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版本更新 · v2arXiv:2605.28063

Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts

Yuyue Wang, Xihua Wang, Xin Cheng, Yijing Chen, Ruihua Song

原文摘要

Generating unified audio that naturally integrates speech and sounds from free-form text prompts remains challenging. For example, the prompt ``A fast drum roll ends with a cymbal crash, after which a man shouts: `Come on!'.'' describes several events that should appear in order and form a coherent recording rather than independent tracks that lack coherent temporal and semantic interactions. Current methods broadly follow two paradigms: pipeline methods couple separate text-to-sound and text-to-speech models, while unified models rely on a single generator that still requires structured, multi-field prompt inputs. Both paradigms require upstream prompt decomposition or rewriting, limiting free-form flexibility and risking cascading errors. To enable direct generation from free-form prompts within a single model, we introduce PlanAudio, an end-to-end framework that maps unconstrained free-form text to unified audio without intermediate prompt preprocessing. To address the challenges of direct free-form generation, PlanAudio integrates latent planning into a single autoregressive framework, predicting continuous audio layout before acoustic token synthesis. We construct a large-scale training corpus and introduce Free-Form Unified Audio Generation Benchmark (FUAG) for evaluation, both spanning speech, sound, and joint speech-and-sound generation. Experiments show that a single PlanAudio outperforms existing pipeline and unified baselines on speech-and-sound generation, while remaining competitive with specialist models on speech and sound generation. Further analysis validates the effectiveness of audio layout planning over textual planning, acoustic planning, and direct generation. Sample Available: https://planaudio.github.io/PlanAudio2026/.

cs.SD · cs.AI · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.14917

BFS-PO: Best-First Search for Large Reasoning Models

Fiorenzo Parascandolo, Wenhui Tan, Enver Sangineto, Xiaoyi Yu, Ruihua Song, Rita Cucchiara

原文摘要

Large Reasoning Models (LRMs) have shown excellent performance in reasoning tasks using long Chains of Thought (CoTs). However, this has also led to a significant increase of computational costs and the generation of verbose output, a phenomenon known as overthinking. The tendency to overthink is often exacerbated by Reinforcement Learning (RL) algorithms such as GRPO/DAPO. In this paper, we propose BFS-PO, an RL algorithm which alleviates this problem using a Best-First Search exploration strategy. Specifically, the reasoning chains generated at training time by BFS-PO are organized into a search tree, within which the shortest correct sequence is selected as the best and expanded using a backtracking mechanism based on maximum entropy nodes. In this way, we bias the exploration of the solution space towards the search for increasingly shorter solutions, training the LRM to generate progressively more concise answers. Using different benchmarks and base LRMs, we show that BFS-PO can simultaneously increase the LRM accuracy and shorten its reasoning chains.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2509.24773

ViSSFlow: Unifying Video-Conditioned Sound and Speech Generation via Joint Learning

Xin Cheng, Yuyue Wang, Xihua Wang, Yihan Wu, Kaisi Guan, Yijing Chen, Peng Zhang, Xiaojiang Liu 等 10 位作者

原文摘要

Video-conditioned audio generation, including Video-to-Sound (V2S) and Visual Text-to-Speech (VisualTTS), has traditionally been studied as two separate tasks despite their shared objective of generating audio conditioned on visual content. Existing methods rely on task-specific architectures and training paradigms, while the possibility of a unified generative framework remains largely unexplored due to heterogeneous conditioning signals and limited joint training data. In this paper, we propose ViSSFlow, a unified flow-matching framework for both V2S and VisualTTS. To effectively integrate diverse conditioning modalities within a Diffusion Transformer (DiT), we introduce a disentangled condition aggregation mechanism that exploits the complementary roles of attention layers, using cross-attention to model semantic conditions and self-attention to capture temporally dense visual cues. Furthermore, contrary to the common assumption that joint optimization of V2S and VisualTTS causes task interference, we show that a single model can be trained end-to-end without sacrificing task-specific performance. To address the scarcity of paired training data covering both sound and speech generation, we further propose a simple yet effective feature-level data synthesis strategy that enables robust joint learning. Extensive experiments on V2S, VisualTTS, and joint generation benchmarks demonstrate that ViSSFlow consistently outperforms state-of-the-art task-specific baselines while providing a unified solution for video-conditioned audio generation. Code and demos are provided in Supplementary Material.

eess.AS · cs.AI · cs.CL · cs.CV · cs.SD

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.06352

CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks

Fanzhe Meng, Guoxin Chen, Jiale Zhao, Shuang Sun, Yuzhi Lin, Wayne Xin Zhao, Ruihua Song, Ji-Rong Wen 等 9 位作者

原文摘要

Training terminal agents requires executable and verifiable tasks that are not merely solvable, but appropriately challenging for learning. Executable validation establishes feasibility, yet does not reveal how a task behaves relative to a given solver setting. In this paper, we present CalibForge, an autonomous terminal-task synthesis system that uses verified solver behavior to revise candidate tasks through adversarial solver calibration. Multi-solver calibration targets disagreement within a heterogeneous solver pool, whereas contrastive solver calibration targets a designated strong-pass/weak-fail relation; both operationalize a solver-relative learnable zone anchored in demonstrated solvability. Using CalibForge, we construct 5,431 calibrated terminal tasks. Our ablations show that both strategies yield more effective supervision than authoring and validation alone or ordinary single-solver feedback. Models trained on the full collection achieve 31.84% and 47.57% on Terminal-Bench 2.1. The largest improvements over the corresponding base model reach 23.97 percentage points on Terminal-Bench 2.1, 26.54 points on SWE-bench Pro, and 30.04 points on Doc2Repo. Together, these results support solver-relative learnability as a practical target for constructing effective and transferable agent training data.

cs.LG · cs.CL

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.647ACL Anthology · ACL 2026

DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing

Qian Cao, Yahui Liu, Wei Bi Independent Researcher, Yi Zhao, Ruihua Song, Xiting Wang Renmin University of China, Ruiming Tang, Guorui Zhou Kuaishou- 快手科技 等 9 位作者

原文摘要

Reinforcement learning (RL)-based enhancement of large language models (LLMs) often leads to reduced output diversity, undermining their utility in open-ended tasks like creative writing. Current methods lack explicit mechanisms for guiding diverse exploration and instead prioritize optimization efficiency and performance over diversity. This paper proposes an RL framework structured around a semi-structured long Chain-of-Thought (CoT), in which the generation process is decomposed into explicitly planned intermediate steps. We introduce a Diverse Planning Branching method that strategically introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories. Experimental results on creative writing benchmarks demonstrate that our approach significantly improves output diversity without compromising generation quality, consistently outperforming existing baselines.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表