GenAI4ELab/papercli-papers huggingface dataset:
floq: Training Critics via Flow-Matching for Scaling Compute in Value-Based RL
Authors: Bhavya Kumar Agrawalla; Michal Nauman; Khush Agrawal; Aviral Kumar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A hallmark of modern large-scale machine learning techniques is the use of training objectives that provide dense supervision to intermediate computations, such as teacher forcing the next token in language models or denoising step-by-step in diffusion models. This enables models to learn complex functions in a generalizable manner. Motivated by this observation, we investigate the benefits of iterative computation for temporal difference (TD) methods in reinforcement learning (RL). Typically, they represent value functions in a monolithic fashion, without iterative compute. We introduce floq (flow-matching Q-functions), an approach that parameterizes the Q-function using a velocity field and trains it with techniques from flow-matching, typically used in generative modeling. This velocity field underneath the flow is trained using a TD-learning objective, which bootstraps from values produced by a target velocity field, computed by running multiple steps of numerical integration. Crucially, floq allows for more fine-grained control and scaling of the Q-function capacity than monolithic architectures, by appropriately setting the number of integration steps. Across a suite of challenging offline RL benchmarks and online fine-tuning tasks, floq improves performance by nearly 1.8x. floq scales capacity far better than standard TD-learning architectures, highlighting the potential of iterative computation for value learning.
PRISON: Unmasking the Criminal Potential of Large Language Models
Authors: Xinyi Wu; Geng Hong; Pei Chen; Yueyue Chen; Xudong Pan; Min Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As large language models (LLMs) advance, concerns about their misconduct in complex social contexts intensify. Existing research has overlooked the systematic assessment of LLMs’ criminal potential in realistic interactions, where criminal potential is defined as the risk of producing harmful behaviors such as deception and blame-shifting under adversarial settings that could facilitate unlawful activities. Therefore, we propose a unified framework PRISON, to quantify LLMs' criminal potential across five traits: False Statements, Frame-Up, Psychological Manipulation, Emotional Disguise, and Moral Disengagement. Using structured crime scenarios grounded in reality, we evaluate both criminal potential and anti-crime ability of LLMs. Results show that state-of-the-art LLMs frequently exhibit emergent criminal tendencies, such as proposing misleading statements or evasion tactics, even without explicit instructions. Moreover, when placed in a detective role, models recognize deceptive behavior with only 44\% accuracy on average, revealing a striking mismatch between expressing and detecting criminal traits. These findings underscore the urgent need for adversarial robustness, behavioral alignment, and safety mechanisms before broader LLM deployment.
AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models
Authors: Changwoo Baek; Jouwon Song; Sohyeon Kim; Kyeongbo Kong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Vision-Language Models (LVLMs) have adopted visual token pruning strategies to mitigate substantial computational overhead incurred by extensive visual token sequences. While prior works primarily focus on either attention-based or diversity-based pruning methods,  in-depth analysis of these approaches' characteristics and limitations remains largely unexplored. In this work, we conduct thorough empirical analysis using effective rank (erank) as a measure of feature diversity and attention score entropy to investigate visual token processing mechanisms and analyze the strengths and weaknesses of each approach. Our analysis reveals two insights:  (1) Our erank-based quantitative analysis shows that many diversity-oriented pruning methods  preserve substantially less feature diversity than intended; moreover, analysis using the CHAIR  dataset reveals that the diversity they do retain is closely tied to increased hallucination 
frequency compared to attention-based pruning.  (2) We further observe that attention-based approaches are more effective on simple images where  visual evidence is concentrated, while diversity-based methods better handle complex images with  distributed features.  Building on these empirical insights, we show that incorporating image-aware adjustments into existing hybrid pruning strategies consistently improves their performance.  We also provide a minimal instantiation of our empirical findings through a simple adaptive 
pruning mechanism, which achieves strong and reliable performance across standard benchmarks as  well as hallucination-specific evaluations. Our project page available at https://cvsp-lab.github.io/AgilePruner.
Stable and Scalable Deep Predictive Coding Networks with Meta-Prediction Errors
Authors: Myoung Hoon Ha; Hyunjun Kim; Yoondo Sung; Youngha Jo; Min S. Kang; Sang Wan Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Predictive Coding Networks (PCNs) offer a biologically inspired alternative to conventional deep neural networks.
However, their scalability is hindered by severe training instabilities that intensify with network depth. 
Through dynamical mean-field analyses, we identify two fundamental pathologies that impede deep PCN training: 
(1) prediction error (PE) imbalance that leads to uneven learning across layers, characterized by error concentration at network boundaries; and
(2) exploding and vanishing prediction errors (EVPE) sensitive to weight variance.
To address these challenges, we propose Meta-PCN, a unified framework that incorporates two synergistic components: 
(1) a loss based on meta-prediction error, which minimizes PEs of PEs to linearize the nonlinear inference dynamics; and 
(2) weight regularization that employs normalization to regulate weight variance and mitigate EVPE.
Extensive experimental validation on CIFAR-10/100 and TinyImageNet demonstrates that Meta-PCN \rev{achieves} statistically significant improvements over conventional PCNs, outperforming backpropagation in most tested configurations, while preserving the local learning rules of PCNs.
NarrLV: Towards a Comprehensive Narrative-Centric Evaluation for Long Video Generation
Authors: Xiaokun Feng; Haiming Yu; Meiqi Wu; Shiyu Hu; Jintao Chen; Chen Zhu; Jiahong Wu; Xiangxiang Chu; Kaiqi Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the rapid development of foundation video generation technologies, long video generation models have exhibited promising research potential thanks to expanded content creation space. Recent studies reveal that the goal of long video generation tasks is not only to extend video duration but also to accurately express richer narrative content within longer videos. However, due to the lack of evaluation benchmarks specifically designed for long video generation models, the current assessment of these models primarily relies on benchmarks with simple narrative prompts (e.g., VBench). To the best of our knowledge, our proposed NarrLV is the first benchmark to comprehensively evaluate the Narrative expression capabilities of Long Video generation models. Inspired by film narrative theory, (i) we first introduce the basic narrative unit maintaining continuous visual presentation in videos as Temporal Narrative Atom (TNA), and use its count to quantitatively measure narrative richness. Guided by three key film narrative elements influencing TNA changes, we construct an automatic prompt generation pipeline capable of producing evaluation prompts with a flexibly expandable number of TNAs. (ii) Then, based on the three progressive levels of narrative content expression, we design an effective evaluation metric using the MLLM-based question generation and answering framework. (iii) Finally, we conduct extensive evaluations on existing long video generation models and the foundation generation models that underpin them. Experimental results demonstrate that our metric aligns closely with human judgments. The derived evaluation outcomes reveal the detailed capability boundaries of current video generation models in narrative content expression.
Pseudo-Non-Linear Data Augmentation: A Constrained Energy Minimization Viewpoint
Authors: Pingbang Hu; Mahito Sugiyama
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a simple yet novel data augmentation method for general data modalities based on energy-based modeling and principles from information geometry. Unlike most existing learning-based data augmentation methods, which rely on learning latent representations with generative models, our proposed framework enables an intuitive construction of a geometrically aware latent space that represents the structure of the data itself, supporting efficient and explicit encoding and decoding procedures. We then present and discuss how to design latent spaces that will subsequently control the augmentation with the proposed algorithm. Empirical results demonstrate that our data augmentation method achieves competitive performance in downstream tasks compared to other baselines, while offering fine-grained controllability that is lacking in the existing literature.
On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning
Authors: Yifan Zhang; Yifeng Liu; Huizhuo Yuan; Yang Yuan; Quanquan Gu; Andrew C Yao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Policy gradient algorithms have been successfully applied to enhance the reasoning capabilities of large language models (LLMs). KL regularization is ubiquitous, yet the design surface, choice of KL direction (forward vs. reverse), normalization (normalized vs. unnormalized), and estimator ($k_1/k_2/k_3$), is scattered across the literature and often intertwined with off-policy estimation. We ask a focused question: under the off-policy setting, what weighting is required for each KL variant so that the surrogate we optimize yields the exact gradient of the intended KL-regularized objective? We answer this with a compact, unified derivation we call the Regularized Policy Gradient (\textbf{RPG}) view. RPG (i) unifies normalized and unnormalized KL variants and shows that the widely-used $k_3$ penalty is exactly the unnormalized KL; (ii) specifies conditions under which REINFORCE-style losses with stop-gradient are gradient-equivalent to fully differentiable surrogates; (iii) identifies and corrects an off-policy importance-weighting mismatch in GRPO's KL term; and (iv) introduces RPG-Style Clip, a truncated-importance-sampling step within RPG-REINFORCE that enables stable, off-policy policy-gradient training at scale. On mathematical reasoning benchmarks (AIME24, AIME25), RPG-REINFORCE with RPG-Style Clip improves accuracy by up to $+6$ absolute percentage points over DAPO. Notably, RPG is a \emph{stable and scalable} RL algorithm for LLM reasoning, realized via (a) a KL-correct objective, (b) truncated importance sampling, and (c) an iterative reference-policy update scheme.
Steer Away From Mode Collisions: Improving Composition In Diffusion Models
Authors: Debottam Dutta; Jianchong Chen; RAJALAXMI RAJAGOPALAN; Yu-Lin Wei; Romit Roy Choudhury
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose to improve multi-concept prompt fidelity in text-to-image diffusion
models. We begin with common failure cases—prompts like “a cat and a dog”
that sometimes yields images where one concept is missing, faint, or colliding
awkwardly with another. We hypothesize that this happens when the diffusion
model drifts into mixed modes that over-emphasize a single concept it learned
strongly during training. Instead of re-training, we introduce a corrective sampling 
strategy that steers away from regions where the joint prompt behavior overlaps 
too strongly with any single concept in the prompt. The goal is to steer
towards “pure” joint modes where all concepts can coexist with balanced visual
presence. We further show that existing multi-concept guidance schemes can operate 
in unstable weight regimes that amplify imbalance; we characterize favorable 
regions and adapt sampling to remain within them. Our approach, CO3 (Concept Contrasting Corrector), 
is plug-and-play, requires no model tuning, and complements
standard classifier-free guidance. Experiments on diverse multi-concept prompts indicate improvements
in concept coverage, balance and robustness, with fewer dropped or distorted concepts 
compared to standard baselines and prior compositional methods. Results
suggest that lightweight corrective guidance can substantially mitigate brittle 
semantic alignment behavior in modern diffusion systems. Code is available at
https://github.com/debottam-dutta7/co3
Bayesian Neural Networks for Functional ANOVA Model
Authors: Seokhun Park; Choeun Kim; Jihu Lee; Yunseop Shin; Insung Kong; Yongdai Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the increasing demand for interpretability in machine learning, functional ANOVA decomposition has gained renewed attention as a principled tool for breaking down high-dimensional function into low-dimensional components that reveal the contributions of different variable groups.
Recently, Tensor Product Neural Network (TPNN) has been developed and applied as basis functions in the functional ANOVA model, referred to as ANOVA-TPNN.
A disadvantage of ANOVA-TPNN, however, is that the components to be estimated must be specified in advance, which makes it difficult to incorporate higher-order TPNNs into the functional ANOVA model due to computational and memory constraints.
In this work, we propose Bayesian-TPNN, a Bayesian inference procedure for the functional ANOVA model with TPNN basis functions, enabling the detection of higher-order components with reduced computational cost compared to ANOVA-TPNN.
We develop an efficient MCMC algorithm and demonstrate that Bayesian-TPNN performs well by analyzing multiple benchmark datasets.
Theoretically, we prove that the posterior of Bayesian-TPNN is consistent.
LLM2Fx-Tools: Tool Calling for Music Post-Production
Authors: SeungHeon Doh; Junghyun Koo; Marco A. Martínez-Ramírez; Woosung Choi; Wei-Hsiang Liao; Qiyu Wu; Juhan Nam; Yuki Mitsufuji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper introduces LLM2Fx-Tools, a multimodal tool-calling framework that generates executable sequences of audio effects (Fx-chain) for music post-production. LLM2Fx-Tools uses a large language model (LLM) to understand audio inputs, select audio effects types, determine their order, and estimate parameters, guided by chain-of-thought (CoT) planning. We also present LP-Fx, a new instruction-following dataset with structured CoT annotations and tool calls for audio effects modules. Experiments show that LLM2Fx-Tools can infer an Fx-chain and its parameters from pairs of unprocessed and processed audio, enabled by autoregressive sequence modeling, tool calling, and CoT reasoning. We further validate the system in a style transfer setting, where audio effects information is transferred from a reference source and applied to new content. Finally, LLM-as-a-judge evaluation demonstrates that our approach generates appropriate CoT reasoning and responses for music production queries. To our knowledge, this is the first work to apply LLM-based tool calling to audio effects modules, enabling interpretable and controllable music production.
What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data
Authors: Rajiv Movva; Smitha Milli; Sewon Min; Emma Pierson
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Human feedback can alter language models in unpredictable and undesirable ways, as practitioners lack a clear understanding of what feedback data encodes. While prior work studies preferences over certain attributes (e.g., length or sycophancy), automatically extracting relevant features without pre-specifying hypotheses remains challenging. We introduce *What's In My Human Feedback?* (WIMHF), a method to explain feedback data using sparse autoencoders. WIMHF characterizes both (1) the preferences a dataset is capable of measuring and (2) the preferences that the annotators actually express. Across 7 datasets, WIMHF identifies a small number of human-interpretable features that account for the majority of the preference prediction signal achieved by black-box models. These features reveal a wide diversity in what humans prefer, and the role of dataset-level context: for example, users on Reddit prefer informality and jokes, while annotators in HH-RLHF and PRISM disprefer them. WIMHF also surfaces potentially unsafe preferences, such as that LMArena users tend to vote against refusals, often in favor of toxic content. The learned features enable effective *data curation*: re-labeling the harmful examples in Arena yields large safety gains (+37%) with no cost to general performance. They also allow fine-grained *personalization*: on the Community Alignment dataset, we learn annotator-specific weights over subjective features that improve preference prediction. WIMHF provides a human-centered analysis method for practitioners to better understand and use preference data.
CAGE: A Framework for Culturally Adaptive Red-Teaming Benchmark Generation
Authors: Chaeyun Kim; YongTaek Lim; Kihyun Kim; Junghwan Kim; Minwoo Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing red-teaming benchmarks, when adapted to new languages via direct translation, fail to capture socio-technical vulnerabilities rooted in local culture and law, creating a critical blind spot in LLM safety evaluation. To address this gap, we introduce CAGE (Culturally Adaptive Generation), a framework that systematically adapts the adversarial intent of proven red-teaming prompts to new cultural contexts. At the core of CAGE is the Semantic Mold, a novel approach that disentangles a prompt's adversarial structure from its cultural content. This approach enables the modeling of realistic, localized threats rather than testing for simple jailbreaks. As a representative example, we demonstrate our framework by creating KoRSET, a Korean benchmark, which proves more effective at revealing vulnerabilities than direct translation baselines. CAGE offers a scalable solution for developing meaningful, context-aware safety benchmarks across diverse cultures.
AbdCTBench: Learning Clinical Biomarker Representations from Abdominal Surface Geometry
Authors: Muhammad Ahmed Chaudhry; Suhana Bedi; Pola Lydia Lagari; Brian T Layden; William Galanter; Ayis Pyrros; Sanmi Koyejo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Body composition analysis through CT and MRI imaging provides critical insights for cardio-metabolic health assessment but remains limited by accessibility barriers including radiation exposure, high costs, and infrastructure requirements. We present AbdCTBench, a large-scale dataset containing 23,506 CT-derived abdominal surface meshes from 18,719 patients, paired with 87 comorbidity labels, 31 specific diagnosis codes, and 16 CT-derived biomarkers. Our key insight is that external surface geometry is predictive of internal tissue composition, enabling accessible health screening through consumer devices. We establish comprehensive benchmarks across seven computer vision architectures (ResNet-18/34/50, DenseNet-121, EfficientNet-B0, ViT-Small, Swin Transformer-Base), demonstrating that models can learn robust surface-to-biomarker representations directly from 2D mesh projections. Our best-performing models achieve clinically relevant accuracy: age prediction with MAE 6.22 years (R²=0.757), mortality prediction with AUROC 0.839, and diabetes (with chronic complications) detection with AUROC 0.801. Notably, smaller architectures consistently matched or surpassed larger models, while medical-domain pre-training (RadImageNet) and self-supervised pre-training (DINOv2) showed competitive but not superior performance. AbdCTBench represents the largest publicly available dataset bridging external body geometry with internal clinical measurements, enabling future research in accessible medical AI. We plan to release the dataset, evaluation protocols, and baseline models to accelerate research in representation learning for medical applications, immediately following the review period.
Multimodal Classification via Total Correlation Maximization
Authors: Feng Yu; Xiangyu Wu; Yang Yang; Jianfeng Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal learning integrates data from diverse sensors to effectively harness information from different modalities. However, recent studies reveal that joint learning often overfits certain modalities while neglecting others, leading to performance inferior to that of unimodal learning. Although previous efforts have sought to balance modal contributions or combine joint and unimodal learning—thereby mitigating the degradation of weaker modalities with promising outcomes—few have examined the relationship between joint and unimodal learning from an information-theoretic perspective.
    In this paper, we theoretically analyze modality competition and propose a method for multimodal classification by maximizing the total correlation between multimodal features and labels. By maximizing this objective, our approach alleviates modality competition while capturing inter-modal interactions via feature alignment. Building on Mutual Information Neural Estimation (MINE), we introduce **T**otal **C**orrelation **N**eural **E**stimation (**TCNE**) to derive a lower bound for total correlation. Subsequently, we present TCMax, a hyperparameter-free loss function that maximizes total correlation through variational bound optimization. Extensive experiments demonstrate that TCMax outperforms state-of-the-art joint and unimodal learning approaches. Our code is available at https://anonymous.4open.science/r/TCMax_Experiments.
SpatialHand: Generative Object Manipulation from 3D Prespective
Authors: Zehan Wang; Jialei Wang; Siyu Chen; Ziang Zhang; Luping Liu; Xize Cheng; Kaihang Pan; Hengshuang Zhao; Zhou Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce SpatialHand, a novel framework for generative object insertion with precise 3D control. Current generative object manipulation methods primarily operate within the 2D image plane, but often fail to grasp 3D scene complexities, leading to ambiguities in an object's 3D position, orientation, and occlusion relations. SpatialHand addresses this by conceptualizing object insertion from a true ``3D perspective," enabling manipulation with a complete 6 Degrees-of-Freedom (6DoF) controllability. Specifically, our solution naturally and implicitly encodes the 6DoF pose condition by decomposing it into 2D location (via masked image), depth (via composited depth map), and 3D orientation (embedded into latent features). To overcome the scarcity of paired training data, we develop an automated data construction pipeline using synthetic 3D assets, rendering, and subject-driven generation, complemented by visual foundation models for pose estimation. We further design a multi-stage training scheme to progressively drive SpatialHand to robustly follow multiple complex conditions. Extensive experiments reveal our approach's superiority over existing alternatives and its great potential for enabling more versatile and intuitive AR/VR-like object manipulation within images.