GenAI4ELab/papercli-papers huggingface dataset:
Bootstrapping MLLM for Weakly‑Supervised  Class‑Agnostic Object Counting
Authors: Xiaowen Zhang; Zijie Yue; Yong Luo; Cairong Zhao; Qijun Chen; Miaojing Shi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Object counting is a fundamental task in computer vision, with broad applicability in many real-world scenarios. Fully-supervised counting methods require costly point-level annotations per object. Few weakly-supervised methods leverage only image-level object counts as supervision and achieve fairly promising results. They are, however, often limited to counting a single category, \eg person. In this paper, we propose WS-COC, the first MLLM-driven weakly-supervised framework for class-agnostic object counting. 
Instead of directly fine-tuning MLLMs to predict object counts, which can be challenging due to the modality gap, we incorporate three simple yet effective strategies to bootstrap the counting paradigm in both training and testing: First, a divide-and-discern dialogue tuning strategy is proposed to guide the MLLM to determine whether the object count falls within a specific range and progressively break down the range through multi-round dialogue. Second, a compare-and-rank count optimization strategy is introduced to train the
MLLM to optimize the relative ranking of multiple images according to their object counts. Third, a global-and-local counting enhancement strategy aggregates and fuses local and global count predictions to improve counting performance in dense scenes. Extensive experiments on FSC-147, CARPK, PUCPR+, and ShanghaiTech show that WS-COC matches or even surpasses many state-of-art fully-supervised methods while significantly reducing annotation costs.
ATOM: A Pretrained Neural Operator for Multitask Molecular Dynamics
Authors: Luke Thompson; Davy Guan; Slade Matthews; Dai Shi; Junbin Gao; Andi Han
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Molecular dynamics (MD) simulations underpin modern computational drug discovery, materials science, and biochemistry. Recent machine learning models provide high-fidelity MD predictions without the need for repeated quantum-mechanical force calculations, enabling significant speedups over conventional pipelines. Yet many such methods typically enforce strict equivariance and rely on sequential rollouts, thus limiting their flexibility and simulation efficiency. They are also commonly single-task, trained on individual molecules and fixed time frames, which restricts generalization to unseen compounds and extended timesteps. To address these issues, we propose Atomistic Transformer Operator for Molecules (ATOM), a pretrained transformer neural operator for multi-task molecular dynamics. ATOM adopts a quasi-equivariant design that does not require an explicit molecular graph and employs a temporal attention mechanism to enable accurate, parallel decoding of multiple future states. To support operator pretraining across chemicals and timescales, we curate TG80, a large, diverse, and numerically stable MD dataset with over 2.5 million femtoseconds of trajectories across 80 compounds. ATOM achieves state-of-the-art performance on established single-task benchmarks, such as MD17, RMD17, and MD22. After multi-task pretraining on TG80, ATOM shows exceptional zero-shot and robust generalization to unseen molecules across varying time horizons. We believe ATOM represents a significant step toward accurate, efficient, and transferable molecular dynamics models.
P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling
Authors: Pinyi Zhang; Ting-En Lin; Yuchuan Wu; Jingyang Chen; Zongqi Wang; Hua Yang; Xu Ze; Fei Huang; Yongbin Li; Kai Zhang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Personalized alignment of large language models seeks to adapt responses to individual user preferences, typically via reinforcement learning. A key challenge is obtaining accurate, user-specific reward signals in open-ended scenarios. Existing personalized reward models face two persistent limitations: (1) oversimplifying diverse, scenario-specific preferences into a small, fixed set of evaluation principles, and (2) struggling with generalization to new users with limited feedback. To this end, we propose **P-GenRM**, the first **P**ersonalized **Gen**erative **R**eward **M**odel with test-time user-based scaling. P-GenRM transforms preference signals into structured evaluation chains that derive adaptive personas and scoring rubrics across various scenarios. It further clusters users into User Prototypes and introduces a dual-granularity scaling mechanism: at the individual level, it adaptively scales and aggregates each user’s scoring scheme; at the prototype level, it incorporates preferences from similar users. This design mitigates noise in inferred preferences and enhances generalization to unseen users through prototype-based transfer. Empirical results show that  P-GenRM achieves state-of-the-art results on widely-used personalized reward model benchmarks, with an average improvement of ~2.31\%, and demonstrates strong generalization on an out-of-distribution dataset. Notably, Test-time User-based scaling provides an additional ~3\% boost, demonstrating stronger personalized alignment with test-time scalability.
Difference Predictive Coding for Training Spiking Neural Networks
Authors: Ville Karlsson; Nicklas Fianda; Joni-Kristian Kämäräinen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Predictive coding networks (PCNs) offer a local-learning alternative to backpropagation in which layers communicate residual errors, aligning well with biological computation and neuromorphic hardware. In this work we introduce Difference Predictive Coding (DiffPC), a spike-native PC formulation for spiking neural networks. DiffPC replaces dense floating-point messages with sparse ternary spikes, provides spike-compatible target and error updates, and employs adaptive threshold schedules for event-driven operation. We validate DiffPC on fully connected and convolutional architectures, demonstrating competitive performance on MNIST (99.3\%) and Fashion-MNIST (89.6\%), and outperforming a backpropagation baseline on CIFAR-10. Crucially, this performance is achieved with high communication sparsity, reducing data movement by over two orders of magnitude compared to standard predictive coding. DiffPC thus establishes a faithful, hardware-aligned framework for communication-efficient training on neuromorphic platforms.
QKV Projections Require a Fraction of Their Memory
Authors: Malik Khalaf; Yara Shamshoum; Nitzan Hodos; Yuval Sieradzki; Assaf Schuster
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The Multi-Head Attention mechanism is central to LLM operation, and multiple works target its compute and memory efficiency during training.
While most works focus on approximating the scaled dot product, the memory consumption of the linear projections that compute the $Q$, $K$, and $V$ tensors from the input $x$ is often overlooked.
To address this, we propose Point-Approximate Matrix Multiplication (PAMM), a novel tensor compression technique that compresses the activations of the $Q,K,V$ projections in attention layers by a factor of up to $\times 512$, effectively erasing their memory footprint, while achieving similar or better final perplexity. PAMM is fully composable with efficient attention techniques such as FlashAttention, making it a practical and complementary method for memory-efficient LLM training.
Modality-free Graph In-context Alignment
Authors: Wei Zhuo; Siqiang Luo
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: In-context learning (ICL) converts static encoders into task-conditioned reasoners, enabling adaptation to new data from just a few examples without updating pretrained parameters. This capability is essential for graph foundation models (GFMs) to approach LLM-level generality. Yet current GFMs struggle with cross-domain alignment, typically relying on modality-specific encoders that fail when graphs are pre-vectorized or raw data is inaccessible. In this paper, we introduce **M**odality-**F**ree **G**raph **I**n-context **A**lignment (MF-GIA), a framework that makes a pretrained graph encoder promptable for few-shot prediction across heterogeneous domains without modality assumptions. MF-GIA captures domain characteristics through gradient fingerprints, which parameterize lightweight transformations that align pre-encoded features and indexed labels into unified semantic spaces. During pretraining, a dual prompt-aware attention mechanism with episodic objective learns to match queries against aligned support examples to establish prompt-based reasoning capabilities. At inference, MF-GIA performs parameter-update-free adaptation using only a few-shot support set to trigger cross-domain alignment and enable immediate prediction on unseen domains. Experiments demonstrate that MF-GIA achieves superior few-shot performance across diverse graph domains and strong generalization to unseen domains. The code is available at https://github.com/JhuoW/MF-GIA.
Segment Any Events with Language
Authors: Seungjun Lee; Gim Hee Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scene understanding with free-form language has been widely explored within diverse modalities such as images, point clouds, and LiDAR. However, related studies on event sensors are scarce or narrowly centered on semantic-level understanding. We introduce **SEAL**, the first Semantic-aware Segment Any Events framework that addresses Open-Vocabulary Event Instance Segmentation (OV-EIS). Given the visual prompt, our model presents a unified framework to support both event segmentation and open-vocabulary mask classification at multiple levels of granularity, including instance-level and part-level. To enable thorough evaluation on OV-EIS, we curate four benchmarks that cover *label granularity* from coarse to fine class configurations and *semantic granularity* from instance-level to part-level understanding. Extensive experiments show that our SEAL largely outperforms proposed baselines in terms of performance and inference speed with a parameter-efficient architecture. In the Appendix, we further present a simple variant of our SEAL achieving generic spatiotemporal OV-EIS that does not require any visual prompts from users in the inference. The code will be publicly available.
FASA: FREQUENCY-AWARE SPARSE ATTENTION
Authors: Yifei Wang; Yueqi Wang; Zhenrui Yue; Huimin Zeng; Yong Wang; Ismini Lourentzou; Zhengzhong Tu; Xiangxiang Chu; Julian McAuley
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The deployment of Large Language Models (LLMs) faces a critical bottleneck when handling lengthy inputs: the prohibitive memory footprint of the Key Value (KV) cache. To address this bottleneck, the token pruning paradigm leverages attention sparsity to selectively retain a small, critical subset of tokens. However, existing approaches fall short, with static methods risking irreversible information loss and dynamic strategies employing heuristics that insufficiently capture the query-dependent nature of token importance.
We propose FASA, a novel framework that achieves query-aware token eviction by dynamically predicting token importance.
FASA stems from a novel insight into RoPE: the discovery of functional sparsity at the frequency-chunk (FC) level. Our key finding is that a small, identifiable subset of "dominant" FCs consistently exhibits high contextual agreement with the full attention head. This provides a robust and computationally free proxy for identifying salient tokens.
Building on this insight, FASA first identifies a critical set of tokens using dominant FCs, and then performs focused attention computation solely on this pruned subset.
Across a spectrum of long-context tasks, from sequence modeling to complex CoT reasoning, FASA consistently outperforms all token-eviction baselines and achieves near-oracle accuracy, demonstrating remarkable robustness even under constraint budgets. Notably, on LongBench-V1, FASA reaches nearly 100\% of full-KV performance when only keeping 256 tokens, and  achieves 2.56$\times$ speedup using just 18.9\% of the cache on AIME24.
Einstein Fields: A Neural Perspective To Computational General Relativity
Authors: Sandeep Suresh Cranganore; Andrei Bodnar; Arturs Berzins; Johannes Brandstetter
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce *Einstein Fields*, a neural representation designed to compress computationally intensive *four-dimensional* numerical relativity simulations into compact implicit neural network weights. By modeling the *metric*, the core tensor field of general relativity, Einstein Fields enable the derivation of physical quantities via automatic differentiation. Unlike conventional neural fields (e.g., signed distance, occupancy, or radiance fields), Einstein Fields fall into the class of *Neural Tensor Fields* with the key difference that, when encoding the spacetime geometry into neural field representations, dynamics emerge naturally as a byproduct. Our novel implicit approach demonstrates remarkable potential, including continuum modeling of four-dimensional spacetime, mesh-agnosticity, storage efficiency, derivative accuracy, and ease of use. It achieves up to a $\mathtt{4,000}$-fold reduction in storage memory compared to discrete representations while retaining a numerical accuracy of five to seven decimal places. Moreover, in single precision, differentiation of the Einstein Fields-parameterized metric tensor is up to five orders of magnitude more accurate compared to naive finite differencing methods. We demonstrate these properties on several canonical test beds of general relativity and numerical relativity simulation data, while also releasing an open-source $\mathtt{JAX}$-based library: https://github.com/AndreiB137/EinFields, taking the first steps to studying the potential of machine learning in numerical relativity.
Three Forward, One Backward: Memory-Efficient Full-Rank Fine-Tuning of Large Models via Extra Forward Passes
Authors: Jia Zhang; Yu Bai; Hualin Zhang; Tianshuo Chen; Zhaogeng Liu; zhiqiang xu; Yi Chang; Bin Gu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-tuning large language models (LLMs) has achieved significant success in downstream tasks.
However, as the model size continues to grow, traditional fine-tuning methods have become increasingly impractical due to their high computational and memory costs.
This has motivated researchers to explore parameter-efficient and memory-friendly fine-tuning strategies to enable scalable approaches, with Low-Rank Adaptation (LoRA) standing out as a representative work.
However, the LoRA update is restricted to a low-rank subspace, which results in suboptimal performance compared to the full-parameter update.
Recent research has also explored memory-efficient fine-tuning LLMs using just forward passes while suffer from high variance in gradient estimation and low convergence speed.
To address the issues above, we propose a new alternating optimization framework called LMAO (**L**ow-rank and **M**emory-efficient Zeroth-Order **A**lternating **O**ptimization), which combines the advantages of LoRA and MeZO.
This method alternately updates the low-rank components and zeroth-order directions during training.
By performing three forward propagations and one backward propagation, each update is full-rank, thereby reducing feature loss and enabling efficient fine-tuning under strict memory constraints.
We provide theoretical guarantees on the convergence and convergence rate of this method.
Empirical results demonstrate that, in experiments on multiple models (e.g., OPT, RoBERTa-large), LMAO achieves performance comparable to first-order methods.
This presents a practical and scalable solution for fine-tuning large-scale models.
Our source code is available at [https://github.com/workelaina/LMAO](https://github.com/workelaina/LMAO).
Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement
Authors: Chenyu Lin; Yilin Wen; Du Su; Hexiang Tan; Fei Sun; Muhan Chen; Chenfu Bao; Zhonghou Lv
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Retrieval-augmented generation (RAG) improves performance on knowledge-intensive tasks but can be derailed by wrong, irrelevant, or conflicting retrieved text, causing models to rely on inaccurate evidence and cascade errors. We propose Knowledgeable-R1, a reinforcement-learning framework that explicitly trains large language models to use parametric knowledge (PK) to resist contextual interference while still exploiting external context when it is reliably helpful. Knowledgeable-R1 introduces a joint sampling scheme that generates paired responses with and without retrieval, and learns both local advantages (within each decoding regime) and global advantages under the same input to quantify when to ignore misleading context versus adopt it. We employ an asymmetric advantage transformation that amplifies exploratory behaviors toward parametric knowledge. Experiments show that Knowledgeable-R1 significantly improves robustness and reasoning accuracy in knowledge conflict scenarios and general RAG scenarios, outperforming SOTA baselines by +22.89% in counterfactual scenarios, and without degradation when the retrieved context is fully accurate.
Flow Matching with Semidiscrete Couplings
Authors: Alireza Mousavi-Hosseini; Stephen Y. Zhang; Michal Klein; marco cuturi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Flow models parameterized as time-dependent velocity fields can generate data from noise by integrating an ODE.
These models are often trained using flow matching, i.e. by sampling random pairs of noise and target points $(x_0,x_1)$ and ensuring that the velocity field is aligned, on average, with $x_1-x_0$ when evaluated along a time-indexed segment linking $x_0$ to $x_1$. 
While these noise/data pairs are sampled independently by default, they can also be selected more carefully by matching batches of $n$ noise to $n$ target points using an optimal transport (OT) solver.
Although promising in theory, the OT flow matching (OT-FM) approach (Pooladian et al., 2023, Tong et al., 2024) is not widely used in practice. 
Zhang et al. (2025), pointed out recently that OT-FM truly starts paying off when the batch size $n$ grows significantly, which only a multi-GPU implementation of the Sinkhorn algorithm can handle.
Unfortunately, the pre-compute costs of running Sinkhorn can quickly balloon, requiring $O(n^2/\varepsilon^2)$ operations for every $n$ pairs used to fit the velocity field, where $\varepsilon$ is a regularization parameter that should be typically small to yield better results.
To fulfill the theoretical promises of OT-FM, we propose to move away from batch-OT and rely instead on a semidiscrete formulation that can leverage the fact that the target dataset distribution is usually of finite size $N$. The SD-OT problem is solved by estimating a dual potential vector of size $N$ using SGD; using that vector, freshly sampled noise vectors at train time can then be matched with data points at the cost of a maximum inner product search (MIPS) over the dataset.
Semidiscrete FM (SD-FM) removes the quadratic dependency on $n/\varepsilon$ that bottlenecks OT-FM. SD-FM beats both FM and OT-FM on all training metrics and inference budget constraints, across multiple datasets, on unconditional/conditional generation, or when using mean-flow models.
UrbanFeel：A Comprehensive Benchmark for Temporal and Perceptual Understanding of City Scenes through Human Perspective
Authors: Jun He; Yi Lin; Zilong Huang; Jiacong Yin; Junyan Ye; Yuchuan Zhou; Weijia Li; Xiang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Urban development impacts over half of the global population, making human-centered understanding of its structural and perceptual changes essential for smart city planning. While Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various domains, existing benchmarks that explore their performance in urban environments remain limited, lacking systematic exploration of temporal evolution and subjective perception of urban environment that aligns with human perception. To address these limitations, we propose UrbanFeel, a comprehensive benchmark designed to evaluate the performance of MLLMs in urban development understanding and subjective environmental perception. UrbanFeel comprises 14.3K carefully constructed visual questions spanning three cognitively progressive dimensions: Static Scene Perception, Temporal Change Perception, and Subjective Environmental Perception. We collect multi-temporal single-view and panoramic street-view images from 11 representative cities worldwide, and generate high-quality question-answer pairs through a hybrid pipeline of spatial clustering, rule-based generation, model-assisted prompting, and manual annotation. Through extensive evaluation of 20 state-of-the-art MLLMs, we observe that Gemini-2.5 Pro achieves the best overall performance, with its accuracy approaching human expert levels and narrowing the average gap to just 1.5%. Most models perform well on tasks grounded in scene understanding. In particular, some models even surpass human annotators in pixel-level change detection. However, performance drops notably in tasks requiring temporal reasoning over urban development. Additionally, in the subjective perception dimension, several models reach human-level or even higher consistency in evaluating dimension such as beautiful and safety. Our results suggest that MLLMs are demonstrating rudimentary emotion understanding capabilities. The code and dataset of this work will
be released at https://github.com/Hejun0915/UrbanFeel .
Robust Equation Structure Learning with Adaptive Refinement
Authors: Yunlun Li; Sinno Jialin Pan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Symbolic regression (SR) aims to automate scientific discovery, but often truncates the hypothetico–deductive cycle, focusing on hypothesis and experiment while lacking systematic analysis. We introduce RESTART, a framework that closes this loop by adding a principled analysis stage to diagnose and correct structural errors. RESTART features two core mechanisms: a short-term refinement process that uses boosting to identify unexplained signals and guide an LLM toward targeted corrections, and a long-term structure library that distills successful refinements into reusable code snippets for cumulative knowledge. On LLM-SRBench across Physics, Biology, and Materials Science, RESTART achieves lower error and higher accuracy than state-of-the-art baselines. It also generalizes robustly, recovering near-exact functional forms on out-of-distribution data, representing a significant advance toward fully automated scientific discovery.