GenAI4ELab/papercli-papers huggingface dataset:
Boosting Open Set Recognition Performance through Modulated Representation Learning
Authors: Amit Kumar Kundu; Vaishnavi S Patil; Joseph JaJa
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The open set recognition (OSR) problem aims to identify test samples from novel semantic classes that are not part of the training classes, a task that is crucial in many practical scenarios. However, the existing OSR methods use a constant scaling factor (the temperature) to the logits before applying a loss function, which hinders the model from exploring both ends of the spectrum in representation learning -- from instance-level to class-specific features. In this paper, we address this problem by enabling temperature-modulated representation learning using a set of proposed temperature schedules, including our novel negative cosine schedule. Our temperature schedules allow the model to form a coarse decision boundary at the beginning of training by focusing on fewer neighbors, and gradually prioritizing more neighbors to smooth out the rough edges. This gradual task switching leads to a richer and more generalizable representation space. While other OSR methods benefit by including regularization or auxiliary negative samples, such as with mix-up, thereby adding a significant computational overhead, our schedules can be folded into any existing OSR loss function with no overhead. We implement the novel schedule on top of a number of baselines, using cross-entropy, contrastive and the ARPL loss functions and find that it boosts both the OSR and the closed set performance in most cases, especially on the tougher semantic shift benchmarks.
ToolWeaver: Weaving Collaborative Semantics for Scalable Tool Use in Large Language Models
Authors: Bowen Fang; Wen Ye; Yunyue Su; Jinghao Zhang; Qiang Liu; Yesheng Liu; Xin Sun; Shu Wu; Jiabing Yang; Baole Wei; Liang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Prevalent retrieval-based tool-use pipelines struggle with a dual semantic challenge: their retrievers often employ encoders that fail to capture complex semantics, while the Large Language Model (LLM) itself lacks intrinsic tool knowledge from its natural language pretraining. Generative methods offer a powerful alternative by unifying selection and execution, tasking the LLM to directly learn and generate tool identifiers. However, the common practice of mapping each tool to a unique new token introduces substantial limitations: it creates a scalability and generalization crisis, as the vocabulary size explodes and each tool is assigned a semantically isolated token. This approach also creates a semantic bottleneck that hinders the learning of collaborative tool relationships, as the model must infer them from sparse co-occurrences of monolithic tool IDs within a vast library. To address these limitations, we propose ToolWeaver, a novel generative tool learning framework that encodes tools into hierarchical sequences. This approach makes vocabulary expansion logarithmic to the number of tools. Crucially, it enables the model to learn collaborative patterns from the dense co-occurrence of shared codes, rather than the sparse co-occurrence of monolithic tool IDs. We generate these structured codes through a novel tokenization process designed to weave together a tool's intrinsic semantics with its extrinsic co-usage patterns. These structured codes are then integrated into the LLM through a generative alignment stage, where the model is fine-tuned to produce the hierarchical code sequences. Evaluation results with nearly 47,000 tools show that ToolWeaver significantly outperforms state-of-the-art methods, establishing a more scalable, generalizable, and semantically-aware foundation for advanced tool-augmented agents.
Fed-Duet: Dual Expert-Orchestrated Framework for Continual Federated Vision-Language Learning
Authors: Tao GUO; Junwei Chen; Laizhong Cui
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Pretrained vision-language models (VLMs), such as CLIP, have shown promise in federated learning (FL) by bringing strong multimodal representations to edge devices. However, continual adaptation remains a core challenge in practical federated settings, where task distributions evolve over time and data remain non-IID across clients. In this emerging area, recent works adopt parameter-efficient fine-tuning (PEFT) as a lightweight way to reduce communication overhead, yet they fail to preserve satisfactory performance under continual learning conditions. Meanwhile, traditional federated continual learning (FCL) methods lack the capacity to maintain cross-modal alignment crucial to VLM performance. We introduce Fed-Duet, a novel Dual Expert-orchestrated framework for efficient federated continual learning in vision-language models. Fed-Duet features a dual-expert adaptation mechanism, combining server-coordinated semantic prompts with client-personalized modular adapters. These pathways are dynamically fused via a cross-attention mechanism, enabling effective knowledge transfer while preserving multimodal alignment and mitigating forgetting. We evaluate Fed-Duet across multiple challenging continual learning tasks in federated vision-language settings and demonstrate that it achieves superior performance and stability compared to existing approaches. Our work highlights the importance of coordinated expert composition in enabling scalable and robust multimodal continual learning. The code is available at https://github.com/cocogt96/Fed-Duet.
LEGACY: A Lightweight Dynamic Gradient Compression Strategy for Distributed Deep Learning
Authors: Mostapha Essoullami; El houcine Bergou; Aritra Dutta
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Distributed learning has achieved remarkable success in training deep neural networks (DNNs) on large datasets, but the communication bottleneck limits its scalability. Various compression techniques have been proposed to alleviate this limitation; however, they either use fixed parameters throughout training or rely on complex and computationally intensive methods to adapt compression parameters. Instead of the hard-to-tune hyperparameters required by adaptive compressors, this paper investigates the impact of two fundamental factors in DNN training—the layer size of the networks and their training phases—to design a simple yet efficient dynamic scheduler for any compressor, guiding the selection of compression parameters. We present a **L**ightweight **E**fficient **G**r**A**dient **C**ompression strategy**Y** or LEGACY, which, in theory, can work with any compression technique to produce a simple dynamic counterpart. We benchmark LEGACY on distributed and federated training, involving seven different DNN architectures, ranging from ResNet, Transformer-XL, to GPT-2, across large and challenging datasets, including ImageNet, WikiText-103, and OpenWebText. On ImageNet-1K, with an equivalent average data volume, LEGACY's dynamic compression strategies improve the Top-1 accuracy of ResNet-50 by 7-11% compared to uniform Top-0.1% compression, while on WikiText-103, the layer-based dynamic strategy reduces the perplexity of Transformer-XL by ~26% relative to the same baseline. In addition, we evaluate LEGACY under constrained and federated settings, and demonstrate that it scales effectively to a 100-worker configuration while maintaining strong accuracy under aggressive compression. We publish anonymized code at: https://github.com/LEGACY-compression/LEGACY.
Negotiated Reasoning: On Provably Addressing Relative Over-Generalization
Authors: Junjie Sheng; Yantian Wang; Bo Jin; Hongyuan Zha; Jun Wang; Wenhao Li; Xiangfeng Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We focus on the relative over-generalization (RO) issue in fully cooperative multi-agent reinforcement learning (MARL). Existing methods show that endowing agents with reasoning can help mitigate RO empirically, but there is little theoretical insight. We first prove that RO is avoided when agents satisfy a consistent reasoning requirement. We then propose a new negotiated reasoning framework connecting reasoning and RO with theoretical guarantees. Based on it, we develop an algorithm called Stein variational negotiated reasoning (SVNR), which uses Stein variational gradient descent to form a negotiation policy that provably bypasses RO under maximum-entropy policy iteration. SVNR is further parameterized with neural networks for computational efficiency. Experiments demonstrate that SVNR significantly outperforms baselines on RO-challenged tasks, including Multi-Agent Particle World and MaMuJoCo, confirming its advantage in achieving better cooperation.
MASAM: Multimodal Adaptive Sharpness-Aware Minimization for Heterogeneous Data Fusion
Authors: Zijie Chen; Kejing Yin; Wenfang Yao; William K. Cheung; Jing Qin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal learning requires integrating heterogeneous modalities, such as structured records, visual imagery, and temporal signals. It has been revealed that this heterogeneity causes modality encoders to converge at different rates, making the multimodal learning imbalanced. We empirically observe that such an imbalance is related to the sharpness of the solution. Modality encoders that converge faster could be dragged into sharp regions due to inter-modal interference, degrading the generalization capability of unimodal features learned. Sharpness-Aware Minimization is effective in improving generalization via finding solutions in flat regions. However, its application in multimodal scenarios is challenging: 1) SAM overemphasizes the dominant modality, inducing misaligned perturbations in weaker modalities, and 2) the perturbation gradient calculation is affected by interference from other modalities. To address these issues, we propose Multimodal Adaptive Sharpness-Aware Minimization (MASAM), which optimizes different modalities based on their dominance. We design an Adaptive Perturbation Score (APS) using convergence speed and gradient alignment to identify dominant modalities for SAM application. Our Modality-Decoupled Perturbation Scaling (MDPS) then reduces inter-modal interference during optimization, better aligning each modality with shared information. Extensive empirical evaluations on five multimodal datasets and six downstream tasks demonstrate that MASAM consistently attains flatter solutions, achieves balanced multimodal learning, and subsequently surpasses state-of-the-art methods across diverse datasets and tasks. Code is available at https://github.com/Orange2107/MASAM-Multimodal-Adaptive-SAM.
Efficient Best-of-Both-Worlds Algorithms for Contextual Combinatorial Semi-Bandits
Authors: Mengmeng Li; Philipp J. Schneider; Jelisaveta Aleksic; Daniel Kuhn
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce the first best-of-both-worlds algorithm for contextual combinatorial semi-bandits that simultaneously guarantees $\widetilde{\mathcal{O}}(\sqrt{T})$ regret in the adversarial regime and $\widetilde{\mathcal{O}}(\ln T)$ regret in the corrupted stochastic regime. Our approach builds on the Follow-the-Regularized-Leader (FTRL) framework equipped with a Shannon entropy regularizer, yielding a flexible method that admits efficient implementations.
Beyond regret bounds, we tackle the practical bottleneck in FTRL (or, equivalently, Online Stochastic Mirror Descent) arising from the high-dimensional projection step encountered in each round of interaction. By leveraging the Karush-Kuhn-Tucker conditions, we transform the $K$-dimensional convex projection problem into a single-variable root-finding problem, dramatically accelerating each round. Empirical evaluations demonstrate that this combined strategy not only attains the attractive regret bounds of best-of-both-worlds algorithms but also delivers substantial per-round speed-ups, making it well-suited for large-scale, real-time applications.
AlignFlow: Improving Flow-based Generative Models with Semi-Discrete Optimal Transport
Authors: Lingkai Kong; Molei Tao; Yang Liu; Bryan Wang; Jinmiao Fu; Chien-Chih Wang; Huidong Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Flow-based Generative Models (FGMs) effectively transform noise into complex data distributions. Incorporating Optimal Transport (OT) to couple noise and data during FGM training has been shown to improve the straightness of flow trajectories, enabling more effective inference. However, existing OT-based methods estimate the OT plan using (mini-)batches of sampled noise and data points, which limits their scalability to large and high-dimensional datasets in FGMs. This paper introduces AlignFlow, a novel approach that leverages Semi-Discrete Optimal Transport (SDOT) to enhance the training of FGMs by establishing an explicit, optimal alignment between noise distribution and data points with guaranteed convergence. SDOT computes a transport map by partitioning the noise space into Laguerre cells, each mapped to a corresponding data point. During FGM training, i.i.d. noise samples are paired with data points via the SDOT map. AlignFlow scales well to large datasets and model architectures with negligible computational overhead. Experimental results show that AlignFlow improves the performance of a wide range of state-of-the-art FGM algorithms and can be integrated as a plug-and-play component. Code is available at: https://github.com/konglk1203/AlignFlow.
Relative Value Learning
Authors: Marc Höftmann; Jan Robine; Stefan Harmeling
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In reinforcement learning (RL), critics traditionally learn absolute state values, estimating how good a particular situation is in isolation. Adding any constant to $V(s)$ leaves action preferences unchanged. Thus only value differences are relevant for decision making.
Motivated by this fact, we ask the question whether these differences can be learned directly. For this, we propose \emph{Relative Value Learning} (RV), a framework that considers antisymmetric value differences $\Delta(s_i, s_j) = V(s_i) - V(s_j)$. We define a new pairwise Bellman operator and prove it is a $\gamma$-contraction with a unique fixed point equal to the true value differences, derive well-posed $1$-step/$n$-step/$\lambda$-return targets and reconstruct generalized advantage estimation from pairwise differences to obtain an unbiased policy-gradient estimator (R-GAE). Besides rigorous theoretical contributions, we integrate RV with PPO and achieve competitive performance on the Atari benchmark (49 games, ALE) compared to standard PPO, indicating that relative value estimation is an effective alternative to absolute critics.
Learning Pseudorandom Numbers with Transformers: Permuted Congruential Generators, Curricula, and Interpretability
Authors: Tao Tao; Maissam Barkeshli
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study the ability of Transformer models to learn sequences generated by Permuted Congruential Generators (PCGs), a widely used family of pseudo-random number generators (PRNGs). PCGs introduce substantial additional difficulty over linear congruential generators (LCGs) by applying a series of bit-wise shifts, XORs, rotations and truncations to the hidden state. We show that Transformers can nevertheless successfully perform in-context prediction on unseen sequences from diverse PCG variants, in tasks that are beyond published classical attacks.  In our experiments we scale moduli up to $2^{22}$ using up to $50$ million model parameters and datasets with up to $5$ billion tokens. Surprisingly, we find even when the output is truncated to a single bit, it can be reliably predicted by the model. 
When multiple distinct PRNGs are presented together during training, the model can jointly learn them, identifying structures from different permutations.
We demonstrate a scaling law with modulus $m$: the number of in-context sequence elements required for near-perfect prediction grows as $\sqrt{m}$. 
For larger moduli, optimization enters extended stagnation phases; in our experiments, learning moduli $m \geq 2^{20}$ requires incorporating training data from smaller moduli, demonstrating a critical necessity for curriculum learning. 
Finally, we analyze embedding layers and uncover a novel clustering phenomenon: the top principal components spontaneously group the integers into bitwise rotationally-invariant clusters, revealing how representations can transfer from smaller to larger moduli.
SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention
Authors: Jiahao Li; Jiayi Dong; Peng Ye; Xiaochi Zhou; Haohai Lu; Fei Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling single-cell gene expression across diverse biological and technical conditions is crucial for characterizing cellular states and simulating unseen scenarios. Existing methods often treat genes as independent tokens, overlooking their high-level biological relationships and leading to poor performance. We introduce SAVE, a unified generative framework based on conditional Transformers for multi-condition single-cell modeling. SAVE leverages a coarse-grained representation by grouping semantically related genes into blocks, capturing higher-order dependencies among gene modules. A Flow Matching mechanism and condition-masking strategy further enhance flexible simulation and enable generalization to unseen condition combinations.  We evaluate SAVE on a range of benchmarks, including conditional generation, batch effect correction, and perturbation prediction. SAVE consistently outperforms state-of-the-art methods in generation fidelity and extrapolative generalization, especially in low-resource or combinatorially held-out settings. Overall, SAVE offers a scalable and generalizable solution for modeling complex single-cell data, with broad utility in virtual cell synthesis and biological interpretation.
Control Tax: The Price of Keeping AI in Check
Authors: Mikhail Terekhov; Zhen Ning David Liu; Caglar Gulcehre; Samuel Albanie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The rapid integration of agentic AI into high-stakes real-world applications requires robust oversight mechanisms. The emerging field of AI Control (AIC) aims to provide such an oversight mechanism, but practical adoption depends heavily on implementation overhead. To study this problem better, we introduce the notion of Control tax---the operational and financial cost of integrating control measures into AI pipelines. Our work makes three key contributions to the field of AIC: (1) we introduce a theoretical framework that quantifies the Control Tax and maps classifier performance to safety assurances; (2) we conduct comprehensive evaluations of state-of-the-art language models in adversarial settings, where attacker models insert subtle backdoors into code while monitoring models attempt to detect these vulnerabilities; and (3) we provide empirical financial cost estimates for control protocols and develop optimized monitoring strategies that balance safety and cost-effectiveness while accounting for practical constraints like auditing budgets. Our framework enables practitioners to make informed decisions by systematically connecting safety guarantees with their costs, advancing AIC through principled economic feasibility assessment across different deployment contexts.
A General Spatio-Temporal Backbone with Scalable Contextual Pattern Bank for Urban Continual Forecasting
Authors: Aoyu Liu; Yaying Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the rapid growth of spatio-temporal data fueled by IoT deployments and urban infrastructure expansion, accurate and efficient continual forecasting has become a critical challenge. Most existing Spatio-Temporal Graph Neural Networks rely on static graph structures and offline training, rendering them inadequate for real-world streaming scenarios characterized by graph expansion and distribution shifts. Although Continual Spatio-Temporal Forecasting methods have been proposed to tackle these issues, they often adopt backbones with limited modeling capacity and lack effective mechanisms to balance stability and adaptability. To overcome these limitations, we propose STBP, a novel framework that integrates a general spatio-temporal backbone with a scalable contextual pattern bank. The backbone extracts stable representations in the frequency domain and captures dynamic spatial correlations through lightweight linear graph attention. To support continual adaptation and mitigate catastrophic forgetting, the contextual pattern bank is updated incrementally via parameter expansion, enabling the capture of evolving node-level heterogeneity and relevance. During incremental training, the backbone remains fixed to preserve general knowledge, while the pattern bank adapts to new scenarios and distributions. Extensive experiments demonstrate that STBP outperforms state-of-the-art baselines in both forecasting accuracy and scalability, validating its effectiveness for continual spatio-temporal forecasting. Code is available at https://github.com/Aoyu-Liu/STBP.
Model-based Offline RL via Robust Value-Aware Model Learning with Implicitly Differentiable Adaptive Weighting
Authors: Zhongjian Qiao; Jiafei Lyu; Boxiang Lyu; Yao Shu; Siyang Gao; Shuang Qiu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Model-based offline reinforcement learning (RL) aims to enhance offline RL with a dynamics model that facilitates policy exploration. However, model exploitation could occur due to inevitable model errors, which degrades algorithm performance. Adversarial model learning offers a theoretical framework to mitigate model exploitation by solving a maximin formulation, and RAMBO provides a practical implementation with model gradient. However, we empirically observe that severe Q-value underestimation and gradient explosion can occur in RAMBO with only slight hyperparameter tuning, suggesting that it tends to be overly conservative and suffers from unstable model updates. To address these issues, we propose RObust value-aware Model learning via Implicitly differentiable adaptive weighting (ROMI). Instead of updating the dynamics model with model gradient, ROMI introduces a novel robust value-aware model learning approach. This approach requires the dynamics model to predict future states with values close to the minimum Q-value within a scale-adjustable state uncertainty set, enabling controllable conservatism and stable model updates. To further improve out-of-distribution (OOD) generalization during multi-step rollouts, we propose implicitly differentiable adaptive weighting, a bi-level optimization scheme that adaptively achieves dynamics- and value-aware model learning. Empirical results on D4RL and NeoRL datasets show that ROMI significantly outperforms RAMBO and achieves competitive or superior performance compared to state-of-the-art methods on datasets where RAMBO typically underperforms.