GenAI4ELab/papercli-papers huggingface dataset:
Text summarization via global structure awareness
Authors: Jiaquan Zhang; Chaoning Zhang; Shuxu Chen; Yibei Liu; Chenghao Li; Qigan Sun; Shuai Yuan; Fachrina Dewi Puspitasari; Dongshen Han; Guoqing Wang; Sung-Ho Bae; Yang Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the explosive growth of information, the volume of long documents has surged, and the cost of processing them continues to rise, making text summarization increasingly important. Existing studies primarily focus on model enhancements and sentence-level pruning based on contextual dependencies and semantic patterns. Although some approaches leverage large language models (LLMs) for text summarization and achieve higher accuracy, they incur substantial computational costs and often overlook global structural modeling. Consequently, summarized texts may lose critical logical chains, disrupting coherence and weakening downstream task performance. To address these issues, we propose GloSA-Sum, a novel text summarization framework that performs global structural analysis of texts via topological data analysis (TDA), enabling efficient summarization while preserving semantic cores and logical dependencies. Specifically, we construct a semantic-weighted graph from sentence embeddings, where persistent homology identifies core semantics and logical structures, preserved in a ``protection pool'' as the backbone for summarization. We design a topology-guided iterative strategy, where lightweight proxy metrics approximate sentence importance to avoid repeated high-cost computations, thus preserving structural integrity while improving efficiency. To further enhance long-text processing, we propose a hierarchical strategy that integrates segment-level and global summarization. Experiments on multiple datasets demonstrate that GloSA-sum reduces redundancy while preserving semantic and logical integrity, striking a balance between accuracy and efficiency, and further benefits LLM downstream tasks by shortening contexts while retaining essential reasoning chains.
PU-BENCH: A UNIFIED BENCHMARK FOR RIGOROUS AND REPRODUCIBLE PU LEARNING
Authors: Qiuyi Chen; Haiyang Zhang; Leqi Zhang; Changchun Li; Jia Wang; Wei Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Positive-Unlabeled (PU) learning, a challenging paradigm for training binary classifiers from only positive and unlabeled samples, is fundamental to many applications. While numerous PU learning methods have been proposed, the research is systematically hindered by the lack of a standardized and comprehensive benchmark for rigorous evaluation. Inconsistent data generation, disparate experimental settings, and divergent metrics have led to irreproducible findings and unsubstantiated performance claims. To address this foundational challenge, we introduce **PU-Bench**, the first unified open-source benchmark for PU learning. PU-Bench provides: 1) a unified data generation pipeline to ensure consistent input across configurable sampling schemes, label ratios and labeling mechanisms; 2) an integrated framework of 18 state-of-the-art PU methods; and 3) standardized protocols for reproducible assessment. Through a large-scale empirical study on 8 diverse datasets (**2880** evaluations in total), PU-Bench reveals a complex yet intuitive performance landscape, uncovering critical trade-offs between effectiveness and efficiency, and systematically mapping method robustness against variations in label frequency and selection bias. It is anticipated to serve as a foundational resource to catalyze reproducible, rigorous, and impactful research in the PU learning community. The source code is publicly available at <https://github.com/XiXiphus/PU-Bench>.
PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing
Authors: Yiping Xie; Bo Zhao; Mingtong Dai; Jian-Ping Zhou; Yue Sun; Tao Tan; Weicheng Xie; Linlin Shen; Zitong YU
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Remote photoplethysmography (rPPG) enables non-contact physiological measurement but remains highly susceptible to illumination changes, motion artifacts, and limited temporal modeling. Large Language Models (LLMs) excel at capturing long-range dependencies, offering a potential solution but struggle with the continuous, noise-sensitive nature of rPPG signals due to their text-centric design. To bridge this gap, we introduce PhysLLM, a collaborative optimization framework that synergizes LLMs with domain-specific rPPG components. Specifically, the Text Prototype Guidance (TPG) strategy is proposed to establish cross-modal alignment by projecting hemodynamic features into LLM-interpretable semantic space, effectively bridging the representational gap between physiological signals and linguistic tokens. Besides, a novel Dual-Domain Stationary (DDS) Algorithm is proposed for resolving signal instability through adaptive time-frequency domain feature re-weighting. Finally, rPPG task-specific cues systematically inject physiological priors through physiological statistics, environmental contextual answering, and task description, leveraging cross-modal learning to integrate both visual and textual information, enabling dynamic adaptation to challenging scenarios like variable illumination and subject movements. Evaluation on four benchmark datasets, PhysLLM achieves state-of-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
FMIP: Joint Continuous-Integer Flow For Mixed-Integer Linear Programming
Authors: Hongpei Li; Hui Yuan; Han Zhang; Jianghao Lin; Dongdong Ge; Mengdi Wang; Yinyu Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mixed-Integer Linear Programming (MILP) is a foundational tool for complex decision-making problems. 
However, the NP-hard nature of MILP presents a significant computational challenge, motivating the development of machine learning-based heuristic solutions to accelerate downstream solvers. 
While recent generative models have shown promise in learning powerful heuristics, they suffer from a critical limitation.
That is, they model the distribution of only the integer variables and fail to capture the intricate coupling between integer and continuous variables, creating an information bottleneck and ultimately leading to suboptimal solutions.
To this end, we propose Joint Continuous-Integer Flow for Mixed-Integer Linear Programming (FMIP), which is the first generative framework that models the joint distribution of both integer and continuous variables for MILP solutions.
Built upon the joint modeling paradigm, a holistic guidance mechanism is designed to steer the generative trajectory, actively refining solutions toward optimality and feasibility during the inference process. 
Extensive experiments on eight standard MILP benchmarks demonstrate the superior performance of FMIP against existing baselines, reducing the primal gap by 41.34% on average. 
Moreover, we show that FMIP is fully compatible with arbitrary backbone networks and various downstream solvers, making it well-suited for a broad range of real-world MILP applications.
Enhancing Sparse Event Detection in Healthcare Time-Series via Adaptive Gate of Context–Detail Interaction
Authors: Beomjun Bark; Yun Kwan Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate detection of clinically meaningful events in healthcare time-series data is crucial for reliable downstream analysis and decision support. However, most existing methods struggle to jointly localize event boundaries and classify event types; even detection transformer (DETR)-based approaches show limited performance when confronted with extremely sparse events typical of clinical recordings. To address these challenges, we propose a coarse-to-fine detection framework combining a global context explorer, a local detail inspector, and an adaptive gating module (AGM) that fuses multiple label perspectives. The AGM uses transformed labels—encoding event presence and temporal position—to improve learning on sparse events. This design acts as a switch that selectively activates detailed feature extraction only when an event is likely, thereby reducing noise and improving efficiency in sparse settings. We evaluate our framework on diverse healthcare datasets—including arrhythmia detection, emotion recognition, and human-activity monitoring—and demonstrate substantial performance gains over existing DETR-based models, with particularly strong improvements in sparse event detection. With precise and robust event detection, our framework enables interpretation and actionable insights in real-world clinical applications.
EnvSocial-Diff: A Diffusion-Based Crowd Simulation Model with Environmental Conditioning and Individual-Group Interaction
Authors: Bingxue Zhao; Qi Zhang; Hui Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling realistic pedestrian trajectories requires accounting for both social interactions and environmental context, yet most existing approaches largely emphasize social dynamics. We propose EnvSocial-Diff: a diffusion-based crowd simulation model informed by social physics and augmented with environmental conditioning and individual-group interaction. Our structured environmental conditioning module explicitly encodes obstacles, objects of interest, and lighting levels, providing interpretable signals that capture scene constraints and attractors. In parallel, the individual-group interaction module goes beyond individual-level modeling by capturing both fine-grained interpersonal relations and group-level conformity through a graph-based design. Experiments on multiple benchmark datasets demonstrate that EnvSocial-Diff outperforms the latest state-of-the-art methods, underscoring the importance of explicit environmental conditioning and multi-level social interaction for realistic crowd simulation.
Uniform Discrete Diffusion with Metric Path for Video Generation
Authors: Haoge Deng; Ting Pan; Fan Zhang; Yang Liu; Zhuoyan Luo; Yufeng Cui; Wenxuan Wang; Chunhua Shen; Shiguang Shan; Zhaoxiang Zhang; Xinlong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Continuous-space video generation has advanced rapidly, while discrete approaches lag behind due to error accumulation and long-context inconsistency. In this work, we revisit discrete generative modeling and present Uniform discRete diffuSion with metric pAth (URSA), a simple yet powerful framework that bridges the gap with continuous approaches for the scalable video generation. At its core, URSA formulates the video generation task as an iterative global refinement of discrete spatiotemporal tokens. It integrates two key designs: a Linearized Metric Path and a Resolution-dependent Timestep Shifting mechanism. These designs enable URSA to scale efficiently to high-resolution image synthesis and long-duration video generation, while requiring significantly fewer inference steps. Additionally, we introduce an asynchronous temporal fine-tuning strategy that unifies versatile tasks within a single model, including interpolation and image-to-video generation. Extensive experiments on challenging video and image generation benchmarks demonstrate that URSA consistently outperforms existing discrete methods and achieves performance comparable to state-of-the-art continuous diffusion methods. Code and models are available at https://github.com/baaivision/URSA.
STAR: Similarity-guided Teacher-Assisted Refinement for Super-Tiny Function Calling Models
Authors: Jiliang Ni; Jiachen Pu; Zhongyi Yang; Jingfeng Luo; Conggang Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The proliferation of Large Language Models (LLMs) in function calling is pivotal for creating advanced AI agents, yet their large scale hinders widespread adoption, necessitating transferring their capabilities into smaller ones. However, existing paradigms are often plagued by overfitting, training instability, ineffective binary rewards for multi-solution tasks, and the difficulty of synergizing techniques. We introduce STAR: Similarity-guided Teacher-Assisted Refinement, a novel holistic framework that effectively transfers LLMs' capabilities to super-tiny models. STAR consists of two core technical innovations: (1) Constrained Knowledge Distillation (CKD), a training objective that augments top-k forward KL divergence to suppress confidently incorrect predictions, ensuring training stability while preserving exploration capacity for downstream RL. STAR holistically synergizes these strategies within a cohesive training curriculum, enabling super-tiny models to achieve exceptional performance on complex function calling tasks; (2) Similarity-guided RL (Sim-RL), a RL mechanism that introduces a fine-grained, similarity-based reward. This provides a robust, continuous, and rich signal for better policy optimization by evaluating the similarity between generated outputs and the ground truth. Extensive experiments on challenging and renowned benchmarks demonstrate the effectiveness of our method. Our STAR models establish SOTA in their size classes, significantly outperforming baselines. Remarkably, our 0.6B STAR model achieves the best performance among all open models under 1B, surpassing even several well-known open models at a larger scale. STAR demonstrates a training framework that distills capabilities of LLMs into super-tiny models, paving the way for powerful, accessible, and efficient AI agents.
Contextual and Seasonal LSTMs for Time Series Anomaly Detection
Authors: Lingpei Zhang; Qingming Li; Yong Yang; Jiahao Chen; Rui Zeng; Chenyang Lyu; Shouling Ji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Univariate time series (UTS), where each timestamp records a single variable, serve as crucial indicators in web systems and cloud servers. Anomaly detection in UTS plays an essential role in both data mining and system reliability management. However, existing reconstruction-based and prediction-based methods struggle to capture certain subtle anomalies, particularly small point anomalies and slowly rising anomalies. To address these challenges, we propose a novel prediction-based framework named Contextual and Seasonal LSTMs (CS-LSTMs). CS-LSTMs are built upon a noise decomposition strategy and jointly leverage contextual dependencies and seasonal patterns, thereby strengthening the detection of subtle anomalies. By integrating both time-domain and frequency-domain representations, CS-LSTMs achieve more accurate modeling of periodic trends and anomaly localization. Extensive evaluations on public benchmark datasets demonstrate that CS-LSTMs consistently outperform state-of-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection.
Learning to summarize user information for personalized reinforcement learning from human feedback
Authors: HyunJi Nam; Yanming Wan; Mickel Liu; Peter F. Ahnn; Jianxun Lian; Natasha Jaques
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As everyday use cases of large language model (LLM) AI assistants have expanded, it is becoming increasingly important to personalize responses to align to different users' preferences and goals. While reinforcement learning from human feedback (RLHF) is effective at improving LLMs to be generally more helpful and fluent, it does not account for variability across users, as it models the entire user population with a single reward model, meaning it assumes that everyone's preferences are the same.
We present a novel framework, **P**reference **L**earning **U**sing **S**ummarization (**PLUS**), that uses reinforcement learning (RL) to learn to produce text-based summaries of each user's preferences, characteristics, and past conversations. These summaries condition the reward model, enabling it to make personalized predictions about the types of responses valued by each user. Both the user-summarization model and reward model are trained simultaneously, creating an online co-adaptation loop. We show that in contrast to the standard Bradley–Terry model, summaries produced by PLUS capture diverse aspects of user preferences, achieving a 11–77\% improvement in reward model accuracy. Key strengths of PLUS are: (1) robust performance with new users and conversation topics, achieving a 25\% improvement over the best personalized reward model technique used for RLHF; (2) zero-shot personalization with state-of-the-art proprietary models like GPT-4 (e.g., PLUS-summary-conditioned responses achieved a 72\% win rate compared to 28\% for default GPT-4o); (3) learning from flexible user contexts beyond preference labels, and (4) interpretable representation of users, enabling greater transparency and user control in pluralistic LLM alignment.
Learning Human Habits with Rule-Guided Active Inference
Authors: GONG ZHIREN; Chao Yang; Wendi Ren; Shuang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Humans navigate daily life by combining two modes of behavior: deliberate planning in novel situations and fast, automatic responses in familiar ones. Modeling human decision-making therefore requires capturing how people switch between these modes. We present a framework for learning human habits with rule-guided active inference, extending the view of the brain as a prediction machine that minimizes mismatches between expectations and observations, and computationally modeling of human(-like) behavior and habits. In our approach, habits emerge as symbolic rules that serve as compact, interpretable shortcuts for action. To learn these rules alongside the human models, we design a biologically inspired wake--sleep algorithm. In the wake phase, the agent engages in active inference on real trajectories: reconstructing states, updating beliefs, and harvesting candidate rules that reliably reduce free energy. In the sleep phase, the agent performs generative replay with its world model, refining parameters and consolidating or pruning rules by minimizing joint free energy. This alternating rule–model consolidation lets the agent build a reusable habit library while preserving the flexibility to plan. Experiments on basketball player movements, car-following behavior, medical diagnosis, and visual game strategy demonstrate that our framework improves predictive accuracy and efficiency compared to logic-based, deep learning, LLM-based, model-based RL, and prior active inference baselines, while producing interpretable rules that mirror human-like habits.
Towards Safe and Optimal Online Bidding: A Modular Look-ahead Lyapunov Framework
Authors: Hengquan Guo; Haobo Zhang; Junwei Pan; Shudong Huang; Nianhua Xie; Lei Xiao; Haijie Gu; Jie Jiang; Xin Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper studies online bidding subject to simultaneous budget and return-on-investment (ROI) constraints, which encodes the goal of balancing high volume and profitability. We formulate the problem as a general constrained online learning problem that can be applied to diverse bidding settings (e.g., first-price or second-price auctions) and feedback regimes (e.g., full or partial information), among others. We introduce L2FOB, a Look-ahead Lyapunov Framework for Online Bidding with strong empirical and theoretical performance. By combining optimistic reward and pessimistic cost estimation with the look-ahead virtual queue mechanism, L2FOB delivers safe and optimal bidding decisions. We provide adaptive guarantees: L2FOB achieves $O (\mathcal{E}\_r(T,p)+(\nu^* / \rho)  \mathcal{E}\_c(T,p))$ regret and $O (\mathcal{E}\_r(T,p)+\mathcal{E}\_c(T,p))$ anytime ROI constraint violation, where $\mathcal{E}_r(T,p)$ and $\mathcal{E}_c(T,p)$ are cumulative estimation errors over $T$ rounds, $\rho$ is the average per-round budget, and $\nu^*$ is the offline optimal average reward. We instantiate L2FOB in several online bidding settings, demonstrating guarantees that match or improve upon the best-known results. These results are derived from the novel look-ahead design and Lyapunov stability analysis. Numerical experiments further validate our theoretical guarantees.
Universal Multi-Domain Translation via Diffusion Routers
Authors: Duc Kieu; Kien Do; Tuan Hoang; Thao Minh Le; Tung Kieu; Dang Nguyen; Thin Nguyen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-domain translation (MDT) aims to learn translations between multiple domains, yet existing approaches either require fully aligned tuples or can only handle domain pairs seen in training, limiting their practicality and excluding many cross-domain mappings. We introduce universal MDT (UMDT), a generalization of MDT that seeks to translate between any pair of $K$ domains using only $K-1$ paired datasets with a central domain. To tackle this problem, we propose Diffusion Router (DR), a unified diffusion-based framework that models all central$\leftrightarrow$non-central translations with a single noise predictor conditioned on the source and target domain labels. DR enables indirect non-central translations by routing through the central domain. We further introduce a novel scalable learning strategy with a variational-bound objective and an efficient Tweedie refinement procedure to support direct non-central mappings. Through evaluation on three large-scale UMDT benchmarks, DR achieves state-of-the-art results for both indirect and direct translations, while lowering sampling cost and unlocking novel tasks such as sketch$\leftrightarrow$segmentation. These results establish DR as a scalable and versatile framework for universal translation across multiple domains.
Fast Convergence of Natural Gradient Descent for Over-parameterized Physics-Informed Neural Networks
Authors: Xianliang Xu; Wang Kong; JiahengM; Zhongyi Huang; Ye Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In the context of over-parameterization, there is a line of work demonstrating that randomly initialized (stochastic) gradient descent (GD) converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. However, the convergence rate of GD for training two-layer neural networks exhibits poor dependence on the sample size and the Gram matrix, leading to a slow training process. In this paper, we show that for training two-layer $\text{ReLU}^3$ Physics-Informed Neural Networks (PINNs), the learning rate can be improved from the smallest eigenvalue of the limiting Gram matrix to the reciprocal of the largest eigenvalue, implying that GD actually enjoys a faster convergence rate. Despite such improvements, the convergence rate is still tied to the least eigenvalue of the Gram matrix, leading to slow convergence. We then develop the positive definiteness of Gram matrices with general smooth activation functions and provide the convergence analysis of natural gradient descent (NGD) in training two-layer PINNs, demonstrating that the maximal learning rate can be $\mathcal{O}(1)$ and at this rate, the convergence rate is independent of the Gram matrix. In particular, for smooth activation functions, the convergence rate of NGD is quadratic. Numerical experiments are conducted to verify our theoretical results.