GenAI4ELab/papercli-papers huggingface dataset:
MICLIP: Learning to Interpret Representation in Vision Models
Authors: Yingdong Shi; Zhiyu Yang; Changming Li; Jingyi Yu; Kan Ren
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision models have demonstrated remarkable capabilities, yet their decision-making processes remain largely opaque. Mechanistic interpretability (MI) offers a promising avenue to decode these internal workings.  However, existing interpretation methods suffer from two key limitations. First, they rely on the flawed activation-magnitude assumption, assuming that the importance of a neuron is directly reflected by the magnitude of its activation, which ignores more nuanced causal roles. Second, they are predominantly input-centric, failing to capture the causal mechanisms that drive a model's output. These shortcomings lead to inaccurate and unreliable internal representation interpretations, especially in cases of incorrect predictions. We propose MICLIP (Mechanism-Interpretability via Contrastive Learning), a novel framework that extends CLIP’s contrastive learning to align internal mechanisms of vision models with general semantic concepts, enabling interpretable and controllable representations. Our approach circumvents previous limitations by performing multimodal alignment between a model's internal representations and both its input concepts and output semantics via contrastive learning. We demonstrate that MICLIP is a general framework applicable to diverse representation unit types, including individual neurons and sparse autoencoder (SAE) features.  By enabling precise, causal-aware interpretation, MICLIP not only reveals the semantic properties of a model's internals but also paves the way for effective and targeted manipulation of model behaviors.
Predicting Kernel Regression Learning Curves from Only Raw Data Statistics
Authors: Dhruva Karkada; Joseph Turnbull; Yuxi Liu; James B Simon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study kernel regression with common rotation-invariant kernels on real datasets including CIFAR-5m, SVHN, and ImageNet.
We give a theoretical framework that predicts learning curves (test risk vs. sample size) from only two measurements: the empirical data covariance matrix and an empirical polynomial decomposition of the target function $f_*$.
The key new idea is an analytical approximation of a kernel’s eigenvalues and eigenfunctions with respect to an anisotropic data distribution.
The eigenfunctions resemble Hermite polynomials of the data, so we call this approximation the \textit{Hermite eigenstructure ansatz} (HEA).
We prove the HEA for Gaussian data, but we find that real image data is often ``Gaussian enough’’ for the HEA to hold well in practice, enabling us to predict learning curves by applying prior results relating kernel eigenstructure to test risk.
Extending beyond kernel regression, we empirically find that MLPs in the feature-learning regime learn Hermite polynomials in the order predicted by the HEA.
Our HEA framework is a proof of concept that an end-to-end theory of learning which maps dataset structure all the way to model performance is possible for nontrivial learning algorithms on real datasets.
Beyond Penalization: Diffusion-based Out-of-Distribution Detection and Selective Regularization in Offline Reinforcement Learning
Authors: Qingjun Wang; Hongtu Zhou; Hang Yu; Junqiao Zhao; Yanping Zhao; Chen Ye; Ziqiao Wang; Guang Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Offline reinforcement learning (RL) faces a critical challenge of overestimating the value of out-of-distribution (OOD) actions. 
Existing methods mitigate this issue by penalizing unseen samples, yet they fail to accurately identify OOD actions and may suppress beneficial exploration beyond the behavioral support.
Although several methods have been proposed to differentiate OOD samples with distinct properties, they typically rely on restrictive assumptions about the data distribution and remain limited in discrimination ability.
To address this problem, we propose $\textbf{DOSER}$ ($\textbf{D}$iffusion-based $\textbf{O}$OD Detection and $\textbf{SE}$lective $\textbf{R}$egularization), a novel framework that goes beyond uniform penalization. 
DOSER trains two diffusion models to capture the behavior policy and state distribution, using single-step denoising reconstruction error as a reliable OOD indicator. 
During policy optimization, it further distinguishes between beneficial and detrimental OOD actions by evaluating predicted transitions, selectively suppressing risky actions while encouraging exploration of high-potential ones.
Theoretically, we prove that DOSER is a $\gamma$-contraction and therefore admits a unique fixed point with bounded value estimates. 
We further provide an asymptotic performance guarantee relative to the optimal policy under model approximation and OOD detection errors.
Across extensive offline RL benchmarks, DOSER consistently attains superior performance to prior methods, especially on suboptimal datasets.
VowelPrompt: Hearing Speech Emotions from Text via Vowel-level Prosodic Augmentation
Authors: Yancheng Wang; Osama Hanna; Ruiming Xie; Xianfeng Rui; Maohao Shen; Xuedong Zhang; Christian Fuegen; Jilong Wu; Debjyoti Paul; Arthur Guo; Zhihong Lei; Ozlem Kalinli; Qing He; Yingzhen Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Emotion recognition in speech presents a complex multimodal challenge, requiring comprehension of both linguistic content and vocal expressivity, particularly prosodic features such as fundamental frequency, intensity, and temporal dynamics. Although large language models (LLMs) have shown promise in reasoning over textual transcriptions for emotion recognition, they typically neglect fine-grained prosodic information, limiting their effectiveness and interpretability. In this work, we propose VowelPrompt, a linguistically grounded framework that augments LLM-based emotion recognition with interpretable, fine-grained vowel-level prosodic cues. Drawing on phonetic evidence that vowels serve as primary carriers of affective prosody, VowelPrompt extracts pitch-, energy-, and duration-based descriptors from time-aligned vowel segments, and converts these features into natural language descriptions for better interpretability.
Such a design enables LLMs to jointly reason over lexical semantics and fine-grained prosodic variation. Moreover, we adopt a two-stage adaptation procedure comprising supervised fine-tuning (SFT) followed by Reinforcement Learning with Verifiable Reward (RLVR), implemented via Group Relative Policy Optimization (GRPO), to enhance reasoning capability, enforce structured output adherence, and improve generalization across domains and speaker variations. Extensive evaluations across diverse benchmark datasets demonstrate that VowelPrompt consistently outperforms state-of-the-art emotion recognition methods under zero-shot, fine-tuned, cross-domain, and cross-linguistic conditions, while enabling the generation of interpretable explanations that are jointly grounded in contextual semantics and fine-grained prosodic structure.
DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights
Authors: Saumya Gupta; Scott Biggs; Moritz Laber; Zohair Shafi; Robin Walters; Ayan Paul
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Building efficient and effective generative models for neural network weights has been a research focus of significant interest that faces challenges posed by the high-dimensional weight spaces of modern neural networks and their symmetries. Several prior generative models are limited to generating partial neural network weights, particularly for larger models, such as ResNet and ViT. Those that do generate complete weights struggle with generation speed or require finetuning of the generated models. In this work, we present \ours, a Flow Matching model that operates directly in weight space to generate diverse and high-accuracy neural network weights for a variety of architectures, neural network sizes, and data modalities. The neural networks generated by \ours do not require fine-tuning to perform well and can scale to large networks. We apply Git Re-Basin and TransFusion for neural network canonicalization in the context of generative weight models to account for the impact of neural network permutation symmetries and to improve generation efficiency for larger model sizes. The generated networks excel at transfer learning, and ensembles of hundreds of neural networks can be generated in minutes, far exceeding the efficiency of diffusion-based methods. DeepWeightFlow models pave the way for more efficient and scalable generation of diverse sets of neural networks.
PAT3D: Physics-Augmented Text-to-3D Scene Generation
Authors: Guying Lin; Kemeng Huang; Michael Liu; Ruihan Gao; Hanke Chen; Lyuhao Chen; Beijia Lu; Taku Komura; Yuan Liu; Jun-Yan Zhu; Minchen Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce PAT3D, the first physics-augmented text-to-3D scene generation framework that integrates vision–language models with physics-based simulation to produce physically plausible, simulation-ready, and intersection-free 3D scenes. Given a text prompt, PAT3D generates 3D objects, infers their spatial relations, and organizes them into a hierarchical scene tree, which is then converted into initial simulation conditions. A rigid-body simulator ensures realistic object interactions under gravity, driving the scene toward static equilibrium without interpenetrations. To further enhance scene quality, we introduce a simulation-in-the-loop optimization procedure that guarantees physical stability and non-intersection, while improving semantic consistency with the input prompt. Experiments demonstrate that PAT3D substantially outperforms prior approaches in physical plausibility, semantic accuracy, and visual quality. Beyond high-quality generation, PAT3D uniquely enables simulation-ready 3D scenes for downstream tasks such as scene editing and robotic manipulation. Our code and data are available at https://github.com/Simulation-Intelligence/PAT3D.
Towards Cognitively-Faithful Decision-Making Models to Improve AI Alignment
Authors: Cyrus Cousins; Vijay Keswani; Vincent Conitzer; Hoda Heidari; Jana Schaich Borg; Walter Sinnott-Armstrong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent AI trends seek to align AI models to learned human-centric objectives, such as personal preferences, utility, or societal values. Using standard preference elicitation methods, researchers and practitioners build models of human decisions and judgments, to which AI models are aligned. However, standard elicitation methods often fail to capture the true cognitive processes behind human decision making, such as the use of heuristics or simplifying structured thought patterns. To address this limitation, we take an axiomatic approach to learning cognitively faithful decision processes from pairwise comparisons. Building on the vast literature characterizing cognitive processes that contribute to human decision-making and pairwise comparisons, we derive a class of models in which individual features are first processed with learned rules, then aggregated via a fixed rule, such as the Bradley-Terry rule, to produce a decision. This structured processing of information ensures that such models are realistic and feasible candidates to represent underlying human decision-making processes. We demonstrate the efficacy of this modeling approach by learning interpretable models of human decision making in a kidney allocation task, and show that our proposed models match or surpass the accuracy of prior models of human pairwise decision-making.
Clipped Gradient Methods for Nonsmooth Convex Optimization under Heavy-Tailed Noise: A Refined Analysis
Authors: Zijian Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Optimization under heavy-tailed noise has become popular recently, since it better fits many modern machine learning tasks, as captured by empirical observations. Concretely, instead of a finite second moment on gradient noise, a bounded $\mathfrak{p}$-th moment where $\mathfrak{p}\in(1,2]$ has been recognized to be more realistic (say being upper bounded by $\sigma_{\mathfrak{l}}^{\mathfrak{p}}$ for some $\sigma_{\mathfrak{l}}\geq0$). A simple yet effective operation, gradient clipping, is known to handle this new challenge successfully. Specifically, Clipped Stochastic Gradient Descent (Clipped SGD) guarantees a high-probability rate $\mathcal{O}(\sigma_{\mathfrak{l}}\ln(1/\delta)T^{\frac{1}{\mathfrak{p}}-1})$ (resp. $\mathcal{O}(\sigma_{\mathfrak{l}}^{2}\ln^{2}(1/\delta)T^{\frac{2}{\mathfrak{p}}-2})$) for nonsmooth convex (resp. strongly convex) problems, where $\delta\in(0,1]$ is the failure probability and $T\in\mathbb{N}$ is the time horizon. In this work, we provide a refined analysis for Clipped SGD and offer two rates, $\mathcal{O}(\sigma_{\mathfrak{l}}d_{\mathrm{eff}}^{-\frac{1}{2\mathfrak{p}}}\ln^{1-\frac{1}{\mathfrak{p}}}(1/\delta)T^{\frac{1}{\mathfrak{p}}-1})$ and $\mathcal{O}(\sigma_{\mathfrak{l}}^{2}d_{\mathrm{eff}}^{-\frac{1}{\mathfrak{p}}}\ln^{2-\frac{2}{\mathfrak{p}}}(1/\delta)T^{\frac{2}{\mathfrak{p}}-2})$, faster than the aforementioned best results, where $d_{\mathrm{eff}}\geq1$ is a quantity we call the generalized effective dimension. Our analysis improves upon the existing approach in two respects: better utilization of Freedman's inequality and finer bounds for clipping error under heavy-tailed noise. In addition, we extend the refined analysis to convergence in expectation and obtain new rates that break the known lower bounds. Lastly, to complement the study, we establish new lower bounds for both high-probability and in-expectation convergence. Notably, the in-expectation lower bounds match our new upper bounds, indicating the optimality of our refined analysis for convergence in expectation.
Kevin: Multi-Turn RL for Generating CUDA Kernels
Authors: Carlo Baronio; Pietro Marsella; Ben Pan; Simon Guo; Silas Alberti
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Writing GPU kernels is a challenging task and critical for AI systems' efficiency. It is also highly iterative: domain experts write code and improve performance through execution feedback. Moreover, it presents verifiable rewards like correctness and speedup, making it a natural environment to apply Reinforcement Learning (RL). To explicitly incorporate the iterative nature of this process into training, we develop a flexible multi-turn RL recipe that addresses unique challenges encountered in real-world settings, such as learning from long trajectories and effective reward attribution across turns. We present Kevin - K(ernel D)evin, the first model trained with multi-turn RL for CUDA kernel generation and optimization. In our evaluation setup, Kevin shows significant gains over its base model (QwQ-32B), improving correctness of generated kernels (in pure CUDA) from 56% to 82% and mean speedup from 0.53x to 1.10x of baseline (PyTorch Eager), and surpassing frontier models like o4-mini (0.78x). Finally, we study its behavior across test-time scaling axes: we found scaling serial refinement more beneficial than parallel sampling. In particular, when given more refinement turns, Kevin shows a higher rate of improvement.
PAMDP: Interact to Persona Alignment via a Partially Observable Markov Decision Process
Authors: ZHE YANG; Yi Huang; Si Chen; Xiaoting Wu; Jingyu Yao; Junlan Feng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The interaction process of comprehending user-specific nuances and adapting to their preferences represents a pivotal consideration for Persona Large Language Models, as it more authentically mirrors genuine dialogue dynamics than adherence to general human value alignment. In this paper, we conceptualize this ``Interact to Persona Alignment'' challenge as a Partially Observable Markov Decision Process, abbreviated as PAMDP, wherein the user’s dynamically evolving profile through interaction is treated as an unobservable variable to the assistant. Grounded in this formulation, we propose a dual-critic reinforcement learning framework, with a continuous latent space action representing the assistant’s utterance. We evaluate our approach on both offline datasets and the online simulator, ultimately demonstrating its effectiveness.
Conformalized Decision Risk Assessment
Authors: Wenbin Zhou; Agni Orfanoudaki; Shixiang Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: High-stakes decisions in healthcare, energy, and public policy have long depended on human expertise and heuristics, but are now increasingly supported by predictive and optimization-based tools. A prevailing paradigm in operations research is predict-then-optimize, where predictive models estimate uncertain inputs and optimization models recommend decisions. However, such approaches often sideline human judgment, creating a disconnect between algorithmic outputs and expert intuition that undermines trust and adoption in practice.
To bridge this gap, we propose CREDO, a framework that, for any candidate decision proposed by human experts, provides a distribution-free upper bound on the probability of suboptimality---informed by both the optimization structure and the data distribution. By combining inverse optimization geometry with conformal generative prediction, CREDO delivers statistically rigorous yet practically interpretable risk certificates. This framework allows human decision-makers to audit and validate their decisions under uncertainty, strengthening the alignment between algorithmic tools and human intuition.
Deconstructing Guidance: A Semantic Hierarchy for Precise Diffusion Model Editing
Authors: Wootaek Jeong; Junghyo Sohn; Jee Seok Yoon; Heung-Il Suk
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Text-guided image editing requires more than prompt following—it demands a principled understanding of what to modify versus what to preserve. We investigate the internal guidance mechanism of diffusion models and reveal that the guidance signal follows a structured semantic hierarchy. We formalize this insight as the Semantic Scale Hypothesis: the magnitude of the guidance difference vector ($\Delta\boldsymbol{\epsilon}$) directly encodes the semantic scale of edits. Crucially, this phenomenon is theoretically grounded in Tweedie’s formula, which links score prediction to the variance of the underlying data distribution. Low-variance regions, such as objects, yield large-magnitude differences corresponding to structural edits, whereas high-variance regions, such as backgrounds, yield small-magnitude differences corresponding to stylistic adjustments. Building on this principle, we introduce Prism-Edit, a training-free, plug-and-play module that decomposes the guidance signal into semantic layers, enabling selective and interpretable control. Extensive experiments—spanning direct visualization of the semantic hierarchy, generalization across foundation models, and integration with state-of-the-art editors—demonstrate that Prism-Edit achieves precise, robust, and controllable editing. Our findings establish semantic scale as a foundational axis for understanding and advancing diffusion-based image editing.
Beyond Uniformity: Sample and Frequency Meta Weighting for Post-Training Quantization of Diffusion Models
Authors: Cuong Pham; Dung Anh Hoang; Cuong C. Nguyen; Trung Le; Dinh Phung; Gustavo Carneiro; Thanh-Toan Do
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Post-training quantization (PTQ) is an attractive approach for compressing diffusion models to speed up the sampling process and reduce memory footprint. Most existing PTQ methods uniformly sample data from various time steps in denoising process to construct a calibration set for quantization and consider calibration samples equally important during the quantization process. However, treating all calibration samples equally may not be optimal. One notable property in the denoising process of diffusion models is that low-frequency features are primarily recovered in early stages, while high-frequency features are recovered in later stages of the denoising process. However, none of the previous works on quantization for diffusion models consider this property to enhance the effectiveness of quantized models. In this paper, we propose a novel meta-learning approach for PTQ of diffusion models that jointly optimizes the contributions of calibration samples and the weighting of frequency components at each time step for quantizing noise estimation networks. Specifically, our approach automatically learns to assign optimal weights to calibration samples while selectively focusing on mimicking specific frequency components of data generated by the full-precision noise estimation network at each denoising time step. Extensive experiments on CIFAR-10, LSUN-Bedrooms, FFHQ, and ImageNet datasets demonstrate that our approach consistently outperforms the compared PTQ methods for diffusion models.
Self-Improving Vision-Language-Action Models with Data Generation via Residual RL
Authors: Wenli Xiao; Haotian Lin; Andy Peng; Haoru Xue; Tairan He; Zhengyi Luo; Yuqi Xie; Fengyuan Hu; Linxi Fan; Guanya Shi; Yuke Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Supervised fine-tuning (SFT) has become the de facto post-training strategy for large vision-language-action (VLA) models, but its reliance on costly human demonstrations limits scalability and generalization. We propose Probe, Learn, Distill (PLD), a plug-and-play framework that improves VLAs through residual reinforcement learning and distribution-aware data collection. In Stage 1 (specialist acquisition), we freeze the VLA backbone and train lightweight residual actors via off-policy RL. These specialists take over in states where the base policy fails, thereby probing failure regions of the generalist. In Stage 2 (data collection), we employ a hybrid rollout scheme that biases residual interventions toward states frequently visited by the base policy, aligning collected trajectories with the generalist’s deployment distribution while capturing recovery behaviors. In Stage 3 (fine-tuning), these curated trajectories are distilled back into the generalist with standard SFT, applicable to both flow-matching and autoregressive heads. We evaluate PLD across diverse settings: it achieves a near-saturated 99% task success rate on the LIBERO benchmark, delivers over 50% performance gains in SimplerEnv, and demonstrates practicality on real-world Franka arm manipulation tasks. We further provide ablations showing that residual policy probing and distribution-aware replay are key to collecting deployment-aligned data that improves VLAs’ capabilities on both seen and unseen tasks. Our results demonstrate that RL-generated, policy-aligned data can surpass teleoperation-only demonstrations, offering a scalable path toward self-improving VLA models.
Jacobian Aligned Random Forests
Authors: Sarwesh Rauniyar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Axis-aligned decision trees are fast and stable but struggle on datasets with rotated
or interaction-dependent decision boundaries, where informative splits require linear combinations of features rather than single-feature thresholds. Oblique forests
address this with per-node hyperplane splits, but at added computational cost.
We propose a simple alternative: JARF, Jacobian-Aligned Random Forests. Concretely, we fit a random forest to estimate class probabilities or regression outputs,
compute finite-difference gradients with respect to each feature, form an expected
Jacobian outer product/expected gradient outer product, and use it as a single
global linear preconditioner for all inputs. This preserves the simplicity of axisaligned trees while applying a single global rotation to capture oblique boundaries
and feature interactions that would otherwise require many axis-aligned splits to
approximate. On tabular benchmarks, our preconditioned forest matches or surpasses oblique baselines while training faster. Our results suggest that supervised
preconditioning can deliver the accuracy of oblique forests while keeping the simplicity of axis-aligned trees.