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Applications · Everything Else

Jan Melechovsky, Ambuj Mehrish, Abhinaba Roy, Dorien Herremans

Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of audio artifacts with text-based control. SonicMaster is conditioned on natural language instructions to apply targeted enhancements, or can operate in an automatic mode for general restoration. To train this model, we construct the SonicMaster dataset, a large dataset of paired degraded and high-quality tracks by simulating common degradation types with nineteen degradation functions belonging to five enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our approach leverages a flow-matching generative training paradigm to learn an audio transformation that maps degraded inputs to their cleaned, mastered versions guided by text prompts. Objective audio quality metrics demonstrate that SonicMaster significantly improves sound quality across all artifact categories. Furthermore, subjective listening tests confirm that listeners prefer SonicMaster’s enhanced outputs over other baselines. Demo samples are available through https://msonic793.github.io/SonicMaster/

Social Aspects · Everything Else

Jiangwei Chen, Xinyuan Niu, Rachael Hwee Ling Sim, Zhengyuan Liu, Nancy Chen, Bryan Kian Hsiang Low

Machine unlearning aims to remove the influence of specific training samples due to privacy, copyright or bias concerns. Multi-objective unlearning seeks to ensure the effective forgetting of such samples while preserving the utility of the unlearned model. Existing multi-objective unlearning methods typically optimize a weighted combination of the objectives. They provide no guarantee that any of the objectives can achieve a required performance and do not consider the similarity between the forget data and the remaining retain data. In this work, we quantify how hard it is to reconcile the conflicting objectives arising from overlapping data and provide conditions under which collateral forgetting is unavoidable, that is, when improving forget quality forces retain utility degradation. Utilizing this hardness measure, we propose a hardness-aware multi-objective unlearning algorithm (HAMU) that adapts the unlearning updates based on the per-iteration hardness. Our algorithm is applicable to non-convex models and is easily parallelizable, making it readily deployable in real-world scenarios. We empirically demonstrate HAMU's superior performance over baselines on both image and text datasets using large-scale models.

Optimization · Everything Else

Shenyang Deng, Zhuoli Ouyang, Ruochen Jin, Tianyu Pang, Zihang Liu, Shuhua Yu, Yaoqing Yang

Preconditioned adaptive methods have gained significant attention for training deep neural networks, as they capture rich curvature information. The central challenge in this field lies in balancing preconditioning effectiveness with computational efficiency of implementing the preconditioner. Among recent advances, \textsc{MUON} stands out by using Newton-Schulz iteration to obtain preconditioned updates without explicitly constructing the preconditioning matrix. In this paper, we introduce \textsc{RMNP} (Row Momentum Normalized Preconditioning), an optimizer that replaces Newton-Schulz iteration with a simple row-wise $\ell_2$ normalization operation, motivated by the empirically observed diagonal block structure of the Transformer layerwise Hessian. This substitution reduces the per-iteration complexity from $\mathcal{O}(mn\cdot\min(m,n))$ to $\mathcal{O}(mn)$ for an $m\times n$ weight matrix while maintaining comparable optimization performance. Theoretically, we establish convergence guarantees for \textsc{RMNP} in the non-convex setting that match recent results for \textsc{Muon} optimizers, achieving the information-theoretic minimax optimal complexity. Extensive experiments on large language model pretraining show that \textsc{RMNP} delivers competitive optimization performance compared with \textsc{Muon} while substantially reducing preconditioning process wall-clock time. Our code is available at \href{https://anonymous.4open.science/r/RMNP-317C/}{link}.

Reinforcement Learning · Everything Else

Zihao Wang, Muyao Li, Kaichen He, Haowei Lin, Xiaojian Ma, Anji Liu, Yitao Liang

Prevailing autonomous agents are often constrained by a single, predefined action space, which limits their generalization capabilities across diverse tasks and can introduce compounding errors through decoupled policy execution. To address these limitations, we introduce the Deep Hierarchical Agent (DeepHA), a unified architecture that operates across a mixture of heterogeneous action spaces, flexibly generating actions ranging from high-level semantic skills to low-level motor controls. We further propose a Chain-of-Action (CoA) reasoning framework, which enables the agent to use higher-level abstract actions as structured `thoughts' to guide the generation of more granular, subsequent actions. To manage the computational demands of this deep reasoning in long-horizon tasks, we develop a memory-efficient mechanism that dynamically compresses historical context and leverages Key-Value (KV) caching, reducing context length by approximately 75% without sacrificing performance. We conduct extensive evaluations on a new, large-scale benchmark of over 800 diverse Minecraft tasks. Results show that DHA significantly outperforms prior methods, establishing a new state-of-the-art and demonstrating superior generalization, particularly in complex, multi-step planning tasks. Our work presents a novel, unified framework for building more capable and efficient autonomous agents.

Reinforcement Learning · Everything Else

Zihao Wang, Muyao Li, Kaichen He, Xiangyu Wang, Zhancun Mu, Minghao Liu, Anji Liu, Yitao Liang

A critical challenge in developing capable AI agents is defining their "action space''—the set of possible actions they can take. These spaces can range widely, from generating code and using language skills to operating on latent representations or raw joystick controls. Through a large-scale study in Minecraft, we discovered a major dilemma: no single action space is universally best. The most effective action space is highly task-dependent, which complicates the goal of building one generalist agent that can handle everything. To solve this, we introduce Chain-of-Action (CoA), a novel framework that unifies high-level abstracted actions and low-level control actions within a single model. With CoA, an abstract goal is not just a final command; instead, it serves as an intermediate reasoning step that guides the model to generate the precise, executable actions needed to complete the task. Furthermore, we show that an All-in-One generalist agent, trained on a diverse mix of action spaces using CoA, learns a more generalizable policy. This unified agent achieves a new state-of-the-art, outperforming strong, specialized baselines. To support the research community, we are releasing the OpenHA (Open Hierarchical Agents) suite, which includes our benchmark of over 800 tasks, curated datasets, source code, and all model checkpoints at: \url{https://anonymous.4open.science/anonymize/OpenHA-ACFE}.

Applications · Everything Else

Zhan Liu, Changli Tang, Yuxin WANG, Zhiyuan Zhu, Youjun Chen, Yiwen Shao, TIANZI WANG, Lei Ke, Zengrui Jin, Chao Zhang

Current audio-visual large language models (AV-LLMs) are predominantly restricted to 2D perception, relying on RGB video and monaural audio. This design choice introduces a fundamental dimensionality mismatch that precludes reliable source localization and spatial reasoning in complex 3D environments. We address this limitation by presenting JAEGER, a framework that extends AV-LLMs to 3D space, to enable joint spatial grounding and reasoning through the integration of RGB-D observations and multi-channel first-order ambisonics. A core contribution of our work is the neural intensity vector (Neural IV), a learned spatial audio representation that encodes robust directional cues to enhance direction-of-arrival estimation, even in adverse acoustic scenarios with overlapping sources. To facilitate large-scale training and systematic evaluation, we propose SpatialSceneQA, a benchmark of 61k instruction-tuning samples curated from simulated physical environments. Extensive experiments demonstrate that our approach consistently surpasses 2D-centric baselines across diverse spatial perception and reasoning tasks, underscoring the necessity of explicit 3D modelling for advancing AI in physical environments. Our source code, pre-trained model checkpoints and datasets will be released upon acceptance.

General Machine Learning · Everything Else

Sergei Chuprov, Richard Lange, Leon Reznik, Paulo Shakarian, Raman Zatsarenko, Dmitrii Korobeinikov

This position paper argues for metacognition as a general design principle for creating more accurate, secure, and efficient AI. The metacognitive solution involves systems monitoring their own states and judiciously allocating resources depending on each problem instance's difficulty or cost of mistakes. Drawing inspiration both from past work on resource-rational AI and from well-documented metacognitive strategies in psychology and cognitive science, we identify specific challenges in embedding these strategies into AI design and highlight open theoretical and implementation problems. We showcase these principles through a tangible example of improved learning efficiency, effectiveness, and security in a Federated Learning (FL) case study. We show how these principles can be translated into practice with a novel software framework developed specifically to allow the community to design, deploy, and experiment with metacognition-enabled AI applications.

Theory · Everything Else

Sreenivas Gollapudi, Kostas Kollias, Kamesh Munagala, Aravindan Vijayaraghavan

Conformal prediction provides rigorous, distribution-free uncertainty guarantees, but often yields prohibitively large prediction sets in structured domains such as routing, planning, or sequential recommendation. We introduce graph-based conformal compression, a framework for constructing compact subgraphs that preserve statistical validity while reducing structural complexity. We formulate compression as selecting a smallest subgraph capturing a prescribed fraction of the probability mass, and reduce to a weighted version of densest-k-subgraphs in hypergraphs, in the regime where the subgraph has a large fraction of edges. We design efficient approximation algorithms that achieve constant factor coverage and size trade-offs. Crucially, we prove that our relaxation satisfies a monotonicity property, derived from a connection to parametric minimum cuts, which guarantees the nestedness required for valid conformal calibration. Our results therefore not only highlight an algorithmic regime, distinct from classical densest-k-subgraph hardness settings, where the problem can be approximated efficiently, but also bridge conformal prediction with combinatorial graph compression via monotonicity. We finally validate our algorithmic approach via simulations for trip planning and navigation, and compare to natural baselines.

Social Aspects · Everything Else

Nihar Shah

In this position paper, we enumerate a number of problems with the current peer-review process based on extensive empirical evidence. We argue for two structural reforms: (1) separating publication from presentation via a four-step process that first evaluates correctness, publishes all sound papers, then uses community-based ratings to select presentations; and (2) offering parallel anonymous and non-anonymous review tracks, where the non-anonymous track releases all review data publicly to increase accountability and generate valuable research datasets. We argue how our proposed policies can mitigate these problems. We urge the community to leverage the learnings from the experiments conducted in peer-review processes and incorporate evidence-based policy design.

Applications · Everything Else

Linyong Gan, Zimo Li, Wenxin XU, Xingjian Li, Jianhua Huang, Enmei Tu, Shuhang Chen

Accurate long-horizon vessel trajectory prediction remains challenging due to compounded uncertainty from complex navigation behaviors and environmental factors. Existing methods often struggle to maintain global directional consistency, leading to drifting or implausible trajectories when extrapolated over long time horizons. To address this issue, we propose a semantic-key-point-conditioned trajectory modeling framework, in which future trajectories are predicted by conditioning on a high-level Next Key Point (NKP) that captures navigational intent. This formulation decomposes long-horizon prediction into global semantic decision-making and local motion modeling, effectively restricting the support of future trajectories to semantically feasible subsets. To efficiently estimate the NKP prior from historical observations, we adopt a pretrain-finetune strategy. Extensive experiments on real-world AIS data demonstrate that the proposed method consistently outperforms state-of-the-art approaches, particularly for long travel durations, directional accuracy, and fine-grained trajectory prediction.

Applications · Everything Else

Ruotong Zhao, Zhiyu Chen, Xurui Liu, Haidong Xue, Dong Liang, Jigao Fu, Wu YanBiao, Yuanyi Zhen, Fengli Xu, Yong Li

Literature reviews are essential to reflect the landscape of research fields. Large language models, especially deep research agents, have recently shown strong capabilities in automated literature review generation. However, it remains a challenging task to rigorously evaluate the scientific value of the generated reviews, since human expert annotations are difficult to scale up and LLM-as-a-judge approaches lack of a convincing criteria. To address this gap, we introduce LitReview Arena, a battle-style evaluation platform with a structured protocol tailored to literature review quality. Our protocol imitates academic peer review by recruiting domain experts with research paper-writing experience, and we match each query to reviewers within their expertise. Each battle is judged with dimension-wise outcomes over five literature-review-specific criteria, enabling reproducible and diagnostic comparisons across systems. We collect a large-scale human preference dataset of expert votes (4984 votes×5 dimensions) and systematically measure how far current models are from human drafts. Results show that the most advanced models win only 23.0\% of decisive matches against humans on overall utility, leaving substantial room for improvement. Meanwhile, agentic LLMs, such as Sonar Deep Research, substantially outperform base language models by over 60\%. We also find that existing LLM-as-a-judge evaluation methods are severely misaligned with human experts (Spearman's $\rho \approx 0.467$). Based on the collected preference data, we provide an expert-calibrated evaluator, \emph{LitJudge}, improving alignment to $\rho \approx 0.78$, comparable to inter-expert consistency. Codes and datasets are publicly available at https://anonymous.4open.science/r/LitReview-Arena-3B82/.

Reinforcement Learning · Everything Else

Yuetian Wang, Dianxi Shi, Yuanze Wang, Huanhuan Yang, Shiming Song, Chunping Qiu

Safe reinforcement learning (Safe RL) seeks to optimize long-term performance while ensuring adherence to safety constraints. However, most existing approaches address safety in a simplified manner, typically by linearly combining rewards and costs, which provides limited guidance when safety and performance interact in complex, nonlinear ways. We present USB-RL (Unsupervised Score-Balanced Reinforcement Learning), a model-based framework that learns implicit safety–performance preferences directly from experience. Our approach infers a monotone partial-order score through unsupervised pairwise comparisons of long-horizon outcomes, capturing nuanced trade-offs without relying on manually tuned cost weights. The learned score guides model-based policy optimization by dynamically balancing safety and performance, enabling flexible and adaptive multi-step planning in imagination-based control. Across diverse safety benchmarks, USB-RL achieves strong returns while substantially reducing safety violations, demonstrating stable and interpretable safety–performance trade-offs.

General Machine Learning · Everything Else

Hongbo Yin, Wu Jichun, Zhou Yang, Chi Jiang, Yin Zhang, Yan Zhang

In personalized federated learning (PFL), collaboration graphs specify model aggregation among clients. However, without constraints on the collaboration geometry, training can drift into two degenerate regimes: global consensus or spontaneous clustering. This paper provides a unified dynamical analysis: under the same budget of representative models, collaborative PFL is more expressive and achieves higher-order approximation accuracy than clustered PFL. An upper bound on disagreement further reveals two degeneration mechanisms—overly strong collaboration drives consensus (reducing to standard federated learning), while similarity-driven weight updates make the graph nearly reducible and induce self-clustering (collapsing to clustered PFL). Motivated by these findings, we propose pFedCCG. pFedCCG preserves the expressivity advantage via controlled collaboration geometry (CCG): it builds a static similarity-based collaboration template decoupled from training, optimizes a Markovian collaboration matrix with a prescribed stationary distribution via reversible parameterization and Euclidean projection, and schedules collaboration strength to avoid self-clustering. Experiments across diverse heterogeneity settings show consistent personalization gains and markedly reduced collapse and self-clustering. Code will be available at https://anonymous.4open.science/r/pFedCCG-CB88.

Deep Learning · Everything Else

Hee-Sung Kim, Hyeonseong Kim, Sungyoon Lee

Estimating the generalization gap and developing optimization methods that improve generalization are crucial for deep learning models, for both theoretical understanding and practical applications. Leveraging unlabeled data for these purposes offers significant advantages in real-world scenarios. This paper introduces a novel generalization measure, $\textit{local inconsistency}$, derived from an information-geometric perspective on the parameter space of neural networks. A key feature of local inconsistency is that it can be computed without explicit labels. We establish theoretical underpinnings by connecting local inconsistency to the Fisher information matrix and the loss Hessian. Empirically, we demonstrate that local inconsistency correlates with the generalization gap. Based on these findings, we propose Inconsistency-Aware Minimization (IAM), which incorporates local inconsistency into the training objective. We demonstrate that in standard supervised learning settings, IAM enhances generalization, achieving performance comparable to that of existing methods such as Sharpness-Aware Minimization. Furthermore, IAM exhibits efficacy in semi- and self-supervised learning scenarios, where the local inconsistency is computed from unlabeled data.

Reinforcement Learning · Everything Else

Zhiqiang Kou, Junyang Chen, Xin-Qiang Cai, Xiaobo Xia, Ming-Kun Xie, Dong-Dong Wu, Biao Liu, Yuheng Jia, Xin Geng, Masashi Sugiyama 等

Due to constraints on privacy, cost, and latency, on-premise deployment of small models is increasingly common. However, most practical pipelines stop at supervised fine-tuning (SFT) and fail to reach the reinforcement learning (RL) alignment stage. The main reason is that RL alignment typically requires either expensive human preference annotation or heavy reliance on high-quality reward models with large-scale API usage and ongoing engineering maintenance, both of which are ill-suited to on-premise settings. To bridge this gap, in this paper, we propose a positive-unlabeled (PU) RL distillation method for on-premise small-model deployment. Without human-labeled preferences or a reward model, our method distills the teacher’s preference-optimization capability from black box generations into a locally trainable student. For each prompt, we query the teacher once to obtain an anchor response, locally sample multiple student candidates, and perform anchor-conditioned self-ranking to induce pairwise or listwise preferences, enabling a fully local training loop via direct preference optimization or group relative policy optimization. Theoretical analysis justifies that the induced preference signal by our method is order-consistent and concentrates on near-optimal candidates, supporting its stability for preference optimization. Experiments demonstrate that our method achieves consistently strong performance under a low-cost setting.

Probabilistic Methods · Everything Else

Anton Conrad, Eric Moulines, julien perez

In multi-target regression and multi-class classification, uncertainty is inherently multivariate: prediction regions must capture joint dependencies across correlated outputs. Conformal prediction provides distribution-free guarantees, yet extending it to vector-valued outputs remains challenging—scalar aggregation discards geometric structure, while optimal transport (OT) approaches are computationally demanding and sensitive to outliers. We introduce two conformal methods based on geometric quantiles and spatial ranks: Geometric Conformalized Quantile Regression (GCQR) constructs prediction regions from learned conditional geometric quantiles, while Geometric Rank Conformal Prediction (GRCP) uses the radial rank of vector-valued conformity scores as the nonconformity measure. We propose multiple estimators offering different tradeoffs between computational cost and adaptivity to feature-dependent heterogeneity, with scalable learning via partially input-convex neural networks. On multi-target regression and multi-class classification benchmarks, GCQR and GRCP attain near-nominal coverage with consistently tighter prediction regions than scalarized and multivariate baselines.

Deep Learning · Everything Else

Chentao Lu, Xuhao Ren, Dawei xu, Chuan Zhang, Liehuang Zhu

Federated learning (FL) allows clients to collaboratively train models without exposing private data, but practical FL is simultaneously challenged by data heterogeneity and model heterogeneity. Prior heterogeneous FL (HtFL) approaches often fail to handle fine-grained feature shifts, leading to weak representation alignment and limited cross-client knowledge transfer, which degrades both personalization and generalization. We propose FedARC, an HtFL framework that couples a shared lightweight extractor with client-specific fusion: a trainable projector integrates local and global embeddings, while adaptive residual compensation dynamically corrects feature-level mismatches. To further stabilize aggregation, FedARC performs semantic anchor alignment across clients, and we theoretically prove FedARC converges with a non-convex convergence rate $\mathcal{O}(1/T)$. Experiments on five public benchmarks show that FedARC outperforms nine state-of-the-art HtFL baselines by up to 2.63\% in average accuracy, while maintaining efficient communication and computation.

General Machine Learning · Everything Else

Prayas Agrawal, Prateek Chanda, Ishita Khatri, Ganesh Ramakrishnan, Bamdev Mishra, Pratik Kumar Jawanpuria

Training Large Language Models (LLMs) on heterogeneous datasets requires optimizing domain representations to balance convergence speed and domain coverage. While recent methods reduce computational overhead by selecting high-quality data subsets, they typically perform selection independently per domain or rely on computationally expensive proxy models to determine continuous domain weights. In this paper, we propose a joint sample selection framework that learns better domain representations in the batch directly and formulate the selection objective as joint domain utility maximization subject to partition matroid constraints. This approach enforces domain-specific budgets while maximizing a validation-guided gradient matching utility across all domains simultaneously. Theoretically, we establish that this objective is weakly submodular, allowing us to employ a computationally efficient orthogonal matching pursuit algorithm with provable guarantees. Empirically, we demonstrate that our method significantly outperforms baselines that favor independent domain selection on mathematical reasoning and molecular generation benchmarks. Furthermore, our analysis show that our approach reduces the number of conflicting training gradient pairs significantly as compared to independent selection across domains or domain agnostic selection. Applied to Qwen2.5 and Llama-3 and trained on MethaMathQA and Mol-Instructions, our approach yields robust gains under multiple subset fractions, with improvements on nine math-reasoning and four molecule-generation benchmarks, highlighting cross-domain benefits of joint subset selection.

Theory · Everything Else

Tao Wang, Xiaoting Zhong

Distributional learning problems optimize discrepancies between probability measures, including optimal transport or Sinkhorn divergence, yet are typically optimized using Euclidean first-order methods in parameter space. We show this mismatch is structural rather than algorithmic. We introduce geometry-misalignment, a local condition number that measures distortion between Euclidean geometry and the intrinsic geometry induced by a distributional objective. For a broad class of problems, we establish lower bounds demonstrating that Euclidean first-order methods incur an unavoidable convergence slowdown proportional to misalignment, even under intrinsic strong convexity and smoothness. We further prove geometry-aware preconditioned methods attain matching upper bounds independent of misalignment, yielding a sharp separation between Euclidean optimization and geometry-aware optimization. Beyond convergence rates, we show geometry-misalignment induces an optimization-dependent excess risk term under finite budgets, directly linking optimization geometry with statistical efficiency. We develop a geometry-calibrated optimization framework that estimates misalignment and selectively activates geometry-aware updates when necessary. Experiments on distribution matching for domain adaptation validate the theory, with improvements concentrated in high-misalignment regimes and negligible overhead.

Reinforcement Learning · Everything Else

Ruishuo Chen, Xun Wang, Rui Hu, Zhuoran Li, Longbo Huang

Generative Flow Networks (GFlowNets) excel at sampling diverse, high-reward objects. In many practical applications where active reward queries are infeasible, these models must be trained using static offline datasets. Prevailing training methods typically rely on a proxy model to provide reward feedback for online sampled trajectories. However, constructing a reliable proxy is often challenging due to data scarcity or high evaluation costs. While existing proxy-free approaches attempt to address this, they often impose coarse constraints that limit the model's ability to explore effectively. To overcome these limitations, we propose **Trajectory-Distilled GFlowNet (TD-GFN)**, a novel proxy-free training framework. TD-GFN utilizes inverse reinforcement learning (IRL) to extract dense, transition-level edge rewards from offline trajectories, providing rich structural guidance for efficient exploration. Crucially, to ensure robustness, these rewards guide the policy indirectly through DAG pruning and prioritized backward sampling. This design ensures that gradient updates rely exclusively on ground-truth terminal rewards from the dataset, thereby preventing error propagation. Empirical results demonstrate that TD-GFN significantly outperforms a broad range of existing baselines in both convergence speed and sample quality, establishing a more robust and efficient paradigm for offline GFlowNet training.