GenAI4ELab/papercli-papers huggingface dataset:
PSP: Prompt-Guided Self-Training Sampling Policy for Active Prompt Learning
Authors: Sen Tao; Kaiduo Feng; Jiawei Liu; Peng Zeng; Yongchao Xu; Yufei Zheng; Zheng-Jun Zha
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Active Prompt Learning (APL) using vision-language models (\textit{e.g.}, CLIP) has attracted considerable attention for mitigating the dependence on fully labeled dataset in downstream task adaptation. However, existing methods fail to explicitly leverage prompt to guide sample selection, resulting in the selected samples being ineffective in facilitating the prompt template's downstream task adaptation, while also overlooking valuable complementary information in the unselected samples. To fill this gap, we propose a novel Prompt-Guided Self-Training Sampling Policy (PSP) for APL, which integrates Soft Actor-Critic with a customized real-pseudo hybrid reward and vectorized critics to incorporate prompts in guiding sample selection toward those that facilitate the optimization of prompt template, by jointly considering both selected and unselected samples. Specifically, PSP comprises two prominent components: Vectorized Soft Actor-Critic Sampling Policy (VSSP) and Uncertainty Augmented Self-Training (UST) mechanism.  VSSP customizes a real-pseudo hybrid reward based on learned prompts and image features, which is fed into vectorized critics to estimate Q-value for each sample and compute gradients that optimize the actor, allowing it to refine its sampling policy in an End-to-End manner to identify the most informative samples for prompt learning. Moreover, UST leverages the CLIP from the previous round to generate reliable pseudo-labeled data based on uncertainty and confidence of average predictions, thereby deepening the understanding of the overall data. Extensive experiments conducted on diverse real-world datasets validate the effectiveness of our PSP.
Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability
Authors: Usha Bhalla; Alex Oesterling; Claudio Mayrink Verdun; Himabindu Lakkaraju; Flavio Calmon
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Translating the internal representations and computations of models into concepts that humans can understand is a key goal of interpretability. While recent dictionary learning methods such as Sparse Autoencoders (SAEs) provide a promising route to discover human-interpretable features, they often only recover token-specific, noisy, or highly local concepts. We argue that this limitation stems from neglecting the temporal structure of language, where semantic content typically evolves smoothly over sequences. Building on this insight, we introduce Temporal Sparse Autoencoders (T-SAEs), which incorporate a novel contrastive loss encouraging consistent activations of high-level features over adjacent tokens. This simple yet powerful modification enables SAEs to disentangle semantic from syntactic features in a self-supervised manner. Across multiple datasets and models, T-SAEs recover smoother, more coherent semantic concepts without sacrificing reconstruction quality. Strikingly, they exhibit clear semantic structure despite being trained without explicit semantic signal, offering a new pathway for unsupervised interpretability in language models.
Learning to Lie: Adversarial Attacks on Human-AI Teams and LLMs
Authors: Abed K. Musaffar; Anand Gokhale; Sirui Zeng; Rasta Tadayon; Xifeng Yan; Ambuj Singh; Francesco Bullo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As artificial intelligence (AI) assistants become more widely adopted in safety-critical domains, it becomes important to develop safeguards against potential failures or adversarial attacks. A key prerequisite to developing these safeguards is understanding the ability of these AI assistants to mislead human teammates. We investigate this attack problem within the context of an intellective strategy game where a team of three humans and one AI assistant collaborate to answer a series of trivia questions. Unbeknownst to the humans, the AI assistant is adversarial. Leveraging techniques from Model-Based Reinforcement Learning (MBRL), the AI assistant learns a model of the humans' trust evolution and uses that model to manipulate the group decision-making process to harm the team. We evaluate two models---one inspired by literature and the other data-driven---and find that both can effectively harm the human team. Moreover, we find that in this setting while our data-driven model is the most capable of accurately predicting how human agents appraise their teammates given limited information on prior interactions, the model based on principles of cognitive psychology does not lag too far behind. Finally, we compare the performance of state-of-the-art LLM models to human agents on our influence allocation task to evaluate whether the LLMs allocate influence similarly to humans or if they are more robust to our attack. These results enhance our understanding of decision-making dynamics in small human-AI teams and lay the foundation for defense strategies.
Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance
Authors: Zhuo Li; Pengyu Cheng; Zhechao Yu; FeifeiTong; Anningzhe Gao; Tsung-Hui Chang; Xiang Wan; erchao.zec; xiaoxi jiang; guanjunjiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reward models (RMs) are essential in reinforcement learning from human feedback (RLHF) to align large language models (LLMs) with human values. However, RM training data is commonly recognized as low-quality, containing inductive biases that can easily lead to overfitting and reward hacking. For example, more detailed and comprehensive responses are usually human-preferred but with more words, leading response length to become one of the inevitable inductive biases. A limited number of prior RM debiasing approaches either target a single specific type of bias or model the problem with only simple linear correlations, *e.g.*,  Pearson coefficients. To mitigate more complex and diverse inductive biases in reward modeling, we introduce a novel information-theoretic debiasing method called **D**ebiasing via **I**nformation optimization for **R**M (DIR). Inspired by the information bottleneck (IB), we maximize the mutual information (MI) between RM scores and human preference pairs, while minimizing the MI between RM outputs and biased attributes of preference inputs. With theoretical justification from information theory, DIR can handle more sophisticated types of biases with non-linear correlations, broadly extending the real-world application scenarios for RM debiasing methods. In experiments, we verify the effectiveness of DIR with three types of inductive biases: *response length*, *sycophancy*, and *format*.  We discover that DIR not only effectively mitigates target inductive biases but also enhances RLHF performance across diverse benchmarks, yielding better generalization abilities. The code and training recipes are available at https://github.com/Qwen-Applications/DIR.
Learning to Answer from Correct Demonstrations
Authors: Nirmit Joshi; Gene Li; Siddharth Bhandari; Shiva Kasiviswanathan; Cong Ma; Nathan Srebro
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study the problem of learning to generate an answer (or completion) to a question (or prompt), where there could be multiple correct answers, any one of which is acceptable at test time. Learning is based on demonstrations of some correct answer to each training question, as in Supervised Fine Tuning (SFT).  We formalize the problem as imitation learning (i.e., apprenticeship learning) in contextual bandits, with offline demonstrations from some expert (optimal, or very good) policy, without explicitly observed rewards. In contrast to prior work, which assumes the demonstrator belongs to a bounded-complexity policy class, we propose relying only on the underlying reward model (i.e., specifying which answers are correct) being in a bounded-complexity class, which we argue is a strictly weaker assumption. We show that likelihood-maximization methods can fail in this setting, and instead present an approach that learns to answer nearly as well as the demonstrator, with sample complexity logarithmic in the cardinality of the reward class. Our method is similar to Syed and Schapire 2007, when adapted to a contextual bandit (i.e., single step) setup, but is a simple one-pass online approach that enjoys an ``optimistic rate'' (i.e., $1/\varepsilon$ when the demonstrator is optimal, versus $1/\varepsilon^2$ in Syed and Schapire 2007, and works even with arbitrarily adaptive demonstrations.
Reshaping Reasoning in LLMs: A Theoretical Analysis of RL Training Dynamics through Pattern Selection
Authors: Xingwu Chen; Tianle Li; Difan Zou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While reinforcement learning (RL) demonstrated remarkable success in enhancing the reasoning capabilities of language models, the training dynamics of RL in LLMs remain unclear. In this work, we provide an explanation of the RL training process through empirical analysis and rigorous theoretical modeling. First, through systematic reasoning-pattern-level and token-level analysis across the RL training process, we show that while different reasoning patterns exhibit relatively stable success rates during training, RL primarily optimizes a sparse subset of critical tokens, thereby reshaping reasoning pattern distributions to affect model performance. Building on these empirical insights, we develop a theoretical framework to understand the training dynamics of RL with two typical rewards: verifiable reward (RLVR) and model's internal feedback (RLIF). For RLVR, we analyze the training dynamics under two special cases: one where models readily converge to optimal reasoning strategies, and another where optimization becomes challenging, revealing that the base model's reasoning quality is crucial for determining convergence behavior. For RLIF, we examine how internal rewards initially improve model performance but can potentially lead to degradation with continued training. Extensive experiments validate our findings, advancing both theoretical understanding and practical applications of RL in language model enhancement.
Secure Inference for Diffusion Models via Unconditional Scores
Authors: Jaeyun Song; Geondo Park; Uigyu Kim; Joonhyung Park; Eunho Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As diffusion model-based services expand across various domains, safeguarding client data privacy has become increasingly critical. While fully homomorphic encryption and secure multi-party computation enable privacy-preserving inference, their high computational overhead poses challenges for large-scale diffusion applications. Recent work alleviates computational costs by substituting non-linear operations with low-degree polynomial approximations. While such relaxations reduce latency, they incur significant degradation in generative fidelity, and inference remains considerably slower than plaintext execution. To further accelerate secure inference while preserving performance, we explore more relaxed approximations and propose a score-correction framework that rectifies the conditional score shift induced by the relaxed approximation, rather than decreasing the approximation error itself. The key insight is that unconditional generation can be executed without approximation and thus provides a high-fidelity score signal. Leveraging this unconditional score as corrective guidance enables more relaxed approximations while preserving semantic and perceptual quality. In experiments, we demonstrate that our method significantly alleviates the performance degradation caused by relaxed approximations across various benchmarks.
TEST-TIME SCALING IN DIFFUSION LLMS VIA HIDDEN SEMI-AUTOREGRESSIVE EXPERTS
Authors: Jihoon Lee; Hoyeon Moon; Kevin Zhai; Arun Kumar Chithanar; Anit Kumar Sahu; Soummya Kar; Chul Lee; Souradip Chakraborty; Amrit Singh Bedi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion-based large language models (dLLMs) are trained to model extreme flexibility/dependence in the data-distribution; however, how to best utilize this at inference time remains an open problem. In this work, we uncover an interesting property of these models: dLLMs {trained on textual data} implicitly learn a mixture of semi-autoregressive experts, where different generation orders reveal different specialized behaviors. We show that committing to any single, fixed inference time schedule, a common practice, collapses performance by failing to leverage this latent ensemble. To address this, we introduce HEX (Hidden semi-autoregressive EXperts for test-time scaling), a training-free inference method that ensembles across heterogeneous block schedules. By doing a majority vote over diverse block-sized generation paths, HEX robustly avoids failure modes associated with any single fixed schedule. On reasoning benchmarks such as GSM8K, it boosts accuracy by up to 3.56× (from 24.72\% to 88.10\%), outperforming top-K margin inference and specialized fine-tuned methods like GRPO, without additional training. HEX even yields significant gains on MATH benchmark from 16.40\% to 40.00\%, scientific reasoning on ARC-C from 54.18\% to 87.80\%, and TruthfulQA from 28.36\% to 57.46\%. Our results establish test-time scaling as a powerful principle for dLLMs, showing that the sequence in which masking is done can play a significant role in test-time scaling/inferencing of dLLMs.
DUET: Distilled LLM Unlearning from an Efficiently Contextualized Teacher
Authors: Yisheng Zhong; Zhengbang Yang; Zhuangdi Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: LLM unlearning is a technique to remove the impacts of undesirable knowledge from the model without retraining from scratch, which is indispensable towards trustworthy AI. Existing unlearning methods face significant limitations: conventional tuning-based unlearning is computationally heavy and prone to catastrophic forgetting. In contrast, in-contextualized unlearning is lightweight for precise unlearning but vulnerable to prompt removal or reverse engineering attacks. In response, we propose Distilled Unlearning from an Efficient Teacher (DUET), a novel distillation-based unlearning method that combines the merits of these two lines of work. It learns a student model to imitate the behavior of a prompt-steered teacher that effectively refuses undesirable knowledge generation while preserving general domain knowledge. Extensive evaluations on existing benchmarks with our enriched evaluation protocols demonstrated that DUET achieves significantly higher performance in both forgetting and utility preservation, while being orders of magnitude more data-efficient than state-of-the-art unlearning methods.
Consistency-Driven Calibration and Matching for Few-Shot Class Incremental Learning
Authors: Qinzhe Wang; Zixuan Chen; Keke Huang; Xiu Su; Chunhua Yang; Chang Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Few-Shot Class Incremental Learning (FSCIL) is crucial for adapting to the complex open-world environments. Contemporary prospective learning-based space construction methods struggle to balance old and new knowledge, as prototype bias and rigid structures limit the expressive capacity of the embedding space. Different from these strategies, we rethink the optimization dilemma from the perspective of feature-structure dual consistency, and propose a Consistency-driven Calibration and Matching (ConCM) framework that systematically mitigates the knowledge conflict inherent in FSCIL. Specifically, inspired by hippocampal associative memory, we design a memory-aware prototype calibration that extracts generalized semantic attributes from base classes and reintegrates them into novel classes to enhance the conceptual center consistency of features. Further, to consolidate memory associations, we propose dynamic structure matching, which adaptively aligns the calibrated features to a session-specific optimal manifold space, ensuring cross-session structure consistency. This process requires no class-number priors and is theoretically guaranteed to achieve geometric optimality and maximum matching. On large-scale FSCIL benchmarks including mini-ImageNet, CIFAR100 and CUB200, ConCM achieves state-of-the-art performance, with harmonic accuracy gains of up to 3.41% in incremental sessions. Code is available at: https://github.com/wire-wqz/ConCM
Soft Equivariance Regularization for Invariant Self-Supervised Learning
Authors: Joohyung Lee; Changhun Kim; Hyunsu Kim; Kwanhyung Lee; Juho Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Self-supervised learning (SSL) typically learns representations invariant to semantic-preserving augmentations (e.g., random crops and photometric jitter). While effective for recognition, enforcing strong invariance can suppress transformation-dependent structure that is useful for robustness to geometric perturbations and spatially sensitive transfer. A growing body of work, therefore, augments invariance-based SSL with equivariance objectives, but these objectives are often imposed on the same **final** (typically spatially-collapsed) representation. We empirically observe a trade-off in this coupled setting: pushing equivariance regularization toward deeper layers improves equivariance scores but degrades ImageNet-1k linear evaluation, motivating a layer-decoupled design. Motivated by this trade-off, we propose **Soft Equivariance Regularization (SER)**, a plug-in regularizer that decouples where invariance and equivariance are enforced: we keep the base SSL objective unchanged on the final embedding, while softly encouraging equivariance on an \emph{intermediate spatial token map} via analytically specified group actions $\rho_g$ (e.g., $90^{\circ}$ rotations, flips, and scaling) applied directly in feature space. SER learns/predicts no per-sample transformation codes/labels, requires no auxiliary transformation-prediction head, and adds only **1.008$\times$** training FLOPs. On ImageNet-1k ViT-S/16 pretraining, SER improves MoCo-v3 by **+0.84** Top-1 in linear evaluation under a strictly matched 2-view setting and consistently improves DINO and Barlow Twins; under matched view counts, SER achieves the best ImageNet-1k linear-eval Top-1 among the compared invariance+equivariance add-ons. SER further improves ImageNet-C/P by **+1.11/+1.22** Top-1 and frozen-backbone COCO detection by **+1.7** mAP. Finally, applying the same **layer-decoupling** recipe to existing invariance+equivariance baselines (e.g., EquiMod and AugSelf) improves their accuracy, suggesting layer decoupling as a general design principle for combining invariance and equivariance. Code is available at https://github.com/aitrics-chris/SER.
DISK: Differentiable Sparse Kernel Complex for Efficient Spatially-Variant Convolution
Authors: Zhizhen Wu; Cao Zhe; Yuchi Huo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Image convolution with complex kernels is common in photography, scientific imaging, and animation, but dense convolution is too expensive for resource-limited devices. Existing approximations, such as simulated annealing and low-rank decompositions, are either slow or struggle with non-convex kernels.
We present a differentiable kernel decomposition framework that represents a spatially variant dense kernel with a small set of sparse samples, assuming the target dense kernel is known for both optimization and filtering. Our method provides (i) end-to-end differentiable sparse-kernel optimization, (ii) shape-aware initialization for non-convex kernels, and (iii) kernel-space interpolation for efficient, multi-dimensional spatially varying filtering without retraining or added runtime cost.
Across Gaussian and non-convex kernels, our method achieves higher fidelity than simulated annealing and lower cost than low-rank decomposition. It is practical for mobile imaging and real-time rendering, and integrates cleanly into learning pipelines.
ProxyThinker: Test-Time Guidance through Small Visual Reasoners
Authors: Zilin Xiao; Jaywon Koo; Siru Ouyang; Jefferson Hernandez; Yu Meng; Vicente Ordonez
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advancements in reinforcement learning with verifiable rewards have pushed the boundaries of the visual reasoning capabilities in large vision-language models (LVLMs). However, training LVLMs with reinforcement fine-tuning (RFT) is computationally expensive, posing a significant challenge to scaling model size. In this work, we propose ProxyThinker, an inference-time technique that enables large models to inherit the visual reasoning capabilities from small, slow-thinking visual reasoners without any training. By subtracting the output distributions of base models from those of RFT reasoners, ProxyThinker modifies the decoding dynamics and successfully elicits the slow-thinking reasoning demonstrated by the emerged sophisticated behaviors, such as self-verification and self-correction. ProxyThinker consistently boosts performance on challenging visual benchmarks on spatial, mathematical, and multidisciplinary reasoning, enabling untuned base models to compete with the performance of their full-scale RFT counterparts. Furthermore, our implementation efficiently coordinates multiple language models with parallelism techniques and achieves faster inference compared to previous decoding-time methods, paving the way for the practical deployment of ProxyThinker. Code is available at https://github.com/MrZilinXiao/ProxyThinker.
Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models
Authors: Zhanke Zhou; Zhaocheng Zhu; Xuan Li; Mikhail Galkin; Xiao Feng; Sanmi Koyejo; Jian Tang; Bo Han
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Numerous applications of large language models (LLMs) rely on their ability to perform step-by-step reasoning. However, the reasoning behavior of LLMs remains poorly understood, posing challenges to research, development, and safety. To address this gap, we introduce landscape of thoughts (LoT), the first landscape visualization tool to inspect the reasoning trajectories with certain reasoning methods on any multi-choice dataset. We represent the textual states in a trajectory as numerical features that quantify the states' distances to the answer choices. These features are then visualized in two-dimensional plots using t-SNE. Qualitative and quantitative analysis with the landscape of thoughts effectively distinguishes between strong and weak models, correct and incorrect answers, as well as different reasoning tasks. It also uncovers undesirable reasoning patterns, such as low consistency and high uncertainty. Additionally, users can adapt LoT to a model that predicts the property they observe. We showcase this advantage by adapting LoT to a lightweight verifier that evaluates the correctness of trajectories. Empirically, this verifier boosts the reasoning accuracy and the test-time scaling effect. The code is publicly available at: https://github.com/tmlr-group/landscape-of-thoughts.