#sequence-generation News & Analysis

7 articles tagged with #sequence-generation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

7 articles

AINeutralarXiv – CS AI · 5d ago6/10

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Variational Learning for Insertion-based Generation

Researchers introduce the Insertion Process (IP), a novel generative model that learns optimal insertion orders for variable-length sequence generation, moving beyond fixed-length masked diffusion approaches. The framework uses permutation-based variational inference to jointly optimize what, where, and when to insert tokens, demonstrating improvements in goal-conditioned planning and molecular generation tasks.

AINeutralarXiv – CS AI · May 275/10

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BrickAnything: Geometry-Conditioned Buildable Brick Generation with Structure-Aware Tokenization

BrickAnything is a new AI framework that generates physically buildable brick structures from 3D shapes by combining geometric reconstruction with structural constraints. The method uses structure-aware tokenization to model how bricks attach to each other, improving the feasibility and stability of generated designs compared to existing heuristic approaches.

AINeutralarXiv – CS AI · May 115/10

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Exact Regular-Constrained Variable-Order Markov Generation via Sparse Context-State Belief Propagation

Researchers present a novel computational method for generating sequences constrained by regular automata using variable-order Markov models. The advancement eliminates the need to expand full K-tuple state spaces while maintaining exact inference, achieving linear complexity for fixed models and enabling efficient constrained sequence generation across applications.

AINeutralarXiv – CS AI · May 116/10

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Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning

Researchers introduce Prune-OPD, a framework that optimizes on-policy distillation for AI reasoning models by detecting when student predictions diverge from teacher guidance and dynamically truncating unreliable training sequences. The method reduces training time by 37-68% on challenging math benchmarks while maintaining or improving performance.

AIBullisharXiv – CS AI · May 16/10

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Simple Self-Conditioning Adaptation for Masked Diffusion Models

Researchers propose Self-Conditioned Masked Diffusion Models (SCMDM), a post-training adaptation that improves discrete sequence generation by conditioning each denoising step on previous predictions rather than discarding them. The method achieves nearly 50% perplexity reduction on language models and demonstrates improvements across image synthesis, molecular generation, and genomic modeling without requiring architectural changes or extra computational costs.

🏢 Perplexity

AIBullisharXiv – CS AI · Mar 36/104

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RL for Reasoning by Adaptively Revealing Rationales

Researchers introduce AdaBack, a new reinforcement learning algorithm that uses partial supervision to help AI models learn complex reasoning tasks. The method dynamically adjusts the amount of guidance provided to each training sample, enabling models to solve mathematical reasoning problems that traditional supervised learning and reinforcement learning methods cannot handle.

AIBullisharXiv – CS AI · Mar 44/103

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Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration

Researchers propose DiSE, a self-evaluation method for diffusion large language models (dLLMs) that quantifies confidence by computing token regeneration probabilities. The method enables more efficient quality assessment and introduces a flexible-length generation framework that adaptively controls sequence length based on the model's self-assessment.