GenAI4ELab/papercli-papers huggingface dataset:
Knowing When to Quit: Probabilistic Early Exits for Speech Separation Networks
Authors: Kenny Falkær Olsen; Mads Østergaard; Karl Ulbæk; Søren Føns Nielsen; Rasmus Malik Høegh Lindrup; Bjørn Sand Jensen; Morten Mørup
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In recent years, deep learning-based single-channel speech separation has improved
  considerably, in large part driven by increasingly compute- and parameter-efficient
  neural network architectures. Most such architectures are, however, designed with a
  fixed compute and parameter budget and consequently cannot scale to varying compute
  demands or resources, which limits their use in embedded and heterogeneous
  devices such as mobile phones and hearables.
  To enable such use-cases we design a neural network architecture for speech separation
  and enhancement capable of early-exit, and we propose an uncertainty-aware
  probabilistic framework to jointly model the clean speech signal and error variance
  which we use to derive probabilistic early-exit conditions in terms of desired
  signal-to-noise ratios.
  We evaluate our methods on both speech separation and enhancement tasks where we
  demonstrate that early-exit capabilities can be introduced without compromising
  reconstruction, and that when trained on variable-length audio our early-exit
  conditions are well-calibrated and lead to considerable compute savings when used to
  dynamically scale compute at test time while remaining directly interpretable.
Understanding and Improving Hyperbolic Deep Reinforcement Learning
Authors: Timo Klein; Thomas Lang; Andrii Shkabrii; Alexander Sturm; Kevin Sidak; Lukas Miklautz; Claudia Plant; Yllka Velaj; Sebastian Tschiatschek
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The exponential volume growth of hyperbolic geometry can embed the hierarchical relationships between states in reinforcement learning (RL) with far less distortion than Euclidean space. However, hyperbolic deep RL faces severe optimization challenges, and formal analysis of why optimization fails is lacking. We identify key factors that determine the success and failure of training hyperbolic deep RL agents. By analyzing the gradients of core operations in the Poincaré Ball and Hyperboloid models of hyperbolic geometry, we show that large-norm embeddings destabilize gradient-based training, leading to trust-region violations in proximal policy optimization (PPO). Based on these insights, we introduce Hyper++, a new hyperbolic deep RL agent that consists of three components: (1) feature regularization guaranteeing bounded norms while avoiding the curse of dimensionality from clipping; (2) a categorical value loss for stable critic training; and (3) a more optimization-friendly formulation of hyperbolic network layers. On ProcGen, we show that Hyper++ guarantees stable learning, outperforms prior hyperbolic agents, and reduces wall-clock time by approximately 30%. On Atari-5 with Double DQN, Hyper++ strongly outperforms Euclidean and hyperbolic baselines. We release our code at https://github.com/Probabilistic-and-Interactive-ML/hyper-rl.
Is On-Policy Data always the Best Choice for Direct Preference Optimization-Based LM Alignment?
Authors: Zetian Sun; Dongfang Li; Xuhui Chen; Baotian Hu; Min Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The alignment of language models (LMs) with human preferences is critical for building reliable AI systems. The problem is typically framed as optimizing an LM policy to maximize the expected reward that reflects human preferences. Recently, Direct Preference Optimization (DPO) was proposed as an LM alignment method that directly optimizes the policy from static preference data, and further improved by incorporating on-policy sampling (i.e., preference candidates generated during the training loop) for better LM alignment. However, we show on-policy data is not always optimal, with systematic effectiveness difference emerging between static and on-policy preference candidates. For example, on-policy data can result in a  $3\times$ effectiveness compared with static data for Llama-3, and a $0.4\times$ effectiveness for Zephyr. To explain the phenomenon, we propose the alignment stage assumption, which divides the alignment process into two distinct stages: the preference injection stage, which benefits from diverse data, and the preference fine-tuning stage, which favors high-quality data. Through theoretical and empirical analysis, we characterize these stages and propose an effective algorithm to identify the boundaries between them. We perform experiments on $5$ models (Llama, Zephyr, Phi-2, Qwen, Pythia) and 
$2$ alignment methods (DPO, SLiC-HF) to show the generalizability of alignment stage assumption and boundary measurement.
Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees
Authors: Mohammed Nowaz Rabbani Chowdhury; Kaoutar El Maghraoui; Hsinyu Tsai; Naigang Wang; Geoffrey W. Burr; Liu Liu; Meng Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sparse Mixture-of-Experts (MoE) allows scaling of language and vision models efficiently by activating only a small subset of experts per input. While this reduces computation, the large number of parameters still incurs substantial memory overhead during inference. Post-training quantization has been explored to address this issue. Because uniform quantization suffers from significant accuracy loss at low bit-widths, mixed-precision methods have been recently explored; however, they often require substantial computation for bit-width allocation and overlook the varying sensitivity of model performance to the quantization of different experts. We propose a theoretically grounded expert-wise mixed-precision strategy that assigns bit-width to each expert primarily based on their *change in router’s* $l_2$ *norm* during training. Experts with smaller changes are shown to capture less frequent but critical features, and model performance is more sensitive to the quantization of these experts, thus requiring higher precision. Furthermore, to avoid allocating experts to lower precision that inject high quantization noise, experts with large *maximum intra-neuron variance* are also allocated higher precision. Experiments on large-scale MoE models, including Switch Transformer and Mixtral, show that our method achieves higher accuracy than existing approaches, while also reducing inference cost and incurring only negligible overhead for bit-width assignment.
MoEEdit: Efficient and Routing-Stable Knowledge Editing for Mixture-of-Experts LLMs
Authors: Yupu Gu; Rongzhe Wei; Andy Zhu; Pan Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Knowledge editing (KE) is crucial for making precise modifications to factual knowledge within large language models (LLMs). Existing KE methods, however, are primarily designed for dense architectures, limiting their applicability to the increasingly popular sparse Mixture-of-Experts (MoE) models that power modern scalable LLMs. While MoEs offer remarkable efficiency and capacity scaling, their unique structure introduces new challenges for KE. Naively adapting dense-model editors to MoEs is not only computationally expensive but also induces routing distribution shifts that degrade model stability and consistency. To address these challenges, we introduce MoEEdit, the first systematic framework for routing-stable knowledge editing in MoE LLMs. Our approach reparameterizes expert updates through per-expert null-space projections, ensuring router inputs remain invariant to suppress these shifts, and solves the resulting block-structured optimization with an efficient block coordinate descent (BCD) solver. Experiments demonstrate that MoEEdit achieves state-of-the-art efficacy and generalization, while maintaining high specificity, routing stability, and superior computational and memory efficiency. Our work establishes a robust foundation for scalable and precise knowledge editing in modern sparse LLMs by highlighting the necessity of routing-stable interventions.
MIRACLE: Model-free Imitation and Reinforcement Learning for Adaptive Cut-Selection
Authors: Arjun M.; Rijul Tandon; Agam Gupta; HARIPRASAD KODAMANA; Manojkumar Ramteke
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mixed-Integer Programming (MIP) solvers rely heavily on cutting planes to tighten LP relaxations, but traditional approaches generate thousands of cuts that consume gigabytes of memory while providing minimal benefit. We present an intelligent cut selection framework that achieves a 98.1\% reduction in memory usage while maintaining competitive solving with an objective gap of approximately 0.08\%. Within this RL framework, we use Proximal Policy Optimization (PPO) to learn a behavioral model that imitates the expert solver’s decisions. The adversarially imitated behavioral model drives an agent comprising these key innovations: (i) a cut-selection policy trained via curriculum learning; and (ii) adaptive inference that dynamically adjusts computational budgets. Through comprehensive evaluation across SetCover and diverse MIPLIB problems, we demonstrate consistent speedups (3.78$\times$ average on MIPLIB) and achieve a 100\% success rate on instances where traditional SCIP fails 47-53\% of the time. Our method also reduces peak memory consumption from 3.03GB to 46 MB, enabling optimization in previously inaccessible and other resource-constrained environments where traditional solvers face fundamental limitations.
From Five Dimensions to Many: Large Language Models as Precise and Interpretable Psychological Profilers
Authors: Yi-Fei Liu; Yi-Long Lu; Di He; Hang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Psychological constructs within individuals are widely believed to be interconnected. We investigated whether and how Large Language Models (LLMs) can model the correlational structure of human psychological traits from minimal quantitative inputs. We prompted various LLMs with Big Five Personality Scale responses from 816 human individuals to role-play their responses on nine other psychological scales. LLMs demonstrated remarkable accuracy in capturing human psychological structure, with the inter-scale correlation patterns from LLM-generated responses strongly aligning with those from human data (R² > 0.88). This zero-shot performance substantially exceeded predictions based on semantic similarity and approached the accuracy of machine learning algorithms trained directly on the dataset. Analysis of reasoning traces revealed that LLMs use a systematic two-stage process: First, they transform raw Big Five responses into natural language personality summaries through information selection and compression, analogous to generating sufficient statistics. Second, they generate target scale responses based on reasoning from these summaries. For information selection, LLMs identify the same key personality factors as trained algorithms, though they fail to differentiate item importance within factors. The resulting compressed summaries are not merely redundant representations but capture synergistic information—adding them to original scores enhances prediction alignment, suggesting they encode emergent, second-order patterns of trait interplay. Our findings demonstrate that LLMs can precisely predict individual participants' psychological traits from minimal data through a process of abstraction and reasoning, offering both a powerful tool for psychological simulation and valuable insights into their emergent reasoning capabilities.
Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models
Authors: Yunzhong Qiu; Zhiyao Cen; Zhongyi Pei; Chen Wang; Jianmin Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large time series models (LTMs) have emerged as powerful tools for universal forecasting, yet they often struggle with the inherent diversity and nonstationarity of real-world time series data, leading to an unsatisfactory trade-off between forecasting accuracy and generalization. Rather than continually finetuning new LTM instances for each domain, we propose a data-centric framework, time-series adaptive transformation optimization (TATO), that enables a single frozen pre-trained LTM to adapt to diverse downstream domains through an optimally configured transformation pipeline. Specifically, TATO constructs three representative types of transformations, including context slicing, scale normalization, and outlier correction, to help LTMs better align with target domain characteristics. To ensure robustness, we incorporate carefully selected time series augmentations and a two-stage ranking mechanism that filters out pipelines underperforming on specific metrics. Extensive experiments on state-of-the-art LTMs and widely used datasets demonstrate that TATO consistently and significantly improves domain-adaptive forecasting performance, achieving a maximum reduction in MSE of 65.4\% and an average reduction of 13.6\%. Moreover, TATO is highly efficient, typically completing optimization in under 2 minutes, making it practical for real-world deployment. The source code is available at https://github.com/thulab/TATO.
Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
Authors: Zhuo Liu; Tao Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Brain-like intelligent systems need brain-like learning methods. Equilibrium Propagation (EP) is a biologically plausible learning framework with strong potential for brain-inspired computing hardware. However, existing implementations of EP suffer from instability and prohibitively high computational costs. Inspired by the structure and dynamics of the brain, we propose a biologically plausible Feedback-regulated REsidual recurrent neural network (FRE-RNN) and study its learning performance in the EP framework. Feedback regulation enables rapid convergence by attenuating feedback signals and reducing the disturbance of feedback paths to feedforward paths. The improvement in the convergence property reduces the computational cost and training time of EP by orders of magnitude, delivering performance on par with backpropagation (BP) in benchmark tasks. Meanwhile, residual connections with brain-inspired topologies help alleviate the vanishing gradient problem that arises when feedback pathways are weak in deep RNNs. Our approach substantially enhances the applicability and practicality of EP. The techniques developed here also offer guidance for implementing in-situ learning in physical neural networks.
FAST‑DIPS: Adjoint‑Free Analytic Steps and Hard‑Constrained Likelihood Correction for Diffusion‑Prior Inverse Problems
Authors: Minwoo Kim; Seunghyeok Shin; Hongki Lim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Training-free diffusion priors enable inverse-problem solvers without retraining, but for nonlinear forward operators data consistency often relies on repeated derivatives or inner optimization/MCMC loops with conservative step sizes, incurring many iterations and denoiser/score evaluations. We propose a training-free solver that replaces these inner loops with a hard measurement-space feasibility constraint (closed-form projection) and an analytic, model-optimal step size, enabling a small, fixed compute budget per noise level. Anchored at the denoiser prediction, the correction is approximated via an adjoint-free, ADMM-style splitting with projection and a few steepest-descent updates, using one VJP and either one JVP or a forward-difference probe, followed by backtracking and decoupled re-annealing. We prove local model optimality and descent under backtracking for the step-size rule, and derive an explicit KL bound for mode-substitution re-annealing under a local Gaussian conditional surrogate. We also develop a latent variant and a one-parameter pixel$\rightarrow$latent hybrid schedule. Experiments achieve competitive PSNR/SSIM/LPIPS with up to 19.5$\times$ speedup, without hand-coded adjoints or inner MCMC. Code and data: [here](https://github.com/ququlza/FAST-DIPS)
Cross-Timestep: 3D Diffusion Model with Trans-temporal Memory LSTM and Adaptive Priori Decoding Strategy for Medical Segmentation
Authors: Shangqian Wu; Siyuan Shen; Yahan Li; Zhijian Huang; Ziyu Fan; Yuanpeng Zhang; YI WANG; Lei Deng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models have recently demonstrated significant robustness in medical image segmentation, effectively accommodating variations across different imaging styles. However, their applications remain limited due to: (i) current successes being primarily confined to 2D segmentation tasks—we observe that diffusion models tend to collapse at the early stage when applied to 3D medical tasks; and (ii) the inherently isolated iteration along timesteps during training and inference. To tackle these limitations, we propose a novel framework named Cross-Timestep, which incorporates two key innovations: an Adaptive Priori Decoding Strategy (APDS) and a trans-temporal memory LSTM (tLSTM) mechanism. (i) The APDS provides prior guidance during the diffusion process by employing a Priori Decoder(PD) that focuses solely on the conditional branch, successfully stabilizing the reverse diffusion process. (ii) The tLSTM integrates convolution and linear layers into the LSTM gating structure, and enhances the memory cell mechanism to retain temporal state, explicitly preserving and propagating continuous temporal states across timesteps. Experimental results demonstrate that Cross-Timestep performs favorably on heterogeneous 3D medical datasets. Three experiments further analyze the collapse phenomenon in 3D medical diffusion models and validate that APDS effectively prevents initial-stage collapse without excessively constraining the model, while tLSTM facilitates the performance and scalability of diffusion models.
In-Context Multi-Objective Optimization
Authors: Xinyu Zhang; Conor Hassan; Julien Martinelli; Daolang Huang; Samuel Kaski
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Balancing competing objectives is omnipresent across disciplines, from drug design to autonomous systems. Multi-objective Bayesian optimization is a promising solution for such expensive, black-box problems: it fits probabilistic surrogates and selects new designs via an acquisition function that balances exploration and exploitation. In practice, it requires tailored choices of surrogate and acquisition that rarely transfer to the next problem, is myopic when multi-step planning is often required, and adds refitting overhead, particularly in parallel or time-sensitive loops. We present TAMO, a fully amortized, universal policy for multi-objective black-box optimization. TAMO uses a transformer architecture that operates across varying input and objective dimensions, enabling pretraining on diverse corpora and transfer to new problems without retraining: at test time, the pretrained model proposes the next design with a single forward pass. We pretrain the policy with reinforcement learning to maximize cumulative hypervolume improvement over full trajectories, conditioning on the entire query history to approximate the Pareto frontier. Across synthetic benchmarks and real tasks, TAMO produces fast proposals, reducing proposal time by 50–1000× versus alternatives while matching or improving Pareto quality under tight evaluation budgets. These results show that transformers can perform multi-objective optimization entirely in-context, eliminating per-task surrogate fitting and acquisition engineering, and open a path to foundation-style, plug-and-play optimizers for scientific discovery workflows.
HippoTune: A Hippocampal Associative Loop–Inspired Fine-Tuning Method for Continual Learning
Authors: chenyanxi; Xiuxing Li; Han Yuyang; Zhuo Wang; Qing Li; Ziyu Li; Xiang Li; Chen Wei; Xia Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Studies have shown that catastrophic forgetting primarily stems from the difficulty of reactivating old memories; although parameter-efficient fine-tuning can mitigate forgetting while keeping most model parameters frozen, it still falls short in fully reawakening knowledge of prior tasks. In contrast, humans can efficiently retrieve and flexibly integrate existing experiences when learning new tasks, thereby maintaining stable performance on earlier ones. During cognition, the hippocampal EC–DG–CA3–CA1 circuit engages in multiple rounds of associative recall, and its pattern-separation and memory-completion mechanisms excel at activating historical information. Inspired by this mechanism, we propose HippoTune, a latent-space iterative retrieval strategy that embeds a query–retrieve–feedback loop within each Transformer layer. Starting from the hidden state as an initial query, the model performs a few rounds of soft key–value retrieval, projects the retrieved signals back into the query, and updates it iteratively until convergence or a preset iteration limit. Theoretically, we show this process implements a Krylov-style polynomial approximation, equivalent to a differentiable second-order preconditioner, thereby deepening retrieval in a principled way. Empirically, HippoTune outperforms classical buffer-free PEFT-CL methods by 5–8\% in accuracy across three vision benchmarks, while reducing training FLOPs by 50\%, effectively mitigating forgetting under tight compute constraints. 
Code is available at: https://github.com/yan4xi1/HippoTune.
Tackling Heavy-Tailed Q-Value Bias in Offline-to-Online Reinforcement Learning with Laplace-Robust Modeling
Authors: Ruibo Guo; Lei Liu; Rui Yang; Junjie Shen; Guoping Wu; Jie Wang; Bin Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Offline-to-online reinforcement learning (O2O RL) aims to improve the performance of offline pretrained agents through online fine-tuning. Existing O2O RL methods have achieved advances in mitigating the overestimation of Q-value biases (i.e., biases of cumulative rewards), improving the performance. However, in this paper, we are the first to reveal that Q-value biases of these methods often follow a heavy-tailed distribution during online fine-tuning. Such biases induce high estimation variance and hinder performance improvement.
To address this challenge, we propose a Laplace-based robust offline-to-online RL (LAROO) approach. LAROO introduces a parameterized Laplace-distributed noise and transfers the heavy-tailed nature of Q-value biases into this noise, alleviating heavy tailedness of biases for training stability and performance improvement. Specifically, (1) since Laplace distribution is well-suited for modeling heavy-tailed data, LAROO introduces a parameterized Laplace-distributed noise that can adaptively capture heavy tailedness of any data. (2) By combining estimated Q-values with the noise to approximate true Q-values, LAROO transfers the heavy-tailed nature of biases into the noise, reducing estimation variance. (3) LAROO employs conservative ensemble-based estimates to re-center Q-value biases, shifting their mean towards zero. Based on (2) and (3), LAROO promotes heavy-tailed Q-value biases into a standardized form, improving training stability and performance. Extensive experiments demonstrate that LAROO achieves significant performance improvement, outperforming several state-of-the-art O2O RL baselines.