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TontaubeV1 is an open-weight text-to-speech model released for local long-form generation, supporting English and German with zero-shot voice cloning and low-latency inference on GPUs.
This paper presents a unified benchmark comparing outline-driven frameworks for long-form generation with LLMs, evaluating outlines independently and finding that performance varies with framework-granularity match, supporting the decoupling of outline and writing stages.
This paper studies the trade-off between grounding and coverage in long-form hallucination reinforcement learning, proposing rubric-based rewards to represent required and optional information for questions. A soft combination of grounding, rubric coverage, and relevance yields the best balance between support and richness.
This paper introduces a two-level meta-rubric framework for evaluating factual completeness in open-ended generation, instantiated as the GAMUT benchmark. It features 1,813 questions across 10 domains and finds the benchmark challenging and discriminative, with top models scoring 58.7%.
Introduces SALT, a benchmark with deterministic ground truth for evaluating LLM uncertainty at fine-grained atomic levels in long-form generation. Analysis of over 50 LLMs reveals insights into confidence functions, error propagation, and trade-offs with reasoning.
ScaffoldAgent introduces a utility-guided dynamic outline optimization framework for open-ended deep research, using expansion, contraction, and revision operations to improve long-form report generation and factual grounding.
POLARIS is a training recipe using GRPO with LLM-as-judge rewards and human-reference injection to improve long-form story generation in small models. Applied to Qwen3.5-9B, the resulting POLARIS-9B model matches Qwen3.5-27B performance on creative writing benchmarks while better adhering to length instructions.
This paper introduces Micro-Macro Retrieval (M2R), a retrieve-while-generate framework that reduces hallucination in long-form LLM outputs by ensuring key information stays close to generated text. It uses curriculum learning-based reinforcement learning to train retrieval and grounding skills, showing effectiveness especially in lengthy contexts.