NeurIPS 2026 Papers

AI-generated image detectors face significant challenges when deployed in real-world environments, particularly when test samples are produced by unseen generators that deviate from the source training distribution. Existing detectors often rely on a single type of evidence, such as semantic representations or low-level generation traces, making them vulnerable when the corresponding cue becomes unreliable. Moreover, adapting detectors to newly emerging generators usually requires target labels or retraining, which is costly and impractical. To address these challenges, we propose Dual-Evidence Disagreement-Constrained Adaptation (DEDCA), a test-time adaptation framework for generalized AI-generated image detection. DEDCA constructs a dual-evidence detector by combining CLIP-based semantic evidence with trace evidence extracted from shuffled entropy and residual statistics, and introduces a reliability-aware gate to perform sample-specific adaptive fusion. Our key idea is to exploit the disagreement between the two evidence streams as an unlabeled adaptation signal, enabling the detector to adjust to target-domain shifts without relying on target labels or blindly trusting a single prediction stream. Experiments show that DEDCA achieves superior performance on the GenImage benchmark by outperforming state-of-the-art AI-generated image detectors and test-time adaptation methods with 92.0\% average accuracy.

Fact-checking systems with search-enabled large language models (LLMs) have shown strong potential for verifying claims by dynamically retrieving external evidence. However, the robustness of such systems against adversarial attack remains insufficiently understood. In this work, we study adversarial claim attacks against search-enabled LLM-based fact-checking systems under a realistic input-only threat model. We propose DECEIVE-AFC, an agent-based adversarial attack framework that integrates novel claim-level attack strategies and adversarial claim validity evaluation principles. DECEIVE-AFC systematically explores adversarial attack trajectories that disrupt search behavior, evidence retrieval, and LLM-based reasoning without relying on access to evidence sources or model internals. Extensive evaluations on benchmark datasets and real-world systems demonstrate that our attacks substantially degrade verification performance, reducing accuracy from 78.7% to 53.7%, and significantly outperform existing claim-based attack baselines with strong cross-system transferability.

Foundational visual features such as DINO have played a critical role across modern computer vision, and have recently become key components in multi-view feed-forward geometry estimators. In this work, we demonstrate that by re-distilling these multi-view models-their internal knowledge of 3D geometry-into a single-view estimator, we can obtain enhanced 3D consistent foundational features. Our key idea is to construct a multi-view teacher by fusing pretrained 2D foundation features with multi-view geometric features, and refining the fused representation with a discriminative ranking objective. Through our discriminative distillation framework, we enforce the learned features to be both 3D consistent and locally distinctive, while keeping them aligned with the feature space of the original foundation model to preserve the semantic structure of the pretrained representation. Consistency and local discriminability are critical for 3D computer vision problems such as forming semantic and geometric correspondences across images. To demonstrate the effectiveness of our method, we perform comprehensive experiments spanning multiple angles: direct feature analysis, dense prediction transfer, and explicit 3D lifting and rendering. Across these evaluations, our method consistently produces stronger 3D-aware foundation features that improve multi-view consistency and local discriminability while preserving the semantic transferability of the original representation.

LLM-based coding agents have advanced rapidly on single-process SWE tasks, with frontier models now clustering in the high-70s on SWE-bench Verified. Distributed-system debugging, however, remains an under-explored regime: bugs span processes, nodes, and protocol interactions, with root causes rarely recoverable from source alone and brute-force exploration intractable across non-deterministic interleavings. This leaves two gaps in LLM and agent evaluation: no code-repair benchmark targets distributed-system bugs, and no controlled study isolates how much externally provided debugging context changes agent success on them. We introduce DDBench, a code-repair benchmark of 60 historical bugs mined from 13 open-source distributed systems, partitioned into three difficulty tiers. DDbench evaluates every case under two matched conditions: a symptom-only condition where the agent receives only the bug symptom and repository, and a context-augmented condition where it additionally receives a bounded debugging context (logs, traces, runtime state, and targeted code-investigation notes), isolating the effect of debugging context from model capability. The evaluation of ten LLMs on DDBench reveals several findings. First, distributed debugging exercises a reasoning dimension that single-process benchmarks do not surface: models’ pass rates span 61 pp, and pairwise bootstrap separates 9 of 15 top-tier model pairs at p < 0.05 on DDBench's hardest case-set. Second, bounded debugging context lifts aggregate pass rate by +18.1 pp, and the lift is asymmetric: weaker models gain pass rate, while stronger models gain efficiency. Third, debugging context requires careful curation, as even faithful debugging context can sometimes mislead LLMs.

Communication enhances cooperative multi-agent reinforcement learning (MARL) under partial observability. In real networks, however, each agent's available bandwidth varies over time due to changing link quality, interference, and congestion. Most existing MARL communication policies decide whether to transmit based on message importance or novelty, or are trained under fixed bandwidth conditions. As a result, they cannot adapt sending frequency to bandwidth variations, leading to packet loss when bandwidth decreases or missed information when bandwidth recovers. To address this, we propose Dual-Decoupled Adaptive Communication (DDACOM)---a framework that decouples message sending and message generation from the action policy. The message-sending module ranks each message by its deviation from teammates' information and the sender's history, and uses a dynamic token bucket to convert the probe-estimated safe rate into a real-time token budget, enabling the communication frequency to track the safe rate online. The message-generation module is trained independently with receiver-side attention feedback and counterfactual marginal value, ensuring each transmitted message meaningfully contributes to cooperation. On SMAC and MPE, DDACOM achieves leading cooperative performance among baseline methods. Under dynamic network conditions in ns-3 simulation, DDACOM sustains over \(95\%\) safe-rate utilization and packet loss below \(0.3\%\) while reducing communication frequency to less than half of full broadcast, confirming its adaptability to runtime network variation.

Software vulnerability detection plays a critical role in ensuring system security, where real-world auditing requires not only determining whether a function is vulnerable but also pinpointing the specific lines responsible. However, existing approaches either rely on a single information source---sequential, structural, or semantic---failing to jointly exploit the complementary strengths across modalities, or treat statement-level localization merely as a byproduct of function-level detection without explicit line-level supervision. To address these limitations, we propose DCVD (Dual-Channel Cross-Modal Vulnerability Detection), a unified framework that performs joint function-level detection and statement-level localization. DCVD extracts control-dependency and semantic features through two parallel branches and integrates them via contrastive alignment coupled with bidirectional cross-attention, effectively bridging the cross-modal representation gap. It further introduces explicit supervision signals at both the function and statement levels, enabling collaborative optimization across the two granularities. Extensive experiments on a large-scale real-world vulnerability benchmark demonstrate that DCVD consistently outperforms state-of-the-art methods on both function-level detection and statement-level localization.

Training and evaluation in multi-channel imaging (MCI) remains challenging due to heterogeneous channel configurations arising from varying staining protocols, sensor types, and acquisition settings. This heterogeneity limits the applicability of fixed-channel encoders commonly used in general computer vision. Recent Multi-Channel Vision Transformers (MC-ViTs) address this by enabling flexible channel inputs, typically by jointly encoding patch tokens from all channels within a unified attention space. However, unrestricted token interactions across channels can lead to feature dilution, reducing the ability to preserve channel-specific semantics that are critical in MCI data. To address this, we propose Decoupled Vision Transformer (DC-ViT), which explicitly regulates information sharing using Decoupled Self-Attention (DSA), which decomposes token updates into two complementary pathways: spatial updates that model intra-channel structure, and channel-wise updates that adaptively integrate cross-channel information. This decoupling mitigates informational collapse while allowing selective inter-channel interaction. To further exploit these enhanced channel-specific representations, we introduce Decoupled Aggregation (DAG), which allows the model to learn task-specific channel importances. Extensive experiments across three MCI benchmarks demonstrate consistent improvements over existing MC-ViT approaches.

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.

Modeling neurodegenerative pathology from observational cohorts is challenging because subjects are typically observed as unpaired endpoint snapshots rather than aligned longitudinal trajectories. Existing approaches to pathology transport are either endpoint-conditioned or population-level, yet neither extreme alone yields the best terminal-distribution fit. We introduce the Doob-Bridge Posterior Sampler (DBPS), a controlled-SDE posterior sampler with balanced endpoint-population control that blends individual endpoint guidance with population-level guidance. This ρ-weighted controller, obtained as the Cole–Hopf log-gradient of a geometric interpolation between endpoint and population desirability functions, is the exact optimal feedback policy of a linearly-solvable stochastic optimal control problem with closed-form terminal and running costs. Endpoint-conditioned bridge sampling and population Doob sampling are the ρ = 0 and ρ = 1 boundary cases; ρ ∈ (0, 1) realizes the genuine balanced regime. We evaluate DBPS on tau PET pathology transport in Alzheimer’s disease across ADNI and NACC cohorts (84 brain regions). Compared to representative baselines from all major unpaired-transport families, DBPS achieves state-of-the-art performance across multi-cohort settings (union training: SWD 0.760 vs. 0.821; held-out ADNI: 0.822 vs. 0.859), and remains competitive in single-cohort training (ADNI: 0.868 vs. 0.876). Beyond distribution matching, the learned controls recover canonical Braak-stage pathology organization without anatomical supervision, suggesting that balanced SOC objectives capture biologically meaningful structure in latent neurodegenerative progression.

Diffusion large language models (dLLMs) have shown advantages in text generation, particularly due to their inherent ability for parallel decoding. However, constrained by the quality--speed trade-off, existing inference solutions adopt conservative parallel strategies, leaving substantial efficiency potential underexplored. A core challenge is that parallel decoding assumes each position can be filled independently, but tokens are often semantically coupled. Thus, the correct choice at one position constrains valid choices at others. Without modeling these inter-token dependencies, parallel strategies produce deteriorated outputs. Motivated by this insight, we propose DAWN, a training-free, dependency-aware decoding method for fast dLLM inference. DAWN extracts token dependencies and leverages two key observations: (1) masked positions dependent on unmasked certain positions become more reliable; (2) coupled masked positions strongly influence each other's predictions. Given those findings, DAWN leverages a dependency graph to select more reliable unmasking positions at each iteration, achieving high parallelism with negligible loss in generation quality. Extensive experiments across multiple models and datasets demonstrate that DAWN speedups the inference by **1.80–8.06**× over baselines while preserving the generation quality.

Symbolic regression (SR) aims to discover interpretable mathematical expressions from data and plays a key role in scientific discovery. However, existing methods face a common bottleneck: the enormous search space and severe combinatorial explosion make it difficult to achieve a favorable trade-off between accuracy and algorithmic efficiency. We argue that, in addition to designing increasingly complex solver-specific SR strategies, an equally promising direction is to equip them with a shareable auxiliary strategy that can provide reliably search guidance. To this end, We propose DASS, a solver-agnostic Dynamic Auxiliary Search Strategy that improves SR by constructing and refining a high-potential auxiliary search subspace. DASS represents the auxiliary search space as a functional subspace spanned by a set of high-potential basis function terms. It estimates term utility through multi-environment evaluation, filters unstable guidance, and updates terms' potential score with a Gibbs posterior, thereby calibrating the subspace to provide more reliable guidance for downstream solvers. The High-quality solutions produced by the solver are used to update the set of basis terms. This forms a virtuous closed-loop optimization process: the subspace guides the downstream solver, and the solver feedback reshapes the subspace for subsequent exploration. DASS can be seamlessly integrated with various types of SR solvers. Extensive experiments on LLM-SRBench show that DASS significantly improves the accuracy of original solvers with a manageable increase in runtime.

Subgraph mining aims to discover meaningful subgraphs from a large graph given a seed node, with applications in community detection, functional module identification, anti-money laundering, and knowledge discovery. Existing methods typically adopt static approaches that cannot dynamically and intelligently adapt to diverse graph structures. Moreover, training models for subgraph mining faces inherent challenges including exposure bias and the difficulty of learning when to stop expansion. To address these limitations, we propose DASM (Dynamic Autoregressive Subgraph Mining), a novel two-stage framework that models subgraph mining as a sequential decision process. In the first stage, we employ step-wise supervised learning with multi-label objectives and asymmetric F1 loss to teach the model how to select correct next nodes. In the second stage, we apply Group Relative Policy Optimization (GRPO) to align the model with end-to-end mining quality, enabling it to learn optimal stopping decisions through trajectory-level feedback. Our dynamic autoregressive approach allows the model to make state-aware decisions at each step, automatically determining subgraph boundaries without predefined size constraints. Experiments on multiple datasets demonstrate that DASM significantly outperforms existing baselines in terms of F1 score and IoU.

Randomized controlled trials typically assume that prognostic covariates are known and available at no cost. In practice, obtaining high-dimensional pretreatment data is costly, forcing a trade-off between covariate-adaptive precision and a measurement budget. We introduce Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS), which treats covariate acquisition as a sequential optimization problem embedded within a design-based causal inference task. A budgeted combinatorial Thompson sampler learns which covariates are most prognostic across successive batches; selected covariates then drive rerandomization and regression adjustment to reduce batch-level average treatment effect variance. Our primary theoretical contribution is a decoupling result: adaptive covariate selection based on past batches preserves batch-level randomization validity, and the cumulative inverse-variance weighted estimator achieves at least nominal asymptotic coverage. We further derive a Bayes risk bound for the acquisition layer that matches the minimax lower bound up to logarithmic factors. Empirically, DARTS systematically concentrates budget on informative features, significantly closing the efficiency gap to oracle designs while maintaining strict inferential validity.

Physical control systems, such as building HVAC systems, thermal processes, and power-grid assets, often involve delayed dynamics, safety constraints, and costly online interaction. Because the effect of a control action may appear after several time steps, effective control often requires reasoning over histories of states, actions, and outcomes. Pretrained sequence controllers, such as Decision Transformers, provide a natural way to model this history-dependent control problem by learning policies from offline trajectories, reducing the need for expensive online retraining. However, their performance can degrade when deployment conditions differ from the training data. In building control, for example, actuator wear, sensor drift, occupancy changes, and weather variation can all affect the observed response of the system to the same control action. Online adaptation is therefore needed, but raw prediction error is not always a reliable signal for updating the controller. The key challenge is to identify which part of the observed mismatch is action-sensitive and should guide policy adaptation. To address this challenge, we propose DARPAN, a Drift-Aware Residual Policy Adapter Network for pretrained physical control. DARPAN keeps the pretrained Decision Transformer frozen and performs online adaptation only through lightweight residual adapters, preserving the offline policy and limiting the number of trainable parameters. Instead of updating from raw prediction error, DARPAN uses a controllability filter trained in simulation to extract the action-sensitive part of the residual and filter out mismatch that is weakly related to the controller's actions. During deployment, a frozen predictor estimates the next state, the controllability filter produces a filtered residual, and a short history of filtered residuals forms a drift signature. This signature is used to select an existing adapter or create a new one when a persistent drift pattern appears. We benchmark DARPAN on SustainGym under actuator faults, sensor drift, delays, and external disturbances. DARPAN reduces the drift-induced performance loss by 31.7% and uses 63% fewer online parameters than Online DT. We release anonymized code and experiment configurations at https://huggingface.co/Darpan2026.

We study model-free reinforcement learning (RL) in non-stationary finite-horizon episodic Markov decision processes (MDPs) without prior knowledge of the non-stationarity. We focus on the piecewise stationary (PS) setting, where both rewards and transition dynamics can change at unknown times. We first revisit existing state-of-the-art approaches and identify theoretical and practical limitations that change the current landscape of performance guarantees. To characterize the difficulty of the problem, we establish the first minimax lower bounds for PS-RL in tabular and linear MDPs. We then introduce *Detection Augmented Reinforcement Learning* (DARLING), a modular wrapper for PS-RL that applies to both tabular and linear MDPs, without knowledge of the changes. In tabular MDPs, under change-point separability and reachability conditions, DARLING improves the best known dynamic regret bounds and matches our minimax lower bound. In linear MDPs, DARLING matches the minimax lower bound when the relevant reachability parameters are known, and our analysis clarifies the structural obstacles that distinguish this setting from the tabular case. Finally, through extensive experimentation across diverse non-stationary benchmarks, we show that DARLING consistently surpasses the state-of-the-art methods.

Pathological whole slide images (WSIs) from different hospitals often exhibit severe domain shifts due to variations in scanning devices, staining protocols, and tissue preparation procedures. This leads to significant performance degradation when a trained model is applied to data from different sources. Although unsupervised domain adaptation (UDA) has achieved strong performance in transferring a model trained on a source domain to an unlabeled target domain in natural images, it is much less studied in the computational pathology field. This is due to the unique properties of WSIs, including ultra-high resolution and strong inter-slide and intra-slide heterogeneity. As a result, existing UDA methods are often unstable and may even cause negative transfer in WSI classification. To address this issue, we propose Dual-level Adversarial learning with domain-aware REgularization (DARE) for WSI Classification. Specifically, we design an adaptive pseudo-labeling framework with a teacher-student architecture to provide stable supervision for the target domain and perform adversarial alignment on both patch-level and slide-level embeddings. This enables joint optimization of patch-level and slide-level semantic embeddings for dual-level alignment across domains. We also introduce domain-aware attention masking to regularize attention and improve cross-domain generalization. We evaluate our method on four public WSI datasets through extensive experiments. The results show that our method consistently outperforms state-of-the-art approaches across various domain shifts.

LLM-based theorem proving in Lean 4 has advanced rapidly, but provers are difficult to leverage newly contributed lemmas, because retrieval skews toward popular foundational lemmas, leaving the right premises buried among hundreds of thousands of candidates. Existing neural selectors rank candidates by semantic similarity, with three intrinsic limitations: frequency-skewed retrieval, isolated pairwise ranking, and degradation on out-of-distribution queries such as competition problems and informal language. Given that mathematics is structured by dependencies rather than similarities, we restore this signal to both the training data and the model. We curate two underused dependency resources. We conduct the first systematic mining of 53 Lean Blueprint projects, a previously untapped corpus of 3,805 manually curated informal+formal nodes that capture rare, research-level premises absent from Mathlib4. We further pair it with the extraction of comprehensive, typed, multi-level Mathlib4 dependencies. We then introduce DAPS, a dependency-aware premise selector with a structural neighborhood encoder, a group-level contrastive objective, and a Mathlib-then-Blueprint adaptation procedure. DAPS reaches Recall@32 of 89.31 on Mathlib4-Heldout (+11.47 over the strong selector LeanHammer). DAPS holds its lead across both out-of-distribution benchmarks, and improves three general-purpose LLMs on the informal IMOProofBench. Dependency graphs, benchmarks, and code are available 1 and will be released under a permissive license upon publication.

Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as intermediate findings emerge. Directed acyclic graph (DAG)-based multi-agent systems are well suited to this setting because they support parallel execution and isolate each sub-task within a focused dependency context. Yet existing DAG-based agents typically instantiate a task-level plan before execution and then repair the graph only after failures or missing evidence are observed. This Plan-then-Patch strategy is brittle for deep research: the system commits most strongly when its evidence is weakest, and later revisions often waste computation on branches that should not have been planned in the first place. To bridge this gap, we propose DAGent, a DAG-based multi-agent framework that introduces Evaluate-then-Grow incremental planning. Instead of committing to a full DAG upfront, an Orchestrator grows the task graph one batch at a time, conditioning each new expansion on confidence and uncertainty signals from completed nodes. To support long-horizon evidence use without overloading each sub-task, DAGent maintains a hierarchical context layer that propagates compact QueryDocs by default while preserving full execution traces for on-demand recall. The cleanly recorded DAG topology in turn admits structural RL signals that an outcome-only recipe cannot define; we instantiate this with DAGRPO, a GRPO adaptation that injects topology-conditioned credit on Executor rollouts and a structural compliance regularization on Orchestrator plans. Across BrowseComp-Plus, GAIA, and xbench-DeepSearch, DAGent surpasses the strongest open-source baseline by 5.3 / 5.8 / 2.0 points at the Qwen3-235B-A22B scale, with the lead replicating across four open-source backbones from four vendors and extending to GPT-5 at 327K context. At the Qwen3-8B scale, DAGRPO further improves over a same-budget outcome-only GRPO baseline by 3.0 average Pass@1 points. A same-architecture comparison further shows that incremental, evidence-conditioned planning recovers accuracy at lower per-task token, tool-call, and step footprints than its Plan-then-Patch counterpart, supporting the view that DAGent's gains come from more targeted evidence expansion rather than additional computation. Anonymized code is available at \url{https://anonymous.4open.science/r/DAGent-F2D6}.

Self-supervised Vision Transformers in the DINO family encode rich semantic structure within their self-attention maps, yet existing parameter-efficient fine-tuning (PEFT) methods apply static, content-agnostic transformations that ignore this internal signal. We present DAGA (Dynamic Attention-Guided Adaptation), a PEFT module that repurposes a frozen backbone's emergent attention maps as instance-specific guidance: per-image attention is converted into channel and spatial gates that condition a lightweight bottleneck adapter. With only 1.4\% additional parameters, DAGA reaches 85.9\% top-1 on ImageNet-1K with DINOv3-ViT-B and surpasses recent PEFT baselines, with strong gains on dense prediction (+12.1\% mIoU on ADE20K) and fine-grained classification (+1.6 / +3.5 on CUB-200 / FGVC-Aircraft). DAGA's effectiveness is tied to the semantic quality of the backbone's attention: gains are large on DINOv2/v3 and iBOT but small on MAE, CLIP, and DeiT, positioning it as the first PEFT module to operationalize emergent SSL attention as in-loop guidance and complementing self-distillation methods that refine attention itself. Anonymized code: https://anonymous.4open.science/r/ID1680.