GenAI4ELab/papercli-papers huggingface dataset:
TROLL: Trust Regions Improve Reinforcement Learning for Large Language Models
Authors: Philipp Becker; Niklas Freymuth; Serge Thilges; Fabian Otto; Gerhard Neumann
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Reinforcement Learning (RL) with PPO-like clip objectives has become the standard choice for reward-based fine-tuning of large language models (LLMs). 
Although recent work has explored improved estimators of advantages and normalization, the clipping mechanism itself has remained untouched.
Originally introduced as a proxy for principled KL-based trust regions, clipping is a crude approximation that often causes unstable updates and suboptimal performance. 
We replace the clip objective with a novel discrete differentiable trust region projection, which provides principled token-level KL constraints.
The projection operates on a sparse subset of the model’s most important token logits to balance computational cost and projection effectiveness. 
Our approach, Trust Region Optimization for Large Language Models (TROLL), serves as a direct replacement for PPO-like clipping during training and does not alter the model’s inference behavior.
Across mathematical reasoning and code generation tasks, model families, as well as advantage-estimation methods, TROLL consistently outperforms PPO-like clipping in terms of training speed, stability, and final success rates.
Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting
Authors: Yanfeng Yang; Siwei Chen; Pingping Hu; Zhaotong Shen; Yingjie Zhang; Zhuoran Sun; Shuai Li; Ziqi Chen; Kenji Fukumizu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Probabilistic forecasting of multivariate time series is challenging due to non-stationarity, inter-variable dependencies, and distribution shifts. While recent diffusion and flow matching models have shown promise, they often ignore informative priors such as conditional means and covariances. In this work, we propose Conditionally Whitened Generative Models (CW-Gen), a framework that incorporates prior information through conditional whitening. Theoretically, we establish sufficient conditions under which replacing the traditional terminal distribution of diffusion models, namely the standard multivariate normal, with a multivariate normal distribution parameterized by estimators of the conditional mean and covariance improves sample quality. Guided by this analysis, we design a novel Joint Mean-Covariance Estimator (JMCE) that simultaneously learns the conditional mean and sliding-window covariance. Building on JMCE, we introduce Conditionally Whitened Diffusion Models (CW-Diff) and extend them to Conditionally Whitened Flow Matching (CW-Flow). Experiments on five real-world datasets with six state-of-the-art generative models demonstrate that CW-Gen consistently enhances predictive performance, capturing non-stationary dynamics and inter-variable correlations more effectively than prior-free approaches. Empirical results further demonstrate that CW-Gen can effectively mitigate the effects of distribution shift.
Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection
Authors: Yao Xiao; Weiyan Chen; Jiahao Chen; Zijie Cao; Weijian Deng; Binbin Yang; ZiYi Dong; Xiangyang Ji; Wei Ke; Pengxu Wei; Liang Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current AI-Generated Image (AIGI) detection approaches predominantly rely on binary classification to distinguish real from synthetic images, often lacking interpretable or convincing evidence to substantiate their decisions. This limitation stems from existing AIGI detection benchmarks, which, despite featuring a broad collection of synthetic images, remain restricted in their coverage of artifact diversity and lack detailed, localized annotations. To bridge this gap, we introduce a fine-grained benchmark towards eXplainable AI-Generated image Detection, named X-AIGD, which provides pixel-level, categorized annotations of perceptual artifacts, spanning low-level distortions, high-level semantics, and cognitive-level counterfactuals. These comprehensive annotations facilitate fine-grained interpretability evaluation and deeper insight into model decision-making processes. Our extensive investigation using X-AIGD provides several key insights: (1) Existing AIGI detectors demonstrate negligible reliance on perceptual artifacts, even at the most basic distortion level. (2) While AIGI detectors can be trained to identify specific artifacts, they still substantially base their judgment on uninterpretable features. (3) Explicitly aligning model attention with artifact regions can increase the interpretability and generalization of detectors.
The data and code are available at: https://github.com/Coxy7/X-AIGD.
Enhancing Multivariate Time Series Forecasting with Global Temporal Retrieval
Authors: Fanpu Cao; Lu Dai; Jindong Han; Hui Xiong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multivariate time series forecasting (MTSF) plays a vital role in numerous real-world applications, yet existing models remain constrained by their reliance on a limited historical context. This limitation prevents them from effectively capturing global periodic patterns that often span cycles significantly longer than the input horizon—despite such patterns carrying strong predictive signals. Naïve solutions, such as extending the historical window, lead to severe drawbacks, including overfitting, prohibitive computational costs, and redundant information processing. To address these challenges, we introduce the Global Temporal Retriever (GTR), a lightweight and plug-and-play module designed to extend any forecasting model’s temporal awareness beyond the immediate historical context. GTR maintains an adaptive global temporal embedding of the entire cycle and dynamically retrieves and aligns relevant global segments with the input sequence. By jointly modeling local and global dependencies through a 2D convolution and residual fusion, GTR effectively bridges short-term observations with long-term periodicity without altering the host model architecture. Extensive experiments on six real-world datasets demonstrate that GTR consistently delivers state-of-the-art performance across both short-term and long-term forecasting scenarios, while incurring minimal parameter and computational overhead. These results highlight GTR as an efficient and general solution for enhancing global periodicity modeling in MTSF tasks. Code is available at this repository: https://github.com/macovaseas/GTR.
Efficient-LVSM: Faster, Cheaper, and Better Large View Synthesis Model via Decoupled Co-Refinement Attention
Authors: Xiaosong Jia; Yihang Sun; Junqi You; Songbur Wong; Zichen Zou; Junchi Yan; Zuxuan Wu; Yu-Gang Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Feedforward models for novel view synthesis (NVS) have recently advanced by transformer-based methods like LVSM, using attention among all input and target views. In this work, we argue that its full self-attention design is suboptimal, suffering from quadratic complexity with respect to the number of input views and rigid parameter sharing among heterogeneous tokens.  We propose Efficient-LVSM, a dual-stream architecture that avoids these issues with a decoupled co-refinement mechanism. It applies intra-view self-attention for input views and self-then-cross attention for target views, eliminating unnecessary computation. Efficient-LVSM achieves 29.86 dB PSNR on RealEstate10K with 2 input views, surpassing LVSM by 0.2 dB, with 2× faster training convergence and 4.2× faster inference speed. 
Efficient-LVSM achieves state-of-the-art performance on multiple benchmarks, exhibits strong zero-shot generalization to unseen view counts, and enables incremental inference with KV-cache, thanks to its decoupled designs.
Behavior Learning (BL)
Authors: Zhenyao Ma; Yue Liang; Dongxu Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Inspired by behavioral science, we propose Behavior Learning (BL), a novel general-purpose machine learning framework that learns interpretable 
and identifiable optimization structures from data, ranging from single optimization problems to hierarchical compositions. It unifies predictive performance, intrinsic interpretability, and identifiability, with broad applicability to scientific domains involving optimization.  BL parameterizes a compositional utility function built from intrinsically interpretable modular blocks, which induces a data distribution for prediction and generation. Each block represents and can be written in symbolic form as a utility maximization problem (UMP), a foundational paradigm in behavioral science and a universal framework of optimization. BL supports architectures ranging from a single UMP to hierarchical compositions, the latter modeling hierarchical optimization structures that offer both expressiveness and structural transparency. Its smooth and monotone variant (IBL) guarantees identifiability under mild conditions. Theoretically, we establish the universal approximation property of both BL and IBL, and analyze the M-estimation properties of IBL. Empirically, BL demonstrates strong predictive performance, intrinsic interpretability and scalability to high-dimensional data. Code: https://github.com/MoonYLiang/Behavior-Learning; installable via pip install blnetwork.
FlashVID: Efficient Video Large Language Models via Training-free Tree-based Spatiotemporal Token Merging
Authors: Ziyang Fan; Keyu Chen; Ruilong Xing; Yulin Li; Li Jiang; Zhuotao Tian
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Although Video Large Language Models (VLLMs) have shown remarkable capabilities in video understanding, they are required to process high volumes of visual tokens, causing significant computational inefficiency. Existing VLLMs acceleration frameworks usually compress spatial and temporal redundancy independently, which overlooks the spatiotemporal relationships, thereby leading to suboptimal spatiotemporal compression. The highly correlated visual features are likely to change in spatial position, scale, orientation, and other attributes over time due to the dynamic nature of video. Building on this insight, we introduce FlashVID, a training-free inference acceleration framework for VLLMs. Specifically, FlashVID utilizes Attention and Diversity-based Token Selection (ADTS) to select the most representative tokens for basic video representation, then applies Tree-based Spatiotemporal Token Merging (TSTM) for fine-grained spatiotemporal redundancy elimination. Extensive experiments conducted on three representative VLLMs across five video understanding benchmarks demonstrate the effectiveness and generalization of our method. Notably, by retaining only $\textbf{10}$% of visual tokens, FlashVID preserves $\textbf{99.1}$% of the performance of LLaVA-OneVision. Consequently, FlashVID can serve as a training-free and plug-and-play module for extending long video frames, which enables a $\textbf{10$\times$}$ increase in video frame input to Qwen2.5-VL, resulting in a relative improvement of $\textbf{8.6}$% within the same computational budget. Code is available at https://github.com/Fanziyang-v/FlashVID.
TyphoonMLA: A Mixed Naive-Absorb MLA Kernel For Shared Prefix
Authors: Ahmet Caner Yüzügüler; Ahmet Çelik; Jiawei Zhuang; Lukas Cavigelli
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-Head Latent Attention (MLA) is a recent attention mechanism adopted in state-of-the-art LLMs such as DeepSeek-v3 and Kimi K2. Thanks to its novel formulation, MLA allows two functionally equivalent but computationally distinct kernel implementations: naive and absorb. While the naive kernels (e.g., FlashAttention) are typically preferred in training and prefill for their computational efficiency, existing decoding kernels (e.g., FlashMLA) rely on the absorb method to minimize HBM bandwidth usage. However, the compute-bound nature of the absorb implementations prohibits performance benefits from data reuse opportunities in attention calculations, such as shared prefixes. In this work, we introduce TyphoonMLA, a hybrid approach that combines naive and absorb formulations to harness the strengths of both. TyphoonMLA effectively leverages the shared prefix by applying the naive formulation to the compute-bound parts of attention calculations, while reducing the bandwidth requirements for non-shared parts by using the absorb formulation. As a result, TyphoonMLA improves the throughput of attention calculations in MLA architectures by up to 3× and 3.24× on NPU and GPUs, and boosts end-to-end throughput by up to 1.48× in tokens per second, with only a 3\% overhead in HBM size.
sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals
Authors: Weixuan Yuan; Zengrui Jin; Yichen Wang; Donglin Xie; Ziyi Ye; Chao Zhang; Xuesong Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Tasks ranging from sleep staging to clinical diagnosis traditionally rely on standard polysomnography (PSG) devices, bedside monitors and wearable devices, which capture diverse nocturnal biosignals (e.g., EEG, EOG, ECG, SpO$_2$). However, heterogeneity across devices and frequent sensor dropout pose significant challenges for unified modelling of these multimodal signals. We present sleep2vec, a foundation model for diverse and incomplete nocturnal biosignals that learns a shared representation via cross-modal alignment. sleep2vec is contrastively pre-trained on 42,249 overnight recordings spanning nine modalities using a Demography, Age, Site & History-aware InfoNCE objective that incorporates physiological and acquisition metadata (e.g., age, gender, recording site) to dynamically weight negatives and mitigate cohort-specific shortcuts. On downstream sleep staging and clinical outcome assessment, sleep2vec consistently outperforms strong baselines and remains robust to any subset of available modalities and sensor dropout. We further characterize, to our knowledge for the first time, scaling laws for nocturnal biosignals with respect to modality diversity and model capacity. Together, these results show that unified cross-modal alignment, coupled with principled scaling, enables label-efficient, general-purpose modelling of real-world nocturnal biosignals.
Semantic Visual Anomaly Detection and Reasoning in AI-Generated Images
Authors: Chuangchuang Tan; Xiang Ming; Jinglu Wang; Renshuai Tao; Bin Li; Yunchao Wei; Yao Zhao; Yan Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid advancement of AI-generated content (AIGC) has enabled the synthesis of visually convincing images; however, many such outputs exhibit subtle \textbf{semantic anomalies}, including unrealistic object configurations, violations of physical laws, or commonsense inconsistencies, which compromise the overall plausibility of the generated scenes. Detecting these semantic-level anomalies is essential for assessing the trustworthiness of AIGC media, especially in AIGC image analysis, explainable deepfake detection and semantic authenticity assessment.In this paper,  we formalize \textbf{semantic anomaly detection and reasoning} for AIGC images and  introduce \textbf{AnomReason}, a large-scale benchmark with structured annotations as quadruples \emph{(Name, Phenomenon, Reasoning, Severity)}. Annotations are produced by  a modular multi-agent pipeline (\textbf{AnomAgent}) with lightweight human-in-the-loop verification, enabling scale while preserving quality. At construction time, AnomAgent processed approximately 4.17\,B GPT-4o tokens, providing scale evidence for the resulting structured annotations. We further  show that models fine-tuned on AnomReason achieve consistent gains over strong vision-language baselines under our proposed semantic matching metric (\textit{SemAP} and \textit{SemF1}). Applications to {explainable deepfake detection} and {semantic reasonableness assessment of image generators} demonstrate practical utility. In summary, AnomReason and AnomAgent serve as a foundation for measuring and improving the semantic plausibility of AI-generated images. The code is available at \url{https://github.com/chuangchuangtan/Semantic-Visual-Anomaly-Detection-and-Reasoning}.
Characterizing and Mitigating Reasoning Drift in Large Language Models
Authors: Yufeng Zhang; Xuepeng Wang; Lingxiang Wu; Jinqiao Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While chain-of-thought prompting enables powerful multi-step reasoning in Large Language Models (LLMs), the stochastic nature of the generation process undermines its reliability. In this work, we first analyze thousands of reasoning paths to identify Reasoning Drift, a key failure mode where models get locked into flawed reasoning patterns. We reveal that the manifestation of drift is a complex interplay between universal functional tendencies and unique, model-specific signatures. Based on the diagnosis, we propose Reasoning-Aware Activation Steering, a novel inference-time intervention method to gently nudge the model's activations away from pathological patterns. We pre-compute a library of vectors from contrastive functional transitions and apply them dynamically. Experiments show that our method effectively mitigates the drift problem and boosts accuracy. Additionally, it generalizes to out-of-distribution tasks, demonstrating a deeper capture of valid reasoning principles.
OmniPortrait: Fine-Grained Personalized Portrait Synthesis via Pivotal Optimization
Authors: Dongxu Yue; Bo Lin; Yao Tang; Jiajun Liang; Zhihai He; Chun Yuan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Image identity customization aims to synthesize realistic and diverse portraits of a specified identity, given a reference image and a text prompt. This task presents two key challenges: (1) generating realistic portraits that preserve fine-grained facial details of the reference identity, and (2) maintaining identity consistency while achieving strong alignment with the text prompt. Our findings suggest that existing single-stream methods fail to  capture and guide fine-grained identity details.
To address these challenges, we introduce \textit{OmniPortrait}, a novel diffusion-based framework for fine-grained identity fidelity and high editability in portrait synthesis. Our core idea is pivotal optimization, which leverages dual-stream identity guidance in a coarse-to-fine manner. First, a Pivot ID Encoder is proposed and trained with a face localization loss while avoiding the degradation of editability typically caused by fine-tuning the denoiser. Although this encoder primarily guides coarse-level identity synthesis, it provides a good initialization that serves as the identity pivot for optimization during inference.
Second, we propose Reference-Based Guidance, which performs on-the-fly feature matching and optimization over diffusion intermediate features conditioned on the identity pivot. In addition, our approach is able to generalize naturally to multi-identity customized image generation scenarios. Extensive experiments demonstrate significant improvements in both identity preservation and text alignment, establishing a new benchmark for image identity customization.
TimeSearch-R: Adaptive Temporal Search for Long-Form Video Understanding via Self-Verification Reinforcement Learning
Authors: Junwen Pan; Qizhe Zhang; Rui Zhang; Ming Lu; Xin Wan; Yuan Zhang; Chang Liu; Qi She
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Temporal search aims to identify a minimal set of relevant frames from tens of thousands based on a given query, serving as a foundation for accurate long-form video understanding. Many existing works attempt to progressively narrow the search space. However, these approaches typically rely on a hand-crafted search process, lacking end-to-end optimization for learning optimal search strategies. In this paper, we propose **TimeSearch-R**, which reformulates temporal search as interleaved text–video thinking, seamlessly integrating searching video clips into the reasoning process through reinforcement learning (RL). However, applying RL training methods, such as Group Relative Policy Optimization (GRPO), to video reasoning can result in unsupervised intermediate search decisions. This leads to insufficient exploration of the video content and inconsistent logical reasoning. To address these issues, we introduce GRPO with Completeness Self-Verification (GRPO-CSV), which gathers searched video frames from the interleaved reasoning process and utilizes the same policy model to verify the adequacy of searched frames, thereby improving the completeness of video reasoning.
Additionally, we construct datasets specifically designed for the SFT cold-start and RL training of GRPO-CSV, filtering out samples with weak temporal dependencies to enhance task difficulty and improve temporal search capabilities. Extensive experiments demonstrate that TimeSearch-R achieves substantial improvements on temporal search benchmarks such as Haystack-LVBench and Haystack-Ego4D, long-form video understanding benchmarks like VideoMME, MLVU, and LongVideoBench, as well as video reasoning benchmarks such as Video-Holmes, consistently and significantly outperforming other existing temporal search approaches and text-only reasoning models. Our code is available at *https://github.com/Time-Search/TimeSearch-R*.
From Tokens to Nodes: Semantic-Guided Motion Control for Dynamic 3D Gaussian Splatting
Authors: Jianing Chen; Zehao Li; Yujun Cai; Hao Jiang; Shuqin Gao; Honglong Zhao; Tianlu Mao; Yucheng Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Dynamic 3D reconstruction from monocular videos remains difficult due to the ambiguity  inferring 3D motion from limited views and computational demands of modeling temporally varying scenes. While recent sparse control methods alleviate computation by reducing millions of Gaussians to thousands of control points, they suffer from a critical limitation: they allocate points purely by geometry, leading to static redundancy and dynamic insufficiency. We propose a motion-adaptive framework that aligns control density with motion complexity. Leveraging semantic and motion priors from vision foundation models, we establish patch-token-node correspondences and apply motion-adaptive compression to concentrate control points in dynamic regions while suppressing redundancy in static backgrounds. Our approach achieves flexible representational density adaptation through iterative voxelization and motion tendency scoring, directly addressing the fundamental mismatch between control point allocation and motion complexity. To capture temporal evolution, we introduce spline-based trajectory parameterization initialized by 2D tracklets, replacing MLP-based deformation fields to achieve smoother motion representation and more stable optimization. Extensive experiments demonstrate significant improvements in reconstruction quality and efficiency over existing state-of-the-art methods.