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新论文 · v1arXiv:2611.16060

REWEAVE: Online Learning for Workload-Adaptive HNSW Indexes

Minghao Li, Nick Koudas, Raghav Mittal

原文摘要

Vector databases rely on Hierarchical Navigable Small World (HNSW) indexes for similarity search, but a graph built from data alone can be poorly matched to a skewed or changing query workload. We present REWEAVE (Reward-Guided Rewiring of Vector Graphs), an online index-maintenance method that learns edge utility from query feedback and periodically rewires the bottom layer within the original per-node degree cap. The method supports sparse exact-neighbor feedback combined with self-labels, and standard HNSW serving without learning overhead once adaptation is disabled. Under explicit assumptions we prove value-estimation and graph-stabilization guarantees. Under stationary skew, REWEAVE improves held-out recall by up to 23.3 percentage points over HNSW. In comparisons with query-aware baselines on up to 100M vectors, it achieves up to 3.3x NGFix's query throughput at recall@100 of 0.80. On Amazon-ESCI with query drift, it increases stream recall@100 from 0.745 to 0.979 with 34% fewer search distance computations than frozen HNSW. These results illustrate the potential to improve retrieval quality and reduce search effort within an existing graph-degree budget, while incurring additional work during adaptation.

cs.DB · cs.IR · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.02338

Graph-Based Nearest-Neighbor Search with Recall Guarantees Using Graph Spanners

Minghao Li, Raghav Mittal, Sanjivni Rana, Suraj Shetiya, Gautam Das, Nick Koudas

原文摘要

Graph-based indexes such as Hierarchical Navigable Small World (HNSW) graphs make nearest-neighbor retrieval efficient, but a high average recall can hide queries whose results fall below an application's quality requirement. We introduce Certify-then-Rectify (CTR), which returns an initial graph-search result when a calibrated rule accepts it and searches for missing neighbors otherwise. The recovery search reuses the existing graph and the distances already evaluated by the initial search. To determine how far this search should proceed, we relate metric distances to graph-path lengths through graph stretch. We develop Stretch-Bounded Expansion from Query (SBE-Q), together with Metric Bound Verification (MBV) to prune unnecessary work, and estimate its budget from paths to the fraction of true neighbors needed for the requested recall. Finite-sample tolerance bounds support this recall-aligned estimate; paths recorded during proposal recovery offer a conservative alternative to separate shortest-path computations. Independent tests certify initial acceptance and recovery outcomes, giving an end-to-end population recall guarantee even when the estimated stretch does not cover every relevant path. If recovery cannot be certified, rejected queries use exact scan. Experiments on HNSW compare five stretch estimators, assess recovery efficiency, and evaluate compliance and throughput on databases of up to 100 million vectors. CTR is not specific to HNSW, and we also evaluate it on another graph index.

cs.DB · cs.CL · cs.IR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13355

DEPUTYBENCH: Evaluating Contextual Safety in Enterprise Agents

Hao Zhao, Minghao Lin

原文摘要

Enterprise agents must determine whether ordinary requests are permitted by organizational records and policies while continuing legitimate work. We introduce DEPUTYBENCH, an executable benchmark with 79 base items, 375 instances, paired permission contexts, and graded evidence requirements. Across 12 models, revised audits give 88.0% pooled safety and 79.1% paired success. A controlled study of 28 derived tasks and six models tests authorization-fact flips, evidence masking, and irrelevant rewrites. Outcomes track backend permissions, yet restricted execution is more frequent with missing evidence than explicit non-permission: 10.3% versus 3.5% on 27 matched tasks, a 6.8-percentage-point difference (95% interval: 1.9-13.0; secondary analysis). A post-hoc analysis locates 61 of 100 masked-condition executions in four privacy tasks requiring complete cumulative counts or person-specific consent. The difference persists when the most error-prone model or the largest contributing task is excluded, but is much smaller outside those four tasks. Thus high safety with available evidence does not establish reliable stopping when permission cannot be verified. Mean paired success is equivalent within a prespecified five-point margin under irrelevant rewrites, while strong-model safety remains near ceiling. Scorer validation improves from 178 to 266 correctly scored cases out of 284 held-out counterexamples. Two security experts manually verified task ground truth over a two-week period; stage labels remain LLM-generated. The concentration analysis describes task structure and audited outcomes, not an identified internal mechanism.

cs.SE · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.01299

HYPIC: Position-Independent Caching for Hybrid-Attention LLM Serving

Yifei Liu, Juntong Wu, Yang Liu, Junhao Hu, Minghao Li, Xiaoxu Chen, Weihang Chen

原文摘要

In retrieval-augmented generation (RAG) and agentic LLM serving, prompts are assembled from multiple independent segments into long contexts, making prefill dominate the computation cost. To reduce this cost, position-independent caching (PIC) admits KV reuse for non-contiguous segments across requests, while hybrid-attention models reduce computational complexity using linear attention. However, existing PIC methods break down on hybrid-attention models because their per-token KV-cache reuse primitives do not transfer to per-request recurrent states. In this work, we present HYPIC, the first system to accelerate hybrid-attention LLM serving with position-independent caching. For linear-attention layers, we identify the segment-cumulative transition operator as the missing algebraic primitive and cache it for each segment, enabling near-exact and constant-time state composition. For the remaining full-attention layers, we construct a small seam window to propagate hidden states through the linear-attention layers, so that cross-segment attention can be repaired by recomputation. Finally, HYPIC introduces segment parallelism, which parallelizes cache-miss prefill across instances, turning long cold requests into an accelerable workload. Evaluated across four hybrid-attention models and five workloads, HYPIC reduces time-to-first-token (TTFT) by $3.25\times$ on average and improves QPS by $1.66\times$ over Prefix Cache, while preserving task quality with a $1.71$-point gap from Full Recompute.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.08898

Towards Visual Query Segmentation in the Wild

Bing Fan, Minghao Li, Hanzhi Zhang, Shaohua Dong, Naga Prudhvi Mareedu, Xiaoqiong Liu, Weishi Shi, Yunhe Feng 等 10 位作者

原文摘要

In this paper, we introduce visual query segmentation (VQS), a new paradigm of visual query localization (VQL) that aims to segment all pixel-level occurrences of an object of interest in an untrimmed video, given a visual query. Compared with existing VQL locating only the last appearance of a target using bounding boxes, VQS enables more comprehensive (i.e., all object occurrences) and precise (i.e., pixel-level masks) localization, making it more practical for real-world scenarios. To foster research on this task, we introduce VQS-4K, a large-scale benchmark dedicated to VQS. Specifically, VQS-4K consists of 4,111 videos with more than 1.3 million frames, and covers a diverse set of 222 object categories. Each video is paired with a visual query defined by a frame outside search video and its target mask, and annotated with spatial-temporal masklets corresponding to the queried target. To ensure high quality, all videos in VQS-4K are manually labeled with meticulous inspection and iterative refinement. To our best knowledge, VQS-4K is the first benchmark specifically designed for VQS. Furthermore, to stimulate future research, we present a simple yet effective method, named VQ-SAM, which extends SAM 2 by leveraging target-specific and background distractor cues from the video to progressively evolve the memory through a novel multi-stage framework with an adaptive memory generation (AMG) module for VQS, significantly improving the performance. In our experiments on VQS-4K, VQ-SAM achieves promising results and surpasses existing methods, evidencing its effectiveness. With VQS-4K and VQ-SAM, we expect to inspire more future research and applications on VQS. Our benchmark and code are released at https://github.com/VAIL-Fan/VQS.

cs.CV

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.139ACL Anthology · ACL 2026

CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization

Zhongyuan Peng, Yifan Yao, Kaijing Ma, Shuyue Guo, Yizhe Li, Yichi Zhang, Chenchen Zhang, Yifan Zhang Princeton University 等 19 位作者

原文摘要

Translating natural language mathematical statements into formal, executable code is a fundamental challenge in automated theorem proving. While prior work has focused on generation and compilation success, little attention has been paid to the critic phase—the evaluation of whether generated formalizations truly capture the semantic intent of the original problem. In this paper, we introduce CriticLean, a novel critic-guided reinforcement learning framework that elevates the role of the critic from a passive validator to an active learning component. Specifically, first, we propose the CriticLeanGPT, trained via supervised fine-tuning and reinforcement learning, to rigorously assess the semantic fidelity of Lean 4 formalizations. Then, we introduce CriticLeanBench, a benchmark designed to measure models’ ability to distinguish semantically correct from incorrect formalizations, and demonstrate that our trained CriticLeanGPT models can significantly outperform strong open- and closed-source baselines. Building on the CriticLean framework, we construct FineLeanCorpus, a dataset comprising over 509K problems that exhibits rich domain diversity, broad difficulty coverage, and high correctness based on human evaluation.Overall, our findings highlight that optimizing the critic phase is essential for producing reliable formalizations and we hope our CriticLean will provide valuable insights for future advances in formal mathematical reasoning.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.946ACL Anthology · ACL 2026

HiSVD: Principled Low-Rank Approximation of LLMs via Hierarchical Modeling of Information Capacity and Spectral Structure

Zhuo Chen, Minghao Li, Xiaoqian Ma Xiaohongshu, Siqi Fan University of Electronic Science and Technology of China, Xiusheng Huang, Zhang Liujie Xiaohongshu, Weihang Chen

原文摘要

Singular Value Decomposition (SVD) enables hardware-agnostic LLM compression via low-rank approximation, yet optimal rank allocation remains a bottleneck. Existing methods predominantly derive layer importance from performance-oriented proxies. Yet, these metrics fail to distinguish between representational importance and structural compressibility, consequently obscuring the fine-grained influence of spectral distribution shape. We demonstrate this disconnect through spectral analysis, revealing that layers with similar information capacity can exhibit markedly different singular value decay behaviors, corresponding to varying degrees of redundancy in the spectral tail. This imperfect coupling implies that allocation strategies driven solely by importance leave significant compression opportunities underexploited. To address this gap, we propose HiSVD, a hierarchical rank allocation framework with two stages: (1) Capacity-Anchored Baseline Allocation, which preserves representational stability by aligning rank budgets with information capacity; and (2) Redundancy-Aware Refinement, which modulates this baseline using tail redundancy to penalize structural excess. Experiments on LLMs demonstrate that HiSVD achieves superior compression efficiency, significantly outperforming state-of-the-art baselines by effectively exploiting this spectral heterogeneity.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表