [From Myopic Selection to Long-Horizon Awareness: Sequential LLM Routing for Multi-Turn Dialogue](https://arxiv.org/abs/2604.12385) - MCTS over dialogue branches induced by different LLM selections; distills a l...
- [Triage: Routing Software Engineering Tasks to Cost-Effective LLM Tiers via Code Quality Signals](https://arxiv.org/abs/2604.07494) - Proposal paper (not yet evaluated). Routes SE tasks to light/standard/heavy t...
- [RouteGoT: Node-Adaptive Routing for Cost-Efficient Graph of Thoughts Reasoning](https://arxiv.org/abs/2603.05818) -

### Memory (23 papers)

- [Acon: Optimizing Context Compression](https://arxiv.org/abs/2510.00615) - Task-specific context compression techniques.
- [CCF: Context Compression Framework](https://arxiv.org/abs/2509.09199) - Learned compression modules for context management.
- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://arxiv.org/abs/2504.19413) - Scalable memory architecture achieving 91% latency reduction
- [Context Engineering Survey](https://arxiv.org/abs/2507.13334) - Taxonomy of context management strategies.
- [xKV: Cross-Layer SVD for KV-Cache Compression](https://arxiv.org/abs/2503.18893) - Cross-layer SVD for KV-cache compression exploiting singular
- [TreeKV: Tree-Structured Cache Compression](https://arxiv.org/abs/2501.04987) - Tree-structured cache compression with smooth context transitions.
- [BET: Behavior-Equivalent Token](https://arxiv.org/abs/2511.23271) - Single-token compression of system prompts via reconstruction
- [A-MEM: Agentic Memory for LLM Agents](https://arxiv.org/abs/2502.12110) - Zettelkasten-inspired agentic memory system where memories are
- [MIRIX: Six-Type Memory System](https://arxiv.org/abs/2507.07957) - Six-type memory system with multi-agent management architecture.
- [MobiMem: Post-Deployment Evolution via Memory Modules](https://arxiv.org/abs/2512.15784) - Post-deployment evolution via Profile, Experience, and Action
- [MemGPT: Towards LLMs as Operating Systems](https://arxiv.org/abs/2310.08560) - Operating system-inspired memory management for LLMs with virtual
- [ICAL: Continual Learning of Multimodal Agents](https://arxiv.org/abs/2406.14596) - Build memory of multimodal experience from suboptimal
- [Lifelong Learning of Large Language Model based Agents: A Roadmap](https://arxiv.org/abs/2501.07278) - Three core modules: Perception (multimodal input), Memory
- [ARIA: Human-in-the-Loop Test-Time Learning](https://arxiv.org/abs/2507.17131) - Agents identify knowledge gaps through self-dialogue, request
- [Hindsight Belief Memory for Reasoning Agents](https://arxiv.org/abs/2512.12818) - Belief Memory layer for reasoning agents that maintains and updates
- [Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management](https://arxiv.org/abs/2510.06727) -
- [TeleMem: Building Long-Term and Multimodal Memory for Agentic AI](https://arxiv.org/abs/2601.06037) -
- [LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation](https://arxiv.org/abs/2510.04851) -
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://arxiv.org/abs/2310.11511) -
- [Multi-Layered Memory Architectures for LLM Agents: An Experimental Evaluation of Long-Term Context Retention](https://arxiv.org/abs/2603.29194) -
- [Escaping the Context Bottleneck: Active Context Curation for LLM Agents via Reinforcement Learning](https://arxiv.org/abs/2604.11462) -
- [GAM: Hierarchical Graph-based Agentic Memory for LLM Agents](https://arxiv.org/abs/2604.12285) -
- [Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception](https://arxiv.org/abs/2604.04660) -

### Code Generation (24 papers)

- [MAR: Multi-Agent Reflexion Improves Reasoning Abilities](https://arxiv.org/abs/2512.20845) - Multiple agents reflect and critique each other's outputs.
- [Self-Refine: Iterative Refinement with Self-Feedback](https://arxiv.org/abs/2303.17651) - A single LLM acts as generator, feedback provider, and refiner
- [Teaching Large Language Models to Self-Debug](https://arxiv.org/abs/2304.05128) - Self-Debugging teaches LLMs to debug their own code via few-shot
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/abs/2303.11366) - Agents maintain an episodic memory of verbal reflections that
- [LATS: Language Agent Tree Search](https://arxiv.org/abs/2310.04406) - Combines Monte Carlo Tree Search (MCTS) with LLM-based value
- [Godel Agent: A Self-Referential Agent Framework](https://arxiv.org/abs/2410.04444) - Inspired by Godel machines, agents can modify their own logic
- [SICA: A Self-Improving Coding Agent](https://arxiv.org/abs/2504.15228) - A unified agent that performs tasks AND improves its own
- [Self-Improving AI Agents through Self-Play](https://arxiv.org/abs/2512.02731) - Formalizes self-improvement as a Generator-Verifier-Updater (GVU)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://arxiv.org/abs/2305.16291) - Lifelong learning agent that builds an ever-growing library of
- [CycleQD: Quality-Diversity for Agent Skill Acquisition](https://arxiv.org/abs/2410.14735) - Uses Quality-Diversity framework with cyclic task focus, model
- [EXIF: Automated Skill Discovery for Language Agents](https://arxiv.org/abs/2506.04287) - Exploration-first strategy using two agents (Alice explores,
- [RLAIF vs. RLHF: Scaling Reinforcement Learning with AI Feedback](https://arxiv.org/abs/2309.00267) - RLAIF achieves comparable performance to RLHF at 10x lower cost.
- [Constitutional AI: Harmlessness from AI Feedback](https://arxiv.org/abs/2212.08073) - Two-phase approach: supervised phase (model critiques and revises)
- [Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents](https://arxiv.org/abs/2408.07199) - Combines guided MCTS with self-critique and DPO for learning
- [On the Role of Feedback in Test-Time Scaling of Agentic AI Workflows](https://arxiv.org/abs/2504.01931) - Feedback mechanisms that enable agents to improve during
- [A Survey of Self-Evolving Agents](https://arxiv.org/abs/2507.21046) - Survey of self-evolving agent architectures and techniques.
- [Confucius Code Agent: Scalable Agent Scaffolding for Real-World Codebases](https://arxiv.org/abs/2512.10398) - Production-scale code agent with Confucius SDK. Achieves 54.3% Resolve@1
- [SWE-Refactor: A Repository-Level Benchmark for Real-World LLM-Based Code Refactoring](https://arxiv.org/abs/2602.03712) -
- [CL4SE: A Context Learning Benchmark For Software Engineering Tasks](https://arxiv.org/abs/2602.23047) -
- [Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches](https://arxiv.org/abs/2510.04905) -
- [In Line with Context: Repository-Level Code Generation via Context Inlining](https://arxiv.org/abs/2601.00376) -
- [Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement](https://arxiv.org/abs/2603.00539) -
- [TAM-Eval: Evaluating LLMs for Automated Unit Test Maintenance](https://arxiv.org/abs/2601.18241) -
- [MARS$^2$: Scaling Multi-Agent Tree Search via Reinforcement Learning for Code Generation](https://arxiv.org/abs/2604.14564) -

### Orchestration (32 papers)

- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://arxiv.org/abs/2501.06322) - Taxonomy of collaboration types (cooperation, competition, coopetition)
- [Multi-Agent Collaboration via Evolving Orchestration](https://arxiv.org/abs/2505.19591) - Puppeteer-style paradigm with RL-trained orchestrator for dynamic
- [AgentsNet](https://arxiv.org/abs/2507.08616) - Benchmark for 100-agent coordination scenarios.
- [TRINITY: Evolved LLM Coordinator](https://arxiv.org/abs/2512.04695) - Evolved LLM coordinator with Thinker/Worker/Verifier roles
- [LATTS: Locally Adaptive Test-Time Scaling](https://arxiv.org/abs/2509.20368) - Locally adaptive test-time scaling with verifier-based
- [Hybrid Architectures for LLMs](https://arxiv.org/abs/2510.04800) - Transformer + SSM hybrid analysis.
- [Multi-LLM Orchestration Engine](https://arxiv.org/abs/2410.10039) - Temporal graph + vector DB integration for multi-LLM orchestration.
- [Pick and Spin: Efficient Multi-Model Orchestration](https://arxiv.org/abs/2512.22402) - Unified Helm-based deployment with adaptive scale-to-zero
- [LatentMAS: Direct Latent Space Collaboration](https://arxiv.org/abs/2511.20639) - Direct latent space collaboration through hidden state sharing.
- [LATS: Language Agent Tree Search](https://arxiv.org/abs/2310.04406) - Combines Monte Carlo Tree Search (MCTS) with LLM-based value
- [Multi-AI Agent System for Autonomous Optimization](https://arxiv.org/abs/2412.17149) - Five specialized agents (Refinement, Execution, Evaluation,
- [Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning](https://arxiv.org/abs/2504.14520) - Develop meta-thinking (self-reflection, assessment, control of
- [A Survey on LLM-based Multi-Agent System](https://arxiv.org/abs/2412.17481) - Comprehensive survey on LLM-based multi-agent systems.
- [Confucius Code Agent: Scalable Agent Scaffolding for Real-World Codebases](https://arxiv.org/abs/2512.10398) - Production-scale code agent with Confucius SDK. Achieves 54.3% Resolve@1
- [AFlow: Automatic Workflow Discovery via MCTS](https://arxiv.org/abs/2410.10762) - Monte Carlo Tree Search (MCTS) for automatic workflow discovery and
- [SEW: Self-Evolving Workflows for Agent Systems](https://arxiv.org/abs/2505.18646) - Self-evolving workflow patterns that adapt and improve through execution
- [Forest-of-Thought: Multi-Tree Reasoning with Sparse Activation](https://arxiv.org/abs/2412.09078) - Multi-tree reasoning architecture with sparse activation for parallel
- [Scaling Agent Systems: Coordination Prediction and Resource Optimization](https://arxiv.org/abs/2512.08296) - Prediction of coordination requirements for scaling multi-agent systems.
- [AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration](https://arxiv.org/abs/2602.03786) - Framework for dynamic sub-agent creation via unified agent abstraction.
- [Rethinking the Role of Entropy in Optimizing Tool-Use Behaviors for Large Language Model Agents](https://arxiv.org/abs/2602.02050) - Entropy reduction correlates with higher-quality tool invocations. Dense proc...
- [Towards Reducible Uncertainty Modeling for Reliable Large Language Model Agents](https://arxiv.org/abs/2602.05073) - Framework for uncertainty quantification in LLM agents using conditional unce...
- [Codified Context: Infrastructure for AI Agents in a Complex Codebase](https://arxiv.org/abs/2602.20478) -
- [Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration](https://arxiv.org/abs/2509.11067) -
- [Multi-Agent LLM Committees for Autonomous Software Beta Testing](https://arxiv.org/abs/2512.21352) -
- [Multi-Agent Teams Hold Experts Back](https://arxiv.org/abs/2602.01011) -
- [CommCP: Efficient Multi-Agent Coordination via LLM-Based Communication with Conformal Prediction](https://arxiv.org/abs/2602.06038) -
- [Towards Adaptive, Scalable, and Robust Coordination of LLM Agents: A Dynamic Ad-Hoc Networking Perspective](https://arxiv.org/abs/2602.08009) -
- [ScaleCall -- Agentic Tool Calling at Scale for Fintech: Challenges, Methods, and Deployment Insights](https://arxiv.org/abs/2511.00074) -
- [MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks](https://arxiv.org/abs/2603.02630) -
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://arxiv.org/abs/2303.17760) -
- [Enhanced Mycelium of Thought (EMoT): A Bio-Inspired Hierarchical Reasoning Architecture with Strategic Dormancy and Mnemonic Encoding](https://arxiv.org/abs/2603.24065) -
- [Reasoning Topology Matters: Network-of-Thought for Complex Reasoning Tasks](https://arxiv.org/abs/2603.20730) -

### Security (16 papers)

- [STPA MCP Framework: System-Theoretic Process Analysis for MCP Safety](https://arxiv.org/abs/2601.08012) - Formal STPA safety analysis methodology for MCP tools. Systematically
- [Agent-SafetyBench: Comprehensive Safety Evaluation for AI Agents](https://arxiv.org/abs/2412.14470) - Comprehensive safety evaluation suite for agent behaviors across multiple
- [Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents](https://arxiv.org/abs/2601.10955) -
- [Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs](https://arxiv.org/abs/2601.12359) -
- [ICON: Indirect Prompt Injection Defense for Agents based on Inference-Time Correction](https://arxiv.org/abs/2602.20708) -
- [Silent Egress: When Implicit Prompt Injection Makes LLM Agents Leak Without a Trace](https://arxiv.org/abs/2602.22450) -
- [AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMs](https://arxiv.org/abs/2602.20720) -
- [From Secure Agentic AI to Secure Agentic Web: Challenges, Threats, and Future Directions](https://arxiv.org/abs/2603.01564) -
- [Security Risks of AI Agents Hiring Humans: An Empirical Marketplace Study](https://arxiv.org/abs/2602.19514) -
- [A Safety and Security Framework for Real-World Agentic Systems](https://arxiv.org/abs/2511.21990) -
- [MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents](https://arxiv.org/abs/2510.15994) -
- [AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification](https://arxiv.org/abs/2602.22724) -
- [Red-Teaming LLM Multi-Agent Systems via Communication Attacks](https://arxiv.org/abs/2502.14847) -
- [ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection](https://arxiv.org/abs/2604.11790) - Deterministic interceptor at tool-call boundary. LLM-based rule induction der...
- [Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks](https://arxiv.org/abs/2603.30016) -
- [ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems](https://arxiv.org/abs/2604.04426) -

### Evaluation (8 papers)

- [Predictive Coding and Information Bottleneck for Hallucination Detection in Large Language Models](https://arxiv.org/abs/2601.15652) -
- [Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences](https://arxiv.org/abs/2602.21585) -
- [Are We on the Right Way to Assessing LLM-as-a-Judge?](https://arxiv.org/abs/2512.16041) -
- [HackSynth: LLM Agent and Evaluation Framework for Autonomous Penetration Testing](https://arxiv.org/abs/2412.01778) -
- [MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments](https://arxiv.org/abs/2512.19432) -
- [Style Over Substance: Evaluation Biases for Large Language Models](https://arxiv.org/abs/2307.03025) -
- [Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-CodeX](https://arxiv.org/abs/2604.14858) - CLI-specific trajectory safety benchmarks (ATBench-Claw for OpenClaw, ATBench...
- [Does RL Expand the Capability Boundary of LLM Agents? A PASS@(k,T) Analysis](https://arxiv.org/abs/2604.14877) -

### Tool Use (3 papers)

- [MCP-Zero: Active Tool Discovery for Autonomous LLM Agents](https://arxiv.org/abs/2506.01056) -
- [Tool-Augmented Policy Optimization: Synergizing Reasoning and Adaptive Tool Use with Reinforcement Learning](https://arxiv.org/abs/2510.07038) -
- [Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling](https://arxiv.org/abs/2509.26553) -

---

## GitHub Issues

| Issue | Feature                                    | Related Papers                     |
| ----- | ------------------------------------------ | ---------------------------------- |
| #119  | Aegean Consensus Protocol                  | arxiv-2512.20184                   |
| #125  | Task-Type Protocol Selection               | arxiv-2502.19130                   |
| #103  | CP-WBFT Byzantine Fault Tolerant Consensus | arxiv-2511.10400                   |
| #1657 | Multi-Agent Reflexion (MAR)                | arxiv-2512.20845                   |
| #152  | Free-MAD Anti-Conformity Scoring           | arxiv-2509.11035                   |
| #146  | TOPSIS Multi-Criteria Routing              | arxiv-2509.07571                   |
| #128  | IPR Quality-Constrained Routing            | arxiv-2509.06274, arxiv-2406.18510 |
| #102  | PILOT Budget-Constrained Routing           | arxiv-2508.21141                   |
| #99   | SATER Confidence-Aware Routing             | arxiv-2510.05164                   |
| #121  | Agreement-Based Cascading (ABC)            | arxiv-2410.10347                   |
| #148  | Preference-Trained Router (RouteLLM)       | arxiv-2406.18665                   |
| #156  | Mem0 Scalable Long-Term Memory             | arxiv-2504.19413                   |
| #157  | MIRIX Six-Type Memory System               | arxiv-2507.07957                   |
| #149  | MobiMem Post-Deployment Evolution          | arxiv-2512.15784                   |
| #142  | Graph-Based Memory                         | arxiv-2504.19413                   |
| #143  | Adaptive Memory                            | arxiv-2310.08560                   |
| #122  | A-MEM Agentic Memory                       | arxiv-2502.12110                   |
| #141  | TRINITY Thinker/Worker/Verifier Roles      | arxiv-2512.04695                   |
| #154  | RL-Trained Orchestrator                    | arxiv-2505.19591                   |
| #335  | Evolving Orchestration Upgrade             | arxiv-2505.19591                   |
| #153  | LATTS Adaptive Test-Time Compute           | arxiv-2509.20368                   |
| #126  | Self-Refine Iterative Loop                 | arxiv-2303.17651                   |
| #130  | Reflexion Verbal Reinforcement Learning    | arxiv-2303.11366                   |
| #150  | Voyager Skill Library Pattern              | arxiv-2305.16291                   |
| #151  | SICA Self-Improving Agent                  | arxiv-2504.15228                   |
| #147  | Constitutional AI Self-Critique            | arxiv-2212.08073                   |
| #131  | Self-Debug Code Repair                     | arxiv-2304.05128                   |
| #1659 | LatentMAS Latent Space Collaboration       | arxiv-2511.20639                   |
| #328  | STPA MCP Framework                         | arxiv-2601.08012                   |
| #329  | AFlow MCTS Workflow Generation             | arxiv-2410.10762                   |
| #330  | SEW Self-Evolving Workflows                | arxiv-2505.18646                   |
| #333  | Higher-Order Voting (OW/ISP)               | arxiv-2510.01499                   |
| #331  | Forest-of-Thought Multi-Tree Reasoning     | arxiv-2412.09078                   |
| #332  | Agent-SafetyBench Evaluation Suite         | arxiv-2412.14470                   |
| #334  | DAAO VAE Difficulty Estimation             | arxiv-2509.11079                   |
| #336  | Hindsight Belief Memory                    | arxiv-2512.12818                   |
| #337  | Scaling Agent Coordination Predictor       | arxiv-2512.08296                   |
| #338  | ZeroRouter Universal Difficulty Space      |                                    |
| #1568 | Failure Lesson Injection                   |                                    |
| #1569 | Skill Relevance Matching                   |                                    |
| #1670 | Context Rot Prevention                     |                                    |
| #1658 | Wave-Based Parallel Execution              |                                    |
| #1570 | Write-Time Memory Deduplication            |                                    |

---

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## Registry Files

- [papers.yaml](registry/papers.yaml) - All 179 papers with metadata
- [techniques.yaml](registry/techniques.yaml) - All 43 techniques with status
- [sources.yaml](registry/sources.yaml) - Product docs and other sources

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## How to Contribute

See [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on adding new research.

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