GenAI4ELab/papercli-papers huggingface dataset:
Revenue Maximization Under Sequential Price Competition Via The Estimation Of $s$-Concave Demand Functions
Authors: Daniele Bracale; Moulinath Banerjee; Yuekai Sun; Cong Shi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We consider price competition among multiple sellers over a selling horizon of $T$ periods. In each period, sellers simultaneously offer their prices (which are made public) and subsequently observe their respective demand (not made public). The demand function of each seller depends on all sellers' prices through a private, unknown, and nonlinear relationship. We propose a dynamic pricing policy that uses semi-parametric least-squares estimation and show that when the sellers employ our policy, their prices converge at a rate of $O(T^{-1/7})$ to the Nash equilibrium prices that sellers would reach if they were fully informed. Each seller incurs a regret of $O(T^{5/7})$ relative to a dynamic benchmark policy. A theoretical contribution of our work is proving the existence of equilibrium under shape-constrained demand functions via the concept of $s$-concavity and establishing regret bounds of our proposed policy. Technically, we also establish new concentration results for the least squares estimator under shape constraints. Our findings offer significant insights into dynamic competition-aware pricing and contribute to the broader study of non-parametric learning in strategic decision-making.
OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents
Authors: Hongrui Jia; Jitong Liao; Xi Zhang; Haiyang Xu; Tianbao Xie; Chaoya Jiang; Ming Yan; Si Liu; Wei Ye; Fei Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With advances in decision-making and reasoning capabilities, multimodal agents show strong potential in computer application scenarios. Past evaluations have mainly assessed GUI interaction skills, while tool invocation abilities, such as those enabled by the Model Context Protocol (MCP), have been largely overlooked. Comparing agents with integrated tool invocation to those evaluated only on GUI interaction is inherently unfair.
We present OSWorld-MCP, the first comprehensive and fair benchmark for assessing computer-use agents' tool invocation, GUI operation, and decision-making abilities in a real-world environment. We design a novel automated code-generation pipeline to create tools and combine them with a curated selection from existing tools. Rigorous manual validation yields 158 high-quality tools (covering 7 common applications), each verified for correct functionality, practical applicability, and versatility.
Extensive evaluations of state-of-the-art multimodal agents on OSWorld-MCP show that MCP tools generally improve task success rates (e.g., from 8.3\% to 17.6\% for OpenAI
o3 at 15 steps, from 38.9\% to 45.0\% for Claude 4 Sonnet at 50 steps), underscoring the importance of assessing tool invocation capabilities. However, even the strongest models have relatively low tool invocation rates, Only 33.3\%, indicating room for improvement and highlighting the benchmark's challenge.
By explicitly measuring MCP tool usage skills, OSWorld-MCP deepens understanding of multimodal agents and sets a new standard for evaluating performance in complex, tool-assisted environments. Our code, environment, and data are publicly available at https://osworld-mcp.github.io.
MAVEN: A Mesh-Aware Volumetric Encoding Network for Simulating 3D Flexible Deformation
Authors: Zhe Feng; Shilong Tao; Haonan Sun; Shaohan Chen; Zhanxing Zhu; Yunhuai Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep learning-based approaches, particularly graph neural networks (GNNs), have gained prominence in simulating flexible deformations and contacts of solids, due to their ability to handle unstructured physical fields and nonlinear regression on graph structures. However, existing GNNs commonly represent meshes with graphs built solely from vertices and edges. These approaches tend to overlook higher-dimensional spatial features, e.g., 2D facets and 3D cells, from the original geometry. As a result, it is challenging to accurately capture boundary representations and volumetric characteristics, though this information is critically important for modeling contact interactions and internal physical quantity propagation, particularly under sparse mesh discretization. In this paper, we introduce MAVEN, a \textbf{m}esh-\textbf{a}ware \textbf{v}olumetric \textbf{e}ncoding \textbf{n}etwork for simulating 3D flexible deformation, which explicitly models geometric mesh elements of higher dimension to achieve a more accurate and natural physical simulation. MAVEN establishes learnable mappings among 3D cells, 2D facets, and vertices, enabling flexible mutual transformations. Explicit geometric features are incorporated into the model to alleviate the burden of implicitly learning geometric patterns. Experimental results show that MAVEN consistently achieves state-of-the-art performance across established datasets and a novel metal stretch-bending task featuring large deformations and prolonged contacts.
CollectiveKV: Decoupling and Sharing Collaborative Information in Sequential Recommendation
Authors: Jingyu Li; Zhaocheng Du; Qianhui Zhu; kaiyuan Li; Zhicheng Zhang; Song-Li Wu; Chaolang Li; Pengwen Dai
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sequential recommendation models are widely used in applications, yet they face stringent latency requirements. 
Mainstream models leverage the Transformer attention mechanism to improve performance, but its computational complexity grows with the sequence length, leading to a latency challenge for long sequences.
Consequently, KV cache technology has recently been explored in sequential recommendation systems to reduce inference latency.
However, KV cache introduces substantial storage overhead in sequential recommendation systems, which often have a large user base with potentially very long user history sequences.
In this work, we observe that KV sequences across different users exhibit significant similarities, indicating the existence of collaborative signals in KV. 
Furthermore, we analyze the KV using singular value decomposition (SVD) and find that the information in KV can be divided into two parts: the majority of the information is shareable across users, while a small portion is user-specific.
Motivated by this, we propose CollectiveKV, a cross-user KV sharing mechanism.
It captures the information shared across users through a learnable global KV pool. During inference, each user retrieves high-dimensional shared KV from the pool and concatenates them with low-dimensional user-specific KV to obtain the final KV.
Experiments on five sequential recommendation models and three datasets show that our method can compress the KV cache to only 0.8\% of its original size, while maintaining or even enhancing model performance.
Explaining Grokking and Information Bottleneck through Neural Collapse Emergence
Authors: Keitaro Sakamoto; Issei Sato
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The training dynamics of deep neural networks often defy expectations, even as these models form the foundation of modern machine learning.
Two prominent examples are grokking, where test performance improves abruptly long after the training loss has plateaued, and the information bottleneck principle, where models progressively discard input information irrelevant to the prediction task as training proceeds.
However, the mechanisms underlying these phenomena and their relations remain poorly understood.
In this work, we present a unified explanation of such late-phase phenomena through the lens of neural collapse, which characterizes the geometry of learned representations.
We show that the contraction of population within-class variance is a key factor underlying both grokking and information bottleneck, and relate this measure to the neural collapse measure defined on the training set.
By analyzing the dynamics of neural collapse, we show that distinct time scales between fitting the training set and the progression of neural collapse account for the behavior of the late-phase phenomena.
Finally, we validate our theoretical findings on multiple datasets and architectures.
No Pixel Left Behind: A Detail-Preserving Architecture for Robust High-Resolution AI-Generated Image Detection
Authors: Lianrui Mu; Haoji Hu; Zou Xingze; Jianhong Bai; Jiaqi Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid growth of high-resolution, meticulously crafted AI-generated images poses a significant challenge to existing detection methods, which are often trained and evaluated on low-resolution, automatically generated datasets that do not align with the complexities of high-resolution scenarios. A common practice is to resize or center-crop high-resolution images to fit standard network inputs. However, without full coverage of all pixels, such strategies risk either obscuring subtle, high-frequency artifacts or discarding information from uncovered regions, leading to input information loss. In this paper, we introduce the **H**igh-Resolution **D**etail-**A**ggregation Network (**HiDA-Net**), a novel framework that ensures no pixel is left behind. We use the Feature Aggregation Module (FAM), which fuses features from multiple full-resolution local tiles with a down-sampled global view of the image. These local features are aggregated and fused with global representations for final prediction, ensuring that native-resolution details are preserved and utilized for detection. To enhance robustness against challenges such as localized AI manipulations and compression, we introduce Token-wise Forgery Localization (TFL) module for fine-grained spatial sensitivity and JPEG Quality Factor Estimation (QFE) module to disentangle generative artifacts from compression noise explicitly. Furthermore, to facilitate future research, we introduce **HiRes-50K**, a new challenging benchmark consisting of **50,568** images with up to **64 megapixels**. Extensive experiments show that HiDA-Net achieves state-of-the-art, increasing accuracy by over **13%** on the challenging Chameleon dataset and **8%** on our HiRes-50K.
PCPO: Proportionate Credit Policy Optimization for Preference Alignment of Image Generation Models
Authors: Jeongjae Lee; Jong Chul Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While reinforcement learning has advanced the alignment of text-to-image (T2I) models, state-of-the-art policy gradient methods are still hampered by training instability and high variance, hindering convergence speed and compromising image quality. Our analysis identifies a key cause of this instability: disproportionate credit assignment, in which the mathematical structure of the generative sampler produces volatile and non-proportional feedback across timesteps. To address this, we introduce Proportionate Credit Policy Optimization (PCPO), a
framework that enforces proportional credit assignment through a stable objective reformulation and a principled reweighting of timesteps. This correction stabilizes the training process, leading to significantly accelerated convergence and superior image quality. The improvement in quality is a direct result of mitigating model collapse, a common failure mode in recursive training. PCPO substantially outperforms existing policy gradient baselines on all fronts, including the state-of-the-art DanceGRPO. Code is available at https://github.com/jaylee2000/pcpo/.
DuPO: Enabling Reliable Self-Verification via Dual Preference Optimization
Authors: Shuaijie She; Yu Bao; Yu Lu; Lu Xu; Tao Li; Wenhao Zhu; Jianbing Zhang; Shujian Huang; Shanbo Cheng; Lu Lu; Yuxuan Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present DuPO, a dual learning-based preference optimization framework that generates annotation-free feedback via generalized duality. DuPO addresses two key limitations: Reinforcement Learning with Verifiable Rewards (RLVR)’s reliance on costly labels and applicability restricted to verifiable tasks, and traditional dual learning’s restriction to strictly dual task pairs (e.g., translation and back-translation). Specifically, DuPO decomposes a primal task’s input into known and unknown components, then constructs its dual task to reconstruct the unknown part using the primal output and known information (e.g., reversing math solutions to recover hidden variables), broadening applicability to non-invertible tasks. The quality of this reconstruction serves as a self-supervised reward to optimize the primal task, synergizing with LLMs’ ability to instantiate both tasks via a single model. Empirically, DuPO achieves substantial gains across diverse tasks: it enhances the average translation quality by 2.1 COMET over 756 directions, boosts the mathematical reasoning accuracy by an average of 6.4 points on four challenge benchmarks, and enhances performance by 9.3 points as an inference-time reranker~(trading computation for accuracy). These results position DuPO as a scalable, general, and annotation-free paradigm for LLM optimization.
Neural Collapse in Multi-Task Learning
Authors: Youjun Wang; Boqi Li; Xin Zou; Weiwei Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural collapse (NC) plays a key role in understanding deep neural networks. However, existing empirical and theoretical studies of NC focus on one single task. This paper studies neural collapse in multi-task learning. We consider two standard feature-based multi-task learning scenarios: Single-Source Multi-Task Classification (SSMTC) and Multi-Source Multi-Task Classification (MSMTC). Interestingly, we find that the task-specific linear classifier and features converge to the Simplex Equiangular Tight Frame (ETF) in the setting of MSMTC. In the setting of SSMTC, task-specific linear classifier converges to the task-specific ETF and these task-specific ETFs are mutually orthogonal. Moreover, the shared features across tasks converge to the scaled sum of the weight vectors associated with the task-specific labels in each task's classifier. We also provide the theoretical guarantee for our empirical findings. Through detailed analysis, we uncover the mechanism of MTL where each task learns task-specific latent features that together form the shared features. Moreover, we reveal an inductive bias in MTL that task correlation reconfigures the geometry of task-specific classifiers and promotes alignment among the features learned by each task.
RayI2P: Learning Rays for Image-to-Point Cloud Registration
Authors: Xinjun Li; Wenfei Yang; Zhixin Cheng; Jiacheng Deng; Fei Wang; Chen Qian; Tianzhu Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Image-to-point cloud registration aims to estimate the 6-DoF camera pose of a query image relative to a 3D point cloud map. Existing methods fall into two categories: matching-free methods regress pose directly using geometric priors, but lack fine-grained supervision and struggle with precise alignment; matching-based methods construct dense 2D-3D correspondences for PnP-based pose estimation, but are fundamentally limited by projection ambiguity (where multiple geometrically distinct 3D points project to the same image patch, leading to ambiguous feature representations) and scale inconsistency (where fixed-size image patches correspond to 3D regions of varying physical size, causing misaligned receptive fields across modalities). To address these issues, we propose a novel ray-based registration framework that   first predicts patch-wise 3D ray bundles connecting image patches to the 3D scene and then estimates camera pose via a differentiable ray-guided regression module, bypassing the need for explicit 2D-3D correspondences. This formulation naturally resolves projection ambiguity, provides scale-consistent geometry encoding, and enables fine-grained supervision for accurate pose estimation. Experiments on KITTI and nuScenes show that our approach achieves state-of-the-art registration accuracy, outperforming existing methods.
Glance and Focus Reinforcement for Pan-cancer Screening
Authors: Linshan Wu; Jia-Xin Zhuang; Hao Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Pan-cancer screening in large-scale CT scans remains challenging for existing AI methods, primarily due to the difficulty of localizing diverse types of tiny lesions in large CT volumes. The extreme foreground-background imbalance significantly hinders models from focusing on diseased regions, while redundant focus on healthy regions not only decreases the efficiency but also increases false positives. Inspired by radiologists' glance and focus diagnostic strategy, we introduce GF-Screen, a Glance and Focus reinforcement learning framework for pan-cancer screening. GF-Screen employs a Glance model to localize the diseased regions and a Focus model to precisely segment the lesions, where segmentation results of the Focus model are leveraged to reward the Glance model via Reinforcement Learning (RL). Specifically, the Glance model crops a group of sub-volumes from the entire CT volume and learns to select the sub-volumes with lesions for the Focus model to segment. Given that the selecting operation is non-differentiable for segmentation training, we propose to employ the segmentation results to reward the Glance model. To optimize the Glance model, we introduce a novel group relative learning paradigm, which employs group relative comparison to prioritize high-advantage predictions and discard low-advantage predictions within sub-volume groups, not only improving efficiency but also reducing false positives. In this way, for the first time, we effectively extend cutting-edge RL techniques to tackle the specific challenges in pan-cancer screening. We conduct training and validation on a large-scale pan-cancer dataset comprising 5,117 CT scans. Extensive experiments on 16 internal and 7 external datasets across 9 lesion types demonstrated the effectiveness of GF-Screen. Notably, GF-Screen leads the public validation leaderboard of MICCAI FLARE25 pan-cancer challenge, surpassing the FLARE24 champion solution by a large margin (+25.6% DSC and +28.2% NSD). In addition, through discarding redundant regions, GF-Screen reduces the computation costs by 5.7 times, significantly improving inference efficiency. The superior performance of GF-Screen remarks a novel and practical breakthrough in pan-cancer screening. Code is available at https://github.com/Luffy03/GF-Screen.
Autoregressive-based Progressive Coding for Ultra-Low Bitrate Image Compression
Authors: Ziyuan Zhang; Yichong Xia; Bin Chen; Tianwei Zhang; Hao Wang; Han Qiu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative models have demonstrated significant results in ultra-low bitrate image compression, owing to their powerful capabilities for content generation and texture completion. Existing works primarily based on diffusion models still face challenges such as limited bitrate adaptability and high computational complexity for encoding and decoding. Inspired by the success of Visual AutoRegressive model (VAR), we introduce AutoRegressive-based Progressive Coding (ARPC) for ultra-low bitrate image compression, a progressive image compression framework based on next-scale prediction visual autoregressive model. Based on multi-scale residual vector quantizer, ARPC efficiently encodes the image into multi-scale discrete token maps and controls the bitrates by selecting different scales for transmission. For decompression, ARPC leverages the prior knowledge inherent in the visual autoregressive model to predict the unreceived scales, which is naturally the autoregressive generation process. To further increase the compression ratio, we target the VAR as a probability estimator for lossless entropy coding and propose group-masked bitwise multi-scale residual quantizer to adaptively allocate bits for different scales. Extensive experiments show that ARPC achieves state-of-the-art perceptual fidelity at ultra-low bitrates and high decompression efficiency compared with existing diffusion-based methods.
Efficient Audio-Visual Speech Separation with Discrete Lip Semantics and Multi-Scale Global-Local Attention
Authors: Kai Li; Gao Kejun; Xiaolin Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Audio-visual speech separation (AVSS) methods leverage visual cues to extract target speech and have demonstrated strong separation quality in noisy acoustic environments. However, these methods usually involve a large number of parameters and require high computational cost, which is unacceptable in many applications where speech separation serves as only a preprocessing step for further speech processing. To address this issue, we propose an efficient AVSS method, named **Dolphin**. For visual feature extraction, we develop **DP‑LipCoder**, a dual‑path lightweight video encoder that transforms lip‑motion into discrete audio‑aligned semantic tokens. For audio separation, we construct a lightweight encoder–decoder separator, in which each layer incorporates a global–local attention (GLA) block to efficiently capture multi-scale dependencies. Experiments on three benchmark datasets showed that Dolphin not only surpassed the current state-of-the-art (SOTA) model in separation quality but also achieved remarkable improvements in efficiency: over 50\% fewer parameters, more than 2.4$\times$ reduction in MACs, and over 6$\times$ faster GPU inference speed. These results indicate that Dolphin offers a practical and deployable solution for high-performance AVSS in real-world scenarios. Our code and demo page are publicly available at https://cslikai.cn/Dolphin.
NePTune: A Neuro-Pythonic Framework for Tunable Compositional Reasoning on Vision-Language
Authors: Danial Kamali; Parisa Kordjamshidi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern Vision-Language Models (VLMs) have achieved impressive performance in various tasks, yet they often struggle with compositional reasoning, the ability to decompose and recombine concepts to solve novel problems. While neuro-symbolic approaches offer a promising direction, they are typically constrained by crisp logical execution or predefined predicates, which limit flexibility. In this work, we introduce NePTune, a neuro-symbolic framework that overcomes these limitations through a hybrid execution model that integrates the perception capabilities of foundation vision models with the compositional expressiveness of symbolic reasoning. NePTune dynamically translates natural language queries into executable Python programs that blend imperative control flow with soft logic operators capable of reasoning over VLM-generated uncertainty. Operating in a training-free manner, NePTune, with a modular design, decouples perception from reasoning, yet its differentiable composition operations support fine-tuning. We evaluate NePTune on multiple visual reasoning benchmarks and various domains, utilizing adversarial tests, and demonstrate a significant improvement over base models, as well as its effective compositional generalization and adaptation capabilities in novel environments.
LightRetriever: A LLM-based Text Retrieval Architecture with Extremely Faster Query Inference
Authors: Guangyuan Ma; Yongliang Ma; Xuanrui Gou; Zhenpeng Su; Ming Zhou; Songlin Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs)-based text retrieval retrieves documents relevant to search queries based on vector similarities. Documents are pre-encoded offline, while queries arrive in real-time, necessitating an efficient online query encoder. Although LLMs significantly enhance retrieval capabilities, serving deeply parameterized LLMs slows down query inference throughput and increases demands for online deployment resources. In this paper, we propose LightRetriever, a novel LLM-based retriever with extremely lightweight query encoders. Our method retains a full-sized LLM for document encoding, but reduces the workload of query encoding to no more than an embedding lookup. Compared to serving a full LLM on an A800 GPU, our method achieves over a thousand times of speedup in query encoding and over 10× increase in end-to-end retrieval throughput. Extensive experiments on large-scale retrieval benchmarks show that LightRetriever generalizes well across diverse tasks, maintaining an average of 95% retrieval performance.