GenAI4ELab/papercli-papers huggingface dataset:
MergePRAG: Orthogonal Merging of Passage-experts for Multi-hop Parametric RAG
Authors: Xuebing Liu; Shanbao Qiao; Roseline Nyange; Dongwook Min; Hyun Kim; Seung-Hoon Na
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) can be enhanced with external knowledge through two dominant approaches: (1) **retrieval-augmented generation (RAG)**, which supplements LLMs with in-context retrieved passages, and (2) **parametric knowledge adaptation (PKA)**, which directly updates model parameters with new domain knowledge. Recently, parametric RAG (PRAG) has emerged as a promising framework, extending RAG by translating retrieved passages into parameter updates, thereby mitigating inefficiency and noise sensitivity inherent to RAG. However, existing PRAG methods remain limited to single-pass retrieval, falling short of the **multi-hop RAG** setting that requires iterative retrieval and reasoning. 
We propose **MergePRAG**(*Orthogonal Merging of Passage-experts for Multi-hop PRAG*), a novel framework that sequentially integrates retrieved passages into LLM parameters through a continual merging mechanism, which is advanced by two key proposals: (1) **orthogonal merging** using the Gram–Schmidt process to minimize conflicts between "passage experts", and (2) **critical-layer parameterization** to efficiently encode in-context passages. Experiments on multi-hop open-domain QA and reasoning-aware knowledge editing show that MergePRAG consistently outperforms both standard and state-of-the-art RAGs as well as existing parametric adaptation methods, achieving superior effectiveness and efficiency. All datasets and code will be released at https://github.com/Liu-Xuebing/MhQA_hypernetwork.
Gradient-Based Diversity Optimization with Differentiable Top-$k$ Objective
Authors: Tianyi Zhou; Sebastian Dalleiger; Ece Calikus; Aristides Gionis
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Predicting relevance is a pervasive problem across digital platforms, covering social media, entertainment, and commerce. However, when optimized solely for relevance and engagement, many machine-learning models amplify data biases and produce homogeneous outputs, reinforcing filter bubbles and content uniformity. To address this issue, we introduce a pairwise top-k diversity objective with a differentiable smooth-ranking approximation, providing a model-agnostic way to incorporate diversity optimization directly into standard gradient-based learning. Building on this objective, we cast relevance and diversity as a joint optimization problem, we analyze the resulting gradient trade-offs, and propose two complementary strategies: direct optimization, which modifies the learning objective, and indirect optimization, which reweights training data. Both strategies can be applied either when training models from scratch or when fine-tuning existing relevance-optimized models. We use recommendation as a natural evaluation setting where scalability and diversity are critical, and show through extensive experiments that our methods consistently improve diversity with negligible accuracy loss. Notably, fine-tuning with our objective is especially efficient, requiring only a few gradient steps to encode diversity at scale.
Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models
Authors: Sen Ye; Mengde Xu; Shuyang Gu; Di He; Liwei Wang; Winston Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current research in multimodal models faces a key challenge where enhancing generative capabilities often comes at the expense of understanding, and vice versa. We analyzed this trade-off and identify the primary cause might be the potential conflict between generation and understanding, which creates a competitive dynamic within the model. To address this, we propose the Reason-Reflect-Refine (R3) framework. This innovative algorithm re-frames the single-step generation task into a multi-step process of "generate-understand-regenerate". By explicitly leveraging the model's understanding capability during generation, we successfully mitigate the optimization dilemma, achieved stronger generation results and improved understanding ability which are related to the generation process. This offers valuable insights for designing next-generation unified multimodal models.
When Language Models Lose Their Mind: The Consequences of Brain Misalignment
Authors: Gabriele Merlin; Mariya Toneva
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While brain-aligned large language models (LLMs) have garnered attention for their potential as cognitive models and for potential for enhanced safety and trustworthiness in AI, the role of this brain alignment for linguistic competence remains uncertain. In this work, we investigate the functional implications of brain alignment by introducing brain-misaligned models--LLMs intentionally trained to predict brain activity poorly while maintaining high language modeling performance. We evaluate these models on over 200 downstream tasks encompassing diverse linguistic domains, including semantics, syntax, discourse, reasoning, and morphology. By comparing brain-misaligned models with well-matched brain-aligned counterparts, we isolate the specific impact of brain alignment on language understanding. Our experiments reveal that brain misalignment substantially impairs downstream performance, highlighting the critical role of brain alignment in achieving robust linguistic competence. These findings underscore the importance of brain alignment in LLMs and offer novel insights into the relationship between neural representations and linguistic processing.
Resurfacing the Instance-only Dependent Label Noise Model through Loss Correction
Authors: Mustafa Enes Aydın; Maarten De Vos; Alexander Bertrand
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We investigate the label noise problem in supervised binary classification settings and resurface the underutilized instance-_only_ dependent noise model through loss correction. On the one hand, based on risk equivalence, the instance-aware loss correction scheme completes the bridge from _empirical noisy risk minimization_ to _true clean risk minimization_ provided the base loss is classification calibrated (e.g., cross-entropy). On the other hand, the instance-only dependent modeling of the label noise at the core of the correction enables us to estimate a single value per instance instead of a matrix. Furthermore, the estimation of the transition rates becomes a very flexible process, for which we offer several computationally efficient ways. Empirical findings over different dataset domains (image, audio, tabular) with different learners (neural networks, gradient-boosted machines) validate the promised generalization ability of the method.
Animating the Uncaptured: Humanoid Mesh Animation with Video Diffusion Models
Authors: Marc Benedí San Millán; Angela Dai; Matthias Nießner
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Animation of humanoid characters is essential in various graphics applications, but require significant time and cost to create realistic animations. We propose an approach to synthesize 4D animated sequences of input static 3D humanoid meshes, leveraging strong generalized motion priors from generative video models -- as such video models contain powerful motion information covering a wide variety of human motions. From an input static 3D humanoid mesh and a text prompt describing the desired animation, we synthesize a corresponding video conditioned on a rendered image of the 3D mesh. We then employ an underlying SMPL representation to animate the corresponding 3D mesh according to the video-generated motion, based on our motion optimization. This enables a cost-effective and accessible solution to enable the synthesis of diverse and realistic 4D animations
EMFuse: Energy-based Model Fusion for Decision Making
Authors: Kejie He; Yi-Chen Li; Yang Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Model fusion has emerged as a promising research direction, offering a resource-efficient paradigm that leverages existing pre-trained models to circumvent the need for training from scratch. In this work, we investigate the fusion of models specifically adapted for decision-making tasks. This challenge divides into two distinct, yet related subproblems: the direct fusion of models that act as policy and the fusion of dynamics models that subsequently induce a policy. We suggest that these seemingly divergent subproblems can be unified through the lens of energy-based models (EBMs), which parameterizes a conditional distribution via an energy function where lower energy implies higher probability. Our framework, \textbf{EMFuse}, provides this convergence by leveraging the concept of energy as a common currency for fusion. For direct fusion of policies, such as those in language models, the output distribution is commonly softmax (Boltzmann), which essentially defines the negative logarithmic probability as an energy function. For dynamics models, existing works often train a set of models on the same dataset to obtain robust uncertainty estimation; such an ensemble approach leads to an exponential explosion in computational complexity when it comes to dynamics fusion across multiple sets of models. To overcome this, we introduce the Any-step Dynamics Energy-based Transition Model (ADETM), a novel architecture that performs efficient single-model-per-dataset uncertainty estimation with its energy-based backbone, thereby avoiding this computational explosion. Our EMFuse framework surpasses other baselines by 0.34\% to 6.63\% on single/cross domain discrete decision-making benchmarks, and achieved an extra 2.3 to 7.4 normalized points on average in D4RL MuJoCo continuous-control scenarios. Our code is available at https://github.com/LAMDA-RL/EMFuse.
The Unseen Bias: How Norm Discrepancy in Pre-Norm MLLMs Leads to  Visual  Information Loss
Authors: Bozhou Li; Xinda Xue; Sihan Yang; Yang Shi; Xinlong Chen; Yushuo Guan; Yuanxing Zhang; Wentao Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs), which couple pre-trained vision encoders and language models, have shown remarkable capabilities. However, their reliance on the ubiquitous Pre-Norm architecture introduces a subtle yet critical flaw: a severe norm disparity between the high-norm visual tokens and the low-norm text tokens. In this work, we present a formal theoretical analysis demonstrating that this imbalance is not a static issue. Instead, it induces an "asymmetric update dynamic", where high-norm visual tokens exhibit a ''representational inertia,'' causing them to transform semantically much slower than their textual counterparts. This fundamentally impairs effective cross-modal feature fusion. Our empirical validation across a range of mainstream MLLMs confirms that this theoretical dynamic---the persistence of norm disparity and the resulting asymmetric update rates---is a prevalent phenomenon. Based on this insight, we propose a remarkably simple yet effective solution: inserting a single, carefully initialized LayerNorm layer after the visual projector to enforce norm alignment. Experiments conducted on the LLaVA-1.5 architecture show that this intervention yields significant performance gains not only on a wide suite of multimodal benchmarks but also, notably, on text-only evaluations such as MMLU, suggesting that resolving the architectural imbalance leads to a more holistically capable model.
ExPO-HM: Learning to Explain-then-Detect for Hateful Meme Detection
Authors: Jingbiao Mei; Mingsheng Sun; Jinghong Chen; Pengda Qin; Yuhong Li; Da Chen; Bill Byrne
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hateful memes have emerged as a particularly challenging form of online abuse, motivating the development of automated detection systems. Most prior approaches rely on direct detection, producing only binary predictions. Such models fail to provide the context and explanations that real-world moderation requires. Recent Explain-then-Detect approaches, using Chain-of-Thought prompting or LMM agents, perform worse than simple SFT baselines, and even advanced post-training methods such as GRPO fail to close the gap. Our analysis identifies two key issues of such systems: important policy-relevant cues such as targets and attack types are not hypothesized by the model as a likely explanation; and the binary reward signal is insufficient to guide reasoning. To address these challenges, we propose ExPO-HM (Explain-then-Detect Policy Optimization for Hateful Memes), inspired by the training and evaluation process of human annotators. ExPO-HM combines SFT warmup, GRPO with curriculum learning, and Conditional Decision Entropy (CDE) as both metric and reward for reasoning quality. Across three hateful meme benchmarks, ExPO-HM achieves state-of-the-art performance on binary detection, fine-grained classification, and reasoning quality, with up to 15\% and 17\% F1 improvement over the GRPO and DPO baselines, respectively. By moving hateful meme detection from simple binary alarms to explanation-driven detection, ExPO-HM provides accurate, interpretable, and actionable moderation support. Code available at: https://github.com/JingbiaoMei/ExPO-HM
Recurrent Action Transformer with Memory
Authors: Egor Cherepanov; Aleksei Staroverov; Alexey Kovalev; Aleksandr Panov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformers have become increasingly popular in offline reinforcement learning (RL) due to their ability to treat agent trajectories as sequences, reframing policy learning as a sequence modeling task. However, in partially observable environments (POMDPs), effective decision-making depends on retaining information about past events - something that standard transformers struggle with due to the quadratic complexity of self-attention, which limits their context length. One solution to this problem is to extend transformers with memory mechanisms. We propose the Recurrent Action Transformer with Memory (RATE), a novel transformer-based architecture for offline RL that incorporates a recurrent memory mechanism designed to regulate information retention. We evaluate RATE across a diverse set of environments: memory-intensive tasks (ViZDoom-Two-Colors, T-Maze, Memory Maze, Minigrid-Memory, and POPGym), as well as standard Atari and MuJoCo benchmarks. Our comprehensive experiments demonstrate that RATE significantly improves performance in memory-dependent settings while remaining competitive on standard tasks across a broad range of baselines. These findings underscore the pivotal role of integrated memory mechanisms in offline RL and establish RATE as a unified, high-capacity architecture for effective decision-making over extended horizons.
Exploring Diverse Generation Paths via Inference-time Stiefel Activation Steering
Authors: Dongxuan Zhu; Ly Tran Ho Khanh; Andy Yat-Ming Cheung; Man-Chung Yue; Viet Anh Nguyen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Language models often default to a narrow set of high-probability outputs, leaving their generation paths homogeneous and prone to mode collapse. Sampling-based strategies inject randomness but still struggle to guarantee diversity across multiple concurrent generation runs. We address this limitation by introducing STARS (**ST**iefel-based **A**ctivation Steering for Diverse **R**ea**S**oning), a training-free, inference-time intervention method that transforms activation steering into an exploration engine. At each token, STARS collects the hidden activations of concurrent generation runs and optimizes multiple additive steering directions jointly on the Stiefel manifold. STARS maximizes the geometric volume of the steered activations, while the Stiefel manifold induces orthogonality of the steering interventions. This formulation explicitly promotes divergent activation vectors of concurrent generation runs, and implicitly promotes divergent generation trajectories. This manifold optimization formulation can be solved using a Riemannian gradient descent algorithm with convergence guarantees, but this algorithm is too time-consuming for real-time inference. To guarantee low latency, we further design a lightweight one-step update with an aggressive, closed-form stepsize. For test case generation and scientific discovery benchmarks, STARS consistently outperforms standard sampling methods, achieving greater diversity without sacrificing qualitative performance.
Random Controlled Differential Equations
Authors: Francesco Piatti; Thomas Cass; William F. Turner
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce a training-efficient framework for time-series learning in which
large randomly parameterized controlled and rough differential equations act as
continuous-time reservoirs. These random dynamical systems map input paths to
rich path-dependent representations, while only a linear readout layer is
trained, yielding fast, scalable models with strong inductive bias. Building on this foundation, we propose two variants: (i) Random Fourier CDEs (RF-CDEs): these lift the input signal using random Fourier features prior to the dynamics, providing a kernel-free approximation of RBF-enhanced sequence models; (ii) Random Rough DEs (R-RDEs): these operate directly on rough-path inputs via a log-ODE discretisation, using log-signatures to capture higher-order temporal interactions while remaining stable and efficient. We prove that in the infinite-width limit, these models induce the RBF-lifted signature kernel and the rough signature kernel, respectively, offering a unified perspective on random-feature reservoirs, continuous-time deep architectures, and path-signature theory.

We evaluate both models across a range of time-series benchmarks, demonstrating competitive or superior performance. These methods provide a practical alternative to explicit signature computations, retaining their inductive bias while benefiting from the efficiency of random features. Code is publicly available at: https://github.com/FrancescoPiatti/RandomSigJax.
FastFlow: Accelerating The Generative Flow Matching Models with Bandit Inference
Authors: Divya Jyoti Bajpai; Dhruv Bhardwaj; Soumya Roy; Tejas Duseja; Harsh Agarwal; Aashay Sandansing; Manjesh Kumar Hanawal
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Flow-matching models deliver state-of-the-art fidelity in image and video generation, but the inherent sequential denoising process renders them slower. Existing acceleration methods like distillation, trajectory truncation, and consistency approaches are static, require retraining, and often fail to generalize across tasks. We propose FastFlow, a plug-and-play adaptive inference framework that accelerates generation in flow matching models. FastFlow identifies denoising steps that produce only minor adjustments to the denoising path and approximates them without using the full neural network models used for velocity predictions. The approximation utilizes finite-difference velocity estimates from prior predictions to efficiently extrapolate future states, enabling faster advancements along the denoising path at zero compute cost. This enables skipping computation at intermediary steps. We model the decision of how many steps to safely skip before requiring a full model computation as a multi-armed bandit problem. The bandit learns the optimal skips to balance speed with performance. FastFlow integrates seamlessly with existing pipelines and generalizes across image generation, video generation, and editing tasks. Experiments demonstrate a speedup of over $2.6\times$ while maintaining high-quality outputs. The source code for this work can be found at https://github.com/Div290/FastFlow.
Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning
Authors: Egor Cherepanov; Nikita Kachaev; Alexey Kovalev; Aleksandr Panov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Memory is crucial for enabling agents to tackle complex tasks with temporal and spatial dependencies. While many reinforcement learning (RL) algorithms incorporate memory, the field lacks a universal benchmark to assess an agent's memory capabilities across diverse scenarios. This gap is particularly evident in tabletop robotic manipulation, where memory is essential for solving tasks with partial observability and ensuring robust performance, yet no standardized benchmarks exist. To address this, we introduce MIKASA (Memory-Intensive Skills Assessment Suite for Agents), a comprehensive benchmark for memory RL, with three key contributions: (1) we propose a comprehensive classification framework for memory-intensive RL tasks, (2) we collect MIKASA-Base -- a unified benchmark that enables systematic evaluation of memory-enhanced agents across diverse scenarios, and (3) we develop MIKASA-Robo -- a novel benchmark of 32 carefully designed memory-intensive tasks that assess memory capabilities in tabletop robotic manipulation. Our work introduces a unified framework to advance memory RL research, enabling more robust systems for real-world use.
Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation
Authors: Egor Cherepanov; Nikita Kachaev; Artem Zholus; Alexey Kovalev; Aleksandr Panov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The incorporation of memory into agents is essential for numerous tasks within the domain of Reinforcement Learning (RL). In particular, memory is paramount for tasks that require the use of past information, adaptation to novel environments, and improved sample efficiency. However, the term ``memory'' encompasses a wide range of concepts, which, coupled with the lack of a unified methodology for validating an agent's memory, leads to erroneous judgments about agents' memory capabilities and prevents objective comparison with other memory-enhanced agents. This paper aims to streamline the concept of memory in RL by providing practical precise definitions of agent memory types, such as long-term vs. short-term memory and declarative vs. procedural memory, inspired by cognitive science. Using these definitions, we categorize different classes of agent memory, propose a robust experimental methodology for evaluating the memory capabilities of RL agents, and standardize evaluations. Furthermore, we empirically demonstrate the importance of adhering to the proposed methodology when evaluating different types of agent memory by conducting experiments with different RL agents and what its violation leads to.
ELMUR: External Layer Memory with Update/Rewrite for Long-Horizon RL Problems
Authors: Egor Cherepanov; Alexey Kovalev; Aleksandr Panov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Real-world robotic agents must act under partial observability and long horizons, where key cues may appear long before they affect decision making. However, most modern approaches rely solely on instantaneous information, without incorporating insights from the past. Standard recurrent or transformer models struggle with retaining and leveraging long-term dependencies: context windows truncate history, while naive memory extensions fail under scale and sparsity. 
We propose ELMUR (External Layer Memory with Update/Rewrite), a transformer architecture with structured external memory. Each layer maintains memory embeddings, interacts with them via bidirectional cross-attention, and updates them through an Least Recently Used (LRU) memory module using replacement or convex blending. 
ELMUR extends effective horizons up to 100,000 times beyond the attention window and achieves a 100% success rate on a synthetic T-Maze task with corridors up to one million steps. In POPGym, it outperforms baselines on more than half of the tasks. 
On MIKASA-Robo sparse-reward manipulation tasks with visual observations, it nearly doubles the performance of strong baselines, achieving the best success rate on 21 out of 23 tasks and improving the aggregate success rate across all tasks by about 70% over the previous best baseline.
These results demonstrate that structured, layer-local external memory offers a simple and scalable approach to decision making under partial observability.