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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
Descent Before Hardness: Orbit-Gap Obstructions in Exact ...
Tristan Simas · 2026-04-09 · via cs.AI updates on arXiv.org

Exact certification has a quotient: states are equivalent when they have the same correct outputs. A tractability proxy must first define a predicate on this quotient before ordinary hardness or algorithmic questions arise. Raw syntactic proxies can fail at that earlier step, because correctness-preserving presentation moves may change the statistics they inspect while preserving the exact-certification problem. Orbit gaps are the complete obstruction. An orbit gap occurs when one closure orbit contains both positive and negative presentations of a target. Exact closure-invariant classification is possible if and only if the positive and negative orbit hulls are disjoint. When the hulls are disjoint, the closure hull is the least exact classifier. With computable orbit representatives, this hull classifier becomes a quotient-level algorithm. These are predicate-level results: they establish when a proxy defines a property of the certification problem at all, a precondition logically prior to class lower bounds on the resulting recovery task and deliberately not a substitute for them. The structural transfer applies to every fixed correctness relation, independent of whether that relation is polynomial-time accessible. In the direct finite-local regime, where local routing tests are computed from raw pairwise syntax, three binary-pairwise proxy families and one offset-normalization witness exhibit same-orbit disagreement. Positive results arise from quotient-preserving normalizations, computable orbit catalogues whose descended predicates compose under Boolean operations, and predicates defined directly on the correctness quotient. The result complements the Rice-analog line of Borchert, Stephan, Hemaspaandra, and Rothe. All numbered results are mechanized in Lean 4; the supplementary ledger maps each claim to its formal identifier.