GenAI4ELab/papercli-papers huggingface dataset:
Decomposed Attention Fusion in MLLMs for Training-free Video Reasoning Segmentation
Authors: Su Ho Han; Jeongseok Hyun; Pilhyeon Lee; Minho Shim; Dongyoon Wee; Seon Joo Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal large language models (MLLMs) demonstrate strong video understanding by attending to visual tokens relevant to textual queries. To directly adapt this for localization in a training-free manner, we cast video reasoning segmentation as a video QA task and extract attention maps via rollout mechanism. However, raw attention maps are noisy and poorly aligned with object regions. We propose Decomposed Attention Fusion (DecAF), which refines these maps through two mechanisms: (1) contrastive object-background fusion and (2) complementary video-frame fusion. This method suppresses irrelevant activations and enhances object-focused cues, enabling direct conversion of attention maps into coarse segmentation masks. In addition, we introduce attention-guided SAM2 prompting for obtaining fine-grained masks. Unlike existing methods that jointly train MLLMs with SAM, our method operates entirely without retraining. DecAF outperforms training-free methods and achieves performance comparable to training-based methods on both referring and reasoning VOS benchmarks.
UniLiP: Adapting CLIP for Unified Multimodal Understanding, Generation and Editing
Authors: Hao Tang; Chen-Wei Xie; Xiaoyi Bao; Tingyu Weng; Pandeng Li; Yun Zheng; Liwei Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we propose UniLIP, a unified framework that adapts CLIP for multimodal understanding, generation and editing. Although CLIP excels at understanding, it lacks reconstruction abilities required to be a unified visual encoder. However, previous CLIP-based unified methods fail to balance understanding and reconstruction, leading to semantic degradation or inconsistent reconstructions. In contrast, we introduce a novel two-stage training scheme with a self-distillation strategy that progressively endows CLIP with high-fidelity reconstruction abilities while preserving its original comprehension performance. For enhanced reasoning and consistency in generation and editing, we further develop a dual-condition architecture built upon the MetaQuery framework. Our architecture jointly utilizes multimodal hidden states for rich contextual details and learnable query embeddings to harness the powerful reasoning abilities of Multimodal Large Language Models (MLLMs). Leveraging advanced image representation and architectural design, UniLIP demonstrates superior instruction following and edit fidelity. With only 1B and 3B parameters, UniLIP can outperform larger unified models such as BAGEL (7B) and Uniworld-V1 (12B), achieving state-of-the-art performance of **0.90** on GenEval, **0.63** on WISE, and **3.94** on ImgEdit. These results demonstrate that UniLIP successfully expands the application of CLIP, establishing its continuous features to not only serve as the optimal choice for understanding tasks but also achieve highly competitive performance in generation and editing tasks. Code and models are available at https://github.com/nnnth/UniLIP.
Bridging Draft Policy Misalignment: Group Tree Optimization  for Speculative Decoding
Authors: Shijing Hu; Jingyang Li; Zhihui Lu; Pan Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Speculative decoding accelerates large language model (LLM) inference by letting a lightweight draft model propose multiple tokens that the target model verifies in parallel. Yet existing training objectives optimize only a single greedy draft path, while decoding follows a tree policy that re-ranks and verifies multiple branches. This draft policy misalignment limits achievable speedups.  	
We introduce **Group Tree Optimization** (GTO), which aligns training with the decoding-time tree policy through two components: (i) Draft Tree Reward, a sampling-free objective equal to the expected acceptance length of the draft tree under the target model, directly measuring  decoding performance; (ii) Group-based Draft Policy Training, a stable optimization scheme that contrasts trees from the current and a frozen reference draft model, forming debiased group-standardized advantages and applying a PPO-style surrogate along the longest accepted sequence for robust updates. We further prove that increasing our Draft Tree Reward provably improves acceptance length and speedup.  	
Across dialogue (MT-Bench), code (HumanEval), and math (GSM8K), and multiple LLMs (e.g., LLaMA-3.1-8B, LLaMA-3.3-70B, Vicuna-1.3-13B, DeepSeek-R1-Distill-LLaMA-8B, Qwen3-8B), GTO increases acceptance length by \(7.4\%\) and yields an additional \(7.7\%\) speedup over prior state-of-the-art EAGLE-3. By bridging draft policy misalignment, GTO offers a practical, general solution for efficient LLM inference. Code and draft models are available at https://github.com/hsj576/GTO.
PixNerd: Pixel Neural Field Diffusion
Authors: Shuai Wang; Ziteng Gao; Chenhui Zhu; Weilin Huang; Limin Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The current success of diffusion transformers are built on the compressed latent space shaped by the pre-trained variational autoencoder(VAE). However, this two-stage training paradigm inevitably introduces accumulated errors and decoding artifacts. To avoid these problems, researchers return to pixel space modeling but at the cost of complicated cascade pipelines and increased token complexity.
Motivated by the simple yet effective diffusion transformer architectures on the latent space, we propose to model pixel space diffusion using a large-patch diffusion transformer and employ neural fields to decode these large patches, leading to a single-stage streamlined end-to-end solution, which we coin as pixel neural field diffusion transformer (**PixNerd**). Thanks to the efficient neural field representation in PixNerd, we achieve **1.93 FID** on ImageNet 256x256 and nearly **8x lower latency** without any complex cascade pipeline or VAE. We also extend our PixNerd framework to text-to-image applications. Our PixNerd-XXL/16 achieves a competitive 0.73 overall score on the GenEval benchmark and 80.9 overall score on the DPG benchmark.
Optimal Transport-Induced Samples against Out-of-Distribution Overconfidence
Authors: Keke Tang; Ziyong Du; Xiaofei Wang; Weilong Peng; Peican Zhu; Zhihong Tian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep neural networks (DNNs) often produce overconfident predictions on out-of-distribution (OOD) inputs, undermining their reliability in open-world environments. Singularities in semi-discrete optimal transport (OT) mark regions of semantic ambiguity, where classifiers are particularly prone to unwarranted high-confidence predictions. Motivated by this observation, we propose a principled framework to mitigate OOD overconfidence by leveraging the geometry of OT-induced singular boundaries. Specifically, we formulate an OT problem between a continuous base distribution and the latent embeddings of training data, and identify the resulting singular boundaries. By sampling near these boundaries, we construct a class of OOD inputs, termed optimal transport-induced OOD samples (OTIS), which are geometrically grounded and inherently semantically ambiguous. During training, a confidence suppression loss is applied to OTIS to guide the model toward more calibrated predictions in structurally uncertain regions. Extensive experiments show that our method significantly alleviates OOD overconfidence and outperforms state-of-the-art methods.
An Open-Ended Benchmark and Formal Framework for Adjuvant Research with MLLM
Authors: yi chen; Yu Zhang; Jian Xu; Hua Yue; Xinming Wang; Zequan Lyu; Xu-Yao Zhang; Wei Wei; Cheng-Lin Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Adjuvants play a critical role in modulating immune responses and are central to the development of vaccines and immunotherapies. Yet progress in this field is constrained by data scarcity and incomplete understanding of mechanisms of action, which limit the transition from experience-based design to AI-driven approaches. To address these challenges, we present the first benchmark dedicated to adjuvants, constructed in an open-ended Q\&A format and annotated by domain experts. The benchmark comprises 1,294 Q\&A pairs and 1,364 formal descriptions, providing a resource for evaluating general-purpose multimodal large language models (MLLMs) and for developing domain-specific systems.  
We systematically assess 11 closed-source and 18 open-source MLLMs across dimensions including domain-specific Q\&A, hallucination rejection, data generation, and instruction following. Results indicate that OpenAI-o1 (STS = 0.7495, LLM Score = 7.7) and DeepSeek-R1 (STS = 0.7415, LLM Score = 7.7) achieved the strongest performance among closed- and open-source models, respectively. In addition, we introduce a formal description framework for representing adjuvant design principles and immune mechanisms as structured abstractions, which can serve as building blocks for future domain-specialized MLLMs. Overall, this work provides a first step toward systematically integrating MLLMs into adjuvant research by offering a dedicated benchmark, comparative evaluation of existing models, and a formal foundation for future development. Data and code will be released at https://github.com/banjiuyufen/Adjuvant-Benchmark.
Mechanistic Detection and Mitigation of Hallucination in Large Reasoning Models
Authors: Zhongxiang Sun; Qipeng Wang; Haoyu Wang; Xiao Zhang; Jun Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Reasoning Models (LRMs) have shown impressive capabilities in multi-step reasoning tasks. However, alongside these successes, a more deceptive form of model error has emerged—**Reasoning Hallucination**—where logically coherent but factually incorrect reasoning traces lead to persuasive yet faulty conclusions. Unlike traditional hallucinations, these errors are embedded within structured reasoning, making them more difficult to detect and potentially more harmful. In this work, we investigate reasoning hallucinations from a mechanistic perspective. We propose the **Reasoning Score**, which quantifies the depth of reasoning by measuring the divergence between logits obtained from projecting late layers of LRMs to the vocabulary space, effectively distinguishing shallow pattern-matching from genuine deep reasoning. Using this score, we conduct an in-depth analysis on the ReTruthQA dataset and identify two key reasoning hallucination patterns: early-stage fluctuation in reasoning depth and incorrect backtracking to flawed prior steps. These insights motivate our **R**easoning **H**allucination **D**etection (**RHD**) framework, which achieves state-of-the-art performance across multiple domains. To mitigate reasoning hallucinations, we further introduce **GRPO-R**, an enhanced reinforcement learning algorithm that incorporates step-level deep reasoning rewards via potential-based shaping. Our theoretical analysis establishes stronger generalization guarantees, and experiments demonstrate improved reasoning quality and reduced hallucination rates.
Taming Momentum: Rethinking Optimizer States Through Low-Rank Approximation
Authors: Zhengbo Wang; Jian Liang; Ran He; Zilei Wang; Tieniu Tan
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Modern optimizers like Adam and Muon are central to training large language models, but their reliance on first- and second-order momenta introduces significant memory overhead, which constrains scalability and computational efficiency. 
In this work, we reframe the exponential moving average (EMA) used in these momenta as the training of a linear regressor via online gradient flow. 
Building on this equivalence, we introduce LoRA-Pre, a novel low-rank optimizer designed for efficient pre-training. 
Specifically, LoRA-Pre reduces the optimizer's memory footprint by decomposing the full momentum matrix into a compact low-rank subspace within the online linear learner, thereby maintaining optimization performance while improving memory efficiency. 
We empirically validate LoRA-Pre's efficacy by pre-training models from the Llama architecture family, scaling from 60M to 1B parameters. 
LoRA-Pre achieves the highest performance across all model sizes.
Notably, LoRA-Pre demonstrates remarkable rank efficiency, achieving comparable or superior results using only 1/8 the rank of baseline methods. 
Beyond pre-training, we evaluate LoRA-Pre's effectiveness in fine-tuning scenarios. 
With the same rank, LoRA-Pre consistently outperforms all efficient fine-tuning baselines.
Specifically, compared to standard LoRA, LoRA-Pre achieves substantial improvements of 3.14 points on Llama-3.1-8B and 6.17 points on Llama-2-7B, validating our approach's effectiveness across both pre-training and fine-tuning paradigms.
Our code is publicly available at https://github.com/mrflogs/LoRA-Pre.
Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment
Authors: Anh Tuan Bui; Thuy-Trang Vu; Trung Le; Junae Kim; Tamas Abraham; Rollin Omari; Amardeep Kaur; Dinh Phung
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this paper, we investigate the semantic collapsing problem in generative personalization, an under-explored topic where the learned visual concept ($V$) gradually shifts from its original textual meaning and comes to dominate other concepts in multi-concept input prompts. This issue not only reduces the semantic richness of complex input prompts like "a photo of $V$ wearing glasses and playing guitar" into simpler, less contextually rich forms such as "a photo of $V$" but also leads to simplified output images that fail to capture the intended concept. We identify the root cause as unconstrained optimisation, which allows the learned embedding $V$ to drift arbitrarily in the embedding space, both in direction and magnitude. To address this, we propose a simple yet effective training-free method that adjusts the magnitude and direction of pre-trained embedding at inference time, effectively mitigating the semantic collapsing problem. Our method is broadly applicable across different personalization methods and demonstrates significant improvements in text-image alignment in diverse use cases. Our code is published at \url{https://github.com/tuananhbui89/Embedding-Adjustment}.
REI-Bench: Can Embodied Agents Understand Vague Human Instructions in Task Planning?
Authors: Chenxi Jiang; Chuhao Zhou; Jianfei Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Robot task planning decomposes human instructions into executable action sequences that enable robots to complete a series of complex tasks. Although recent large language model (LLM)-based task planners achieve amazing performance, they assume that human instructions are clear and straightforward. However, real-world users are not experts, and their instructions to robots often contain significant vagueness. Linguists suggest that such vagueness frequently arises from referring expressions (REs), whose meanings depend heavily on dialogue context and environment. This vagueness is even more prevalent among the elderly and children, who are the groups that robots should serve more. This paper studies how such vagueness in REs within human instructions affects LLM-based robot task planning and how to overcome this issue. To this end, we propose the first robot task planning benchmark that systematically models vague REs grounded in pragmatic theory (REI-Bench), where we discover that the vagueness of REs can severely degrade robot planning performance, leading to success rate drops of up to 36.9\%. We also observe that most failure cases stem from missing objects in planners. To mitigate the REs issue, we propose a simple yet effective approach: task-oriented context cognition, which generates clear instructions for robots, achieving state-of-the-art performance compared to aware prompts, chains of thought, and in-context learning. By tackling the overlooked issue of vagueness, this work contributes to the research community by advancing real-world task planning and making robots more accessible to non-expert users, e.g., the elderly and children.
AnesSuite: A Comprehensive Benchmark and Dataset Suite for Anesthesiology Reasoning in LLMs
Authors: Xiang Feng; Wentao Jiang; Zengmao Wang; Yong Luo; Pingbo Xu; Baosheng Yu; Hua Jin; Jing Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The application of large language models (LLMs) in the medical field has garnered significant attention, yet their reasoning capabilities in more specialized domains like anesthesiology remain underexplored. To bridge this gap, we introduce AnesSuite, the first comprehensive dataset suite specifically designed for anesthesiology reasoning in LLMs. The suite features AnesBench, an evaluation benchmark tailored to assess anesthesiology-related reasoning across three levels: factual retrieval (System 1), hybrid reasoning (System 1.x), and complex decision-making (System 2).  Alongside this benchmark, the suite includes three training datasets that provide an infrastructure for continued pre-training (CPT), supervised fine-tuning (SFT), and reinforcement learning with verifiable rewards (RLVR). Leveraging this suite, we develop Morpheus, the first baseline model collection for anesthesiology reasoning.  Despite undergoing limited training with SFT and group relative policy optimization (GRPO), Morpheus not only achieves substantial improvements in anesthesiology that rival larger-scale models, but also demonstrates enhanced reasoning capabilities across general medical and broad-domain benchmarks. Furthermore, through comprehensive evaluations and experiments, we analyze the key factors influencing anesthesiology reasoning performance, including model characteristics, training strategies and training data. Both AnesSuite and Morpheus will be open-sourced at https://github.com/MiliLab/AnesSuite.
Native Reasoning Models: Training Language Models to Reason on Unverifiable Data
Authors: Yuanfu Wang; Zhixuan Liu; Li xiangtian; Chaochao Lu; Chao Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The dominant paradigm for training large reasoning models—combining Supervised Fine-Tuning (SFT) with Reinforcement Learning with Verifiable Rewards (RLVR)—is fundamentally constrained by its reliance on high-quality, human-annotated reasoning data and external verifiers.
This dependency incurs significant data-collection costs, risks embedding human cognitive biases, and confines the reinforcement learning stage to objectively assessable domains like mathematics and coding, leaving a vast landscape of unverifiable tasks unaddressed.
To overcome these limitations, we introduce Native Reasoning Training (NRT), a novel framework that cultivates complex reasoning by having the model generate its own reasoning traces using only standard question-answer pairs, thereby obviating the need for expert-written demonstrations.
NRT reframes the training problem by treating the reasoning process as a latent variable. 
It employs a unified training objective that models reasoning as an optimization problem, intrinsically rewarding paths that increase the model's likelihood of producing the ground-truth answer.
This unified perspective allows us to analyze intrinsic failure modes of prior methods, such as policy collapse, and systematically design more robust reward aggregation functions, creating a self-correcting feedback loop where the model learns to \textit{think} in ways that resolve its own uncertainty.
Empirical evaluation on Llama and Mistral model families demonstrates that NRT achieves state-of-the-art performance among verifier-free methods, significantly outperforming standard SFT baselines and prior verifier-free RL methods.
Our approach yields particularly strong performance gains in complex reasoning domains and exhibits high robustness to policy collapse, offering a general, scalable path toward building more powerful and broadly applicable reasoning systems.
Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization
Authors: Yifan Chang; Jie Qin; Limeng Qiao; Xiaofeng Wang; Zheng Zhu; Lin Ma; Xingang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vector quantization (VQ) is a key component in discrete tokenizers for image generation, but its training is often unstable due to straight-through estimation bias, one-step-behind updates, and sparse codebook gradients, which lead to suboptimal reconstruction performance and low codebook usage. 
In this work, we analyze these fundamental challenges and provide a simple yet effective solution. 
To maintain high codebook usage in VQ networks (VQN) during learning annealing and codebook size expansion, we propose VQBridge, a robust, scalable, and efficient projector based on the map function method. VQBridge optimizes code vectors through a compress–process–recover pipeline, enabling stable and effective codebook training. 
By combining VQBridge with learning annealing, our VQN achieves full (100\%) codebook usage across diverse codebook configurations, which we refer to as FVQ (FullVQ).
Through extensive experiments, we demonstrate that FVQ is effective, scalable, and generalizable: it attains 100\% codebook usage even with a 262k-codebook, achieves state-of-the-art reconstruction performance, consistently improves with larger codebooks, higher vector channels, or longer training, and remains effective across different VQ variants. Moreover, when integrated with LlamaGen, FVQ significantly enhances image generation performance, surpassing visual autoregressive models (VAR) by 0.5 and diffusion models (DiT) by 0.2 rFID, highlighting the importance of high-quality tokenizers for strong autoregressive image generation.
Premise Selection for a Lean Hammer
Authors: Thomas Zhu; Joshua Clune; Jeremy Avigad; Albert Q. Jiang; Sean Welleck
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Neural methods are transforming automated reasoning for proof assistants, yet integrating these advances into practical verification workflows remains challenging. A $\textit{hammer}$ is a tool that integrates premise selection, translation to external automatic theorem provers, and proof reconstruction into one overarching tool to automate tedious reasoning steps. We present LeanPremise, a novel neural premise selection system, and we combine it with existing translation and proof reconstruction components to create LeanHammer, the first end-to-end domain general hammer for the Lean proof assistant. Unlike existing Lean premise selectors, LeanPremise is specifically trained for use with a hammer in dependent type theory. It also dynamically adapts to user-specific contexts, enabling it to effectively recommend premises from libraries outside LeanPremise's training data as well as lemmas defined by the user locally. With comprehensive evaluations, we show that LeanPremise enables LeanHammer to solve 21\% more goals than existing premise selectors and generalizes well to diverse domains. Our work helps bridge the gap between neural retrieval and symbolic reasoning, making formal verification more accessible to researchers and practitioners.