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版本更新 · v3arXiv:2509.03244

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization

Yiming Yao, Fei Liu, Liang Zhao, Xi Lin, Yilu Liu, Qingfu Zhang

原文摘要

Expensive multi-objective optimization is a prevalent and crucial concern in many real-world scenarios, where sample-efficiency is vital due to the limited evaluations to recover the true Pareto front for decision making. Existing works either involve repeatedly fitting Gaussian process surrogates from scratch for each newly encountered problem, or rely on computationally demanding hypervolume-oriented policy learning for amortized optimization, making it challenging to achieve scalable pre-training and efficient yet robust adaptation for diverse emerging real-world applications. To address these challenges, we propose FoMEMO (Foundation Models for Expensive Multi-objective Optimization), which adopts a decomposition-based training paradigm that converts multi-objective optimization into preference-wise aggregated posterior learning tasks, enabling scalable pre-training on hundreds of millions of diverse synthetic datasets without relying on extensive real-world domain experiments. At test time, given observed trajectories from unseen problems and user preferences, the foundation model performs in-context posterior prediction and optimizes the derived acquisition functions for efficient candidate generation, achieving strong generalization and optimization performance without any subsequent model training or updates.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16301

LoCoJAX: Host-Local Coordination for Small-Model RL on TPUs

Loki Chen, Gregory B. Rehm, Yang Song, Hongye Xie, Ming Lei, Chongyang Bai, Bob Kamma, Mingfu Liang 等 16 位作者

原文摘要

Reinforcement learning (RL) increasingly targets small language models, and executing these workloads efficiently across multiple accelerators requires effective coordination. On TPUs, Pathways-based RL uses a single controller to coordinate computation across hosts. This execution model is effective for large models, but its coordination computational costs are harder to amortize when small models repeatedly invoke short computations. To characterize these computational costs, we sweep dense models from 0.6B to 405B parameters on TPU v7x under a decode-heavy workload and measure how host and network computational overhead vary with model size. At 0.6B, host orchestration occupies 36% of step time and network communication 2%, whereas for large models, estimated network computational costs dominate the non-compute time. Motivated by these findings, we present Local Coordination JAX (LoCoJAX), which implements local rollout and reward processing on each TPU host with synchronous global training. In controlled comparisons against a tuned and optimized Pathways baseline on Qwen3-0.6B across three workloads and 8-64 TPU v7x chips, its colocated mode (LoCoJAX-CO) is 1.3-3.6x faster per step; on GSM8K, it completes a step faster on 8 chips than Pathways does on 64. Finally, a Ray-managed disaggregated mode (LoCoJAX-DA) overlaps rollout and training on separate hosts, and placement experiments establish when this overlap compensates for dividing devices and transferring weights: only when rollout dominates the step and receives most of the hosts. On NuminaMath-CoT at 256 chips, giving rollout 75% of hosts cuts step time by about 15% compared with LoCoJAX-CO.

cs.LG · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15941

Lang-SVG: Hierarchical Image Vectorization with Language Priors

Xi Liu, Chaoyi Zhou, Run Wang, Jiaang Li, Feng Luo, Junxiang Huang, Siyu Huang

原文摘要

Image vectorization reconstructs raster images as compact scalable vector graph- ics (SVG), providing a structured and editable code representation of images. However, most existing vectorization methods optimize for pixel-level render- ing fidelity, often producing SVGs that lack alignment with human-perceived object-part hierarchies, making them difficult to manipulate. To address this, we propose Lang-SVG, which introduces an explicit tree structure to organize SVG primitives with semantic labels and parent-child relationships. Lang-SVG incorporates granularity-controllable and open-vocabulary segmentation priors to initialize object-part hierarchies and leverages multimodal large language models (MLLMs) with SVG tree context to assign semantic labels to unlabeled nodes. To reduce redundancy, we introduce depth-adaptive simplification to reduce shape complexity and Tree-Guided Pruning and Merging to eliminate redundant SVG nodes. L1-SAM-Prompting further recovers missing regions to improve recon- struction fidelity and structural completeness. Beyond reconstruction fidelity, we introduce a new evaluation protocol that measures structural quality and semantic part alignment. Experiments demonstrate that Lang-SVG achieves state-of-the-art performance in both rendering fidelity and structural quality. Project website: https://xiliu8006.github.io/Lang-SVG/.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.05659

Structural Correspondence and Universal Approximation in Diagonal plus Low-Rank Neural Networks

Ying Chen, Aoxi Li, Jihun Kim, Javad Lavaei

原文摘要

Structured low-rank and sparse-plus-low-rank parameterizations, motivated by methods such as LoRA and low-rank factorization, impose strong structural constraints on the linear transformations within neural networks. We study the expressive consequences of these constrained structures from the perspective of universal approximation. We first characterize that purely low-rank neural networks, despite being able to exactly interpolate arbitrary finite scalar datasets, can fail to approximate continuous functions that vary along directions ignored by the low-rank hidden mapping. We show that augmenting the low-rank structure with a one-parameter diagonal component suffices to overcome this geometric bottleneck, yielding a Diagonal-plus-Low-Rank (DLoR) structure. We show that a full-rank linear transformation can be reconstructed using DLoR components either by expanding network width through additive decomposition or by extending network depth through multiplicative decomposition. We term this correspondence between additive width expansion and multiplicative depth extension the Structural Correspondence framework. By combining these constructions with local linearization of the activation function, we prove that DLoR neural networks restore universal approximation for general activation functions. These results characterize how architectural width and depth can compensate for severe rank constraints without requiring unrestricted dense hidden transformations. Diagonal terms are required for multiplicative construction, while additive expansion works with purely low-rank terms. We further determine its exact cost: both the minimal number of rank-$r$ DLoR factors representing $W$ and the minimal network depth for that layer equal $\lceilκ(W)/r\rceil$, where $κ(W)=\min_{γ\ne0}rank(W-γI)$, achieving a lower cost than its dense representation when $W$ is identity-centered.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14171

BARS: Budget-Aware Sequential Sampling with Refusal for Majority Identification under Unknown Distributions

Shenxi Liu, Yiwei Li, Mingyang Zhao, Yuhang Tian, Kan Li

原文摘要

Repeated sampling with majority selection is an effective means of enforcing self-consistency in large language models. Recently, a variant has also been introduced that terminates early to save costs when the output distribution is favorable. But it still cannot yield a correct confidence estimate from the answer distribution. For this issue, we formalize this as majority identification under an unknown distribution with a budget and a target error rate, and propose BARS, a sequential rule certifying the top-two gap and returning an early stop, a budgeted majority vote, or a refusal when feasibility cannot be established. A refusal is a statement about the rule and its budget, not a claim that a question is unanswerable, and it tells practitioners when to widen the budget or switch models. A certified mode carries proven bounds on the joint answering error and on refusing an instance the budget could handle, while a practical mode, used in our experiments, replaces them with empirical calibration. On synthetic instances drawn from known distributions over thousands of seeds, the difficulty-driven behavior is recovered and the price of the certified mode is measured.On GSM8K the practical mode draws 22.3\% fewer samples than fixed-8 sampling over the same pool, refused questions included, at the cost of declining 18.8\% of the questions. On MATH, it greatly improves the accuracy of its output answers into 82.5\% at the cost of honestly refusing to answer half of the difficult questions that the model itself is unable to solve.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13860

Uncovering the Memory Hierarchy of Modern NVIDIA GPUs for Accurate Simulation

Ziwen Wang, Jiapeng Zhou, Changxi Liu, Dongsheng Wang, Yun Chen

原文摘要

Emerging artificial intelligence workloads, particularly Large Language Models (LLMs), place unprecedented demands on GPU memory systems. Consequently, architectural innovations increasingly target the memory system to alleviate these bottlenecks. However, existing works still rely on outdated memory hierarchy models that fail to capture the complexities of modern hardware. This fidelity gap leads to inaccurate characterization and could misguide optimization strategies. To bridge this gap, we present GemAtlas, the first systematic reverse engineering of the memory hierarchy in modern NVIDIA GPUs. By developing fine-grained SASS-level benchmarking, we expose critical, previously undocumented micro-architectural details across different cache levels, including cache set associativity, slice-routing and set-indexing hash functions, replacement policies, coherence behavior, and interconnect topologies. We integrate these discovered micro-architecture details into the state-of-the-art GPU simulation framework. The final results demonstrate that GemAtlas reduces the mean absolute percentage error (MAPE) from 31.69% to 22.24% on average across four GPUs. Specifically, GemAtlas reduces the MAPE from 34.04% to 23.98% for real-world LLM operators.

cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13802

Scaling Shapley Energy Attribution to Large LLM Batches

Qi Luo, Ziwen Wang, Kunlin Li, Changxi Liu, Yun Chen

原文摘要

LLM services have become essential infrastructure for applications such as chat assistants and coding agents. Service providers need to attribute resource consumption to individual requests for purposes such as billing. Among these resources, energy is a key metric for assessing the cost of serving each request. However, serving engines batch requests together, and GPUs report only aggregate energy consumption. Providers typically estimate each request's share in proportion to its token consumption, but this approach can produce substantially unfair estimates. The Shapley value provides a principled solution to fair energy allocation, but computing it directly requires $2^N-1$ GPU runs for $N$ requests, which is infeasible for large batches. To address this problem, we propose StepSplit. Across GPU generations, we observe that the energy consumed by each inference step, a forward pass over a batch, consists of a shared energy, largely due to reading model weights, and a request-specific energy. We derive a closed-form Shapley value: each request is charged its own energy plus an equal part of the shared energy of every step in which it participates. A microbenchmark of 2.5-17.8 minutes calibrates both parts of the step energy once per model and GPU configuration. Under static batching, StepSplit uses this closed form in a single pass over the scheduler log. Under continuous batching, removing a request can change how the remaining requests are batched, so StepSplit adds a correction to the closed form, computed by replaying the scheduler on a CPU. We evaluate StepSplit against Shapley values measured using 529,513 GPU runs. Under static batching with batch sizes of 8-256, StepSplit achieves an $L_1$ error of 0.4-4.3%, compared with 13.4-61.3% for token-proportional allocation. Under continuous batching with 8-128 requests, the corresponding errors are 0.5-6.4% and 20.6-71.7%.

cs.DC · cs.AR · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13762

Exploiting What NVIDIA Profilers Hide: Fast and Accurate Analytical GPU Performance Modeling

Ziwen Wang, Qi Luo, Kunlin Li, Changxi Liu, Dongsheng Wang, Yun Chen

原文摘要

GPUs have become essential to many fields, including AI and scientific computing. As GPUs grow more complex, the design space of their hardware configurations becomes increasingly large, yet cycle-level simulators remain too slow to explore it. Analytical models make this exploration much faster. However, existing GPU analytical models fall short of accurately predicting performance across hardware configurations, because they re-estimate or scale every stall of a warp on the target configuration. We observe that fixed-latency stalls stay the same across configurations, whereas queueing stalls vary with the pressure on each hardware unit. Based on this observation, we propose ForeSight, a queueing analytical model that reuses the fixed-latency stalls measured on GPU hardware and recomputes the queueing stalls of each hardware unit from its pressure. To provide ForeSight with the counter values of each hardware unit, we build UnitSight, a profiler that recovers these values, which NVIDIA's official profilers hide by aggregating across units. Our evaluation on hardware configurations of nine NVIDIA GPUs shows that ForeSight predicts kernel execution time with a mean absolute percentage error (MAPE) of 8.3%, compared with 14.1% for the best baseline. ForeSight also predicts the end-to-end execution time of LLM inference on vLLM with a MAPE of 2.0%. It runs 49x faster than the cycle-level simulator Accel-Sim and faster than interval-based models such as GCoM. With the aid of ForeSight, we explore configurations derived from the H20 GPU for LLM inference and find one with 1.78x its performance/cost.

cs.AR · cs.PF

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.14560

DVFace: Spatio-Temporal Dual-Prior Diffusion for Video Face Restoration

Zheng Chen, Bowen Chai, Rongjun Gao, Mingtao Nie, Xi Li, Bingnan Duan, Jianping Fang, Xiaohong Liu 等 10 位作者

原文摘要

Video face restoration aims to enhance degraded face videos into high-quality results with realistic facial details, stable identity, and temporal coherence. Recent diffusion-based methods have brought strong generative priors to restoration and enabled more realistic detail synthesis. However, existing approaches for face videos still rely heavily on generic diffusion priors and multi-step sampling, which limit both facial adaptation and inference efficiency. These limitations motivate the use of one-step diffusion for video face restoration, yet achieving faithful facial recovery alongside temporally stable outputs remains challenging. In this paper, we propose, DVFace, a one-step diffusion framework for real-world video face restoration. Specifically, we introduce a spatio-temporal dual-codebook design to extract complementary spatial and temporal facial priors from degraded videos. We further propose an asymmetric spatio-temporal fusion module to inject these priors into the diffusion backbone according to their distinct roles. Evaluation on various benchmarks shows that DVFace delivers superior restoration quality, temporal consistency, and identity preservation compared to recent methods. Code: https://github.com/zhengchen1999/DVFace.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.00033

Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration

Xinghao Zhu, Zixi Liu, Shalin Jain, Chenran Li, Milad Noori, Michael Andres Lin, Huihua Zhao, John Welsh 等 21 位作者

原文摘要

Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging. We present Contact Wrench Guidance from Human Demonstration in Robotic Dexterous Manipulation (CHORD), a framework for long-horizon manipulation of rigid and articulated objects with reinforcement learning. The key idea is object-centric contact wrench space guidance: we represent human and robot motions by the forces and torques they can induce on the object, enabling similarity to be measured by the induced instantaneous motions. This guidance makes reinforcement learning more scalable for contact-rich dexterous manipulation. We further introduce a large-scale simulation benchmark with 4,739 bimanual dexterous manipulation tasks, constructed from motion-capture datasets and reconstructed in-house videos. Evaluated on 1,831 benchmark tasks, CHORD achieves an average success rate of 82.12%, demonstrating strong scalability. CHORD also generalizes to whole-body manipulation from hand-only and third-person demonstrations, achieving a 90.77% success rate, and the learned policies transfer to the real world in both open-loop and closed-loop settings.

cs.RO · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13620

When to Start Denoising? Adaptive Denoising Entry for Generative Video Compression

Zifu Zhang, Yuqiao Li, Ding Ding, Shengxi Li, Mai Xu, Daowen Li, Kai Li, Ying Chen

原文摘要

Generative video compression offers a promising solution for compressing and reconstructing videos under extremely low bitrate constraints, among which the diffusion-based solutions still achieve the best performance by providing the powerful generative latent prior. However, their effectiveness critically depends on when the compressed latent is introduced into the diffusion process, which directly governs the trade-off between reconstruction fidelity and perceptual realism. To address this limitation, we identify denoising start-timestep selection as a key factor governing this trade-off and formulate it as a geometric optimization problem. Based on this formulation, we propose a novel adaptive diffusion entry method for generative video compression (ADE-GVC), the first timestep-aware generative video compression framework, featuring a lightweight predictor that estimates an adaptive denoising start point from the compressed latent. A temporal adapter further extracts compression-aware conditions and injects them into a pretrained video diffusion model, enabling the reconstruction of videos with both high fidelity and realism. More importantly, we observe a strong quantitative relationship between the predicted timesteps and the distortion between the original and compressed latents, providing empirical support for our optimization formulation. Extensive experiments demonstrate that ADE-GVC achieves the state-of-the-art performance on perceptual metrics and substantially improves reconstruction realism over existing generative video compression methods. Notably, our ADE-GVC achieves an average bitrate savings of 40.99\% (on DISTS) and 95.24\% (on PSNR) compared to the latest open-source baseline GLC-Video.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12635

Beyond Sunny Skies: Using Large Language Models to Predict Urban Traffic in Adverse Weather

Zhu Xiao, Kuan Feng, Tong Li, Chenxi Liu, Gang Wang, Zhuo Tang, Kenli Li

原文摘要

Urban traffic flow prediction is a cornerstone of intelligent transportation systems, yet accurate forecasting under adverse weather conditions remains a significant challenge. Existing data-driven models, while effective for routine patterns, often struggle with the sparse data and complex non-linear shifts associated with rare meteorological events. These methods typically treat environmental contexts as shallow numerical features, failing to capture the underlying semantic reasoning-why and how specific adverse conditions alter mobility behaviors across regions with different functional compositions. To address this limitation, we propose Multi-STG, a unified framework that integrates Large Language Model (LLM)-derived semantic priors with graph-based spatiotemporal learning. Multi-STG introduces an LLM-empowered semantic enhancement module that transforms comprehensive context descriptions such as weather, POIs, and holidays into high-level semantic embeddings, effectively providing a knowledge-driven complement to data scarcity. Furthermore, we design a multimodal attentional fusion mechanism to dynamically weigh heterogeneous inputs and a semantic-enhanced prediction head with gated residual calibration to robustly adjust forecasts based on semantic guidance. Extensive experiments on real-world multi-city private car datasets demonstrate that Multi-STG outperforms state-of-the-art baselines, reducing MAE by 11.82\% on average relative to the strongest baseline across three cities, while maintaining stable performance under sparse and non-stationary conditions.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.14260

Speculative Decoding Reimagined for Multimodal Large Language Models

Luxi Lin, Zhihang Lin, Zhanpeng Zeng, Rongrong Ji

原文摘要

Multimodal Large Language Models (MLLMs) deliver strong performance across vision-language tasks but suffer from high autoregressive decoding latency. Although speculative decoding can accelerate Large Language Models (LLMs) without changing their output distribution, directly applying existing approaches to MLLMs yields limited speedups because text and visual tokens exhibit distinct characteristics. We present Multimodal Speculative Decoding (MSD), a framework specifically designed for efficient MLLM inference. MSD is motivated by two key observations: text and visual tokens should be handled separately during drafting, and an effective draft model requires both strong language modeling and visual perception capabilities. Accordingly, MSD decouples text and visual token processing in the draft model and applies token-specific strategies to improve drafting efficiency. We further introduce a two-stage training strategy. The draft model is first trained on text-only instruction-tuning data to strengthen its language modeling ability and is then progressively exposed to multimodal data to develop visual perception while preserving its language competence. Extensive experiments across multimodal benchmarks demonstrate that MSD achieves up to $2.29\times$ inference speedup for LLaVA-1.5-7B and $2.46\times$ for LLaVA-1.5-13B without sacrificing generation quality. These results establish MSD as an effective and general approach for accelerating multimodal autoregressive generation. Our code is available at https://github.com/Lyn-Lucy/MSD

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12997

TIB: Sample-wise Tempered Information Bottleneck for Multimodal Attribution beyond Alignment Assumption

Qiming Huang, Peixi Liu, Jianbo Jiao

原文摘要

Multimodal (e.g. vision-language, in this work) attribution aims to interpret the models without access to ground-truth explanatory supervision. Existing multimodal attribution methods typically rely on paired modalities as proxy supervision, implicitly assuming that image-text pairs are semantically aligned. Information bottleneck (IB)-based attribution methods follow this general paradigm and instantiate it through a global sufficiency-compression objective. In realistic multimodal datasets, however, this assumption is frequently violated due to partial semantic misalignment and unreliable cross-modal correspondences. Through theoretical analysis, we show that, under such misalignment, information-bottleneck-based attribution objectives exhibit a structural limitation: by assuming equal reliability across all proxy supervision signals, they induce posterior over-contraction and forced explanations. To address this issue, we propose TIB, a sample-wise Tempered Information Bottleneck framework that adaptively modulates attribution strength according to cross-modal reliability, without assuming semantic alignment. Extensive experiments on controlled benchmarks and large-scale datasets show the effectiveness of the proposed approach, while also validating the identified failure mode.

cs.CV · cs.AI · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11364

OOM: Offload-Optimized Full-Parameter MoE Training via Memory-Execution Co-Design

Ruijia Yang, Shiyuan Lin, Chengxi Liao, Zeyi Wen

原文摘要

Offloading model state to host memory and streaming layer weights let a single multi-GPU node train large mixture-of-experts (MoE) models. GPU memory then becomes a performance budget, shared by resident expert state, retained activations, and execution worksets. The best allocation depends on the expert execution path, batch size, and sequence length. We present OOM (Offload-Optimized MoE), a full-parameter MoE training system that co-designs expert execution and memory allocation. For expert-parallel execution, OOM updates resident experts on their owning GPUs and jointly plans state residency and MoE activation retention according to pipeline dependencies. For local execution, OOM bounds GPU worksets through dependency-aware partitioning; in hybrid models, it also retains attention results on the host to avoid costly recomputation and selectively regenerates layer boundaries through cheaper layers to make room. Calibrated cost models guide joint configuration within GPU and host budgets to minimize step time; OOM selects among the configured paths through short executions. On four H100s, OOM trains with 1M-token contexts, whereas FSDP2, ZeRO-Offload, and MegaTrain exhaust GPU memory at 512K. It also trains a 122B-parameter MoE model, on which all three exhaust host memory. On four 24 GB RTX 4090s, it trains 512K-token contexts and 1,024 1K-token sequences per step without gradient accumulation. Across matched-global-batch comparisons, OOM delivers 1.58x and 4.08x geometric-mean (up to 2.14x and 7.77x) throughput over CPU-offloaded FSDP2 on the two platforms while using the least host memory among the evaluated systems -- as little as 1/3.0 of FSDP2's.

cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.04391

SkillAny: Online Skill Learning for Language Agents at Any Intermediate State

Jiaxi Li, Ke Deng, Yun Wang, Jingyuan Huang, Yucheng Shi, Yuanfu Sun, Qiaoyu Tan, Jin Lu 等 9 位作者

原文摘要

Language agents increasingly rely on reusable skills to improve multi-step web automation across related tasks. A growing line of work studies online skill learning, where agents continually induce skills from previous task trajectories and reuse them in future tasks on the fly. However, existing methods mainly reuse skills at the task-level: a fixed set of skills is retrieved based on the initial task instruction and then held fixed throughout execution. This static strategy is misaligned with web execution, where the appropriate next action depends not only on the task goal but also on the current webpage state, which often transitions into situations that the initial skills fail to cover. To address this gap, we introduce SkillAny, an online skill learning framework that enables language agents to learn, retrieve, and invoke reusable skills at any intermediate state during task execution. Three components jointly enable this capability: sliding-window extraction turns completed trajectories into sub-procedures applicable beyond task boundaries, a dual text-code representation makes each skill both retrievable and executable, and step-level task-state retrieval selects skills that match the goal and the current webpage state as execution unfolds. Experiments on WebArena across five domains show that SkillAny consistently outperforms strong baselines, achieving average success rates of 37.5% with GPT-4.1 and 24.3% with Qwen3-4B, corresponding to relative gains of 10.6% and 10.0% over the strongest baseline, respectively. The code is available at https://github.com/plusnli/online-skill-learning.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.05093

From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows

Cenwei Zhang, Lin Zhu, Manxi Lin, Lei You

原文摘要

Feature attributions often hide a critical modeling choice: they explain a prediction along a counterfactual path from a reference state to an input. Different baselines, interpolations, and generative trajectories define different paths and can therefore produce different explanations. We study this path ambiguity as a modeling problem. Our central question is whether the path can be chosen by a distribution-level transport process, rather than by a hand-designed interpolation or by the sensitivity geometry of the model being explained. We separate attribution into fixed-path credit allocation and path selection. For a fixed path, we prove that the Aumann-Shapley line integral is the unique attribution rule under fixed-path axioms and coordinate-trace separability. For path selection, we minimize kinetic action over flows that transport a reference distribution to the data distribution, yielding a transport-geodesic attribution principle. We instantiate this framework using Rectified Flow and derive stability bounds quantifying how vector-field approximation errors affect the resulting attributions. Experiments show that Reflow improves cross-seed attribution stability, while learned transport paths yield spatially coherent explanations with competitive deletion faithfulness among the evaluated path-based methods. Our code is available at https://github.com/cenweizhang/OTFlowSHAP.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03601

DiabAgent: Toward Agentic Physiological Control

Chengxi Li, Shaoxuan Li, Deyao Hong, Qingle Liu, Yi Huang, Puheng Bian, Minzhi Xu, Lanyue Zhang 等 16 位作者

原文摘要

As pretraining scales, medical language models have advanced rapidly in clinical question answering. Their ability to achieve effective physiological control through sustained intervention still lacks systematic quantitative evaluation. Type 1 diabetes provides a concrete setting: impaired insulin secretion creates dependence on exogenous insulin, with dosing continually adjusted using information about meals, exercise, illness, and glucose measurements. We introduce DiabAgent, a research suite for agentic physiological control comprising the Agentic Physiological Control (APC) framework, the DiabetSim simulator, and the SweetBench benchmark. APC controls glucose through medical-device tools, using ongoing observations to infer individual states, accumulate personal experience, and choose treatment actions. We build DiabetSim to simulate different patients, events, and device conditions for scalable virtual testing and training. Using DiabetSim, we construct SweetBench by instantiating 32 scenario designs as 212 glucose-control tasks. With Claude Opus 5.5 and a fixed decision interval of one hour, APC achieves a COGI of 73.5, compared with 71.2 for the open-source closed-loop baseline OpenAPS. Ablations further show that individual adaptation and contextual information improve glucose control. Combining high-frequency rule-based control with lower-frequency agent decisions shows potential for balancing control performance and computational cost. Our work provides a framework, simulator, and evaluation foundation for agents to participate in human physiological control.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10559

CompositionEvidence: Controlling Geometric Evidence for Cinematic Composition Reasoning with Frozen Vision-Language Models

Yichen He, Xin Huang, Yuxi Liu

原文摘要

Cinematic composition questions depend on in-frame geometry that vision-language models (VLMs) use inconsistently. External perception tools can expose this geometry, but naive prompt injection can overwrite useful visual predictions. On ShotBench composition, pre-question geometry reduces Qwen2.5-VL-7B from 49.5% to 46.3%; corrupted measurements lower it to 33.6%. CompositionEvidence derives nine reproducible features from frozen detection, depth, and line experts and supplies their shared 24-dimensional representation through two interfaces. A training-free channel appends gated neutral evidence after the question, reaching 56.2% (+6.7, exact McNemar p=0.0018) and 48.4% under corrupted evidence. A minimal-training channel maps the same representation to four soft tokens with a 7.37M-parameter projector while leaving the VLM frozen; it reaches 61.0% (+11.5). Across nine VLMs, including five commercial models queried through official provider APIs, zero-shot accuracy ranges from 43.8% to 63.0%, and the text channel changes it by +6.7 to -6.9 points; the highest baseline is among the regressions. The effect of explicit geometry depends on measurement reliability and on how the receiving model integrates it.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09041

DobotTrack: Target-Grounded Vision-Language-Action Learning for Embodied Person Tracking

Shengjun Deng, Yinghan Zhang, Chengrui Wei, Liu Liang, Songyuan Liu, Pengkun Zheng, Yajun Zheng, Ying Zhang 等 11 位作者

原文摘要

Language-guided embodied person tracking requires a mobile agent to identify a target from a referring description and consistently follow the target under egocentric observations. We present DobotTrack, a target-grounded vision-language-action (VLA) framework that integrates auxiliary grounding supervision with efficient temporal modeling. We systematically investigate five auxiliary tasks---person segmentation, bounding-box grounding, visual-spatial classification (VSC), future DINO feature prediction, and future-frame prediction---and adopt complementary mask and bounding-box supervision to enhance target delineation and localization. We introduce Q-identity, a target-conditioned historical visual compression module that leverages target-grounding predictions to selectively preserve identity-discriminative appearance features and spatial cues in compact historical tokens. On EVT-Bench, our 0.8B-parameter model achieves an average success rate of 77.9%, exceeding OmTrackVLA by 16.9 percentage points across the three tasks. We further construct a real-world person-tracking dataset collected using a quadruped robot, comprising 126 episodes and 596,323 mask-annotated frames. The target masks enable automatic derivation of bounding boxes and visual-spatial labels encoding horizontal position and apparent image scale, providing additional grounding supervision without separate manual annotation of these labels. Together, our framework and annotation pipeline support learning target-grounded tracking policies from simulated and real-world data. Code is available at https://github.com/Dobot-Team/dobot_track.

cs.RO · cs.CV

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版本更新 · v2arXiv:2611.08251

VoxCanvas-41M: A Large-Scale Bilingual Dataset for Instruction-Based Speech Editing and Its Learning Dynamics

Zhikang Niu, Wenming Tu, Yalin Li, Yanru Huo, Junxi Liu, Tianrui Wang, Ruiqi Yan, Junxuan Liang 等 11 位作者

原文摘要

Recent advances in speech generation have spurred the development of speech editing techniques. However, achieving high-quality speech editing remains challenging due to two key limitations. First, the major hurdle is the lack of large-scale, high-quality speech editing datasets. Constructing such datasets is costly, while open-source, scalable data pipelines remain limited. Second, the learning dynamics of speech editing models remain poorly understood. In particular, how models acquire editing capabilities and the relationship between generation and editing remain unclear. To address these challenges, we introduce VoxCanvas-41M, a large-scale bilingual dataset comprising approximately 41 million speech editing pairs. VoxCanvas-41M covers two major categories: content editing, including single-span, multi-span, and location-reference editing, and paralinguistic editing, covering the insertion and deletion of 39 classes of nonverbal events such as laughter, sighs, and coughs. Building on VoxCanvas-41M, we train VoxCanvas-Edit, an instruction-based speech editing model, demonstrating the effectiveness of our dataset in improving editing performance. Furthermore, we systematically investigate the learning dynamics of speech editing, examining how models learn different editing operations and how data scale, task interactions, and training strategies affect editing performance. The dataset, pipeline, and demos are publicly available.

cs.SD · eess.AS

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新论文 · v1arXiv:2611.08802

Scale as a Modality: Multi-Scale Pixel-Space Diffusion Transformers

Ruibo Chen, Kaishen Wang, Tianyi Xiong, Chenxi Liu, Yanshuo Chen, Heng Huang, Jian Du

原文摘要

Latent diffusion models rely on pretrained autoencoders that introduce lossy compression and constrain spatial granularity. Pixel-space diffusion avoids this bottleneck and enables flexible tokenization at multiple spatial scales, yet existing approaches largely adopt two-level coarse-to-fine architectures with limited cross-scale interaction. We introduce MS-pDiT, a multi-scale pixel-space diffusion transformer that treats spatial scale as a modality. MS-pDiT tokenizes the same noisy image at multiple patch sizes, processes each scale in a dedicated transformer stream, and enables bidirectional information exchange through compacted MM-DiT-style joint attention, with attention cost independent of the finest patch size. This design establishes the number of scales as a new architectural scaling dimension. On ImageNet $256\times256$, increasing the number of scales from two to five consistently improves FID from 4.39 to 2.31, 2.26, and 2.19, while adding an intermediate scale substantially outperforms increasing depth under comparable parameter budgets. Further analysis highlights the importance of bidirectional cross-scale interaction and late introduction of fine-grained streams. MS-pDiT achieves FID scores of 1.43 and 1.44 on ImageNet $256\times256$ and $512\times512$, respectively, and naturally extends to text-to-image generation, achieving 0.86 on GenEval and 83.9 on DPG-Bench.

cs.CV

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版本更新 · v4arXiv:2608.05466

Recursive Synthesis for Long-Horizon Terminal Tasks

Zhongzhi Li, Yucheng Shi, Zongxia Li, Ruhan Wang, Anhao Li, Jiaxi Li, Junyao Yang, Lei Ke 等 14 位作者

原文摘要

High-quality long-horizon training data for terminal agents is costly and difficult to scale, as manual authoring is expensive and direct LLM generation often produces inconsistent instructions, environments, solutions, and verifiers. We present Recursive Synthetic Terminal Tasks (RST), a recursive verified synthesis framework for constructing long-horizon terminal-agent tasks at scale. Starting from verified seed tasks, RST extends the reference solution, realigns the verifier and instruction to the new workflow, validates the result in a fresh sandbox, and reuses accepted tasks as seeds for subsequent rounds. Across fifteen recursive rounds, RST produces 37,484 synthesized terminal-agent tasks at roughly $0.05 per task. Task difficulty increases substantially over rounds: the median reference solution grows from 67 to 374 lines, the median number of executed commands grows from 40 to 244, and DeepSeek-V4-Pro pass@4 drops from 90% at R1 to 2.5% at R15. To demonstrate training utility, we collect rejection-sampled Qwen3.5 trajectories on the synthesized tasks and use them for supervised fine-tuning. Fine-tuning on these trajectories improves Qwen3.5-27B and Qwen3.5-122B-A10B by up to 10 points on Terminal-Bench 2, Terminal-Bench Hard, and Long-Horizon Terminal Bench, while agentic PPO lifts Qwen3.5-27B to 49.44%, 32.00%, and 22.07% on the three benchmarks, corresponding to relative gains of 20.0%, 41.2%, and 21.9% over the base model. Moreover, after 15 rounds, the recursion shows no ceiling: synthesis yield and validation rates remain stable as difficulty keeps climbing, indicating that the process can continue well beyond the scale reported here.

cs.AI · cs.LG

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版本更新 · v2arXiv:2610.31835

SAGE：临时交通管制下施工区通行路径预测的视觉-语言与几何混合框架

SAGE: A hybrid vision-language and geometry framework for work-zone pathway prediction under temporary traffic control

Jiaxi Liu, Keshu Wu, Weizhe Tang, Bofeng Cao, Sikai Chen, Xiaopeng Li, Steven T. Parker, Bin Ran

AI 速读 基于摘要

SAGE 将视觉语言模型（VLM）的轨迹作为对几何候选路径的偏好，在共享候选空间中分离路径构建、路径偏好与控制合规，以预测施工区临时交通管制下的通行路径。

cs.CV · cs.RO

arXiv ↗PDF ↗
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Temporary traffic control redirects vehicles through work zones, requiring automated vehicles to connect an interpretation of the visible devices to a continuous passing path. Geometric predictors provide several plausible paths, yet selecting the one consistent with the temporary control remains difficult. We propose SAGE (Semantic-to-Action Grounded Execution), a read-decide-execute-check framework that separates path construction, path preference, and control compliance within a shared candidate space. The central idea is to use a vision-language model's (VLM's) trajectory as a preference over intact geometric candidates. A role-decoupled generator constructs the candidates, and rank fusion combines each candidate's geometric score with its agreement with the VLM trajectory. Supervision structured around the Manual on Uniform Traffic Control Devices (MUTCD) trains SAGE-VLM to state the lane status and hazard side before its passage decision and trajectory. The predicted hazard side enters image-space predicates derived from MUTCD provisions, which support a final intervention bounded in score and displacement. On 2,306 real-world ROADWork validation episodes, the candidates include a path within 10 px of the human demonstration in 87% of episodes, yet selection regret accounts for 53% of the geometric ranker's error. SAGE reduces average and final displacement errors by 8.3% and 12.0% relative to the ranker and hard-constraint violations by 37%. The trajectory opinion improves all four tested generator-selector combinations. SAGE is comparable to state-of-the-art VLM modules in accuracy, with a faster-responding VLM, and outperforms four other adapted driving VLMs in displacement error and safety measures. Project page: https://hhjj233.github.io/SAGE-page/.

新论文 · v1arXiv:2611.08011

Route-DMD: Distribution Matching Distillation with Task-Routed Mechanism for Speech Foundation Models

Yanru Huo, Zhikang Niu, Wenming Tu, Junxi Liu, Tian Tan, Shan Yang, Yipeng Kang, Ziyang Ma 等 10 位作者

原文摘要

Speech foundation models built on flow matching handle generation, editing, and processing in one system, but every request costs 20-50 iterative steps, and classifier-free guidance (CFG) doubles each of them. Distribution matching distillation (DMD) trains a student that approaches the teacher's quality in a few steps, yet existing speech distillation work applies it to generation alone. We show that a distribution-matching objective does not serve every task: in separation, the noised states of erroneous student outputs deviate far from the teacher's training distribution, where its prediction is unreliable. Route-DMD therefore makes the objective task-dependent, routing the affected tasks to a regression loss and keeping decoupled distribution matching elsewhere. The student is initialized by consistency distillation, and teacher rollouts use adaptive projected guidance (APG) instead of CFG. Without CFG, the 4-step student matches the 32-step teacher on generation, editing, and processing tasks at a 4.5$\times$ end-to-end speedup.

cs.SD

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新论文 · v1arXiv:2611.07550

SEROA: Stable Encrypted-Traffic Representation and Online Adaptation for Service-Aware Networks

Zongxi Liu, Shuai Mao, Jie Chuai, Hongyang Du

原文摘要

Mobile networks are evolving toward service-aware operation to support heterogeneous applications with diverse quality-ofservice (QoS) and quality-of-experience (QoE) requirements. In practice, however, the widespread use of encryption makes service types difficult to observe directly, which limits the deployment of service-aware optimization in operational networks. Recent raw-byte representation learning methods have improved offline encrypted-traffic classification, but their robustness in realistic deployment remains limited due to temporal drift and the lack of explicit labels for online adaptation. This paper proposes Stable Encrypted-Traffic Representation and Online Adaptation (SEROA), a deployment-oriented framework for encryptedtraffic classification in service-aware mobile networks. In the offline stage, SEROA exploits protocol structure to learn temporally stable representations by applying masked modeling to stable fields, protocolaware transformations and contrastive learning to unstable fields, and time-adversarial learning to suppress time-specific cues. In the online stage, SEROA uses feedback records containing QoE measurements, wireless states, and allocation decisions to update the deployed classifier through contextual-bandit learning. Experiments show that SEROA consistently outperforms representative pretrained baselines under both random and time-based evaluation protocols, and remains effective under QoE-driven online adaptation. These results demonstrate the value of stable representation learning and online adaptation for practical service-aware mobile networks.

cs.NI

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新论文 · v1arXiv:2611.07452

Understanding Self-Supervised Representation Learning for Encrypted Traffic

Zongxi Liu, Shuai Mao, Jie Chuai, Hongyang Du

原文摘要

SSL has been adopted for encrypted traffic classification to learn representations from unlabeled traces and reduce reliance on manual feature engineering. However, recent studies show that data leakage can inflate reported performance and that pretrained models can underperform shallow classifiers once leakage is controlled. These findings raise questions about what information SSL can learn from encrypted traffic and how different pretraining mechanisms exploit this information. In this paper, we systematically investigate these questions by examining the sources of learnable information, the effectiveness of contextual pretraining, alternative self-supervision mechanisms, and the limits of representation learning. We first find that visible protocol fields retain contextual dependencies that are strongest between adjacent bytes, while payload interventions establish the classification value of retained plaintext structure. Guided by these insights, we investigate byte-level contextual learning through ByteBERT and TrafficDCNet, showing that aligning prediction granularity with local dependencies produces more discriminative representations. We further examine cross-view contrastive learning through AlignTraffic and find that complementary supervision improves representation quality beyond contextual prediction alone. Across four public classification tasks, AlignTraffic outperforms shallow learning by 5.97% to 30.70%, with additional evaluations on industrial traffic demonstrating its effectiveness in broader settings. Finally, evaluations on Tor traffic and individual packets reveal limitations when application information is concealed or unevenly distributed. These findings provide a systematic understanding of how observable information and self-supervision mechanisms shape the effectiveness and scope of representation learning for encrypted traffic.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05036

ToolSpace: Scaling Tool-Centric Environment Synthesis for Long-Horizon Agents

Guanting Dong, Zhichao Sun, Xinyu Yang, Rui Wang, Xin Deng, Jiajie Jin, Zhixiong Yang, Junhao Huang 等 25 位作者

原文摘要

Large language models are deployed as long-horizon agents that compose diverse tools across MCP services into executable workflows. Existing MCP environment-scaling methods are data-centric: they first synthesize a database and scenario, then derive tools and tasks from them. As a result, tool interfaces may not match real ones, tool compositions are not grounded in execution, and tasks rarely span more than one environment. We propose ToolSpace, a tool-centric framework that reverses this order. ToolSpace mines data, state, and retry dependencies from adjacent calls in execution traces of real MCP services and open agent corpora, using deterministic rules rather than LLM judgment, and clusters the resulting weighted tool-composition graph into environments. Databases are synthesized only for state-mutating environments, while query and stateless tools replay recorded returns. ToolSpace then performs state-aware random walks with rollback and decompiles each chain into a natural-language task with assertion-based checks, keeping only samples that replay successfully from the initial state. Applied to AutomationBench and tau3-Bench Banking, the pipeline produces harder Pro versions on which 7 frontier models drop by up to 38.2 points. Across seven long-horizon tool-use benchmarks, ToolSpace raises Qwen3.6-35B-A3B from 23.0 to 41.0 average score (43.2 with RL) and GLM-5.2 from 44.6 to 56.6, on par with Claude Opus 5. Ablations show that execution-witnessed edges clearly outperform LLM-judged ones, and performance keeps improving with data and environment scale.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.02542

World-Model-Augmented Visual Locomotion for Humanoids on Foothold-Constrained Terrain

Yuxi Liu, Lijun Han, Ziming Wang, Ao Zhang, Cong Yang, Wei Sui

原文摘要

Foothold-constrained terrain limits feasible footholds to sparse, discontinuous, or narrow support regions, as in gaps, stepping stones, stairs, and balance beams. In such settings, a single misplaced step can cause a fall. Reliable traversal therefore requires precise foot placement and anticipation of upcoming terrain. To address this challenge, we present World-Model-Augmented Visual Locomotion (WM-LOCO), which augments a visual locomotion policy with predictive context from a jointly trained world model. Given onboard depth observations and proprioception, a recurrent state-space model (RSSM) learns action-conditioned latent dynamics that predict future proprioceptive states, egocentric observations, and task rewards. The resulting recurrent feature is then fed into the proximal policy optimization (PPO) actor-critic, providing predictive context for anticipatory foot placement on foothold-constrained terrain. WM-LOCO achieves average success rates of 96.0% on gaps, 96.9% on stepping stones, 94.0% on stairs, and 92.1% on balance beams, outperforming the evaluated baselines while improving gait efficiency and smoothness. Furthermore, WM-LOCO achieves zero-shot sim-to-real transfer to a Unitree G1, with 92.5% success across these four terrain classes.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.12969

Compact Attention: Exploiting Structured Spatio-Temporal Sparsity for Fast Video Generation

Qirui Li, Guangcong Zheng, Qi Zhao, Jie Li, Bin Dong, Yiwu Yao, Xuewei Li, Zequn Qin 等 9 位作者

原文摘要

The quadratic computational complexity of self-attention mechanisms imposes a critical constraint on transformer-based video generation, particularly for synthesizing ultra-long and high-definition sequences. While most sparse attention techniques employ fixed or heuristic patterns, they often struggle to accommodate the heterogeneous and layer-specific redundancies inherent in video diffusion transformers (VDiTs). We find that different attention heads consistently concentrate most of their attention mass in different coarse query-key regions, a behavior we call functional specialization. Although individual attention values vary with the generated content, these regions remain similar across prompts and random seeds. Leveraging these insights, we introduce Compact Attention, a hardware-aware sparse attention framework for efficient long-video synthesis. We quantify this stability and use it to construct searchable, reusable, head-specific masks. Based on the observation, we propose a deformable dual-window sparse pattern mechanism that flexibly approximates local and axial attention regions within one tile-aligned mask family. To avoid manually choosing a mask for each head, we develop a recall-driven offline auto-search algorithm that identifies high-quality sparse masks while leveraging temporal stability for mask reuse. Extensive evaluations on Wan2.1 (14B) and Hunyuan-Video demonstrate that Compact Attention achieves 2.25x/3.03x attention speedups over FlashAttention-2 and 1.20x/1.74x over FlashAttention-3, respectively, while maintaining visual quality and temporal consistency comparable to full-attention baselines. Project Page: https://yo-ava.github.io/Compact-Attention.github.io/

cs.CV

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