GenAI4ELab/papercli-papers huggingface dataset:
Direct Doubly Robust Estimation of Conditional Quantile Contrasts
Authors: Josh Givens; Song Liu; Henry Reeve; Katarzyna Reluga
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Within heterogeneous treatment effect (HTE) analysis, various estimands have been proposed to capture the effect of a treatment conditional on covariates. Recently, the conditional quantile comparator (CQC) has emerged as a promising estimand, offering quantile-level summaries akin to the conditional quantile treatment effect (CQTE) while preserving some interpretability of the conditional average treatment effect (CATE).
It achieves this by summarising the treated response conditional on both the covariates and the untreated response. Despite these desirable properties, the CQC's current estimation is limited by the need to first estimate the difference in conditional cumulative distribution functions and then invert it. 
This inversion obscures the CQC estimate, hampering our ability to both model and interpret it. To address this, we propose the first direct estimator of the CQC, allowing for explicit modelling and parameterisation.
This explicit parameterisation enables better interpretation of our estimate while also providing a means to constrain and inform the model. We show, both theoretically and empirically, that our estimation error depends directly on the complexity of the CQC itself, improving upon the existing estimation procedure. Furthermore, it retains the desirable double robustness property with respect to nuisance parameter estimation. We further show our method to outperform existing procedures in estimation accuracy across multiple data scenarios while varying sample size and nuisance error. Finally, we apply it to real-world data from an employment scheme, uncovering a reduced range of potential earnings improvement as participant age increases.
One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs
Authors: Yuanzhi Zhu; Ruiqing Wang; Shilin Lu; Hanshu Yan; Junnan Li; Kai Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in diffusion and flow-based generative models have demonstrated remarkable success in image restoration tasks, achieving superior perceptual quality compared to traditional deep learning approaches. However, these methods either require numerous sampling steps to generate high-quality images, resulting in significant computational overhead, or rely on common model distillation, which usually imposes a fixed fidelity-realism trade-off and thus lacks flexibility. In this paper, we introduce OFTSR, a novel flow-based framework for one-step image super-resolution that can produce outputs with tunable levels of fidelity and realism. Our approach first trains a conditional flow-based super-resolution model to serve as a teacher model. We then distill this teacher model by applying a specialized constraint. Specifically, we force the predictions from our one-step student model for same input to lie on the same sampling ODE trajectory of the teacher model. This alignment ensures that the student model's single-step predictions from initial states match the teacher's predictions from a closer intermediate state. Through extensive experiments on datasets including FFHQ (256$\times$256), DIV2K, and ImageNet (256$\times$256), we demonstrate that OFTSR achieves state-of-the-art performance for one-step image super-resolution, while having the ability to flexibly tune the fidelity-realism trade-off. 
Code and pre-trained models are available at \href{https://github.com/yuanzhi-zhu/OFTSR}{https://github.com/yuanzhi-zhu/OFTSR} and \href{https://huggingface.co/Yuanzhi/OFTSR}{https://huggingface.co/Yuanzhi/OFTSR}, respectively.
Tree-sliced Sobolev IPM
Authors: Viet-Hoang Tran; Thanh Tran; Thanh Chu; Duy-Tung Pham; Trung-Khang Tran; Tam Le; Tan Minh Nguyen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent work shows Tree-Sliced Optimal Transport to be an efficient and more expressive alternative to Sliced Wasserstein (SW), improving downstream performance. Tree-sliced metrics compare probability distributions by projecting measures onto tree metric spaces; a central example is the Tree-Sliced Wasserstein (TSW) distance, which applies the $1$-Wasserstein metric after projection. However, computing tree-based $p$-Wasserstein for general $p$ is costly, largely confining practical use to $p=1$. This restriction is a significant bottleneck, as higher-order metrics ($p > 1$) are preferred in gradient-based learning for their more favorable optimization landscapes. In this work, we revisit Sobolev integral probability metrics (IPM) on trees to obtain a practical generalization of TSW. Building on the insight that a suitably regularized Sobolev IPM admits a closed-form expression, we introduce TS-Sobolev, a tree-sliced metric that aggregates regularized Sobolev IPMs over random tree systems and remains tractable for all $p \ge 1$; for $p>1$, TS-Sobolev has the same computational complexity as TSW at $p=1$. Notably, at $p=1$ it recovers TSW exactly. Consequently, TS-Sobolev serves as a drop-in replacement for TSW in practical applications, with an additional flexibility in changing $p$. Furthermore, we extend this framework to define a corresponding metric for probability measures on hyperspheres. Experiments on Euclidean and spherical datasets show that TS-Sobolev and its spherical variant improve downstream performance in gradient flows, self-supervised learning, generative modeling, and text topic modeling over recent SW and TSW variants. Our code is available at [https://github.com/thanhquangtran/TS-Sobolev](https://github.com/thanhquangtran/TS-Sobolev).
Mixing Importance with Diversity: Joint Optimization for KV Cache Compression in Large Vision-Language Models
Authors: Xuyang Liu; Xiyan Gui; Yuchao Zhang; Linfeng Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent large vision-language models (LVLMs) demonstrate remarkable capabilities in processing extended multi-modal sequences, yet the resulting key-value (KV) cache expansion creates a critical memory bottleneck that fundamentally limits deployment scalability.
While existing KV cache compression methods focus on retaining high-importance KV pairs to minimize storage, they often overlook the modality-specific semantic redundancy patterns that emerge distinctively in multi-modal KV caches.
In this work, we first analyze how, beyond simple importance, the KV cache in LVLMs exhibits varying levels of redundancy across attention heads. 
We show that relying solely on importance can only cover a subset of the full KV cache information distribution, leading to potential loss of semantic coverage.
To address this, we propose MixKV, a novel method that mixes importance with diversity for optimized KV cache compression in LVLMs. MixKV adapts to head-wise semantic redundancy, selectively balancing diversity and importance when compressing KV pairs. Extensive experiments demonstrate that MixKV consistently enhances existing methods across multiple LVLMs. Under extreme compression (budget=64), MixKV improves baseline methods by an average of 5.1% across five multi-modal understanding benchmarks and achieves remarkable gains of 8.0% and 9.0% for SnapKV and AdaKV on GUI grounding tasks, all while maintaining comparable inference efficiency. Furthermore, MixKV extends seamlessly to LLMs with comparable performance gains.
SWE-RM: Execution-free Feedback for Software Engineering Agents
Authors: KaShun SHUM; Binyuan Hui; Jiawei Chen; Lei Zhang; X. W.; Jiaxi Yang; Yuzhen Huang; Junyang Lin; Junxian He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Execution-based feedback like unit testing is widely used in the development of coding agents through test-time scaling (TTS) and reinforcement learning (RL). This paradigm requires scalable and reliable collection of unit test cases to provide accurate feedback, and the resulting feedback is often sparse and cannot effectively distinguish between trajectories that are both successful or both unsuccessful. In contrast, execution-free feedback from reward models can provide more fine-grained signals without depending on unit test cases. Despite this potential, execution-free feedback for realistic software engineering (SWE) agents remains underexplored. Aiming to develop versatile reward models that are effective across TTS and RL, however, we observe that two verifiers with nearly identical TTS performance can nevertheless yield very different results in RL. Intuitively, TTS primarily reflects the model’s ability to select the best trajectory, but this ability does not necessarily generalize to RL. To address this limitation, we identify two additional aspects that are crucial for RL training: classification accuracy and calibration. We then conduct comprehensive controlled experiments to investigate how to train a robust reward model that performs well across these metrics. In particular, we analyze the impact of various factors such as training data scale, policy mixtures, and data source composition.
Guided by these investigations, we introduce SWE-RM, an accurate and robust reward model adopting a mixture-of-experts architecture with 30B total parameters and 3B activated during inference. SWE-RM substantially improves SWE agents on both TTS and RL performance. For example, it increases the accuracy of Qwen3-Coder-Flash from 51.6% to 62.0%, and Qwen3-Coder-Max from 67.0% to 74.6% on SWE-Bench Verified using TTS, achieving new state-of-the-art performance among open-source models. On RL training, SWE-RM lifts the resolve rate of execution-based counterparts by 3 absolute points on SWE-Bench Verified.
Token-level Data Selection for Safe LLM Fine-tuning
Authors: Yanping Li; Zhening Liu; Zijian Li; Zehong Lin; Jun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-tuning large language models (LLMs) on custom datasets has become a standard approach for adapting these models to specific domains and applications. However, recent studies have shown that such fine-tuning can lead to significant degradation in the model's safety. Existing defense methods operate at the sample level and often suffer from an unsatisfactory trade-off between safety and utility. To address this limitation, we perform a systematic token-level diagnosis of safety degradation during fine-tuning. Based on this, we propose token-level data selection for safe LLM fine-tuning (TOSS), a novel framework that quantifies the safety risk of each token by measuring the loss difference between a safety-degraded model and a utility-oriented model. This token-level granularity enables accurate identification and removal of unsafe tokens, thereby preserving valuable task-specific information. In addition, we introduce a progressive refinement strategy, TOSS-Pro, which iteratively enhances the safety-degraded model's ability to identify unsafe tokens. Extensive experiments demonstrate that our approach robustly safeguards LLMs during fine-tuning while achieving superior downstream task performance, significantly outperforming existing sample-level defense methods. Our code is available at https://github.com/Polly-LYP/TOSS.
Disentangling Length Bias in Preference Learning via Response-Conditioned Modeling
Authors: Jianfeng Cai; Jinhua Zhu; Ruopei Sun; Yue Wang; Li Li; Wengang Zhou; Houqiang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning from Human Feedback (RLHF) has achieved considerable success in aligning large language models (LLMs) by modeling human preferences with a learnable reward model and employing a reinforcement learning algorithm to maximize the reward model's scores. However, these reward models are susceptible to exploitation through various superficial confounding factors, with length bias emerging as a particularly significant concern. Moreover, while the pronounced impact of length bias on preference modeling suggests that LLMs possess an inherent sensitivity to length perception, our preliminary investigations reveal that fine-tuned LLMs consistently struggle to adhere to explicit length instructions. To address these two limitations, we propose a novel framework wherein the reward model explicitly differentiates between human semantic preferences and response length requirements. Specifically, we introduce a $\textbf{R}$esponse-$\textbf{c}$onditioned $\textbf{B}$radley-$\textbf{T}$erry (Rc-BT) model that enhances the model's capability in length bias mitigating and length instruction following, through training on our augmented dataset. Furthermore, we propose the Rc-RM and Rc-DPO algorithm to leverage the Rc-BT model for reward modeling and direct policy optimization (DPO) of LLMs, simultaneously mitigating length bias and promoting adherence to length instructions. Extensive experiments across various models and datasets demonstrate the effectiveness and generalizability of our approach.
UltraViCo: Breaking Extrapolation Limits in Video Diffusion Transformers
Authors: Min Zhao; Hongzhou Zhu; Yingze Wang; Bokai Yan; Jintao Zhang; Guande He; Ling Yang; Chongxuan Li; Jun Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite advances, video diffusion transformers still struggle to generalize beyond their training length, a challenge we term video length extrapolation. We identify two failure modes: model-specific periodic content repetition and a universal quality degradation.
Prior works attempt to solve repetition via positional encodings, overlooking quality degradation and achieving only limited extrapolation. In this paper, we revisit this challenge from a more fundamental view—attention maps, which directly govern how context influences outputs. We identify that both failure modes arise from a unified cause: attention dispersion, where tokens beyond the training window dilute learned attention patterns. This leads to quality degradation and repetition emerges as a special case when this dispersion becomes structured into periodic attention patterns, induced by harmonic properties of positional encodings. Building on this insight, we propose UltraViCo, a training-free, plug-and-play method that suppresses attention for tokens beyond the training window via a constant decay factor. By jointly addressing both failure modes, we outperform a broad set of baselines largely across models and extrapolation ratios, pushing the extrapolation limit from $~2\times$ to $4\times$. Remarkably, it improves Dynamic Degree and Imaging Quality by 233\% and 40.5\% over the previous best method at $4\times$ extrapolation. Furthermore, our method generalizes seamlessly to downstream tasks such as controllable video synthesis and editing.
THOR: Tool-Integrated Hierarchical Optimization via RL for Mathematical Reasoning
Authors: Qikai Chang; Zhenrong Zhang; Pengfei Hu; Jun Du; Jiefeng Ma; Yicheng Pan; Jianshu Zhang; Quan Liu; Jianqing Gao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have made remarkable progress in mathematical reasoning, but still continue to struggle with high-precision tasks like numerical computation and formal symbolic manipulation. Integrating external tools has emerged as a promising approach to bridge this gap. Despite recent advances, existing methods struggle with three key challenges: constructing tool-integrated reasoning data, performing fine-grained optimization, and enhancing inference. To overcome these limitations, we propose THOR (Tool-Integrated Hierarchical Optimization via RL). First, we introduce TIRGen, a multi-agent based pipeline for constructing high-quality datasets of tool-integrated reasoning paths, aligning with the policy and generalizing well across diverse models. Second, to perform fine-grained hierarchical optimization, we introduce an RL strategy that jointly optimizes for both episode-level problem solving and step-level code generation. This is motivated by our key insight that the success of an intermediate tool call is a strong predictor of the final answer's correctness. Finally, THOR incorporates a self-correction mechanism that leverages immediate tool feedback to dynamically revise erroneous reasoning paths during inference. Our approach demonstrates strong generalization across diverse models, performing effectively in both reasoning and non-reasoning models. It further achieves state-of-the-art performance for models of a similar scale on multiple mathematical benchmarks, while also delivering consistent improvements on code benchmarks. Our code will be publicly available at https://github.com/JingMog/THOR.
Unveiling the Basin-Like Loss Landscape in Large Language Models
Authors: Huanran Chen; Zeming Wei; Yao Huang; Yichi Zhang; Yinpeng Dong; Jun Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We discover the emergence of basins in the loss landscape of large language models. As model scale increases, LLMs become progressively more resilient to random perturbations in the parameter space, giving rise to expansive stability regions where models exhibit nearly identical performance, but outside of which their capabilities collapse. We observe that pre-training creates a basic capability basin, and subsequent alignment fine-tuning forms specific capability basins (e.g., safety, math, coding). Thus, we argue that benign fine-tuning confined to the basin should preserve prior capabilities. Besides, we also analyze the loss landscape for worst-case directions, which is consistently sharp and detrimental. We find that adversarial fine-tuning moves along the nearly worst-case directions, thus rapidly degrading model capabilities. Finally, we provide a theoretical analysis demonstrating that the basin size bounds the performance degradation of any fine-tuning, including the adversarial ones, while also guaranteeing the model robustness w.r.t. input perturbations, suggesting the benefit of enlarging basins.
Dataset Color Quantization: A Training-Oriented Framework for Dataset-Level Compression
Authors: YU CHENYUE; Lingao Xiao; Jinhong Deng; Ivor Tsang; Yang He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large-scale image datasets are fundamental to deep learning, but their high storage demands pose challenges for deployment in resource-constrained environments. While existing approaches reduce dataset size by discarding samples, they often ignore the significant redundancy within each image -- particularly in the color space. To address this, we propose Dataset Color Quantization (DCQ), a unified framework that compresses visual datasets by reducing color-space redundancy while preserving information crucial for model training. DCQ achieves this by enforcing consistent palette representations across similar images, selectively retaining semantically important colors guided by model perception, and maintaining structural details necessary for effective feature learning. Extensive experiments across CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet-1K show that DCQ significantly improves training performance under aggressive compression, offering a scalable and robust solution for dataset-level storage reduction.
ProtoKV: Long-context Knowledges Are Already Well-Organized Before Your Query
Authors: Zhiyuan Yu; Shijian Xiao; Zhangyue Yin; Xiaoran Liu; Lekai Xing; Wenzhong Li; Nguyen Cam-Tu; Sanglu Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern Large Language Models (LLMs) face fundamental challenges in processing long text sequences due to the quadratic complexity of attention mechanisms. Key-Value (KV) cache retention strategies mitigate this issue by selectively preserving salient KV pairs for autoregressive generation. However, existing methods fail to adequately and efficiently preserve the semantic integrity of the compressed representations. In this paper, we discover a prevalent phenomenon in LLM: within the key embedding space, while most tokens exhibit similarity with their contextual neighbors (we term position-determined tokens), a small subset of special tokens serving as semantic anchors consistently show local deviation property and form one or several clusters (we term semantic-anchored tokens). Motivated by this observation, we propose ProtoKV that separately processes these two token categories for KV cache compression. Within this framework, we first construct semantic prototypes based on the inherent characteristics of the two token categories, and subsequently form clusters of semantically similar tokens as basic compression units. This approach preserves semantic integrity with high computational efficiency. Experiments on LongBench demonstrate that ProtoKV achieves 2.11% higher accuracy than state-of-the-art methods under matched memory constraints.
Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement
Authors: Yu-Che Tsai; Kuan-Yu Chen; Yuan-Chi Li; Yuan-Hao Chen; Ching-Yu Tsai; Shou-De Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing large language model (LLM)-based embeddings typically adopt an encoder-only
paradigm, treating LLMs as static feature extractors and overlooking their core gener-
ative strengths. We introduce GIRCSE (Generative Iterative Refinement for Contrastive
Sentence Embeddings), a novel framework that leverages autoregressive generation to iter-
atively refine semantic representations. By producing sequences of soft tokens optimized
under a contrastive objective, GIRCSE captures latent concepts and implicit semantics
that encoder-only methods often miss. To guide this process, we propose an Iterative
Contrastive Refinement (ICR) objective that encourages each refinement step to yield bet-
ter representations. Extensive experiments show that GIRCSE outperforms strong LLM-
based embedding baselines on the MTEB embedding benchmark. Moreover, GIRCSE ex-
hibits an emergent test-time scaling property: generating more tokens at inference steadily
improves embedding quality. Our results establish generative iterative refinement as a new
paradigm for representation learning.
RefAny3D: 3D Asset-Referenced Diffusion Models for Image Generation
Authors: Hanzhuo Huang; Qingyang Bao; Zekai Gu; Zhongshuo Du; Cheng Lin; Yuan Liu; Sibei Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we propose a 3D asset-referenced diffusion model for image generation, exploring how to integrate 3D assets into image diffusion models. Existing reference-based image generation methods leverage large-scale pretrained diffusion models and demonstrate strong capability in generating diverse images conditioned on a single reference image. However, these methods are limited to single-image references and cannot leverage 3D assets, constraining their practical versatility. To address this gap, we present a cross-domain diffusion model with dual-branch perception that leverages multi-view RGB images and point maps of 3D assets to jointly model their colors and canonical-space coordinates, achieving precise consistency between generated images and the 3D references. Our spatially aligned dual-branch generation architecture and domain-decoupled generation mechanism ensure the simultaneous generation of two spatially aligned but content-disentangled outputs, RGB images and point maps, linking 2D image attributes with 3D asset attributes. Experiments show that our approach effectively uses 3D assets as references to produce images consistent with the given assets, opening new possibilities for combining diffusion models with 3D content creation.