GenAI4ELab/papercli-papers huggingface dataset:
vAttention: Verified Sparse Attention via Sampling
Authors: Aditya Desai; Kumar Krishna Agrawal; Shuo Yang; Alejandro Cuadron; Luis Gaspar Schroeder; Matei Zaharia; Joseph E. Gonzalez; Ion Stoica
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: State-of-the-art sparse attention methods for reducing decoding latency fall into two main categories: approximate top-$k$ (and its extension, top-$p$) and recently introduced sampling-based estimation. However, these approaches are fundamentally limited in their ability to approximate full attention: they fail to provide consistent approximations across heads and query vectors and, most critically, lack guarantees on approximation quality, limiting their practical deployment. We observe that top-$k$ and random sampling are complementary: top-$k$ performs well when attention scores are dominated by a few tokens, whereas random sampling provides better estimates when attention scores are relatively uniform. Building on this insight and leveraging the statistical guarantees of sampling, we introduce vAttention, the first practical sparse attention mechanism with user-specified $(\epsilon, \delta)$ guarantees on approximation accuracy. These guarantees make vAttention a compelling step toward practical, reliable deployment of sparse attention at scale. By unifying top-k and sampling, vAttention outperforms both individually, delivering a superior quality–efficiency trade-off. Our experiments show that vAttention significantly improves the quality of sparse attention (e.g., $\sim$4.5 percentage points for Llama-3.1-8B-Inst and Deepseek-R1-Distill-Llama-8B on RULER-HARD ), and effectively bridges the gap between full and sparse attention (e.g., across datasets, it matches full model quality at 10x–20x sparsity). We also demonstrate that it can be deployed in long-generation scenarios to achieve fast decoding without compromising model quality (e.g., vAttention achieves full model quality on AIME2024 at 10\% sparsity with up to 32K token generations).
Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated?
Authors: Coen Adler; Yuxin Chang; Samar Abdi; Felix Draxler; Padhraic Smyth
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The recent development of foundation models for time series data has generated considerable interest in using such models across a variety of applications. Although foundation  models achieve state-of-the-art predictive performance, their calibration properties remain relatively underexplored, despite the fact that calibration can be critical for many practical applications. In this paper, we investigate the calibration-related properties of five recent time series foundation models and two competitive baselines. We perform a series of systematic evaluations assessing model calibration (i.e., over- or under-confidence), effects of varying prediction heads, and calibration under long-term autoregressive forecasting. We find that time series foundation models are consistently better calibrated than baseline models and tend not to be either systematically over- or under-confident, in contrast to the overconfidence often seen in other deep learning models.
FACT: Fine-grained Across-variable Convolution for Multivariate Time Series Forecasting
Authors: Huiqiang Wang; Jieming Shi; Li Qing
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling the relationships among variables has become increasingly important, particularly in high-dimensional multivariate time series forecasting tasks. However, most existing methods primarily focus on capturing coarse-grained correlations between variables, overlooking a finer and more dynamic aspect: the variable interactions often manifest differently as time progresses.
To address this limitation, we propose FACT, an Fine-grained Across-variable Convolution architecture for multivariate Time series forecasting that explicitly models fine-grained variable interactions from both the time and frequency domains. 
Technically, we introduce a depth-wise convolution block DConvBlock, which leverages a depth-wise convolution architecture with channel-specific kernels to model dynamic variable interactions at each granularity.
To further enhance efficiency, we reconfigure the original one-dimensional variables into a two-dimensional space, reducing the variable distance and the required model layers. Then DConvBlock incorporates multi-dilated 2D convolutions with progressively increasing dilation rates, enabling the model to capture fine-grained and dynamic variable interactions while efficiently attaining a global reception field.
Extensive experiments on twelve benchmark datasets demonstrate that FACT not only achieves state-of-the-art forecasting accuracy but also delivers substantial efficiency gains, significantly reducing both training time and memory consumption compared to attention mechanism.
MetaCaptioner: Towards Generalist Visual Captioning with Open-source Suites
Authors: Zhenxin Lei; Zhangwei Gao; Changyao Tian; Erfei Cui; Guanzhou Chen; Danni Yang; Yuchen Duan; Zhaokai Wang; Wenhao Li; Weiyun Wang; Xiangyu Zhao; Jiayi Ji; Yu Qiao; Wenhai Wang; Gen Luo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generalist visual captioning goes beyond a simple appearance description task, but requires integrating a series of visual cues into a caption and handling various visual domains.  In this task, current open-source models present a large performance gap with commercial ones, which limits various applications such as data synthesis.  To bridge the gap,  this paper proposes CapFlow, a novel multi-agent collaboration workflow.  CapFlow demonstrates for the first time that, by capitalizing on open-source models, it is possible to achieve caption quality on par with GPT-4.1 in various domains with an 89.5\% reduction in costs.   By leveraging CapFlow as the data synthesizer, we produce high-quality visual captions from image and video domains at scale, and obtain a generalist visual captioner via fine-tuning, namely MetaCaptioner. Through extensive experiments, we show that MetaCaptioner not only achieves comparable captioning capabilities with commercial models but also reaches top-tier multimodal performance in the open-source community. We hope CapFlow and MetaCaptioner can benefit future multimodal research by providing a strong and cost-effective visual captioning solution. Our source code and models will be publicly released.
Tequila: Trapping-free Ternary Quantization for Large Language Models
Authors: Hong Huang; Decheng Wu; Rui Cen; Guanghua Yu; Zonghang Li; Kai Liu; Jianchen Zhu; Peng Chen; Xue Liu; Dapeng Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Quantization techniques are essential for the deployment of Large Language Models (LLMs) on edge devices. However, prevailing methods often rely on mixed-precision multiplication that lacks efficient hardware support, making it not feasible. Ternary weight quantization addresses this by constraining weights to {-1, 0, 1}, replacing expensive multiplications with hardware-efficient additions. However, such aggressive compression leads to significant accuracy degradation, even after costly quantization-aware training with massive data.  We identify the core issue as _**deadzone trapping**: a large number of weights are trapped at the deadzone boundary._ This occurs because these weights receive only noisy, less informative gradients, preventing stable escape from the deadzone and severely impeding model capacity and optimization. To address this issue, we propose **Tequila**, a trapping-free quantization optimization method that reactivates deadzone-trapped weights by repurposing them as dynamic biases. This allows the repurposed weights to provide a continuous signal in the forward pass and, critically, receive direct, meaningful gradient signals during backpropagation, thereby enhancing model capacity and optimization with nearly _zero_ inference overhead. Extensive evaluations demonstrate that Tequila outperforms state-of-the-art (SOTA) ternary quantization methods across five benchmarks. Specifically, on the ARC benchmark, it achieves $>4$% accuracy gain over the SOTA baseline, nearly matching full-precision performance (within $<1$% gap) with an $3.0\times$ inference speedup. Consequently, Tequila offers a highly practical and efficient implementation for the deployment of advanced LLMs in resource-constrained environments. The code is available at https://github.com/Tencent/AngelSlim/tree/tequila/TernaryQuant .
Bidirectional Predictive Coding
Authors: Gaspard Oliviers; Mufeng Tang; Rafal Bogacz
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Predictive coding (PC) is an influential computational model of visual learning and inference in the brain. Classical PC was proposed as a top-down generative model, where the brain actively predicts upcoming visual inputs, and inference minimises the prediction errors. Recent studies have also shown that PC can be formulated as a discriminative model, where sensory inputs predict neural activities in a feedforward manner. However, experimental evidence suggests that the brain employs both generative and discriminative inference, while unidirectional PC models show degraded performance in tasks requiring bidirectional processing. In this work, we propose bidirectional PC (bPC), a PC model that incorporates both generative and discriminative inference while maintaining a biologically plausible circuit implementation. We show that bPC matches or outperforms unidirectional models in their specialised generative or discriminative tasks, by developing an energy landscape that simultaneously suits both tasks. We also demonstrate bPC's superior performance in two biologically relevant tasks including multimodal learning and inference with missing information, suggesting that bPC resembles biological visual inference more closely.
Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling
Authors: Tal Daniel; Carl Qi; Dan Haramati; Amir Zadeh; Chuan Li; Aviv Tamar; Deepak Pathak; David Held
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: We introduce Latent Particle World Model (LPWM), a self-supervised object-centric world model scaled to real-world multi-object datasets and applicable in decision-making. LPWM autonomously discovers keypoints, bounding boxes, and object masks directly from video data, enabling it to learn rich scene decompositions without supervision. Our architecture is trained end-to-end purely from videos and supports flexible conditioning on actions, language, and image goals. LPWM models stochastic particle dynamics via a novel latent action module and achieves state-of-the-art results on diverse real-world and synthetic datasets. Beyond stochastic video modeling, LPWM is readily applicable to decision-making, including goal-conditioned imitation learning, as we demonstrate in the paper. Code, data, pre-trained models and video rollouts are available: https://taldatech.github.io/lpwm-web
KernelFusion: Zero-Shot Blind Super-Resolution via Patch Diffusion
Authors: Oliver Heinimann; Tal Zimbalist; Assaf Shocher; michal Irani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Traditional super-resolution (SR) methods assume an "ideal'' downscaling SR-kernel (e.g., bicubic downscaling) between the high-resolution (HR) image and the low-resolution (LR) image. Such methods fail once the LR images are generated differently. Current blind-SR methods aim to remove this assumption, but are still fundamentally restricted to rather simplistic downscaling SR-kernels (e.g., anisotropic Gaussian kernels), and fail on more complex (out of distribution) downscaling degradations. However, using the correct SR-kernel is often more important than using a sophisticated SR algorithm. In "KernelFusion'', we introduce a zero-shot diffusion-based method that uses an unrestricted kernel. Our method recovers the unique image-specific SR-kernel directly from the LR input image, while simultaneously recovering its corresponding HR image.  KernelFusion exploits the principle that the correct SR-kernel is the one that  maximizes patch similarity across different scales of the LR image.  We first train an image-specific patch-based diffusion model on the single LR input image, capturing its unique internal patch statistics. We then reconstruct a larger HR image with the same learned patch distribution, while simultaneously recovering the correct downscaling  SR-kernel that maintains this cross-scale relation between the HR and LR images. Empirical results demonstrate that KernelFusion handles complex downscaling degradations where existing Blind-SR methods fail, achieving robust kernel recovery and superior SR quality. By breaking free from predefined kernel assumptions and training distributions, KernelFusion establishes a new paradigm of zero-shot Blind-SR that can handle unrestricted, image-specific kernels previously thought impossible.
Beware Untrusted Simulators -- Reward-Free Backdoor Attacks in Reinforcement Learning
Authors: Ethan Rathbun; Wo Wei Lin; Alina Oprea; Christopher Amato
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Simulated environments are a key piece in the success of Reinforcement Learning (RL), allowing practitioners and researchers to train decision making agents without running expensive experiments on real hardware. Simulators remain a security blind spot, however, enabling adversarial developers to alter the dynamics of their released simulators for malicious purposes. Therefore, in this work we highlight a novel threat, demonstrating how simulator dynamics can be exploited to stealthily implant action-level backdoors into RL agents. The backdoor then allows an adversary to reliably activate targeted actions in an agent  upon observing a predefined "trigger", leading to potentially dangerous consequences. Traditional backdoor attacks are limited in their strong threat models, assuming the adversary has near full control over an agent's training pipeline, enabling them to both alter and observe  agent's rewards. As these assumptions are infeasible to implement within a simulator, we propose a new attack "Daze" which is able to reliably and stealthily implant backdoors into RL agents trained for real world tasks without altering or even observing their rewards. We provide formal proof of Daze's effectiveness in guaranteeing attack success across general RL tasks along with extensive empirical evaluations on both discrete and continuous action space domains. We additionally provide the first example of RL backdoor attacks transferring  to real, robotic hardware. These developments motivate further research into securing all components of the RL training pipeline to prevent malicious attacks.
BTZSC: A Benchmark for Zero-Shot Text Classification Across Cross-Encoders, Embedding Models, Rerankers and LLMs
Authors: Ilias Aarab
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Zero-shot text classification (ZSC) offers the promise of eliminating costly task-specific annotation by matching texts directly to human-readable label descriptions. While early approaches have predominantly relied on cross-encoder models fine-tuned for natural language inference (NLI), recent advances in text-embedding models, rerankers, and instruction-tuned large language models (LLMs) have challenged the dominance of NLI-based architectures. Yet, systematically comparing these diverse approaches remains difficult. Existing evaluations, such as MTEB, often incorporate labeled examples through supervised probes or fine-tuning, leaving genuine zero-shot capabilities underexplored. To address this, we introduce __BTZSC__, a comprehensive benchmark of $22$ public datasets spanning sentiment, topic, intent, and emotion classification, capturing diverse domains, class cardinalities, and document lengths. Leveraging BTZSC, we conduct a systematic comparison across four major model families, NLI cross-encoders, embedding models, rerankers and instruction-tuned LLMs, encompassing $38$ public and custom checkpoints. Our results show that: (i) modern rerankers, exemplified by _Qwen3-Reranker-8B_, set a new state-of-the-art with macro $F_1 = 0.72$; (ii) strong embedding models such as _GTE-large-en-v1.5_ substantially close the accuracy gap while offering the best trade-off between accuracy and latency; (iii) instruction-tuned LLMs at 4-12B parameters achieve competitive performance (macro $F_1$ up to $0.67$), excelling particularly on topic classification but trailing specialized rerankers; (iv) NLI cross-encoders plateau even as backbone size increases; and (v) scaling primarily benefits rerankers and LLMs over embedding models. BTZSC and accompanying evaluation code are publicly released to support fair and reproducible progress in zero-shot text understanding.
LLaVAction: evaluating and training multi-modal large language models for action understanding
Authors: Haozhe Qi; Shaokai Ye; Alexander Mathis; Mackenzie W Mathis
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding human behavior requires measuring behavioral actions. Due to its complexity, behavior is best mapped onto a rich, semantic structure such as language. Emerging multimodal large language models (MLLMs) are promising candidates, but their fine-grained action understanding ability has not been fully examined. In this work, we reformulate EPIC-KITCHENS-100, one of the largest and most challenging egocentric action recognition datasets, into a MLLM benchmark (EPIC-KITCHENS-100-MQA). We demonstrate that when we sample difficult answers based on specialist models as distractors, leading MLLMs struggle to recognize the correct actions. How can we increase the performance of MLLMs? We curated a supervised finetuning dataset that includes `hard' action recognition, temporal detection, captioning, and free-form question answering to improve models' diverse action understanding capabilities. We introduce a new model called LLaVAction that adds an action token to boost models' attention on visual tokens and a two-stage pipeline to obtain structured actions. LLaVAction greatly improves the MLLMs' ability of action understanding, achieving strong improvements on both MLLM benchmarks (21 points in accuracy over GPT-4o on EPIC-KITCHENS-100-MQA) and established action recognition benchmarks, suggesting that our methods prepare MLLMs to be a promising path forward for complex action tasks. 
Code, data, the benchmark, and models are available at https://github.com/AdaptiveMotorControlLab/LLaVAction.
ComGS: Efficient 3D Object-Scene Composition via Surface Octahedral Probes
Authors: Jian Gao; Mengqi Yuan; Yifei Zeng; Chang Zeng; Zhihao Li; Zhenyu Chen; Weichao Qiu; Xiao-Xiao Long; Hao Zhu; Xun Cao; Yao Yao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Gaussian Splatting (GS) enables immersive rendering, but realistic 3D object–scene composition remains challenging. Baked appearance and shadow information in GS radiance fields cause inconsistencies when combining objects and scenes. Addressing this requires relightable object reconstruction and scene lighting estimation. For relightable object reconstruction, existing Gaussian-based inverse rendering methods often rely on ray tracing, leading to low efficiency. We introduce Surface Octahedral Probes (SOPs), which store lighting and occlusion information and allow efficient 3D querying via interpolation, avoiding expensive ray tracing. SOPs provide at least a 2x speedup in reconstruction and enable real-time shadow computation in Gaussian scenes. For lighting estimation, existing Gaussian-based inverse rendering methods struggle to model intricate light transport and often fail in complex scenes, while learning-based methods predict lighting from a single image and are viewpoint-sensitive. We observe that 3D object–scene composition primarily concerns the object’s appearance and nearby shadows. Thus, we simplify the challenging task of full scene lighting estimation by focusing on the environment lighting at the object’s placement. Specifically, we capture a 360° reconstructed radiance field of the scene at the location and fine-tune a diffusion model to complete the lighting. Building on these advances, we propose ComGS, a novel 3D object–scene composition framework. Our method achieves high-quality, real-time rendering at around 26 FPS, produces visually harmonious results with vivid shadows, and requires only 36 seconds for editing. The code and dataset are available at https://nju-3dv.github.io/projects/ComGS/.
CoT-Evo: Evolutionary Distillation of Chain-of-Thought for Scientific Reasoning
Authors: Kehua Feng; Keyan Ding; Zhihui Zhu; Lei Liang; Qiang Zhang; Huajun Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While chain-of-thought (CoT) distillation from advanced large language models (LLMs) has proven effective in general reasoning tasks, it struggles in scientific domains where even advanced models often produce incorrect or superficial reasoning due to high complexity and specialized knowledge requirements. Directly distilling from such flawed outputs results in low-quality training data and limits the performance of smaller student models. To overcome this, we propose CoT-Evo, an evolutionary CoT distillation framework. It begins by constructing a diverse pool of reasoning trajectories from multiple LLM thinkers, enriches them with automatically retrieved domain knowledge, and iteratively refines the trajectories using novelty-driven selection, reflective recombination and mutation. The refinement is guided by a fitness function that evaluates answer correctness, coherence, and effective knowledge utilization. This results in a high-quality CoT dataset tailored for scientific reasoning. We employ this evolved dataset to fine-tune a compact model, which achieves state-of-the-art performance on scientific reasoning benchmarks. Our work establishes a scalable approach to synthesizing high-fidelity scientific reasoning data from diverse and fallible LLMs.
Rethinking Residual Errors in Compensation-based LLM Quantization
Authors: Shuaiting Li; Juncan Deng; Kedong Xu; Rongtao Deng; Hong Gu; Minghan Jiang; Haibin Shen; Kejie Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Methods based on weight compensation, which iteratively apply quantization and weight compensation to minimize the output error, have recently demonstrated remarkable success in quantizing Large Language Models (LLMs). 
The representative work, GPTQ, introduces several key techniques that make such iterative methods practical for LLMs with billions of parameters.
GPTAQ extends this approach by introducing an asymmetric calibration process that aligns the output of each quantized layer with its full-precision counterpart, incorporating a residual error into the weight compensation framework.
In this work, we revisit the formulation of the residual error.
We identify a sub-optimal calibration objective in existing methods: during the intra-layer calibration process, they align the quantized output with the output from compensated weights, rather than the true output from the original full-precision model. Therefore, we redefine the objective to precisely align the quantized model's output with the original output of the full-precision model at each step. We then reveal that the residual error originates not only from the output difference of the preceding layer but also from the discrepancy between the compensated and original weights within each layer, which we name the 'compensation-aware error'.
By inheriting the neuron decomposition technique from GPTAQ, we can efficiently incorporate this compensation-aware error into the weight update process. Extensive experiments on various LLMs and quantization settings demonstrate that our proposed enhancements integrate seamlessly with both GPTQ and GPTAQ, significantly improving their quantization performance. Our code is publicly available at https://github.com/list0830/ResComp.