ICLR 2026 论文列表

Reinforcement learning with verifiable rewards (RLVR) succeeds in reasoning tasks (e.g., math and code) by checking the final verifiable answer (i.e., a verifiable dot signal). However, extending this paradigm to open-ended generation is challenging because there is no unambiguous ground truth. Relying on single-dot supervision often leads to inefficiency and reward hacking. To address these issues, we propose reinforcement learning with verifiable reference-based rewards (RLVRR). Instead of checking the final answer, RLVRR extracts an ordered linguistic signal from high-quality references (i.e, reward chain). Specifically, RLVRR decomposes rewards into two dimensions: content, which preserves deterministic core concepts (e.g., keywords), and style, which evaluates adherence to stylistic properties through LLM-based verification. In this way, RLVRR combines the exploratory strength of RL with the efficiency and reliability of supervised fine-tuning (SFT). Extensive experiments on more than 10 benchmarks with Qwen and Llama models confirm the advantages of our approach. RLVRR (1) substantially outperforms SFT trained with ten times more data and advanced reward models, (2) unifies the training of structured reasoning and open-ended generation, and (3) generalizes more effectively while preserving output diversity. These results establish RLVRR as a principled and efficient path toward verifiable reinforcement learning for general-purpose LLM alignment.

Auto-regressive (AR) models have recently made notable progress in image generation, achieving performance comparable to diffusion-based approaches. However, their computational intensity and sequential nature impede on-device deployment, causing disruptive latency. We address this via a cloud-device collaboration framework \textbf{CIAR}, which utilizes on-device self-verification to handle two key properties of visual synthesis: \textit{the vast token vocabulary} required for high-fidelity images and \textit{inherent spatial redundancy} which leads to extreme predictability in homogeneous regions, while object boundaries exhibit high uncertainty. Uniform verification wastes resources on such redundant tokens. Our solution centers on an on-device token uncertainty quantifier, which adopts continuous probability intervals to accelerate processing and make it feasible for large visual vocabularies instead of conventional discrete solution sets. Additionally, we incorporate a Interval-enhanced decoding module to further speed up decoding while maintaining visual fidelity and semantic consistency via a distribution alignment training strategy. Extensive experiments demonstrate that CIAR achieves a 2.18× speed-up and reduces cloud requests by 70\%, while preserving image quality compared to existing methods.

The rapid advancement of video generation models has enabled the creation of highly realistic synthetic media, raising significant societal concerns regarding the spread of misinformation. However, current detection methods suffer from critical limitations. They often rely on preprocessing operations like fixed-resolution resizing and cropping, which not only discard subtle, high-frequency forgery artifacts but can also cause distortion and significant information loss. Furthermore, these methods are frequently trained and evaluated on outdated datasets that fail to capture the sophistication of modern generative models. To address these challenges, we introduce two key contributions: a new large-scale dataset and benchmark, as well as a novel detection framework. We present a comprehensive dataset of over 140K videos from 15 state-of-the-art open-source and leading commercial generators. Specifically, we curate the Magic Videos Testset, featuring ultra-realistic videos produced by six latest generators through a meticulous generation and filtering pipeline. In addition, we propose a novel detection framework built on the Qwen2.5-VL Vision Transformer, which processes videos at their native spatial resolution and temporal duration. This native-scale approach preserves high-frequency details and spatiotemporal inconsistencies that are often lost during conventional preprocessing. Extensive experiments show that our method achieves state-of-the-art performance across multiple benchmarks. Our work underscores the importance of native-scale processing and establishes a robust new baseline for AI-generated video detection.

Existing video-to-audio (V2A) generation methods predominantly rely on text prompts alongside visual information to synthesize audio. However, two critical bottlenecks persist: semantic granularity gaps in training data (e.g., conflating acoustically distinct sounds like different dog barks under coarse labels), and textual ambiguity in describing microacoustic features (e.g., "metallic clang" failing to distinguish impact transients and resonance decay). These bottlenecks make it difficult to perform fine-grained sound synthesis using text-controlled modes. To address these limitations, we propose **AC-Foley**, an audio-conditioned V2A model that directly leverages reference audio to achieve precise and fine-grained control over generated sounds. This approach enables: fine-grained sound synthesis (e.g., footsteps with distinct timbres on wood, marble, or gravel), timbre transfer (e.g., transforming a violin’s melody into the bright, piercing tone of a suona), zero-shot generation of sounds (e.g., creating unique weapon sound effects without training on firearm datasets) and better audio quality. By directly conditioning on audio signals, our approach bypasses the semantic ambiguities of text descriptions while enabling precise manipulation of acoustic attributes. Empirically, AC-Foley achieves state-of-the-art performance for Foley generation when conditioned on reference audio, while remaining competitive with SOTA video-to-audio methods even without audio conditioning.

Existing diffusion models excel at generating diverse content, but remain confined to fixed image shapes and lack the ability to flexibly control spatial attributes such as viewpoint, field-of-view (FOV), and resolution. To fill this gap, we propose Arbitrary-Shaped Image Generation (ASIG), the first generative framework that enables precise spatial attribute control while supporting high-quality synthesis across diverse image shapes (e.g., perspective, panoramic, and fisheye). ASIG introduces two key innovations: (1) a mesh-based spherical latent diffusion to generate a complete scene representation, with seam enforcement denoising strategy to maintain semantic and spatial consistency across viewpoints; and (2) a spherical neural field to sample arbitrary regions from the scene representation with coordinate conditions, enabling distortion-free generation at flexible resolutions. To this end, ASIG enables precise control over spatial attributes within a unified framework, enabling high-quality generation across diverse image shapes. Experiments demonstrate clear improvements over prior methods specifically designed for individual shapes.

Agentic tasks, which require multistep problem solving with tool use and adaptive reasoning, are becoming increasingly central to the advancement of NLP and AI. Although benchmarks such as GAIA and BrowseComp have advanced agent evaluation, their scalability remains limited by the high cost of human annotation. We introduce TaskCraft, the first automated workflow for generating scalable, multitool, and verifiable agentic tasks of difficulty. TaskCraft progressively complexifies atomic tasks through depth-based and width-based extensions, with incremental validation via rejection sampling and LLM-based linguistic analysis, ensuring both scalability and efficiency. The generated tasks enable trajectory sampling within state-of-the-art workflows, supporting end-to-end SFT and RL training. Experimental results on multiple LLMs show that TaskCraft data substantially improves multi-hop reasoning and agentic capabilities. Further scaling with TaskCraft tasks and applying RL training yields additional gains, achieving state-of-the-art performance on four agentic benchmarks. The resulting dataset comprises 41k tool-intensive tasks across varied difficulty levels, including 12.6k tool-interaction trajectories and 5k multihop decompositions.

Neural vocoders have recently advanced waveform generation, yielding natural and expressive audio. Among these approaches, iSTFT-based vocoders have recently gained attention. They predict a complex-valued spectrogram and then synthesize the waveform via iSTFT, thereby avoiding learned upsampling stages that can increase computational cost. However, current approaches use real-valued networks that process the real and imaginary parts independently. This separation limits their ability to capture the inherent structure of complex spectrograms. We present ComVo, a Complex-valued neural Vocoder whose generator and discriminator use native complex arithmetic. This enables an adversarial training framework that provides structured feedback in complex-valued representations. To guide phase transformations in a structured manner, we introduce phase quantization, which discretizes phase values and regularizes the training process. Finally, we propose a block-matrix computation scheme to improve training efficiency by reducing redundant operations. Experiments demonstrate that ComVo achieves higher synthesis quality than comparable real-valued baselines, and that its block-matrix scheme reduces training time by 25%. Audio samples and code are available at https://hs-oh-prml.github.io/ComVo/.

Recent advances in the audio language modeling (ALM) domain tackle audio understanding and text-to-audio generation as separate tasks. Very few studies attempt to unify these tasks -- an essential step toward advanced multimodal reasoning. This paper introduces Unified Audio Language Model (UALM), which aims to unify audio understanding, text-to-audio generation, and multimodal reasoning in a single model. To achieve this goal, we first present UALM-Gen, a text-to-audio language model that directly predicts audio tokens and is comparable to state-of-the-art diffusion-based models. We then demonstrate, using proper data blending, training recipes, and inference techniques, that our single UALM model matches the quality of state-of-the-art specialized models in audio understanding, text-to-audio generation, and text reasoning. Furthermore, we present UALM-R1, a multimodal reasoning model that utilizes both text and audio in the intermediate thinking steps to facilitate complex generation tasks. To our knowledge, this is the first demonstration in audio research of cross-modal generative reasoning, with its effectiveness confirmed by subjective evaluations.

Cover songs constitute a vital aspect of musical culture, preserving the core melody of an original composition while reinterpreting it to infuse novel emotional depth and thematic emphasis. Although prior research has explored the reinterpretation of instrumental music through melody-conditioned text-to-music models, the task of cover song generation remains largely unaddressed. In this work, we reformulate our cover song generation as a conditional generation, which simultaneously generates new vocals and accompaniment conditioned on the original vocal melody and text prompts. To this end, we present SongEcho, which leverages Instance-Adaptive Element-wise Linear Modulation (IA-EiLM), a framework that incorporates controllable generation by improving both conditioning injection mechanism and conditional representation. To enhance the conditioning injection mechanism, we extend Feature-wise Linear Modulation (FiLM) to an Element-wise Linear Modulation (EiLM), to facilitate precise temporal alignment in melody control. For conditional representations, we propose Instance-Adaptive Condition Refinement (IACR), which refines conditioning features by interacting with the hidden states of the generative model, yielding instance-adaptive conditioning. Additionally, to address the scarcity of large-scale, open-source full-song datasets, we construct Suno70k, a high-quality AI song dataset enriched with comprehensive annotations. Experimental results across multiple datasets demonstrate that our approach generates superior cover songs compared to existing methods, while requiring fewer than 30% of the trainable parameters. The code, dataset, and demos are available at [https://github.com/lsfhuihuiff/SongEcho_ICLR2026](https://github.com/lsfhuihuiff/SongEcho_ICLR2026).

Generating realistic, context-aware two-person motion conditioned on diverse modalities remains a fundamental challenge for graphics, animation and embodied AI systems. Real-world applications such as VR/AR companions, social robotics and game agents require models capable of producing coordinated interpersonal behavior while flexibly switching between interactive and reactive generation. We introduce DualFlow, the first unified and efficient framework for multi-modal two-person motion generation. DualFlow conditions 3D motion generation on diverse inputs, including text, music, and prior motion sequences. Leveraging rectified flow, it achieves deterministic straight-line sampling paths between noise and data, reducing inference time and mitigating error accumulation common in diffusion-based models. To enhance semantic grounding, DualFlow employs a novel Retrieval-Augmented Generation (RAG) module for two-person motion that retrieves motion exemplars using music features and LLM-based text decompositions of spatial relations, body movements, and rhythmic patterns. We use contrastive rectified flow objective to further sharpen alignment with conditioning signals and add synchronization loss to improve inter-person temporal coordination. Extensive evaluations across interactive, reactive, and multi-modal benchmarks demonstrate that DualFlow consistently improves motion quality, responsiveness, and semantic fidelity. DualFlow achieves state-of-the-art performance in two-person multi-modal motion generation, producing coherent, expressive, and rhythmically synchronized motion. Code will be released upon acceptance.

This paper introduces LLM2Fx-Tools, a multimodal tool-calling framework that generates executable sequences of audio effects (Fx-chain) for music post-production. LLM2Fx-Tools uses a large language model (LLM) to understand audio inputs, select audio effects types, determine their order, and estimate parameters, guided by chain-of-thought (CoT) planning. We also present LP-Fx, a new instruction-following dataset with structured CoT annotations and tool calls for audio effects modules. Experiments show that LLM2Fx-Tools can infer an Fx-chain and its parameters from pairs of unprocessed and processed audio, enabled by autoregressive sequence modeling, tool calling, and CoT reasoning. We further validate the system in a style transfer setting, where audio effects information is transferred from a reference source and applied to new content. Finally, LLM-as-a-judge evaluation demonstrates that our approach generates appropriate CoT reasoning and responses for music production queries. To our knowledge, this is the first work to apply LLM-based tool calling to audio effects modules, enabling interpretable and controllable music production.

Controllable music generation remains a significant challenge, with existing methods often requiring model retraining or introducing audible artifacts. We introduce MusicRFM, a framework that adapts Recursive Feature Machines (RFMs) to enable fine-grained, interpretable control over frozen, pre-trained music models by directly steering their internal activations. RFMs analyze a model's internal gradients to produce interpretable "concept directions", or specific axes in the activation space that correspond to musical attributes like notes or chords. We first train lightweight RFM probes to discover these directions within MusicGen's hidden states; then, during inference, we inject them back into the model to guide the generation process in real-time without per-step optimization. We present advanced mechanisms for this control, including dynamic, time-varying schedules and methods for the simultaneous enforcement of multiple musical properties. Our method successfully navigates the trade-off between control and generation quality: we can increase the accuracy of generating a target musical note from 0.23 to 0.82, while text prompt adherence remains within approximately 0.02 of the unsteered baseline, demonstrating effective control with minimal impact on prompt fidelity.

Audio Language Models (ALM) have emerged as the dominant paradigm for speech and music generation by representing audio as sequences of discrete tokens. Yet, unlike text tokens, which are invertible, audio tokens are extracted from lossy codecs with a limited bitrate. As a consequence, increasing audio quality requires generating more tokens, which imposes a trade-off between fidelity and computational cost. We address this issue by studying Continuous Audio Language Models (CALM). These models instantiate a large Transformer backbone that produces a contextual embedding at every timestep. This sequential information then conditions an MLP that generates the next continuous frame of an audio VAE through consistency modeling. By avoiding lossy compression, CALM achieves higher quality at lower computational cost than their discrete counterpart. Experiments on speech and music demonstrate improved efficiency and fidelity over state-of-the-art discrete audio language models, facilitating lightweight, high-quality audio generation. Samples are available at iclr-continuous-audio-language-models.github.io. Finally, we release Pocket TTS, an open-source 100M-parameter text-to-speech model that can run faster than real time on a laptop CPU: github.com/kyutai-labs/pocket-tts.

Computer-Aided Design (CAD), renowned for its flexibility and precision, serves as the foundation of digital design. Recently, some efforts adopt Large Language Models (LLMs) for generating parametric CAD command sequences from text instructions. However, our study reveals that LLMs pre-trained on large-scale general data are not proficient at directly outputting task-specific CAD sequences. Instead of relying on direct generation, we introduce a Plan then Act process where user instructions are first parsed into a chain-like operational plan via an LLM, which is then used to generate accurate command sequences. Specifically, we propose PTA, a new bi-level CAD command sequence generation method. The PTA consists of two critical stages: high-level plan generation and low-level command generation. During the high-level stage, an LLM-based Planner completes the planning process, parsing user instructions into a high-level operation plan. Following this, at the low-level generation stage, we introduce an Actioner equipped with a requirement-aware mechanism to extract design requirements (e.g., dimensions, geometric relationships) from user instructions. This extracted information is used to guide the low-level command sequence generation, improving the alignment of the generated sequences with user requirements. Experimental results demonstrate that our PTA outperforms existing methods in both quantitative and qualitative evaluations. Our source code will be made publicly available.

Unified multimodal models (UMMs) have emerged as a powerful paradigm in fundamental cross-modality research, demonstrating significant potential in both image understanding and generation. However, existing research in the face domain primarily faces two challenges: **(1) fragmentation development**, with existing methods failing to unify understanding and generation into a single one, hindering the way to artificial general intelligence. **(2) lack of fine-grained facial attributes**, which are crucial for high-fidelity applications. To handle those issues, we propose UniF$^2$ace, the first UMM specifically tailored for fine-grained face understanding and generation. **First**, we introduce a novel theoretical framework with a Dual Discrete Diffusion (D3Diff) loss, unifying masked generative models with discrete score matching diffusion and leading to a more precise approximation of the negative log-likelihood. Moreover, this D3Diff significantly enhances the model's ability to synthesize high-fidelity facial details aligned with text input. **Second**, we propose a multi-level grouped Mixture-of-Experts architecture, adaptively incorporating the semantic and identity facial embeddings to complement the attribute forgotten phenomenon in representation evolvement. **Finally**, to this end, we construct UniF$^2$aceD-1M, a large-scale dataset comprising *130K* fine-grained image-caption pairs and *1M* visual question-answering pairs, spanning a much wider range of facial attributes than existing datasets. Extensive experiments demonstrate that UniF$^2$ace outperforms existing models with a similar scale in both understanding and generation tasks, with 7.1% higher Desc-GPT and 6.6% higher VQA-score, respectively. Code is available in the supplementary materials.

We present Aurelius, a new framework that enables relation aware text-to-audio (TTA) generation research at scale. Given the lack of essential audio event and relation corpora, \emph{Aurelius} contributes a large-scale audio event corpus \emph{AudioEventSet} and another large-scale relation corpus \emph{AudioRelSet}. Comprising 110 event categories, AudioEventSet maximally covers all commonly heard audio events and each event is unique, realistic and of high-quality. AudioRelSet consists of 100 relations, comprehensively covering the relations that present in the physical world or can be neatly described by text. As the two corpora provide audio event and relation independently, they can be combined to create massive <text,audio> pairs with our pair generation strategy to support relation aware TTA investigation at scale. We comprehensively benchmark all existing TTA models from both general and relation aware evaluation perspective. We further provide in-depth investigation on scaling up existing TTA models' relation aware generation by either training from scratch or leveraging cross-domain general TTA knowledge. The introduced corpora and the findings through investigation in this work potentially facilitate future research on relation aware TTA generation.

Automatic tool generation (ATG) has emerged as a key approach to enable the automatic adaptation across diverse tasks within a single generalist agent. Despite their potential, we argue that current ATG agents, often built on reactive paradigms, fail to effectively adapt to realistic environments requiring long-term reasoning and stateful interaction, particularly in office ecosystems. We empirically show that current ATG agents underperform by up to 27.43\%. This performance degradation stems from three fundamental limitations of prevailing agent paradigms: (1) a failure to build a coherent world model from long, partially observable contexts; (2) a memory-less execution model where stateless actions fail to track state evolution during iterative tasks; and (3) a static capability generation model focusing on one-shot tool generation for immediate needs, thereby forcing redundant regeneration for similar steps. To address these fundamental limitations, we propose ROGA, which instantiates a new agent paradigm for long-horizon, stateful environments. ROGA moves beyond simple reactive loops by introducing four foundational algorithmic innovations: (1) \textbf{Active World Modeling}, an iterative process where the agent actively probes the environment to construct its own world model; (2) a \textbf{Persistent Symbolic Memory} that explicitly tracks the state evolution for temporal reasoning; and (3) a \textbf{Dynamic Capability Evolution} model for long-term adaptation and meta-learning on the agent's own capabilities. Comprehensive experiments on widely used benchmarks show that ROGA consistently outperforms existing ATG agents by up to 13.64\%. These results underscore ROGA's potential to advance the ATG paradigm, delivering a practical pathway toward building sustainable generalist agents in realistic environments.

Academic posters are vital for scholarly communication, yet their manual creation is time-consuming. However, automated academic poster generation faces significant challenges in preserving intricate scientific details and achieving effective visual-textual integration. Existing approaches often struggle with semantic richness, structural nuances, and lack standardized benchmarks for evaluating generated academic posters comprehensively. To address these limitations, we introduce P2P, the first flexible, LLM-based multi-agent framework that generates high-quality, HTML-rendered academic posters directly from research papers. P2P employs three specialized agents—for visual element processing, content generation, and final poster assembly—each integrated with dedicated checker modules to enable iterative refinement and ensure output quality. To foster advancements and rigorous evaluation in this domain, we argue that generated posters must be assessed from two complementary perspectives: objective fidelity and subjective quality. So we establish P2Peval, a comprehensive benchmark featuring 1738 checklist items and a dual evaluation methodology (Fine-Grained and Universal). Our Fine-Grained Evaluation uses human-annotated checklists to objectively measure the faithful preservation of verifiable content from the source paper. Concurrently, our Universal Evaluation captures subjective, holistic quality by training a model to align with human aesthetic preferences across key design principles. We evaluate a total of 35 models. To power these advancements, we also release P2Pinstruct, the first large-scale instruction dataset comprising over 30,000 high-quality examples tailored for the academic paper-to-poster generation task. Furthermore, our contributions aim to streamline research dissemination while offering a principled blueprint for evaluating complex, creative AI-generated artifacts. The code is on the anonymous GitHub, https://anonymous.4open.science/r/c-163Canonymous/README.md.

Multi-track music generation has garnered significant research interest due to its precise mixing and remixing capabilities. However, existing models often overlook essential attributes such as rhythmic stability and synchronization, leading to a focus on differences between tracks rather than their inherent properties. In this paper, we introduce SyncTrack, a synchronous multi-track waveform music generation model designed to capture the unique characteristics of multi-track music. SyncTrack features a novel architecture that includes track-shared modules to establish a common rhythm across all tracks and track-specific modules to accommodate diverse timbres and pitch ranges. Each track-shared module employs two cross-track attention mechanisms to synchronize rhythmic information, while each track-specific module utilizes learnable instrument priors to better represent timbre and other unique features. Additionally, we enhance the evaluation of multi-track music quality by introducing rhythmic consistency through three novel metrics: Inner-track Rhythmic Stability (IRS), Cross-track Beat Synchronization (CBS), and Cross-track Beat Dispersion (CBD). Experiments demonstrate that SyncTrack significantly improves the multi-track music quality by enhancing rhythmic consistency. Code is available at https://anonymous.4open.science/r/SyncTrack/.