NeurIPS 2026 论文列表

The rapid proliferation of personalization techniques in Stable Diffusion has driven the widespread deployment of adversarial perturbations as a protective measure against unauthorized data mimicry. Existing diffusion-based purification methods primarily rely on the noising and denoising paradigm and have proven somewhat effective in neutralizing these protections. However, they suffer from a severe trade-off between purification efficacy and perceptual fidelity, especially under large perturbation budgets. To mitigate this problem, in this paper, we propose Dynamic Sparse Semantic Anchoring (DSSA), a novel purification framework. By starting the reverse denoising process from pure Gaussian noise, DSSA removes the initial adversarial perturbations and circumvents the dilemma of selecting an optimal noising level. To prevent the loss of original semantics caused by pure noise generation, we propose a dynamic sparse anchoring strategy that directly integrates sparse pixels from the adversarial sample to guide the reconstruction. Additionally, we design a perception-guided early-stopping mechanism to prevent the perturbation resurgence caused by this direct pixel integration. Extensive experiments across diverse datasets and protection schemes demonstrate that our method consistently outperforms existing baselines, achieving state-of-the-art performance in efficacy and fidelity, successfully neutralizing even advanced adaptive protections.

High-compression tokenizers are essential for scaling latent image generative models. However, aggressive compression creates a fundamental tradeoff between reconstruction fidelity and generation efficiency: high compression image encoder always increase the learning diffucity of diffusion training, resulting slow model convergence. Recent representation autoencoders speed-up the diffusion training by improving the latent feature's expressive capability by replacing VAE encoders with pretrained semantic encoders, yet they are typically limited to moderate compression and lose pixel-level details necessary for faithful reconstruction. To achieve both high compression and fast diffusion training, we propose DC-SAE, a Decoupled Compact Semantic Autoencoder designed for high-compression image generation with accelerated diffusion model convergence. DC-SAE consists of two key components: (1) a macro-level architecture design that leverages semantic encoders to enable higher compression ratios, and (2) a pixel-level encoder that preserves low-level details, ensuring high-fidelity image reconstruction. We empirically demonstrate that DC-SAE performs strongly on image generation tasks, achieving both compact latent representations and efficient training dynamics. % Specifically, on ImageNet dataset with $512 \times 512$ resolution, DC-SAE achieves $32\times$ spatial compression, with \textbf{29.79} PSNR and \textbf{3.05} gFID, substantially outperforming the previous state-of-the-art high-compression tokenizer baselines DC-AE by 13.5\% and 59.7\% on PSNR and gFID respectively, maintaining comparable throughput and faster diffusion model training convergence.

Charts convey dense quantitative and relational information, yet general chart parsing remains challenging due to fragmented evaluation and limited structural reasoning. Existing benchmarks focus on narrow sub-tasks with inconsistent output formats (e.g., Markdown, CSV, JSON, SVG, code), hindering fair comparison and often overlooking real-world scenarios such as printed or hand-drawn photos. Meanwhile, end-to-end multimodal models tend to learn shallow pixel-to-text mappings without explicit structure understanding, making them fragile under visual variations. To address these issues, we present a unified framework that connects evaluation and modeling. On the evaluation side, we introduce **ChartArena**, a comprehensive bilingual benchmark covering eight chart families, including both numeric charts (bar, line, pie, radar, box plot, combination) and diagrammatic structures (flowchart, mind map). The dataset is constructed via a human-agent collaborative annotation pipeline, where model-assisted drafts are refined through multi-stage human verification to ensure structural consistency and annotation reliability. Furthermore, we design a format-agnostic evaluation protocol that maps different outputs into two canonical semantic spaces: a normalized triple view and a directed graph view, and evaluates them with structure-aware metrics. On the modeling side, we propose **Chart Atomic Primitives (CAP)**, a lightweight semantic scaffold that introduces structural inductive bias during supervised fine-tuning. By guiding the model to infer structure before generating outputs, CAP leads to more consistent and robust parsing behavior. Extensive experiments show that models trained with CAP achieve reliable performance on both rendered charts and real-world images. Code, benchmark, and models will be made publicly available.

While Large Language Models demonstrate remarkable proficiency in high-level semantic planning, they remain limited in handling fine-grained, low-level web component manipulations. To address this limitation, extensive research has focused on enhancing model grounding capabilities through techniques such as Reinforcement Learning. However, rather than compelling agents to adapt to human-centric interfaces, we propose constructing interaction interfaces specifically optimized for agents. This paper introduces Component Interface for Agent (CI4A), a semantic encapsulation mechanism that abstracts the complex interaction logic of UI components into a set of unified tool primitives accessible to agents. We implemented CI4A within Ant Design, an industrial-grade front-end framework, covering 23 categories of commonly used UI components. Furthermore, we developed a hybrid agent featuring an action space that dynamically updates according to the page state, enabling flexible invocation of available CI4A tools. Leveraging the CI4A-integrated Ant Design, we refactored and upgraded the WebArena benchmark to evaluate existing SoTA methods. Experimental results demonstrate that the CI4A-based agent significantly outperforms existing approaches, achieving a new SoTA task success rate of 86.3\%, alongside substantial improvements in execution efficiency.

Visual foundation models (VFMs) provide strong region-level representations for semantic segmentation, substantially improving high-level semantic understanding. However, accurate boundary recovery remains challenging, especially for fine structures and semantic transitions. A key reason is that conventional pixel-level supervision largely treats pixels independently and lacks explicit modeling of local structure and neighboring-pixel relationships. To address this issue, we propose a Local Structure (LS) regularization framework that constrains the second-order local organization of semantic transition regions. Unlike existing boundary losses that mainly supervise contour location, distance, overlap, or topology, LS models boundary neighborhoods through a structure-tensor representation of semantic probability fields. It decomposes local boundary structure into two complementary cues: Local Structure Magnitude (LSM), which measures the strength of anisotropic class transitions, and Local Structure Orientation (LSO), which describes their dominant geometric orientation. LS is model-agnostic, compatible with different VFM-backbone-decoder combinations, and introduces no extra inference cost. Extensive experiments on four diverse datasets show that LS consistently improves boundary-sensitive performance while also enhancing overall IoU, demonstrating its effectiveness for boundary-structure learning in semantic segmentation.

Contextual bandits require balancing nonlinear reward modeling with online efficiency. Tree ensembles capture nonlinearities but require periodic retraining and large replay buffers. Linear models update efficiently per observation with $O(1)$ memory, but are fundamentally restricted to linear reward structures. We propose Residual Quantization (RQ) as a representation layer to bridge this gap. An offline-trained RQ codebook maps continuous contexts into discrete centroid assignments across $\ell$ levels, set dynamically through a shadow mechanism. This enables a spectrum of additive bandit algorithms that achieve nonlinear expressivity with strictly bounded memory. Across 13 datasets, RQ variants beat their non-RQ counterparts on 11 of 13 datasets, often by wide margins, while matching doubling-retrain XGBoost and neural baselines at up to $1000\times$ less memory.

Post-training quantization (PTQ) compresses large language models (LLMs) but remains largely semantic-agnostic, relying on global activation magnitude to guide precision allocation. This overlooks the functional heterogeneity of Transformer representations, where semantically meaningful channels are sparsely activated while structurally dominant channels exhibit large but context-insensitive activations. We propose Agent-guided Semantic-aware Quantization (ASQ), a PTQ framework that conditions on semantically informative positions identified by an auxiliary LLM. A dual-pathway scoring mechanism captures both activation selectivity and attention routing toward these positions, producing channel-wise importance scores for scaling. This yields a closer approximation to a semantically conditioned objective and consistently improves low-bit (e.g., W4A16) quantization performance across LLMs. Extensive experiments show consistent gains over state-of-the-art PTQ, preserving general reasoning and even surpassing FP16 performance on several benchmarks in specialized domains, while enabling training-free, privacy-preserving self-quantization with zero inference overhead.