NeurIPS 2026 Papers

Artificially generated speech is increasingly embedded in everyday life. Voice cloning in particular enables applications where identity preservation is important, such as completing a recording, dubbing in a new language, or preserving the voices of individuals with speech loss. However, in our work, we find that despite the term, voice cloning does not faithfully "clone" an individual's voice. Instead, we find that widely-used voice cloning models systematically apply style transfer to source voices. As rated by human annotators, cloned voices are perceived as more authoritative, warm, customer-service-like, and human-like compared to their sources. Human annotators also report greater trust in cloned voices than source voices, and a greater willingness to disclose sensitive personal information to them. Our work furthermore shows that voice cloning leads to homogenization of speaker characteristics, as measured by reduced variance in accent, speaking rate, and the audio embedding space. Together, our results highlight a new set of limitations and risks of voice cloning technology and their potential impact on human behavior.

While recent autoregressive video diffusion models achieve remarkable streaming quality, they remain confined to low resolutions (e.g., $480$P), leaving efficient, scalable, real-time high-resolution video generation a fundamental open challenge. To bridge this gap, we present Ultra Flash, a cascaded streaming framework capable of real-time high-resolution video generation. Ultra Flash achieves ${\sim}30$ FPS at 1K resolution and ${\sim}18$ FPS at 2K resolution on a single B200 GPU through three key contributions: (1) an architecture-preserving T2V-to-TV2V super-resolution training paradigm coupled with an AIGC-oriented data degradation pipeline that effectively preserves the generative capability of the base model, enabling enhanced high-resolution detail when cascaded after mainstream low-resolution generative models; (2) a causal streaming latent upsampler paired with a high-resolution decoder, which enhances spatiotemporal coherence while enabling efficient latent spatial scaling and precise high-resolution decoding with negligible computational overhead; and (3) a cascade high-resolution streaming video generation optimization scheme that first performs hybrid-reward-enhanced sparse causalization and single-step distillation of the super-resolution model, then introduces cascaded streaming self-forcing preference optimization with dynamic cache management, jointly enhancing overall coherence, improving quality, and enabling real-time high-resolution streaming video generation. Extensive experiments demonstrate that Ultra Flash reliably produces ultra-high-resolution streaming video while maintaining state-of-the-art visual quality and superior efficiency.

The analysis of electrostatic induction effects on transmission lines in real-world power distribution systems requires inferring induced surface charge density $\sigma$ on overhead-line corridors at deployment scale, where dense $\mathcal{O}(N^3)$ boundary-element solvers for the underlying Poisson boundary integral equation (BIE) are infeasible. We present a recipe for training cheap PDE surrogates from canonical-case closed forms (flat ground, grounded sphere) plus sparse dense-solver fine-tuning where the canonical case breaks like in our case study. The recipe also avoids a structural degeneracy between training operator $A_\theta$ and solution $\sigma$ in self-supervised operator learning that tends to lead to internally consistent but non-physical solutions. The recipe combines a closed-form local-tangent-plane baseline plus a small neural correction; a frozen analytic kernel; an $SE(3)$- and scale-invariant dimensionless feature stack derived from a $2$-term identity $\sigma = -(\nabla\phi\cdot\hat{n})/(2\pi) + H\phi/(4\pi)$, exact on flat ground and the grounded sphere and supervised training against this identity. An $8$-parameter linear head recovers the analytic curvature coefficient $+1/(4\pi)$ from data to within $2\%$; holds to $1\%$ relative RMS in $\sigma$ across $12$ orders of magnitude in source-charge scale, where DeepONet, Set Transformer, and FNO baselines miss the analytic answer by orders of magnitude out-of-distribution; and matches direct-collocation boundary-element ground truth on a synthetic curved patch to $3.8\%$ relative RMS. A regime-of-validity result delineates when the canonical $2$-term identity applies; outside it (terrain curvature radius $\kappa^{-1}$ much greater than source clearance height $|h|$, the corridor regime), we extend the recipe with sparse dense-BEM-target supervision. On several industrial transmission-line corridors with native triangulated meshes, the extended recipe attains $9.2\%$ median relative RMS to dense BEM on held-out corridors, $20\times$ tighter than the analytic 2-term plug-in. Deployment: a $1.7$kB MLP, seconds per corridor on a single CPU core.

Do large language models represent psychological constructs internally, or do they only generate outputs that resemble psychometric structure? We address this question using a cross-persona paradigm across 14 models (Llama3 and Qwen2.5, 0.5B--14B, base and instruct). Given responses on one psychological scale from real individuals ($N = 272$), models predict responses on six other scales. At the behavioral level, LLMs reproduce human cross-scale correlation structure and systematically amplify it, with model-generated correlations exceeding human estimates even after correcting for measurement attenuation. We then examine whether this structure is reflected internally. Contrastive direction analysis reveals an organized geometry in activation space aligned with psychometric relationships. This structure emerges in large instruct models but is not observed in base models. Across models, geometry strength predicts behavioral amplification ($r = 0.68$, $p = 0.008$; partial $r = 0.69$ controlling for log size), independent of model size. To relate internal structure to output behavior, a matched activation-probing paradigm shows that representational amplification is less variable than behavioral amplification ($1.38$--$1.77$ vs $0.53$--$1.53$). A synthetic control with known ground-truth structure shows that this range survives subtraction of a ridge-probe baseline ($\approx 1.3$), with adjusted slopes still predicting behavior at $r = 0.88$. We term the systematic representation-to-behavior gap \emph{readout attenuation}. Together, these findings suggest that LLMs encode structured representations aligned with psychological constructs, while differences in output primarily reflect how these representations are read out.

LiDAR is a critical sensor for autonomous driving and robotics, yet it remains vulnerable to adverse conditions such as fog, rain, transparent objects, and low-reflectance surfaces. Full-waveform LiDAR (FWL) addresses these limitations by capturing the complete return waveform as a histogram, preserving rich reflection characteristics including multi-path echoes and weak signals. While recent Transformer-based approaches have demonstrated promise on FWL data, two fundamental bottlenecks remain: high computational cost from redundant tokens in low-intensity background regions, and limited generalization due to scarce annotated training data. We address both bottlenecks by exploiting the physical structure inherent to FWL data. First, we propose FWL-ToPM, a token pruning and merging mechanism that leverages intensity distributions to selectively reduce tokens while retaining critical waveform content. Second, we introduce FWLAug, a physically consistent data augmentation framework that preserves time-of-flight waveform structure and frustum geometry to improve generalization across diverse environments. Evaluated on the Ghost-FWL benchmark, FWL-ToPM achieves over 8$\times$ speedup at real-time throughput alongside a nearly 9-point F1 gain, establishing the first real-time FWL Transformer that simultaneously improves accuracy. FWLAug further improves generalization to both standard and out-of-distribution environments. Together, these methods push FWL-based perception toward practical deployment.

AI-generated image detection has become increasingly important as generative models produce highly realistic visual content. Existing detectors mainly rely on visual artifacts or visual features extracted from pretrained multimodal models, while largely overlooking the structural differences between real and generated images in the joint image--text space. In this paper, we reveal a systematic Cross-modal Alignment Shift (CAS): generated images tend to exhibit stronger and more concentrated alignment with text than semantically matched real images. We verify this phenomenon through retrieval preference, image--text residual structure, spectral concentration, and matching entropy. Motivated by this finding, we propose a CAS-based detection framework(CASNet) that learns an amplified multimodal model with a text-guided alignment-shift objective. The amplification-induced feature increment is extracted as an explicit cross-modal cue and coupled with amplified visual representations, enabling the detector to exploit both alignment-level structural evidence and visual discriminative cues. Extensive experiments across diffusion models, GANs, autoregressive models, and commercial generators demonstrate consistent improvements over state-of-the-art detectors. In particular, on recent commercial generators, our method improves ACC and AP by 5.48\% and 6.13\%, respectively.

Text and 2D-conditioning interfaces provide weak, ambiguous control over spatial transformations in image editing -- particularly under large object motions and camera changes. Prior work has used 3D primitives such as boxes, but only as loose conditioning signals indicating approximate object location rather than specifying the transformation. We instead use 3D boxes as structured specifications: the user provides the input and output boxes of the edit, casting editing as a well-posed geometry problem. This ``thinking in boxes'' interface, where each box face is color-coded to convey 3D orientation, gives precise control over translation, rotation, scaling, and viewpoint changes in real images while preserving scene and object identity, and recovering previously unseen object regions. To ground transformations in scene appearance, we introduce a depth-aligned planar floor as a global reference frame, shaded with depth-aware cues. Conditioned on this structure, an image generator produces consistent results under large transformations. Trained in two stages -- on synthetic multi-object scenes and a small set of real-world videos from Objectron -- the system generalizes to complex, in-the-wild real images. Our method operates directly on real photographs and substantially outperforms recent state-of-the-art methods on large 3D edits.

We study the stability of data exchange among competing firms. Unlike tangible goods, data is non-rival: sharing it does not deprive the giver of its use. On one hand, competing firms-- often operating as machine-learning–driven predictors that collect observations from a shared environment to solve similar tasks-- can improve their predictive accuracies from pooling data. On the other hand, they compete for market share, which may depend on their relative predictive performance. This raises the central question of this paper: which data exchanges are stable in such competitive environments? We formalize this via a model of $n$ firms exchanging fractions of their datasets, with predictive performance feeding into market competition. We adopt core stability as our solution concept, accounting for externalities via worst-case responses by non-participating firms. Our main results reveal a sharp contrast driven by demand structure: (i) in *fully rational* markets, where customers flock entirely to the top-performing firm, a core-stable exchange always exists for any monotone continuous prediction and utility functions; (ii) in *partially rational* markets with logit-style demand, core-stable exchanges may fail to exist, and even approximate cores can be empty. Our impossibility results suggest that internal profit-sharing within coalitions can restore stability in partially rational markets. We formalize this intuition by showing that when co-operating coalitions equally redistribute market-share revenues among members, a core-stable data exchange exists. Finally, we propose a natural exchange dynamic and demonstrate empirically that it converges to a core-stable data exchange in fully rational markets on simulated instances derived from real datasets.

Reinforcement learning (RL) has gained growing attention in large language model (LLM) post-training, yet RL training remains fragile and can suffer from instability or collapse. One vital cause is \textit{training-inference mismatch}: % different from traditional RL, the engineering design of LLM adopts separate inference and training engines for generation efficiency and training precision, which in practice exhibits inconsistent probabilities for the same trajectories on training and inference sides, even with synchronized model parameters. This naturally induces a special type of off-policyness ever existing and poisoning the training. Prior works have made various efforts in addressing the off-policyness to stabilize the training policies under the mismatch. In this paper, we point out the \textit{object misalignment} neglected by existing works that an effective update to the policy in the training engine not necessarily ensures the improvement of the inference policy, i.e., the one used in deployment. To this end, we propose a new policy optimization objective for LLM RL, named \textbf{Monotonic Inference Policy Improvement (MIPI)}. Following this principle, we introduce {Monotonic Inference Policy Update (MIPU)}, a two-step LLM RL framework that constructs sampler-referenced candidate updates and selectively accepts synchronized candidates using an inference-side gap proxy. Experiments under high mismatch show that {MIPU} improves average reasoning performance and training stability.

Understanding the benefits, risks, and impacts of general-purpose AI assistants requires looking at real conversations---not curated benchmarks or surveys. Yet AI use narratives rely on limited proprietary reports, or on few data sources, fragmented across platforms, models, and time. We introduce the AI Observatory, a public measurement platform that aggregates the most comprehensive set of real AI conversation sources, and develop a common taxonomy of 145 features, spanning function, topic, sensitive use, interaction style, multi-turn dynamics, and conversation structure. Across 23,158 conversations and 85,633 turns, we find that real AI use is highly heterogeneous: sources differ substantially in tasks, structures, and sensitive-use distributions, with no single source that generalizes. We further show that occupational summaries such as Anthropic's Clio capture an important but incomplete slice of use: 48\% of the conversations we analyze would be filtered out for being non-occupational, omitting substantial personal, social, cultural, and safety-relevant interaction. Real AI use is also not static: we find tokens and turns per conversation all rise sharply between 2023 and 2025, and we expose how model variants within the same developer support distinct usage regimes. The Observatory provides a new taxonomy, reusable annotation tools, and a public platform for reproducible measurement of how AI assistants are really used and evolving in the wild: \url{https://project-ai-observatory.vercel.app/}.

We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from "in-the-wild" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 19 real-world categories, ranging from short everyday operations to workflows exceeding 50 steps, and covering 1,280 unique commands. From these, we curate a Verified subset of 200 representative, manually reviewed tasks. Comprehensive benchmarking on TerminalWorld-Verified across eight frontier models and six agents reveals that current systems still struggle with authentic terminal workflows, achieving a maximum pass rate of only 62.5%. Moreover, TerminalWorld captures real-world terminal capabilities distinct from existing expert-curated benchmarks (e.g., Terminal-Bench), with only a weak correlation to their scores (Pearson $r=0.20$). The automated engine makes TerminalWorld authentic and scalable by construction, enabling it to evaluate agents in real-world terminal environments as developer practices evolve. Data and code are available at https://github.com/EuniAI/TerminalWorld.

Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos containing moments relevant to a given text query. Despite recent progress, existing PRVR methods suffer from two key limitations: a fixed video decomposition scheme that causes semantic dilution, and weak cross-domain robustness due to task-specific training. In this paper, we propose TF-PRVR, the first training-free framework for PRVR designed to improve real-world generalization. TF-PRVR leverages frozen vision-language features to construct video-specific hierarchical representations. It derives temporal semantic signals from frame-level features and applies frequency-based multi-scale analysis to identify adaptive temporal boundaries, producing hierarchical segments with coherent event-level semantics. Built on these segments, TF-PRVR constructs a unified multi-scale graph and propagates query relevance across temporally and semantically related nodes. A moment-aware scoring strategy then aggregates temporally aligned relevance across scales, emphasizing consistently supported moments while suppressing isolated false responses. Without task-specific training, TF-PRVR preserves the general-purpose alignment capability of pre-trained vision-language models and avoids dataset-specific overfitting. Extensive experiments on standard PRVR benchmarks demonstrate competitive retrieval performance and strong robustness under cross-domain evaluation, suggesting a practical direction for real-world PRVR.

Mixture-of-Experts (MoE) large language models (LLMs) suffer significant inference and storage overheads, which makes extremely low-bit quantization (sub 2-bit) highly desirable. However, existing methods for dense LLMs typically quantize each expert independently and require it to approximate the original mapping over the entire input space. We argue that this formulation is overly conservative and structurally mismatched for MoE: 1) experts in the same MoE layer are not fully independent but operate on a shared hidden representation space. Under low-bit budgets, independently binarizing each expert will distort the activation-sensitive input geometry, which leads to severe performance degradation, and 2) due to routing-induced specialization, each expert only serves a concentrated subset of tokens, whose activations typically occupy a much narrower subspace than that of dense layers. More importantly, when the weights approach 1-bit, the dominant deployment bottleneck is no longer the weights themselves, but the metadata (e.g., scaling factors, bitmap, grouping index) which can raise the real effective inference precision to around 3-bit. This issue, however, has largely been overlooked in prior works. Building on these perspectives, we introduce a novel extremely low-bit quantization framework for MoE-LLMs, called TriS-MoE, which considers Structure, Subspace and System. Specifically, we first extract a shared high-precision input-side backbone to preserve activation-sensitive input geometry, while binarizing the remaining components to save costs. Secondly, we propose to redirect expert quantization errors into the null space of routed activations to minimize output perturbation on the subspace actually used by each expert. Finally, we co-design a bitmap compression method based on Golomb-Rice coding which approaches the Shannon entropy lower bound and a specialized streaming loading mechanism to reduce metadata overhead during inference. Extensive experiments on MoE-LLMs demonstrate our TriS-MoE outperforms the strongest baseline by 8.31% average accuracies, and also achieves an average 6.32$\times$ reduction in inference memory on real systems, enabling Qwen3.5-35B-A3B and Mixtral-8$\times$7B to run on a single consumer-GPU.

We propose a stable algorithm for solving maximum coverage problems under a cardinality constraint of $k$ in a record-level stability model, where adjacent instances $I\sim I'$ differ by the incidence of one universe element. Our algorithm computes a negative-entropy-regularized maximizer of the capped concave relaxation over the hypersimplex and rounds it with a common-seed rounding map from hypersimplex correlated sampling. The resulting sensitivity bound is $\min\{2k, \alpha_k k/\lambda\}$, where $\lambda$ is the regularization parameter and $\alpha_k = O(\log k)$. For utility, the expected coverage has approximation factor $1-\frac{1}{e}$ with an additive error of order $O(\lambda k \log \frac{m}{k})$, where $m$ is the number of input sets. We also report experiments on synthetic and real-world instances.

In neural inverse reconstruction, the forward model is typically treated as a fixed simulator that maps a neural scene representation to measurements. This work studies how the parameterization of the forward operator itself shapes the optimization landscape of coherent inverse problems. We introduce Moray, a reconstruction framework built on spectrally parameterized forward operators that bypass conventional time-domain synthesis and instead evaluate measurements directly through closed-form spectral kernels. This parameterization induces a substantially shorter and better-conditioned differentiable graph compared to the time-domain approaches. We further introduce Phase-Coherent Manifold Parameterization, a scene representation that jointly learns scene reflectivity and a deformable surface manifold, allowing the reconstruction to adapt to geometric deviations from the assumed imaging plane and thereby reducing common artifacts in coherent reconstruction. We instantiate these ideas for 77 GHz radar imaging using real measurements from a synthetic-aperture mmWave system. Across multiple challenging scenes, Moray consistently improves reconstruction fidelity, suppresses background artifacts, and degrades more gracefully under data limitations.More broadly, the results suggest that, in neural inverse problems, exposing appropriate analytical structure of the sensing physics to the optimizer can be as important as the choice of neural representation itself.

Compound AI systems promise capabilities beyond those of individual models, yet their success depends critically on effective orchestration. Existing routing approaches face two limitations: (1) input-level routers make coarse query-level decisions that ignore evolving task requirements; (2) RL-trained orchestrators are expensive to adapt and often suffer from routing collapse, repeatedly invoking one option in multi-turn scenarios. We introduce SkillOrchestra, a framework for skill-aware orchestration. Instead of directly learning a routing policy end-to-end, SkillOrchestra learns fine-grained skills from execution experience and models agent-specific competence and cost under those skills. At deployment, the orchestrator infers the skill demands of the current interaction and selects agents that best satisfy them under an explicit performance-cost trade-off. Extensive experiments across ten benchmarks demonstrate that SkillOrchestra outperforms SoTA RL-based orchestrators by up to 27.5% with 700x and 300x learning cost reduction compared to Router-R1 and ToolOrchestra, respectively. These results show that explicit skill modeling enables scalable, interpretable, and sample-efficient orchestration, offering a principled alternative to data-intensive RL-based approaches.

Representational Engineering (RepE) aims to expose and steer high-level concept directions in language model activations, and recent work shows that hidden states can be transferred across heterogeneous architectures via trained neural adapters. Whether specific concepts survive such transfer remains an open question. We study one such concept, \textit{truth}, by mapping source-model activations through autoencoder adapters into target-model representation space, varying injection strength $\alpha \in [0, 1]$, and reading the result with the target's native truth probe across 21 cross-family trajectories from four model families, with comparison against a closed-form rigid alignment baseline on a representative subset. We find that ordinal truth ranking is preserved through partial injection (median AUROC near the native ceiling through $\alpha \in [0, 0.7]$) but collapses sharply at full injection ($\alpha{=}1$), with parametric separability dropping $\sim$$14\times$ while rank order partially survives. The apparent accuracy overshoot under partial injection decomposes cleanly into two effects: a large recalibration component dominated by native probe miscalibration ($R^2 = 0.75$), and a small but universal genuine separability gain present in all 21 trajectories. These findings characterize cross-family truth structure as partially compatible, ordinally stable, but non-isomorphic: preserved enough under partial injection that learned and rigid alignment methods both surface meaningful truth signal, but with full replacement representing a hard failure mode that practitioners should treat as a deployment boundary.

Two finite-domain implementations, including neural-network parameterizations restricted to an evaluation domain, can agree on every input while implementing the computation through different mechanisms, such as independent lookup storage or a shared reusable rule. We formalize this distinction as a statement about the local Kullback-Leibler geometry of an implementation chart: a mechanism-specified local family of perturbations around a fixed predictive distribution. For finite input-output distributions with full support, we prove a logit-Fisher pullback theorem: the Hessian rank of a chart equals the dimension of its tangent image in the quotient logit space, so the quadratic active contribution to the local learning coefficient is half this rank under the analytic/dead-kernel conditions stated below. We prove that this rank is invariant under local reparameterizations, dead parameters, and redundant duplications, but changes when the allowed perturbation class changes; hence it is a first-order chart-germ invariant, not a function-only invariant. For mechanisms specified by exact finite-group sharing, we give a closed-form orbit-count formula for the active dimension. Lookup and shared cyclic modular addition are then immediate corollaries: both implement the same softened conditional distribution, yet their active ranks are $p^2(p-1)$ and $p-1$, respectively. The results clarify when Bayesian/Singular-Learning-Theory complexity can distinguish extensionally equivalent algorithms and when chart dependence is a real modeling choice rather than a defect.

Computer-Using Agents (CUAs) are rapidly extending large language models (LLMs) beyond text-based reasoning toward action execution in more complex environments, such as web browsers and graphical user interfaces (GUIs). However, existing web and GUI agent benchmarks often rely on simplified settings, isolated tasks, or short-horizon interactions, making it difficult to assess capabilities of agents in realistic professional workflows. Software-as-a-Service (SaaS) environments are a natural choice for CUA evaluation, as they host a large share of modern digital work and naturally involve dynamic system states, cross-application coordination, domain-specific knowledge, and long-horizon dependencies. To this end, we introduce \textbf{SaaS-Bench}, a benchmark built on 23 deployable SaaS systems across six professional domains, containing 106 tasks grounded in realistic work scenarios. These tasks require long-horizon execution, cover both text-only and multimodal settings, and are evaluated with weighted verification checkpoints that measure strict task completion and partial progress. Experiments show that representative LLM-based agents struggle on SaaS-Bench, with even the strongest model completing fewer than 4\% of tasks end-to-end, exposing limitations in planning, state tracking, cross-application context maintenance, and error recovery.