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新论文 · v1arXiv:2611.04240

VerfGraph: Efficient Zero-Knowledge Verifiable Graph Query Evaluation System

Tianyi Ma, Yuncong Zhang, Yuanzhuo Yu, Boshi Yuan, Shi-Feng Sun

原文摘要

Graph databases are often used to manage relationship-rich data, whose graph structure and attributes may be sensitive. In many applications, a database owner or a service provider evaluates queries over a private graph for a client. Because the graph cannot be fully disclosed and independently re-executing the query may be impractical, the client cannot directly verify the returned result. This creates a need for verifiable graph query evaluation that preserves data confidentiality. We present VerfGraph, a zero-knowledge system for verifiable graph query evaluation, which enables a client to verify that the query result was correctly computed over a committed graph data, without revealing private graph records or intermediate query values beyond the public inputs and what is implied by the query result. Building on the expansion-centric model introduced by the state-of-the-art zero-knowledge verifiable graph query evaluation system ZKGraph (ICDE'26), VerfGraph achieves more efficient and expressive zero-knowledge verifiable graph query evaluation by advancing design in two key ways. First, we propose novel operator-proof methods for core expansion and shortest-path operations that reduce costly sorting and comparison checks and eliminate edge canonicalization for bidirectional expansion. Furthermore, we introduce correctness proofs for count and weighted shortest-path operators, broadening the class of graph queries that can be verified. We evaluate VerfGraph using the LDBC Social Network Benchmark. Compared with ZKGraph on complex IC queries, VerfGraph achieves $33.9\times$-$59.7\times$ speedups in proof generation and reduces peak prover memory by factors of $4.5\times$-$6.6\times$, while maintaining millisecond-level verification and producing sub-MB proofs.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04239

LIRA: A More Interpretable Dynamic Loss-Informed Rank Allocation for Low-Rank Adaptation

Sihao Wang, Bozheng Li, Xuemiao Xu, Haoxin Yang, Yujie Xu, Jie Tang

原文摘要

Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large pretrained models. However, standard LoRA typically assigns a fixed rank profile despite layer- and stage-dependent adaptation needs, while recent adaptive-rank variants often rely on heuristic or first-order signals that provide only an indirect link between rank reallocation and local loss behavior. This leaves the rationale behind each rank reallocation insufficiently interpretable. Motivated by this gap, we propose LIRA (Loss-Informed Rank Allocation), a dynamic-rank LoRA method designed to make online rank reallocation more interpretable. LIRA scores each layer by the estimated magnitude of the local loss perturbation associated with its recent LoRA-factor movements under a second-order approximation, making the basis for each rank change interpretable term by term. By periodically applying DELTA (Displacement-Evaluated Loss-Trajectory Allocation), a displacement-based algorithm for LoRA rank reallocation, LIRA produces discrete effective ranks under matched budgets while remaining compatible with standard (A,B) adapters. On RoBERTa-base GLUE under the La-LoRA/LoRA protocol, LIRA outperforms the uniform-LoRA and adaptive-rank baselines reported under this protocol; Trajectory EMA attains performance competitive with Fixed-point Fisher at lower estimation cost.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.07684

Disentangling Dual Image References in Frequency Aware Diffusion Models for Personalized Generation

Haipeng Liu, Yang Wang, Meng Wang

原文摘要

Personalized image generation aims to synthesize text-driven images conditioned on reference images, while mainly casting the generation as image customization for foreground and style transfer for background. Previous arts of diffusion models suffers from the text misalignment with background for image customization and foreground for style transfer during the denoising process. Such facts, as we observed, rooted from the entanglement among hybrid frequency bands during the denoising process. In particular, the low-frequency band primarily captures color information, mid-frequency band encodes global structure, while high-frequency band corresponds to local texture. Besides, the early (high-noise) denoising stage mainly focuses on the low-and-mid frequency bands, while the late (low-noise) denoising stage progressively exhibits the high-frequency band, which, all together, overwhelm the text prompt guidance, suffering from the heavy reliance on reference image. To address such salient limitation, in this paper, we study personalized generation based on dual references - customization and color style reference - to propose a paradigm to disentangle these Dual image references within Frequency-aware Diffusion Models, dubbed Dual-FDM, to simultaneously tackle two crucial personalized image generation tasks: customization style transfer and color style transfer, by disentangling different frequency bands via mask strategy within frequency domain. We further compute the ratio of information entropy of the substituted frequency bands to adaptively modulate the denoising timesteps between the early and late stages. Extensive experiments validate the superiority of Dual-FDM over the state-of-the-art diffusion models for personalized image generation. Our code can be accessed from https://github.com/htyjers/Dual-FDM.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.08227

StatLUT: Statistical Feature-Driven Multimodal 3DLUT Generation for Photorealistic Style Transfer

Yifan Wang, Zhixiang Hao, Yu Wang, Congchao Zhu

原文摘要

Photorealistic Style Transfer (PST) aims to transfer the color and tonal style of a reference to a content image while strictly preserving its structural integrity. However, existing deep learning-based methods inherently suffer from semantic entanglement caused by pre-trained image encoders, leading to unnatural spatial distortions. Moreover, current pixel-level mapping paradigms often ignore color gamut topology, resulting in color banding, while also lacking the multimodal capability for precise, tone-specific text-driven control. To address these bottlenecks, we propose StatLUT, an innovative multimodal framework for 3D LUT generation. First, we bypass traditional encoders and introduce a Lab-Extractor to derive spatially-agnostic statistical features, fundamentally decoupling color distributions from structural semantics to ensure artifact-free rendering. Second, we formulate LUT generation as a Transformer-based Seq2Seq translation task, utilizing a Multi-dimensional Residual Mapper (MR-Mapper) to predict topologically smooth 3D LUTs. Finally, to break the unimodal barrier, we propose the H-Diffuser, a lightweight Diffusion Transformer that directly synthesizes statistical features from tone-descriptive text prompts, enabling controllable and robust color grading. Extensive experiments on standard benchmarks demonstrate that StatLUT significantly outperforms state-of-the-art methods in both visual quality and quantitative metrics, pioneering a highly robust and versatile paradigm for multimodal photorealistic style transfer.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.07173

InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models

Hongyu Chen, Letian Ruan, Zilin Xu, Yuchen Li, Xinyu Chen, Bingsheng He, Jingwen Leng, Jieru Zhao 等 10 位作者

原文摘要

LoRA enables efficient customization of LLMs and is widely used in multi-tenant and multi-task serving. However, emerging model architectures such as MoE significantly increase LoRA memory cost, making existing coupled LoRA serving designs poorly scalable and prone to tail-latency inflation. We present InfiniLoRA, a disaggregated LoRA serving system that decouples LoRA execution from base-model inference. InfiniLoRA introduces a shared LoRA Server with parallelism-aware execution, SLO-driven provisioning, and critical-path optimizations, including GPU-initiated communication and hardware-specialized LoRA kernels. Experiments show that InfiniLoRA can achieve an average $3.05\times$ increase in serviceable request rate under strict latency SLOs, and improve the percentage of LoRA adapters satisfying the SLO requirement by 54.0\%.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04235

TCINet: Tissue-Conditioned Adaptive Cross-Branch Interaction Network for Nuclei Instance Segmentation

Zhenghang Qian, Yang Liu, Yanzhi Yu, Peiwu Qin, Yibing Shen, Joongwon Chae, Lihui Luo, Zhongde Zhang 等 10 位作者

原文摘要

Separating touching nuclei requires local boundary detail, while cell type classification requires broader morphological context. These requirements also vary with the surrounding tissue environment. We propose the Tissue-Conditioned Adaptive Cross-Branch Interaction Network (TCINet), a single-stage model with Task-Specific Feature Adapters (TSFA) and Tissue-Conditioned Task-Aware Cross-Branch Interaction (TC-TACBI). TSFA produces branch-specific features at skip connections, and TC-TACBI controls three asymmetric paths among nucleus-presence (NP), horizontal-vertical (HV), and nucleus-type (NT) branches using predicted tissue probabilities. On PanNuke under standard three-fold cross-validation, TCINet achieves multiclass and binary panoptic quality scores of 0.519 and 0.692, respectively. Zero-shot tests on MoNuSeg yield a detection F1 score of 0.876. On PanNuke fold 0, adding TC-TACBI increases mPQ from 0.505 to 0.525. Code is available at https://github.com/celestial1321/TCINet.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04234

The Unreported Variable:Collector Selectivity in Data Placement Evaluation

Nannan Zhao, Jiameng Zhang, Taoyu Zhong, Bo Wang, Hao Wang, Zhijie Huang, Shujie Han, Xiaonan Zhao 等 9 位作者

原文摘要

A solid-state drive cannot overwrite data in place, so it cleans full blocks by copying out still-valid data, costing speed and lifetime. Interfaces such as NVMe Flexible Data Placement (FDP) let the host label data by expected lifetime so data dying together is stored together, and published schemes report cutting these copies by 20--75%. Those studies rarely state which blocks the drive chooses to clean. Varying only this choice in simulation, we show it decides how much a label is worth: label value peaks when the drive searches moderately for blocks holding little valid data, falls when it always takes the block with the least, and turns negative when it ignores how much valid data blocks hold. This holds in 47 of 48 synthetic configurations, and one real mail-server trace shows the same shape at about half the magnitude. Priced as spare capacity, a two-class label is worth 65% more spare when each scheme runs at its best setting, but becomes a net loss at 20% label error, typical of learned classifiers. Labels stating which data die together keep most of their value at 16 FDP handles and gain as cleaning improves. Evaluations must report the drive's cleaning policy.

cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04233

LINDA: Low Resource Foot Ulcer Segmentation using Local Similarity and Iterative Neural Dynamics

Trishaani Acharjee, Sudip Chakrabarty, Rajdeep Chatterjee, Tathagata Bandyopadhyay

原文摘要

Foot ulcers are a leading cause of lower limb amputation, and the highest burden often falls on low and middle income regions where specialists and computing hardware are both scarce. Wound monitoring tools are most useful there when they are accurate yet small enough to run on ordinary devices, but accuracy usually comes from heavy networks. We present LINDA, a compact model that grounds segmentation in an explicit structural prior instead of raw intensity. A Local Self-Similarity (LSS) map, computed directly from the image and stable under local lighting changes, is fused into a ResNet-34 encoder, and a small Liquid Time-Constant module refines the boundary over four iterative steps. With every model trained under one protocol on the FUSeg 2021 dataset, LINDA obtains the best Dice (86.68\%), IoU (79.67\%), and boundary accuracy (HD95 6.37 pixels) in our comparison while using only 25.70M parameters, fewer than the U-Net and transformer baselines. As the LSS map is a fixed function of the input, it exposes the structural cues the model follows in a readable form. Together this makes LINDA an accurate, inspectable, and lightweight option for wound assessment in resource-constrained care.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04231

Constructing a Regional Slang Lexicon for Electricity Demands via Soft-Label Bidirectional Anchoring and Active Learning

Rong Chang, Lijing Shi, Zhaoxiang Zang

原文摘要

Power-grid customer service increasingly relies on community social media, where customers describe outages, billing, and equipment faults in informal regional dialect. Such messages exhibit non-standard morphology, semantic drift, and out-of-vocabulary expressions that make manual lexicon construction costly and degrade conventional NLP tools. We propose a weak-supervision framework constructing a regional slang lexicon from curated dialect anchors and power-domain seed terms. Candidates are generated via Pointwise Mutual Information with soft-label bidirectional anchoring, retaining dialect-anchored terms while recording power-domain relevance as separate metadata, and are scored by combining Sentence-BERT semantic similarity with a jieba-based wordhood measure. Uncertainty-aware active learning with rule-based and retrieval-augmented LLM oracles is applied under three query strategies, and the resulting lexicon is evaluated by a higher-capability LLM calibrated against human expert annotations via Cohen's $κ$. On 11,177 real WeChat messages from Yuxi (Yunnan, China), the framework achieves an evaluator-estimated dialect precision of DP~$=$~1.0 with zero false positives. An ablation shows that soft-label layering retains 751\% more candidates than strict power-gating at identical precision, expanding the candidate pool from 41 to 349 terms. Human calibration ($n=46$) yields $κ_{\text{dialect}}=1.0$ and $κ_{\text{power}}=0.85$. The resulting 25--33-term lexicon, 4--5 of which are power-relevant, provides a preliminary resource for dialect-aware power-grid service NLP.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04230

Exact Optimality Certification of QPLIB 2096 through Complete Set Partitioning

Jingyang Du, Zixiong Yi, Yang Shen

原文摘要

We present an exact computational proof that the frozen QPLIB_2096 instance has global minimum 7,025,720. The solution and certification approach was explored and refined through ZiYor, a self-evolving AI system. The authors provide the mathematical account and exact validation reported here. The original formulation has 300 binary variables, 6,900 triangle inequalities, and 25 nonconvex quadratic constraints. A coefficient-level reconstruction identifies its variables with the unordered pairs of 25 items. The triangle inequalities encode partitions, while each quadratic constraint becomes a bound on the total weight of edges incident to one partition block. This yields a set-partitioning formulation over all capacity-feasible blocks, with a separate edge-cost graph determining the objective. Exact enumeration examines 245,505 subsets of sizes one through six and finds 14,981 feasible blocks. Every six-element subset is infeasible; nonnegative capacity weights exclude all larger blocks. An integer price vector satisfies the dual inequality for every feasible block and has sum 2,229,456. Subtracting this sum from the total objective edge cost 9,255,176 gives a lower bound of 7,025,720, attained by the supplied six-block partition. Fresh solver-free checks validate the candidate against both frozen model formats and verify the certificate. The result is an instance-specific optimality proof with an exact primal and dual witness; no cross-instance performance conclusion is drawn.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04228

DualWorldBench: Can Agents Plan Deliveries across Symbolic and Grounded Worlds?

Yang Chen, Bin Wen, Danyang Peng, Hong-Jie You, Zi-Qiao Shang, Lan-Zhe Guo

原文摘要

Recent multimodal agents have shown promising progress in embodied planning and web interaction. However, existing evaluations often rely on simplified tasks with limited combinatorial structure, leaving it unclear whether current agents can robustly integrate perception and reasoning for multi-goal planning under temporal, physical, and multimodal constraints. We introduce DualWorldBench, a unified benchmark for evaluating agent planning under controlled symbolic--temporal and grounded--topological complexity. Built around a shared pickup--delivery interface, DualWorldBench consists of three complementary components: TextWorld, which isolates symbolic planning under varying task compositions and release dynamics; SimWorld, which evaluates grounded path planning under controlled topological variation with text, image, or multimodal inputs; and DualWorld-VQA, a diagnostic suite spanning perception, scene understanding, and planning. Experiments reveal a common gap between local understanding and globally consistent planning. In TextWorld, agents struggle to derive optimal task sequences under interacting temporal constraints; in SimWorld, grounded observations are not reliably converted into feasible action sequences. DualWorld-VQA further confirms that perception and topology understanding do not necessarily translate into constrained planning. Together, these results identify the central challenge as composing symbolic or grounded evidence into globally feasible and efficient multi-goal plans. These findings establish DualWorldBench as a challenging testbed for agents with tighter perception--reasoning--planning integration. All datasets and code are publicly available at our project website https://dualworld.netlify.app/.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.14238

Online Beck--Fiala Down to Logarithmic Sparsity

Dylan J. Altschuler, Konstantin Tikhomirov

原文摘要

The celebrated (offline) Beck--Fiala conjecture asserts that every matrix $A\in\{0,1\}^{n\times T}$ with at most $d$ nonzero entries in each column has discrepancy $O(\sqrt d)$. In this note, we consider the {\it online} version of the conjecture. For every fixed $η>0$, we give a randomized online algorithm that achieves $O_η(\sqrt d)$ prefix discrepancy for every fixed sequence of $d$-sparse vectors $a_t\in[-1,1]^n$, except with probability at most \[ C_ηT\exp\left[-\frac{c_ηd}{\log^{2+η}(ed)}\right]. \] In particular, this gives an optimal $O(\sqrt d)$ discrepancy upper bound for $d\geq C_η(\log T)(\log\log T)^{2+η}$. This range of sparsity is optimal up to $\log\log T$ factors. The result also resolves in strong form the {\it online Spencer problem}, corresponding to the case $d = n$. The algorithm is based on a compactly supported Metropolis walk, constructed by combining ideas from several recent works on online vector balancing. The proof was generated in conversation with ChatGPT 5.6 Pro; the authors provided high-level guidance in several rounds of prompting, followed by manual checking and rewriting of the proof.

math.CO · cs.DM · cs.DS · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04226

DiMuRe: Diagnosis-Guided Multilevel Recovery for Robotic Task Execution

Renkai Yi, Yuwei Wu, Haoyu Zhang, Penghao Dai, Zhi Gao, Jin Chen, Che Sun

原文摘要

Disturbances can invalidate the conditions required by a robot's next action even when much of its task program remains useful. A recovery system must therefore choose a response suited to the current state. We present DiMuRe (diagnosis-guided multilevel recovery), a framework for robot-use agents that uses phase-specific evidence and method preconditions to select program reuse or predefined recovery skills, including bounded vision-language-action (VLA) grasping. After the original program exits, a multimodal large language model (MLLM) can generate corrective code from images and execution feedback. Local verification checks the outcome of each recovery attempt. We also introduce LIBERO-Disturb, a benchmark for robustness and recovery under mid-execution disturbances, supporting program-based agents and VLA policies. On 801 scheduled disturbed Encore development cases, Local DiMuRe (program reuse and recovery skills) achieves 77.65% complete-task success, compared with 58.68% for the original program under the same physical-step allowance. Full DiMuRe achieves 81.90% with terminal replanning, an additional step allowance, and early handoff. Local DiMuRe also uses fewer physical steps per scheduled episode than the original program.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04225

IntentGEO: Intent-aware Generative Engine Optimization for Recommendation

Runlin Lei, Shiqi Zhang, Xiaokui Xiao, Zhewei Wei

原文摘要

Large language models (LLMs) increasingly shape recommendations across chatbots, e-commerce, and user-generated content (UGC) platforms, creating demand for Generative Engine Optimization (GEO) to improve item visibility in generated responses. Existing GEO studies are mainly content-centric, optimizing visibility under general-purpose queries. However, a shared item description usually serves users with different needs and decision goals, resulting in its visibility gains being dependent on the request. To investigate how changing intent affects visibility, we formulate IntentGEO to evaluate shared rewrites across need specificity and decision progression. We revisit existing GEO methods and find that their visibility gains vary across user intents. More critically, learning-based prompt optimization can underperform its handwritten seed on average. Tracing these updates reveals prompt drift from explicit recommendation instructions toward generic promotional tactics. To address this drift, we propose AMO (Anchored Meta-Optimization), which preserves recommendation instructions in a fixed core while learning appended strategies from citation feedback. Across four domains and ten intent states, AMO improves average citation-rank gain by 35% over the strongest prior learning-based baseline and leads on nine states. Ablations show that learned additions improve on the fixed core alone and that AMO outperforms full-prompt optimization initialized from that core. It also achieves the highest gains in an agentic recommendation pipeline and, after separate adaptation, on Doubao, a commercially deployed, closed-source online assistant. These findings highlight user intent as a key dimension of GEO evaluation and support anchored adaptation across recommendation needs and decision stages.

cs.IR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04222

GC-MoE: Overlapping Expert-Parallel Token Dispatch with FP8/FP4 Grouped GEMM via CUDA Green Contexts

Yong Li, Kan Liu, Tao Lan

原文摘要

Under expert parallelism, mixture-of-experts (MoE) layers pay all-to-all token dispatch, grouped GEMMs, and a combine step in every layer. In decode each rank holds only 1-256 tokens: dispatch latency and weight traffic dominate while tensor-core utilization is inherently low. In fused operators such as DeepGEMM's Mega MoE, dispatch and computation overlap only by sharing every SM: dispatch warps compete with TMA/MMA and epilogue warps for registers, shared memory, and issue slots; every phase shares one CTA resource budget, synchronization spans the whole SM array, and every replay ends with a workspace-clean pass and a cross-rank barrier. We present GC-MoE, an open-source FP8/FP4 MoE inference operator for NVIDIA Hopper and Blackwell GPUs that uses CUDA Green Contexts to split the device into a small communication partition and a large compute partition. Each role gets its own kernel, resource budget, and synchronization domain, so dispatch and GEMM tune independently. A three-node CUDA Graph (four on Hopper with fused shared experts) launches the dispatch and grouped-GEMM kernels as concurrent roots; a token-ring handshake starts computation on the first filled ring blocks, and double-buffered receive counters remove the trailing cross-rank barrier between replays. The compute partition runs operand-swapped tcgen05 block-scaled MMA on Blackwell and register-A WGMMA with in-register FP4 decoding on Hopper, with fused SwiGLU, requantization, and optional shared experts. On 8 H20 GPUs GC-MoE accelerates DeepSeek-V4.1-Flash decode by 1.188x on average on both the routed and shared-expert suites (peaks 1.296x and 1.353x); on 8 B300 GPUs it accelerates GLM-5.2 and DeepSeek-V4.1-Flash by 1.106x (1.123x with fused shared experts, peak 1.209x), beating Mega MoE at all 54 measured points at matched accuracy. GC-MoE reserves only 20 of 148 SMs on B300 for dispatch.

cs.DC · cs.AI · cs.AR · cs.LG · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04221

Mamba3R: Fixed-Capacity Selective State Memory for Long-Sequence 3D Reconstruction

Zhenxiang Xiong, Tianchen Deng, Nailin Wang, Lei Zhao, Guole Shen, Weidong Che

原文摘要

Streaming 3D reconstruction must maintain geometric quality over long inputs within a fixed GPU budget. A pretrained recurrent model provides bounded state, but freezing its backbone also fixes its learned state-observation interactions. We introduce Mamba3R, a jointly designed state-memory adapter that provides a trainable adaptation path without updating the backbone or task heads. A selective state-space mixer jointly transforms native state features and a fixed-capacity auxiliary memory, explicit read and write paths carry information across frames, and a decomposed feedback path regulates what the added memory feeds back. Only 1.28 million adapter parameters are trained. Under a shared periodic-reset protocol, it lowers trajectory error and improves accumulated surface and depth accuracy over recurrent baselines on public indoor and outdoor datasets and robot-recorded traversals, gaining up to 40 percent on one metric. Matched comparisons support both the choice of the mixing operator and the utility of carrying the auxiliary memory across frames. Allocated memory remains unchanged and throughput moves by at most 1.4 percent from one to two thousand frames. Replaying its recorded traversals offline, the platform's RTX 4060 averages 11.7 frames per second within 5.1 GiB with the same checkpoint, a mean throughput covering the 10 Hz sensor rate.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2506.04235

A Comprehensive Benchmark of Computational Models for Antibody--Antigen Binding Affinity

Xinyan Zhao, Yi-Ching Tang, Victor J Cantu, Junseok Lee, Adam E Stogsdill, Ibraheem M Hamdi, Ashwin Kumar Ramesh, Zhiqiang An 等 10 位作者

原文摘要

Computational antibody design requires benchmarks that measure functional binding rather than similarity to natural sequences. Here we present AbBiBench, which treats the antibody--antigen complex as the evaluation unit and harmonizes 216,560 experimental measurements spanning 13 antibodies and 9 antigens. We compare 20 computational approaches without task-specific training or fine-tuning. The analyses show that global structure-conditioned inverse folding yields the strongest and most consistent affinity rankings, whereas antibody-specific fine-tuning does not consistently improve performance. Across models, rankings are more useful for prioritizing likely affinity gains than for excluding weak variants. In a subsequent case study, multi-objective evaluation of model-generated F045-092 CDR-H3 variants for H1N1 binding revealed a trade-off between predicted binding potential and evolutionary sequence plausibility; all 21 selected candidates produced H1-reactive ELISA signals. AbBiBench provides an open framework for developing and evaluating function-aware antibody design methods.

q-bio.QM · cs.AI · cs.CE · cs.LG · q-bio.BM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04219

Reliability-Role Collaborative Learning for Semi-Supervised Cardiac Image Segmentation

Zhongrui Xie, Wenfeng Zhang, Wei Hu, Ruitao Xie, Wenjie Wang, Zhouyun Xie, Yi Zhang, Tao Jia

原文摘要

Semi-supervised cardiac image segmentation aims to exploit abundant unlabeled images together with limited annotations, but the reliability of pseudo-label supervision is highly uneven across spatial regions. In a dual-network setting, interior regions are usually more stable, whereas boundary regions, low-confidence areas, and disagreements between networks can introduce noisy supervision. However, existing methods typically impose uniform supervision on all unlabeled pixels, overlooking reliability differences and limiting discriminative learning in challenging areas. To address this issue, we propose R2CL (Reliability-Role Collaborative Learning), a framework for semi-supervised cardiac image segmentation. R2CL models confidence relationships between two networks to assign roles to unlabeled regions: mutually uncertain regions are used for entropy regularization, confidence-discrepant regions for directional knowledge transfer, and mutually confident regions as reliable supervision anchors. Reliability-Role Mixed Cross Learning (RR-MCL) implements these roles through region-mixed training, directional cross-network supervision, and entropy regularization over mutually uncertain regions. Reliability-Transition Prototype Contrastive Learning (RT-PCL) constructs query features from foreground-adjacent reliability-transition regions and learns network-specific class prototypes from mutually confident regions, improving feature discrimination around ambiguous boundaries. High-confidence Context Refinement (HCR) exploits reliable interior pixels and trusted dense blocks within mutually confident regions to complement stable contextual supervision. R2CL achieves stable improvements in region-overlap and boundary-related metrics across multiple datasets, with more evident advantages on the foreground-sparse MVAA-CT dataset.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04218

Shared Context Candidate Scoring with Jointly Adapted Readout Networks

Jingyang Du, Yiben Fu, Yang Shen

原文摘要

Repeated decisions over a shared document create two distinct opportunities: reusing document computation and adapting how question representations score candidate answers. We present Q64, a candidate-scoring system that preserves a frozen 64-layer Qwen3.8-27B text backbone, reuses its hybrid prefix state and intermediate document representations, and jointly trains a residual adapter with a four-layer decoder and candidate-conditioned readout. We used ZiYor, a self-evolving AI system, to explore and refine the Q64 design. We specified the scoring method, implemented the system, and evaluated its accuracy and latency. The final adaptation stage updates 88.66 million parameters using 8,192 selected training views. On a fixed 5,112-item evaluation combining ARC-Challenge, ANLI-R3, and stratified subsets of MMLU-Pro and CMMLU, joint adaptation improves four-benchmark macro accuracy from 66.46% to 74.32%. It remains below the native 27B baseline at 75.68% and AnyJev Qwen3-32B L0 at 76.68%. On a separate synthetic workload with 4,096 document tokens and 50 questions, median cold-request latency is 2.237 seconds, compared with 5.383 seconds for the native baseline and 56.872 seconds for the measured batched AnyJev configuration. Short single-question requests do not share this advantage. The evidence supports an accuracy-latency tradeoff in the evaluated systems, rather than a backbone reduction or an isolated component advantage. Public benchmarks informed earlier development, and real-document multi-question validation remains open.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04217

LENS: Reliability-Aware Stock Prediction via LLM-Extracted Priors and News-Driven Semantic Graphs

Hao Zhou, Tiejun Ma, Luis Felipe Costa Sperb

原文摘要

Predicting stock returns requires modeling persistent company relations and time-varying market signals. Graph Neural Network (GNN) approaches combine static priors with dynamic relational graphs, but treat every extracted edge as a trustworthy binary fact, ignoring heterogeneous reliability across relational sources. We propose LENS (LLM-Extracted priors and News-driven Semantics), a static--dynamic graph framework for stock return prediction and ranking that models relational reliability as a first-class signal across the pipeline. On the static side, we extract a multi-relational company graph from a Large Language Model and aggregate it over multiple runs with confidence weighting to downweight low-consistency edges. On the dynamic side, we build a news-driven semantic graph with exponential-decay temporal aggregation to capture changing market context under uneven coverage. A context-dependent gated fusion module rebalances the two graphs at the per-stock, per-day level. On S&P 500, LENS improves AR@5 by 16.4% and SR@5 by 22.6% over the strongest baseline, and on NASDAQ 100 it achieves comparable improvements (15.5% and 20.9% respectively). Ablations isolate each component's contribution, and a human-validation study of sampled relations supports that the aggregation is grounded, providing evidence that reliability-aware LLM relations and news-based semantic graphs carry complementary information for graph-based financial prediction.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2412.03774

Refining Concentration Bounds for Gaussian Quadratic Chaos with Applications

Kamyar Moshksar

原文摘要

This paper studies concentration of measure for Gaussian quadratic chaos in the non-asymptotic regime. We begin by tightening the classic Hanson-Wright inequality, where we increase its absolute constant $κ$ from 0.125 to at least 0.1457 in the symmetric case. We present a sharper version of Laurent-Massart inequality (LMI), which increases $κ$ from the largest available value of $1-\frac{\sqrt{3}}{2}\approx 0.134$ to $\frac{9-\sqrt{17}}{32}\approx 0.152$ in the positive-semidefinite case. We develop a sequence of inequalities indexed by $m\ge1$ that involve Schatten norms of the underlying matrix. The case $m=1$ recovers HWI. The bounds undergo a phase transition in the sense that if the tail parameter is smaller than a critical threshold $τ_c$, then $m=1$ is the tightest, whereas if it is larger than $τ_c$, then $m=\infty$ is the tightest. This leads to a novel bound called the $m_\infty$ bound. All HWI, LMI and the $m_\infty$ inequality rely on Markov's inequality. By avoiding Markov's inequality, we introduce the strong and weak $χ^2$ inequalities. We provide a list of concentration bounds that depend only on the operator norm in the positive-definite case, namely, the $m_\infty$ bound, the weak $χ^2$ bound, relaxed versions of HWI and LMI, and the so-called large-deviations bound. The sharpest among these is shown to be always either the $m_\infty$ bound or the weak $χ^2$ bound. By utilizing conditioning, modified versions of $m_\infty$, $χ^2$, and HW inequalities of various orders are proposed, all of which can be significantly tighter than the original inequalities. By leveraging the Ruben-Kotz expansion for the distribution of a positive-definite quadratic form, we introduce the Ruben-Kotz inequalities (RKIs) for general symmetric quadratic forms. A key advantage of RKIs is that they provide an explicit upper bound on the approximation gap.

math.PR · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.09737

Q-Score: A Quantum-Native Scoring Function for Molecular Docking

Kangyu Zheng, Yidong Zhou, Ruihao Li, Zixin Ding, Zhiding Liang, Shaohua Li

原文摘要

Molecular docking predicts how a small molecule binds to a protein and is a key bottleneck in drug discovery. Classical scoring functions sum empirical pairwise contacts, blind to quantum-mechanical effects like orbital charge transfer that govern binding specificity. We introduce Q-Score, encoding GNN-predicted orbital donor-acceptor energies into a weighted graph and scoring binding by solving a maximum-weight vertex clique problem via Digitized-Counterdiabatic QAOA. Each interaction anchor maps to one qubit and compatibility constraints become edges. Across 11 protein targets, DC-QAOA recovers the exact optimum on 8 at 10 qubits. On 1000 AI-generated molecules, Q-Score is orthogonal to classical scoring with Spearman rho of 0.05, driven by orbital quality with rho of 0.90, and free of molecular-weight bias, enriching for strong orbital interactions at twice the random rate. DC-QAOA achieves a mean approximation ratio of 0.94 with 52 percent exact. Execution of 1000 circuits on IBM Eagle confirms 6-qubit solvability on NISQ hardware.

physics.chem-ph · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04215

Tokenizer Myths and Where To Find Them

Rylan Schaeffer, Sanmi Koyejo

原文摘要

Tokenization is a fundamental component of modern language models: before a model sees data, its tokenizer has already determined how the data appears. We identify and challenge three myths in the tokenization research literature. Myth #1: Bits-Per-Byte (BPB) is agnostic to the tokenizer and/or the vocabulary size: BPB is tokens per byte times bits per token, and the two factors do not cancel; using untrained models, we show that BPB prefers some tokenizers over others: the model has learned nothing, but the tokenizer--model pair has. Myth #2: lower BPB means a better model: On untrained models, BPB strongly prefers some vocabulary sizes, even though accuracy remains at chance, and on real models trained on the same data, BPB correlates weakly with downstream performance. Myth #3: different "tokenizer-agnostic" metrics yield consistent results: The "unigram-normalized loss" is not tokenizer-agnostic; its reported correlation of 0.989 with BPB comes from a confounder and falls to 0.057 once fixed; from the same data the two metrics select different vocabularies at each model size. We present a tokenizer trilemma: no metric can score only the true distribution best, give the same predictions the same score, and tie all untrained models, so choosing a metric is choosing a research question. We conclude with a caution for reading the tokenization literature and a conjecture for future work.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.15754

A Survey on Reconfigurable Intelligent Surfaces Towards Practical Deployments: Security and Privacy Perspectives

Ziyu Chen, Yitong Shen, Jingzhe Zhang, Yao Zheng, Yili Ren, Xuyu Wang, Shiwen Mao, Hanqing Guo

原文摘要

Reconfigurable Intelligent Surfaces (RIS) have emerged as a transformative technology capable of reshaping wireless environments through dynamic manipulation of electromagnetic waves. Although RISs have been widely studied for communication, sensing, and localization, their security and privacy implications are still fragmented across application-specific studies, prototypes, and testbeds. This survey provides a deployment-oriented review of RIS applications, with a focus on the security and privacy threats, vulnerabilities, and defensive strategies relevant to practical use. We analyze scenarios with two types of systems (with and without legitimate RIS) and two types of attackers (with and without malicious RIS), and clarify how RIS introduces new attack surfaces to practical systems, including eavesdropping, jamming, and spoofing attacks. In response, we review defenses against RIS-related attacks in these systems, such as protocol- and algorithm-level protections, unauthorized RIS detection, and interference/disruption-based mitigation. We also discuss scenarios in which the legitimate user applies an additional RIS to defend against attacks. To support future research, we also curate papers and available open resources at https://awesome-ris-security.github.io/. By highlighting RIS's functionality and its security/privacy challenges and opportunities, this survey aims to guide researchers and engineers toward the development of secure, resilient, and privacy-preserving RIS-enabled practical wireless systems and environments.

cs.CR · cs.NI · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04211

Two-Layer Random ReLU Networks Can Separate Disjoint Classes

Yichen Liu, Jianbin Yang

原文摘要

Set separation by continuous linear functionals is fundamental in functional analysis and convex optimization and underlies binary classification in machine learning. Support vector machines provide maximum-margin affine separators for linearly separable data, while kernel methods represent nonlinear decision rules through linear separation in a reproducing-kernel Hilbert space. We study when two classes, not necessarily convex or linearly separable in the input space, can be separated with positive margin by finite ReLU networks and by random ReLU features. At the deterministic level, we prove that positive-margin separation by a finite ReLU network is equivalent to the existence of finite closed convex covers whose opposite-label members have positive pairwise distances, and exactly two hidden layers suffice. For random separation, suppose each class is represented as a finite union of nonempty closed convex cells, possibly unbounded. If every opposite-label cell pair has positive distance and its closed difference cone is pointed, then two independent random ReLU layers yield a common positive-margin affine separator with high probability. The first layer separates the feature image of each negative cell from the feature image of the positive class by a hyperplane through the origin. It also controls all cross-class distances uniformly. The second layer combines cellwise scores through Chebyshev polynomials and realizes the resulting polynomial using Gaussian Hermite features, followed by a uniform finite-width approximation. With the remaining geometric, bias, and confidence parameters fixed, each sufficient width is quadratic in the largest Gaussian width of a spherical difference-cone section, hence at most linear in its span dimension. An example shows that this squared Gaussian-width dependence is sharp for both layers.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2601.16836

ColorConceptBench: A Benchmark for Probabilistic Color-Concept Understanding in Text-to-Image Models

Chenxi Ruan, Yihan Hou, Yu Xiao, Guosheng Hu, Wei Zeng

原文摘要

Text-to-image (T2I) models have advanced considerably in generating high-quality images from textual descriptions. However, their ability to associate colors with concepts remains largely constrained to explicit color names or codes, while their capacity to handle \emph{implicit concepts}, such as emotions and visual states, remains underexplored. To address this gap, we introduce ColorConceptBench, an expert-annotated benchmark that systematically evaluates color-concept associations through probabilistic color distributions. ColorConceptBench moves beyond explicit color specifications by examining how models interpret 1,281 implicit color concepts, grounded in 6,584 human annotations. Our evaluation of nine leading T2I models reveals that performance varies substantially across semantic categories, and models exhibit a significant lack of sensitivity to abstract semantics. These limitations persist even when applying classifier-free guidance scaling at inference time, suggesting that achieving human-like color understanding demands a shift in how models learn and represent implicit semantic meaning.

cs.CV · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04207

Point interpolation and distributional universality in flow-based generative models

Antonio Álvarez-López, Jingpu Cheng, Liyan Zhang, Qianxiao Li

原文摘要

Motivated by the expressive power of neural ordinary differential equations in flow-based generative modeling, we study the approximation of probability measures by pushforwards generated by parameterized families of Lipschitz vector fields. For Wasserstein distances, we establish a quantitative relationship between finite-sample point interpolation and distributional approximation, and derive universality criteria based on $L^p$-approximation of transport maps. In particular, affine-invariant families containing a non-affine vector field are universal from compactly supported absolutely continuous base measures. For total variation, we show that coordinate-wise dynamics are insufficient in dimensions $d\geq 2$ and establish sufficient conditions based on one-dimensional Sobolev approximation and shear-type mixing. We further examine the role of point interpolation, establishing when different regimes relate to one another and when approximate interpolation can be made exact.

stat.ML · cs.LG · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39562

Raincheck: Deferred Multimodal Prefill for Efficient Multi-Turn MLLM Inference

Wenjie Huang, Jun Zhang, Hongyaoxing Gu, Haoyang Huang, Tianqi Xu, Xinyue Hao, Xinyi Hu, Xinle Du 等 13 位作者

原文摘要

Multi-turn conversations with multimodal large language models (MLLMs) reuse input context, but long multimodal inputs increase time to first token (TTFT). Query-aware hard pruning reduces prefill tokens but permanently excludes information that later questions may require. We propose Raincheck, a framework for deferred multimodal prefill that decouples initial token selection from later information availability. An existing pruning method selects tokens for initial prefill; Raincheck preserves and progressively prefills unselected tokens in source order using unused compute capacity during decode. Unfinished prefill continues after answer return during available scheduling gaps. Attention visibility control and affine state mapping enable context updates after generation begins in full-attention and hybrid recurrent models. Prefill-decode fusion reduces repeated weight reads. We evaluate Qwen3-VL-8B and Qwen3.5-9B with 30% initial multimodal-token retention. Across both models and four pruning methods, Raincheck recovers on average 77.7% and 60.4% of the quality lost to matched hard pruning on MultiVerse and ConvBench, respectively. On MultiVerse, the SGLang implementation reduces mean first-turn TTFT by up to 24.1% relative to full prefill, with less than 0.5% increase in mean time per output token (TPOT).

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2507.10938

Graph-Aggregated Prototype Learning for Remote Sensing Semantic Change Detection

Zhengyi Xu, Haoran Wu, Wen Jiang, Jie Geng

原文摘要

Semantic change detection (SCD) extends the binary change detection task to provide not only the change locations but also the detailed "from-to" categories in multi-temporal remote sensing data. Such detailed semantic insights into changes offer considerable advantages for a wide array of applications. However, since SCD involves the simultaneous optimization of multiple tasks, the model is prone to negative transfer due to task-specific learning difficulties and conflicting gradient flows. To address this issue, we propose Graph-Aggregated Prototype Learning for Remote Sensing Semantic Change Detection(GAPL-SCD). In this framework, a multi-task joint optimization method is designed to optimize the primary task of semantic segmentation and change detection, along with the auxiliary task of graph aggregation prototype learning. Adaptive weight allocation and gradient rotation methods are used to alleviate the conflict between training tasks and improve multi-task learning capabilities. Specifically, the graph aggregation prototype learning module constructs an interaction graph using high-level features. Prototypes serve as class proxies, enabling category-level domain alignment across time points and reducing interference from irrelevant changes. Additionally, the proposed self-query multi-level feature interaction and bi-temporal feature fusion modules further enhance multi-scale feature representation, improving performance in complex scenes. Experimental results on the SECOND and Landsat-SCD datasets demonstrate that our method achieves state-of-the-art performance, with significant improvements in accuracy and robustness for SCD task.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04206

Are We Just Applying LLMs? What SE Research Studies, Underexplores, and Could Do Next in the LLM Era

Zeinadsadat Saghi, Sadra Sabouri, Souti Chattopadhyay

原文摘要

Is AI taking over software engineering research? By 2025, AI appeared in more than half of the research-track papers at ICSE, FSE, and ASE. Yet ubiquity alone cannot show whether AI expanded the publication portfolio, displaced other work, or redirected the intellectual agenda. We investigate this shift through a census of 4,790 research papers (three SE conferences; 2018--2026) and free-form responses from 244 SE researchers discussing the future of the SE research community (ICSE 2026 FoSE survey). We found that while AI is taking over, it is not simply replacing non-AI research; it is unevenly reorganizing SE research around its capabilities. AI accounted for 93.1% of publication growth, but expansion in some venues and problem areas concealed contraction in others. The LLM wave also changed the field's tools more than its problem boundaries: 92.6% of LLM-wave AI papers revisit established SE problems, while most new problems arise from the introduction of LLMs in the SE process themselves. Engagement with AI remains predominantly downstream: 95.7% of AI papers apply AI or evaluate and support its use, while only 4.3% interrogate the paradigm or develop alternatives. The FoSE survey reveals a similar imbalance between recognition and response. AI-driven change was the second-most frequent concern about the field's future 20.2%, yet only 7.0% of responses mentioned AI when researchers were asked to identify one change the community should make. Together, our findings reveal a field reorganizing an inherited agenda around AI faster than it is questioning that reorganization. We discuss how SE can respond by building cumulatively across technological waves, strengthening evaluation and assurance, developing SE-native foundations, and studying software engineering in an AI-transformed world.

cs.SE

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arXiv ↗PDF ↗