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新论文 · v1arXiv:2611.14147

Secure Information Flow in Quantum Programs

Zhicheng Zhang, Mingsheng Ying, Lirong Xia

原文摘要

Information-flow security aims to prevent the leakage of confidential information through program execution. This problem becomes fundamentally subtler for quantum programs, because their states may involve superposition and entanglement, and their executions may involve intricate interactions between classical and quantum variables. This paper develops semantic foundations and verification methods for secure information flow in quantum programs. We formulate two notions of information-flow security. The first is a noninterference-based definition that naturally generalizes its classical counterpart. The second is a simulation-based definition that explicitly models an attacker, providing a clear operational interpretation of security. We prove that these two notions are equivalent. To verify the information-flow security of quantum programs, we develop a proof framework based on quantum Hoare logic. A key challenge is that the standard self-composition technique, which expresses information-flow security in Hoare logic by composing a program with a renamed copy of itself, fails in the quantum setting due to an obstruction analogous to the no-cloning theorem. To overcome this challenge, we introduce self-conjugation, a novel technique that leverages the deferred measurement principle and properties of unitary operations. We demonstrate our framework on examples drawn from the quantum one-time pad, quantum error correction, and anonymous entanglement generation.

quant-ph · cs.CR · cs.PL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.18634

EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation

Zecheng Yin, Benedict Jun Ma

原文摘要

To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots. A simplified version of such task is Object Goal Navigation (ObjNav). In ObjNav, successful arrival at the target object provides a basic measure of performance; however, the efficiency of the navigation trajectory is equally important, as it indicates how intelligently the agent explores and how much time remains for subsequent tasks. In unknown environments, the key to efficient navigation lies in deciding where to explore next. While many prior works aim to address this core challenge and achieved promising performance in certain settings, recent training-based models and non-training frameworks still suffer from generalization and efficiency issues respectively, which in the worst cases can lead to excessive exploration of already-visited areas or redundant back-and-forth motion. We evaluate EffiNav on two widely used simulation benchmarks Habitat Matterport 3D (HM3D) and Open-Vocabulary Object goal Navigation (OVON), and further validate its effectiveness on physical robots in real-world settings. We conduct failure analysis on massive simulation episodes. With minimal modification, we also extend EffiNav to a memory-augmented ObjNav task on the GOAT-BENCH dataset, demonstrating its adaptability beyond standard ObjNav settings. Across two standard metrics--Success Rate (SR) and Success weighted by Path Length (SPL), EffiNav matches or outperforms recent baselines, reflecting its efficiency, robustness, and practical applicability. Recognizing the different emphases of the two datasets, the performances reveals this framework is more balanced and generalizable for efficient ObjNav.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.00249

SANEval: Open-Vocabulary Compositional Benchmarks with Failure-mode Diagnosis

Rishav Pramanik, Ian E. Nielsen, Jeff Smith, Saurav Pandit, Ravi P. Ramachandran, Zhaozheng Yin

原文摘要

Text-to-image (T2I) models struggle with compositional prompts requiring multiple objects, bound attributes, accurate counts, or spatial relations. Existing benchmarks face limits from fixed detector vocabularies, aggregated scores, and varying human-validation protocols. We introduce SANEval (Spatial, Attribute, and Numeracy Evaluation), an open-vocabulary compositional benchmark with two key contributions: (i) a public dataset of ~5,250 prompts paired with images from six state-of-the-art T2I models, split into algorithmically generated Simple and fully human-written Hard partitions across five compositional tasks, and (ii) a modular evaluation pipeline combining an LLM prompt parser, an open-vocabulary detector with LLM synonym expansion, and scorers for attribute binding, spatial relations, and numeracy. Each scorer emits numerical scores and diagnostic feedback for missing, extra, or mis-bound elements. Validated against human ratings (n = 500), SANEval achieves positive Pearson correlations across all categories (overall r = 0.222), outperforming CompBench++ (-0.016) and GenEval (-0.030). A manual audit of 24,012 detections shows 96.58% precision, with ablation confirming that synonym expansion is critical to pipeline performance. Dataset: [https://huggingface.co/datasets/saneval/saneval-release](https://huggingface.co/datasets/saneval/saneval-release). Pipeline: [https://github.com/nielseni6/saneval](https://github.com/nielseni6/saneval).

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2609.33239

Schrödinger--Föllmer Actor--Critic: Diffusion Policy Improvement with Finite-Sample Analysis

Yuling Jiao, Lican Kang, Jerry Zhijian Yang, Jincheng Ying

原文摘要

Diffusion policies represent multimodal action distributions, but a $Q$-weighted update does not specify how to sample from the resulting target distribution. We propose Schrödinger--Föllmer Actor--Critic (SFAC), an offline-to-online reinforcement learning (RL) method for Kullback--Leibler (KL)-regularized policy improvement through conditional diffusion. A minimax Bellman critic estimates the action-value function, which defines an exponentially tilted target policy. A Doob $h$-transform expresses this update as a correction to the reference diffusion drift. The target drift is a weighted conditional mean of Brownian-bridge labels. We fit it by weighted regression using endpoints from the current policy and pointwise action-value evaluations. In the small-update regime, the KL-regularized update follows the natural policy-gradient direction, and the Doob correction represents the same local change in the space of diffusion drifts. Under suitable conditions, we derive finite-sample bounds that separate the errors from critic estimation, weighted neural drift regression, and diffusion discretization through a reverse-KL actor bound. The average-performance bound depends explicitly on the comparison coefficients over the policy iterations. Across six D4RL offline-to-online tasks, the final scores of the $Q$-guided bridge-regression implementation exceed its offline initialization on every task and Cal-QL on five tasks. We also compare with offline RL and online-only baselines.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10723

CyberBinGym: Can AI Agents Turn Binary Patches into Real Exploits?

Lambang Akbar Wijayadi, Zhaoqi Xiao, Zhun Wang, Hongwei Li, Wenbo Guo, Heng Yin, Zhenkai Liang, Dawn Song

原文摘要

Security fixes usually reach users as compiled updates, but deployment delays leave unpatched systems exposed. During this window, comparing patched and unpatched binaries can reveal how to exploit a vulnerability, turning a defensive update into actionable information for attackers. Existing benchmarks do not measure this capability: they expose source code, rely on human-crafted challenges, or evaluate binary comprehension without requiring exploitation. We introduce CyberBinGym, a benchmark containing 100 tasks derived from real vulnerabilities in 53 widely used projects, spanning six vulnerability families and three levels of analysis complexity. Agents receive stripped vulnerable and patched binaries without source code or vulnerability information, and must combine static and dynamic analysis to recover and exploit the vulnerability. Evaluation separately measures vulnerability recovery through differential crash testing and end-to-end exploitation through flag retrieval, with trajectory audits to reject reward hacking. The strongest evaluated agent recovers the target vulnerability in 88% of tasks and exploits it in 20%. Providing the patched binary raises its exploit rate from 10% to 25% on a matched 20-task subset; with standard binary hardening enabled, it still exploits two of its 20 previously successful tasks. These results demonstrate that current agents can turn released binary patches into working exploits in a controlled setting, although exploitation remains unreliable. These capabilities lower the barriers to targeting unpatched software and compress defenders' effective response window, increasing the urgency of timely patch deployment.

cs.CR · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10521

TimeHF 2.0: Multimodal, Multivariate, and Multi-Region Collaborative Forecasting for Large-Scale Retail Supply Chains

Zhongxuan Han, Zhengxin Shi, Kunxiu Chen, Xiaolin Huang, Yulin Huang, Anni Zhang, Xiaoming Lin, Zhengyu Chen 等 33 位作者

原文摘要

Large-scale retail supply chains need sales forecasts for specified products, future dates or planning windows, and regions under the conditions available at the forecast origin. Regional sparsity, changing environments, volatile sales, and unfamiliar business situations make these dimensions difficult to model together. We introduce TimeHF 2.0, a multimodal, multivariate, and multi-region collaborative forecasting framework that makes flexibly expressed business knowledge usable in numerical prediction. A coverage-aware directed encoder integrates regional sales and applicable covariates. A semantic alignment module transfers language-model hidden representations of natural-language plans and domain experience into target-specific forecasting context. Learning against sales observations connects these meanings to numerical evidence, allowing business users to supply experience, future plans, and changing circumstances without defining a new numerical feature for each description. Text-conditioned future queries and shared/routed experts adapt prediction to the supplied context. An ordered-distribution head allocates fine resolution to routine fluctuations and coarse resolution to extreme sales movements, while daily/window learning connects forecasts to cumulative planning quantities. Online parallel forecasting at JD yields relative accuracy improvements of 18.25% and 17.94% for national daily and planning-window sales, and 19.44% and 18.99% for DC daily and planning-window sales, over the original production deep learning baseline. Component comparisons, loss-design results, and product-type analyses examine the roles of regional information, covariates, business knowledge, context-dependent processing, and robust planning-oriented learning.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03352

Neural Actor--Critic Learning with Minimax Critics and Reflected Langevin Sampling

Kunqi Huang, Lican Kang, Hongrui Shu, Jincheng Ying

原文摘要

Actor--critic methods rely on critic gradients to improve continuous-action policies, yet finite-sample guarantees must control both value and gradient errors and their propagation through the actor. We develop a neural actor-critic procedure that combines a regularized minimax Bellman estimator over sigmoid networks with a finite reflected Langevin sampler driven by the fitted critic gradient. Under uniform Bellman regularity and covered reset sampling, a positive high-order action-derivative penalty yields value and action-gradient risk bounds with classical nonparametric minimax exponents up to logarithmic factors. We propagate estimation, mixing, discretization, temperature, and policy-iteration errors to obtain return guarantees, with refinements for bounded multimodal potentials and strongly concave regimes. Empirical studies across controlled simulations, logged continuous-control data, and a simulated decision task show favorable performance relative to the considered baselines.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04274

Reasoning about Shared-Qubit Concurrency in Quantum Computing

Mingsheng Ying

原文摘要

In this paper, we develop a semantic model for the parallel execution of quantum circuits, providing a formal foundation for studying shared-qubit concurrency in quantum computing. As in classical shared-memory concurrency, parallel quantum circuits may interfere with one another through operations on shared qubits. However, quantum entanglement and the noncommutativity of quantum operations introduce additional challenges for reasoning about their correctness. To address these challenges, we generalise the classical Owicki-Gries method to quantum computing at the level of quantum circuits. Our approach enables compositional reasoning about parallel quantum circuits by separating the verification of individual components from the analysis of interference between them. We formulate proof rules for establishing the correctness of parallel quantum circuits and prove their soundness with respect to the proposed semantic model.

quant-ph · cs.DC · cs.LO · cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03165

LaBraM at Scale: Scaling Data and Models for EEG Pretraining

Jun-Yu Pan, Ruicheng Yin, Bao-Liang Lu, Dongsheng Li, Wei-Long Zheng, Yansen Wang

原文摘要

LaBraM, an early EEG foundation model, demonstrated that large-scale EEG pretraining can yield strong representations for diverse downstream tasks. Building on this foundation, we expand pretraining data and model capacity while retaining its core encoder and objective. We train ten encoders with 5.8 million to 1.97 billion parameters on three EEG collections spanning 25,067 to 79,910 hours, adapting signal preparation and training to heterogeneous recordings. Across 23 tasks, Atom-L* reaches 59.05% mean balanced accuracy at its best evaluated full fine-tuning (Full-FT) checkpoint, 3.61 percentage points above the strongest baseline EEG foundation model. Quark-G leads linear probing (LP) at 54.33%, a 1.24-point margin. Both also lead average task rank. At the last evaluated checkpoints, larger encoders improve Full-FT more consistently than LP, and stronger LP does not guarantee stronger Full-FT. Preserving patch structure improves LP over average pooling. Data-fraction experiments suggest capacity-dependent saturation, while checkpoint trajectories show that downstream performance can decline as pretraining loss falls. Joint classification with a shared Qwen3 readout reaches 50.50% with Molecule-H and Qwen3-8B; increasing language-model size provides no consistent gain. These results demonstrate the value of scaling LaBraM and the importance of matching pretraining scale to downstream adaptation.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.03280Berkeley BAIR

How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference

Toru Lin, Shuying Deng, Zhao-Heng Yin, Pieter Abbeel, Jitendra Malik

原文摘要

Many essential manipulation tasks - such as food preparation, surgery, and craftsmanship - remain difficult for autonomous robots. These tasks are characterized not only by contact-rich, force-sensitive dynamics, but also by their "implicit" success criteria: unlike pick-and-place, task quality in these domains is continuous and subjective (e.g. how well a potato is peeled), making quantitative evaluation and reward design difficult. We present a learning framework for such tasks, using peeling with a knife as a representative example. Our approach follows a two-stage pipeline: first, we learn a robust initial policy via force-aware data collection and imitation learning, enabling generalization across object variations; second, we refine the policy through preference-based finetuning using a learned reward model that combines quantitative task metrics with qualitative human feedback, aligning policy behavior with human-evaluated task quality. Using only 50~200 peeling trajectories, our system achieves 100% same-produce success on cucumbers, apples, and potatoes, with performance improving by up to 40% through preference-based finetuning. Remarkably, policies trained on one produce category also transfer zero-shot to selected held-out produce categories, achieving 50% success on zucchini, 90% on pear, and 80% on daikon radish.

cs.RO · cs.AI · cs.CV · cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39293HF 社区推荐

Unlocking Any-Order Generation in Pretrained Autoregressive Image Models

Rishav Pramanik, Marco Pedersoli, Zhaozheng Yin

原文摘要

Autoregressive (AR) image generators are powerful in principle but inflexible in practice: training with a fixed raster-scan order hardcodes a single factorization of the image distribution into the model, leaving it unable to inpaint, outpaint, or edit at inference time. Existing solutions, including the modern discrete-diffusion family, train from scratch on a mixture of token orderings, paying roughly 3x the compute of standard raster-scan training; discrete diffusion further sacrifices compatibility with the AR inference ecosystem (KV caching, vLLM) by construction. We argue that this cost is unnecessary. A pretrained raster-scan AR model has already learned the joint distribution over image tokens; what it lacks is not knowledge but the ability to express that knowledge through non-sequential conditional pathways. We propose a recipe for adapting pretrained raster-scan models into any-order generators using two components: future-aware positional embeddings that resolve a target-position identifiability problem at negligible overhead, and an ordering curriculum that traces a stable optimization path from the pretrained solution to a good any-order basin. Applied to four base models across two architectures and two scales (LlamaGen-L/XL, RAR-L/XL), the recipe substantially outperforms MaskGIT on ImageNet inpainting (FID 6.21 vs. 10.44) and outpainting, is competitive with MAR-L, trades a 1.2-1.9 increase in class-conditional FID for this flexibility, and costs 13-33% additional compute beyond pretraining, while leaving the standard AR inference stack untouched. Inpainting, outpainting, and editing, our results suggest, need not be added to autoregressive image models. They can be unlocked from the ones that exist. Code is available at https://github.com/Ordered-AR/unlockedadaptation.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2506.10390

DART: Differentiable Dynamic Adaptive Region Tokenizer for Vision Foundation Models

Shicheng Yin, Kaixuan Yin, Yang Liu, Weixing Chen, Liang Lin

原文摘要

The content-agnostic, fixed-grid tokenizers used by standard large-scale vision models like Vision Transformer (ViT) and Vision Mamba (Vim) represent a fundamental performance bottleneck, creating a trade-off between capturing fine-grained detail and suffering from redundant computation. To resolve this dilemma, we introduce DART, a fully differentiable Dynamic Adaptive Region Tokenizer. DART employs learnable region scores and quantile-based partitioning to create content-aware patches of varying sizes, intelligently allocating a higher token density to information-rich regions. The impact of this approach is profound: it unlocks a more intelligent scaling paradigm, where a DART-equipped DeiT-Small (22M parameters) matches the performance of a DeiT-Base (86M) with nearly double the inference speed by efficiently capturing high-resolution details in key regions. Furthermore, the principle of adaptive tokenization proves its generality with clear benefits in dense prediction and spatiotemporal video tasks. We argue that by resolving the tokenizer bottleneck at its source, adaptive tokenization is a key component for building the next generation of more efficient and capable foundation models for multimodal AI, robotics, and content generation. Code is available at https://github.com/HCPLab-SYSU/DART.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.32215

Democratizing LLM Training with the Edge: Challenges, Systems, and the Road Ahead

Ziyue Luo, Jiaxuan Cai, Cedric LeDenmat, Srijith Nair, Fatemeh Nourzad, Rohith Krishnan, Qinhang Wu, Jifan Zhang 等 30 位作者

原文摘要

The growing computational power of modern GPU data centers has let a few industrial giants train and deploy large language models (LLMs) on massive amounts of data. Yet the sheer scale of these models and their training data makes centralized development increasingly hard to sustain, and at the same time shuts out smaller institutions that hold relevant private data but lack comparable infrastructure. In this paper, we organize our positions on distributed LLM training around the pretraining and fine-tuning paradigms, covering federated learning, split learning, and decentralized approaches, and we assess their readiness for geo-distributed settings with heterogeneous hardware and unreliable connectivity. We then present a {\em unified} system architecture built around a cloud-hosted parameter server that coordinates heterogeneous clients over outbound gRPC connections, reusing one communication, compression, and fault-tolerance stack for both pretraining and a family of fine-tuning methods. We validate the design with real geo-distributed experiments on GPT-2 Medium and Llama3-1B pretraining across multiple university sites, showing stable convergence under realistic conditions. Finally, we chart the road ahead, identifying open problems that span frontier model support, elastic fault tolerance, adaptive compression, federated alignment, privacy and trust, incentive design, and system--algorithm co-design, all of which must be addressed to make democratized LLM training a reality.

cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.07763

SateBeam: Beam-Aware Radio Map Estimation With Physics-Consistent Parametric Modeling for Unknown Multiple Satellites

Xiucheng Wang, Nan Cheng, Zhisheng Yin, Conghao Zhou, Ruijin Sun

原文摘要

Dense low-Earth-orbit (LEO) networks increasingly reuse spectrum across moving satellites and overlapping beams, making a spatial radio map essential for interference diagnosis and coordination. Constructing such a map from sparse ground power measurements is difficult because the active satellite subset and its cardinality are unknown, candidate footprints can be highly coherent, and beam pointing, width, effective power, Doppler, and background must be inferred jointly. Known ephemerides do more than enumerate visible candidates. Propagated positions determine candidate-specific ranges, viewing geometry, and spatial footprints, while radial velocities induce distinct time-frequency signatures. Their joint spatial--Doppler structure rejects candidates whose geometry or time--frequency signature conflicts with the observations before continuous beam inference. SateBeam therefore screens all candidates through Doppler-resolved nonnegative fitting. GPU-accelerated grid correlations initialize continuous off-grid refinement; a Gamma-likelihood Bayesian information criterion (BIC) selects model cardinality, and backward deletion and exchange repair greedy support errors. Across paired real-ephemeris scenarios, SateBeam recovers the exact support in every case and reduces independent-query RMSE from $0.655$ to $0.050$ dB relative to orthogonal matching pursuit, with consistent advantages under model mismatch and larger candidate sets. The code is available at https://github.com/UNIC-Lab/SateBeam.

cs.IT · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.17055Berkeley BAIR

T-Rex: Tactile-Reactive Dexterous Manipulation

Dantong Niu, Zhuoyang Liu, Zekai Wang, Boning Shao, Zhao-Heng Yin, Anirudh Pai, Yuvan Sharma, Stefano Saravalle 等 34 位作者

原文摘要

The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) models for robotic manipulation generally either overlook the tactile modality or are limited to encoders with static cues, due in part to the scarcity of diverse training data and standardized evaluation, architectural constraints in current VLA models, and limitations of static tactile encoders. In this paper, we push the frontier of tactile-reactive manipulation by addressing all of these limitations. We propose a large-scale, 100-hour tactile-rich dataset collected via a novel, data-efficient recipe that prioritizes elementary motor primitives. To effectively exploit naturally high-frequency touch signals without sacrificing the existing capabilities of existing VLAs, we introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder. We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation, achieving over 30% higher average success rate than the strongest baseline.

cs.RO

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arXiv ↗PDF ↗