GenAI4ELab/papercli-papers huggingface dataset:
SNAPHARD CONTRAST LEARNING
Authors: Changpu Meng; Jie Yang; Wanqing Li; Yi Guo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In recent years, Contrastive Learning (CL) has garnered significant attention due to its efficacy across various domains, spanning from visual and textual modalities. A fundamental aspect of CL is aligning the representations of anchor instances with relevant positive samples while simultaneously separating them from negative ones. Prior studies have extensively explored diverse strategies for generating and sampling contrastive (i.e., positive/negative) pairs. Despite the empirical success, the theoretical understanding of the CL approach remains under-explored, leaving questions such as the rationale behind contrastive-pair sampling and its contributions to the model performance unclear.
This paper addresses this gap by providing a comprehensive theoretical analysis from the angle of optimality conditions and introducing the SnaPhArd Contrast Learning (SPACL). Specifically, SPACL prioritizes hard positive and hard negative samples during constructing contrastive pairs and computing the contrastive loss, rather than treating all samples equally. Experimental results across two downstream tasks demonstrate that SPACL consistently outperforms or competes favorably with state-of-the-art methods, showcasing its robustness and efficacy. A comprehensive ablation study further examines the effectiveness of SPACL's individual components to verify the theoretic findings.
Test-Time Adaptation for LLM Agents via Environment Interaction
Authors: Arthur Chen; Zuxin Liu; Jianguo Zhang; Akshara Prabhakar; Zhiwei Liu; Shelby Heinecke; Silvio Savarese; Victor Zhong; Caiming Xiong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language model (LLM)-based agents struggle to generalize to novel and complex environments, such as unseen websites or new sets of functions, due to a fundamental mismatch between their pre-training and test-time conditions.
This challenge stems from two distinct failure modes: a syntactic misunderstanding of environment-specific components like observation formats, and a semantic misunderstanding of state-transition dynamics, which are only revealed at test time.
To address these issues, we propose two distinct strategies for adapting LLM agents by leveraging environment-specific information from interaction that is available during deployment.
First, an online syntactic alignment (SA) method parameterizes environmental nuances by learning a lightweight adaptation vector that biases the model's output distribution, enabling rapid alignment with an environment response format.
Second, a deployment-time dynamics grounding (DG) method employs a persona-driven exploration phase to systematically probe and learn the environment's causal dynamics before task execution, equipping the agent with an in-context world model.
We evaluate these strategies across diverse agentic benchmarks, including function calling and web navigation.
Our empirical results show the effectiveness of both strategies across all benchmarks with minimal computational cost.
We find that dynamics grounding is particularly effective in complex environments where unpredictable dynamics pose a major obstacle, demonstrating a robust path toward more generalizable and capable LLM-based agents.
For example, on the WebArena multi-site split, this method increases the agent's success rate from 2\% to 23\%.
We release our code.
RAG4DMC: Retrieval-Augmented Generation for Data-Level Modality Completion
Authors: Ningxin He; Yongheng Deng; Sheng Yue; Yongjian Fu; Zhangzehui; Tiegang Gao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-modal datasets are critical for a wide range of applications, but in practice, they often suffer from missing modalities. This motivates the task of Missing Modality Completion (MMC), which aims to reconstruct missing modalities from the available ones to fully exploit multi-modal data. While pre-trained generative models offer a natural solution, directly applying them to domain-specific MMC is often ineffective, and fine-tuning suffers from limitations like limited complete samples, restricted API access, and high cost. To address these issues, we propose RAG4DMC, a retrieval-augmented generation framework for data-level MMC. RAG4DMC builds a dual knowledge base from complete in-dataset samples and external public datasets, enhanced with feature alignment and clustering-based filtering to mitigate modality and domain shifts. A multi-modal fusion retrieval mechanism combining intra-modal retrieval with cross-modal fusion then provides relevant context to guide generation, followed by a candidate selection mechanism for coherent completion. Extensive experiments on general and domain-specific datasets demonstrate that our method produces more accurate and semantically coherent missing-modality completions, resulting in substantial improvements in downstream image–text retrieval and image captioning tasks.
SkillFactory: Self-Distillation for Learning Cognitive Behaviors
Authors: Zayne Rea Sprague; Jack Lu; Manya Wadhwa; Sedrick Keh; Mengye Ren; Greg Durrett
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reasoning models leveraging long chains of thought employ various cognitive skills, such as verification of their answers, backtracking, retrying by an alternate method, and more. Previous work has shown that when a base language model exhibits these skills, training that model further with reinforcement learning (RL) can learn to leverage them. How can we get models to leverage skills that aren't exhibited by base models? Our work, SkillFactory, is a method for fine-tuning models to roughly learn these skills during a supervised fine-tuning (SFT) stage prior to RL. Our approach does not rely on distillation from a stronger model, but instead uses samples from the model itself, rearranged to provide training data in the format of those skills. These "silver" SFT traces may be imperfect, but are nevertheless effective for priming a model to acquire skills during RL. Our evaluation shows that (1) starting from SkillFactory SFT initialization helps a model to generalize to harder variants of a task post-RL, despite lower performance pre-RL; (2) cognitive skills are indeed used by the model; (3) RLed SkillFactory models are more robust to regression on out-of-domain tasks than RLed base models. Our work suggests that inductive biases learned prior to RL help models learn robust cognitive skill use.
CAR-LoRA: Training Compression-Aware and Robust LoRA Adapters for Evolving LLMs
Authors: Rana Shahroz; Zhen Tan; Ruichen Zhang; Hua Wei; Tianlong Chen; Charles Fleming
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The deployment of large language models (LLMs) for specialized tasks on resource-constrained edge devices like smartphones and sensors presents a significant scalability problem. To run on such hardware, these massive models must be compressed using techniques like \emph{quantization or pruning} to reduce their memory and computational footprint. Concurrently, foundational LLMs are periodically updated by their developers with new data, making their $\textit{internal parameters shift over time}$. While parameter-efficient methods like Low-Rank Adaptation (LoRA) streamline personalization by fine-tuning only a small fraction of parameters, the resulting adapters are $\textbf{brittle}$; a LoRA trained for one specific compression scheme is incompatible with another, and an adapter trained on an older base model performs poorly on an updated one. This forces a costly cycle of retraining for each unique device and every new model release. To address this, we introduce a novel framework that creates a single, universally portable adapter that is both $\textbf{\textit{(i)} compression-aware and \textit{(ii)} temporally robust}$. We achieve this by augmenting the training process with a variety of simulated compression techniques during a single run, utilizing a quantized forward pass to build resilience while maintaining a full-precision backward pass for stable gradient optimization. $\textit{This method yields a unified adapter robust to diverse compression artifacts and the subtle parameter shifts from model evolution}$. Extensive experiments on models such as $\texttt{Llama-2, Llama-3.1, Gemma-2}$, and $\texttt{Mistral}$ across reasoning benchmarks like $\textit{SQA, MATH, and GSM8K}$ demonstrate that our single adapter achieves performance comparable to specialized adapters ($\textit{e.g.}$, QLoRA) that are individually retrained for each compression scheme. Furthermore, we show this single adapter maintains its high performance when applied to future, evolved versions of the base model, eliminating the need for periodic retraining. Our work pioneers an efficient paradigm for edge AI, creating portable model patches that bridge the gap between cloud-based personalization, the diverse hardware ecosystem, and the lifecycle of evolving LLMs.
The Lattice Representation Hypothesis of Large Language Models
Authors: Bo Xiong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical operations in embedding geometry.  Our framework unifies the Linear Representation Hypothesis with Formal Concept Analysis (FCA), showing that linear attribute directions with separating thresholds induce a concept lattice via half-space intersections. This geometry enables symbolic reasoning through geometric meet (intersection) and join (union) operations, and admits a canonical form when attribute directions are linearly independent. Experiments on WordNet sub-hierarchies provide empirical evidence that LLM embeddings encode concept lattices and their logical structure, revealing a principled bridge between continuous geometry and symbolic abstraction.
Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs
Authors: Zhongyang Li; Ziyue Li; Tianyi Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sparse Mixture-of-Experts (MoE) have been widely adopted in recent large language models since it can efficiently scale up the model capability without increasing the inference cost. However, evaluations on broad downstream tasks reveal a consistent suboptimality of the routers in existing MoE LLMs, which results in a severe performance gap (e.g., 10-20% in accuracy) to the optimal routing. In this paper, we show that aligning the manifold of routing weights with that of task embedding via post-training can effectively reduce the gap and improve MoE LLMs’ generalization performance. Our method, “Routing Manifold Alignment (RoMA)”, introduces an additional manifold regularization term in the post-training objective and only requires lightweight finetuning of routers (with other parameters frozen). Specifically, the regularization encourages the routing weights of each sample to be close to those of its successful neighbors (whose routing weights lead to correct answers) in a task embedding space. Consequently, samples targeting similar tasks will share similar expert choices across layers. Building such bindings between tasks and experts over different samples is essential to achieve better generalization. Moreover, RoMA demonstrates the advantage of unifying the task understanding (by embedding models) with solution generation (by MoE LLMs). In experiments, we finetune routers in two recent MoE LLMs using RoMA. Evaluations on diverse benchmarks and extensive comparisons with baselines show the substantial improvement brought by RoMA.
Distilling and Adapting:  A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks
Authors: Alana Deng; Sugitha Janarthanan; Yan Sun; Zihao Jing; Pingzhao Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information.  To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-aware graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher–student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate that our framework outperforms state-of-the-art methods in interaction prediction for MBNs, providing a powerful tool for exploring various biological interactions and advancing personalized therapeutics.
Is Finer Better? The Limits of Microscaling Formats in Large Language Models
Authors: Andrea Fasoli; Monodeep Kar; Chi-Chun Liu; Swagath Venkataramani; Viji Srinivasan; Leland Chang; Naigang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Microscaling data formats leverage per-block tensor quantization to enable aggressive model compression with limited loss in accuracy. Unlocking their potential for efficient training and inference necessitates hardware-friendly implementations that handle matrix multiplications in a native format and adopt efficient error-mitigation strategies. Herein, we reported the emergence of a surprising behavior associated with microscaling quantization, whereas the output of a quantized model degrades as block size is decreased below a given threshold. This behavior clashes with the expectation that a smaller block size should allow for a better representation of the tensor elements. We investigate this phenomenon both experimentally and theoretically, decoupling the sources of quantization error behind it. Experimentally, we analyze the distributions of several Large Language Models and identify the conditions driving the anomalous behavior. Theoretically, we lay down a framework showing remarkable agreement with experimental data from pretrained model distributions and ideal ones. Overall, we show that the anomaly is driven by the interplay between narrow tensor distributions and the limited dynamic range of the quantized scales. Based on these insights, we propose the use of FP8 unsigned E5M3 as a novel hardware-friendly format for the scales in FP4 microscaling data types. We demonstrate that UE5M3 achieves comparable performance to the conventional FP8 unsigned E4M3 scales while obviating the need of global scaling operations on weights and activations.
Fair Decision Utility in Human-AI Collaboration: Interpretable Confidence Adjustment for Humans with Cognitive Disparities
Authors: Jiashi Gao; Kexin Liu; Xinwei Guo; Junlei Zhou; Jiaxin Zhang; Xiangyu Zhao; Guanhua Chen; Xin Yao; Xuetao Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In AI-assisted decision-making, human decision-makers finalize decisions by taking into account both their human confidence and AI confidence regarding specific outcomes.  In practice, they often exhibit heterogeneous cognitive capacities, causing their confidence to deviate, sometimes significantly, from the actual label likelihood. We theoretically demonstrate that existing AI confidence adjustment objectives, such as *calibration* and *human-alignment*, are insufficient to ensure fair utility across groups of decision-makers with varying cognitive capacities. Such unfairness may raise concerns about social welfare and may erode human trust in AI systems.
To address this issue, we introduce a new concept in AI confidence adjustment: *inter-group-alignment*. By theoretically bounding the utility disparity between human decision-maker groups as a function of  *human-alignment* level and *inter-group-alignment* level, we establish an interpretable fairness-aware objective for AI confidence adjustment. Our analysis suggests that achieving utility fairness in AI-assisted decision-making requires both *human-alignment* and *inter-group-alignment*. Building on these objectives, we propose a multicalibration-based AI confidence adjustment approach tailored to scenarios involving human decision-makers with heterogeneous cognitive capacities. We further provide theoretical justification showing that our method constitutes a sufficient condition for achieving both *human-alignment* and *inter-group-alignment*.
We validate our theoretical findings through extensive experiments on four real-world tasks. The results demonstrate that AI confidence adjusted toward both *human-alignment* and *inter-group-alignment* significantly improves utility fairness across human decision-maker groups, without sacrificing overall utility.
*The implementation code is available at* https://github.com/WEILaboratory/AI-Ethics-Safety-PaperCode/tree/main/Fair_HAI (ICLR2026).
Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization
Authors: Chengli Tan; Yubo Zhou; Haishan Ye; Guang Dai; Junmin Liu; Zengjie Song; Jiangshe Zhang; Zixiang Zhao; Yunda Hao; Yong Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving. However, many studies suggest that they are prone to being poorly calibrated and have a propensity for overconfidence, which may have disastrous consequences. In this paper, unlike standard training such as stochastic gradient descent, we show that the recently proposed sharpness-aware minimization (SAM) counteracts this tendency towards overconfidence. The theoretical analysis suggests that SAM allows us to learn models that are already well-calibrated by implicitly maximizing the entropy of the predictive distribution. Inspired by this finding, we further propose a variant of SAM, coined as CSAM, to ameliorate model calibration. Extensive experiments on various datasets, including ImageNet-1K, demonstrate the benefits of SAM in reducing calibration error. Meanwhile, CSAM performs even better than SAM and consistently achieves lower calibration error than other approaches.
Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs
Authors: Song Bian; Tao Yu; Shivaram Venkataraman; Youngsuk Park
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM) performance. Yet, as these models grow increasingly powerful and widely deployed, the cost of inference has become a pressing concern. Despite its importance, the trade-off between model accuracy and inference efficiency remains underexplored. In this work, we examine how key architectural factors, hidden size, the allocation of parameters between MLP and attention (mlp-to-attention ratio), and grouped-query attention (GQA), influence both inference cost and accuracy. We introduce a conditional scaling law that augments the Chinchilla framework with architectural information, along with a search framework for identifying architectures that are simultaneously inference-efficient and accurate. To validate our approach, we train more than 200 models spanning 80M to 3B parameters and 8B to 100B training tokens, and fit the proposed conditional scaling law. Our results show that the conditional scaling law reliably predicts optimal architectural choices and that the resulting models outperform existing open-source baselines. Under the same training budget, optimized architectures achieve up to 2.1\% higher accuracy and 42\% greater inference throughput compared to LLaMA-3.2.
Pose-RFT: Aligning MLLMs for 3D Pose Generation via Hybrid Action Reinforcement Fine-Tuning
Authors: Bao Li; Xiaomei Zhang; Miao Xu; Zhaoxin Fan; Xiangyu Zhu; Zhen Lei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generating 3D human poses from multimodal inputs such as text or images requires models to capture both rich semantic and spatial correspondences. While pose-specific multimodal large language models (MLLMs) have shown promise, their supervised fine-tuning (SFT) paradigm struggles to resolve the task's inherent ambiguity. Its reliance on objectives like SMPL parameter regression creates a critical alignment gap, compromising the model's ability to achieve the required semantic and spatial fidelity. To close the gap, we propose Pose-RFT, a framework that shifts the learning paradigm from supervised imitation to reward-driven reinforcement fine-tuning (RFT). We address the core technical challenge of this task: a 
hybrid action space requiring joint optimization of discrete language and continuous pose outputs. To this end, we introduce HyGRPO, a hybrid reinforcement learning algorithm that enables stable optimization by performing group-wise reward normalization over sampled responses. Pose-RFT incorporates task-specific reward functions to guide optimization towards spatial alignment in image-to-pose generation and semantic consistency in text-to-pose generation.
Extensive experiments on multiple pose generation benchmarks demonstrate that Pose-RFT significantly improves performance over existing pose-specific MLLMs, validating the effectiveness of our approach in closing the alignment gap for 3D pose generation.
Multi-View Encoders for Performance Prediction in LLM-Based Agentic Workflows
Authors: Patara Trirat; Wonyong Jeong; Sung Ju Hwang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but optimizing LLM-based agentic systems remains challenging due to the vast search space of agent configurations, prompting strategies, and communication patterns. Existing approaches often rely on heuristic-based tuning or exhaustive evaluation, which can be computationally expensive and suboptimal. This paper proposes **Agentic Predictor**, a lightweight predictor for efficient agentic workflow evaluation. Agentic Predictor is equipped with a *multi-view workflow encoding* technique that leverages multi-view representation learning of agentic systems by incorporating code architecture, textual prompts, and interaction graph features. To achieve high predictive accuracy while significantly reducing the number of required workflow evaluations for training a predictor, Agentic Predictor employs *cross-domain unsupervised pretraining*. By learning to approximate task success rates, Agentic Predictor enables fast and accurate selection of optimal agentic workflow configurations for a given task, significantly reducing the need for expensive trial-and-error evaluations. Experiments on a carefully curated benchmark spanning three domains show that our predictor outperforms several strong graph-based baselines in both predictive accuracy and workflow utility, highlighting the potential of performance predictors in streamlining the design of LLM-based agentic workflows.