GenAI4ELab/papercli-papers huggingface dataset:
Hierarchical Value-Decomposed Offline Reinforcement Learning for Whole-Body Control
Authors: Zhilong Zhang; Yunpeng Mei; Xinghao Du; Hongjie Cao; Haonan Wang; Pengyuan Min; Chenyu Wang; Pengfei Chen; Chenbo Xin; Yijie Wang; Wenyu Luo; Yihao Sun; Yidi Wang; Lei Yuan; Gang Wang; Yang Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scaling imitation learning to high-DoF whole-body robots is fundamentally constrained by the scarcity of expert demonstrations. In contrast, large amounts of suboptimal data are readily available and offer a practical way to alleviate supervision bottlenecks in real-world whole-body control. However, leveraging such data introduces two central challenges: how to extract informative signals from imperfect trajectories, and how to cope with the increased learning complexity induced by high-dimensional control. To overcome this, we propose **HVD** (Hierarchical Value-Decomposed Offline Reinforcement Learning). The offline RL formulation provides principled data selection over suboptimal datasets, enabling the policy to prioritize high-value behaviors while down-weighting harmful ones. Complementarily, hierarchical value decomposition organizes learning along the robot’s kinematic structure, improving credit assignment and reducing learning complexity in high-DoF systems. Built on a Transformer-based architecture, HVD supports *multi-modal* and *multi-task* learning, allowing flexible integration of diverse sensory inputs. To enable realistic evaluation and training, we further introduce **WB-50**, a 50-hour dataset of teleoperated and policy rollout trajectories annotated with rewards and preserving natural imperfections, including partial successes, corrections, and failures. Experiments show HVD significantly outperforms existing baselines in success rate across complex whole-body tasks. Our results suggest effective policy learning for high-DoF systems can emerge not from perfect demonstrations, but from structured learning over realistic, imperfect data. Our code is available at https://github.com/LAMDA-RL/HVD.
Continuous Audio Language Models
Authors: Simon Rouard; Manu Orsini; Axel Roebel; Neil Zeghidour; Alexandre Défossez
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: 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.
BiasScope: Towards Automated Detection of Bias in LLM-as-a-Judge Evaluation
Authors: Peng Lai; Zhihao Ou; Yong Wang; Longyue Wang; Jian Yang; Yun Chen; Guanhua Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: LLM-as-a-Judge has been widely adopted across various research and practical applications, yet the robustness and reliability of its evaluation remain a critical issue. A core challenge it faces is bias, which has primarily been studied in terms of known biases and their impact on evaluation outcomes, while automated and systematic exploration of potential unknown biases is still lacking. Nevertheless, such exploration is crucial for enhancing the robustness and reliability of evaluations. To bridge this gap, we propose BiasScope, a LLM-driven framework for automatically and at scale discovering potential biases that may arise during model evaluation. BiasScope can uncover potential biases across different model families and scales, with its generality and effectiveness validated on the JudgeBench dataset. It overcomes the limitations of existing approaches, transforming bias discovery from a passive process relying on manual effort and predefined bias lists into an active and comprehensive automated exploration. Moreover, based on BiasScope, we propose JudgeBench-Pro, an extended version of JudgeBench and a more challenging benchmark for evaluating the robustness of LLM-as-a-judge. Strikingly, even powerful LLMs as evaluators show error rates above 50\% on JudgeBench-Pro, underscoring the urgent need to strengthen evaluation robustness and to mitigate potential biases further.
Beyond Text-to-Image: Liberating Generation with a Unified Discrete Diffusion Model
Authors: Qingyu Shi; Jinbin Bai; Zhuoran Zhao; Wenhao Chai; Kaidong Yu; Jianzong Wu; Shuangyong Song; Yunhai Tong; Xiangtai Li; Xuelong Li; Shuicheng YAN
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unified generation models aim to handle diverse tasks across modalities—such as text-to-image generation and image-to-text generation—within a single architecture and decoding paradigm. Autoregressive unified models suffer from slow inference due to sequential decoding, and non-autoregressive unified models suffer from weak generalization due to limited pretrained backbones. We introduce Muddit, a unified discrete diffusion transformer that enables fast and parallel generation across both text and image modalities. Unlike prior unified diffusion models trained from scratch, Muddit integrates strong visual priors from a pretrained text-to-image backbone with a lightweight text decoder, enabling flexible and high-quality multimodal generation under a unified architecture. Empirical results show that Muddit achieves competitive or superior performance compared to models in both quality and efficiency. This work also highlights the potential of purely discrete diffusion, when equipped with strong visual priors, as a scalable and effective backbone for unified generation.
OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs
Authors: Caorui Li; Yu Chen; Yiyan Ji; Jin Xu; Zhenyu Cui; Shihao Li; Yuanxing Zhang; Zhenghao Song; Dingling Zhang; Heying; Haoxiang Liu; Yuxuan Wang; Qiufeng Wang; Jiafu Tang; Zhenhe Wu; Jiehui Luo; Zhiyu Pan; Weihao Xie; Chenchen Zhang; Zhaohui Wang; Jiayi Tian; Yanghai Wang; Zhe Cao; Minxin Dai; ke wang; Runzhe Wen; Yinghao Ma; Yaning Pan; Sungkyun Chang; Termeh Taheri; Haiwen Xia; Christos Plachouras; Emmanouil Benetos; Yizhi LI; Ge Zhang; Jian Yang; Tianhao Peng; Zili Wang; Minghao Liu; Junran Peng; Zhaoxiang Zhang; Jiaheng Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in multimodal large language models (MLLMs) have demonstrated substantial potential in video understanding. However, existing benchmarks fail to comprehensively evaluate synergistic reasoning capabilities across audio and visual modalities, often neglecting either one of the modalities or integrating them in a logically inconsistent manner. To bridge this gap, we introduce OmniVideoBench, a large-scale and rigorously designed benchmark dedicated to assessing synergistic audio-visual understanding, with a strong emphasis on modality complementarity and logical consistency. Specifically, OmniVideoBench comprises 1000 high-quality question-answer(QA) pairs, each annotated with step-by-step reasoning traces, derived from 628 diverse videos ranging from several seconds to 30 minutes, and manually verified to guarantee complete correctness and uniqueness. Moreover, OmniVideoBench encompasses 13 carefully designed question types, covering temporal reasoning, spatial localization, counting, causal inference, summarization, and beyond, thereby capturing the essential challenges of video understanding. Evaluation of multiple MLLMs on OmniVideoBench reveals a pronounced gap between model performance and human reasoning, with open-source models lagging significantly behind their closed-source counterparts, underscoring the inherent difficulty of genuine audio-visual reasoning. We will release OmniVideoBench to foster the development of MLLMs with stronger and more generalizable reasoning capabilities.
TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
Authors: Gideon Stein; Niklas Penzel; Tristan Piater; Joachim Denzler
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Causal Discovery (CD) is a powerful framework for scientific inquiry. 
Yet, its practical adoption is hindered by a reliance on strong, often unverifiable assumptions and a lack of robust performance assessment.
To address these limitations and advance empirical CD evaluation, we present **TCD-Arena**, a modularized, highly customizable, and extendable testing kit to assess the robustness of time series CD algorithms against stepwise more severe assumption violations. 
For demonstration, we conduct an extensive empirical study comprising around 30 million individual CD attempts and reveal nuanced robustness profiles for 33 distinct assumption violations. 
Further, we investigate CD ensembles and find that they have the potential to improve general robustness, which has implications for real-world applications. 
With this, we strive to ultimately facilitate the development of CD methods that are reliable for a diverse range of synthetic and potentially real-world data conditions.
Rethinking Global Text Conditioning in Diffusion Transformers
Authors: Nikita Starodubcev; Daniil Pakhomov; Zongze Wu; Ilya Drobyshevskiy; Yuchen Liu; Zhonghao Wang; Yuqian Zhou; Zhe Lin; Dmitry Baranchuk
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion transformers typically incorporate textual information via (i) attention layers and (ii) a modulation mechanism using a pooled text embedding. Nevertheless, recent approaches discard modulation-based text conditioning and rely exclusively on attention.
In this paper, we address whether modulation-based text conditioning is necessary and whether it can provide any performance advantage. Our analysis shows that, in its conventional usage, the pooled embedding contributes little to overall performance, suggesting that attention alone is generally sufficient for faithfully propagating prompt information. However, we reveal that the pooled embedding can provide significant gains when used from a different perspective—serving as guidance and enabling controllable shifts toward more desirable properties. This approach is training-free, simple to implement, incurs negligible runtime overhead, and can be applied to various diffusion models, bringing improvements across diverse tasks, including text-to-image/video generation and image editing.
Tracking Equivalent Mechanistic Interpretations Across Neural Networks
Authors: Alan Sun; Mariya Toneva
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mechanistic interpretability (MI) is an emerging framework for interpreting neural networks. Given a task and model, MI aims to discover a succinct algorithmic process, an interpretation, that explains the model's decision process on that task. However, MI is difficult to scale and generalize. This stems in part from two key challenges: there is no precise notion of a valid interpretation; and, generating interpretations is often an ad hoc process. In this paper, we address these challenges by defining and studying the problem of interpretive equivalence: determining whether two different models share a common interpretation, without requiring an explicit description of what that interpretation is. At the core of our approach, we propose and formalize the principle that two interpretations of a model are equivalent if all of their possible implementations are also equivalent. We develop an algorithm to estimate interpretive equivalence and case study its use on Transformer-based models. To analyze our algorithm, we introduce necessary and sufficient conditions for interpretive equivalence based on models' representation similarity. We provide guarantees that simultaneously relate a model's algorithmic interpretations, circuits, and representations. Our framework lays a foundation for the development of more rigorous evaluation methods of MI and automated, generalizable interpretation discovery methods.
Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
Authors: Francesco D'Amico; Dario Bocchi; Matteo Negri
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scaling laws in deep learning -- empirical power-law relationships linking model performance to resource growth -- have emerged as simple yet striking regularities across architectures, datasets, and tasks. These laws are particularly impactful in guiding the design of state-of-the-art models, since they quantify the benefits of increasing data or model size, and hint at the foundations of interpretability in machine learning. However, most studies focus on asymptotic behavior at the end of training. In this work, we describe a richer picture by analyzing the entire training dynamics:  we identify two novel \textit{dynamical} scaling laws that govern how performance evolves as function of different norm-based complexity measures. Combined, our new laws recover the well-known scaling for test error at convergence. Our findings are consistent across CNNs, ResNets, and Vision Transformers trained on MNIST, CIFAR-10 and CIFAR-100. Furthermore, we provide analytical support using a single-layer perceptron trained with logistic loss, where we derive the new dynamical scaling laws, and we explain them through the implicit bias induced by gradient-based training.
Fast Catch-Up, Late Switching: Optimal Batch Size Scheduling via Functional Scaling Laws
Authors: Jinbo Wang; Binghui Li; Zhanpeng Zhou; Mingze Wang; Yuxuan Sun; Jiaqi Zhang; Xunliang Cai; Lei Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Batch size scheduling (BSS) plays a critical role in large-scale deep learning training, influencing both optimization dynamics and computational efficiency. Yet, its theoretical foundations remain poorly understood.
In this work, we show that the **functional scaling law (FSL)** framework introduced in [Li et al. (2025a)](https://arxiv.org/abs/2509.19189) provides a principled lens for analyzing BSS. Specifically, we characterize the optimal BSS under a fixed data budget and show that its structure depends sharply on task difficulty. For easy tasks, optimal schedules keep increasing batch size throughout.  In contrast, for hard tasks, the optimal schedule  maintains small batch sizes for most of training and switches to large batches only in a late stage. 
To explain the emergence of late switching, we uncover a dynamical mechanism—the **fast catch-up** effect—which also manifests in large language model (LLM) pretraining. After switching from small to large batches, the loss rapidly aligns with the constant large-batch trajectory. Using FSL, we show that this effect stems from rapid forgetting of accumulated gradient noise, with the catch-up speed determined by task difficulty. Crucially, this effect implies that *large batches can be safely deferred to late training* without sacrificing performance, while substantially reducing data consumption. Finally,
extensive LLM pretraining experiments—covering both Dense and MoE architectures with up to **1.1B** parameters and **1T** tokens—validate our theoretical predictions. Across all settings, late-switch schedules consistently outperform constant-batch and early-switch baselines.
FlashDLM: Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion
Authors: Zhanqiu Hu; Jian Meng; Yash Akhauri; Mohamed S. Abdelfattah; Jae-sun Seo; Zhiru Zhang; Udit Gupta
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion language models offer parallel token generation and inherent bidirectionality, promising more efficient and powerful sequence modeling compared to autoregressive approaches. However, state-of-the-art diffusion models (e.g., Dream 7B, LLaDA 8B) suffer from slow inference. While they match the quality of similarly sized Autoregressive (AR) Models (e.g., Qwen2.5 7B, Llama3 8B), their iterative denoising requires multiple full-sequence forward passes, resulting in high computational costs and latency, particularly for long input prompts and long-context scenarios. Furthermore, parallel token generation introduces token incoherence problems, and current sampling heuristics suffer from significant quality drops with decreasing denoising steps. We address these limitations with two training-free techniques. First, we propose *FreeCache*, a Key-Value (KV) approximation caching technique that reuses stable KV projections across denoising steps, effectively reducing the computational cost of DLM inference. 
Second, we introduce *Guided Diffusion*, a training-free method that uses a lightweight pretrained autoregressive model to supervise token unmasking, dramatically reducing the total number of denoising iterations without sacrificing quality.
We conduct extensive evaluations on open-source reasoning benchmarks, and our combined methods deliver an average of 12.14$\times$ end-to-end speedup across various tasks with negligible accuracy degradation. For the first time, diffusion language models achieve a comparable and even faster latency as the widely adopted autoregressive models. Our work successfully paved the way for scaling up the diffusion language model to a broader scope of applications across different domains. Our code and implementation are available at 
https://github.com/ZhanqiuHu/flash-dlm-experimental.
Distillation of Large Language Models via Concrete Score Matching
Authors: Yeongmin Kim; Donghyeok Shin; Mina Kang; Byeonghu Na; Il-chul Moon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) deliver remarkable performance but are costly to deploy, motivating knowledge distillation (KD) for efficient inference. Existing KD objectives typically match student and teacher probabilities via softmax, which blurs valuable logit information. While direct logit distillation (DLD) mitigates softmax smoothing, it fails to account for logit shift invariance, thereby restricting the solution space. We propose Concrete Score Distillation (CSD), a discrete score-matching objective that overcomes both softmax-induced smoothing and restrictions on the optimal solution set. We resolve the training instability and quadratic complexity of discrete score-matching in autoregressive LLMs, and the resulting CSD objective aligns relative logit differences across all vocabulary pairs between student and teacher with flexible weighting. We provide both mode-seeking and mode-covering instances within our framework and evaluate CSD on task-agnostic instruction-following, task-specific, and general chat capability distillation using GPT-2-1.5B, OpenLLaMA-7B, and Gemma-7B-IT, Qwen2.5-7B-IT, and Gemma2-9B-IT teachers. Experiments show that CSD consistently surpasses recent KD objectives, achieves favorable fidelity–diversity trade-offs, and yields complementary gains when combined with on-policy techniques, demonstrating its scalability and effectiveness for LLM distillation. Code: https://github.com/aailab-kaist/CSD.
OmniCVR: A Benchmark for Omni-Composed Video Retrieval with Vision, Audio, and Text
Authors: Junyang Ji; Shengjun Zhang; Da Li; Yuxiao Luo; Yan Wang; Di Xu; Biao Yang; Wei Yuan; Fan Yang; Zhihai He; Wenming Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Composed video retrieval presents a complex challenge: retrieving a target video based on a source video and a textual modification instruction. This task demands fine-grained reasoning over multimodal transformations. However, existing benchmarks predominantly focus on vision–text alignment, largely overlooking the rich semantic signals embedded in audio—such as speech, music, and environmental sounds—which are often decisive for comprehensive video understanding. To bridge this gap, we introduce **OmniCVR**, a large-scale benchmark for omni-composed video retrieval that establishes vision, audio, and text as first-class modalities. OmniCVR is constructed via a scalable, automated pipeline integrating content-aware segmentation, omni-modal annotation, and a rigorous dual-validation protocol involving both large language models and human experts. The benchmark comprises vision-centric, audio-centric, and integrated queries, with the latter forming the majority to accurately reflect real-world multimodal complexity. Furthermore, we propose **AudioVLM2Vec**, an audio-aware extension of VLM2Vec. By incorporating explicit audio semantics, AudioVLM2Vec achieves state-of-the-art performance, highlighting fundamental limitations in the audio reasoning capabilities of current multimodal retrieval systems.
Goal Reaching with Eikonal-Constrained Hierarchical Quasimetric Reinforcement Learning
Authors: Vittorio Giammarino; Ahmed H Qureshi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Goal-Conditioned Reinforcement Learning (GCRL) mitigates the difficulty of reward design by framing tasks as goal reaching rather than maximizing hand-crafted reward signals. In this setting, the optimal goal-conditioned value function naturally forms a quasimetric, motivating Quasimetric RL (QRL), which constrains value learning to quasimetric mappings and enforces local consistency through discrete, trajectory-based constraints. We propose Eikonal-Constrained Quasimetric RL (Eik-QRL), a continuous-time reformulation of QRL based on the Eikonal Partial Differential Equation (PDE). This PDE-based structure makes Eik-QRL trajectory-free, requiring only sampled states and goals, while improving out-of-distribution generalization. We provide theoretical guarantees for Eik-QRL and identify limitations that arise under complex dynamics. To address these challenges, we introduce Eik-Hierarchical QRL (Eik-HiQRL), which integrates Eik-QRL into a hierarchical decomposition. Empirically, Eik-HiQRL achieves state-of-the-art performance in offline goal-conditioned navigation and yields consistent gains over QRL in manipulation tasks, matching temporal-difference methods.