GenAI4ELab/papercli-papers huggingface dataset:
CR-CTC: Consistency regularization on CTC for improved speech recognition
Authors: Zengwei Yao; Wei Kang; Xiaoyu Yang; Fangjun Kuang; Liyong Guo; Han Zhu; Zengrui Jin; Zhaoqing Li; Long Lin; Daniel Povey
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Connectionist Temporal Classification (CTC) is a widely used method for automatic speech recognition (ASR), renowned for its simplicity and computational efficiency. However, it often falls short in recognition performance.  In this work, we propose the Consistency-Regularized CTC (CR-CTC), which enforces consistency between two CTC distributions obtained from different augmented views of the input speech mel-spectrogram. We provide in-depth insights into its essential behaviors from three perspectives: 1) it conducts self-distillation between random pairs of sub-models that process different augmented views; 2) it learns contextual representation through masked prediction for positions within time-masked regions, especially when we increase the amount of time masking; 3) it suppresses the extremely peaky CTC distributions, thereby reducing overfitting and improving the generalization ability. Extensive experiments on LibriSpeech, Aishell-1, and GigaSpeech datasets demonstrate the effectiveness of our CR-CTC. It significantly improves the CTC performance, achieving state-of-the-art results comparable to those attained by transducer or systems combining CTC and attention-based encoder-decoder (CTC/AED). We release our code at \url{https://github.com/k2-fsa/icefall}.
ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains
Authors: Yein Park; Chanwoong Yoon; Jungwoo Park; Donghyeon Lee; Minbyul Jeong; Jaewoo Kang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large language models (LLMs) have brought significant changes to many aspects of our lives.
However, assessing and ensuring their chronological knowledge remains challenging.
Existing approaches fall short in addressing the temporal adaptability of knowledge, often relying on a fixed time-point view. 
To overcome this, we introduce ChroKnowBench, a benchmark dataset designed to evaluate chronologically accumulated knowledge across three key aspects: multiple domains, time dependency, temporal state.
Our benchmark distinguishes between knowledge that evolves (e.g., personal history, scientific discoveries, amended laws) and knowledge that remain constant (e.g., mathematical truths, commonsense facts). 
Building on this benchmark, we present ChroKnowledge (Chronological Categorization of Knowledge), a novel sampling-based framework for evaluating LLMs' non-parametric chronological knowledge.
Our evaluation led to the following observations: 
(1) The ability of eliciting temporal knowledge varies depending on the data format that model was trained on.
(2) LLMs partially recall knowledge or show a cut-off at temporal boundaries rather than recalling all aspects of knowledge correctly.
Thus, we apply our ChroKnowPrompt, an in-depth prompting to elicit chronological knowledge by traversing step-by-step through the surrounding time spans.
We observe that it successfully recalls objects across both open-source and proprietary LLMs, demonstrating versatility, though it faces challenges with dynamic datasets and unstructured formats.
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
Authors: Najwa Laabid; Severi Rissanen; Markus Heinonen; Arno Solin; Vikas Garg
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Graph diffusion models, dominant in graph generative modeling, remain underexplored for graph-to-graph translation tasks like chemical reaction prediction. We demonstrate that standard permutation equivariant denoisers face fundamental limitations in these tasks due to their inability to break symmetries in noisy inputs. To address this, we propose \emph{aligning} input and target graphs to break input symmetries while preserving permutation equivariance in non-matching graph portions. Using retrosynthesis (i.e., the task of predicting precursors for synthesis of a given target molecule) as our application domain, we show how alignment dramatically improves discrete diffusion model performance from $5$\% to a SOTA-matching $54.7$\% top-1 accuracy. Code is available at https://github.com/Aalto-QuML/DiffAlign.
Influence-Guided Diffusion for Dataset Distillation
Authors: Mingyang Chen; Jiawei Du; Bo Huang; Yi Wang; Xiaobo Zhang; Wei Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Dataset distillation aims to streamline the training process by creating a compact yet effective dataset for a much larger original dataset. However, existing methods often struggle with distilling large, high-resolution datasets due to prohibitive resource costs and limited performance, primarily stemming from sample-wise optimizations in the pixel space. Motivated by the remarkable capabilities of diffusion generative models in learning target dataset distributions and controllably sampling high-quality data tailored to user needs, we propose framing dataset distillation as a controlled diffusion generation task aimed at generating data specifically tailored for effective training purposes. By establishing a correlation between the overarching objective of dataset distillation and the trajectory influence function, we introduce the Influence-Guided Diffusion (IGD) sampling framework to generate training-effective data without the need to retrain diffusion models. An efficient guided function is designed by leveraging the trajectory influence function as an indicator to steer diffusions to produce data with influence promotion and diversity enhancement. Extensive experiments show that the training performance of distilled datasets generated by diffusions can be significantly improved by integrating with our IGD method and achieving state-of-the-art performance in distilling ImageNet datasets. Particularly, an exceptional result is achieved on the ImageNet-1K, reaching 60.3\% at IPC=50. Our code is available at https://github.com/mchen725/DD_IGD.
ELICIT: LLM Augmentation Via External In-context Capability
Authors: Futing Wang; Jianhao Yan; Yue Zhang; Tao Lin
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Enhancing the adaptive capabilities of large language models is a critical pursuit in both research and application.
Traditional fine-tuning methods require substantial data, computational resources, and specific capabilities, while in-context learning is limited by the need for appropriate demonstrations and efficient token usage.
    Inspired by the expression of in-context learned capabilities through task vectors and the concept of modular capability or knowledge, we propose ELICIT, a framework consisting of two modules designed to effectively store and reuse task vectors to enhance the diverse adaptive capabilities of models without additional training or inference tokens.
    Our comprehensive experiments and analysis demonstrate that our pipeline is highly transferable across different input formats, tasks, and model architectures.
    Externally storing and reusing vectors that represent in-context learned capabilities not only shows the potential to extract modular capabilities but also significantly enhances the performance, versatility, adaptability, and scalability of large language models, paving the way for more efficient and effective use of these models in a wide range of applications.
RecFlow: An Industrial Full Flow Recommendation Dataset
Authors: Qi Liu; Kai Zheng; Rui Huang; Wuchao Li; Kuo Cai; Yuan Chai; Yanan Niu; Yiqun Hui; Bing Han; Na Mou; Hongning Wang; Wentian Bao; Enyun Yu; Guorui Zhou; Han Li; Yang Song; Defu Lian; Kun Gai
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Industrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS benchmark datasets primarily focus on the exposure space, where novel RS algorithms are trained and evaluated. However, when these algorithms transition to real-world industrial RS, they face two critical challenges: (1) handling unexposed items—a significantly larger space than the exposed one, profoundly impacting their practical performance; and (2) overlooking the intricate interplay between multiple stages of the recommendation pipeline, resulting in suboptimal system performance. To bridge the gap between offline RS benchmarks and real-world online environments, we introduce RecFlow—an industrial full-flow recommendation dataset. Unlike existing datasets, RecFlow includes samples not only from the exposure space but also from unexposed items filtered at each stage of the RS funnel. RecFlow comprises 38 million interactions from 42,000 users across nearly 9 million items with additional 1.9 billion stage samples collected from 9.3 million online requests over 37 days and spanning 6 stages. Leveraging RecFlow, we conduct extensive experiments to demonstrate its potential in designing novel algorithms that enhance effectiveness by incorporating stage-specific samples. Some of these algorithms have already been deployed online at KuaiShou, consistently yielding significant gains. We propose RecFlow as the first comprehensive whole-pipeline benchmark dataset for the RS community, enabling research on algorithm design across the entire recommendation pipeline, including selection bias study, debiased algorithms, multi-stage consistency and optimality, multi-task recommendation, and user behavior modeling.
Offline Hierarchical Reinforcement Learning via Inverse Optimization
Authors: Carolin Schmidt; Daniele Gammelli; James Harrison; Marco Pavone; Filipe Rodrigues
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Hierarchical policies enable strong performance in many sequential decision-making problems, such as those with high-dimensional action spaces, those requiring long-horizon planning, and settings with sparse rewards. 
However, learning hierarchical policies from static offline datasets presents a significant challenge.
Crucially, actions taken by higher-level policies may not be directly observable within hierarchical controllers, and the offline dataset might have been generated using a different policy structure, hindering the use of standard offline learning algorithms.
In this work, we propose $\textit{OHIO}$: a framework for offline reinforcement learning (RL) of hierarchical policies. 
Our framework leverages knowledge of the policy structure to solve the $\textit{inverse problem}$, recovering the unobservable high-level actions that likely generated the observed data under our hierarchical policy.
This approach constructs a dataset suitable for off-the-shelf offline training.
We demonstrate our framework on robotic and network optimization problems and show that it substantially outperforms end-to-end RL methods and improves robustness.  We investigate a variety of instantiations of our framework, both in direct deployment of policies trained offline and when online fine-tuning is performed. Code and data are available at https://ohio-offline-hierarchical-rl.github.io.
QA-Calibration of Language Model Confidence Scores
Authors: Putra Manggala; Atalanti A. Mastakouri; Elke Kirschbaum; Shiva Kasiviswanathan; Aaditya Ramdas
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: To use generative question-and-answering (QA) systems for decision-making and in any critical application, these systems need to provide well-calibrated confidence scores that reflect the correctness of their answers. Existing calibration methods aim to ensure that the confidence score is, *on average*, indicative of the likelihood that the answer is correct.  We argue, however, that this standard (average-case) notion of calibration is difficult to interpret for decision-making in generative QA. To address this, we generalize the standard notion of average calibration and introduce QA-calibration, which ensures calibration holds across different question-and-answer groups. We then propose discretized posthoc calibration schemes for achieving QA-calibration. We establish distribution-free guarantees on the performance of this method and validate our method on confidence scores returned by elicitation prompts across multiple QA benchmarks and large language models (LLMs).
ARLON: Boosting Diffusion Transformers with Autoregressive Models for Long Video Generation
Authors: Zongyi Li; Shujie HU; Shujie LIU; Long Zhou; Jeongsoo Choi; Lingwei Meng; Xun Guo; Jinyu Li; Hefei Ling; Furu Wei
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Text-to-video (T2V) models have recently undergone rapid and substantial advancements. Nevertheless, due to limitations in data and computational resources, achieving efficient generation of long videos with rich motion dynamics remains a significant challenge. 
To generate high-quality, dynamic, and temporally consistent long videos, this paper presents ARLON,  a novel framework that boosts diffusion Transformers with autoregressive (\textbf{AR}) models for long (\textbf{LON}) video generation, by integrating the coarse spatial and long-range temporal information provided by the AR model to guide the DiT model effectively.
Specifically, ARLON incorporates several key innovations: 
1) A latent Vector Quantized Variational Autoencoder (VQ-VAE) compresses the input latent space of the DiT model into compact and highly quantized visual tokens, bridging the AR and DiT models and balancing the learning complexity and information density;
2) An adaptive norm-based semantic injection module integrates the coarse discrete visual units from the AR model into the DiT model, ensuring effective guidance during video generation; 
3) To enhance the tolerance capability of noise introduced from the AR inference, the DiT model is trained with coarser visual latent tokens incorporated with an uncertainty sampling module. 
Experimental results demonstrate that ARLON significantly outperforms the baseline OpenSora-V1.2 on eight out of eleven metrics selected from VBench, with notable improvements in dynamic degree and aesthetic quality, while delivering competitive results on the remaining three and simultaneously accelerating the generation process. In addition, ARLON achieves state-of-the-art performance in long video generation, outperforming other open-source models in this domain. 
Detailed analyses of the improvements in inference efficiency are presented, alongside a practical application that demonstrates the generation of long videos using progressive text prompts. Project page: \url{http://aka.ms/arlon}.
Rethinking Shapley Value for Negative Interactions in Non-convex Games
Authors: Wonjoon Chang; Myeongjin Lee; Jaesik Choi
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We study causal interactions for payoff allocation in cooperative game theory, including quantifying feature attribution for deep learning models. Most feature attribution methods mainly stem from the criteria of the Shapley value, which assigns fair payoffs to players based on their expected contribution in a cooperative game. However, interactions between players in the game do not explicitly appear in the original formulation of the Shapley value. In this work, we reformulate the Shapley value to clarify the role of interactions and discuss implicit assumptions from a game-theoretical perspective. Our theoretical analysis demonstrates that when negative interactions exist—common in deep learning models—the efficiency axiom can lead to the undervaluation of attributions or payoffs. We suggest a new allocation rule that decomposes contributions into interactions and aggregates positive parts for non-convex games. Furthermore, we propose an approximation algorithm to reduce the cost of interaction computation which can be applied to differentiable functions such as deep learning models. Our approach mitigates counterintuitive attribution outcomes observed in existing methods, ensuring that features critical to a model’s decision receive appropriate attribution.
CO-MOT: Boosting End-to-end Transformer-based Multi-Object Tracking via Coopetition Label Assignment and Shadow Sets
Authors: Feng yan; Weixin Luo; Yujie Zhong; Yiyang Gan; Lin Ma
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not surpassed non-end-to-end tracking-by-detection methods. One possible reason lies in the training label assignment strategy that consistently binds the tracked objects with tracking queries and assigns few newborns to detection queries. Such an assignment, with one-to-one bipartite matching, yields an unbalanced training, _i.e._, scarce positive samples for detection queries, especially for an enclosed scene with the majority of the newborns at the beginning of videos. As such, e2e-MOT will incline to generate a tracking terminal without renewal or re-initialization, compared to other tracking-by-detection methods.
To alleviate this problem, we propose **Co-MOT**, a simple yet effective method to facilitate e2e-MOT by a novel coopetition label assignment with a shadow concept. Specifically, we add tracked objects to the matching targets for detection queries when performing the label assignment for training the intermediate decoders. For query initialization, we expand each query by a set of shadow counterparts with limited disturbance to itself.
With extensive ablation studies, Co-MOT achieves superior performances without extra costs, _e.g._, 69.4% HOTA on DanceTrack and 52.8% TETA on BDD100K. Impressively, Co-MOT only requires 38% FLOPs of MOTRv2 with comparable performances, resulting in the 1.4× faster inference speed. Source code is publicly available at [GitHub](https://github.com/BingfengYan/CO-MOT).
What's New in My Data? Novelty Exploration via Contrastive Generation
Authors: Masaru Isonuma; Ivan Titov
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Fine-tuning is widely used to adapt language models for specific goals, often leveraging real-world data such as patient records, customer-service interactions, or web content in languages not covered in pre-training.
These datasets are typically massive, noisy, and often confidential, making their direct inspection challenging.
However, understanding them is essential for guiding model deployment and informing decisions about data cleaning or suppressing any harmful behaviors learned during fine-tuning.
In this study, we introduce the task of novelty discovery through generation, which aims to identify novel domains of a fine-tuning dataset by generating examples that illustrate these properties.
Our approach － Contrastive Generative Exploration (CGE) － assumes no direct access to the data but instead relies on a pre-trained model and the same model after fine-tuning.
By contrasting the predictions of these two models, CGE can generate examples that highlight novel domains of the fine-tuning data.
However, this simple approach may produce examples that are too similar to one another, failing to capture the full range of novel domains present in the dataset.
We address this by introducing an iterative version of CGE, where the previously generated examples are used to update the pre-trained model, and this updated model is then contrasted with the fully fine-tuned model to generate the next example, promoting diversity in the generated outputs.
Our experiments demonstrate the effectiveness of CGE in detecting novel domains, such as toxic language, as well as new natural and programming languages.
Furthermore, we show that CGE remains effective even when models are fine-tuned using differential privacy techniques.
Durable Quantization Conditioned Misalignment Attack on Large Language Models
Authors: Peiran Dong; Haowei Li; Song Guo
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: As large language models (LLMs) are increasingly deployed on resource-constrained edge devices, quantization techniques have been widely adopted to reduce model size and computational requirements. However, this process can expose models to new vulnerabilities. In this work, we introduce the Quantization Conditioned Misalignment (Q-Misalign) attack, a novel threat in which safety misalignment remains dormant in a full-precision LLM but becomes exploitable post-quantization. We demonstrate that our Q-Misalign attack effectively bypasses safety mechanisms and enables the generation of harmful content in quantized models while maintaining full-precision performance. Furthermore, we propose a contrastive task vector-based approach to enhance attack durability, ensuring that vulnerabilities persist even after downstream fine-tuning. Experimental results show that Q-Misalign attack significantly increases jailbreak success rates in quantized models, while preserving model utility and safety alignment in full precision. Our findings highlight a critical gap in current LLM safety measures and call for more robust defenses in quantization-aware scenarios.
Joint Gradient Balancing for Data Ordering in Finite-Sum Multi-Objective Optimization
Authors: Hansi Yang; James Kwok
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: In finite-sum optimization problems, the sample orders for parameter updates can significantly influence the convergence rate of optimization algorithms. While numerous sample ordering techniques have been proposed in the context of single-objective optimization, the problem of sample ordering in finite-sum multi-objective optimization has not been thoroughly explored. To address this gap, we propose a sample ordering method called JoGBa, which finds the sample orders for multiple objectives by jointly performing online vector balancing on the gradients of all objectives. Our theoretical analysis demonstrates that this approach outperforms the standard baseline of random ordering and accelerates the convergence rate for the MGDA algorithm. Empirical evaluation across various datasets with different multi-objective optimization algorithms further demonstrates that JoGBa can achieve faster convergence and superior final performance than other data ordering strategies.
Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
Authors: Yuankai Luo; Hongkang Li; Qijiong Liu; Lei Shi; Xiao-Ming Wu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations—termed node identifiers (node IDs)—to tackle inference challenges on large-scale graphs. By employing vector quantization, we compress continuous node embeddings from multiple layers of a Graph Neural Network (GNN) into discrete codes, applicable under both self-supervised and supervised learning paradigms. These node IDs capture high-level abstractions of graph data and offer interpretability that traditional GNN embeddings lack. Extensive experiments on 34 datasets, encompassing node classification, graph classification, link prediction, and attributed graph clustering tasks, demonstrate that the generated node IDs significantly enhance speed and memory efficiency while achieving competitive performance compared to current state-of-the-art methods. Our source code is available at https://github.com/LUOyk1999/NodeID.