GenAI4ELab/papercli-papers huggingface dataset:
Primal-Dual Policy Optimization for Linear CMDPs with Adversarial Losses
Authors: Kihyun Yu; Seoungbin Bae; Dabeen Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Existing work on linear constrained Markov decision processes (CMDPs) has primarily focused on stochastic settings, where the losses and costs are either fixed or drawn from fixed distributions. However, such formulations are inherently vulnerable to adversarially changing environments. To overcome this limitation, we propose a primal-dual policy optimization algorithm for online finite-horizon {adversarial} linear CMDPs, where the losses are adversarially chosen under full-information feedback and the costs are stochastic under bandit feedback. Our algorithm is the \emph{first} to achieve sublinear regret and constraint violation bounds in this setting, both bounded by $\widetilde{\mathcal{O}}(K^{3/4})$, where $K$ denotes the number of episodes. The algorithm introduces and runs with a new class of policies, which we call weighted LogSumExp softmax policies, designed to adapt to adversarially chosen loss functions. Our main result stems from the following key contributions: (i) a new covering number argument for the weighted LogSumExp softmax policies, and (ii) two novel algorithmic components---periodic policy mixing and a regularized dual update---which allow us to effectively control both the covering number and the dual variable. We also report numerical results that validate our theoretical findings on the performance of the algorithm.
Semantic-Enhanced Time-Series Forecasting via Large Language Models
Authors: Hao Liu; Zhang xiaoxing; Chun Yang; Xiaobin Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Time series forecasting plays a significant role in finance, energy, meteorology, and IoT applications. Recent studies have leveraged the generalization capabilities of large language models (LLMs) to adapt to time series forecasting, achieving promising performance. However, existing studies focus on token-level modal alignment, instead of bridging the intrinsic modality gap between linguistic knowledge structures and time series data patterns, greatly limiting the semantic representation. To address this issue, we propose a novel Semantic-Enhanced LLM (SE-LLM) that explores the inherent periodicity and anomalous characteristics of time series to embed into the semantic space to enhance the token embedding. This process enhances the interpretability of tokens for LLMs, thereby activating the potential of LLMs for temporal sequence analysis. Moreover, existing Transformer-based LLMs excel at capturing long-range dependencies but are weak at modeling short-term anomalies in time-series data. Hence, we propose a plugin module embedded within self-attention that models long-term and short-term dependencies to effectively adapt LLMs to time-series analysis. Our approach freezes the LLM and reduces the sequence dimensionality of tokens, greatly reducing computational consumption. Experiments demonstrate the superiority performance of our SE-LLM against the state-of-the-art (SOTA) methods.
Unveiling Super Experts in Mixture-of-Experts Large Language Models
Authors: Zunhai Su; Qingyuan Li; HaoZhang; Weihao Ye; Qibo Xue; Yulei Qian; Ngai Wong; Kehong Yuan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Leveraging the intrinsic importance differences among experts, recent research has explored expert-level compression techniques to enhance the efficiency of Mixture-of-Experts (MoE) large language models (LLMs).
However, existing approaches often rely on empirical heuristics to identify critical experts, while lacking a deeper understanding into the heterogeneous importance of experts and the inner workings of MoE LLMs.
In this study, we report, for the first time, the discovery and systematic investigation of a distinct subset of experts that play a pivotal role in the model's forward inference.
These experts are prevalent in open-source MoE LLMs, and despite their extremely limited number, pruning them results in a substantial decline in model performance (e.g., prune just three out of 6,144 causes Qwen3-30B-A3B to generate repetitive and uninformative outputs).
We refer to these experts as Super Experts (SEs).
Our comprehensive analysis provides progressively deeper insights into SEs: 
(i) SEs are characterized by rare but extreme activation outliers in the output of the down\_proj, which give rise to massive activations in the
hidden states between decoder layers.
Moreover, the distribution of SEs is model-specific, data-agnostic, and remains unaffected by post-training processes.
(ii) By pruning SEs, we assess their significance across a variety of tasks, revealing their considerable impact on the model's overall performance, particularly in mathematical reasoning.
(iii) We further investigate why compressing SEs exerts such a pronounced impact. 
We show that, in MoE LLMs, SEs serve as the primary source of the systematic outlier mechanism in Transformers, and that compressing them profoundly disrupts this process, ultimately causing the collapse of attention sinks. 
These findings advance the understanding of the internal dynamics of MoE LLMs, filling an important gap in the current knowledge.
In addition, we developed an automated tool for rapid and accurate SE profiling.
The code is provided in the supplementary materials.
Interaction-aware Representation Modeling With Co-Occurrence Consistency for Egocentric Hand-Object Parsing
Authors: YUEJIAO SU; Yi Wang; Lei Yao; Yawen Cui; Lap-Pui Chau
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A fine-grained understanding of egocentric human-environment interactions is crucial for developing next-generation embodied agents.
One fundamental challenge in this area involves accurately parsing hands and active objects.
While transformer-based architectures have demonstrated considerable potential for such tasks, several key limitations remain unaddressed:
1) existing query initialization mechanisms rely primarily on semantic cues or learnable parameters, demonstrating limited adaptability to changing active objects across varying input scenes;
2) previous transformer-based methods utilize pixel-level semantic features to iteratively refine queries during mask generation, which may introduce interaction-irrelevant content into the final embeddings;
and 3) prevailing models are susceptible to ``interaction illusion'', producing physically inconsistent predictions.
To address these issues, we propose an end-to-end Interaction-aware Transformer (InterFormer), which integrates three key components, i.e., a Dynamic Query Generator (DQG), a Dual-context Feature Selector (DFS), and the Conditional Co-occurrence (CoCo) loss. 
The DQG explicitly grounds query initialization in the spatial dynamics of hand-object contact, enabling targeted generation of interaction-aware queries for hands and various active objects.
The DFS fuses coarse interactive cues with semantic features, thereby suppressing interaction-irrelevant noise and emphasizing the learning of interactive relationships.
The CoCo loss incorporates hand-object relationship constraints to enhance physical consistency in prediction. Our model achieves state-of-the-art performance on both the EgoHOS and the challenging out-of-distribution mini-HOI4D datasets, demonstrating its effectiveness and strong generalization ability.
Code and models are publicly available at https://github.com/yuggiehk/InterFormer.
Token-Importance Guided Direct Preference Optimization
Authors: Ning Yang; Hai Lin; Yibo Liu; Baoliang Tian; Guoqing Liu; Haijun Zhang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Aligning Large Language Models (LLMs) with human preferences is crucial for safe and effective AI interactions. While popular methods like Direct Preference Optimization (DPO) have simplified alignment, they remain sensitive to data noise and overlook the differential importance of individual tokens. Existing token-level approaches often rely on probability prediction or simplistic weighting schemes to obtain token importance, which still cannot fully address these issues. To solve this problem, we propose the Token-Importance Guided Direct Preference Optimization (TI-DPO), a framework that achieves fine-grained semantic control through two synergistic innovations. 
First, we propose a novel hybrid weighting mechanism that combines gradient attribution with a Gaussian prior, ensuring both the accuracy and robustness of token importance scores. Second, we employ a triplet loss to provide structured guidance for the optimization, explicitly guiding model outputs to approach preferred responses and diverge from non-preferred ones. Experimental results show that TI-DPO achieves higher accuracy and stronger generative diversity, providing more stable and computationally efficient solutions compared with DPO and other RLHF methods.
DTO-KD: Dynamic Trade-off Optimization for Effective Knowledge Distillation
Authors: Zeeshan Hayder; Ali Cheraghian; Lars Petersson; Mehrtash Harandi; Richard Hartley
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Knowledge Distillation (KD) is a widely adopted framework for compressing large models into compact student models by transferring knowledge from a high-capacity teacher. Despite its success, KD presents two persistent challenges: (1) the trade-off between optimizing for the primary task loss and mimicking the teacher's outputs, and (2) the gradient disparity arising from architectural and representational mismatches between teacher and student models. In this work, we propose Dynamic Trade-off Optimization for Knowledge Distillation (DTO-KD), a principled multi-objective optimization formulation of KD that dynamically balances task and distillation losses at the gradient level. Specifically, DTO-KD resolves two critical issues in gradient-based KD optimization: (i) gradient conflict, where task and distillation gradients are directionally misaligned, and (ii) gradient dominance, where one objective suppresses learning progress on the other. Our method adapts per-iteration trade-offs by leveraging gradient projection techniques to ensure balanced and constructive updates. We evaluate DTO-KD on large-scale benchmarks including ImageNet-1K for classification and COCO for object detection. Across both tasks, DTO-KD consistently outperforms prior KD methods, yielding state-of-the-art accuracy and improved convergence behavior. Furthermore, student models trained with DTO-KD exceed the performance of their non-distilled counterparts, demonstrating the efficacy of our multi-objective formulation for KD.
Generalizable End-to-End Tool-Use RL with Synthetic CodeGym
Authors: Weihua Du; Hailei Gong; Zhan Ling; Kang Liu; Lingfeng Shen; Xuesong Yao; Yufei Xu; Dingyuan Shi; Yiming Yang; Jiecao Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Tool-augmented large language models (LLMs), hereafter LLM agents, leverage external tools to solve diverse tasks and interface with the real world. However, current training practices largely rely on supervised fine-tuning (SFT) over static trajectories or reinforcement learning (RL) on narrow tasks, which generalize poorly beyond development settings and lead to brittleness with new tools and unseen workflows. Because code execution reflects many structural patterns of real-world workflows, we use coding problems as a structured substrate to build tool-use agent training environments with diverse task configurations. To this end, we introduce **CodeGym**, a scalable framework that synthesizes diverse, verifiable, and controllable multi-turn tool-use environments for agent RL, enabling LLM agents to explore and master various workflows actively. CodeGym converts static coding problems into interactive environments by extracting atomic functions or logic into callable tools, yielding verifiable tasks that span various tool-execution workflows. Models of varying sizes and chain-of-thought configurations trained in CodeGym exhibit consistent out-of-distribution generalizability; for example, Qwen2.5-32B-Instruct achieves an absolute accuracy gain of 8.7 points on the OOD benchmark $\tau$-Bench. These results highlight CodeGym as a step toward scalable general-purpose RL environments for training tool-use behaviors that align with real-world agent workflows. Our code is publicly available at https://github.com/StigLidu/CodeGym.
Learning From Dictionary: Enhancing Robustness of Machine-Generated Text Detection in Zero-Shot Language via Adversarial Training
Authors: Yuanfan Li; Qi Zhou; Zexuan Xie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Machine-generated text (MGT) detection is critical for safeguarding online content integrity and preventing the spread of misleading information. 
Although existing detectors achieve high accuracy in monolingual settings, they exhibit severe performance degradation on zero-shot languages and are vulnerable to adversarial attacks. 
To tackle these challenges, we propose a robust adversarial training framework named 
**T**ranslation-based 
**A**ttacker 
**S**trengthens 
Mul**T**ilingual 
Def**E**nder (TASTE). 
TASTE comprises two core components: an attacker that performs code-switching by querying translation dictionaries to generate adversarial examples, and a detector trained to resist these attacks while generalizing to unseen languages. 
We further introduce a novel Language-Agnostic Adversarial Loss (LAAL), which encourages the detector to learn language-invariant feature representations and thus enhances zero-shot detection performance and robustness against unseen attacks. 
Additionally, the attacker and detector are synchronously updated, enabling continuous improvement of defensive capabilities. 
Experimental results on 9 languages and 8 attack types show that our TASTE surpasses 8 SOTA detectors, improving the average F1 score by **0.064** and reducing the average Attack Success Rate (ASR) by **3.8\%**.
Our framework offers a promising approach for building robust, multilingual MGT detectors with strong generalization to real-world adversarial scenarios.
Our codes are available in https://github.com/Liyuuuu111/MGT-Eval, and our datasets and pretrained checkpoint are available in https://drive.google.com/file/d/1w1hbdiZMS_JzPntVMWM3qrTQ4KxJf-t6.
Reducing Class-Wise Performance Disparity via Margin Regularization
Authors: Beier Zhu; Kesen Zhao; Jiequan Cui; Qianru Sun; Yuan Zhou; Xun Yang; Hanwang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data—posing concerns for reliable deployment. While prior efforts have explored empirical remedies, a theoretical understanding of such performance disparities in classification remains limited. In this work, we present Margin Regularization for performance disparity Reduction ( $MR^2$ ), a theoretically principled  regularization for classification by dynamically adjusting margins in both the logit and representation spaces. Our analysis establishes a novel margin-based, class-sensitive generalization bound that reveals how per-class feature variability contributes to error, motivating the use of larger margins for ''hard'' classes. Guided by this insight,$MR^2$ optimizes per-class logit margins proportional to feature spread and penalizes excessive representation margins to enhance intra-class compactness.
Experiments on seven datasets—including ImageNet—and diverse pre-trained backbones (MAE, MoCov2, CLIP) demonstrate demonstrate that our $MR^2$ not only improves overall accuracy but also significantly boosts ''hard'' class performance without trading off ''easy'' classes, thus reducing the performance disparities. Codes are available in https://github.com/BeierZhu/MR2.
STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation
Authors: Zijin Liu; Xu Geng; Wenshuai Xu; Xiang Zhao; Yan Xia; You Song
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurate crowd simulation is crucial for public safety management, emergency evacuation planning, and intelligent transportation systems. However, existing methods, which typically model crowds as a collection of independent individual trajectories, are limited in their ability to capture macroscopic physical laws. This microscopic approach often leads to error accumulation and compromises simulation stability. Furthermore, deep learning-driven methods tend to suffer from low inference efficiency and high computational overhead, making them impractical for large-scale, efficient simulations. To address these challenges, we propose the Spatio-Temporal Decoupled Differential Equation Network (STDDN), a novel framework that guides microscopic trajectory prediction with macroscopic physics. We innovatively introduce the continuity equation from fluid dynamics as a strong physical constraint. A Neural Ordinary Differential Equation (Neural ODE) is employed to model the macroscopic density evolution driven by individual movements, thereby physically regularizing the microscopic trajectory prediction model. We design a density-velocity coupled dynamic graph learning module to formulate the derivative of the density field within the Neural ODE, effectively mitigating error accumulation. We also propose a differentiable density mapping module to eliminate discontinuous gradients caused by discretization and introduce a cross-grid detection module to accurately model the impact of individual cross-grid movements on local density changes. The proposed STDDN method has demonstrated significantly superior simulation performance compared to state-of-the-art methods on long-term tasks across four real-world datasets, as well as a major reduction in inference latency.
Mapping Post-Training Forgetting in Language Models at Scale
Authors: Jackson Harmon; Andreas Hochlehnert; Matthias Bethge; Ameya Prabhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scaled post‑training now drives many of the largest capability gains in language models (LMs), yet its effect on pretrained knowledge remains poorly understood. Not all forgetting is equal: Forgetting one fact (e.g., a U.S. president or an API call) does not “average out” when recalling another. Hence, we propose a sample-wise paradigm to measure what is forgotten and when backward transfer occurs. Our metric counts 1→0 transitions (correct before post‑training, incorrect after) to quantify forgetting and 0→1 transitions to quantify backward transfer. Traditional task averages conflate these effects and obscure large changes. For multiple‑choice benchmarks, we add chance‑adjusted variants that subtract the expected contribution of random guessing from pre‑ and post‑training accuracies. We apply this framework across post‑training stages, model sizes, and data scales. Our large‑scale analysis across nearly 30 model pairs and 100 sub-benchmarks with up to 32,768 generated tokens per sample shows that: (1) Domain-continual pretraining induces moderate forgetting with low-to-moderate backward transfer; (2) RL/SFT post-training applied to base models and instruction tuning yields moderate-to-large backward transfer on math and logic with overall low-to-moderate forgetting; (3) Applying RL/SFT to instruction‑tuned models is sensitive on data scale: at small scales, both forgetting and backward transfer are small; at larger scales, effects are mixed and warrant further study with better controls; (4) Model merging does not reliably mitigate forgetting. Overall, our framework offers a practical yardstick for mapping how post‑training alters pretrained knowledge at scale -- enabling progress towards generally capable AI systems.
Plan then Act: Bi-level CAD Command Sequence Generation
Authors: Qiangya Guo; Gang Dai; Zhuoman Liu; Shuangping Huang; Yunqing Hu; Huiyuan Zhang; Tianshui Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Computer-Aided Design (CAD), renowned for its flexibility and precision, serves as the foundation of digital design. Recently, some efforts adopt Large Language Models (LLMs) for generating parametric CAD command sequences from text instructions. However, our study reveals that LLMs pre-trained on large-scale general data are not proficient at directly outputting task-specific CAD sequences. Instead of relying on direct generation, we introduce a Plan then Act process where user instructions are first parsed into a chain-like operational plan via an LLM, which is then used to generate accurate command sequences. Specifically, we propose PTA, a new bi-level CAD command sequence generation method. The PTA consists of two critical stages: high-level plan generation and low-level command generation. During the high-level stage, an LLM-based Planner completes the planning process, parsing user instructions into a high-level operation plan. Following this, at the low-level generation stage, we introduce an Actioner equipped with a requirement-aware mechanism to extract design requirements (e.g., dimensions, geometric relationships) from user instructions.  This extracted information is used to guide the low-level command sequence generation, improving the alignment of the generated sequences with user requirements. Experimental results demonstrate that our PTA outperforms existing methods in both quantitative and qualitative evaluations.  Code is available at https://github.com/QiferG/Plan-then-Act.
All That Glitters Is Not Gold: Key-Secured 3D Secrets within 3D Gaussian Splatting
Authors: Yan Ren; Shilin Lu; Adams Wai-Kin Kong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in 3D Gaussian Splatting (3DGS) have revolutionized scene reconstruction, opening new possibilities for 3D steganography by hiding 3D secrets within 3D covers. The key challenge in steganography is ensuring imperceptibility while maintaining high-fidelity reconstruction. However, existing methods often suffer from detectability risks and utilize only suboptimal 3DGS attributes, limiting their full potential. We propose a novel end-to-end key-secured 3D steganography framework (KeySS) that jointly optimizes a 3DGS model and a key-secured decoder for secret reconstruction. Our approach reveals that Gaussian attributes contribute unequally to secret hiding. The framework incorporates a key-controllable mechanism enabling multi-secret hiding and unauthorized access prevention, while systematically exploring optimal attribute update to balance fidelity and security. To rigorously evaluate steganographic imperceptibility beyond conventional 2D metrics, we introduce 3D-Sinkhorn distance analysis, which quantifies distributional differences between original and steganographic Gaussian parameters in the representation space. Extensive experiments show that our method achieves state-of-the-art performance in 3D reconstruction while ensuring high levels of steganographic security. The framework is highly efficient and readily extensible to multi-GPU training. Our code will be publicly available.