GenAI4ELab/papercli-papers huggingface dataset:
Operator Learning with Domain Decomposition for Geometry Generalization in PDE Solving
Authors: Jianing Huang; Kaixuan Zhang; Youjia Wu; Ze Cheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Neural operators have become increasingly popular in solving partial differential equations (PDEs) due to their superior capability to capture intricate mappings between function spaces over complex domains. However, the data-hungry nature of operator learning inevitably poses a bottleneck for their widespread applications. At the core of the challenge lies the absence of transferability of neural operators to new geometries. To tackle this issue, we propose operator learning with domain decomposition, a local-to-global framework to solve PDEs on arbitrary geometries. Under this framework, we devise an iterative scheme Schwarz Neural Inference (SNI). This scheme allows for partitioning of the problem domain into smaller subdomains, on which local problems can be solved with neural operators, and stitching local solutions to construct a global solution. Additionally, we provide a theoretical analysis of the convergence rate and error bound. We conduct extensive experiments on several representative linear and nonlinear PDEs with diverse boundary conditions and achieve remarkable geometry generalization compared to alternative methods.These analysis and experiments demonstrate the proposed framework's potential in addressing challenges related to geometry generalization and data efficiency. The code is publicly available at this repository: https://github.com/questionstorer/sni.
SYNC: Measuring and Advancing Synthesizability in Structure-Based Drug Design
Authors: Yunfan Liu; Lirong Wu; Zhifeng Gao; Yufei Huang; Cheng Tan; Haitao Lin; Zicheng Liu; Changxi Chi; Chang Yu; Stan Z. Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Designing 3D ligands that bind to a given protein pocket with high affinity is a fundamental task in Structure-Based Drug Design (SBDD). However, the lack of synthesizability of 3D ligands has been hindering progress toward experimental validation; moreover, computationally evaluating synthesizability is a non-trivial task. In this paper, we first benchmark eight classical synthesizability metrics across 11 SBDD methods. The comparison reveals significant inconsistencies between these metrics, making them impractical and inaccurate criteria for guiding SBDD methods toward synthesizable drug design. Therefore, we propose a simple yet effective SE(3)-invariant \textit{\underline{SYN}thesizability \underline{C}lassifier} (SYNC) to enable better synthesizability estimation in SBDD, which demonstrates superior generalizability and speed compared to existing metrics on five curated datasets. Finally, with SYNC as a plug-and-play module, we establish a synthesizability classifier-driven SBDD paradigm through guided diffusion and Direct Preference Optimization, where highly synthesizable molecules are directly generated without compromising binding affinity. Extensive experiments also demonstrate the effectiveness of SYNC and the advantage of our paradigm in synthesizable SBDD. Code is available at \url{https://github.com/XYxiyang/SYNC}.
Chain-of-Context Learning: Dynamic Constraint Understanding for Multi-Task VRPs
Authors: Shuangchun Gui; Suyu Liu; Xuehe Wang; Zhiguang Cao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-task Vehicle Routing Problems (VRPs) aim to minimize routing costs while satisfying diverse constraints. Existing solvers typically adopt a unified reinforcement learning (RL) framework to learn generalizable patterns across tasks. However, they often overlook the constraint and node dynamics during the decision process, making the model fail to accurately react to the current context. To address this limitation, we propose Chain-of-Context Learning (CCL), a novel framework that progressively captures the evolving context to guide fine-grained node adaptation. Specifically, CCL constructs step-wise contextual information via a Relevance-Guided Context Reformulation (RGCR) module, which adaptively prioritizes salient constraints. This context then guides node updates through a Trajectory-Shared Node Re-embedding (TSNR) module, which aggregates shared node features from all trajectories' contexts and uses them to update inputs for the next step. By modeling evolving preferences of the RL agent, CCL captures step-by-step dependencies in sequential decision-making. We evaluate CCL on 48 diverse VRP variants, including 16 in-distribution and 32 out-of-distribution (with unseen constraints) tasks. Experimental results show that CCL performs favorably against the state-of-the-art baselines, achieving the best performance on all in-distribution tasks and the majority of out-of-distribution tasks.
GarmentGPT: Compositional Garment Pattern Generation via Discrete Latent Tokenization
Authors: Fangsheng Weng; Junhao Chen; Xiang Li; Jie Qin; Hanzhong Guo; ShaochunHao; Xiaoguang Han
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Apparel is a fundamental component of human appearance, making garment digitalization critical for digital human creation. However, sewing pattern creation traditionally relies on the intuition and extensive experience of skilled artisans. This manual bottleneck significantly hinders the scalability of digital garment creation. Existing generative approaches either operate as data replicators without intrinsic understanding of garment construction principles (e.g., diffusion models), or struggle with low-level regression of raw floating-point coordinates (e.g., Vision-Language Models).
We present GarmentGPT, the first framework to operationalize latent space generation for sewing patterns. Our approach introduces a novel pipeline where a RVQ-VAE tokenizes continuous pattern boundary curves into discrete codebook indices. A fine-tuned Vision-Language Model then autoregressively predicts these discrete token sequences instead of regressing coordinates, enabling high-level compositional reasoning. This paradigm shift aligns generation with the knowledge-driven, symbolic reasoning capabilities of large language models.
To address the data bottleneck for real-world applications, we develop a Data Curation Pipeline that synthesizes over one million photorealistic images paired with GarmentCode, and establish the Real-Garments Benchmark for comprehensive evaluation. Experiments demonstrate that GarmentGPT significantly outperforms existing methods on structured datasets (95.62\% Panel Accuracy, 81.84\% Stitch Accuracy), validating our discrete compositional paradigm's advantages.
Code is available at \url{https://github.com/ChimerAI-MMLab/Garment-GPT}.
Enhancing Geometric Perception in VLMs via Translator-Guided Reinforcement Learning
Authors: Hao Yu; Shuning Jia; Guanghao Li; Wenhao Jiang; Chun Yuan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-language models (VLMs) often struggle with geometric reasoning due to their limited perception of fundamental diagram elements. To tackle this challenge, we introduce GeoPerceive, a benchmark comprising diagram instances paired with domain-specific language (DSL) representations, along with an efficient automatic data generation pipeline. This design enables the isolated evaluation of geometric perception independently from reasoning. To exploit the data provided by GeoPerceive for enhancing the geometric perception capabilities of VLMs, we propose GeoDPO, a translator-guided reinforcement learning framework. GeoDPO employs an NL-to-DSL translator, which is trained on synthetic pairs generated by the data engine of GeoPerceive, to bridge natural language and DSL. This translator facilitates the computation of fine-grained, DSL-level scores, which serve as reward signals in reinforcement learning. We assess GeoDPO on both in-domain and out-of-domain datasets, spanning tasks in geometric perception as well as downstream reasoning. Experimental results demonstrate that, while supervised fine-tuning (SFT) offers only marginal improvements and may even impair performance in out-of-domain scenarios, GeoDPO achieves substantial gains: $+26.5\\%$ on in-domain data, $+8.0\\%$ on out-of-domain data, and $+39.0\\%$ on downstream reasoning tasks. These findings underscore the superior performance and generalization ability of GeoDPO over SFT. All codes are released at https://github.com/Longin-Yu/GeoPerceive to ensure reproducibility.
Beyond In-Domain Detection: SpikeScore for Cross-Domain Hallucination Detection
Authors: Yongxin Deng; Zhen Fang; Sharon Li; Ling Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hallucination detection is critical for deploying large language models (LLMs) in real-world applications. Existing hallucination detection methods achieve strong performance when the training and test data come from the same domain, but they suffer from poor cross-domain generalization. In this paper, we study an important yet overlooked problem, termed generalizable hallucination detection (GHD), which aims to train hallucination detectors on data from a single domain while ensuring robust performance across diverse related domains. In studying GHD, we simulate multi-turn dialogues following LLMs' initial response and observe an interesting phenomenon: hallucination-initiated multi-turn dialogues universally exhibit larger uncertainty fluctuations than factual ones across different domains. Based on the phenomenon, we propose a new score SpikeScore, which quantifies abrupt fluctuations in multi-turn dialogues. Through both theoretical analysis and empirical validation, we demonstrate that SpikeScore achieves strong cross-domain separability between hallucinated and non-hallucinated responses. Experiments across multiple LLMs and benchmarks demonstrate that the SpikeScore-based detection method outperforms representative baselines in cross-domain generalization and surpasses advanced generalization-oriented methods, verifying the effectiveness of our method in cross-domain hallucination detection.
Scheduling Your LLM Reinforcement Learning with Reasoning Trees
Authors: Hong Wang; Zhezheng Hao; Jian Luo; Chenxing Wei; Yao Shu; Lei Liu; Cheaterlin; Hande Dong; Jiawei Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Using Reinforcement Learning with Verifiable Rewards (RLVR) to optimize Large Language Models (LLMs) can be conceptualized as progressively editing a query's 'Reasoning Tree'. This process involves exploring nodes (tokens) and dynamically modifying the model's policy at each node. When combined with data scheduling, this process yields further gains in data efficiency and accuracy.
However, existing RLVR data scheduling methods typically rely on path-based metrics to rank queries, overlooking the reasoning tree structures of these queries.
In this paper, we introduce a novel metric, namely **Reasoning Score (r-score)**, which measures the query's learning difficulty based on the structure of its reasoning tree.
Based on the r-score, we propose the **Reasoning Tree Schedule (Re-Schedule)**, a scheduling algorithm that constructs a curriculum progressing from structurally simple (high r-score) to complex (low r-score) queries.
Experiments on six math-reasoning benchmarks show that Re-Schedule significantly improves average accuracy, achieving gains of up to 3.2\%.
These strong results validate our approach and demonstrate that a structural understanding of the reasoning tree provides a more powerful and principled foundation for RLVR data scheduling.
Reasoning in Space via Grounding in the World
Authors: Yiming Chen; Zekun Qi; Wenyao Zhang; Xin Jin; Li Zhang; Peidong Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we claim that 3D visual grounding is one of the cornerstones of spatial reasoning and introduce the $\textit{Grounded-Spatial Reasoner (GS-Reasoner)}$ to explore the effective spatial representations that bridge the gap between them. Existing 3D LLMs suffer from the absence of a unified 3D representation capable of jointly capturing semantic and geometric information. This deficiency is manifested either in poor performance on grounding or in an excessive reliance on external modules, ultimately hindering the seamless integration of grounding and spatial reasoning. To address this, we propose a simple yet effective \emph{dual-path pooling} mechanism that tightly aligns geometric features with both semantic and positional cues, constructing a unified image patch-based 3D representation that encapsulates all essential information without extra tokens. Leveraging this holistic representation, GS-Reasoner is the first 3D LLMs that achieves autoregressive grounding entirely without external modules while delivering performance comparable to state-of-the-art models, establishing a unified and self-contained framework for 3D spatial reasoning. To further bridge grounding and spatial reasoning, we introduce the $\textit{Grounded Chain-of-Thought (GCoT)}$ dataset. This dataset is meticulously curated to include both 3D bounding box annotations for objects referenced in reasoning questions and step-by-step reasoning paths that integrate grounding as a core component of the problem-solving process. Extensive experiments demonstrate that GS-Reasoner achieves impressive results on 3D visual grounding, which in turn significantly enhances its spatial reasoning capabilities, leading to state-of-the-art performance.
Delving into Spectral Clustering with Vision-Language Representations
Authors: Bo Peng; Yuanwei Hu; Bo Liu; Ling Chen; Jie Lu; Zhen Fang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spectral clustering is known as a powerful technique in unsupervised data analysis. 
The vast majority of approaches to spectral clustering are driven by a single modality, leaving the rich information in multi-modal representations untapped. 
Inspired by the recent success of vision-language pre-training, this paper enriches the landscape of spectral clustering from a single-modal to a multi-modal regime. 
Particularly, we propose Neural Tangent Kernel Spectral Clustering that leverages cross-modal alignment in pre-trained vision-language models. 
By anchoring the neural tangent kernel with positive nouns, i.e., those semantically close to the images of interest, we arrive at formulating the affinity between images as a coupling of their visual proximity and semantic overlap. 
We show that this formulation amplifies within-cluster connections while suppressing spurious ones across clusters, hence encouraging block-diagonal structures. 
In addition, we present a regularized affinity diffusion mechanism that adaptively ensembles affinity matrices induced by different prompts.
Extensive experiments on \textbf{16} benchmarks---including classical, large-scale, fine-grained and domain-shifted datasets---manifest that our method consistently outperforms the state-of-the-art by a large margin.
Knowledge Fusion of Large Language Models via Modular SkillPacks
Authors: Guodong DU; Zhuo Li; Xuanning Zhou; Junlin Li; Zesheng Shi; Wanyu Lin; Ho-Kin Tang; Xiucheng Li; Fangming Liu; Wenya Wang; Min Zhang; Jing Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cross-capability transfer represents a key challenge in large language model (LLM) research, particularly in multi-task integration, model compression, and knowledge fusion. Recent works such as FuseLLM and FuseChat have shown the potential of transferring multiple model capabilities to lightweight models, thereby enhancing adaptability and efficiency. This motivates our investigation into more efficient methods for cross-capability transfer. However, existing merging approaches primarily focus on small, homogeneous models, limiting their applicability.
For large, heterogeneous models, knowledge distillation with full-parameter fine-tuning often overlooks the student model’s inherent capability and risks catastrophic forgetting, while PEFT methods struggle to effectively absorb knowledge from source LLMs.
To address these issues, we introduce **GraftLLM**, a novel grafting-based method that stores source model capabilities in a target model + SkillPack format. This approach preserves general capabilities, reduces parameter conflicts, and supports forget-free continual learning and model fusion. We employ a module-aware adaptive compression strategy for parameter updates, ensuring efficient storage while **preserving task-specific knowledge**. The resulting SkillPack serves as a compact and transferable knowledge carrier, ideal for **heterogeneous LLM fusion**.
Experiments across various scenarios demonstrate that GraftLLM outperforms existing techniques in knowledge transfer, knowledge fusion, and forget-free learning, providing a scalable and efficient solution for cross-capability transfer.
Beyond Fixed: Training-Free Variable-Length Denoising for Diffusion Large Language Models
Authors: Jinsong Li; Xiaoyi Dong; Yuhang Zang; Yuhang Cao; Jiaqi Wang; Dahua Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion Large Language Models (DLLMs) are emerging as a powerful alternative to the dominant Autoregressive Large Language Models, offering efficient parallel generation and capable global context modeling. However, the practical application of DLLMs is hindered by a critical architectural constraint: the need for a statically predefined generation length.  This static length allocation leads to a problematic trade-off: insufficient lengths cripple performance on complex tasks, while excessive lengths incur significant computational overhead and sometimes result in performance degradation.  While the inference framework is rigid, we observe that the model itself possesses internal signals that correlate with the optimal response length for a given task.  To bridge this gap, we leverage these latent signals and introduce DAEDAL, a novel training-free denoising strategy that enables Dynamic Adaptive Length Expansion for Diffusion Large Language Models.  DAEDAL operates in two phases: 1) Before the denoising process, DAEDAL starts from a short initial length and iteratively expands it to a coarse task-appropriate length, guided by a sequence completion metric.  2) During the denoising process, DAEDAL dynamically intervenes by pinpointing and expanding insufficient generation regions through mask token insertion, ensuring the final output is fully developed. Extensive experiments on DLLMs demonstrate that DAEDAL achieves performance comparable, and in some cases superior, to meticulously tuned fixed-length baselines, while simultaneously enhancing computational efficiency by achieving a higher effective token ratio. By resolving the static length constraint, DAEDAL unlocks new potential for DLLMs, bridging a critical gap with their Autoregressive counterparts and paving the way for more efficient and capable generation.
Policy Likelihood-based Query Sampling and Critic-Exploited Reset for Efficient Preference-based Reinforcement Learning
Authors: Jongkook Heo; Jaehoon Kim; Young Jae Lee; Min Gu Kwak; Youngjoon Park; Seoung Bum Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Preference-based reinforcement learning (PbRL) enables agent training without explicit reward design by leveraging human feedback. Although various query sampling strategies have been proposed to improve feedback efficiency, many fail to enhance performance because they select queries from outdated experiences with low likelihood under the current policy. Such queries may no longer represent the agent's evolving behavior patterns, reducing the informativeness of human feedback. To address this issue, we propose a policy likelihood-based query sampling and critic-exploited reset (PoLiCER). Our approach uses policy likelihood-based query sampling to ensure that queries remain aligned with the agent’s evolving behavior. However, relying solely on policy-aligned sampling can result in overly localized guidance, leading to overestimation bias, as the model tends to overfit to early feedback experiences. To mitigate this, PoLiCER incorporates a dynamic resetting mechanism that selectively resets the reward estimator and its associated Q-function based on critic outputs. Experimental evaluation across diverse locomotion and robotic manipulation tasks demonstrates that PoLiCER consistently outperforms existing PbRL methods. Our code is available at https://github.com/JongKook-Heo/PoLiCER.
Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimization
Authors: Manish Acharya; David Hyde
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The sliced Wasserstein distance (SW) reduces optimal transport on $\mathbb{R}^d$ to a sum of one-dimensional projections, and thanks to this efficiency, it is widely used in geometry, generative modeling, and registration tasks.  Recent work shows that quasi-Monte Carlo constructions for computing SW (QSW) yield direction sets with excellent approximation error.  This paper presents an alternate, novel approach: learning directions with Bayesian optimization (BO), particularly in settings where SW appears inside an optimization loop (e.g., gradient flows).  We introduce a family of drop-in selectors for projection directions: **BOSW**, a one-shot BO scheme on the unit sphere; **RBOSW**, a periodic-refresh variant; **ABOSW**, an adaptive hybrid that seeds from competitive QSW sets and performs a few lightweight BO refinements; and **ARBOSW**, a restarted hybrid that periodically relearns directions during optimization.  Our BO approaches can be composed with QSW and its variants (demonstrated by ABOSW/ARBOSW) and require no changes to downstream losses or gradients.  We provide numerical experiments where our methods achieve state-of-the-art performance, and on the experimental suite of the original QSW paper, we find that ABOSW and ARBOSW can achieve convergence comparable to the best QSW variants with modest runtime overhead.  We release code with fixed seeds and configurations to support faithful replication (see supplementary material).
DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning
Authors: Weize Liu; Yongchi Zhao; Yijia Luo; Mingyu Xu; Jiaheng Liu; Yanan Li; Xiguo Hu; ZhiqiBai; Yuchi Xu; Wenbo Su; Bo Zheng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) perform strongly on many language tasks but still struggle with complex multi-step reasoning across disciplines. Existing reasoning datasets often lack disciplinary breadth, reasoning depth, and diversity, as well as guiding principles for question synthesis. We propose DESIGNER: a DESIGN-logic-guidEd Reasoning data synthesis pipeline that leverages naturally available, extensive raw documents to generate multidisciplinary questions. The central insight is the notion of Design Logic, a form of reusable meta-knowledge that encapsulates the structured process human experts use to transform knowledge into complex exam questions, enabling LLMs to generate new questions with the same complex reasoning patterns from entirely different source texts with explicit control over difficulty, diversity, and question types. We use LLMs to reverse-engineer and abstract over 120,000 Design Logics from existing questions across various disciplines. By designing a two-stage retrieve-and-generate mechanism to match these Design Logics with raw corpus, we synthesized two large-scale reasoning datasets that span 75 disciplines: DLR-Book (3.04 million questions from the book corpus) and DLR-Web (1.66 million questions from the web corpus). Data analysis indicates that the questions synthesized by our method exhibit greater difficulty and diversity compared to those in the baseline datasets. Supervised fine-tuning (SFT) on Qwen3 and Llama3 with our data substantially improves multidisciplinary reasoning and outperforms baseline datasets. Notably, by applying SFT on the base versions of these models using only our data, we even surpass their official final models that have undergone the full post-training.