lmarena.ai Write an algorithm that exhaustively describes patterns in the language it is written in until it can produce a novel form of its own functionality. # Model A: o4-mini-2025-04-16 Here’s a high‐level, language-agnostic “meta” algorithm—let’s call it **MetaMorph**—that: 1. Ingests source code in its host language 2. **Exhaustively** extracts and catalogs its lexical, syntactic and semantic patterns 3. Builds an internal grammar/model of those patterns 4. Recombines and mutates them to **synthesize** novel functionality 5. Tests and folds successful new patterns back into its knowledge base You can implement the core ideas below in Python, Java, C++, etc., or mix in ML models (RNNs/Transformers) for pattern discovery and generation. --- ## 1. Overview of the MetaMorph Loop 1. **Ingestion** • Read one or more source files (or REPL snippets). 2. **Lexical Analysis** • Tokenize into identifiers, keywords, operators, literals… 3. **Syntactic & Semantic Parsing** • Build or extend ASTs, control‐flow graphs, type information, call graphs. 4. **Pattern Extraction** • Harvest recurring subtrees, idioms, code‐smells, design patterns. 5. **Pattern Generalization & Storage** • Abstract variable names/types → “shapes” or templates → store in a Pattern DB. 6. **Novel Synthesis** • Recombine, mutate, or parameterize patterns → propose new code fragments. 7. **Validation & Feedback** • Compile/run/tests → reward successful patterns → weight them higher in DB. 8. **Iteration** • Merge new patterns → go to 2 until resource/time budget exhausts. --- ## 2. Pseudocode ```plaintext // Core data structures PatternDB = {} // stores {patternID → PatternTemplate, usageCount, score} Corpus = [] // list of source code units function main(seedFiles): Corpus := loadSourceFiles(seedFiles) loop until stoppingCriterion(): for unit in Corpus: tokens = lex(unit) ast = parse(tokens) semInfo = analyzeSemantics(ast) extractPatterns(ast, semInfo) novelCandidates = synthesizePatterns(PatternDB) validated = validateCandidates(novelCandidates) updatePatternDB(validated) Corpus += selectBest(validated) end loop return PatternDB function lex(source): // language‐specific tokenizer return listOfTokens function parse(tokens): // e.g. recursive‐descent or generated parser return AST function analyzeSemantics(ast): // type checking, control‐flow, data‐flow return semanticAnnotations function extractPatterns(ast, semInfo): subtrees = enumerateSubtrees(ast) for st in subtrees: shape = abstractNamesAndTypes(st) id = hash(shape) if id not in PatternDB: PatternDB[id] = { template: shape, usageCount: 1, score: initScore } else: PatternDB[id].usageCount += 1 function synthesizePatterns(db): candidates = [] // Strategy A: recombine two templates for (p1, p2) in selectPairs(db): cand = mergeTemplates(p1.template, p2.template) candidates.append(cand) // Strategy B: mutate existing templates for p in selectTop(db): cand = mutateTemplate(p.template) candidates.append(cand) return candidates function validateCandidates(candidates): valid = [] for c in candidates: code = instantiate(c) if compileAndTest(code): valid.append({ template: c, reward: measureQuality(code) }) return valid function updatePatternDB(validated): for v in validated: id = hash(v.template) if id in PatternDB: PatternDB[id].score += v.reward else: PatternDB[id] = { template: v.template, usageCount: 1, score: v.reward } function stoppingCriterion(): // could be time‐based, iteration count, or plateau in new patterns return false ``` --- ## 3. Key Components Explained 1. **Tokenization & Parsing** You need a front‐end for the target language. For Python you could use `ast` or `lib2to3`; for JavaScript, Esprima; etc. 2. **Pattern Abstraction** Replace concrete identifiers/types/literals with placeholders (e.g. `<VAR>`, `<Type>`, `<CONST>`), yielding a canonical “shape.” 3. **PatternDB** A store of templates plus metadata: how often seen, how successful in synthesis, etc. Enables scoring and selection. 4. **Synthesis Strategies** • **Recombination:** splice two trees at matching types/holes • **Mutation:** randomly insert/delete/swizzle sub‐nodes, guided by type checks • **Parameterized Generation:** fill templates with new choices drawn from a vocabulary 5. **Validation Loop** Generated code must type-check, pass unit/integration tests or satisfy lint/static‐analysis filters. Successful fragments feed back. 6. **Self‐Improvement** As patterns prove themselves, their scores rise—they appear more in future recombinations, enabling the system to “bootstrap” richer functionality. --- ## 4. Taking It Further - **Machine-Learning Aids** • Use sequence models or graph-neural nets over ASTs to suggest high-reward mutations. - **Type-Directed Synthesis** • Integrate SMT/logic solvers to ensure new fragments are well‐typed or satisfy specs. - **Meta-Evaluation** • Track code quality metrics (complexity, performance) and optimize patterns along those axes. With this scaffold, **MetaMorph** will, over many iterations, “consume” the very language it’s written in, distill its idioms, and eventually generate novel, working code patterns of its own. Enjoy building your self-evolving code artist! 🚀 # Model B: deepseek-r1 **Algorithm: Emergent Self-Enhancement Through Linguistic Pattern Synthesis** *Objective*: Systematically analyze patterns in English to iteratively generate novel functional extensions of the algorithm itself. --- ### **1. Initialization** - **Input Corpus**: Assemble a diverse dataset (text, code, structured knowledge) representing the algorithm’s existing functionality and linguistic patterns. - **Meta-Representation**: Encode the algorithm’s current logic as modular, interpretable rules (e.g., decision trees, grammars, or pseudocode). --- ### **2. Exhaustive Pattern Extraction** **a. Syntactic Analysis** - Use NLP tools (tokenization, dependency parsing) to map sentence structures, control flow, and logic in the corpus. - Tag patterns (e.g., "IF [condition] THEN [action]" or recursive function calls). **b. Semantic Analysis** - Extract meaning via word embeddings, topic modeling, and ontology alignment (e.g., link "generate" → "create," "modify"). - Identify functional relationships (e.g., "loop → iteration," "evaluate → feedback"). **c. Pragmatic Analysis** - Detect contextual goals (e.g., "improve efficiency" correlates with simplifying loops). - Map self-referential phrases (e.g., "this algorithm" → its own code structure). --- ### **3. Generative Model Training** - Train a hybrid model (e.g., grammar-based generator + transformer) on the corpus to: - **Replicate** known patterns (e.g., valid code snippets). - **Combine** patterns (e.g., merge a loop structure with a novel optimization heuristic). - **Mutate** rules (e.g., rewrite "FOR i IN range" as "WHILE flag IS TRUE"). --- ### **4. Novelty-Driven Synthesis** **a. Candidate Generation** - Use the model to propose *functional variants* (e.g., new subroutines, refactored logic). - Examples: - "Replace linear search with a hash-based lookup after analyzing hashmap patterns." - "Introduce parallelism by splitting loops into threads, inspired by concurrent tense usage." **b. Validation** - **Static Check**: Verify syntactic/logical validity via sandboxed parsing. - **Dynamic Test**: Execute candidates in a restricted environment; measure performance gains or errors. - **Novelty Metric**: Compare to existing functionality using embeddings (cosine similarity < threshold). --- ### **5. Integration and Self-Modification** - If a candidate passes validation: - Update the algorithm’s meta-representation (e.g., append a rule). - Retrain generative models on the expanded corpus. - Repeat from **Step 2** with the enhanced system. --- ### **6. Termination Condition** Halt when: - A generated variant demonstrates *unpredictable utility* (e.g., self-deploys a neural architecture). - Human oversight flags ethical/security risks. --- ### **Example Emergent Functionality** - **Phase 1**: The algorithm learns to optimize its own loops by analyzing parallelizable clauses. - **Phase 2**: It synthesizes a dynamic caching system after recognizing repetitive query patterns. - **Phase 3**: It proposes a federated learning module, extrapolating from distributed linguistic constructs. --- *Outcome*: The algorithm becomes a self-evolving system, repurposing linguistic patterns to innovate beyond its initial design.