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新论文 · v1arXiv:2611.16272

A Binary Search Tree with the Unified Bound

Yang Cao, Haoran Qi, Hanzhi Wang

原文摘要

We give a deterministic online binary search tree (BST) that satisfies the unified bound of Iacono [SODA 2001], which subsumes the working-set and dynamic-finger bounds: it serves every sequence $X$ of $m$ searches on $n$ keys at total cost $O(m+\mathrm{UB}(X))$, and every single search at cost $O(\log n)$. Previously, no BST was known to satisfy the unified bound, even offline. As a consequence, the dynamic optimality conjecture implies Iacono's conjecture that splay trees satisfy the unified bound. We also show that the unified bound is $O((m+n)(1+\log c_σ))$ on every sequence of searches that avoids a fixed permutation $σ$, where $c_σ$ is the Füredi-Hajnal limit of $σ$. Hence every BST that satisfies the unified bound, including ours, serves such sequences at this cost. For permutations this bound is tight, and it improves the previous upper bound $O(nc_σ^2)$ on the optimal cost, due to Berendsohn, Kozma and Opler [STOC 2024]. Finally, we extend the tree to support insertions and deletions: a sequence $X$ of $m$ operations, starting from $n_0$ keys, costs $O(n_0+m+\mathrm{UB}(X))$ in total, where $\mathrm{UB}$ is now the extension of the unified bound to insertions and deletions by Bădoiu, Cole, Demaine and Iacono [TCS 2007]. Hence, if splay trees are dynamically optimal for searches, insertions and deletions, then they serve every such sequence at this total cost as well.

cs.DS

版本更新 · v2arXiv:2509.16176

Agentic Aerial Cinematography: From Dialogue Cues to Cinematic Trajectories

Yifan Lin, Sophie Ziyu Liu, Ran Qi, George Z. Xue, Xinping Song, Chao Qin, Hugh H. -T. Liu

原文摘要

Agents have demonstrated strong capabilities in digital environments, yet their reasoning abilities alone are insufficient to accomplish tasks in the physical world. We present Agentic Aerial Cinematography: From Dialogue Cues to Cinematic Trajectories (ACDC), an agentic framework that bridges this gap for autonomous aerial cinematography. ACDC provides an execution harness that grounds an agent's interpretation of cinematic intent and visual judgment in scene geometry and flight constraints. Given a natural-language request and a 3D scene representation, the agent selects relevant viewpoints and guides their refinement through visual feedback, while optimization and motion planning translate these decisions into executable drone trajectories. Experiments across a variety of simulated indoor scenes and hardware-in-the-loop flight tests demonstrate how this framework connects open-ended user intent to prompt-aligned video tours and physical drone execution.

cs.RO

新论文 · v1arXiv:2611.14601

VASTRA: Efficient Agentic Top-K Interactive Video Analysis

Tushar Tyagi, Aditya Raghavan, Haoran Qiu, Francisco Romero

原文摘要

Vision-language models (VLMs) enable natural language video retrieval, bypassing traditional hand-engineered pipelines. Integrating agentic reasoning frameworks (e.g., ReAct) further advances this paradigm by allowing models to dynamically inspect video content rather than relying on a single visual pass. Applying this pattern naively to video retrieval inherits problems that video systems cannot afford: results vary from run to run under identical conditions, and cost and latency scale with every reasoning step the agent takes rather than with the work the query actually requires. We present VASTRA, an agentic video retrieval system whose execution is dictated by query complexity rather than an open-ended reasoning loop: cheap, reliably answerable queries are ranked directly from pre-ingested summaries, while harder queries trigger targeted verification only where relevance is genuinely uncertain. This keeps control flow external to the model rather than latent in a growing chain of thought, establishing deterministic control flow by construction and end-to-end rank reproducibility under greedy serving. Across 40 benchmark queries, VASTRA exposes explicit presets: VASTRA-Fast runs 12.2x faster (4.2 s vs. 51.2 s) and 10.4x cheaper ($0.0044 vs. $0.0458) than best performing ReAct agent at a 5.0-point mAP reduction, while VASTRA-Max matches peak ReAct agent accuracy (0.653 vs. 0.649 mAP) at comparable latency, trading higher spend ($0.1022 vs. $0.0458) for zero run-to-run variance.

cs.DB

新论文 · v1arXiv:2611.13176

SHADOWPREFILL: Keeping Your Next Model Warm for Per-Step Model Routing in Agentic LLM Serving

Haoran Qiu, Rohan Mahapatra, Rodrigo Fonseca, Mark Russinovich

原文摘要

Per-step model routers send each LLM call of an agent to the cost-optimal model dynamically within each session. However, every model switch discards prefix-cache reuse: KV caches are per model, so the new model must prefill the relevant context before it emits a token. Across representative agentic workloads, including a production coding-agent trace with a mean context of 63K tokens, the prefix hit rate falls from 80-89% on same model calls to 44-65% after a switch, and a switch call takes 1.5-3.2x as long. We present SHADOWPREFILL, which prefills the session's context on the model the router has not chosen, in capacity that instances serving normal traffic leave idle, so the next switch finds its prefix cached. SHADOWPREFILL harvests the per-iteration SLO slack of requests in flight, protects routed calls, spills shadow KV to host memory, and falls back to a cold switch whenever the shadow is incomplete. With LLMRouter routing 24 model pairs over five agentic benchmark suites, SHADOWPREFILL cuts switch-call latency by 8.5-21.4%, LLM-call latency by 3.7-17.7%, and routed token cost by 22.5-46.1%, while normal traffic keeps its SLO and energy per request rises by 0.4%.

cs.DC

新论文 · v1arXiv:2611.11800

Ultrasound-Assisted Biometric Watermarking Against Face Video Deepfakes

Guodong Liu, Lei Wang, Dongxu Liu, Qianran Qiao, Meng Li, Haipeng Dai, He Huang, Guihai Chen

原文摘要

Currently, artificial intelligence-generated content (AIGC) poses a severe threat to facial video deepfakes, and many AIGC companies employ video watermarking to mitigate unauthorized fake video usage, but its effectiveness is limited due to watermark tampering and private-model forgery. Additionally, some schemes actively add watermarks to user videos. However, current approaches focus primarily on the quality of watermark embedding in offline scenarios, and no existing work offers a solution for protecting human facial videos in real-time online applications. To fill this gap, we propose, to the best of our knowledge, a novel biometric video watermarking method, UltraGuard, for the first time. It uses the smartphone's built-in speaker and microphone to emit ultrasonic signals and capture biometric features, which are then fused with spatio-temporal dynamic features derived from the video content. The fused features are encoded as watermarks and embedded into videos in real time, enabling dynamic, content-aware watermarking throughout transmission and propagation. Extensive experimental validation demonstrates that UltraGuard can effectively defend against malicious attacks such as forgery and editing, both at the source and during propagation, proving the system's efficacy. In the future, it can be deployed on users' mobile devices to provide real-time protection for users' facial videos, thereby preventing security and privacy breaches caused by various malicious attacks.

cs.CR · cs.AI

新论文 · v1arXiv:2611.11777

Towards Trustworthy Mobile Speech Interaction: Ensuring Voice Authenticity and Transmission Integrity

Guodong Liu, Lei Wang, Dongxu Liu, Qianran Qiao, Haipeng Dai, He Huang, Guihai Chen

原文摘要

The rapid progress of generative speech models has made high-fidelity voice cloning and editing cheap, fast, and widely accessible, weakening the security of voiceprint-based authentication in mobile speech interactions. Existing speech watermarking techniques focus on offline protection or watermarking synthetic speech, and thus cannot verify whether a live utterance originates from the genuine speaker at capture time. We present \textit{MoBiMark}, a real-time \textbf{mo}bile \textbf{bi}ologic speech water\textbf{mark}ing system that binds ultrasonic articulatory signatures to speech content and embeds a dynamic watermark during capture. MoBiMark emits a multi-frequency ultrasonic probe on commodity smartphones, extracts echo features from the vocal tract, and fuses them with a content-dependent speech embedding to produce a dual-layer dynamic watermark combining a persistent user identifier (UID) and a per-utterance speech identifier (SID), enabling both source authentication and propagation-time tamper detection. A jointly trained embedder and detector injects the watermark while preserving perceptual quality, and system-level optimization with INT8 quantization enables lightweight on-device deployment. Evaluated on 50 users across four smartphone models, MoBiMark achieves an average 99.79% accuracy in source and propagation attacks, with negligible impact on speech quality and intelligibility.

cs.SD · cs.AI

新论文 · v1arXiv:2611.10379

RefineSplat: Reconstruction-Guided Object-Centric Gaussian Splatting for Open-Vocabulary Querying

Yiren Lu, Disheng Liu, Sreya Srinidhi, Yunlai Zhou, Yiran Qiao, Chaoda Song, Tuo Liang, Jing Ma 等 10 位作者

原文摘要

Open-vocabulary 3D scene understanding aims to segment and retrieve arbitrary objects in 3D scenes using natural language queries. Most existing methods typically follow a splat-then-segment paradigm, where 3D Gaussian Splatting is first reconstructed and then augmented with labels or language features for subsequent segmentation. However, this pipeline often suffers from view inconsistency, ambiguous object boundaries, and weak object-level correspondence, especially in complex static and dynamic scenes. Building on our prior Segment-then-Splat framework, we present RefineSplat, a reconstruction-guided extension that retains its object-centric paradigm: it first tracks objects in 2D and then reconstructs each object with a dedicated set of 3D Gaussians, preserving explicit object-Gaussian correspondence for unified open-vocabulary segmentation and retrieval. To improve robustness, we further close the loop between 2D tracking and 3D reconstruction. Since off-the-shelf video trackers can produce unreliable masks that propagate errors into object reconstruction, we introduce a reconstruction-guided feedback mechanism that refines object tracking using rendered masks and multi-view consistency. Moreover, instead of assigning each object a single language embedding, we propose a viewpoint-aware embedding assignment strategy, where each object maintains multiple view-clustered embeddings to capture view-dependent semantic cues. We also introduce Blender-OVS, a synthetic benchmark of static and dynamic sequences with exact object meshes and point clouds, enabling direct evaluation of extracted geometry. Experiments on static and dynamic benchmarks show improved rendered-view segmentation and lower object-level Chamfer distances, with competitive scene reconstruction quality.

cs.CV

版本更新 · v2arXiv:2610.34487

A Multi-Domain Study on the Effectiveness of LLMs at Writing Property-Based Tests

M M Abid Naziri, Feiran Qin, Khush Patel, Marcelo d'Amorim

原文摘要

A property-based test (PBT) checks a property of a program on many generated inputs, but writing one takes a property, a generator of valid inputs, and an oracle. LLM-based PBT writers produce all three, yet they have been evaluated only on Python libraries. We study how two of them, Proptest-AI and Agentic-PBT, generalize to ten programs in five domains: deep learning libraries, databases, system software, compilers, and LLM serving engines. Most of these programs take text in their own language, such as queries, configurations, and programs. Agentic-PBT covers more branches than Proptest-AI on seven of the ten programs with Opus 5 and on all ten with GLM-5.3-Flash. Domain-specific fuzzers cover more of the compilers and of PostgreSQL and less of PyTorch than Agentic-PBT. We filed 180 bug reports, 44 of which duplicate reports that others filed after the models' knowledge cutoffs; maintainers have confirmed 65 of the others, in all five domains, and fixed 23. We draw lessons for building and evaluating such writers: low observability hurts them, and mature fuzzers paired with LLM-written properties could check that each feature behaves as documented.

cs.SE

版本更新 · v2arXiv:2604.05484

CoEnv: Code-Driven Exploration in Real-to-Sim Digital Twins for Multi-Arm Manipulation

Li Kang, Yutao Fan, Rui Li, Heng Zhou, Yiran Qin, Zhemeng Zhang, Songtao Huang, Xiufeng Song 等 14 位作者

原文摘要

General-purpose coding agents can now write, debug, and refine entire robot programs, but only if they have somewhere to practice. Physical hardware is a poor practice ground: exploratory rollouts are slow to reset and, when several arms share a workspace, unsafe. Generic simulation benchmarks are safe but do not match the geometry, assets, or viewpoints of the workspace where the robot must actually work. We present CoEx (Code-driven Exploration), which lets the agent practice in an executable digital twin of its own deployment workspace. The twin is built by CoEnv, our real-to-sim framework, which reconstructs the physical scene from multi-view RGBD into a physics simulation. Inside it, a centralized code agent explores at the program level, writing a complete multi-arm program, running it in the twin, and refining it against grounded verification (privileged state checks, checkpoint predicates on risky steps, and adaptive viewpoints against inter-arm occlusion). Verified programs transfer to hardware through collision-checked deployment. On five real-world multi-arm tasks with up to three heterogeneous robots, CoEx reaches 70% average success, against 50% for the same system without the code agent (where a VLM must choose every primitive step by step), outperforms two prior VLM-planning frameworks, and produces verified episodes at 3.8-11.7x the ablated rate. Because every verified rollout is also a demonstration, exploration leaves reusable experience behind: a $π_{0.5}$ policy fine-tuned purely on 200 agent-generated rollouts reaches 100% and 70% real-robot success on two tasks, with no human demonstrations.

cs.RO · cs.CV

新论文 · v1arXiv:2611.02154

Faster Algorithms for Disjoint Paths in DAGs and Two Disjoint Shortest Paths

Yang Cao, Haoran Qi, Hanzhi Wang

原文摘要

For $k$ vertex-disjoint paths with prescribed endpoints in a directed acyclic graph (DAG), the $O(mn^{k-1})$-time pebbling algorithm of Fortune, Hopcroft, and Wyllie [TCS 1980] has remained the fastest for every $k\geq3$ for over four decades, even when the paths must be shortest. We break this bound for every $k\geq3$ with randomized algorithms running in expected $O_k(m+n^{k-3+ω+\varepsilon})$ time for every $\varepsilon>0$, where $ω<2.372$ is the matrix multiplication exponent; this is $O_k(m+n^{k-0.62})$. For two disjoint shortest paths, we find the paths as fast as we detect them: in expected linear time on DAGs and on undirected graphs with polynomially bounded integer weights, and in expected $O(\min\{mn,n^{ω+\varepsilon}\})$ time on directed graphs whose cycles have positive weight, which on dense graphs is $o(mn)$ even for detection. All algorithms evaluate a polynomial whose monomials are the solutions: a new first-collision circuit speeds up the evaluation in DAGs, and a correlated extraction step at cuts crossed once by every path finds a solution at the cost of a constant number of evaluations.

cs.DS