NeurIPS 2026 论文列表

AI-generated videos are becoming increasingly realistic, yet most detection methods focus on spatial artifacts within individual frames and require synthetic training data from known generators, causing them to fail on unseen generators. We identify two previously unexplored temporally distributed forensic microstructures that reveal whether a video was AI-generated. These traces arise from how a video's visual field and motion evolve over time, capturing subtle deviations that generators introduce when approximating real-world temporal dynamics. To expose them, we train two simple predictors on real video only, modeling visual evolution (VE-FSD) and motion evolution (ME-FSD), and analyze their prediction residuals. We fit 3D spatio-temporal autoregressive models to these residuals, producing compact descriptors we call Video Forensic Self-Descriptions (VFSD). VFSDs of real videos naturally cluster together and separate from AI-generated ones, enabling zero-shot detection and source attribution without synthetic training data. Experiments across multiple datasets and 35 generators show VFSD achieves state-of-the-art performance on both tasks.

We consider two-layer neural networks trained in the feature-learning regime using gradient descent, and relate the output of the finite-width network $f_{\hat{\rho}}$ to its infinite-width counterpart $f_{\rho^{MF}}$, which evolves in the mean-field dynamics. While constant-time horizon bounds for $\|f_{\rho^{MF}} -f_{\hat{\rho}}\|$ may be obtained via standard Grönwall estimates, the long-time behavior of the fluctuation is a more delicate matter. Uniform-in-time bounds often rely on (local) strong convexity in the landscape or Logarithmic Sobolev inequalities present in noisy gradient dynamics. % In this work, we study a noiseless setting and do not make assumptions on the geometry of the landscape near the optimum. Instead, we impose a weaker condition on the convergence rate of the mean-field deterministic Wasserstein-gradient-flow dynamics. In this work, we establish non-asymptotic weak propagation-of-chaos that holds uniformly in time, obtained by exploiting instead the convergence rate of the mean-field deterministic Wasserstein-gradient-flow dynamics. Specifically, denoting by $L_t$ the mean-field loss at time $t$ and $m$ the number of neurons, under standard regularity assumptions and the condition $\int_0^\infty L_t^{1/2} dt =O_d(1)$, we obtain the uniform in time bound $\|f_{\rho^{MF}} -f_{\hat{\rho}}\|^2 \lesssim \text{poly}(d) m^{-\min(1,c/6)}$ whenever $L_t \lesssim t^{-c}$. Our result holds in a noiseless setting and does not make any assumptions on the geometry of the landscape near the optimum. A key implication of our result is that whenever the convergence rate of the mean-field, population-loss dynamics is faster than $1/t^2$, we can attain a loss of $\epsilon$ with only $\text{poly}(d/\epsilon)$ neurons, training samples, and GD steps.

Despite the promise of Neural Combinatorial Optimization (NCO), recent literature has increasingly coupled *decision paradigms*, *training objectives*, and *problem classes* with specific backbone architectures, limiting generic model design and fair cross-paradigm evaluation. We propose UECO, a simple yet effective **U**nified **E**ncoder for **C**ombinatorial **O**ptimization with a structure-aware attention mixture scheme, where a lightweight module defined upon graph-based locality is interleaved with conventional (global) node-focused attention blocks to dynamically evolve edge representations and inject local topological information into attention scores. Instead of fusing static edge features in a single step, UECO thereby integrates local and global messages in a progressive and problem-agnostic manner. UECO is orthogonal to task-specific decoders, and works seamlessly with *Local Construction* (LC), *Global Prediction* (GP), and *Adaptive Expansion* (AE) paradigms, across node- or edge-oriented problems, symmetric or asymmetric tasks, and sparse or dense graphs, for supervised, reinforcement, or generative learning. Extensive experiments show that, via plug-in substitution of encoders, UECO consistently improves solution and generalization quality over diverse backbones and task scales on TSP, ATSP, CVRP, ACVRP, MIS, MaxClique, and MaxCut.

Current video-to-4D methods struggle with complex topology changes, transparent materials, thin structures, and inner surfaces. We present Trellis4D, a dynamic mesh generation framework by inheriting the expressive representation of Trellis2, adapting it from image-to-3D to video-conditioned 4D generation. Our design arises from two key questions: (a) how to enable Trellis2's frame-local attention to share information across frames while preserving its pretrained quality on rare cases such as transparent objects and inner surfaces, and (b) how to inject temporal information into a purely 3D positional encoding without breaking pretrained capabilities. We address (a) with a sliding-window cross-frame attention and anchor on the first frame. The first frame is generated by the base Trellis2 model and injected into our model, letting it inherit Trellis2's quality in rare cases through cross-frame attention. We address (b) with a 4D temporal encoding that repurposes redundant low-frequency spatial RoPE bands for time, extending the encoding from 3D with no additional parameters. Extensive experiments show the effectiveness of Trellis4D for high-quality dynamic mesh generation on ActionBench and our own challenging complex dynamics set.

Transmissive scenes are ubiquitous in daily life, yet reconstructing and rendering them remains highly challenging due to the inherent entanglement between near-field reflections from the surrounding environment on the transmissive surface, and the transmitted content of the scene behind it. This coupling gives rise to dual surface geometries and dual radiance components within each observation, posing ambiguities for standard methods. We present TransmissiveGS, a novel framework for disentangled reconstruction and rendering of transmissive scenes. Specifically, we model the scene with a dual-Gaussian representation and introduce a deferred shading function to jointly render the two Gaussian components. To separate reflection and transmission, we exploit the inherent multi-view inconsistency of reflections and leverage the residuals from reconstructing multi-view consistent content as cues for disentangled geometry and appearance modeling. We further propose a reflection light field that enables high-fidelity estimation of near-field reflections. During training, we introduce a high-frequency regularization to preserve fine details. We also contribute a new synthetic dataset for evaluating transmissive surface reconstruction. Experiments on both synthetic and real-world scenes demonstrate that TransmissiveGS consistently outperforms prior Gaussian Splatting-based methods in both reconstruction and rendering quality for transmissive scenes.

Many emerging agentic paradigms require agents to collaborate with one another (or people) to achieve shared goals. However, existing approaches to learning policies for such collaborative problems produce brittle solutions that fail when paired with new partners. We attribute these failures to a combination of free-riding during training and a lack of strategic robustness. To address these problems, we study the concept of strategic risk aversion and interpret it as a principled inductive bias for generalizable cooperation with unseen partners. While strategically risk-averse players are robust to deviations in their partner's behavior by design, we show that, in collaborative games, they also (1) can have better equilibrium outcomes than those at classical game-theoretic concepts like Nash, and (2) exhibit less or no free-riding. Inspired by these insights, we develop a multi-agent reinforcement learning (MARL) algorithm that integrates strategic risk aversion into standard policy optimization methods. Our empirical results across collaborative benchmarks (including an LLM collaboration task) validate our theory and demonstrate that our approach consistently achieves reliable collaboration with heterogeneous and previously unseen partners across collaborative tasks.

Recent multimodal large language models (MLLMs) increasingly rely on visual chain-of-thought to perform region-grounded reasoning over images. However, existing approaches ground regions via either textified coordinates—causing modality mismatch and semantic fragmentation—or fixed-granularity patches that both limit precise region selection and often require non-trivial architectural changes. In this paper, we propose Numerical Visual Chain-of-Thought (NV-CoT), a framework that enables MLLMs to reason over images using continuous numerical coordinates. NV-CoT expands the MLLM action space from discrete vocabulary tokens to a continuous Euclidean space, allowing models to directly generate bounding-box coordinates as actions with only minimal architectural modification. The framework supports both supervised fine-tuning and reinforcement learning. In particular, we replace categorical token policies with a Gaussian (or Laplace) policy over coordinates and introduce stochasticity via reparameterized sampling, making NV-CoT fully compatible with GRPO-style policy optimization. Extensive experiments on three benchmarks against eight representative visual reasoning baselines demonstrate that NV-CoT significantly improves localization precision and final answer accuracy, while also accelerating training convergence, validating the effectiveness of continuous-action visual reasoning in MLLMs. The code is provided in the Supplementary Materials.

The task of visual reasoning relative pose identification (VRRPI) has exposed critical weaknesses in the multi-view 3D spatial reasoning capabilities of modern vision-language models (VLMs). We present a systematic analysis of VLMs behavior in VRRPI, uncovering stable output biases where VLMs disproportionately predict specific motion patterns regardless of visual evidence. To mitigate these inherent biases, we propose self knowledge re-expression with iterative debiasing (SKR-ID), an annotation-free pipeline that adapts VLMs for VRRPI using only randomly extracted unannotated image pairs. SKR-ID transitions VLMs' output mechanism from generic next-token prediction to task-optimized classification heads. The pipeline proceeds in two stages: (1) a cold-start initialization using debiasing object-centric prompts to anchor motion inference in 2D visual evidence; and (2) a recursive logit-ranking mechanism that enforces statistical parity by using the median logit value as a dynamic threshold. Experimental evaluations across frontier VLMs on standard benchmarks demonstrate that SKR-ID significantly mitigates systematic biases and unearths latent 3D reasoning ability. Our method achieves substantial gains, improving open-sourced VLMs' Macro-F1 scores by at least 8.3% and at most 23.0%, proving that VLMs possess significant untapped multi-view 3D reasoning potential.

Every neuron in a trained network outputs a number that can be separated into its sign (positive or negative) and its magnitude (absolute size). We call the set of all neuron signs the \textit{sign code}. This raises a key question: does classification depend more on the sign code or on the magnitudes? For overparameterized vision classifiers with sign-preserving activations, the answer is overwhelmingly the sign code. A $k$-nearest neighbor classifier operating only on this binary sign code achieves accuracy comparable to the full model across different architectures, activation functions, and datasets. Moreover, replacing all magnitudes in every layer with random noise while keeping the original signs barely changes residual network performance. In contrast, flipping just 20\% of the signs reduces accuracy to chance. Models generalize well when same-class inputs share similar sign codes. When this overlap is disrupted by a regularizer, training accuracy remains high, but test performance collapses. A margin decomposition helps explain these results: the sign-only contribution to each logit grows as $\sqrt{d}$ in network width, dominating the magnitude residual. The natural unit of analysis for these networks may not be through their continuous weight space but instead through their discrete sign codes.

In this paper, we introduce a semi-random model in online optimization, which interpolates naturally between the standard (adversarial) and random-order arrival model, which we call the online with a random sample (ORS) model: the full input may be chosen adversarially, but a random $p$-fraction of the input is selected and presented to the algorithm in random-order, before the remaining non-sampled elements are presented (in adversarial order). While similar in spirit to the AOS (adversarial order with a sample) model introduced by Kaplan, Naori and Raz, in our ORS model, the sampled $p$-fraction is revealed online instead of offline, and hence, competitiveness is measured with respect to the full input. Our central focus in the paper is applying the ORS model to the secretary problem and its natural $(k,1)$ variant. For the secretary problem, we present an optimal $pe^{-p}$-competitive algorithm, while for the $(k,1)$-secretary problem, we obtain a competitive algorithm whose performance converges to optimal competitiveness as $p\rightarrow 1$. We also include a $O(\log (1/p))$-competitive algorithm for facility location.

Recent mechanistic work has uncovered learned algorithms within neural networks, from modular arithmetic to search and planning in game-playing agents. But does algorithmic structure guarantee algorithmic behavior? We investigate this in Leela Chess Zero, the strongest neural chess engine, where prior work identified learned look-ahead. By extending the logit lens to its move-selecting policy network, we discover that correct puzzle solutions—including immediate checkmates—often appear in intermediate layers but are systematically overridden in the final output, a phenomenon we term *forgotten puzzles*. Replicating prior analyses on these positions, we find that look-ahead operates normally—future moves of the correct continuation are represented, causally important, and linearly decodable—ruling out a failure of the algorithm itself. Instead, late layers increasingly shift toward prioritizing safe play over aggression. To test whether this shift drives the override, we steer the model against these preferences and recover 61.7% of forgotten puzzles, providing causal evidence that safety priors override algorithmically computed solutions. These findings demonstrate that algorithmic structure does not guarantee algorithmic behavior: a model can internally solve a problem and still output the wrong answer.

AI generated predictions increasingly inform decision making in critical tasks, and therefore must be trustworthy. One widely used measure of trustworthiness is calibration, which requires that the predictions match the true frequencies and can be treated like real probabilities of a given outcome. However, defining calibration is subtle, and designing good measures of calibration error has been an active topic of recent research. The first goal is to find calibration measures that are *actionable*, meaning they can inform decision makers about their utility loss when predictions are treated as true probabilities, which is known as swap regret. The second goal is to find calibration measures that are *testable*, meaning that calibration error can be measured from a small sample of predictions and outcomes. Although these are very basic requirements, there is no existing calibration measure that fully satisfies both properties, and all existing measures relax actionability by bounding a weaker notion of swap regret, or relax testability by having suboptimal estimation error. We introduce a new calibration measure, Soft-Binned Calibration Decision Loss (SCDL), which we prove is fully actionable without weakening either requirement, and testable with nearly optimal error rate. In addition, SCDL satisfies other desired properties such as continuity and consistency. We also provide a set of experiments confirming that the theoretical advantages of SCDL compared to other measures lead to better performance in practice.

We study Flow Matching in a semi-discrete setting where a Gaussian source is transported toward a discrete target supported on finitely many points. This semi-discrete regime is the theoretical setting behind the use of Flow Matching for generative modeling, where the target distribution is represented by a finite dataset. In this semi-discrete regime, the exact Flow Matching velocity field is available in closed form, which makes it possible to analyze the geometry induced by the terminal flow map independently of optimization and approximation effects. We investigate the terminal assignment regions, namely the preimages of the target atoms under the terminal flow. We show that these regions are open, simply connected and, under an additional assumption, homeomorphic to the unit ball. At the same time, a planar four-point example shows that these cells can differ sharply from Laguerre cells arising in semi-discrete optimal transport: they may be non-convex, have curved boundaries, and exhibit different boundedness and adjacency patterns. These results clarify the geometry intrinsically induced by the exact semi-discrete Flow Matching objective before neural approximation enters the picture.

Source-free domain adaptation (SFDA) adapts a pre-trained source model to an unlabeled target domain without access to source data. However, existing SFDA studies have predominantly focused on classification tasks, leaving time series forecasting (TSF), a fundamentally different problem with continuous outputs and complex temporal dependencies, largely unexplored. In this work, we identify unique characteristics and key challenges of SFDA in TSF: 1) forecasting labels correspond to future temporal segments of the same underlying signal, enabling us to split target input and construct a self-supervised learning task, thereby training an accurate short-term forecasting model to provide high-quality pseudo-labels; 2) however, such supervision remains inherently *local* rather than *global* for long-horizon source model adaptation. To fill this gap, we propose the notion of Spectral Energy Allocation (SEA) pattern, defined over a short-long signal pair. The SEA pattern provides a structured correspondence that bridges signals across different temporal horizons, enabling reliable short-horizon pseudo-labels to guide long-horizon adaptation. Extensive experiments demonstrate that our method substantially outperforms baselines across different forecasting backbones.

LLM agents that execute multi-step tool calls must satisfy two objectives simultaneously: completing the user's task (utility) and conforming to domain policies and correctness constraints (safety). These objectives are in tension -- blocking an unsafe action prevents a violation but can leave the agent stranded with no principled way to recover. Existing approaches sacrifice one objective for the other. We introduce Soteria, a framework that reconciles safety and utility through verified hierarchical planning and runtime enforcement of specifications. Before execution, the agent generates a structured plan that is formally verified against the specifications prior to any tool invocation. During execution, the verified plan serves dual roles: it ensures that the agent takes specification-conformant trajectories and provides guidance when unsafe actions are blocked. Across multiple benchmarks covering a diverse range of tool-use tasks, Soteria achieves perfect specification conformance while improving utility by up to $5\times$ over existing guardrail approaches, demonstrating that safety and utility need not be traded off.

Synthetic data can scale visual pretraining, but current selection criteria only partially explain which datasets train strong representations. We propose using the energy profile of a dataset---the distribution of log-probability levels it occupies under a normalised real-image density model---as an audit and curation principle for synthetic data. To operationalise this view, we introduce Energy-Level Distribution Distance (Eld), which compares real and synthetic energy distributions. Real images span sparse high-likelihood scenes and dense low-likelihood textures, so useful synthetic data should cover this energy range rather than collapse to likely but low-variation samples. Across diverse synthetic datasets, Eld exposes mismatches missed by FID and predicts pretraining performance beyond FID, LPIPS variation, and feature-space recall, including across CNN and ViT self-supervised settings. We then use Eld to resample and mix synthetic sources, matching the real-image energy histogram while preserving diversity and improving downstream transfer.

At the heart of existing language model agents is a fixed orchestrator program which is responsible for the state transition between consecutive turns. This paper introduces \emph{self-programmed execution} (SPE), an agent architecture in which the model completion is itself the orchestrator program, and the harness evaluates this program but does not impose its own orchestration policy. I formalize this idea using agentic machines: an SPE state is one from which a model completion can load any state of an embedded copy of the machine, meaning that it is subject to no fixed turn-to-turn orchestration policy. Realizing SPE in practice is nontrivial because the same data is both model context and executable program. I therefore introduce \textsc{Spell}, a Lisp-based language in which programs can edit and re-evaluate themselves, and effectful expressions like model invocations are structured such that re-evaluating an edited program does not replay its side effects. Experiments with existing models, not trained for SPE or \textsc{Spell}, show that frontier models can operate in this regime and accomplish challenging agentic tasks. These results demonstrate how an LM can act as an agent without any fixed orchestration policy, and they raise the question of what self-orchestration strategies might be learned by a model trained for self-programmed execution.

Large language model (LLM)-powered multi-agent systems have been shown to effectively solve real-world end-to-end problems through collaboration among diverse agents. However, as LLM capabilities advance and user demands continue to grow, the range of problems that agents can address expands dramatically, while the population of available agents and tools continues to increase, giving rise to open and evolving ecosystems of autonomous agents. In this paradigm, agents are likely to become increasingly specialized, each focusing on particular capabilities or problem types. In such a setting, a central challenge is how to efficiently assemble an appropriate team of agents to take on different responsibilities from a massive agent pool for a given task. In this work, we study this problem in a controlled setting with 300 task-oriented agents. SMART (Scalable Multi-Agent Role-conditioned Teaming) first plans a task-conditioned collaboration topology and candidate role set, then constructs a compact role-conditioned candidate pool, and finally uses a LLM-free Monte Carlo Tree Search (MCTS) to assign agents to roles online via metadata-based rollouts. This design enables high flexibility without incurring prohibitive inference costs. Across multiple experiments, our approach achieves superior performance on GSM8K (93.64\%), MATH (61.73\%), HumanEval (93.89\%), and MBPP (87.68\%) using gpt-4o-mini under a limited budget.

3D animal reconstruction in the wild remains challenging due to large species variation, frequent occlusions, and the prevalence of multi-animal scenes, while existing methods predominantly focus on single-animal settings. We present SAM 3D Animal, the first promptable framework for multi-animal 3D reconstruction from a single image. Built on the SMAL+ parametric animal model, our method jointly reconstructs multiple instances and supports flexible prompts in the form of keypoints and masks which enable more reliable disambiguation in crowded and occluded scenes. To train such a model, we further introduce Herd3D, a multi-animal 3D dataset containing over 5K images, designed to increase diversity in species, interactions, and occlusion patterns. Experiments on the Animal3D, APTv2, and Animal Kingdom datasets show that our framework achieves state-of-the-art results over both existing model-based and model-free methods, demonstrating a scalable and effective solution for prompt-driven animal 3D reconstruction in the wild.