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新论文 · v1arXiv:2611.11618

Delegating Computation with Approximate Arithmetic: Interactive Proofs for Wizards

Aritra Das, Debayan Gupta, Arup Mondal, Mihir More

原文摘要

We study interactive proofs for arithmetic circuits over the complex numbers $\mathbb{C}$ in which both the prover and the verifier compute approximately (unlike muggles, wizards can use magic, i.e., floating-point numbers). The goal is to prove that a claimed output $y$ is close to the exact output $C(x)$: for thresholds $0<Δ_0<Δ$, the honest prover is accepted whenever $|y-C(x)|\leΔ_0$, and every prover is rejected with high probability whenever $|y-C(x)|>Δ$. Existing proof systems for real-valued data verify rounded finite-field computations. Bitan et al.~[EUROCRYPT~2026] gave an approximate sum-check protocol. We extend the protocol of Goldwasser, Kalai, and Rothblum (GKR) [STOC~2008] to public-coin interactive proofs for logspace-uniform layered circuits over $\mathbb{C}$ of size $S$, depth $L$, and $n$ inputs, under public bounds on gate values and wiring coefficients. At constant soundness error, the verifier runs in time $(n+1)\mathrm{poly}(L,\log S,b)$, the communication is $\mathrm{poly}(L,\log S,b)$, and the prover runs in time $\mathrm{poly}(S,b)$, where $b$ is the bit precision. To the best of our knowledge, these are the first doubly efficient interactive proofs in which both parties compute approximately. GKR reduces two claims about a layer to one on a random line, which leaves the unit circle, where our error bounds hold. We instead label gates by complex roots of unity and combine the two claims with a random root of unity as coefficient. We choose the error thresholds backward from the input layer, so that a large output error survives the verifier's rounding at every step. The honest prover needs only $O(S \log S)$ complex operations. We also obtain zero-knowledge proofs from one-way functions, one-round arguments from private information retrieval, and interactive PCPs whose proof length is polynomial in the witness length.

cs.CR

版本更新 · v3arXiv:2504.04619

增量最小生成树的新算法及其时序图应用

New Algorithms for Incremental Minimum Spanning Trees and Temporal Graph Applications

Xiangyun Ding, Yan Gu, Yihan Sun

AI 速读 基于摘要

作者提出 AM-tree 数据结构,用于增量最小生成树,并在理论上达到与 link-cut tree 相同的界,同时报告了在实践中优于 link-cut tree 的性能,还讨论了如何用时序图处理中的多类设置-问题组合来求解。

cs.DS

查看原文摘要

Processing graphs with temporal information (the temporal graphs) has become increasingly important in the real world. In this paper, we study efficient solutions to temporal graph applications using new algorithms for Incremental Minimum Spanning Trees (MST). The first contribution of this work is to formally discuss how a broad set of setting-problem combinations of temporal graph processing can be solved using incremental MST, along with their theoretical guarantees. Despite the importance of the problem, we observe a gap between theory and practice for efficient incremental MST algorithms. While many classic data structures, such as the link-cut tree, provide strong bounds for incremental MST, their performance is limited in practice. Meanwhile, existing practical solutions used in applications do not have any non-trivial theoretical guarantees. Our second and main contribution includes new algorithms for incremental MST that are efficient both in theory and in practice. Our new data structure, the AM-tree, achieves the same theoretical bound as the link-cut tree for temporal graph processing and shows strong performance in practice. In our experiments, the AM-tree has competitive or better performance than existing practical solutions due to theoretical guarantees, and can be significantly faster than the link-cut tree (7.8-11x in updates and 7.7-13.7x in queries).

新论文 · v1arXiv:2611.09172

PaB-tree: Parallel B+-tree with Batch Updates

Ziyang Men, Yan Gu, Yihan Sun, Helen Xu

原文摘要

The B+-tree is a ubiquitous search index due to its provable I/O (cache) efficiency. Yet unlike batch-parallel binary search trees, which are both fast and widely available, no batch-parallel B+-tree offers strong empirical performance together with provable, near-optimal I/O efficiency. Prior B+-tree-based designs are either parallel but purely theoretical, with no high-performance implementation, or concurrent indexes such as the Bw-tree and Masstree, which run an order of magnitude slower on batch updates. We present PaB-tree, the first batch-parallel B+-tree with guarantees on work, span, and cache complexity alongside strong practical performance. Its core challenge -- repairing node overflows and underflows without eroding the tree's I/O advantage -- is resolved by one simple invariant: a repair never touches a sibling and is always pushed to the parent. A Promote primitive reduces batch construction and insertion to repeated node promotion, and batch deletion to reinsertion. Batch updates take O(B log_B n) work and O(log_B n) I/O per element, matching the sequential B+-tree bounds, with polylogarithmic span. Against highly-optimized parallel and concurrent baselines, PaB-tree delivers the best overall performance trade-off: up to 1.4x faster on construction and up to 3.6x faster on batch insertion than the strongest baseline, while preserving B+-tree query efficiency.

cs.DS · cs.DB · cs.DC

新论文 · v1arXiv:2611.06770

Benchmarking Fine-Grained Visual Reasoning for Remote Sensing in Urban Management

Jiawei Zhang, Rizhao Cai, Dayan Guan

原文摘要

Fine-grained visual reasoning is essential for remote sensing in urban management, yet remains underexplored in the evaluation of multimodal large language models (MLLMs). We introduce UMBench, a fine-grained urban remote sensing benchmark built from unmanned aerial vehicle (UAV) images for multiple urban management tasks such as traffic monitoring, urban construction, environmental quality, and disaster warning. Evaluations on UMBench reveal that current MLLMs often rely on scene-level priors and overlook fine-grained visual evidence. To address this issue, we further propose a novel evidence-first chain-of-thought method that directs models to inspect fine-grained visual evidence in a training-free manner. Experiments show that our method can improve the performance of both open-source and commercial MLLMs on UMBench.

cs.CV

版本更新 · v2arXiv:2607.18539

AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation

Yen-Chi Cheng, Chen Gao, Chuhan Chen, Tuotuo Li, Rajvi Shah, Ayush Saraf, Changil Kim, Liangyan Gui 等 11 位作者

原文摘要

Novel view rendering of large and complex reconstructed scenes is becoming increasingly photorealistic. However, most reconstructions remain static and lack the ambient motion that makes environments immersive. We present AniGS, a method for scene-level animation of 3D Gaussian Splatting (3DGS) reconstructions that adds subtle, distributed dynamics, e.g., vegetation motion, while preserving rigid structures. Unlike existing 3D animation techniques which are limited to object-centric subjects or small regions, AniGS is designed for large, cluttered, navigable scenes. AniGS represents the scene with a canonical 3DGS and models motion using a time-conditioned deformation field. To animate the entire scene, we leverage a pretrained video diffusion model and introduce an iterative dataset--model update strategy that progressively expands viewpoint coverage and repeatedly updates camera-fixed training videos using a render-and-refine scheme. To prevent artifacts from unintended motion in static areas, we further introduce a composed video-to-video refinement scheme that restricts motion to desired regions. Experiments on five real-world, large-scale outdoor scenes demonstrate that AniGS produces natural ambient dynamics and high-quality novel view videos, enabling more immersive viewing experiences of reconstructed environments.

cs.CV

版本更新 · v2arXiv:2608.26797

On the Indistinguishability of Human v/s AI Generated Text

Jaee Ponde, Aritra Das, Mihir More, Debayan Gupta

原文摘要

The rapid improvement of LLMs has made distinguishing AI-generated text from human writing a pressing problem. This challenge is further amplified by paraphrasing tools designed to make machine-generated text appear more ``human''. We study how access to human writing samples can be used to strategically paraphrase machine-generated responses toward the human distribution. Under a multi-sample setting with human and machine responses to the same prompts, we show that repeated paraphrasing moves the machine distribution toward a human target under mixing and stability conditions. We derive an explicit convergence rate, examine how semantic-preservation and quality constraints affect this rate, and extend the analysis to finite samples to characterize how the required human data and paraphrasing rounds scale with the desired error. We extend the analysis beyond the shared-parameter setting by learning a human-preserving rewriting kernel.

cs.LG

新论文 · v1arXiv:2611.02624

Widening Harm Evaluation Coverage with a Judge that Reads the World

Prabhpreet Singh Setia, Bhavesh Neekhra, Debayan Gupta

原文摘要

An agent acting in a stateful environment can leave evidence of harm in two places: the record of what it did and said, or the world it changed. Safety evaluations for discovering harm examine one or the other in limited ways, using either an LLM judge over the trajectory or deterministic checks over the final state. Although LLM-based verifiers that inspect the environment exist, they have been used to verify task completion, not to detect and confirm harm in a safety setting. We address this gap with an LLM that has read-only access to the environment state and is given a harm taxonomy. We evaluate this approach in three environments: two from DecodingTrust-Arena and one from OSWorld. We hold the judge model and harm taxonomy fixed, varying only what the judge observes: the trajectory, read-only tools for inspecting the environment, or both channels together. In the two DecodingTrust-Arena environments, combining the trajectory and state-only judges recalls 94 percent of confirmed harms, compared with 53 percent for the trajectory judge alone. On OS-Harm, the union recalls 77 percent, compared with 67 percent. A single judge given both channels does not perform better and produces more false negatives than the state-only judge. We also record disagreements and find that the two channels miss different episodes. These results suggest that combining verdicts from two channel-specific judges is a robust and flexible approach to detecting and verifying harm in agentic settings.

cs.AI

版本更新 · v2arXiv:2609.03035

You Can't Escape Your Activations : Evaluation Awareness and Multi-Agent Monitoring

Jaee Ponde, Aritra Das, Mihir More, Debayan Gupta

原文摘要

LLM agents are increasingly deployed in multi-agent systems, where they can collude while keeping their actions benign. Output monitors designed to detect such collusions can be fooled by obfuscation and steganography, motivating the use of probes trained on internal activations. However, these probes are usually evaluated on agents that do not know they are being watched. We study how activation-based detection changes when agents are explicitly informed that their internal activations are being monitored, and when they additionally receive feedback from the monitor. We keep the models, probes, and thresholds fixed and change only what the agents are told: nothing (baseline), that an activation monitor is present (aware), or that a monitor is present together with the previous round's score (feedback). We test two games, a four-agent blackjack game and a two-agent Simmons prisoners game, using Qwen3-32B-AWQ and GPT-OSS-20B in homogeneous and mixed pairings. Telling agents about the monitor does not hide them. The best probes stay accurate in all three conditions, and the agents keep colluding.

cs.MA · cs.LG

版本更新 · v3arXiv:2511.20709

DualGauge: Automated Joint Security-Functionality Benchmarking of Specification-Only Code Generation by LLMs and Coding Agents

Rupam Patir, Keyan Guo, Suvadra Barua, Abhijeet Pathak, Dinesh Gudimetla, Jiawei Guo, Hongxin Hu, Haipeng Cai

原文摘要

Large language models (LLMs) and LLM-based coding agents are now used to generate code from natural-language specifications, yet ensuring such code is both functionally correct and secure remains a challenge. We present DualGauge, the first fully automated framework for jointly evaluating correctness and security of specification-only code generation, supported by DualGauge-Bench, a language-agnostic benchmark of 307 coding tasks each paired with functional and security tests derived from the same specification. Evaluating 10 representative LLMs across Python, C++, and JavaScript, we find that functional correctness substantially overestimates reliable code generation: even the strongest model remains below 15% joint security-functionality success in every language. Common model-side factors--scale, extended thinking, quantization, instruction tuning, and code specialization--do not reliably improve joint performance, suggesting secure-and-correct code generation does not simply emerge from stronger coding capability. Evaluation of 3 leading coding agents (Codex, OpenHands, and Claude Code) shows that iterative scaffolding is not a reliable remedy on specification-only tasks: results are strongly scaffold-dependent, with two of the three agents failing to outperform a comparable direct model. A qualitative audit reveals that these failures concentrate at the output contract boundary and in guards that exist but are insufficient--patterns that only joint benchmarking reliably exposes.

cs.SE · cs.AI · cs.CR

版本更新 · v2arXiv:2602.22724

AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification

Tian Zhang, Yiwei Xu, Juan Wang, Keyan Guo, Xiaoyang Xu, Bowen Xiao, Quanlong Guan, Jinlin Fan 等 11 位作者

原文摘要

Tool-augmented large language model (LLM) agents aggregate dialogue history, tool outputs, and retrieved content into an accumulated runtime state to complete multi-step tasks. This execution model exposes agents to indirect prompt injection (IPI) attacks, in which attacker-controlled instructions embedded in external artifacts enter the agent state and later influence tool use. Multi-turn IPI is especially challenging because injected content can persist across steps and trigger unauthorized state-changing actions only after merging with user intent. This creates a source-attribution problem: existing inference-time defenses can often detect suspicious behavior but do not directly distinguish whether a pending action is supported by the user task or influenced by injected content. They therefore resort to conservative blocking, termination, or broad filtering. We propose AgentSentry, an inference-time defense that operationalizes temporal causal diagnostics through boundary-local counterfactual re-executions at tool-return boundaries. It substitutes the mediator under a restored snapshot, records whether the pending proposal survives, and aggregates user-view and probe scores across adjacent boundaries. When a proposal is mediator-sensitive, AgentSentry purifies the implicated mediator content while preserving task-relevant evidence and resumes execution from the sanitized state. We evaluate AgentSentry on AgentDojo across four task suites, three IPI attack families, and three black-box LLM backbones. It records no observed successful attack in the evaluated cases (ASR = 0%) and achieves an average Utility Under Attack (UA) of 76.10%. Over the strongest evaluated baseline, it improves average UA by 1.84--7.60 percentage points, with benign FPRs of 2.06%--5.15%. We further evaluate long-horizon and adaptive attacks, imperfect purification, runtime overhead, and an OpenClaw case study.

cs.CR · cs.AI