GenAI4ELab/papercli-papers huggingface dataset:
When MLLMs Meet Compression Distortion: A Coding Paradigm Tailored to MLLMs
Authors: Jinming Liu; Zhaoyang Jia; Jiahao Li; Bin Li; Xin Jin; Wenjun Zeng; Yan Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The increasing deployment of powerful Multimodal Large Language Models (MLLMs), typically hosted on cloud platforms, urgently requires effective compression techniques to efficiently transmit signal inputs (e.g., images, videos) from edge devices with minimal bandwidth usage. However, conventional image codecs are optimized for fidelity to serve the Human Visual System (HVS) and ill-suited for MLLMs, in which diverse downstream tasks are jointly considered. In this paper, we first systematically analyze the impact of compression artifacts on several mainstream MLLMs. We find that: Compression distortion unevenly impacts different-level image features, leading to varying effects on MLLMs' downstream tasks depending on their feature-level reliance. Motivated by this discovery, we propose an image Codec TAilored to MLLMs (CoTAM) designed to adaptively protect multi-level features and suit different demands of downstream tasks. The encoder leverages CLIP's shallow-layer attention to generate an importance map for bit allocation, preserving critical semantic regions. Concurrently, the decoder integrates a lightweight adapter with a multi-level loss function to ensure the faithful reconstruction both of low-level details and high-level semantic context for robust synthesis of cross-level features. Extensive experiments validate that our method achieves up to 35.99\% bitrate saving while maintaining the same performance on the MLLM tasks, outperforming previous SOTA neural codecs. The code is released at https://github.com/jmliu206/CoTAM.
Sci2Pol: Evaluating and Fine-tuning LLMs on Scientific-to-Policy Brief Generation
Authors: Weimin Wu; Alexander C Furnas; Eddie Yang; Gefei Liu; Akhil Pandey Akella; Xuefeng Song; Dashun Wang; Han Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose Sci2Pol-Bench and Sci2Pol-Corpus, the first benchmark and training dataset for evaluating and fine-tuning large language models (LLMs) on policy brief generation from a scientific paper.
We build Sci2Pol-Bench on a five-stage taxonomy to mirror the human writing process: 
(i) Autocompletion, (ii) Understanding, (iii) Summarization, (iv) Generation, and (v) Verification. 
It features 18 tasks in multiple-choice and open-ended formats.
Specifically, for the Generation stage, we show that BERTScore and ROUGE scores fail to capture the quality of brief writing, and introduce a new LLM-based evaluation metric aligned with expert judgement.
Using this benchmark, we evaluate 13 leading open-source and commercial LLMs to uncover key limitations.
To improve LLM performance on brief writing, we curate the Sci2Pol-Corpus for fine-tuning.
We start by linking each cited scientific paper to its corresponding policy document, drawn from 5.6 million policy records.
This produces 140,000 candidate pairs.
We then employ an LLM-as-a-judge to filter high-quality examples, followed by in-context polishing using three expert-written samples as references.
This process yields a final set of 639 new pairs.
Finally, we fine-tune three models on Sci2Pol-Corpus: LLaMA-3.1-8B, Gemma-12B, and Gemma-27B.
Fine-tuning leads to consistent performance improvements across Sci2Pol-Bench.
Notably, after fine-tuning, Gemma-27B surpasses the much larger GPT-4o and DeepSeek-V3 (671B).
These demonstrate the effectiveness of our corpus in bridging the gap between science and policy.
SparseD: Sparse Attention for Diffusion Language Models
Authors: Zeqing Wang; Gongfan Fang; Xinyin Ma; Xingyi Yang; Xinchao Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While diffusion language models (DLMs) offer a promising alternative to autoregressive models (ARs), existing open-source DLMs suffer from high inference latency. This bottleneck is mainly due to the attention’s quadratic complexity with respect to context length in computing all query–key pairs.
Intuitively, to reduce this complexity, a natural strategy is to restrict attention to sparse patterns that retain only the most relevant connections. 
Such approaches are well-established in ARs, where attention follows fixed and clearly defined sparse patterns.
However, in DLMs, we observe distinct sparsity behaviors: (1) attention patterns vary across heads, (2) attention patterns in each head remain highly similar across denoising steps, and (3) early denoising steps are critical for generation.
These findings render sparse attention methods designed for ARs largely incompatible with DLMs, as they fail to capture head-specific structures and risk degrading generation when applied in early denoising steps.
To address these challenges, we propose **SparseD**, a novel sparse attention method for DLMs. 
Leveraging the observations, SparseD only requires pre-computing head-specific sparse patterns one time, and reuses them across all steps. This prevents recomputing sparse patterns at each denoising step.
Meanwhile, SparseD uses full attention in the early steps, then switches to sparse attention later to maintain generation quality. 
Together, these establish SparseD as a practical and efficient solution for deploying DLMs in long-context applications. Experimental results demonstrate that SparseD  achieves lossless acceleration, delivering up to $1.50\times$ speedup over FlashAttention at a 64k context length with 1,024 denoising steps. Code is available at https://github.com/INV-WZQ/SparseD.
RNE: plug-and-play diffusion inference-time control and energy-based training
Authors: Jiajun He; José Miguel Hernández-Lobato; Yuanqi Du; Francisco Vargas
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models generate data by removing noise gradually, which corresponds to the time-reversal of a noising process.
However, access to only the denoising kernels is often insufficient.
In many applications, we need the knowledge of the marginal densities along the generation trajectory, which enables tasks such as inference-time control.
To address this gap, in this paper, we introduce the Radon-Nikodym Estimator (RNE).
Based on the concept of the density ratio between path distributions, it reveals a fundamental connection between marginal densities and transition kernels, providing a flexible plug-and-play framework that unifies (1) diffusion density estimation, (2) inference-time control, and (3) energy-based diffusion training under a single perspective.
Experiments demonstrated that RNE delivers strong results in inference-time control applications, such as annealing and model composition, with promising inference-time scaling performance, and achieves simple yet efficient regularisation for training energy-based diffusion models.
Additionally, our proposed RNE is modality-agnostic and applicable not only to continuous diffusion models but also to their discrete diffusion counterparts.
MotionWeaver: Holistic 4D-Anchored Framework for Multi-Humanoid Image Animation
Authors: Xirui Hu; Yanbo Ding; Jiahao Wang; Tingting Shi; Yali Wang; Guo Zhi Zhi; Weizhan Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Character image animation, which synthesizes videos of reference characters driven by pose sequences, has advanced rapidly but remains largely limited to single-human settings. Existing methods struggle to generalize to multi-humanoid scenarios, which involve diverse humanoid forms, complex interactions, and frequent occlusions. We address this gap with two key innovations. First, we introduce unified motion representations that extract identity-agnostic motions and explicitly bind them to corresponding characters, enabling generalization across diverse humanoid forms and seamless extension to multi-humanoid scenarios. Second, we propose a holistic 4D-anchored paradigm that constructs a shared 4D space to fuse motion representations with video latents, and further reinforces this process with hierarchical 4D-level supervision to better handle interactions and occlusions. We instantiate these ideas in MotionWeaver, an end-to-end framework for multi-humanoid image animation. To support this setting, we curate a 46-hour dataset of multi-human videos with rich interactions, and construct a 300-video benchmark featuring paired humanoid characters. Quantitative and qualitative experiments demonstrate that MotionWeaver not only achieves state-of-the-art results on our benchmark but also generalizes effectively across diverse humanoid forms, complex interactions, and challenging multi-humanoid scenarios.
Adversarial Encoding Perturbation and Synthesis for Set Representation Auxiliary Learning
Authors: Yankai Chen; Xinni Zhang; Henry Peng Zou; Bowei He; Yangning Li; Philip S. Yu; Irwin King; Xue Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sets are a fundamental data structure, and learning their vectorized representations is crucial for many computational problems. Existing methods typically focus on intra-set properties such as permutation invariance and cardinality independence. While effective at preserving basic intra-set semantics, these approaches may be insufficient in explicitly modeling inter-set correlations, which are critical for tasks requiring fine-grained comparisons between sets. In this work, we propose SRAL, a Set Representation Auxiliary Learning framework for capturing inter-set correlations that is compatible with various downstream tasks. SRAL conceptualizes sets
as high-dimensional distributions and leverages the 2-Sliced-Wasserstein distance to derive their distributional discrepancies into set representation encoding. More
importantly, we introduce a novel adversarial auxiliary learning scheme. Instead of
manipulating the input data, our method perturbs the set encoding process itself and
compels the model to be robust against worst-case perturbations through a min-max
optimization. Our theoretical analysis shows that this objective, in expectation,
directly optimizes for the set-wise Wasserstein distances, forcing the model to
learn highly discriminative representations. Comprehensive evaluations across
four downstream tasks examine SRAL’s performance relative to baseline methods,
showing consistent effectiveness in both inter-set relation-sensitive retrieval and
intra-set information-oriented processing tasks.
InputDSA: Demixing, then comparing recurrent and externally driven dynamics
Authors: Ann Huang; Mitchell Ostrow; Satpreet Harcharan Singh; Leo Kozachkov; Ila R Fiete; Kanaka Rajan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In control problems and basic scientific modeling, it is important to compare observations with dynamical simulations. 
For example, comparing two neural systems can shed light on the nature of emergent computations in the brain and deep neural networks. Recently, Ostrow et al. (2023) introduced Dynamical Similarity Analysis (DSA), a method to measure the similarity of two systems based on their recurrent dynamics rather than geometry or topology. However, DSA does not consider how inputs affect the dynamics, meaning that two similar systems, if driven differently, may be classified as different. Because real-world dynamical systems are rarely autonomous, it is important to account for the effects of input drive. To this end, we introduce a novel metric for comparing both intrinsic (recurrent) and input-driven dynamics, called InputDSA (iDSA). InputDSA extends the DSA framework by estimating and comparing both input and intrinsic dynamic operators using a variant of Dynamic Mode Decomposition with control (DMDc) based on subspace identification. We demonstrate that InputDSA can successfully compare partially observed, input-driven systems from noisy data. We show that when the true inputs are unknown, surrogate inputs can be substituted without a major deterioration in similarity estimates. We apply InputDSA on Recurrent Neural Networks (RNNs) trained with Deep Reinforcement Learning, identifying that high-performing networks are dynamically similar to one another, while low-performing networks are more diverse. Lastly, we apply InputDSA to neural data recorded from rats performing a cognitive task, demonstrating that it identifies a transition from input-driven evidence accumulation to intrinsically-driven decision-making. Our work demonstrates that InputDSA is a robust and efficient method for comparing intrinsic dynamics and the effect of external input on dynamical systems.
Mean Estimation from Coarse Data: Characterizations and Efficient Algorithms
Authors: Alkis Kalavasis; Anay Mehrotra; Manolis Zampetakis; Felix Zhou; Ziyu Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Coarse data arise when learners observe only partial information about samples; namely, a set containing the sample rather than its exact value. This occurs naturally through measurement rounding, sensor limitations, and lag in economic systems. We study Gaussian mean estimation from coarse data, where each true sample $x$ is drawn from a $d$-dimensional Gaussian distribution with identity covariance, but is revealed only through the set of a partition containing $x$. When the coarse samples, roughly speaking, have ``low'' information, the mean cannot be uniquely recovered from observed samples (i.e., the problem is not *identifiable*). Recent work by Fotakis et al. (2021) established that *sample*-efficient mean estimation is possible when the unknown mean is *identifiable* and the partition consists of only *convex* sets. Moreover, they showed that without convexity, mean estimation becomes NP-hard. However, two fundamental questions remained open: 
1. When is the mean identifiable under convex partitions?
2. Is *computationally* efficient estimation possible under identifiability and convex partitions?

This work resolves both questions. We provide a geometric characterization of when a convex partition is identifiable, showing it depends on whether the convex sets form ``slabs'' in a direction. Second, we give the first polynomial-time algorithm for finding $\varepsilon$-accurate estimates of the Gaussian mean given coarse samples from an unknown convex partition, matching the optimal $\widetilde{O}(d/\varepsilon^2)$ sample complexity. Our results have direct applications to robust machine learning, particularly robustness to observation rounding. As a concrete example, we derive a sample- and computationally- efficient algorithm for linear regression with market friction, a canonical problem in using ML in economics, where exact prices are unobserved and one only sees a range containing the price (Rosett, 1959).
PE-SGD: Differentially Private Deep Learning via Evolution of Gradient Subspace for Text
Authors: Tianyuan Zou; Zinan Lin; Sivakanth Gopi; Yang Liu; Ya-Qin Zhang; Robert Sim; Xin Deng; Sergey Yekhanin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) and its variants like DP-Adam ensure data privacy by injecting noise into per-sample gradients. Although effective with large private datasets, their performance degrades significantly when private training data is limited. Recent works leverage public data to learn a gradient subspace and project noisy private sample gradients on to this subspace, achieving improved performance. However, they have overlooked two crucial aspects: the limitation of using a fixed projection subspace throughout training and the importance of choosing where to inject noise. Therefore, we propose Private Evolution aided Stochastic Gradient Descent (***PE-SGD***), a differentially private training framework effective for scenarios with limited private data. ***PE-SGD*** uses an evolutionary strategy to update the gradient projection subspace during training process. We also identify a more effective noise injection point for better alignment between approximate DP-protected gradient and real private gradient. This enables ***PE-SGD*** to outperform DP-SGD and other baselines, particularly in the regime of limited private data and small privacy budget.
Contact-guided Real2Sim from Monocular Video with Planar Scene Primitives
Authors: Zihan Wang; Jiashun Wang; Jeff Tan; Yiwen Zhao; Jessica K. Hodgins; Shubham Tulsiani; Deva Ramanan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce CRISP, a method that recovers simulatable human motion and scene geometry from monocular video. Prior work on joint human--scene reconstruction relies on data-driven priors and joint optimization with no physics in the loop, or recovers noisy geometry with artifacts that cause motion-tracking policies with scene interactions to fail. In contrast, our key insight is to fit simulation-ready convex planar primitives to a depth-based point cloud reconstruction of the scene via a simple clustering pipeline over depth, normals, and flow. To reconstruct scene geometry that might be occluded during interactions, we use human--scene contact modeling (e.g., using human posture to reconstruct the occluded seat of a chair). Finally, we ensure that human and scene reconstructions are physically plausible by using them to drive a humanoid controller via reinforcement learning. Our approach reduces motion-tracking failure rates from 55.2\% to 6.9\% on human-centric video benchmarks (EMDB, PROX), while delivering 43\% faster RL simulation throughput. This demonstrates CRISP's ability to generate physically valid human motion and interaction environments at scale, advancing real-to-sim applications for robotics. Code and interactive demos are available at our project website: https://crisp-real2sim.github.io/CRISP-Real2Sim
Why Do Unlearnable Examples Work: A Novel Perspective of Mutual Information
Authors: Yifan Zhu; Yibo Miao; Yinpeng Dong; Xiao-Shan Gao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The volume of freely scraped data on the Internet has driven the tremendous success of deep learning. Along with this comes the rising concern about data privacy and security. Numerous methods for generating unlearnable examples have been proposed to prevent data from being illicitly learned by unauthorized deep models by impeding generalization. However, the existing approaches primarily rely on
empirical heuristics, making it challenging to enhance unlearnable examples with solid explanations. In this paper, we analyze and improve unlearnable examples from a novel perspective: mutual information reduction. We demonstrate that effective unlearnable examples always decrease mutual information between clean features and poisoned features, and when the network gets deeper, the unlearnability goes better together with lower mutual information. Further, we prove from a covariance reduction perspective that minimizing the conditional covariance of intra-class poisoned features reduces the mutual information between distributions. Based on the theoretical results, we propose a novel unlearnable method called Mutual Information Unlearnable Examples (MI-UE) that reduces covariance by maximizing the cosine similarity among intra-class features, thus impeding the generalization effectively. Extensive experiments demonstrate that our approach significantly outperforms previous state-of-the-art methods, even under defense mechanisms.
Learning Unified Representation of 3D Gaussian Splatting
Authors: Yuelin Xin; Yuheng Liu; Xiaohui Xie; Xinke Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A well-designed vectorized representation is crucial for the learning systems natively based on 3D Gaussian Splatting. While 3DGS enables efficient and explicit 3D reconstruction, its parameter-based representation remains hard to learn as features, especially for neural-network-based models. Directly feeding raw Gaussian parameters into learning frameworks fails to address the non-unique and heterogeneous nature of the Gaussian parameterization, yielding highly data-dependent models. This challenge motivates us to explore a more principled approach to represent 3D Gaussian Splatting in neural networks that preserves the underlying color and geometric structure while enforcing unique mapping and channel homogeneity. In this paper, we propose an embedding representation of 3DGS based on continuous submanifold fields that encapsulate the intrinsic information of Gaussian primitives, thereby benefiting the learning of 3DGS.
Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks
Authors: Yubo Li; Ramayya Krishnan; Rema Padman
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have revolutionized conversational AI, yet their robustness in extended multi-turn dialogues remains poorly understood. Existing evaluation frameworks focus on static benchmarks and single-turn assessments, failing to capture the temporal dynamics of conversational degradation that characterize real-world interactions. In this work, we present a large-scale survival analysis of conversational robustness, modeling failure as a time-to-event process over 36,951 turns from 9 state-of-the-art LLMs on the MT-Consistency benchmark. Our framework combines Cox proportional hazards, Accelerated Failure Time (AFT), and Random Survival Forest models with simple semantic drift features. We find that abrupt prompt-to-prompt semantic drift sharply increases the hazard of inconsistency, whereas cumulative drift is counterintuitively \emph{protective}, suggesting adaptation in conversations that survive multiple shifts. AFT models with model–drift interactions achieve the best combination of discrimination and calibration, and proportional hazards checks reveal systematic violations for key drift covariates, explaining the limitations of Cox-style modeling in this setting. Finally, we show that a lightweight AFT model can be turned into a turn-level risk monitor that flags most failing conversations several turns before the first inconsistent answer while keeping false alerts modest. These results establish survival analysis as a powerful paradigm for evaluating multi-turn robustness and for designing practical safeguards for conversational AI systems.
GAGA: Gaussianity-Aware Gaussian Approximation for Efficient 3D Molecular Generation
Authors: Jingxiang Qu; Wenhan Gao; Ruichen Xu; Yi Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Gaussian Probability Path based Generative Models (GPPGMs) generate data by reversing a stochastic process that progressively corrupts samples with Gaussian noise. Despite state-of-the-art results in 3D molecular generation, their deployment is hindered by the high cost of long generative trajectories, often requiring hundreds to thousands of steps during training and sampling. In this work, we propose a principled method, named GAGA, to improve generation efficiency without sacrificing training granularity or inference fidelity of GPPGMs. Our key insight is that different data modalities obtain sufficient Gaussianity at markedly different steps during the forward process. Based on this observation, we analytically identify a characteristic step at which molecular data attains sufficient Gaussianity, after which the trajectory can be replaced by a closed-form Gaussian approximation. Unlike existing accelerators that coarsen or reformulate trajectories, our approach preserves full-resolution learning dynamics while avoiding redundant transport through truncated distributional states. Experiments on 3D molecular generation benchmarks demonstrate that our GAGA achieves substantial improvement on both generation quality and computational efficiency.