GenAI4ELab/papercli-papers huggingface dataset:
Dynamic Multimodal Activation Steering for Hallucination Mitigation in Large Vision-Language Models
Authors: Jianghao Yin; Qin Chen; Kedi Chen; Jie Zhou; Xingjiao Wu; Liang He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Vision-Language Models (LVLMs) exhibit outstanding performance on vision-language tasks but struggle with hallucination problems.
Through in-depth analysis of LVLM activation patterns, we reveal two key findings: 1) truthfulness and visual perception capabilities predominantly engage different subsets of attention heads within the model architecture; and 2) truthfulness steering vectors vary significantly across different semantic contexts. Based on these observations, we propose Dynamic Multimodal Activation Steering, a training-free approach for hallucination mitigation. Our method constructs a semantic-based truthfulness steering vector database and computes visual perception steering vectors, enabling context-aware interventions during inference by dynamically selecting the most relevant steering vectors based on input semantic similarity and applying them to the most influential attention heads. We conduct comprehensive experiments across multiple models and datasets, demonstrating that our approach significantly enhances model performance, outperforming existing state-of-the-art methods.
VisualPrompter: Semantic-Aware Prompt Optimization with Visual Feedback for Text-to-Image Synthesis
Authors: Shiyu Wu; Mingzhen Sun; Weining Wang; Yequan Wang; Jing Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The notable gap between user-provided and model-preferred prompts poses a significant challenge for generating high-quality images with text-to-image models, compelling the need for prompt engineering.
Current studies on prompt engineering can effectively enhance the style and aesthetics of generated images. 
However, they often neglect the semantic alignment between generated images and user descriptions, resulting in visually appealing but content-wise unsatisfying outputs. 
In this work, we propose VisualPrompter, a novel training-free prompt engineering framework that refines user inputs to model-preferred sentences. 
VisualPrompter utilizes an automatic self-reflection module that identifies absent concepts in the generated images, followed by a target-specific prompt optimization mechanism that revises the prompts in a fine-grained manner. 
By deconstructing prompts, introducing new elements at the atomic semantic level, and then reassembling them, our framework is able to maintain semantic consistency and integrity throughout the optimization process.
Extensive experiments demonstrate the effectiveness of VisualPrompter, which achieves new state-of-the-art performance on multiple benchmarks for text-image alignment evaluation. 
Additionally, our framework features a plug-and-play design, making it highly adaptable to various generative models. 
Our code is available at https://github.com/teheperinko541/VisualPrompter.
Inferring the Invisible: Neuro-Symbolic Rule Discovery for Missing Value Imputation
Authors: Wendi Ren; Ke Wan; Junyu Leng; Shuang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: One of the central challenges in artificial intelligence is reasoning under partial observability, where key values are missing but essential for understanding and modeling the system. This paper presents a neuro-symbolic framework for latent rule discovery and missing value imputation. In contrast to traditional latent variable models, our approach treats missing grounded values as latent predicates to be inferred through logical reasoning. By interleaving neural representation learning with symbolic rule induction, the model iteratively discovers—both conjunctive and disjunctive rules—that explain observed patterns and recover missing entries. Our framework seamlessly handles heterogeneous data, reasoning over both discrete and continuous features by learning soft predicates from continuous values. Crucially, the inferred values not only fill in gaps in the data but also serve as supporting evidence for further rule induction and inference—creating a feedback loop in which imputation and rule mining reinforce one another. Using a staged block-coordinate gradient descent, the system learns these rules end-to-end by iteratively optimizing over parameter blocks in an alternating fashion. Experiments on both synthetic and real-world datasets demonstrate that our method effectively imputes missing values while uncovering meaningful, human-interpretable rules that govern system dynamics.
WRING Out The Bias: A Rotation-Based Alternative To Projection Debiasing
Authors: Walter Gerych; Cassandra Parent; Quinn Perian; Rafiya Javed; Justin Solomon; Marzyeh Ghassemi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language models (VLMs), including CLIP, are known to encode biases such as learning spurious correlations that falsely associate background attributes with particular labels. Debiasing approaches typically aim to isolate and remove subspaces corresponding to a target concept via projecting the embedding away from the concept. This strategy succeeds in debiasing VLM embeddings with respect to the concepts considered but can amplify biased shortcuts in unconsidered concepts. In practice, it is impossible to enumerate all possible biases, meaning that an increase in bias can go unobserved during evaluation. We propose a debiasing approach for a set of known concepts such that the relation to the remaining, unconsidered, concepts is minimally changed. We achieve this by rotating the VLM's embeddings within only a relevant subspace, rather than removing these subspaces, which mitigates unintended bias amplification.
Latent Wavelet Diffusion For Ultra High-Resolution Image Synthesis
Authors: Luigi Sigillo; Shengfeng He; Danilo Comminiello
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preservation of fine-grained visual detail. We present $\textit{Latent Wavelet Diffusion (LWD)}$, a lightweight training framework that significantly improves detail and texture fidelity in ultra-high-resolution (2K-4K) image synthesis. LWD introduces a novel, frequency-aware masking strategy derived from wavelet energy maps, which dynamically focuses the training process on detail-rich regions of the latent space. This is complemented by a scale-consistent VAE objective to ensure high spectral fidelity. The primary advantage of our approach is its efficiency: LWD requires no architectural modifications and adds zero additional cost during inference, making it a practical solution for scaling existing models. Across multiple strong baselines, LWD consistently improves perceptual quality and FID scores, demonstrating the power of signal-driven supervision as a principled and efficient path toward high-resolution generative modeling.
FlexiVoice: Enabling Flexible Style Control in Zero-Shot TTS with Natural Language Instructions
Authors: Dekun Chen; Xueyao Zhang; Yuancheng Wang; Kenan Dai; Li Ma; Zhizheng Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This study proposes FlexiVoice, a text-to-speech (TTS) synthesis system capable of flexible style control with zero-shot voice cloning. The speaking style is controlled by a natural-language instruction and the voice timbre is provided by a speech reference in zero-shot manner. FlexiVoice is built with an LLM core, which takes text as input, and also takes an optional natural language instruction and an optional speech reference to control style and timbre, respectively. FlexiVoice is equipped with a novel Progressive Post-Training (PPT) scheme that progressively unlocks accurate and flexible controllability. In particular, it first employs Direct Preference Optimization (DPO) to enable FlexiVoice to accurately follow both natural language instruction and speech reference simultaneously. It then uses a multi-objective Group Relative Policy Optimization (GRPO) to disentangle style instruction, reference timbre, and textual content. Finally, it adapts instruction GRPO for more advanced instruction following. Experimental results show that FlexiVoice surpasses competing baselines and demonstrates strong capability in decoupling control factors. Human evaluations further confirm its naturalness, controllability, and robustness.
Audio samples are available at https://flexi-voice.github.io/.
Understanding the Learning Phases in Self-Supervised Learning via Critical Periods
Authors: JangHyeon Lee; Philipe Ambrozio Dias; Yao-Yi Chiang; Dalton Lunga
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Self-supervised learning (SSL) has emerged as a powerful pretraining strategy to learn transferable representations from unlabeled data. Yet, it remains unclear how long SSL models should be pretrained to yield such representations. Contrary to the prevailing heuristic that longer pretraining translates to better downstream performance, we observe a transferability trade-off: across diverse SSL settings, intermediate checkpoints can yield stronger out-of-domain (OOD) generalization, whereas additional pretraining primarily benefits in-domain (ID) performance. From this observation, we hypothesize that SSL progresses through learning phases that can be characterized via the lens of critical periods (CP). Prior work on CP has shown that supervised models exhibit an early phase of high plasticity, followed by a consolidation phase where adaptability declines but task-specific performance increases. Since traditional CP analysis was developed for supervised settings, we rethink it for SSL in two ways. First, we inject deficits to perturb the pretraining data and assess their lasting impact on representation quality via downstream tasks. Second, we compute the Fisher Information on pretext objectives to track plasticity, quantifying how sensitive model parameters are to the pretext task. Our experiments suggest that SSL models may exhibit their own CP, with CP closure coinciding with a sweet spot for broad downstream transferability. Leveraging these insights, we introduce CP-guided checkpoint selection as a strategy for selecting checkpoints that offer stronger OOD transferability. Finally, to balance the transferability trade-off, we present CP-guided self-distillation, which selectively distills layer representations from the intermediate checkpoint into their overspecialized counterparts in the final checkpoint.
Multifidelity Simulation-based Inference for Computationally Expensive Simulators
Authors: Anastasia N. Krouglova; Hayden R Johnson; Basile Confavreux; Michael Deistler; Pedro J. Goncalves
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Across many domains of science, stochastic models are an essential tool to understand the mechanisms underlying empirically observed data. Models can be of different levels of detail and accuracy, with models of high-fidelity (i.e., high accuracy) to the phenomena under study being often preferable. However, inferring parameters of high-fidelity models via simulation-based inference is challenging, especially when the simulator is computationally expensive. We introduce a multifidelity approach to neural posterior estimation that uses transfer learning to leverage inexpensive low-fidelity simulations to efficiently infer parameters of high-fidelity simulators. Our method applies the multifidelity scheme to both amortized and non-amortized neural posterior estimation. We further improve simulation efficiency by introducing a sequential variant that uses an acquisition function targeting the predictive uncertainty of the density estimator to adaptively select high-fidelity parameters. On established benchmark and neuroscience tasks, our approaches require up to two orders of magnitude fewer high-fidelity simulations than current methods, while showing comparable performance. Overall, our approaches open new opportunities to perform efficient Bayesian inference on computationally expensive simulators.
Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis
Authors: Tyler Farghly; Patrick Rebeschini; George Deligiannidis; Arnaud Doucet
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The success of denoising diffusion models raises important questions regarding their generalisation behaviour, particularly in high-dimensional settings. Notably, it has been shown that when training and sampling are performed perfectly, these models memorise training data—implying that some form of regularisation is essential for generalisation. Existing theoretical analyses primarily rely on algorithm-independent techniques such as uniform convergence, heavily utilising model structure to obtain generalisation bounds. In this work, we instead leverage the algorithmic aspects that promote generalisation in diffusion models, developing a general theory of algorithm-dependent generalisation for this setting. Borrowing from the framework of algorithmic stability, we introduce the notion of score stability, which quantifies the sensitivity of score-matching algorithms to dataset perturbations. We derive generalisation bounds in terms of score stability, and apply our framework to several fundamental learning settings, identifying sources of regularisation. In particular, we consider denoising score matching with early stopping (denoising regularisation), sampler-wide coarse discretisation (sampler regularisation), and optimising with SGD (optimisation regularisation). By grounding our analysis in algorithmic properties rather than model structure, we identify multiple sources of implicit regularisation unique to diffusion models that have so far been overlooked in the literature.
Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting
Authors: Mert Kayaalp; Ali Caner Turkmen; Oleksandr Shchur; Pedro Mercado; Abdul Fatir Ansari; Michael Bohlke-Schneider; Bernie Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Is bigger always better for time series foundation models? With the question in mind, we explore an alternative to training a single, large monolithic model: building a portfolio of smaller, pretrained forecasting models. By applying ensembling or model selection over these portfolios, we achieve competitive performance on large-scale benchmarks using much fewer parameters. We explore strategies for designing such portfolios and find that collections of specialist models consistently outperform portfolios of independently trained generalists. Remarkably, we demonstrate that post-training a base model is a compute-effective approach for creating sufficiently diverse specialists, and provide evidences that ensembling and model selection are more compute-efficient than test-time fine-tuning.
GRL-SNAM: Geometric Reinforcement Learning with Differential Hamiltonians for Navigation and Mapping in Unknown Environments
Authors: Aditya Sai Ellendula; Yi Wang; Minh Phuong Nguyen; Chandrajit L. Bajaj
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present GRL-SNAM, a geometric reinforcement learning framework for Simultaneous Navigation and Mapping in unknown environments. GRL-SNAM differs from traditional SLAM and other reinforcement learning methods by relying exclusively on local sensory observations without constructing a global map. Our approach formulates navigation and mapping as coupled dynamics on generalized Hamiltonian manifolds: sensory inputs are translated into local energy landscapes that encode reachability, obstacle barriers, and deformation constraints, while policies for sensing, planning, and reconfiguration evolve stagewise under Differential Policy Optimization. A reduced Hamiltonian serves as an adaptive score function, updating kinetic/potential terms, embedding barrier constraints, and continuously refining trajectories as new local information arrives. We evaluate GRL-SNAM on 2D deformable navigation tasks, where a hyperelastic robot learns to squeeze through narrow gaps, detour around obstacles, and generalize to unseen environments. We compare our method against local reactive baselines (PF, CBF, staged DWA), global A* references (rigid, clearance-aware), and deep RL baselines (PPO, TRPO, SAC) under identical stagewise sensing constraints. GRL-SNAM achieves strong path quality while using the minimal map coverage, preserves clearance, and generalizes to unseen layouts. This demonstrates that our Hamiltonian-structured RL framework enables high-quality navigation through minimal exploration via local energy refinement rather than global mapping.
Narrow Finetuning Leaves Clearly Readable Traces in Activation Differences
Authors: Julian Minder; Clément Dumas; Stewart Slocum; Helena Casademunt; Cameron Holmes; Robert West; Neel Nanda
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Finetuning on narrow domains has become an essential tool to adapt Large Language Models (LLMs) to specific tasks and to create models with known unusual properties that are useful for safety research. Model diffing--the study of differences between base and finetuned models--is a promising approach for understanding how finetuning modifies neural networks.
In this paper, we show that narrow finetuning creates easily readable biases in LLM activations that can be detected using simple model diffing tools, suggesting that the finetuning data is overrepresented in the model's activations.
In particular, analyzing activation differences between base and finetuned models on the first few tokens of random text  and steering with this difference allows us to recover the format and general content of the finetuning data.  We call this the Activation Difference Lens (ADL).
We demonstrate that these analyses significantly enhance an LLM-based interpretability agent's ability to identify subtle finetuning objectives through interaction with base and finetuned models.
Our analysis spans synthetic document finetuning for false facts, emergent misalignment, subliminal learning, and taboo guessing game models across different architectures (Gemma, LLaMA, Qwen) and scales (1B to 32B parameters).
Our work: (1) demonstrates that researchers should be aware that narrow finetuned models will represent their training data and objective very saliently, (2) warns AI safety and mechanistic interpretability researchers that these models might not be a realistic proxy for studying broader finetuning, despite current literature widely using them.
While we show that mixing pretraining data into the finetuning corpus is enough to remove this bias, a deeper investigation is needed to understand the side effects of narrow finetuning and develop truly realistic case studies for model-diffing, safety and interpretability research.
ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment
Authors: Xiaoqiang Lin; Arun Verma; Zhongxiang Dai; Daniela Rus; See-Kiong Ng; Bryan Kian Hsiang Low
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The recent success in using human preferences to align large language models (LLMs) has significantly improved their performance in various downstream tasks, such as question answering, mathematical reasoning, and code generation. However, achieving effective LLM alignment depends on high-quality datasets of human preferences. Collecting these datasets requires human preference annotation, which is costly and resource-intensive, necessitating efficient active data selection methods. Existing methods either lack a strong theoretical foundation or depend on restrictive assumptions about the reward function, such as linear latent reward functions. To this end, we propose an algorithm, ActiveDPO, that uses a theoretically grounded data selection criterion for non-linear reward functions while directly leveraging the LLM itself to parameterize the reward model used for active data selection. As a result, ActiveDPO explicitly accounts for the LLM's influence on data selection, unlike methods that select data without considering the LLM that is being aligned, thereby leading to more effective and efficient data collection. Our extensive experiments demonstrate that ActiveDPO outperforms existing methods across various models and real-world preference datasets.
LRIM: a Physics-Based Benchmark for Provably Evaluating Long-Range Capabilities in Graph Learning
Authors: Joël Mathys; Henrik Christiansen; Federico Errica; Takashi Maruyama; Francesco Alesiani
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurately modeling long-range dependencies in graph-structured data is critical for many real-world applications. However, incorporating long-range interactions beyond the nodes' immediate neighborhood in a $\textit{scalable}$ manner remains an open challenge for graph machine learning models. Existing benchmarks for evaluating long-range capabilities either cannot $\textit{guarantee}$ that their tasks actually depend on long-range information or are rather limited.
Therefore, claims of long-range modeling improvements based on said performance remain questionable.
We introduce the Long-Range Ising Model Graph Benchmark, a physics-based benchmark utilizing the well-studied Ising model whose ground truth $\textit{provably}$ depends on long-range dependencies. Our benchmark consists of ten datasets that scale from 256 to 65k nodes per graph, and provide controllable long-range dependencies through tunable parameters, allowing precise control over the hardness and ``long-rangedness". We provide model-agnostic evidence that local information is insufficient, further validating the design choices of our benchmark. Via experiments on classical message-passing architectures and graph transformers, we show that both perform far from the optimum, especially those with scalable complexity. Our goal is that our benchmark will foster the development of scalable methodologies that effectively model long-range interactions in graphs.
A General Framework for Black-Box Attacks Under Cost Asymmetry
Authors: Mahdi Salmani; Alireza Abdollahpoorrostam; Seyed-Mohsen Moosavi-Dezfooli
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Traditional decision-based black-box adversarial attacks on image classifiers aim to generate adversarial examples by slightly modifying input images while keeping the number of queries low, where each query involves sending an input to the model and observing its output. Most existing methods assume that all queries have equal cost. However, in practice, queries may incur *asymmetric costs*; for example, in content moderation systems, certain output classes may trigger additional review, enforcement, or penalties, making them more costly than others. While prior work has considered such asymmetric cost settings, effective algorithms for this scenario remain underdeveloped. 
In this paper, we introduce asymmetric black-box attacks, a new family of decision-based attacks that generalize to the asymmetric query-cost setup. We develop new methods for boundary search and gradient estimation when crafting adversarial examples. Specifically, we propose *Asymmetric Search (AS)*, a more conservative alternative to binary search that reduces reliance on high-cost queries, and *Asymmetric Gradient Estimation (AGREST)*, which shifts the sampling distribution in Monte Carlo style gradient estimation to favor low-cost queries.
We design efficient algorithms that reduce total attack cost by balancing different query types, in contrast to earlier methods such as *stealthy attacks* that focus only on limiting expensive (high-cost) queries. We perform both theoretical analysis and empirical evaluation on standard image classification benchmarks. Across various cost regimes, our method consistently achieves lower total query cost and smaller perturbations than existing approaches, reducing the perturbation norm by up to 40\% in some settings.