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cs.AI updates on arXiv.org

Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method CocoaBench: Evaluating Unified Digital Agents in the Wild MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis Efficient Training for Cross-lingual Speech Language Models Shared Emotion Geometry Across Small Language Models: A Cross-Architecture Study of Representation, Behavior, and Methodological Confounds A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities Uncertainty-Aware Web-Conditioned Scientific Fact-Checking When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies When Verification Fails: How Compositionally Infeasible Claims Escape Rejection Mem$^2$Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation AOP-Smart: A RAG-Enhanced Large Language Model Framework for Adverse Outcome Pathway Analysis Advancing Polish Language Modeling through Tokenizer Optimization in the Bielik v3 7B and 11B Series TInR: Exploring Tool-Internalized Reasoning in Large Language Models Do BERT Embeddings Encode Narrative Dimensions? A Token-Level Probing Analysis of Time, Space, Causality, and Character in Fiction Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models Learning and Enforcing Context-Sensitive Control for LLMs Efficient Process Reward Modeling via Contrastive Mutual Information Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment Bridging Linguistic Gaps: Cross-Lingual Mapping in Pre-Training and Dataset for Enhanced Multilingual LLM Performance Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models LLMs Should Incorporate Explicit Mechanisms for Human Empathy
Exploring Collatz Dynamics with Human-LLM Collaboration
Edward Y. Chang · 2026-03-10 · via cs.AI updates on arXiv.org

We present a comprehensive structural analysis of the Collatz conjecture through ~1014 computational experiments yielding 630 formal results. By systematically deploying 29 distinct mathematical paradigms--including transfer operator spectral theory, S-unit equations, p-adic interpolation, martingale methods, modular sieving, formal language theory, cascade algebra, discrete logarithm obstruction, and Diophantine approximation--we establish a Paradigm Exhaustion Theorem: every known framework for promoting distributional convergence ("almost all orbits descend") to pointwise convergence ("all orbits descend") encounters an irreducible structural obstruction when applied to the Syracuse map. On the unconditional side, we prove: (i) the Syracuse transfer operator has a uniform spectral gap for all M, implying equidistribution modulo any power of 2; (ii) any nontrivial cycle of length L satisfies D > 2^F for all L >= 3, giving ord_D(2) > F and F+1 distinct residues mod D; (iii) divergent starting points have natural density 0 and Hausdorff dimension ~0.68; (iv) the formal language of divergent-compatible v-sequences is not context-free; (v) cylinder-averaged density-1 convergence is proved unconditionally via spectral contraction on the invariant core I_2; (vi) a discrete logarithm triple filter achieves 100% cycle blockage for all L tested. We identify the Distributional-to-Pointwise Gap as the irreducible core and prove it equivalent to the divergence component. The modular sieve is permanently nonempty via the Mersenne Bypass. The present work is not a proof of the Collatz conjecture; it characterizes why the conjecture resists proof. The 29-paradigm exhaustion constitutes the most comprehensive structural survey of Collatz attack surfaces to date. Produced through human-LLM collaboration; see Section 12.