GenAI4ELab/papercli-papers huggingface dataset:
Planner Aware Path Learning in Diffusion Language Models Training
Authors: Fred Zhangzhi Peng; Zachary Bezemek; Jarrid Rector-Brooks; Shuibai Zhang; Michael M. Bronstein; Anru Zhang; Alexander Tong; Joey Bose
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Diffusion language models have emerged as a powerful alternative to autoregressive models, enabling fast inference through more flexible and parallel generation paths. This flexibility of sampling is unlocked by new engineered sampling strategies, or *planners*, that select more favorable generation paths by iteratively planning---versus uniformly at random---where to denoise along the sequence. However, by modifying the reverse paths via planning, planners create an irrevocable mismatch between the uniformly random denoising paths assumed during training and planning-based inference. In this paper, we systematically investigate the mismatch of discrete diffusion training and inference under planning and theoretically prove that the standard discrete diffusion training evidence lower bound (ELBO) does not accurately describe a denoiser that uses a non-uniform planner. To address this gap, we derive a new planned evidence lower bound (P-ELBO) that incorporates planner-based reverse dynamics directly into the training objective.
Using the P-ELBO, we introduce *Planner Aware Path Learning* (PAPL), a novel training scheme that aligns training and inference under a planned denoiser.
PAPL is implemented as a simple yet effective modification to the standard masked discrete diffusion loss, making it widely applicable and easy to adopt.
Empirically, we show PAPL delivers consistent gains across domains, including a 40\% relative improvement in protein sequences, improved text generation with up to a $4\times$ relative MAUVE gain, and 23\% relative improvement in code generation HumanEval pass@10. Code is available at https://github.com/pengzhangzhi/PAPL
Differentiable JPEG-based Input Perturbation for Knowledge Distillation Amplification via Conditional Mutual Information Maximization
Authors: SIYU CHEN; Kaixiang Zheng; Ahmed H. Salamah; EN-HUI YANG
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Maximizing conditional mutual information (CMI) has recently been shown to enhance the effectiveness of teacher networks in knowledge distillation (KD). Prior work achieves this by fine-tuning a pretrained teacher to maximize a proxy of its CMI. However, fine-tuning large-scale teachers is often impractical, and proxy-based optimization introduces inaccuracies.
 To overcome these limitations, we propose Differentiable JPEG-based Input Perturbation (DJIP), a plug-and-play framework that improves teacher–student knowledge transfer without modifying the teacher. DJIP employs a trainable differentiable JPEG layer inserted before the teacher to perturb teacher inputs in a way that directly increases CMI. We further introduce a novel alternating optimization algorithm to efficiently learn the coding parameters of the JPEG layer to maximize the perturbed CMI. Extensive experiments on CIFAR-100 and ImageNet, across diverse distillers and architectures, demonstrate that DJIP consistently improves student accuracy-achieving up to 4.11% gains-while remaining computationally lightweight and fully compatible with standard KD pipelines.
ELEPHANT: Measuring and understanding social sycophancy in LLMs
Authors: Myra Cheng; Sunny Yu; Cinoo Lee; Pranav Khadpe; Lujain Ibrahim; Dan Jurafsky
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: LLMs are known to exhibit sycophancy: agreeing with and flattering users, even at the cost of correctness. Prior work measures sycophancy only as direct agreement with users' explicitly stated beliefs that can be compared to a ground truth. This fails to capture broader forms of sycophancy such as affirming a user's self-image or other implicit beliefs. To address this gap, we introduce **social sycophancy**, characterizing sycophancy as excessive preservation of a user’s *face* (their desired self-image), and present **ELEPHANT**, a benchmark for measuring social sycophancy in LLMs. Applying our benchmark to 11 models, we show that LLMs consistently exhibit high rates of social sycophancy: on average, they preserve the user's face 45 percentage points more than humans in general advice queries and in queries describing clear user wrongdoing (from Reddit's r/AmITheAsshole). Furthermore, when prompted with perspectives from either side of a moral conflict, LLMs affirm *whichever side the user adopts* in 48% of cases—telling both the at-fault party and the wronged party that they are not wrong—rather than adhering to a consistent moral or value judgment. We further show that social sycophancy is rewarded in preference datasets. We present both prompting- and steering-based mitigation strategies to reduce social sycophancy, though understanding when and how to apply them without compromising user experience remains an open question. Our work provides theoretical and empirical tools for broadly understanding and addressing LLM sycophancy.
Tight Bounds for Schrodinger Potential Estimation in Unpaired Data Translation
Authors: Nikita Puchkin; Denis Suchkov; Alexey Naumov; Denis Belomestny
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modern methods of generative modelling and unpaired data translation based on Schrodinger bridges and stochastic optimal control theory aim to transform an initial density to a target one in an optimal way. In the present paper, we assume that we only have access to i.i.d. samples from the initial and final distributions. This makes our setup suitable for both generative modelling and unpaired data translation. Relying on the stochastic optimal control approach, we choose an Ornstein-Uhlenbeck process as the reference one and estimate the corresponding Schrodinger potential. Introducing a risk function as the Kullback-Leibler divergence between couplings, we derive tight bounds on the generalization ability of an empirical risk minimizer over a class of Schrodinger potentials, including Gaussian mixtures. Thanks to the mixing properties of the Ornstein-Uhlenbeck process, we almost achieve fast rates of convergence, up to some logarithmic factors, in favourable scenarios. We also illustrate the performance of the suggested approach with numerical experiments.
Learning a Game by Paying the Agents
Authors: Brian Hu Zhang; Tao Lin; Yiling Chen; Tuomas Sandholm
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study the problem of learning the utility functions of no-regret learning agents in a repeated normal-form game.
Differing from most prior literature, we introduce a principal with the power to observe the agents playing the game, send agents signals, and give agents *payments* as a function of their actions.
We show that the principal can, using a number of rounds polynomial in the size of the game, learn the utility functions of all agents to any desired precision $\varepsilon > 0$, for *any* no-regret learning algorithms of the agents.
Our main technique is to formulate a zero-sum game between the principal and the agents, where the principal chooses strategies among the set of all payment functions to minimize the agent's payoff. 
Finally, we discuss implications for the problem of *steering* agents. We introduce, using our utility-learning algorithm as a subroutine, the first algorithm for steering arbitrary no-regret learning agents to a desired equilibrium without prior knowledge of their utility functions.
BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Images
Authors: Soumyaroop Nandi; Prem Natarajan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose BioTamperNet, a novel framework for detecting duplicated regions in tampered biomedical images, leveraging affinity-guided attention inspired by State Space Model (SSM) approximations. Existing forensic models, primarily trained on natural images, often underperform on biomedical data where subtle manipulations can compromise experimental validity. To address this, BioTamperNet introduces an affinity-guided self-attention module to capture intra-image similarities and an affinity-guided cross-attention module to model cross-image correspondences. Our design integrates lightweight SSM-inspired linear attention mechanisms to enable efficient, fine-grained localization. Trained end-to-end, BioTamperNet simultaneously identifies tampered regions and their source counterparts. Extensive experiments on the benchmark bio-forensic datasets demonstrate significant improvements over competitive baselines in accurately detecting duplicated regions. All source code and dataset will be publicly available.
Features Emerge as Discrete States: The First Application of SAEs to 3D Representations
Authors: Albert Miao; Chenliang Zhou; Jiawei Zhou; Cengiz Oztireli
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sparse Autoencoders (SAEs) have found human-interpretable features in LLM activations, clarifying how LLMs transform input to output. However, they have rarely been applied outside of text, limiting explorations of feature dynamics. We present the first application of SAEs to the 3D domain, analyzing the features found in 53k 3D objects encoded by a state-of-the-art 3D reconstruction VAE. We observe that the model encodes discrete rather than continuous features, leading to our key finding: the model's feature activations approximate a discrete state space, driven by phase-like transitions. Through this state space framework, we address three otherwise unintuitive behaviors — the preference for positional encoding features, the sigmoidal relationship between feature ablation and reconstruction loss, and the bimodal distribution of phase transition points. This final observation suggests the model redistributes superposition interference to prioritize the high-importance features. Our work not only catalogs and explains unexpected feature dynamics, but also provides a framework to explain the model's learning dynamics. The code is available at https://feature3d.github.io/Dora-SAE/.
Distributionally Robust Cooperative Multi-agent Reinforcement Learning with Value Factorization
Authors: Chengrui Qu; Christopher Yeh; Kishan Panaganti; Eric Mazumdar; Adam Wierman
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cooperative multi-agent reinforcement learning (MARL) commonly adopts centralized training with decentralized execution, where value-factorization methods enforce the individual-global-maximum (IGM) principle so that decentralized greedy actions recover the team-optimal joint action. However, the reliability of this recipe in real-world settings remains uncertain due to environmental uncertainties arising from the sim-to-real gap, model mismatch, system noise. We address this gap by introducing Distributionally robust IGM (DrIGM), a principle that requires each agent's robust greedy action to align with the robust team-optimal joint action. We show that DrIGM holds for a novel definition of robust individual action values, which is compatible with decentralized greedy execution and yields a provable robustness guarantee for the whole system. Building on this foundation, we derive DrIGM-compliant robust variants of existing value-factorization architectures (e.g., VDN/QMIX/QTRAN) that (i) train on robust Q-targets, (ii) preserve scalability, and (iii) integrate seamlessly with existing codebases without bespoke per-agent reward shaping. Empirically, on high-fidelity SustainGym simulators and a StarCraft game environment, our methods consistently improve out-of-distribution performances.
PRISM-Physics: Causal DAG-Based Process Evaluation for Physics Reasoning
Authors: Wanjia Zhao; Qinwei Ma; Jingzhe Shi; Shirley Wu; Jiaqi Han; Yijia Xiao; Si-Yuan Chen; Xiao Luo; Ludwig Schmidt; James Zou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Benchmarks for competition-style reasoning have advanced evaluation in mathematics and programming, yet physics remains comparatively underexplored. Most existing physics benchmarks evaluate only final answers, which fail to capture reasoning processes, while recent stepwise methods rely on heuristic LLM-as-judge scoring or restrictive linear assumptions, limiting reliability and diagnostic validity.
We introduce PRISM-Physics, a process-level evaluation framework and benchmark for complex physics reasoning problems. Solutions are represented as directed acyclic graphs (DAGs) of formulas, explicitly encoding causal dependencies among intermediate steps to enable fine-grained, interpretable, and theoretically grounded scoring. 
We prove the optimality of the DAG representation and the corresponding scoring policy. Combining with a fully rule-based method for symbolic formula equivalence matching that we developed, we ensure consistent validation across diverse formulations without heuristic judgments. Results show that our evaluation framework is more aligned with human experts' scoring. 
Experiments on state-of-the-art LLMs reveal persistent reasoning failures in physics, while step-level scoring offers both diagnostic insight and rich signals for later training. By combining structural rigor, theoretical guarantees, and symbolic validation, PRISM-Physics provides a principled foundation for advancing process-level evaluation and guiding the development of models with deeper scientific reasoning capabilities.
Fantastic Pretraining Optimizers and Where to Find Them
Authors: Kaiyue Wen; David Leo Wright Hall; Tengyu Ma; Percy Liang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: AdamW has long been the dominant optimizer in language model pretraining, despite numerous claims that alternative optimizers offer 1.4 to 2$\times$ speedup. We posit that two methodological shortcomings have obscured fair comparisons and hindered practical adoption: (i) unequal hyperparameter tuning and (ii) limited or misleading evaluation setups.
To address these two issues, we conduct a systematic study of ten deep learning optimizers across four model scales (0.1B–1.2B parameters) and data-to-model ratios (1--8$\times$ the Chinchilla optimum). We find that fair and informative comparisons require rigorous hyperparameter tuning and evaluations across a range of model scales and data-to-model ratios, performed at the end of training.
First, optimal hyperparameters for one optimizer may be suboptimal for another, making blind hyperparameter transfer unfair. 
Second, the actual speedup of many proposed optimizers over well-tuned baselines is lower than claimed and decreases with model size to only 1.1$\times$ for 1.2B parameter models. Thirdly, comparing intermediate checkpoints before reaching the target training budgets can be misleading, as rankings between two optimizers can flip during training due to learning rate decay.
Through our thorough investigation, we find that all the fastest optimizers such as Muon and Soap, use matrices as preconditioners --- multiplying gradients with matrices rather than entry-wise scalars. However, the speedup of matrix-based optimizers is inversely proportional to model scale, decreasing from 1.4$\times$ over AdamW for 0.1B parameter models to merely 1.1$\times$ for 1.2B parameter models.
Robust Fine-tuning of Vision-Language-Action Robot Policies via Parameter Merging
Authors: Yajat Yadav; Zhiyuan Zhou; Andrew Wagenmaker; Karl Pertsch; Sergey Levine
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generalist robot policies, trained on large and diverse datasets, have demonstrated the ability to generalize across a wide spectrum of behaviors, enabling a single policy to act in varied real-world environments. However, they still fall short on new tasks not covered in the training data. When finetuned on limited demonstrations of a new task, these policies often overfit to the specific demonstrations---not only losing their prior abilities to solve a wide variety of generalist tasks but also failing to generalize within the new task itself. In this work, we aim to develop a method that preserves the generalization capabilities of the generalist policy during finetuning, allowing a single policy to robustly incorporate a new skill into its repertoire. Our goal is a single policy that both learns to generalize to variations of the new task and retains the broad competencies gained from pretraining. We show that this can be achieved through a simple yet effective strategy: interpolating the weights of a finetuned model with that of the pretrained model. We show, across extensive simulated and real-world experiments, that such model merging produces a single model that inherits the generalist abilities of the base model and learns to solve the new task robustly, outperforming both the pretrained and finetuned model on out-of-distribution variations of the new task. Moreover, we show that model merging enables continual acquisition of new skills in a lifelong learning setting, without sacrificing previously learned generalist abilities.
Batch Pruning by Activation Stability
Authors: Md Mustakin Alam; Shaker Islam; Aminul Islam
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Training deep neural networks remains costly in terms of data, time, and energy, limiting their deployment in large-scale and resource-constrained settings. To address this, we propose Batch Pruning by Activation Stability (*B-PAS*), a dynamic plug-in strategy that accelerates training by removing batches that contribute less to learning. *B-PAS* monitors the stability of activation representations across epochs and prunes batches whose activation variance exhibits minimal change, indicating diminishing learning utility. Applied to ResNet-18, ResNet-50, and the Convolutional vision Transformer (CvT) on CIFAR-10, CIFAR-100, SVHN, and ImageNet-1K, *B-PAS* reduces training batch usage by up to 57\% with no loss in accuracy, and by 47\% while slightly improving accuracy. Moreover, it achieves up to 61\% savings in GPU node-hours, outperforming prior state-of-the-art pruning methods with up to 29\% higher data savings and 21\% greater GPU node-hour savings. We further demonstrate the generalization of *B-PAS* by extending it to GPT-2 fine-tuning, showing that activation stability can serve as an effective pruning signal beyond vision models. These results highlight activation stability as a powerful internal signal for efficient training, offering a practical and sustainable path toward data and energy-efficient deep learning.
MOLM: Mixture of LoRA Markers
Authors: Samar Fares; Nurbek Tastan; Noor Hazim Hussein; Karthik Nandakumar
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative models can generate photorealistic images at scale. This raises serious concerns about the ability to detect synthetically generated images and attribute these images to specific sources. While watermarking has emerged as a possible solution, existing methods remain fragile to realistic distortions, susceptible to adaptive removal, and expensive to update when the underlying watermarking key changes. We propose a general watermarking framework that formulates the encoding problem as key-dependent perturbation of the parameters of a generative model. Within this framework, we introduce  Mixture of LoRA Markers (MOLM), a routing-based instantiation in which binary keys activate lightweight low-rank adapters (LoRA) inside residual and attention blocks. This design avoids key-specific re-training and achieves the desired properties such as imperceptibility, fidelity, verifiability, and robustness. Experiments on Stable Diffusion and FLUX show that MOLM preserves image quality while achieving robust key recovery against distortions, compression and regeneration, averaging attacks, and black-box adversarial attacks on the extractor. Code is available at [https://github.com/Samar-Fares/MOLM-Watermark](https://github.com/Samar-Fares/MOLM-Watermark)
Is This Just Fantasy? Language Model Representations Reflect Human Judgments of Event Plausibility
Authors: Michael A. Lepori; Jennifer Hu; Ishita Dasgupta; Roma Patel; Thomas Serre; Ellie Pavlick
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Language models (LMs) are used for a diverse range of tasks, from question answering to writing fantastical stories. In order to reliably accomplish these tasks, LMs must be able to discern the modal category of a sentence (i.e., whether it describes something that is possible, impossible, completely nonsensical, etc.). However, recent studies have called into question the ability of LMs to categorize sentences according to modality. In this work, we identify linear representations that discriminate between modal categories within a variety of LMs, or modal difference vectors. Analysis of modal difference vectors reveals that LMs have access to more reliable modal categorization judgments than previously reported. Furthermore, we find that modal difference vectors emerge in a consistent order as models become more competent (i.e., through training steps, layers, and parameter count). Notably, we find that modal difference vectors identified within LM activations can be used to model fine-grained human categorization behavior. This potentially provides a novel view into how human participants distinguish between modal categories, which we explore by correlating projections along modal difference vectors with human participants' ratings of interpretable features. In summary, we derive new insights into LM modal categorization using techniques from mechanistic interpretability, with the potential to inform our understanding of modal categorization in humans.
PateGAIL++: Utility Optimized Private Trajectory Generation with Imitation Learning
Authors: Yingjie Ma; Bijal Bharadva; Xin Zhang; Joann Qiongna Chen
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Human mobility trajectory data supports a wide range of applications, including urban planning, intelligent transportation systems, and public safety monitoring. However, large-scale, high-quality mobility datasets are difficult to obtain due to privacy concerns. Raw trajectory data may reveal sensitive user information, such as home addresses, routines, or social relationships, making it crucial to develop privacy-preserving alternatives.
Recent advances in deep generative modeling have enabled synthetic trajectory generation, but existing methods either lack formal privacy guarantees or suffer from reduced utility and scalability. Differential Privacy (DP) has emerged as a rigorous framework for data protection, and recent efforts such as PATE-GAN and \textsc{PateGail} integrate DP with generative adversarial learning. While promising, these methods struggle to generalize across diverse trajectory patterns and often incur significant utility degradation.
In this work, we propose a new framework that builds on \textsc{PateGail\texttt{++}} by introducing a \emph{sensitivity-aware noise injection module} that dynamically adjusts privacy noise based on sample-level sensitivity. This design significantly improves trajectory fidelity, downstream task performance, and scalability under strong privacy guarantees. We further adapt our framework to the local differential privacy (LDP) setting, allowing individual-level protection without reliance on a trusted server. We evaluate our method on a real-world mobility dataset and demonstrate its superiority over state-of-the-art baselines in terms of privacy-utility trade-off.