Papers Text Generation
“Text Generation” 태그가 달린 논문 6,012편 · 필터 해제
When State Becomes an Attack Surface: State-Semantic Injection in LLM-Driven Embodied Agents
Large Language Models (LLMs) have demonstrated capabilities in in-context learning, task decomposition, step-by-step reasoning, and code generation, driving their gradual evolution from text generation models into the co…
Text GenerationCode GenerationPosterText: Towards Unified Visual Text Generation and Editing for E-commerce Poster
Automated e-commerce poster design requires both high-quality poster generation and flexible editing of existing designs. However, most existing methods either target end-to-end poster generation or follow multi-stage de…
Reinforcement LearningText GenerationSpark-to-Paper: End-to-End Research Paper Generation as a Composable Skill
Turning a research idea into a complete paper requires more than text generation: the system must retrieve literature, design and execute experiments, revise claims according to evidence, produce publication-ready figure…
Paper generationText GenerationECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation
Electrocardiography (ECG) is one of the most widely used non-invasive tools for diagnosing cardiovascular disease, but transforming multi-lead ECG recordings into reliable clinical reports remains challenging. Automating…
Text GenerationBalancing Efficiency and Efficacy: Training-Free Attention-Guided Switching Between Explicit and Latent Thoughts for MLLMs
Reasoning in Multimodal Large Language Models (MLLMs) requires both fine-grained visual perception and rigorous logical deduction. Explicit text-based Chain-of-Thought (CoT) is computationally expensive and prone to visu…
Text GenerationBridging Online and Offline Handwriting via Differentiable Physical Rendering
Realistic handwritten text generation plays an important role in numerous applications, such as font design, biometric authentication, and robotic calligraphy. Existing methods are typically divided into two independent …
Text GenerationAURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling
Language remains an outlier in generative modeling: while images, video, and audio are increasingly modeled in continuous latent spaces, text generation still relies predominantly on discrete tokens. Existing continuous …
Text GenerationRelative Parameter Importance in Task-Agnostic Replay-Free Continual Learning
Achieving continual learning (CL) with deep neural networks requires balancing stability and plasticity while enabling knowledge transfer. In this work, we focus on offline learning algorithms under the constraints: (I) …
Incremental LearningText ClassificationContinual LearningText GenerationORCA: ORgan-Centroid Aggregation for Training-Free 3D CT Visual Token Compression
A 3D CT scan entering a vision-language model produces a long sequence of visual tokens, often thousands to tens of thousands per volume, and this sequence must be compressed before a language model can consume it. Token…
Visual Question AnsweringText GenerationFairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations
This work presents Fairness Pruning, a lightweight structural intervention method designed for the management and future mitigation of demographic bias in large language models (LLMs). As a foundational empirical validat…
General KnowledgeText GenerationLatent-Kernel Discrete Flow Maps for Few-Step Generation
Discrete diffusion and flow-matching models denoise a sequence over many steps, but to keep each step cheap, they factorize the transition across positions and decide every token independently. This makes few-step genera…
Text GenerationWhere Quality Breaks in Compressed Short-Text Generation: Staged Bottleneck Localization
Compressed short-text generators can fail in two different places: the codec may discard information before generation starts, or the latent generator may produce weak codes. Without separating these failure modes, resea…
Text GenerationPrefReward: Learning User Preference Matrix for Personalized Text Generation
Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. However, most existing personalization approaches rely on implicit re…
Text GenerationAttention-Guided Saliency Maps for Interpreting Visualization Literacy in VLMs
Understanding how vision-language models (VLMs) interpret data visualizations remains an open problem, and is increasingly important as these models are used for analytical tasks where reliable reasoning is essential. We…
Answer GenerationText GenerationDebiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling
As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI. Existing T2I evaluation metrics and mu…
Visual Question AnsweringReinforcement LearningText GenerationAdaptive Multi-Step Lookahead Decoding for Diffusion Language Models
Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternative to autoregressive decoding. Recent lookahead-based decoding methods improve …
Text GenerationLarge Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality
Large Language Models (LLMs) perform differently on identical programming tasks when prompted in different natural languages, a phenomenon known as language bias. While this behavior has been widely studied for general t…
Code GenerationText GenerationLaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models
Diffusion-based Large Language Models(DLLMs) enable parallel generation via Semi-Autoregressive (SAR) decoding in text generation. However, current methods suffer from severe operator-level redundancy: they recompute the…
Text GenerationLLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
Large language models (LLMs) excel at pattern recognition and text generation, but their capacity for abductive inference - inferring latent hypotheses that explain observed behavior - remains poorly understood. Here, we…
Text GenerationBeyond Parallel Tracking: Interactive Multi-Feature Fusion Drives Semantic Reconstruction from Non-invasive Brain Recordings
Continuous semantic reconstruction from non-invasive neural recordings remains limited by the representational mismatch between semantic feature spaces and neural coding patterns, which severely impedes cross-modal align…
Text Generation