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NeurIPS 2026

VAMIRec: Value-Aware Memory Intervention for Continual Recommendation

Xudong Zhao, Xiaolong Xu, Haolong Xiang, Xiaoyu Xia, Jiaqiang Zhang, Xuyun Zhang, Lianyong Qi, Wanchun Dou, Amin Beheshti

Continual recommendation captures dynamic user interests amid sequential interactions, enabling models to adapt to new observations while preserving prior valuable knowledge. Existing continual recommendation methods primarily preserve or reuse historical knowledge through replay, distillation, or regularization to mitigate forgetting during incremental updates. However, noisy or obsolete inherited representations render indiscriminate preservation detrimental by inducing memory conflicts with current stage preferences. Moreover, rapid shifts in user interests require timely and selective memory updates, while coarse global mechanisms fail to provide such fine-grained responsiveness. To address these issues, we propose VAMIRec, a value-aware memory intervention framework for continual recommendation. VAMIRec first constructs a memory state by separating inherited user memory into a slow anchor component and a fast-changing drift component. We further design a value-aware action evaluation module to generate three candidate operations, and estimate conservative action values by accounting for uncertainty and intervention cost. Finally, we develop a policy-guided memory intervention module that distills the training-time action supervision into an inference-safe policy and applies the selected memory action before all-item ranking. Extensive experiments on three real-world datasets demonstrate that VAMIRec consistently outperforms the state-of-the-art baselines.

NeurIPS 2026

VALOR: Vector-Aware Low-Rank Restructuring of Neural Networks for RISC-V Inference

Zhihao Xu, Xiaoning Du, Bixin Li, Wang Lulu, Li Liao, Zhou Ying

The RISC-V Vector Extension (RVV) provides SIMD-style vector execution for accelerating deep learning (DL) inference on edge processors. However, existing low-rank compression methods mainly select ranks according to accuracy preservation and theoretical floating-point operation (FLOP) reduction, without considering whether the selected ranks are profitable under a target RVV effective vector length. As a result, a low-rank factorization that reduces FLOPs may still introduce tail-handling overhead or even increase the RVV instruction count under specific vector configurations. To address this problem, we propose VALOR, a low-rank restructuring framework for efficient DL deployment on RISC-V processors. Given a pretrained model and a target RVV configuration, VALOR identifies decomposable layers in the pretrained model, filters rank candidates using an RVV instruction-profitable condition, and searches for a global restructuring strategy with minimal accuracy loss. Furthermore, to avoid repeated end-to-end evaluation, VALOR uses a Hessian-aware accuracy predictor that combines layer-wise factorization error with inter-layer coupling. Experiments on Spike and a real RISC-V vector processor, SpacemiT X60, show that VALOR achieves a better accuracy-efficiency trade-off than baseline methods.

NeurIPS 2026

VAANI: Capturing the language landscape for an inclusive digital India

Sujith Pulikodan, Abhayjeet Singh, Agneedh Basu, Nihar Desai, Pavan K J, Pranav D Bhat, Raghu Dharmaraju, Ritika Gupta, Sathvik Udupa, Saurabh Kumar, Sumit Sharma, Visruth Sanka, Dinesh Tewari, Harsh Dhand, Amrita Kamat, Sukhwinder Singh, Shikhar Vashishth, Partha Talukdar, Raj Acharya, Prasanta Ghosh

Voice-based technologies have the potential to bridge digital accessibility gaps; however, existing datasets fail to capture the linguistic and regional diversity of Indic languages. We present Project VAANI, a large-scale multimodal dataset designed to represent India’s linguistic landscape across 165 districts. Speech data is collected using image-based prompts to elicit spontaneous responses, while images are curated through a separate pipeline covering diverse themes across regions. The dataset undergoes a rigorous multi-stage quality control process, combining automated and manual evaluation to ensure high audio quality and transcription accuracy. We release approximately 289K images, 31,255 hours of speech, and 2,043 hours of transcribed audio spanning 105 languages from 28 states and 3 union territories. Many of these languages are represented at this scale for the first time, making VAANI a foundational resource for inclusive speech technology. The dataset enables the development of robust, multilingual, and multimodal models, and supports research in speech recognition, language understanding, and cross-modal learning for underrepresented languages.

NeurIPS 2026

V2VFusion: Text-Controlled Video-to-Video Diffusion for Degradation-Aware Video Fusion

Jiajun Chen, Han Xu, Yunfei Huang, Zhuoya Zhang, Guangcan Liu, Jiayi Ma

Video fusion integrates complementary information in multiple source video streams into a single sequence for complete scene perception. Existing methods perform frame-wise fusion and lack unified spatio-temporal modeling across sequences, damaging temporal coherence across sequences. When dealing with degradations, they also ignore temporal dynamics and overload a single network with diverse degradations, leading to limited and imbalanced degradation suppression. To address these issues, we propose V2VFusion, the first unified video-to-video fusion framework that directly generates temporally coherent, high-quality fused videos from degraded multi-source inputs. By reformulating video fusion as a video-to-video conditional generation problem in latent diffusion space, our approach holistically models spatio-temporal dependencies across entire sequences and intrinsically ensures sequence-level consistency. On this basis, a text-guided degradation-conditioned ControlNet bridges high-level textual degradation descriptions with low-level visual priors, enabling interpretable and controllable guidance for structure-preserving fusion beyond purely data-driven conditioning. To handle heterogeneous and compound degradations, a state-modulated degradation-aware hierarchical mixture-of-experts module decouples expert selection into diffusion-state-aware filtering and degradation-aware routing. It prevents unreliable routing and fosters expert specialization to mitigate representation conflicts among diverse degradations. Experiments on infrared-visible, multi-exposure, and multi-focus video fusion tasks and complex degradations validate that V2VFusion surpasses existing methods in fusion quality and temporal stability.

NeurIPS 2026

V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation

Yan-Bo Lin, Jonah Casebeer, Long Mai, Aniruddha Mahapatra, Gedas Bertasius, Nicholas J. Bryan

Generating music that temporally aligns with video events is challenging for existing text-to-music models, which lack fine-grained temporal control. We introduce V2M-ZERO, a video-to-music generation approach that generates time-aligned3 music with disentangled time synchronization and semantic control (e.g. genre, mood) from video while requiring zero video-music pairs at training time. Our method is motivated by a key observation: temporal synchronization requires matching when and how much change occurs, not what changes. While musical and visual events differ semantically, they exhibit shared temporal structure that can be captured independently within each modality. We capture this structure through event curves computed from intra-modal similarity using pretrained music and video encoders. By measuring temporal change within each modality independently, these curves provide comparable representations across modalities. This enables a simple training strategy: fine-tune a text-to-music model on music-event curves, then substitute video-event curves at inference without cross-modal training or paired data. Across OES-Pub, MovieGenBench-Music, and AIST++, V2M-ZERO achieves state-of-the-art performance without any paired music-video data, surpassing the strongest prior baselines per metric with 5–9% higher audio quality, 13–15% better semantic alignment, 21–52% improved temporal synchronization, and 28% higher beat alignment on dance videos. We find similar results via a large crowd-source subjective listening test. Our results vali-20 date that temporal alignment through within-modality features is not only effective for video-to-music generation but leads to better performance over paired cross-modal supervision. Furthermore, our approach enables independent controls for timing and music style (e.g. genre, mood) for more controllable generation.

NeurIPS 2026

V1-Inspired Dynamic Vision System: A Bio-Plausible Video Embedding Framework with Decoupled Shape–Color Pathways and Long-Range Spatiotemporal Perception

Beier Meng

Large-scale video–text contrastive learning has become the de facto standard for video understanding, yet mainstream methods almost universally adopt the linear patch embedding of vision Transformers (ViT) or evenly sliced tubelets as the front end. We revisit this front end from the biological principles of the primary visual cortex (area V1) and introduce V1DynamicVisionSystem (V1-DVS), a fully end-to-end trainable, bio-plausible video embedding framework. Its main contributions are: (i) a three-pathway parallel architecture that maps onto layer 4C orientation selectivity, layer 4B motion direction selectivity, and the cytochrome-oxidase Blob color-opponent system; (ii) an explicit shape–color decoupling, with isotropic Blob processing running in parallel with anisotropic Gabor orientation processing; (iii) long-range spatiotemporal convolution that captures inter-frame optical flow through 3D filters and the Adelson–Bergen motion-energy model rather than naive frame stacking; and (iv) bidirectional cross-pathway attention, allowing motion and color features to mutually modulate, as in V1→V4 inter-areal feedback. Pretrained on CC3M, WebVid-10M, ActivityNet and Ego4D and evaluated under a fully self-supervised objective(SimCLR + temporal-order verification + masked-token prediction), V1-DVS outperforms four strong baselines—linear patch embedding, mean temporal pooling, tubelet embedding, and spatiotemporal latent encoding—on six metrics spanning retrieval and embedding-space geometry, validating the practical value of biologically grounded structure for video embedding.

NeurIPS 2026

V-LUMEN: Visual Lookup Memory for Embedding Scaling in Vision-Language Models

Miso Choi, Daekeun Kim, Eunji Kim, Jungbeom Lee

Embedding scaling has recently emerged as a promising direction for increasing the capacity of language models through embedding-level representations, rather than solely expanding transformer depth or width. However, existing embedding scaling methods are primarily designed for discrete text tokens, leaving it unclear whether similar mechanisms can be extended to vision-language models, where visual representations are continuous, high-dimensional, and spatially structured. In this paper, we introduce V-LUMEN, a visual lookup memory framework for embedding-level scaling in vision-language models. V-LUMEN augments visual-language representations with reusable visual embeddings retrieved from an external memory indexed by discretized visual patterns, thereby expanding accessible representational capacity without directly increasing the dense computation of the backbone model. To address the unique challenges of visual representations, V-LUMEN introduces spatial aggregation for constructing structured visual memory keys and text-conditioned hashing for retrieving task-relevant visual memories based on the input query. Together, these components enable memory retrieval that is both spatially aware and instruction-conditioned. Experiments across diverse reasoning-intensive multimodal benchmarks show that V-LUMEN effectively augments vision-language models through external visual memory. Without backbone adaptation, V-LUMEN substantially improves the vanilla Qwen3-VL-2B-Instruct model, and further achieves competitive or better performance against parameter-matched MoE and LoRA baselines, demonstrating the potential of visual embedding scaling as an alternative scaling axis for vision-language models.

NeurIPS 2026

UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence

Hongwei Zhang, qiqiang zhong, Jiangxia Cao, Junfeng Shu, Yiyang Lv, Huanjie Wang, Liwei Guan, Jing Yao, Yiyu Wang, Liu Zhaojie, Han Li

Modeling ultra-long user behavior sequences is an essential task for capturing evolving preferences in modern recommender systems, and this research direction has contributed solid gains in the past several years. However, recommendation systems always serve enormous traffic, and extending user sequences sharply increases computational cost, which creates a difficult trade-off between efficiency and effectiveness. To scale to longer user sequences while keeping lightweight serving computation, existing works can be divided into two paradigms: (1) $\textit{Search-based Top-$K$ selection}$, which constructs an $\textbf{item-specific}$ subsequence for each candidate item to avoid facing the ultra-long sequence directly; and (2) $\textit{pre-trained user-interest compression}$, which maps an ultra-long user sequence into a small group of $\textbf{item-agnostic}$ user interest memories so that the online model can perceive user long-term interests via this highly compressed dense memory. Besides the two technical routes (totally item-specific or item-agnostic), we argue that there exists an intermediate path not well explored: preserving partial relevance between the user sequence and the target item, while exposing only limited signals to guide the direction of interest compression. This design does not pursue item-specific user interest compression, but seeks semantic-group shared general user interest memory according to item attributes, where semantically similar items share the same compressed user interest memory. Motivated by this, we propose $\textbf{UxSID}$, a novel framework that bridges this gap by facilitating a target item semantic-aware interaction between $\textbf{U}$ser histories and candidate $\textbf{S}$emantic $\textbf{ID}$s (SIDs). Specifically, UxSID employs a dual-level attention strategy: it first extracts item-agnostic user interests from raw sequences, and then performs a semantic-specific query over global behaviors and those agnostic interests to generate semantic-specific preferences. By adopting this end-to-end architecture, UxSID generates offline embeddings that balance computational parsimony with target items' semantic awareness, while strictly preserving parity with online inference in constant time. Extensive public benchmarks and large-scale A/B tests demonstrate that UxSID achieves state-of-the-art performance, driving a 0.337\% revenue lift in advertising. Open source link: $\url{https://anonymous.4open.science/r/UxSID/}$

NeurIPS 2026

Utility-Driven Clustered Federated Learning via Class-wise Interaction Attribution

Zhongxu Wang, Yichen Li, Haozhao Wang, Hanlin Cai, chenqi, Ruixuan Li, Ozgur Akan

Clustered Federated Learning (CFL) mitigates statistical heterogeneity by grouping clients with similar data distributions. However, existing CFL frameworks predominantly rely on proxy metrics, such as gradient or data similarities, which collapse fine-grained class-level similarity into macroscopic client-level scores. This coarse-grained aggregation inherently masks critical class-wise conflicts, inadvertently exposing the federation to severe negative transfer. In this paper, we propose a fundamental paradigm shift from heuristic similarity to *explicit value attribution*. We introduce **FedCap**, a novel utility-driven CFL framework that explicitly quantifies class-wise marginal utilities to capture the true generalization impact of local updates. Guided by this attribution mechanism, FedCap employs a conflict-aware clustering algorithm to systematically eliminate intra-cluster negative transfer, while orchestrating safe, supply-demand-guided representation sharing across clusters. Crucially, we provide rigorous theoretical guarantees demonstrating the robustness of our underlying attribution mechanism against four major types of distribution shifts. Extensive experiments across diverse heterogeneity settings show that FedCap consistently achieves state-of-the-art accuracy. The code is available for anonymous access at https://anonymous.4open.science/r/FedCap-605E.

NeurIPS 2026

Useful Memories Become Faulty When Continuously Updated by LLMs

Dylan Zhang, Yanshan Lin, Zhengkun Wu, Yihang Sun, Bingxuan Li, Dianqi Li, Hao Peng

Learning from past experience requires forming abstractions that can be reused in future problems. Recent work on agentic-memory systems has explored a practical route to continuously improving LLM agents after deployment without parameter updates: a textual abstraction is derived from each history and stored in memory, then continuously updated with more interactions. Yet we show that such textual abstractions produced by today's LLMs are often faulty, even when derived from useful experiences. Over time, the memory becomes less useful and even harmful. More surprisingly, even when abstracting from ground-truth solutions, GPT-5.4 fails on 54% of a set of ARC-AGI problems it had previously solved without memory. We attribute this to the iterative update process: the same trajectories yield qualitatively different memories under different update schedules, while an episodic-only control over those trajectories remains competitive with the consolidators we test---pinning the failure on the consolidation step, not the underlying experience. In our ARC-AGI Stream environment designed to trace memory consolidation behavior, when agents are given additional actions, they tend to keep episodic memory and double the accuracy of their forced-consolidation counterparts; removing consolidation entirely (episodic management only) matches this auto mode. These results suggest that current LLMs should not recursively rewrite their own experience into stable long-term knowledge. Robust agent memory should treat raw episodes as first-class evidence and make abstraction selective, delayed, and explicitly gated rather than mandatory after every interaction.

NeurIPS 2026

UpSafe℃: Upcycling for Controllable Safety in Large Language Models

Yuhao Sun, Lingyun Yu, Zhuoer Xu, Kun Yang, shiwen cui, Yongdong Zhang, Hongtao Xie

Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks. Existing safety techniques---including external guardrails, inference-time guidance, and post-training alignment---each face limitations in balancing safety, utility, and controllability. In this work, we propose UpSafe℃, a unified framework for enhancing LLM safety through safety-aware upcycling. Our approach first identifies safety-critical layers and upcycles them into a sparse Mixture-of-Experts (MoE) structure, where the router acts as a soft guardrail that selectively activates original MLPs and added safety experts. We further introduce a two-stage SFT strategy to strengthen safety discrimination while preserving general capabilities. To enable flexible control at inference time, we introduce a safety temperature mechanism, allowing dynamic adjustment of the trade-off between safety and utility. Experiments across multiple benchmarks, base model, and model scales demonstrate that UpSafe℃ achieves robust safety improvements against harmful and jailbreak inputs, while maintaining competitive performance on general tasks. Moreover, analysis shows that safety temperature provides fine-grained inference-time control that achieves controllable trade-off between utility and safety. Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control. Code: https://anonymous.4open.science/r/UpCycle-70B3.

NeurIPS 2026

Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation

Zhiqi Yu, Zhangquan Chen, Mengting Liu, Heye Zhang, Liangqiong Qu

Reinforcement Learning with Verifiable Rewards (RLVR), particularly GRPO, has become the standard for eliciting LLM reasoning. However, its efficiency in exploration and difficulty adaptation remains an open challenge. In this work, we identify an implicit advantage symmetry inherent in Group Relative Advantage Estimation (GRAE) as a structural property that provides a new perspective for understanding these bottlenecks. This symmetry induces two critical limitations: (i) at the group level, strict symmetry in weights between correct and incorrect trajectories leaves unsampled action logits unchanged, thereby hindering the exploration of novel correct solution. (ii) at the sample level, the algorithm implicitly prioritizes medium-difficulty samples, remaining agnostic to the non-stationary demands of difficulty focus. Through controlled experiments, we reveal that this symmetric property is sub-optimal, yielding two pivotal insights: (i) asymmetrically down-weighting the advantages of correct trajectories encourages essential exploration but risks instability; (ii) learning efficiency can be boosted by a curriculum-like transition—prioritizing simpler samples initially before gradually shifting to complex ones. Motivated by these findings, we propose Asymmetric GRAE (A-GRAE), which dynamically modulates exploration incentives and sample-difficulty focus. Experiments across seven benchmarks demonstrate that A-GRAE consistently improves GRPO and its variants across both LLMs and MLLMs.

NeurIPS 2026

Untrusted Content Masking for Web Agents with Security Guarantees

Kristina Nikolić, Egor Zverev, Javier Rando, Matthew Jagielski, Edoardo Debenedetti, Florian Tramer

Defenses that provide security guarantees against prompt injection attacks require strict isolation between an agent's task planning and data processing capabilities. This prevents third-party content from overwriting trusted instructions. In text-based environments such as tool-use APIs, agents can plan from interface definitions without ever processing untrusted data. Web agents, however, face a fundamental challenge: they must observe the rendered page to perceive their environment, but that page already contains untrusted third-party content. In this paper, we present Untrusted Content Masking, a simple, effective approach that enables web agents to observe their environment and plan without directly processing untrusted content. We leverage a key structural insight: a webpage's Document Object Model (DOM) structure alone suffices to identify untrusted regions. Our framework exploits this by redacting such regions before they reach the agent, and restricting interaction to a sandboxed interface with strict privilege separation.

NeurIPS 2026

Unsupervised Unlearnable Segmentation via Semantic Structure Disruption

Jiale Cai, Zhihao Li, Gezheng Xu, Ruiyi Fang, Hao ZHENG, Zhimin Mei, Song Tang, Charles Ling, Boyu Wang

Unlearnable examples (UEs) aim to prevent machine learning models from extracting useful information in protected training data. Extending UEs to segmentation is practically important but challenging, because pixel-level annotations are expensive to obtain and therefore cannot be assumed available when generating protective perturbations. This motivates us to study unsupervised unlearnable segmentation, where UE perturbations must be generated without segmentation annotations. Since dense prediction fundamentally relies on region-level affinity and cross-layer feature correspondence, effective segmentation data protection faces a twofold design challenge: perturbations should induce misleading alternatives to these structural priors, while the induced structure must remain easy to fit so that downstream segmentation models can adopt it during training. To address these challenges, we propose **DRIFT** (**D**isrupting **R**egion-level and **I**nterlayer **F**eature s**T**ructure) an unsupervised method that generates unlearnable examples for segmentation. Specifically, DRIFT constructs pseudo region-level affinity and pseudo cross-layer correspondence from clean-image features. It then optimizes perturbations to steer the extracted features of protected images toward the misleading pseudo-structure rather than the clean one, while an auxiliary structure learner, trained to predict pseudo-region assignments, explicitly encourages the induced structure to remain easy to fit. Extensive experiments across four segmentation datasets and multiple architectures demonstrate that DRIFT provides strong protection, consistently degrading the generalization performance of models trained on protected data and remaining competitive with the supervised UE baseline.

NeurIPS 2026

Unsupervised Physics Informed Decomposition of Incomplete Time-Resolved Spectroscopy

Tom Muir, Qifeng Liu, Mohammadrahim Kazemzadeh, William Mills, Ales Leonardis, Huabing Yin, Alexander Krull

Raman spectroscopy is often hindered by strong autofluorescence backgrounds, which are typically handled by waiting for fluorescence to bleach before recording the Raman spectrum. Here, we instead show that short, incomplete recordings of the bleaching process already contain enough information to recover the underlying Raman signal. We present the first unsupervised model that operates on short, incomplete, variable-length sequences of time-resolved spectra, decomposing them into Raman spectra and autofluorescence components while simultaneously removing measurement noise. Our VAE-based method simultaneously learns (i) a bank of fluorophore spectra comprising the baseline, (ii) a model of the measurement noise, and (iii) the distribution of Raman spectra and decay behavior. We evaluate our method on spectral reconstruction and downstream peak detection, where it outperforms state-of-the-art approaches on multiple simulated benchmarks as well as on a newly collected real-world dataset of spectral time series. We further show that fluorescence decay dynamics themselves contain discriminative information that may be exploited in future work.

NeurIPS 2026

Unsupervised Concept Discovery with Dirichlet Concept Diffusion Models

Yuchong Geng, Ao Tang

We show that a generative model can discover concepts from data in an unsupervised setting while learning to generate. The Dirichlet Concept Diffusion Model (DCDM) embeds concept discovery into diffusion-based generation. DCDM learns concept centers and infers a Dirichlet distribution over them for each input. The resulting weighted center shapes the forward diffusion mean and reverse denoising process, so components are learned through the same evidence lower bound used to model the data. The method uses no class labels, attribute annotations, captions, pretrained text-to-image priors, or semantic concept supervision. Analysis shows how the formulation preserves concept-level information along diffusion paths and reduces denoising ambiguity, creating pressure for concept centers to capture stable modes. Experiments across diverse image domains show that the learned components form coherent prototypes, support interventions, and reflect recurring visual structure.

NeurIPS 2026

Unrolled gradients in disguise: bridging interpolation-based and Jacobian regularization for stable neural dynamics

Maya Janvier, Etienne Meunier

Hybrid models and neural differential equations (NDEs) are becoming increasingly important for modelling physical systems; however, they often encounter stability and accuracy issues during long-term integration. Training on unrolled trajectories is known to limit these divergences but quickly becomes too computationally expensive due to the need to compute gradients over an iterative process, making this method impractical for large-scale physical systems (e.g. weather and climate forecasting). Two popular paradigms for improving the stability of learned dynamics are to train on interpolated or noisy samples or to regularize the Jacobian of the learned representation. In this work, we demonstrate that these paradigms can be understood under a common lens and that they both correspond to an approximation of the gradients obtained in an unrolled learning strategy. Building on this understanding, we derive a principled and complete approach to regularize training and demonstrate its impact on the stability of learned dynamical systems.

NeurIPS 2026

Unpaired Canonical Correlation Analysis

Nir Ben-Ari, Ronen Talmon, Uri Shaham

Canonical Correlation Analysis (CCA) is a fundamental method for multiview shared space learning. However, its strict reliance on paired data poses a significant limitation, as such data is often difficult to obtain or entirely unavailable. In this paper, we present Unpaired CCA (UCCA), a novel method that learns linear projections to maximize the correlation of the true underlying pairing without access to any paired samples during training. We first establish theoretical results connecting the Quadratic Assignment Problem (QAP) to CCA. Leveraging these theoretical insights, we derive a practical method to maximize correlation exclusively from unpaired data. To the best of our knowledge, UCCA is the first approach to learn maximally correlated projections in a strictly unpaired setting. We validate UCCA on real-world multi-modal datasets, demonstrating that it significantly outperforms recent unpaired alignment baselines in recovering the underlying true correlation. This work fills a critical gap between traditional statistical multiview learning and the growing field of unpaired data learning.

NeurIPS 2026

Unlocking the Duality between Flow and Field Matching

Daniil Shlenskii, Alexander Varlamov, Nazar Buzun, Aleksandr Korotin

Conditional Flow Matching (CFM) unifies conventional generative paradigms such as diffusion and flow-based models. Interaction Field Matching (IFM) is a recently proposed framework that generalizes Electrostatic Field Matching (EFM), rooted in Poisson Flow Generative Models (PFGM). While both frameworks define generative dynamics, they start from different objects: CFM specifies a conditional probability path in data space, whereas IFM specifies a physics-inspired interaction field in an augmented data space. This raises a basic question: **are CFM and IFM genuinely different, or are they two descriptions of the same underlying dynamics?** We show that they coincide for a natural subclass of IFM, which we call *forward-only* IFM. Specifically, we construct mappings between CFM and forward-only IFM and show that they induce the same generative dynamics. We further show that general IFM is strictly more expressive: it includes EFM and other interaction fields that cannot be realized within the standard CFM formulation. Our findings suggest developing generative models using both interpretations rather than treating them separately. Moreover, they highlight a novel direction for generative modeling based on backward-oriented field lines, which lies outside the conventional CFM formalism and may lead to new generative properties.

NeurIPS 2026

Unlocking Compositional Generalization in Continual Few-Shot Learning

Phu Quy Nguyen Lam, Phu Hoa Pham, Dao S Minh, Chi Nguyen Tran, Trung Kiet Huynh, Long Tran-thanh

Object-centric representations promise a key property for few-shot learning: Rather than treating a scene as a single unit, a model can decompose it into individual object-level parts that can be matched and compared across different concepts. In practice, this potential is rarely realized. Continual learners either collapse scenes into global embeddings, or train with part-level matching objectives that tie representations too closely to seen patterns, leaving them unable to generalize to truly novel concepts. In this paper, we identify this fundamental structural conflict and pioneer a new paradigm that strictly decouples representation learning from compositional inference. Leveraging the inherent patch-level semantic geometry of self-supervised Vision Transformers (ViTs), our framework employs a dual-phase strategy. During training, slot representations are optimized entirely toward holistic class identity, preserving highly generalizable, object-level geometries. At inference, preserved slots are dynamically composed to match novel scenes. We demonstrate that this paradigm offers dual structural benefits: The frozen backbone naturally prevents representation drift, while our lightweight, holistic optimization preserves the features' capacity for novel-concept transfer. Extensive experiments validate this approach, achieving state-of-the-art unseen-concept generalization and minimal forgetting across standard continual learning benchmarks.

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