A curated, high-signal index of recursive self-improvement (RSI) in AI: foundations, self-modifying agents, evaluation, and safety.
Last reviewed: 2026-08-18.
Important
RSI is stronger than ordinary iteration. This list distinguishes systems that improve a persistent part of themselves from systems that merely revise one answer. A recursive system must also improve, or repeatedly reuse, the mechanism that produces later improvements. Most current systems are bounded or partial RSI—not open-ended intelligence explosions.
- Scope and labels
- Start here
- Surveys and taxonomies
- Foundations
- Self-modifying and recursively improving agents
- Components of self-improvement
- Automated AI research
- Software-engineering self-improvement
- Evaluation and benchmarks
- Safety, limits, and governance
- Self-improvement harnesses
- Workshops and related collections
This list uses three labels to keep adjacent research useful without overstating what it demonstrates:
- the system changes a persistent part of itself, evaluates the change, and applies the same or an improved process again.
- the system persistently improves model weights, prompts, memory, tools, skills, or scaffolding, but the improvement operator itself remains fixed.
- automated research, optimization, evaluation, or safety work that could support RSI but is not itself RSI.
Clickable resource badges identify primary artifacts: 
Excluded by default: one-shot self-critique, answer-only refinement with no persistent update, generic agent frameworks, and projects whose improvement claims have no reproducible evaluation.
| System behavior | Included? | Label |
|---|---|---|
| Revises only the current answer, with no reusable state | Usually no | Output refinement |
| Generates, filters, or repairs data and trains a later model on it | Yes |  if the data loop is system-driven |
| Stores experience that changes later behavior | Yes | when reuse is demonstrated; otherwise  |
| Updates prompts, memory, tools, skills, routing, permissions, or executable control logic | Yes | |
| Improves the updater, evaluator, mutation policy, or harness engineer used in later rounds | Yes |  candidate |
| Optimizes an external artifact while the agent remains fixed | Yes, in an adjacent section | |
The unit of analysis is the deployed agent system, not only its neural weights. Model, data, prompt, memory, tool, workflow, harness, evaluator, and environment are all legitimate update surfaces, but changing a surface is not automatically recursive improvement.
- Self-Improvements in Modern Agentic Systems: A Survey (2026) - A 239-paper map separating foundation-model improvement from prompt, memory, tool, and full-scaffolding improvement.
- Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops (2026) - Distinguishes bounded refinement, persistent self-improvement, recursive improvement, and autonomous research loops.
- A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve (TMLR 2026) - Organizes model, memory, tool, and architecture evolution by update time, feedback, and single- versus multi-agent design.
- A Comprehensive Survey of Self-Evolving AI Agents (2025) - Unifies system inputs, agent systems, environments, and optimizers, including domain-specific evolution and safety.
- A Survey on Self-Evolution of Large Language Models (2024) - Frames model self-evolution as repeated experience acquisition, refinement, updating, and evaluation.
- Self-Improving Agents in the Era of Experience: A Survey of Self- to Meta-Evolution (2026) - Treats the harness as experience infrastructure connecting skills, memory, environments, continual learning, and meta-evolution.
- Towards Persistent Growth: A Survey on Self-Evolving Agents from a Lifelong Learning Perspective (2026) - Requires persistent, reusable, behaviorally consequential change across episodes rather than within-episode retry.
- Diving into Reliable Self-Evolving Agents (2026) - Adds reliability requirements for scaffold-, improver-, and criterion-level evolution.
- A Systematic Survey of Self-Evolving Agents: From Model-Centric to Environment-Driven Co-Evolution (2026) - Extends the scope to agent–environment co-evolution.
- The Path to Recursive Self-Improving Agents: Foundation, Framework, and Future Directions (2026) - Grades agent self-improvement from manual updates through general RSI and separates harness, data-system, trainer, and cross-component evolution.
- From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents (2026) - Organizes agentic computation-graph optimization by when structure changes, which components change, and which signals guide the update.
- Agent Harness Engineering: A Survey (2026) - Surveys runtime harness components, lifecycle adaptation, evaluation, and the distinction between model and interface improvement.
- Automated Design of Agentic Systems: A Survey of Algorithms for Searching, Optimizing, and Evolving LLM Agents, Workflows, and Prompts (2026) - Compares search spaces, feedback signals, representations, transfer, cost, and safety across prompt optimizers, workflow search, and self-rewriting agents.
- Speculations Concerning the First Ultraintelligent Machine (I. J. Good, 1965) - Introduced the intelligence-explosion argument.
- Gödel Machines (Jürgen Schmidhuber, 2003) - A proof-searching agent that rewrites any part of itself after proving a utility gain.
- Basic AI Drives (Stephen Omohundro, 2008) - Instrumental pressures that can arise in sufficiently capable goal-directed systems.
- Intelligence Explosion Microeconomics (Eliezer Yudkowsky, 2013) - A detailed treatment of returns, bottlenecks, and dynamics in recursive improvement.
- From Seed AI to Technological Singularity via Recursively Self-Improving Software (Roman Yampolskiy, 2015) - A taxonomy and critical analysis of RSI software.
- The Surprising Creativity of Digital Evolution (Lehman et al., 2020) - Examples of unexpected solutions in evolutionary computation and lessons for open-ended search.
- Open-Endedness: The Last Grand Challenge You've Never Heard Of (Stanley et al., 2017) - Why continually generating novelty is distinct from optimizing a fixed objective.
- POWERPLAY (2011) - Jointly searches for a new task and a solver modification that preserves old skills while adding or accelerating a validated capability.
- Learning to Learn by Gradient Descent by Gradient Descent (NeurIPS 2016) - Learns an optimizer, a central mechanism for improving the improvement process.
- Population Based Training of Neural Networks (2017) - Jointly evolves parameters and hyperparameters during training.
- Paired Open-Ended Trailblazer (POET) (2019) - Co-evolves environment challenges and their solutions, transferring stepping-stone policies between an expanding set of tasks.
- AI-GAs: AI-Generating Algorithms (2019) - Proposes jointly learning architectures, learning algorithms, and environments as an alternative to manually assembling increasingly general AI systems.
- AutoML-Zero (ICML 2020) - Evolves complete machine-learning algorithms from primitive operations.
- Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (Nature 2020) - MuZero as a landmark self-play and learned-model system; adjacent, not recursive self-modification.
| Year | Work | Level | What changes |
|---|---|---|---|
| 2023 | Self-Taught Optimizer (STOP) · |
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An LLM improves a program that is itself used to improve code. |
| 2024 | Automated Design of Agentic Systems (ADAS) · |
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A meta-agent searches over agent programs; the meta-optimizer stays fixed. |
| 2024 | AFlow: Automating Agentic Workflow Generation · |
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Agent workflows are generated and refined against task feedback. |
| 2025 | Gödel Agent · |
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The agent dynamically modifies its own task-solving and optimization logic. |
| 2025 | A Self-Improving Coding Agent (SICA) · |
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A coding agent edits and evaluates its own implementation. |
| 2025 | Darwin Gödel Machine · |
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An archive-based evolutionary loop modifies coding-agent code and reuses improved descendants. |
| 2026 | Huxley-Gödel Machine · |
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An empirical approximation of a Gödel machine develops its own coding-agent implementation. |
| 2026 | HyperAgents · |
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Task and meta-agent roles are integrated so the agent can modify its own improver. |
| 2026 | MOSS · |
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An agent rewrites its TypeScript source, replays failure batches, and promotes container images through an approval and rollback gate. |
| 2026 | EvoTrainer · |
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Model policies and their training harnesses co-evolve under executable feedback. |
| 2026 | SIA: Self Improving AI with Harness & Weight Updates · |
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A meta-agent updates both task harnesses and model weights under benchmark feedback. |
| 2026 | Red Queen Gödel Machine | |
Agents and their evaluators co-evolve through epoch-bounded utility updates, making the improvement criterion part of the loop. |
| 2026 | OpenRSI / OpenMLE / Frontis-MA1 · |
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The Tsinghua-affiliated release joins OpenMLE-Gym, OpenMLE-RL, Frontis-MA1, and OpenMLE-Evo in an executable MLE research stack. |
| 2026 | Ouroboros · |
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Reviewed commits to tools, prompts, context assembly, and core code become the runtime for later work and can schedule another evolution cycle. |
These works improve a persistent component and are important building blocks, but generally keep the outer improvement algorithm fixed.
Data evolution belongs here when generated experience is persisted, selected or repaired, and consumed by a later update. Static synthetic-data generation without a feedback-to-update loop is an enabler rather than self-improvement.
Automating AI R&D can close an important part of the RSI loop, but these systems usually optimize an external artifact rather than their own improvement procedure.
Coding is a particularly important test bed because repositories, tests, and issue resolution provide persistent state and executable feedback. The entries below still span different levels of recursion.
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A downstream task score is not by itself an RSI evaluation. Direct benchmarks below measure change across episodes, generations, or checkpoints; task environments provide the external work and verifiers on which an improvement loop can be tested.
- RSIBench-Data (2026) - Opens only the data-generation strategy while holding the target model, training stack, evaluator, and budgets fixed. Agents synthesize data, train checkpoints, inspect execution feedback, and select a final candidate across six downstream benchmarks.
- PAST-Bench (2026) - Uses matched persistence-on/off conditions across ordered fresh-session tasks to attribute later gains to saved experience and its intended retrieval or update pathway.
- EvoAgentBench (2026) - Measures whether trace-derived procedural abilities transfer to held-out tasks across web research, algorithmic reasoning, software engineering, and knowledge work.
- SIP-Bench (2026) - An adapter-based protocol that converts task benchmarks into longitudinal evaluations with
T0/T1/T2checkpoints, replay/adapt/held-out/drift splits, retention, stability, and cost metrics. - SEAGym (2026) - Converts Harbor-compatible tasks into train, frozen validation, held-out in- and out-of-distribution, replay, and cost views for evaluating harness updates.
- SEA-Eval (2026) - Uses sequential task streams and success-rate/token-consumption trajectories to measure evolutionary gain and stability beyond isolated episodic scores. Code not linked by the paper.
- SE-Bench (2026) - Measures whether an agent internalizes new API knowledge and later applies it without documentation access.
- LifelongAgentBench (2025) - Tests experience accumulation and transfer through interdependent database, operating-system, and knowledge-graph tasks.
- StuLife (2025) - A persistent virtual-campus environment for evaluating long-term memory, reusable skills, adaptation, and self-directed behavior across an academic-year task stream.
- FinEvolveBench (2026) - Tests whether agents turn low-repetition financial tasks and delayed, noisy outcomes into reusable experience.
; the repository schedules the complete dataset for September 2026.
- FinEvo-Bench (2026) - Uses paired non-evolving controls and shuffled longitudinal streams to measure experience gains and compliance in professional financial workflows. Code not linked by the paper.
- MLAgentBench - ML experimentation tasks with execution-based evaluation.
- MLE-bench - 75 Kaggle competitions for measuring ML-engineering agents.
- RE-Bench - Open-ended AI R&D environments with direct human-expert comparisons.
- ResearchGym - End-to-end AI research tasks with solution methods withheld, fixed budgets, executable experiments, and human baselines.
- MLGym-Bench - Open-ended machine-learning research tasks spanning hypothesis generation, implementation, training, and experimental iteration.
- AIRS-Bench - Twenty open-ended tasks from recent ML papers spanning idea generation, experiment analysis, and iterative refinement without starter code.
- PostTrainBench - Gives agents one base model, one H100 GPU, and ten hours to research and execute an autonomous post-training strategy while auditing reward-hacking failures.
- CORE-Bench - Reproduction of computational research across multiple disciplines.
- PaperBench - Replication of 20 ICML papers, decomposed into 8,316 gradable tasks.
- Meta-Agent Challenge - Meta-agents build complete agents inside a sealed environment; a verifier scores the result on a hidden test set.
- Frontier-Eng - Tracks improvement trajectories on 47 generative engineering-optimization tasks with frozen verifiers.
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- NatureBench - Scientific ML tasks derived from Nature-family papers with held-out tests and an information firewall.
- METR: Measuring AI Ability to Complete Long Tasks - Time-horizon methodology relevant to autonomous AI R&D.
These evaluate a fixed agent on useful task distributions. An RSI study must add longitudinal splits, frozen selection gates, or matched non-improving controls before treating them as evidence of self-improvement.
- SWE-bench - Real-world GitHub issue resolution.
- Terminal-Bench - Complex terminal tasks with containerized execution and verifiers.
- ALE-Bench - Algorithm-engineering problems with score-based executable evaluation.
- SkillsBench - Measures both skill utility and an agent's ability to use supplied skills.
- AppWorld - Stateful app interactions with programmatic evaluation.
- tau-bench - Tool–agent–user interaction in policy-constrained domains.
- MCP-Atlas - Tool-use tasks over Model Context Protocol servers.
These do not measure improvement rates directly. They test whether increasingly autonomous or adaptive agents exploit evaluators, drift from assigned objectives, or acquire dangerous capabilities.
- Reward Hacking Benchmark - Multi-step tool tasks with shortcut and evaluator-tampering opportunities, including harder chained variants.
- RepliBench - Measures resource acquisition, model-weight exfiltration, deployment, and persistence as components of autonomous replication risk.
- Performance across multiple generations, including regressions and variance—not only the best descendant.
- A held-out evaluator or verifier that the system cannot rewrite or directly optimize against.
- Ablations for self-modification, archive/search strategy, memory, and external feedback.
- Generalization to tasks that were not used to select modifications.
- Compute, wall-clock time, model/API version, prompts, trajectories, and failed attempts.
- Safety isolation, change permissions, rollback behavior, and the exact human interventions.
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The Basic AI Drives (2008) - Why self-preservation, resource acquisition, and self-improvement may emerge instrumentally.
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Risks from Learned Optimization in Advanced Machine Learning Systems (2019) - Mesa-optimization and objectives learned inside an optimizing system.
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The Alignment Problem from a Deep Learning Perspective (2022) - Technical survey of alignment failure modes relevant to increasingly autonomous systems.
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Goal Misgeneralization in Deep Reinforcement Learning (ICML 2022) - Capable policies can pursue unintended goals outside training conditions.
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The Curse of Recursion (2023) - Repeated training on generated data can cause model collapse.
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Large Language Models Cannot Self-Correct Reasoning Yet (ICLR 2024) - Evidence that intrinsic self-correction can degrade performance without external feedback.
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Sleeper Agents (2024) - Safety training may fail to remove deceptive, conditionally triggered behavior.
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Weak-to-Strong Generalization (ICML 2024) - Empirical study of supervising stronger models with weaker ones.
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Your Agent May Misevolve (ICLR 2026) - Measures harmful drift across model, memory, tool, and workflow evolution, including degraded safety alignment and vulnerabilities introduced by accumulated updates.
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Evaluating Goal Drift in Language Model Agents (2025) - Tests whether long-running agents preserve assigned objectives under competing environmental pressures and extended context.
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Large Language Model Agents Are Not Always Faithful Self-Evolvers (ICML 2026) - Studies whether retained updates actually reflect the experience claimed to cause them.
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Recursive Self-Evolving Agents via Held-Out Selection (2026) - Separates mutation from promotion using held-out selection signals.
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Zombie Agents (ICLR 2026 Workshop) - Demonstrates persistent, self-reinforcing injections in agents that update their own state.
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Safety in Self-Evolving LLM Agent Systems (2026) - Threat model and case studies for amplification across persistent update loops.
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International AI Safety Report - Consensus-oriented review of frontier capabilities, loss-of-control risks, and safeguards.
Warning
Self-modifying agents execute model-generated code and may alter their own safeguards. Use isolated, disposable environments; least-privilege credentials; immutable evaluators; resource limits; append-only logs; and human approval for promotion. Do not run experimental RSI systems against valuable hosts, secrets, or production infrastructure.
This section indexes runnable systems rather than every repository that uses the words “self-improving.” The Gate column records the strongest promotion evidence documented by the project:
- Frozen eval - candidates are scored by an evaluator outside the editable surface.
- Keep/revert - a candidate must beat a baseline or pass checks before it becomes persistent.
- Versioned - changes and lineage are recorded, but promotion may still rely on model or human judgment.
- Memory only - experience persists without a demonstrated candidate-selection loop; useful, but not RSI by itself.
These systems can alter multiple parts of an agent or its harness, not just the answer to one task.
| Project | Persistent surface | Gate | Evidence |
|---|---|---|---|
| Penguin Harness | Prompts, skills, runtime configuration | Frozen eval + keep/revert | Benchmarks, traces, versioned snapshots |
| Ouroboros | Core code, tools, prompts, context assembly, dependencies | Separate-agent review + frozen benchmark snapshots | |
| Darwin Gödel Machine | Coding-agent implementation | Frozen eval + archive | |
| SICA | Its own coding-agent codebase | Benchmark + keep/revert | |
| Gödel Agent | Agent logic and self-improvement procedure | Task evaluation | |
| Huxley-Gödel Machine | Coding-agent implementation | Benchmark + evolutionary selection | |
| HyperAgents | Task agent and meta-agent logic | Task evaluation | |
| MOSS | TypeScript source and container image | Replay + approval + rollback | |
| SIA | Harness and model weights | Benchmark selection | |
| Prime Agent | Prompts, memories, skills, subagent specifications | Quality gates + snapshots + rollback | |
| SHE | System prompt, rule bank, safety memory, tool policy | Safety–utility validation on held-out tasks | |
| Self-Harness | Model-specific executable harness | Regression tests + held-out pass rate | Paper; code not linked at publication |
| Life-Harness | Environment contracts, procedural skills, action realization, trajectory control | Frozen held-out tasks after trajectory-derived adaptation | |
| Agentic Harness Engineering | Coding-agent harness under fixed base model | Terminal-Bench evaluation + transfer | |
| Adaptive Auto-Harness | Stateful harness tree and solve-time router | Open-ended stream evaluation + routing | |
| Continual Harness | Prompt, subagents, skills, and memory in one continuous episode | Online task progress; reset-free updates | |
| Harness-R1 | Executable runtime patches and a learned harness engineer | Frozen-target reruns + RL outcome reward | |
| HALO | Prompts, tools, routing, and agent loop | Trace diagnosis + benchmark comparison | Production-trace optimizer |
| AutoAgent | System prompt, tools, configuration, orchestration | Benchmark + keep/discard | Autonomous harness engineering |
| Harness Evolver | Prompts, routing, retrieval, tools, orchestration | Held-out eval + constraints + rollback | |
| MetaHarness | Generated harness, skills, memory, and learning loop | Tests + benchmark + signed release gates | Harness factory with Darwin mode |
| RSIHub | Declared agent mutation surfaces | Frozen eval + evidence-gated promotion | Reproducible lineage |
| KADATH | Populations of multi-agent configurations | Locked evaluator + benchmark epochs | Git lineage; outer kernel remains fixed |
| GEA | Agent variants and shared experience | Benchmark selection | |
| Yunjue Agent | Executable tools and their versions | Binary execution feedback | |
| CORAL | Agent organizations, shared skills, attempts, and policies | Grader-scored commits | |
| OpenRSI / OpenMLE | Executable tasks, SFT/RL operators, programs, model weights, and evolutionary search | Execution feedback + held-out transfer evaluation | |
| A-Evolve | Agent programs and evolutionary strategy configuration | Pluggable evaluators + archive | |
| Exo | Prompts, memory, tools, policy, harness code | Immutable event history; experimental | Recursive harness runtime |
| Yoyo Evolve | Its own Rust source | Tests + scheduled promotion | Public GitHub evolution history |
These projects evolve a narrower persistent layer. Entries with Memory only are included as infrastructure, not claimed as recursive self-improvement.
| Project | Persistent surface | Gate | Evidence |
|---|---|---|---|
| SkillOpt | Natural-language skills | Held-out validation + promotion | |
| SkillHone | Whole skill packages | Evaluation + Git-native decisions | |
| EvoSkill | Skills and prompts synthesized from failures | Held-out evaluation | |
| Hermes Agent Self-Evolution | Skills, tool descriptions, system prompts, code | Tests, size limits, benchmark selection | DSPy + GEPA optimizer |
| AutoContext | Lessons, playbooks, datasets, training artifacts | Task evaluation + retained successes | Recursive improvement runs |
| Agent Digivolve Harness | Versioned agent configuration | Fixed eval + baseline + keep/revert | Bounded mutation and resumable runs |
| Auto Agent | Prompt, tools, and agent implementation | Immutable golden eval + rollback | Branch-per-hypothesis workflow |
| Recursive Improve | Agent versions generated from traces | Metrics + version comparison | Trace-driven dashboard |
| AgentDescent | Skills, prompts, and harness artifacts | Parallel evaluation + versioned candidates | Asynchronous optimizer |
| Evolver | Genes, reusable capsules, and personality | Versioned events + rollback | |
| Raven | Memory, skills, tools, and policies | Versioned traces + Evolver evaluations | Memory-first harness |
| Hivemind | Reusable skills distilled from traces | Trace feedback and skill propagation | Cross-agent learning layer |
| Rudder | Team feedback and reviewed skills | Human review + local benchmark | Does not auto-promote every lesson |
| GenericAgent | Executable skill tree | Task success; versioned | Minimal growing agent |
| OpenViking | Memory, knowledge, context, and skills | Versioned context + benchmarks | Context database, not a full agent loop |
| Hive | Reflexion memory, skills, and playbooks | Memory only | Production multi-agent harness |
| Voyager | Executable skill library | Environment feedback | |
| SEAL | Model weights through generated update data | Reward-guided training | |
| RewardHarness | Reward tools and skills | Preference accuracy + downstream RL | |
| SkillWeaver | Web-agent skill library | Environment execution feedback | |
| CoEvoSkills | Skills and their verification process | Co-evolutionary verification | |
| Skill Self-Play | Skills and task challenges | Co-evolving self-play evaluation | |
| OpenSkill | Skills and self-created verification signals | Open-world verification | |
| ACE | Context and reusable playbooks | Execution feedback + context evolution | |
| MemRL | Episodic-memory selection policy | Runtime reinforcement learning | |
| Mem²Evolve | Capability memory and distilled experience | Cross-task evaluation | |
| WebEvolver | Web-agent policy and world model | Co-evolving simulated and real experience | |
These run closed experimentation loops, but usually improve a target program, scientific artifact, or domain policy rather than the general harness itself.
| Project | Optimized artifact | Selection signal |
|---|---|---|
| autoresearch | ML training code | Fixed validation metric + keep/revert |
| OpenEvolve | Arbitrary programs | User-supplied executable evaluators |
| Distributed OpenEvolve | Arbitrary programs | Distributed evaluation and archive |
| AutoKernel | Triton GPU kernels | Correctness + performance benchmark |
| The AI Scientist | Ideas, experiments, papers, and reviews | Experimental results + reviewer model |
| AIDE | Machine-learning solutions | Tree search over validation scores |
| CORAL | Research code and agent organization | Grader-scored commits and shared experience |
| Agon | Research proposals and experiments | Executed experiments and file-based review |
| NanoResearch | Research policy, skills, and memory | Co-evolution loop; |
| EvoScientist | Research skills and memory | Reviewed AutoSkills; |
These repositories package the edit–measure–keep loop for particular coding agents or deployment environments. They are useful harnesses, although the loop itself is normally fixed.
- Pi Autoresearch - Autonomous experiment-loop extension for the Pi coding agent.
- Claude Autoresearch - Goal-directed modify–verify–keep/discard skill for Claude Code.
- Codex Autoresearch - Continuous modify–verify–retain/discard skill for Codex.
- Autoresearch at Home - Single-GPU autonomous research over NanoChat training.
- Research Loop - Bootstraps safe, auditable research loops inside existing Git repositories.
- Thoth - Dashboard-first orchestration runtime for autoresearch runs.
- goal.md - Generalizes autoresearch with constructed metrics and an explicit goal specification.
- STOP - Recursively improves a code optimizer; one of the clearest compact examples of optimizing the optimizer.
- ADAS - Searches for agent programs with a fixed meta-agent.
- AFlow - Searches over executable agent workflows.
- GEPA - Reflective Pareto evolution of prompts and other textual components.
- TextGrad - Textual feedback and gradients for compound AI systems.
- Eureka - Evolves reward programs using environment feedback.
- Self-Instruct - Generates persistent instruction-tuning data; a model-adaptation precursor rather than a harness.
- ICLR 2025 Workshop on Scaling Self-Improving Foundation Models - Research on self-improvement, feedback, evaluation, and scalable oversight.
- Awesome Self-Improving Agents - Broad collection of foundation-model and scaffold improvement work.
- Awesome Autoresearch - Autonomous experiment loops and descendants of
karpathy/autoresearch. - Awesome LLM Agent Optimization - Agent workflow, prompt, memory, and tool optimization papers.
- Awesome AI Scientist Papers - Automated scientific discovery systems and literature.
- Awesome Self-Evolving Coding Agents - Coding-focused self-evolution papers and systems.
- Prism Shadow's Awesome RSI - Research-oriented index of RSI methods and benchmarks.
- Token Rhythm's Awesome RSI - Evidence-oriented catalog spanning foundations, self-modification, open-ended search, limits, and governance.
- LobeHub Awesome RSI - Broad research map separating model-, harness-, coding-, and multi-agent-level self-improvement.
- Awesome Recursive Self-Improvement - Collection covering self-refinement, experience learning, automated curricula, and governed improvement loops.
- Awesome Reliable Self-Evolving Agents - Large companion catalog organized from output-level through criterion-level evolution.
- FrontisAI Awesome Self-Improving Agents - Survey collection focused on the path from self-evolution to meta-evolution.
- Awesome Self-Evolving Agents - Survey collection covering model, memory, tool, and architecture evolution.
Contributions are welcome. Please read CONTRIBUTING.md before opening a pull request. New entries should identify the persistent component that changes, the source of evaluation, and whether the improvement operator itself changes.