GenAI4ELab/papercli-papers huggingface dataset:
Learning to Generate Unit Test via Adversarial Reinforcement Learning
Authors: Dongjun Lee; Changho Hwang; Kimin Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unit testing is a core practice in programming, enabling systematic evaluation of programs produced by human developers or large language models (LLMs). Given the challenges in writing comprehensive unit tests, LLMs have been employed to automate unit test generation, yet methods for training LLMs to produce high-quality unit tests remain underexplored. In this work, we propose UTRL, a novel reinforcement learning (RL) framework that trains an LLM to generate high-quality unit test given a programming instruction. Our key idea is to iteratively train two LLMs, the unit test generator and the code generator, in an adversarial manner via RL: (1) the unit test generator is trained to maximize a discrimination reward, encouraging it to produce tests that reveal faults in the code generator’s solutions; and (2) the code generator is trained to maximize a code reward, encouraging it to produce solutions that pass the unit tests generated by the unit test generator. In our experiment, we demonstrate that unit tests generated by Qwen3-4B trained via UTRL show higher quality compared to unit tests generated by the same model trained via supervised fine-tuning on ground-truth unit tests, yielding code evaluations that more closely align with those induced by the ground-truth tests. Moreover, Qwen3-4B trained with UTRL outperforms frontier models like GPT-4.1 and GPT-4o in generating high-quality unit tests, highlighting the effectiveness of UTRL in training LLMs for the unit test generation.
Cutting the Skip: Training Residual-Free Transformers
Authors: Yiping Ji; James Martens; Jianqiao Zheng; Ziqin Zhou; Peyman Moghadam; Xinyu Zhang; Hemanth Saratchandran; Simon Lucey
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformers have achieved remarkable success across a wide range of applications, a feat often attributed to their scalability. Yet training them without residual (skip) connections remains notoriously difficult. While skips stabilize optimization, they also disrupt the hierarchical structure of representations, raising the long-standing question of whether transformers can be trained efficiently without them. In this work, we address this problem by analyzing the Jacobian of a skipless transformer block, showing why residuals improve conditioning and revealing that their stabilization benefits can be recovered through a principled initialization strategy. Building on this insight, we introduce the first method that enables stable and efficient training of skipless transformers without altering the standard architecture. We validate our approach on Vision Transformers (ViTs) in both supervised and self-supervised settings, demonstrating that skipless ViTs trained with our initialization overcome the usual optimization barriers, learn richer hierarchical representations, and outperform strong residual baselines on dense prediction benchmarks. These results show that skip connections are not a fundamental requirement for training ViTs and open new avenues for hierarchical representation learning in vision models.
IGU-LoRA: Adaptive Rank Allocation via Integrated Gradients and Uncertainty-Aware Scoring
Authors: Xuan Cui; HuiYue Li; Run Zeng; Yunfei Zhao; Jinrui Qian; Wei Duan; Bo Liu; Zhanpeng Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As large language models (LLMs) scale to billions of parameters, full-parameter fine-tuning becomes compute- and memory-prohibitive. Parameter-efficient fine-tuning (PEFT) mitigates this issue by updating only a small set of task-specific parameters while keeping the base model frozen. Among PEFT approaches, low-rank adaptation (LoRA) is widely adopted; however, it enforces a uniform rank across layers despite substantial variation in layer importance, motivating layerwise rank allocation. Recent adaptive-rank variants (e.g., AdaLoRA) allocate ranks based on importance scores, yet typically rely on instantaneous gradients that capture only local sensitivity, overlooking non-local, pathwise effects within the same layer, which yields unstable and biased scores. To address this limitation, we introduce IGU-LoRA, an adaptive-rank LoRA that (i) computes within-layer Integrated Gradients (IG) sensitivities and aggregates them into a layer-level score for rank allocation, and (ii) applies an uncertainty-aware scheme using exponential moving averages with deviation tracking to suppress noisy updates and calibrate rank selection. Theoretically, we prove an upper bound on the composite trapezoidal rule approximation error for parameter-space IG under a pathwise Hessian-Lipschitz condition, which informs the quadrature budget. Across diverse tasks and architectures, IGU-LoRA consistently outperforms strong PEFT baselines at matched parameter budgets, improving downstream accuracy and robustness. Ablations confirm the contributions of pathwise within-layer sensitivity estimates and uncertainty-aware selection to effective rank allocation.
Learning Admissible Heuristics for A*:  Theory and Practice
Authors: Ehsan Futuhi; Nathan R. Sturtevant
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Heuristic functions are central to the performance of search algorithms such as A*, where \emph{admissibility}—the property of never overestimating the true shortest-path cost—guarantees solution optimality. Recent deep learning approaches often disregard full admissibility and provide limited guarantees on generalization beyond the training data. We address both of these limitations. First, we pose heuristic learning as a constrained optimization problem and introduce \emph{Cross-Entropy Admissibility (CEA)}, a loss function that enforces admissibility during training. When evaluated on the Rubik’s Cube domain, our method yields heuristics with near-perfect admissibility and significantly stronger guidance than compressed pattern database (PDB) heuristics. On the theoretical side, we derive a new upper bound on the expected suboptimality of A*. By leveraging PDB abstractions and the structural properties of graphs such as the Rubik’s Cube, we tighten the bound on the number of training samples needed for A* to generalize to unseen states. Replacing a general hypothesis class with a ReLU neural network gives bounds that depend primarily on the network’s width and depth, rather than on graph size. Using the same network, we also provide the first generalization guarantees for \emph{goal-dependent} heuristics.
Temporal Graph Thumbnail: Robust Representation Learning with Global Evolutionary Skeleton
Authors: Weining Shi; Zhisen Wen; Qinggang Zhang; Chentao Zhang; Zhihong Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Temporal graphs are commonly employed as conceptual models for capturing time-evolving interactions in real-world systems. Representation learning on such non-Euclidean data typically depends on aggregating information from neighbors, and the presence of temporal dynamics further complicates this process. However, neighbors often contain noisy information in practice, making the unreliable propagation of knowledge and may even lead to the model failure. Although existing methods employ adaptive spatiotemporal neighbor sampling strategies or temporal dependency modeling frameworks to enhance model robustness, their constrained sampling scope limits handling of severe noise and long-term dependencies. This limitation can be attributed to a fundamental cause: neglecting global evolution inherently overlooks the temporal regularities encoded in continuous dynamics. To address this, we propose the **T**emporal **G**raph **T**humbnail (**TGT**), encapsulating a temporal graph’s global evolutionary skeleton as a thumbnail to characterize temporal regularities and enhance model robustness. Specifically, we model the thumbnail by leveraging von Neumann graph entropy and node mutual information to extract essential evolutionary skeleton from the raw temporal graph, and subsequently use it to guide optimization for model learning. In addition to rigorous theoretical derivation, extensive experiments demonstrate that TGT achieves superior capability and robustness compared to baselines, particularly in rapidly evolving and noisy environments. The code is available at https://anonymous.4open.science/r/TGT-BDF2.
LumosX: Relate Any Identities with Their Attributes for Personalized Video Generation
Authors: Jiazheng Xing; Fei Du; Hangjie Yuan; Pengwei Liu; Hongbin Xu; Hai Ci; Ruigang Niu; Weihua Chen; Fan Wang; Yong Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in diffusion models have significantly improved text-to-video generation, enabling personalized content creation with fine-grained control over both foreground and background elements. However, precise face–attribute alignment across subjects remains challenging, as existing methods lack explicit mechanisms to ensure intra-group consistency. Addressing this gap requires both explicit modeling strategies and face-attribute-aware data resources.  We therefore propose LumosX, a framework that advances both data and model design. On the data side, a tailored collection pipeline orchestrates captions and visual cues from independent videos, while multimodal large language models (MLLMs) infer and assign subject-specific dependencies. These extracted relational priors impose a finer-grained structure that amplifies the expressive control of personalized video generation and enables the construction of a comprehensive benchmark. On the modeling side, Relational Self-Attention and Relational Cross-Attention intertwine position-aware embeddings with refined attention dynamics to inscribe explicit subject–attribute dependencies, enforcing disciplined intra-group cohesion and amplifying the separation between distinct subject clusters. Comprehensive evaluations on our benchmark demonstrate that LumosX achieves state-of-the-art performance in fine-grained, identity-consistent, and semantically aligned personalized multi-subject video generation.
UrbanGS: Efficient and Scalable Architecture for Geometrically Accurate Large-Scene Reconstruction
Authors: Changbai Li; Haodong Zhu; Hanlin Chen; Xiuping Liang; Tongfei Chen; Shuwei Shao; Linlin Yang; Huobin Tan; Baochang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While 3D Gaussian Splatting (3DGS) delivers high-quality, real-time rendering for bounded scenes, its extension to large-scale urban environments introduces critical challenges in geometric consistency, memory efficiency, and computational scalability. We present UrbanGS, a scalable reconstruction framework that effectively addresses these challenges for city-scale applications.
We propose a Depth-Consistent D-Normal Regularization module. In contrast to existing approaches that rely solely on monocular normal estimators—which effectively update rotation parameters but poorly optimize other geometric attributes—our method integrates D-Normal constraints with external depth supervision. This enables comprehensive updates of all geometric parameters. By further incorporating an adaptive confidence weighting mechanism based on gradient consistency and inverse depth deviation, our approach significantly enhances multi-view depth alignment and geometric coherence.
To improve scalability, we introduce a Spatially Adaptive Gaussian Pruning (SAGP) strategy, which dynamically adjusts Gaussian density based on local geometric complexity and visibility to reduce redundancy. Additionally, a unified partitioning and view assignment scheme is designed to eliminate boundary artifacts and optimize computational load. Extensive experiments on multiple urban datasets demonstrate that UrbanGS achieves superior performance in rendering quality, geometric accuracy, and memory efficiency, offering a systematic solution for high-fidelity large-scale scene reconstruction.
Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward
Authors: Xinyu Tang; Zhenduo Zhang; Yurou Liu; Xin Zhao; zujie wen; Zhiqiang Zhang; JUN ZHOU
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in large language models (LLMs) have utilized reinforcement learning with verifiable rewards (RLVR) to improve reasoning capabilities.
However, scaling these methods typically requires massive data and extensive rollout computations, leading to high training costs and low data efficiency.
To mitigate this issue, we propose DEPO, a Data-Efficient Policy Optimization approach that combines optimized strategies for both offline and online data selection.
In the offline phase, we curate a high-quality subset of training data based on multiple objectives, including diversity, influence, and difficulty.
During online RLVR training, we propose a sample-level explorability metric to dynamically filter out samples with low exploration potential, thereby reducing substantial rollout computational costs.
Additionally, we employ a replay mechanism for under-explored samples to ensure sufficient training, which enhances the final convergence performance.
Experiments on five reasoning benchmarks show that DEPO consistently outperforms existing methods in both offline and online data selection scenarios.
Notably, using only 20% of the training data, our approach achieves a 1.85 $\times$ speed-up on AIME24 and a 1.66 $\times$ speed-up on AIME25 compared to GRPO trained on the full dataset.
PICABench: How Far are We from Physical Realistic  Image Editing?
Authors: Yuandong Pu; Le Zhuo; Songhao Han; Jinbo Xing; Kaiwen Zhu; Shuo Cao; Bin Fu; Si Liu; Hongsheng Li; Yu Qiao; Wenlong Zhang; Xi Chen; Yihao Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Image editing has achieved remarkable progress recently. Modern editing models could already follow complex instructions to manipulate the original content. However, beyond completing the editing instructions, the accompanying physical effects are the key to the generation realism. For example, removing an object should also remove its shadow, reflections, and interactions with nearby objects. Unfortunately, existing models and benchmarks mainly focus on instruction completion but overlook these physical effects. So, at this moment, how far are we from physically realistic image editing? To answer this, we introduce PICABench, which systematically evaluates physical realism across eight sub-dimension(spanning optics, mechanics, and state transitions) for most of the common editing operations(add, remove, attribute change, etc). We further propose the PICAEval, a reliable evaluation protocol that uses VLM-as-a-judge with per-case, region-level human annotations and questions. Beyond benchmarking, we also explore effective solutions by learning physics from videos and construct a training dataset PICA-100K.After evaluating most of the mainstream models, we observe that physical realism remains a challenging problem with large rooms to explore. We hope that our benchmark and proposed solutions can serve as a foundation for future work moving from naive content editing toward physically consistent realism.
Not All Bits Are Equal: Scale-Dependent Memory Optimization Strategies for Reasoning Models
Authors: Junhyuck Kim; Ethan Ewer; Taehong Moon; Jongho Park; Dimitris Papailiopoulos
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While 4-bit quantization has emerged as a memory-optimal choice for non-reasoning models and zero-shot tasks across scales, we show that this universal prescription fails for reasoning models, where KV cache rather than model size can dominate memory. 
Through systematic experiments on mathematical, code generation, and knowledge-intensive reasoning tasks, we find a scale-dependent trade-off: models with an effective size below 8-bit 4B parameters achieve better accuracy by allocating memory to larger weights, rather than longer generation, while larger models benefit from the opposite strategy. 
This scale threshold also determines when parallel scaling becomes memory-efficient and whether KV cache eviction outperforms KV quantization. 
Our findings show that memory optimization for LLMs cannot be scale-agnostic, while providing principled guidelines: for small reasoning models, prioritize model capacity over test-time compute, while for large ones, maximize test-time compute. 
Our results suggest that optimizing reasoning models for deployment requires fundamentally different strategies than those established for non-reasoning ones.
Prior-free Tabular Test-time Adaptation
Authors: Rundong He; Jieming Shi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep neural networks (DNNs) have been effectively deployed in tabular data modeling for various applications. However, these models suffer severe performance degradation when distribution shifts exist between training and test tabular data. While test-time adaptation (TTA) serves as a promising solution to distribution shifts, existing TTA methods primarily focus on visual modalities and demonstrate poor adaptation when directly applied to tabular modality. Recent efforts have proposed tabular-specific TTA approaches to mitigate distribution shifts on tabular data. Nevertheless, these methods inherently assume the accessibility of source domain or prior and fail to fundamentally address feature shift while overlooking unique characteristics of tabular data, leading to suboptimal adaptation. In this paper, we focus on the problem of \textit{prior-free tabular test-time adaptation} where no access to source data and any prior knowledge is allowed, and we propose a novel method, \underline{P}rior-\underline{F}ree \underline{T}abular \underline{T}est-\underline{T}ime \underline{A}daptation (PFT$_3$A), which has three designs to simultaneously address label shift and feature shift without source domain or prior access. Specially, PFT$_3$A contains the \textit{Class Prior Estimating} module for estimating source-target class priors to calibrate prediction, eliminating dependency on source class prior and mitigating label shift; the \textit{Robust Feature Learning} module for learning robust feature by aligning source-like and target-like features to mitigate feature shift; the \textit{Representative Subspace Exploration} module for eliminating redundant features by projecting feature into subspace to enhance feature alignment. Extensive experiments demonstrate the effectiveness and generalization of PFT$_3$A in tabular TTA tasks. The implementation is at \url{https://github.com/rundohe/PFT3A}.
Factuality Matters: When Image Generation and Editing Meet Structured Visuals
Authors: Le Zhuo; Songhao Han; Yuandong Pu; Boxiang Qiu; Sayak Paul; Yue Liao; Yihao Liu; Jie Shao; Xi Chen; Si Liu; Hongsheng Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While modern visual generation models excel at creating aesthetically pleasing natural images, they struggle with producing or editing structured visuals like charts, diagrams, and mathematical figures, which demand composition planning, text rendering, and multimodal reasoning for factual fidelity. To address this, we present the first comprehensive, systematic investigation of this domain, encompassing data construction, model training, and an evaluation benchmark. First, we construct a large-scale dataset of 1.3 million high-quality structured image pairs derived from executable drawing programs and augmented with chain-of-thought reasoning annotations. Building on it, we train a unified model that integrates a VLM with FLUX.1 Kontext via a lightweight connector for enhanced multimodal understanding. A three-stage training curriculum enables progressive feature alignment, knowledge infusion, and reasoning-augmented generation, further boosted by an external reasoner at inference time. Finally, we introduce StructBench, a novel benchmark for generation and editing with over 1,700 challenging instances, and an accompanying evaluation metric, StructScore, which employs a multi-round Q\&A protocol to assess fine-grained factual accuracy. Evaluations of 15 models reveal that even leading closed-source systems remain far from satisfactory. Our model attains strong editing performance, and inference-time reasoning yields consistent gains across diverse architectures. By releasing the dataset, model, and benchmark, we aim to advance unified multimodal foundations for structured visuals.
IVEBench: Modern Benchmark Suite for Instruction-Guided Video Editing Assessment
Authors: Yinan Chen; Jiangning Zhang; Teng Hu; Yuxiang Zeng; Zhucun Xue; Qingdong He; Chengjie Wang; Yong Liu; Xiaobin Hu; Shuicheng YAN
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Instruction-guided video editing has emerged as a rapidly advancing research direction, offering new opportunities for intuitive content transformation while also posing significant challenges for systematic evaluation. Existing video editing benchmarks fail to support the evaluation of instruction-guided video editing adequately and further suffer from limited source diversity, narrow task coverage and incomplete evaluation metrics. To address above limitations, we introduce IVEBench, a modern benchmark suite specifically designed for instruction-guided video editing assessment. IVEBench comprises a diverse database of 600 high-quality source videos, spanning seven semantic dimensions, and covering video lengths ranging from 32 to 1,024 frames. It further includes 8 categories of editing tasks with 35 subcategories, whose prompts are generated and refined through large language models and expert review. Crucially, IVEBench establishes a three-dimensional evaluation protocol encompassing video quality, instruction compliance and video fidelity, integrating both traditional metrics and multimodal large language model-based assessments. Extensive experiments demonstrate the effectiveness of IVEBench in benchmarking state-of-the-art instruction-guided video editing methods, showing its ability to provide comprehensive and human-aligned evaluation outcomes. All data and code will be made publicly available.
Null-Space Filtering for Data-Free Continual Model Merging: Preserving Stability, Promoting Plasticity
Authors: Zihuan Qiu; Lei Wang; Yang Cao; Runtong ZHANG; Bing Su; Yi Xu; Fanman Meng; Linfeng Xu; Qingbo Wu; Hongliang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Data-free continual model merging (DFCMM) aims to fuse independently fine-tuned models into a single backbone that evolves with incoming tasks without accessing task data.
This paper revisits two fundamental desiderata for DFCMM: stability, avoiding interference with earlier tasks, and plasticity, adapting faithfully to each new task. This poses a challenge that existing approaches fail to address: how to bridge data-level desiderata with parameter-space optimization to ensure stability and plasticity in the absence of task data.
To this end, we propose NUFILT(NUll-space FILTering) a data-free framework that directly links these desiderata into parameter-space optimization. Our key observation is that task vectors approximately align with representation subspaces, providing structural surrogates for enforcing stability and plasticity. 
Accordingly, we design a null-space projector that preserves prior responses by filtering overlapping components of new task vectors, ensuring stability.
We further introduce a lightweight LoRA adapter that injects complementary task-specific signals to enable plasticity.
The adapter is trained with a projection-based surrogate loss that preserves consistency with prior knowledge while introducing novel directions. 
This joint filtering–adaptation process enables the backbone to absorb new knowledge while retaining existing behaviors, with updates fused back in a layer-wise linear fashion without extra parameters or inference cost.
Theoretically, we establish approximate subspace alignment guarantees that justify null-space filtering. Empirically, NUFILT achieves state-of-the-art performance with minimal forgetting on both vision and NLP benchmarks, improving average accuracy by 4–7% over OPCM and WUDI-Merging, while narrowing the gap to fine-tuning and reducing computation overhead. The code is available at: https://github.com/zihuanqiu/NUFILT