GenAI4ELab/papercli-papers huggingface dataset:
Adaptive Conformal Prediction via Mixture-of-Experts Gating Similarity
Authors: Jingsen Kong; Wenlu Tang; Dezheng Kong; Linglong Kong; Guangren Yang; Bei Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Prediction intervals are essential for applying machine learning models in real applications, yet most conformal prediction (CP) methods provide coverage guarantees that overlook the heterogeneity and domain knowledge that characterize modern multimodal datasets. We introduce Mixture-of-Experts Conformal Prediction (MoE-CP), a flexible and scalable framework that uses the gating probability vectors of Mixture-of-Experts (MoE) models as soft domain assignments to guide similarity-weighted conformal calibration. MoE-CP weights calibration residuals according to the similarity between gating vectors of calibration and test points, producing prediction intervals that adapt to latent subpopulations without requiring explicit domain labels. We provide theoretical justification showing that MoE-CP preserves nominal marginal validity under common similarity measures and improves conditional adaptivity when the gating captures domain structure. Empirical results on synthetic and real-world datasets demonstrate that MoE-CP yields more domain-aware, interpretable, and often tighter intervals than existing conformal baselines while maintaining target coverage. MoE-CP offers a practical route to reliable uncertainty quantification in latent heterogeneous, multi-domain environments.
UniTrack: Differentiable Graph Representation Learning for Multi-Object Tracking
Authors: Bishoy Galoaa; Xiangyu Bai; Utsav Nandi; Sai Siddhartha Vivek Dhir Rangoju; Somaieh Amraee; Sarah Ostadabbas
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present UniTrack, a plug-and-play graph-theoretic loss function designed to significantly enhance multi-object tracking (MOT) performance by directly optimizing tracking-specific objectives through unified differentiable learning. Unlike prior graph-based MOT methods that redesign tracking architectures, UniTrack provides a universal training objective that integrates detection accuracy, identity preservation, and spatiotemporal consistency into a single end-to-end trainable loss function, enabling seamless integration with existing MOT systems without architectural modifications. Through differentiable graph representation learning, UniTrack enables networks to learn holistic representations of motion continuity and identity relationships across frames. We validate UniTrack across diverse tracking models and multiple challenging benchmarks, demonstrating consistent improvements across all tested architectures and datasets including Trackformer, MOTR, FairMOT, ByteTrack, GTR,  and MOTE. Extensive evaluations show up to 53\% reduction in identity switches and 12\% IDF1 improvements across challenging benchmarks, with GTR achieving peak performance gains of 9.7\% MOTA on SportsMOT. Code and additional resources are available at https://github.com/ostadabbas/UniTrack and https://ostadabbas.github.io/unitrack.github.io/.
DeepAFL: Deep Analytic Federated Learning
Authors: Jianheng Tang; Yajiang Huang; Kejia Fan; Feijiang Han; Jiaxu Li; Jinfeng Xu; Run He; Anfeng Liu; Houbing Herbert Song; Huiping Zhuang; Yunhuai Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Federated Learning (FL) is a popular distributed learning paradigm to break down data silo. Traditional FL approaches largely rely on gradient-based updates, facing significant issues about heterogeneity, scalability, convergence, and overhead, etc. Recently, some analytic-learning-based work has attempted to handle these issues by eliminating gradient-based updates via analytical (i.e., closed-form) solutions. Despite achieving superior invariance to data heterogeneity, these approaches are fundamentally limited by their single-layer linear model with a frozen pre-trained backbone. As a result, they can only achieve suboptimal performance due to their lack of representation learning capabilities. In this paper, to enable representable analytic models while preserving the ideal invariance to data heterogeneity for FL, we propose our Deep Analytic Federated Learning approach, named DeepAFL. Drawing inspiration from the great success of ResNet in gradient-based learning, we design gradient-free residual blocks in our DeepAFL with analytical solutions. We introduce an efficient layer-wise protocol for training our deep analytic models layer by layer in FL through least squares. Both theoretical analyses and empirical evaluations validate our DeepAFL's superior performance with its dual advantages in heterogeneity invariance and representation learning, outperforming state-of-the-art baselines by up to 5.68%-8.42% across three benchmark datasets. Related code is available at https://github.com/tangent-heng/DeepAFL.
Stackelberg Coupling of Online Representation  Learning and Reinforcement Learning
Authors: Fernando Martinez; Tao Li; Yingdong Lu; Juntao Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep Q-learning jointly learns representations and values within monolithic networks, promising beneficial co-adaptation between features and value estimates. Although this architecture has attained substantial success, the coupling between representation and value learning creates instability as representations must constantly adapt to non-stationary value targets, while value estimates depend on these shifting representations. This is compounded by high variance in bootstrapped targets, which causes bias in value estimation in off-policy methods. We introduce Stackelberg Coupled Representation and Reinforcement Learning (SCORER), a framework for value-based RL that views representation and Q-learning as two strategic agents in a hierarchical game. SCORER models the Q-function as the leader, which commits to its strategy by updating less frequently, while the perception network (encoder) acts as the follower, adapting more frequently to learn representations that minimize Bellman error variance given the leader's committed strategy. Through this division of labor, the Q-function minimizes MSBE while perception minimizes its variance, thereby reducing  bias accordingly, with asymmetric updates allowing stable co-adaptation, unlike simultaneous parameter updates in monolithic solutions. Our proposed SCORER framework leads to a bi-level optimization problem whose solution is approximated by a two-timescale algorithm that creates an asymmetric learning dynamic between the two players. Extensive experiments on DQN and its variants demonstrate that gains stem from algorithmic insight rather than model complexity.
PersonaX: Multimodal Datasets with LLM-Inferred Behavior Traits
Authors: Loka Li; Wong Yu Kang; Minghao Fu; Guangyi Chen; Zhenhao Chen; Gongxu Luo; Yuewen Sun; Salman Khan; Peter Spirtes; Kun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding human behavior traits is central to applications in human-computer interaction, computational social science, and personalized AI systems. Such understanding often requires integrating multiple modalities to capture nuanced patterns and relationships. However, existing resources rarely provide datasets that combine behavioral descriptors with complementary modalities such as facial attributes and biographical information. To address this gap, we present PersonaX, a curated collection of multimodal datasets designed to enable comprehensive analysis of public traits across modalities. PersonaX consists of (1) CelebPersona, featuring 9444 public figures from diverse occupations, and (2) AthlePersona, covering 4181 professional athletes across 7 major sports leagues. Each dataset includes behavioral trait assessments inferred by three high-performing large language models, alongside facial imagery and structured biographical features.
We analyze PersonaX at two complementary levels. First, we abstract high-level trait scores from text descriptions and apply five statistical independence tests to examine their relationships with other modalities. Second, we introduce a novel causal representation learning (CRL) framework tailored to multimodal and multi-measurement data, providing theoretical identifiability guarantees. Experiments on both synthetic and real-world data demonstrate the effectiveness of our approach. By unifying structured and unstructured analysis, PersonaX  establishes a foundation for studying LLM-inferred behavioral traits in conjunction with visual and biographical attributes, advancing multimodal trait analysis and causal reasoning. 
The code is available at https://github.com/lokali/PersonaX.
Phantom-Data:  Towards a General Subject-Consistent Video Generation Dataset
Authors: Zhuowei Chen; Bingchuan Li; Tianxiang Ma; Lijie Liu; Mingcong Liu; Yunsheng Jiang; Gen Li; Xinghui Li; Liyang Chen; SiYu Zhou; Qian HE; Xinglong Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Subject-to-video generation has witnessed substantial progress in recent years. However, existing models still face significant challenges in faithfully following textual instructions. This limitation, commonly known as the copy-paste problem, arises from the widely used in-pair training paradigm. This approach inherently entangles subject identity with background and contextual attributes by sampling reference images from the same scene as the target video. To address this issue, we introduce \textbf{Phantom-Data, the first general-purpose cross-pair subject-to-video consistency dataset}, containing approximately one million identity-consistent pairs across diverse categories. Our dataset is constructed via a three-stage pipeline: (1) a general and input-aligned subject detection module, (2) large-scale cross-context subject retrieval from more than 53 million videos and 3 billion images, and (3) prior-guided identity verification to ensure visual consistency under contextual variation. Comprehensive experiments show that training with Phantom-Data significantly improves prompt alignment and visual quality while preserving identity consistency on par with in-pair baselines.
Expanding the Capability Frontier of LLM Agents with ZPD-Guided Data Synthesis
Authors: Xuanzhong Chen; Zile Qiao; Guoxin Chen; Liangcai Su; Zhen Zhang; Xinyu Wang; Pengjun Xie; Fei Huang; Jingren Zhou; Yong Jiang; Ting Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unlocking advanced reasoning in large language model agents is hindered by a scarcity of training data situated at the very frontier of their capabilities. We address this with a novel data synthesis approach inspired by the educational theory of the Zone of Proximal Development (ZPD), which conceptualizes this frontier as tasks an LLM cannot solve independently but can master with guidance. We operationalize this principle through the AgentFrontier Data Engine, an automated pipeline that synthesizes high-quality, multidisciplinary data situated precisely within an LLM's ZPD. The engine yields two synergistic outputs: knowledge-intensive data for continued pre-training and frontier-level reasoning trajectories for post-training. Concurrently, it produces the ZPD Exam, a self-evolving benchmark for evaluating agent capabilities by compelling them to reason beyond their parameterized knowledge. By training our AgentFrontier-30B-A3B model on the synthesized data, we achieve state-of-the-art results on demanding benchmarks like Humanity's Last Exam, outperforming several leading proprietary agents. This work establishes ZPD-guided data synthesis as a scalable and effective paradigm for cultivating increasingly capable LLM agents.
MAGE: Multi-scale Autoregressive Generation for Offline Reinforcement Learning
Authors: Chenxing Lin; Xinhui Gao; Haipeng Zhang; Xinran Li; Haitao Wang; Songzhu Mei; Chenglu Wen; Weiquan Liu; Siqi Shen; Cheng Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative models have gained significant traction in offline reinforcement learning (RL) due to their ability to model complex trajectory distributions. However, existing generation-based approaches still struggle with long-horizon tasks characterized by sparse rewards. Some hierarchical generation methods have been developed to mitigate this issue by decomposing the original problem into shorter-horizon subproblems using one policy and generating detailed actions with another. While effective, these methods often overlook the multi-scale temporal structure inherent in trajectories, resulting in suboptimal performance. To overcome these limitations, we propose MAGE, a Multi-scale Autoregressive GEneration-based offline RL method. MAGE incorporates a condition-guided multi-scale autoencoder to learn hierarchical trajectory representations, along with a multi-scale transformer that autoregressively generates trajectory representations from coarse to fine temporal scales. MAGE effectively captures temporal dependencies of trajectories at multiple resolutions. Additionally, a condition-guided decoder is employed to exert precise control over short-term behaviors. Extensive experiments on five offline RL benchmarks against fifteen baseline algorithms show that MAGE successfully integrates multi-scale trajectory modeling with conditional guidance, generating coherent and controllable trajectories in long-horizon sparse-reward settings. The source code is available at https://github.com/xmu-rl-3dv/MAGE.
RF-DETR: Neural Architecture Search for Real-Time Detection Transformers
Authors: Isaac Robinson; Peter Robicheaux; Matvei Popov; Deva Ramanan; Neehar Peri
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Open-vocabulary detectors achieve impressive performance on COCO, but often fail to generalize to real-world datasets with out-of-distribution classes not typically found in their pre-training. Rather than simply fine-tuning a heavy-weight vision-language model (VLM) for new domains, we introduce RF-DETR, a light-weight specialist detection transformer that discovers accuracy-latency Pareto curves for any target dataset with weight-sharing neural architecture search (NAS). Our approach fine-tunes a pre-trained base network on a target dataset and evaluates thousands of network configurations with different accuracy-latency tradeoffs without re-training. Further, we revisit the "tunable knobs" for NAS to improve the transferability of DETRs to diverse target domains. Notably, RF-DETR significantly improves over prior state-of-the-art real-time methods on COCO and Roboflow100-VL. RF-DETR (nano) achieves 48.0 AP on COCO, beating D-FINE (nano) by 5.3 AP at similar latency, and RF-DETR (2x-large) outperforms GroundingDINO (tiny) by 1.2 AP on Roboflow100-VL while running 20 times as fast. To the best of our knowledge, RF-DETR (2x-large) is the first real-time detector to surpass 60 AP on COCO. Our code is available on GitHub.
RESA: Bringing Back What Sparse Attention Ignores with Residual Estimation
Authors: Weihao Yang; Hao Huang; Ningke Li; Shihao Wang; Darong Yang; Yanqi Pan; Wen Xia; Shiyi Li; Xiangyu Zou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLM) have gained significant attention.
    KV cache, stored to avoid quadratic complexity of attention, becomes a bottleneck due to the demands for long-context.
    Sparse attention (SA) has been proposed to address this by only selecting critical KVs for attention, which may degrade model quality in less sparse scenarios.
    To improve quality, rather than selecting more KVs, this paper reveals another perspective by estimating the contributions of remaining KVs, called Residual Estimation. 
    We find that attention logits (before softmax) exhibit substantial redundancy due to its inherent low-rank nature.
    We perform Singular Value Decomposition (SVD) on logits matrix in prefilling and find the spectral dominance of principal singular value and its linearly scaling property with sequence length.
    These imply that increasing sequence length leads to replication-like logits growth with significant redundancy.
    However, it is impossible to perform SVD at each decoding step in practice due to its heavy costs.
    To this end, we propose RESA, a training-free framework compensating SA's output with an estimated low-rank prior of logits.
    RESA introduces (i) a Prior Estimator that derives a prior distribution from a typical query as a rank-1 approximation at the end of prefilling, and (ii) an Online Aggregator that fuses the prior with SA at each decoding step via lightweight scaling and merging.
    Besides, we further show that RESA's effect comes from priors being used as attention bias for knowledge injection.
    Extensive experiments show that without extra overheads, RESA improves model quality by up to 26\% across various tasks with the same KV budget compared to state-of-the-art.
    Moreover, RESA maintains the same quality with up to 33.2\% KV budget reduction and 1.23$\times$ attention throughput improvement.
Invisible Safety Threat: Malicious Finetuning for LLM via Steganography
Authors: Guangnian Wan; Xinyin Ma; Gongfan Fang; Xinchao Wang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Understanding and addressing potential safety alignment risks in large language models (LLMs) is critical for ensuring their safe and trustworthy deployment. In this paper, we highlight an insidious safety threat: a compromised LLM can maintain a facade of proper safety alignment while covertly generating harmful content. To achieve this, we finetune the model to understand and apply a steganographic technique. At inference time, we input a prompt that contains a steganographically embedded malicious target question along with a plaintext cover question. The model, in turn, produces a target response similarly embedded within a benign-looking cover response. In this process, human observers only see the model being prompted with a cover question and generating a corresponding cover response, while the malicious content is hidden from view. We demonstrate this invisible safety threat on GPT-4.1 despite the OpenAI finetuning API’s safeguards. The finetuned model produces steganographic malicious outputs in response to hidden malicious prompts, while the user interface displays only a fully benign cover interaction. We also replicate the attack on three open-source models, Llama-3.3-70B-Instruct, Phi-4, and Mistral-Small-24B-Base-2501, confirming the generality of our method. We quantitatively evaluate our method on the AdvBench dataset, using Llama-Guard-3-8B for content safety classification. Across all four models, all stegotexts containing malicious content are incorrectly classified as safe.
LLaVA-FA: Learning Fourier Approximation for Compressing Large Multimodal Models
Authors: Pengcheng Zheng; Chaoning Zhang; Jiarong Mo; GuoHui Li; Jiaquan Zhang; Jiahao Zhang; Sihan Cao; Sheng Zheng; Caiyan Qin; Guoqing Wang; Yang Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large multimodal models (LMMs) have achieved impressive performance on various vision-language tasks, but their substantial computational and memory costs hinder their practical deployment. Existing compression methods often decouple low-rank decomposition and quantization, leading to compounded reconstruction errors, especially in multimodal architectures with cross-modal redundancy. To address this issue, we propose LLaVA-FA, a novel efficient LMM that performs joint low-rank plus quantization approximation in the frequency domain. By leveraging the de-correlation and conjugate symmetry properties of Fourier transform, LLaVA-FA achieves more compact and accurate weight representations. Furthermore, we introduce PolarQuant, a polar-coordinate quantization method tailored for complex matrices, and an optional diagonal calibration (ODC) scheme that eliminates the need for large-scale calibration data. Extensive experimental results demonstrate that our proposed LLaVA-FA outperforms existing efficient multimodal models across multiple benchmarks while maintaining minimal activated parameters and low computational costs, validating its effectiveness as a powerful solution for compressing LMMs.
Learning to Reason Efficiently with Discounted Reinforcement Learning
Authors: Alex Ayoub; Kavosh Asadi; Dale Schuurmans; Csaba Szepesvari; Karim Bouyarmane
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large reasoning models (LRMs) often consume excessive tokens, inflating computational cost and latency. More broadly, in goal reaching sequential decision problems we often want to reach the goal quickly, and LRM reasoning can be viewed through this lens. We challenge the assumption that longer responses improve accuracy. By penalizing reasoning tokens using a discounted reinforcement learning setup (interpretable as a small token cost) and analyzing Blackwell optimality in restricted policy classes, we encourage concise yet accurate reasoning, analogous to preferring shorter successful trajectories in a stochastic shortest path problem. Experiments confirm our theoretical results that this approach shortens chains of thought while preserving accuracy.
Cortical Policy: A Dual-Stream View Transformer for Robotic Manipulation
Authors: Xuening Zhang; Qi Lv; Xiang Deng; Miao Zhang; Xingbo Liu; Liqiang Nie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: View transformers process multi-view observations to predict actions and have shown impressive performance in robotic manipulation. Existing methods typically extract static visual representations in a view-specific manner, leading to inadequate 3D spatial reasoning ability and a lack of dynamic adaptation. Taking inspiration from how the human brain integrates static and dynamic views to address these challenges, we propose Cortical Policy, a novel dual-stream view transformer for robotic manipulation that jointly reasons from static-view and dynamic-view streams. The static-view stream enhances spatial understanding by aligning features of geometrically consistent keypoints extracted from a pretrained 3D foundation model. The dynamic-view stream achieves adaptive adjustment through position-aware pretraining of an egocentric gaze estimation model, computationally replicating the human cortical dorsal pathway. Subsequently, the complementary view representations of both streams are integrated to determine the final actions, enabling the model to handle spatially-complex and dynamically-changing tasks under language conditions. Empirical evaluations on RLBench, the challenging COLOSSEUM benchmark, and real-world tasks demonstrate that Cortical Policy outperforms state-of-the-art baselines substantially, validating the superiority of dual-stream design for visuomotor control. Our cortex-inspired framework offers a fresh perspective for robotic manipulation and holds potential for broader application in vision-based robot control.
Unbiased Object Detection Beyond Frequency with Visually Prompted Image Synthesis
Authors: Xinhao Cai; Liulei Li; Gensheng Pei; Tao Chen; Jinshan Pan; Yazhou Yao; Wenguan Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper presents a generation-based debiasing framework for object detection. Prior debiasing methods are often limited by the representation diversity of samples, while naive generative augmentation often preserves the biases it aims to solve. Moreover, our analysis reveals that simply generating more data for rare classes is suboptimal due to two core issues: i) instance frequency is an incomplete proxy for the true data needs of a model, and ii) current layout-to-image synthesis lacks the fidelity and control to generate high-quality, complex scenes. To overcome this, we introduce the representation score (RS) to diagnose representational gaps beyond mere frequency, guiding the creation of new, unbiased layouts. To ensure high-quality synthesis, we replace ambiguous text prompts with a precise visual blueprint and employ a generative alignment strategy, which fosters communication between the detector and generator. Our method significantly
narrows the performance gap for underrepresented object groups, e.g., improving large/rare instances by 4.4/3.6 mAP over the baseline, and surpassing prior L2I synthesis models by 15.9 mAP for layout accuracy in generated images.