NeurIPS 2026 论文列表

Learned representations (or embeddings) are the foundation of modern machine learning systems, yet they are typically trained as fixed-size embeddings, without accounting for varying downstream resource or task constraints. Matryoshka Representation Learning (MRL) addresses this limitation by learning nested representations, but requires costly end-to-end retraining and does not support post hoc expansion, while Contrastive Sparse Representations (CSR) reduce training overhead via lightweight adaptors but operate at a 4x larger dimensionality. Additionally, all prior works restrict their framework towards compression only. In this work, we introduce \textbf{H}yperbolic \textbf{E}lastic \textbf{R}epresentation \textbf{L}earning (HERL), a framework for learning dimension-adaptive embeddings (compression and expansion) through a lightweight adaptor without retraining the base encoder. Our key idea is to exploit the exponential capacity of hyperbolic geometry by learning a non-linear mapping from the encoder space to hyperbolic space. We train a simple MLP to downsample the representations before projecting them onto a Lorentzian and learn adaptive representations that respect the Lorentzian geometry. Our approach is model-agnostic and effective in both supervised and unsupervised settings. Through extensive experiments across image and text modalities, we show that HERL preserves downstream performance across a wide range of embedding sizes, exhibits smooth interpolation between dimensions, and achieves these benefits at a fraction of the computational cost.

Touch is fundamental to dexterous manipulation, yet most egocentric human data increasingly used for robot learning lacks tactile information. Directly collecting large-scale tactile data is challenging due to sensor limitations, while human video data is abundant, contact-rich, and easily scalable. This motivates a natural question: can tactile signals be inferred purely from vision? To address this, we introduce **EgoTac**, a generalizable model that predicts rich **tac**tile information directly from **ego**centric human videos. EgoTac is trained on a unified corpus of over 5.7M image-tactile pairs, covering both continuous force measurements and binary contacts. By learning from this diverse dataset, EgoTac captures nuanced touch dynamics across varied interactions. Experiments demonstrate strong performance: in-domain prediction achieves an average force error below 0.06N. On out-of-domain contact prediction benchmarks, EgoTac consistently outperforms the state-of-the-art contact estimator. It also captures the rise and fall patterns of real tactile data and enables zero-shot predictions on unconstrained real-world videos. Scaling analyses further reveal that both data diversity and volume improve performance steadily. Overall, EgoTac provides a scalable pathway to extract tactile priors from egocentric human videos, enabling broadly applicable tactile-aware robot learning.

We introduce EgoSurgHands, an egocentric 3D hand-pose dataset and benchmark for open-surgery training tasks captured with Meta Project Aria glasses on standard surgical training pads. To our knowledge, EgoSurgHands is the first public benchmark for surgical training that is simultaneously egocentric, sensor-grounded, and human-validated. EgoSurgHands contains 41,909 training hand-instance rows across 55 procedure recordings and 9,476 inter-annotator-agreement (IAA)-validated rows across 13 held-out procedure recordings from 4 wearers, 13 distinct tasks, 3 glove colors, and 3 stages of training. Each frame includes Aria intrinsics, SLAM extrinsics, MPS (Machine Perception Services) landmarks, per-joint confidence, timestamps, and eye gaze. Validation 3D keypoints are initialized from Aria MPS and independently corrected by four expert annotators under a multi-rater IAA workflow, yielding evaluation ground truth independent of any tested model's training signal. Using EgoSurgHands, we benchmark six state-of-the-art hand reconstructors under zero-shot transfer: HaMeR, WiLoR, WildHands, JointTransformer, SimpleHand, and MeshGraphormer. Ego-pretrained models consistently outperform non-egocentric baselines: WildHands improves over the non-ego HaMeR reference by 4.51 mm PA-MPJPE, while JointTransformer achieves the best zero-shot result at 14.95 mm PA-MPJPE. These results show that generalist egocentric pretraining transfers to surgical egocentric tasks, but also reveal a substantial remaining surgical-domain gap. As a lightweight diagnostic adaptation experiment, a 58K-parameter two-headed (pose + translation) residual corrector further reduces error on frozen backbones, reaching 10.40 mm PA-MPJPE and 75.8 mm absolute MPJPE on WildHands ($-$49.2\% and $-$85.8\% vs zero-shot). We release the dataset, benchmark code, checkpoints, and multi-rater annotation framework at [URL withheld for double-blind review].

Continuous episodic memory is a core capability for autonomous agents operating in dynamic, real-world environments, yet current streaming video benchmarks provide limited tools for diagnosing what models remember and for how long. We introduce Egostream, a diagnostic benchmark for streaming episodic memory evaluation in egocentric vision. \egostream organizes 2,250 curated questions along seven cognitive dimensions: detail, spatial, temporal, event, social, causal, and prospective memory. We introduce the Answer Validity Window (AVW), which specifies the temporal span an answer remains valid as the observed scene evolves. This allows us to expand the questions into 8,528 recall-conditioned evaluations, enabling controlled testing from instant to ultra-long-term recall while separating genuine model forgetting from natural world-state changes. We rigorously establish baseline performance through a unified streaming MLLM framework that compares several state-of-the-art memory-management mechanisms, covering sliding windows, attention sinks, KV-cache pruning, merging, and offloading. Experiments within a unified Qwen3-VL backbone reveal that comparable aggregate accuracies mask starkly different memory profiles. For instance, token pruning preserves fine-grained details and temporal structure significantly better than token merging, while quantized offloading rescues ultra-long-term recall. Ultimately, all mechanisms operate well below real-time (>1s per frame), and top performing methods ceil at about 45\% accuracy, exposing critical gaps in current architectures. Egostream provides the diagnostic testbed needed to close these gaps.

The complex interactive dependencies between human behavior and 3D scenes make accurately predicting human intent a major challenge for human motion prediction (HMP). While recent studies use human gaze to infer intent, gaze signals suffer from ambiguity, drift, and a reliance on eye-tracking hardware. Research in cognitive science indicates that egocentric views inherently encode potential interactions, while head motion serves as a crucial precursor in motion planning. Motivated by this, we propose EgoHMP, a novel framework that uses egocentric views and head motions as robust carriers of interaction intent. By translating this intent into an envisioning of future interactions, EgoHMP achieves precise HMP in 3D scenes. EgoHMP first employs a vision-motion alignment encoder to align the visual and motion feature spaces. Based on these aligned features, an egocentric intent predictor with a modality-aware modulation mechanism adaptively suppresses invalid interaction cues to predict an accurate interaction probability map. Furthermore, to enhance the stability and realism of the generated motions, we propose a contact-aware motion decoder. Specifically, inspired by the human cognitive process of planning trajectories prior to action execution, we first predict a global trajectory to serve as a self-prompt, effectively mitigating the optimization conflict in diffusion models. Subsequently, we introduce a multi-stage denoiser, which relieves motion artifacts by incorporating human contact modeling through a multi-stage optimization strategy. Finally, a GCN-based motion decoder is employed to synthesize physically plausible and semantically consistent motions. Extensive experiments demonstrate that EgoHMP achieves state-of-the-art performance.

As AI agents increasingly operate in open, real-world environments, they require a deep synergy of multimodal perception, tool invocation with multi-hop reasoning, and dynamic interaction with users. However, existing benchmarks fail to jointly evaluate these capabilities due to challenges in designing strictly coupled multi-capability tasks, simulating natural and task-constrained user feedback, and ensuring objective evaluation of dynamic interaction. To bridge this gap, we introduce EgoBench, the first interactive multimodal benchmark for tool-using agents. EgoBench comprises 1,590 egocentric-video-grounded tasks covering five daily scenarios, along with a user-agent-tool interactive environment for evaluation. We implement a three-stage synergistic pipeline through which each task is designed to enforce the joint application of visual perception and tool-augmented multi-hop reasoning. We additionally develop a multi-agent simulated user to evaluate agents' interaction capabilities, which generates high-fidelity, task-aligned responses to agents. Furthermore, we establish a deterministic joint validation framework that guarantees objective assessment through process-based and result-based equivalence. Benchmarking eight video-MLLM agents on EgoBench reveals a severe performance ceiling: the best-performing model achieves only a 34.95% average Joint Success Rate across the three interaction modes. Finally, we conduct a multi-dimensional error analysis to disentangle failure modes, exposing capability bottlenecks for advancing future AI agents.

Children acquire language with remarkable speed and robustness from limited linguistic input in ways that surpass today's best large language models. Recent research suggests this efficiency stems from learning through multimodal sensory experiences where semantic alignment between what children see and hear grounds language in the external world. However, vision-language models (VLMs) trained on curated web data fail to generalize to the sparse, weakly-aligned egocentric streams produced by wearable devices, embodied agents, and infant head-cams—and no fixed evaluation pipeline exists for measuring progress on this regime. We train VLMs on datasets with varying degrees of semantic alignment between visual and linguistic inputs, including naturalistic infant egocentric videos, and evaluate them with a comprehensive suite spanning multimodal language grounding and unimodal vision and language tasks. At the core of this suite is _Machine-DevBench_, a corpus-grounded benchmark of lexical and grammatical competence, automatically generated from the model's training vocabulary across logarithmic frequency bins to eliminate the train/eval mismatch and low statistical power of prior developmental benchmarks. Our results show that current VLM paradigms hinge on the tight semantic alignment of curated data and fail to exploit the weakly-aligned signal that dominates naturalistic egocentric input—the very regime in which humans thrive. To motivate progress, we introduce the EgoBabyVLM Challenge to drive the development of models capable of grounded language learning from the kind of naturalistic data that human infants experience.

The increasing popularity of *reasoning* models---language models that output a series of reasoning or thought tokens before producing an answer---is justified, in part, by theoretical results showing that chain-of-thought (CoT) transformers can simulate Turing machines, and thus perform arbitrary computation. However, the Turing machine, while suitable for a complexity theoretic analysis, isn't convenient, intuitive, or efficient when discussing algorithms. Algorithms are typically designed and analyzed at a higher level of abstraction, captured by the *Word RAM* model with random-access memory and unit-cost operations on $O(\log n)$-bit words. As a result, Word RAM algorithms can be substantially more efficient than their Turing machine counterparts, raising the question: *Can CoT transformers efficiently simulate Word RAM algorithms?* For instance, can they sort $n$ items in $O(n \log n)$ steps or run Dijkstra's algorithm in $O(E + V \log V)$ steps? We answer affirmatively, up to poly-logarithmic overhead. We first establish this for finite-precision transformers with poly-logarithmic width and rightmost unique hard attention, then strengthen the result to two more practical settings with finite width and log-precision: *continuous* CoT, where reasoning takes the form of vectors rather than tokens, and a *hybrid* architecture in which transformer layers sit atop a recurrent (linear RNN) layer. In all three cases, we find that CoT *can* efficiently simulate any Word RAM algorithm with only a poly-logarithmic overhead in $n$. This overhead reduces to log-square when the Word RAM has a ``flat'' instruction set, and only logarithmic for multiplication-free flat instructions---in stark contrast to known CoT simulations of Turing machines, which require quadratic overhead over Word RAM.

Speculative decoding accelerates LLM inference but suffers from performance degradation when target models are fine-tuned for specific domains. A naive solution is to retrain draft models for every target model, which is costly and inefficient. To address this, we introduce a practical parameter- and data-efficient framework named Efficient Draft Adaptation, abbreviated as EDA, for efficiently adapting draft models. EDA introduces three innovations: (1) a decoupled architecture that utilizes shared and private components to model the shared and target-specific output distributions separately, enabling parameter-efficient adaptation by updating only the lightweight private component; (2) a data regeneration strategy that utilizes the fine-tuned target model to regenerate training data, thereby improving the alignment between training and speculative decoding, leading to higher average acceptance length; (3) a sample selection mechanism that prioritizes high-value data for efficient adaptation. Our experiments show that EDA effectively restores speculative performance on fine-tuned models, achieving superior average acceptance lengths with significantly reduced training costs compared to full retraining. Code will be released upon acceptance.

Reinforcement learning (RL) has become a representative post-training paradigm for large language models (LLMs), enabling strong reasoning and agentic capabilities. However, its rollout generation remains a dominant training bottleneck because it relies on sequential autoregressive (AR) decoding, where a small number of long-tailed responses often determine completion time. Speculative decoding (SD) can reduce inference latency while preserving model quality by rapidly drafting tokens and accepting them through parallel verification. Applying SD to RL rollouts, however, introduces challenges absent from standard LLM inference: (i) algorithmically, the continuously evolving target model makes static drafters stale; (ii) system-wise, rollout decoding moves across regimes, from large active batches where SD can be compute-bound and ineffective to shrinking-batch tails where SD becomes beneficial. Existing RL-SD approaches address aspects of this problem, but either yield low effective accepted lengths that limit speedup or rely on auxiliary drafters requiring pretraining and online adaptation, increasing system complexity. We present EfficientRollout, an SD framework designed to accelerate RL rollouts while addressing these challenges. EfficientRollout induces a quantized drafter directly from the target model, keeping it coupled to the evolving policy without separate drafter training before or during RL. It then uses a dynamic SD toggle policy that enables SD only in beneficial regimes identified by system-aware roofline modeling. It further adapts drafting budgets using acceptance-behavior signals observed during training, better realizing the potential accepted length. Under realistic RL workload, EfficientRollout reduces rollout and end-to-end latency by up to 19.6% and 12.7%, respectively, over a standard accelerated AR rollout baseline, while preserving final model quality.

Training Neural ODEs requires backpropagating through an ODE solve. The state-of-the-art backpropagation method is recursive checkpointing that balances recomputation with memory cost. Here, we introduce a class of algebraically reversible ODE solvers that significantly improve upon both the time and memory cost of recursive checkpointing. The reversible solvers presented calculate exact gradients, are high-order and numerically stable -- strictly improving on previous reversible architectures. On scientific modeling and time series classification experiments, reversible solvers reduce the training time by more than $2\times$ while using at least $10\times$ less memory on average than recursive checkpointing.

Reliable AI evaluation is a prerequisite for measuring progress and for deployment in safety-critical settings. Unfortunately, even gathering frontier system outputs on a full suite of relevant benchmarks can be expensive, let alone adding expert review or running evaluation inside of an optimization loop. Existing approaches to sample-efficient evaluation address this concern only partially: they typically assume abundant calibration data, pretrained error-relevant embeddings, or a fixed benchmark. Meanwhile, optimization loops require repeated evaluation and the ability to surface a long tail of errors; in fact, in real world settings, the long tail of errors often drives human-in-the-loop iterative system improvement. Working in the regime in which the only available information is system inputs and one or more cheap surrogate measures of system error, we derive an optimal importance sampler for error estimation and analyze its theoretical properties. Going further, we show how the same sampler can naturally discover and rank error patterns. On standard benchmarks for frontier model question answering (MMLU-Pro) and agentic coding (TerminalBench 2.0) we show our method consistently outperforms standard Monte Carlo on estimating aggregate error, recovering failure modes and calculating their prevalence, and detecting poison agent/environment injections.

Extending 3D Gaussian Splatting (3DGS) to 4D physical simulation remains challenging. Based on the Material Point Method (MPM), existing methods either rely on manual parameter tuning or distill dynamics from video diffusion models, limiting the generalization and optimization efficiency. Recent attempts using LLMs/VLMs suffer from a text/image-to-3D perceptual gap, yielding unstable physics behavior. In addition, they often ignore the surface structure of 3DGS, leading to implausible motion. We propose FastPhysGS, a fast and robust framework for physics-based dynamic 3DGS simulation: (1) Instance-aware Particle Filling (IPF) with Monte Carlo Importance Sampling (MCIS) to efficiently populate interior particles while preserving geometric fidelity; (2) Bidirectional Graph Decoupling Optimization (BGDO), an adaptive strategy that rapidly optimizes material parameters predicted from a VLM. Experiments show FastPhysGS achieves high-fidelity physical simulation in 1 minute using only 7 GB runtime memory, outperforming prior works with broad potential applications.

We study the classical problem of linear regression under heteroskedastic noise, where observations arise from multiple sources with unknown and potentially unequal variances. Specifically, we consider a setting with $n$ sources, each providing a linear measurement of an unknown $d$-dimensional parameter vector $\beta^*$, where each source is associated with an unknown noise variance. We focus on the classic "subset-of-signals'' model, where one assumes that $m$ of the sources have a relatively low noise, formally, with variance of at most 1. Our goal is to accurately estimate $\beta^\*$ despite the presence of high-variance sources. We show that if $m$ is sufficiently large, it is possible to recover $\beta^*$ with sub-constant error. Specifically, we prove a recovery error bound of $\tilde{O} \left(\frac{\sqrt{nd}}{m}\right)$, significantly improving upon previously known results for this setting.

Log-Gaussian Cox Processes (LGCP) are essential for modeling spatial point patterns but suffer from high computational costs and intractable likelihoods. We propose VoGCAM, an efficient Variational Voronoi Gaussian Coordinate Ascent Maximization framework for fitting LGCPs. Our approach first approximates the intractable integral in the LGCP likelihood using a flexible Voronoi tessellation, which incorporates observed points as integration nodes. By applying variational Gaussian approximation, we derive an Evidence Lower Bound (ELBO) that admits an explicit and closed-form expression. To optimize this objective, we develop a novel coordinate ascent algorithm that updates parameter blocks via Newton and fixed-point methods. We further enhance scalability by adopting a Nearest Neighbor Gaussian Process (NNGP) prior and utilizing the Woodbury formula to reduce matrix inversion costs. Theoretically, we prove the existence and uniqueness of the optimal solution and establish the convergence of our algorithm. Numerical experiments on synthetic and real-world data demonstrate that VoGCAM offers superior computational efficiency and inferential accuracy over state-of-the-art methods like INLA and VIFRK.

Optimal Transport (OT) plans provide correspondences between distributions supporting alignment tasks in various domains. Sliced transport plans have been recently proposed as a computationally efficient alternative to OT plans. These methods optimize a one-dimensional projection (slice) to obtain a conditional transport plan that minimizes the transport cost in the ambient space. Despite their efficiency, it remains unclear whether learned slicers transfer to new distribution pairs under shift, an issue central to evolving data and repeated OT computations over related distributions. We study the min-Sliced Transport Plan (min-STP) framework and examine slicer transferability: can a slicer learned on one distribution pair produce effective transport plans for unseen pairs? Theoretically, we show that optimized slicers remain close under slight perturbations of the data distributions, enabling efficient transfer across related tasks. To further improve scalability, we introduce a minibatch formulation of min-STP and provide statistical guarantees on its accuracy. Empirically, we demonstrate that the transferable min-STP achieves strong one-shot matching performance and facilitates amortized training for point cloud and image analysis. Our code is available at https://anonymous.4open.science/r/Min-STP-CF74.

Spiking neural networks (SNNs) are a promising paradigm for efficient neuromorphic computing, yet their training remains challenging due to the non-differentiability nature of spike firing. Backpropagation through time (BPTT) with surrogate gradients (SG) has become a mainstream approach due to its superior performance. However, this method incurs high computational and memory demands during training, while its update rule depends on specific neuronal dynamics. Furthermore, when targeting neuromorphic hardware implementation, the temporal error-propagation process and the full membrane-potential access required by SG are difficult to realize; these can introduce additional readout overhead, measurement noise, or perturbations to the system dynamics. To address these issues, we propose the input-driven derivative-free training (IDDFT) method. Rather than relying on membrane-potential-based surrogate derivatives, IDDFT constructs derivative-free error signals by applying a relaxed nonlinearity to the inputs, thereby avoiding the temporal error-propagation process and reducing the dependence of the update rule on specific neuronal dynamics. By eliminating access to internal membrane-potential states, our method reduces computational and memory costs, enhances compatibility with neuromorphic hardware, and enables training of black-box SNN models. We validate the IDDFT method through theoretical analysis and systematic experiments, demonstrating its effectiveness as an input-driven, derivative-free mechanism for constructing update signals. When integrated with a biologically inspired training strategy, IDDFT maintains comparable performance while substantially enhancing robustness to the choice of relaxed nonlinearities. Based on the resulting bio-inspired combined framework, we conduct evaluations on CIFAR-10, CIFAR-100, CIFAR10-DVS, and Tiny-ImageNet. Experimental results demonstrate that our method achieves performance comparable to BPTT and state-of-the-art methods, while exhibiting remarkable robustness under adversarial attacks.

Reinforcement-learning policies for robot locomotion can fail at deployment when operating conditions shift (e.g., rough or slippery terrain, payload changes, sensing degradation), and retraining or manual recalibration is often infeasible under on-device latency and memory constraints. We study test-time adaptation (TTA) for robotic control: adapting a pretrained policy online and self-supervisedly during execution, without access to rewards. Our starting point is a lightweight baseline that updates only the actor using a frozen pretrained critic as a deployment-time performance surrogate. Building on this, we introduce TEMPO, a test-time entropy-regularized, memory-guided policy optimization method that stabilizes critic-driven updates under severe shift. TEMPO combines (i) an entropy-promoting regularizer on the critic’s predictive distribution to mitigate overconfident extrapolation, and (ii) a score-based memory, inspired by prioritized replay, that prioritizes low-value (“hard”) observations and applies importance sampling to focus limited computation on the most informative deployment conditions. Across Go1, Go2, and G1 in MuJoCo Playground under four representative shifts and multiple architectures, TEMPO yields consistent gains, reaching up to 80\% improvement in a single-device setting. We further validate on a physical Unitree Go2 with an added calf payload, where fully on-device adaptation improves performance over the non-adapted baseline across real-world episodes.

LLM-based workflows compose specialized agents to execute complex tasks, and these agents usually share substantial context, allowing KV-Cache reuse to save computation. Existing approaches either manage KV-Cache at agent level and fail to exploit the reuse opportunities within workflows, or manage cache at the workflow level but assume that each workflow calls a static sequence of agents. However, practical workflows are typically dynamic, where the sequence of invoked agents and thus induced cache reuse opportunities depend on the context of each task. To serve such dynamic workflows efficiently, we build a system dubbed PBKV (\textbf{P}rediction-\textbf{B}ased \textbf{KV}-Cache Management). For each workflow, PBKV predicts the agent invocations in several future steps by fusing the guidance from historical workflows and context of the target workflow. Based on the predictions, PBKV estimates the reuse potential of cache entries and keeps the high-potential entries in GPU memory. To be robust to prediction errors, PBKV utilizes the predictions conservatively during both cache eviction and prefetching. Experiments on three workflow benchmarks show that PBKV achieves up to $1.85\times$ speedup over LRU on dynamic workflows, and up to $1.26\times$ speedup over the SOTA baseline KVFlow on the static workflow.