Skip to content

docs: correct primary-source paper references and metrics #77

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
autogame-17 merged 2 commits into from
Oct 20, 2026
+7 −7
Merged

docs: correct primary-source paper references and metrics #77

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
cfdf7da
docs: correct primary-source paper references and metrics
autogame-17 Oct 20, 2026
8b0c22f
Merge latest main before paper-reference maintenance
autogame-17 Oct 20, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 7 additions & 7 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -231,7 +231,7 @@ Projects connecting AI agents to physical devices, robotics, and real-world envi
- [Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?](https://arxiv.org/abs/2511.13646) (arXiv'25) - First live agent that autonomously evolves itself during runtime. 77.4% on SWE-bench Verified.
- [SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution](https://arxiv.org/abs/2603.13428) (ICML'26) - Benchmark revealing performance drops from >80% on isolated tasks to 38.03% in continuous evolution settings.
- [Symbolic Learning Enables Self-Evolving Agents](https://arxiv.org/abs/2406.18532) (arXiv'24) - Agents that evolve through symbolic representation learning.
- [Building Self-Evolving Agents via Experience-Driven Lifelong Learning](https://arxiv.org/abs/2504.01072) (arXiv'25) - Framework and benchmark for lifelong agent learning.
- [Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark](https://arxiv.org/abs/2508.19005) (arXiv'25) - Framework and benchmark for experience-driven lifelong agent learning.
- [Darwin Godel Machine](https://arxiv.org/abs/2505.22954) (arXiv'25) - Agents that rewrite their own code through evolutionary pressure.
- [EvoAgent: Self-evolving Agent with Continual World Model](https://arxiv.org/abs/2502.05907) (arXiv'25) - Continual world model for long-horizon task evolution.
- [Absolute Zero: Reinforced Self-play Reasoning with Zero Data](https://arxiv.org/abs/2505.03335) (arXiv'25) - Self-play reasoning without any training data.
Expand All @@ -248,13 +248,13 @@ Projects connecting AI agents to physical devices, robotics, and real-world envi
- [Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory](https://arxiv.org/abs/2504.19413) (arXiv'25) - Production architecture. 26% improvement on LOCOMO, 91% latency reduction.
- [TeleMem: Long-Term and Multimodal Memory for Agentic AI](https://arxiv.org/abs/2601.06037) - Multimodal memory achieving 19% higher accuracy, 43% fewer tokens, 2.1x speedup over Mem0. (arXiv 2026)
- [A-MEM: Agentic Memory for LLM Agents](https://arxiv.org/abs/2502.12110) (arXiv'25) - Self-organizing memory with autonomous management.
- [Agent Workflow Memory](https://arxiv.org/abs/2409.07429) (ICML'24) - Memory tied to agent workflow patterns.
- [Agent Workflow Memory](https://arxiv.org/abs/2409.07429) (ICML'25) - Memory tied to agent workflow patterns.
- [MemoryBank: Enhancing Large Language Models with Long-Term Memory](https://arxiv.org/abs/2305.10250) (AAAI'24) - Structured long-term memory for LLMs.
- [Compress to Impress](https://arxiv.org/abs/2402.11975) (ICLR'25) - Compression-based memory for extended dialogues.
- [Compress to Impress](https://aclanthology.org/2025.coling-main.51/) (COLING'25) - Compression-based memory for extended dialogues.

### Prompt and Behaviour Evolution

- [ARTEMIS: Evolutionary Optimization for LLM Agent Configurations](https://arxiv.org/abs/2512.09108) (arXiv'25) - Semantically-aware genetic operators for joint agent config optimization. 13.6% on competitive programming.
- [ARTEMIS: Evolutionary Optimization for LLM Agent Configurations](https://arxiv.org/abs/2512.09108) (arXiv'25) - Semantically-aware genetic operators for joint agent config optimization. 13.6% relative acceptance-rate improvement on the AtCoder Heuristic Contest, from 66.0% to 75.0%.
- [E-SPL: Unifying Evolutionary Prompt Search and Reinforcement Learning](https://arxiv.org/abs/2602.14697) (arXiv'26) - Joint RL weight updates with genetic operators for system prompt evolution.
- [EvoPrompt: Connecting LLMs with Evolutionary Algorithms](https://arxiv.org/abs/2309.08532) (ICLR'24) - Evolutionary algorithms for prompt optimization.
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://arxiv.org/abs/2309.16797) (ICML'24) - Prompts that evolve themselves recursively.
Expand All @@ -264,7 +264,7 @@ Projects connecting AI agents to physical devices, robotics, and real-world envi
### Tool and Code Evolution

- [AlphaEvolve](https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/AlphaEvolve.pdf) (Google'25) - LLM-driven evolutionary code improvement.
- [Learning Evolving Tools for Large Language Models](https://arxiv.org/abs/2410.06617) (ICLR'25) - Tools that co-evolve with agent capabilities.
- [Learning Evolving Tools for Large Language Models](https://arxiv.org/abs/2410.06617) (ICLR'25) - Agents adapt tool-use behavior to changing external APIs using execution feedback.
- [CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning](https://arxiv.org/abs/2305.14318) (EMNLP'23) - Agents that create their own tools.
- [ToolRL: Reward is All Tool Learning Needs](https://arxiv.org/abs/2504.13958) (arXiv'25) - Reinforcement learning for tool use optimization.

Expand All @@ -276,9 +276,9 @@ Projects connecting AI agents to physical devices, robotics, and real-world envi
### Safety, Red-Teaming, and Alignment

- [AgenticRed: Optimizing Agentic Systems for Automated Red-teaming](https://arxiv.org/abs/2601.13518) (arXiv'26) - Evolutionary red-teaming workflow design. 96% attack success on Llama-2-7B.
- [Agent vs. Agent: Automated Red-Teaming for Custom Agentic Workflows](https://aclanthology.org/2025.emnlp-industry.62/) (EMNLP'25) - AgentHarm-Gen for adversarial task generation. 162% increase in attack success rate.
- [Agent vs. Agent: Automated Red-Teaming for Custom Agentic Workflows](https://aclanthology.org/2025.emnlp-industry.62/) (EMNLP'25) - AgentHarm-Gen for adversarial task generation. Reports up to a 162% relative improvement in attack success rate on o4-mini.
- [AGENTSAFE: Unified Framework for Ethical Assurance and Governance](https://arxiv.org/abs/2512.03180) (arXiv'25) - Design, runtime, and audit controls covering the agentic loop.
- [OpenGuardrails: Context-Aware AI Guardrails Platform](https://arxiv.org/abs/2510.19169) (arXiv'25) - Context-aware safety detection and model-manipulation prevention.
- [OpenGuardrails: Context-Aware AI Guardrails Platform](https://arxiv.org/abs/2510.19169) (arXiv'25) - Per-request configurable detection for content-safety violations, model-manipulation attacks, and sensitive-data leakage.
- [Jailbreaking LLMs' Safeguard with Universal Magic Words for Text Embedding Models](https://arxiv.org/abs/2501.18280) (arXiv'25) - Universal suffix that manipulates text-embedding similarity to bypass safety guardrails across ChatGPT, DeepSeek, and Qwen.

### Embodied AI and Robotics
Expand Down
Loading