跳到正文
研踪PaperTrail

论文库

检索计算机领域论文，按研究方向、作者或关键词筛选。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+0已有论文修订
+98新增 AI 导读

arXiv 最近完成扫描：2026-11-15 23:30 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

统计 2026-11-15 00:16 — 2026-11-16 00:16（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-15 23:30最近发现新增版本：2026-11-14 15:09库内论文最新更新时间：2026-11-13 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

23,348 已收录论文19,580 AI 相关6,729 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 39 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-15 23:27（北京时间）
新论文 · v1arXiv:2611.16922

Batch, Pack, and Prove: More Efficient Verifiable Computation for CKKS

Ignacio Cascudo, Anamaria Costache, Daniele Cozzo, Dario Fiore, Antonio Guimarães, Eduardo Soria-Vazquez

原文摘要

An important challenge in homomorphic encryption (HE) is ensuring the integrity of the encrypted computations. Although verifiable computation can address this in theory, making it practical is far from trivial. [Cascudo et al. CRYPTO 2025] recently proposed a solution to this problem for the CKKS scheme. In particular, they propose new succinct arguments that efficiently prove arithmetic circuits and range checks over the ring $R_q = \mathbb{Z}_q[Y]/(Y^N+1)$, and combine them to prove computations on CKKS ciphertexts. Despite the important progress, their work faces scalability challenges: the size of the polynomial commitment proofs, a large $O(N)$ overhead in range proofs, and the lack of an explicit method for efficient -- sublinear in circuit size -- verification. In this paper we present new techniques that remove all these bottlenecks. First, we provide a more efficient arithmetization of CKKS computations. Second, we introduce BatchFold, a new polynomial commitment for $R_q$ that supports batched evaluations and achieves shorter proofs. Third, we design packing, a novel algebraic technique that eliminates the need to commit to massive polynomials and removes the $O(N)$ overhead in range proofs. Finally, we introduce SparsePack, an efficient protocol for proving the evaluations of sparse, circuit-dependent polynomials, leveraging both packing and batched evaluations. We evaluate the efficiency of our solution on a generic neural network architecture, achieving proofs several orders of magnitude smaller and faster than prior approaches.

cs.CR

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33267

Scoped Semantic Invariance as a Foundation for LLM Reliability

Rosario Uceda-Sosa, Maria Chang, Karthikeyan Natesan Ramamurthy, Moninder Singh

原文摘要

LLMs often answer semantically equivalent queries inconsistently, even when the underlying facts are known. We formalize consistency as semantic invariance over equivalence classes of queries and show that invariance under arbitrary meaning-preserving transformations is undecidable in general. This motivates scoped semantic invariance, where model behavior is evaluated relative to bounded semantic domains. We operationalize this framework using knowledge graphs (KGs), which act as explicit semantic control structures defining local semantic constraints and grounding contexts. Across the MITRE D3FEND ontology and eight Wikidata- derived domains, we show that inconsistencies are structured rather than random, concentrating in localized hotspot subgraphs organized around recurring TREE, STAR, and CYCLE patterns that remain stable across models and domains. We further show that selective graph grounding improves correctness and cross-model consistency, while indiscriminate grounding introduces regressions. A layered grounding strategy achieves the strongest monotonicity profile, preserving correct knowledge while reducing inconsistency. Finally, we show that hotspot structures discovered from simple consistency probes transfer to more realistic compositional reasoning tasks.

cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.09767

Listen to the Features: Voice Anonymization Driven by Content Embedding Matching over Signal Reconstruction

Adrien Schneider, Kacper Zabkowski, Anderson Augusma, Fr{é}d{é}rique Letu{é}, Maria Camila Pinzon, Dominique Vaufreydaz

原文摘要

The paper presents a voice anonymization model focusing on preserving content rather than producing realistic speech. It relies on content embeddings extracted from a frozen pretrained wav2vec2 encoder. These embeddings are decoded into an anonymized signal using vector quantization and a HiFi-GAN vocoder, both trained on LibriTTS without any waveform reconstruction loss or speaker embedding mapping. The training objective enforces that embeddings of the anonymized signal match those of the original one. While training, an auxiliary speaker classification branch with a gradient reversal layer is used to discard speakerspecific information. Results show that this straightforward embedding-based approach achieves very low WER (2.53) with an anonymization performance (EER 13.39) ranking within first level for VPC. Notably, emotions are partially preserved (UAR 43.91), even without a supporting training objective, while the anonymized voice is audible without reconstruction loss.

eess.SP · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13562

An evidence-traceable benchmark of clinical reasoning in large language models

Zhiqun Zhao, Xiaodong Yang, Bruce Gu, Ming Li, Yi Fu, Song Li, Qi Mei, Zhi Liu 等 16 位作者

原文摘要

Benchmark scores for medical large language models (LLMs) are difficult to interpret when scoring criteria cannot be traced to clinical evidence. Here we developed Med Evidence Bench (MEB; https://www.medevidencebench.org.cn/), comprising 1,536 physician-constructed items across three levels: guideline knowledge, standardized clinical scenarios and real-world laboratory scenarios. Each criterion stating a medical claim is linked to a versioned source-text span. Across ten LLMs, the Evidence Trace Score (ETS) was 0.975-0.997 for guideline knowledge, 0.292-0.658 for standardized scenarios and 0.182-0.447 for real-world scenarios, and rankings varied across levels. For real-world scenarios, scores for identifying clinical information (mean, 0.770) exceeded those for matching evidence (0.165) or formulating recommendations (0.160). On 1,296 criterion-level judgments, automated grading agreed with the majority vote of three physicians (macro-F1, 0.930; 95% confidence interval, 0.915-0.945), although agreement was lower for evidence application. These findings from Chinese-language materials indicate that guideline recall does not ensure patient-specific evidence use and do not establish clinical effectiveness.

cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.18738

Do Language Models Share Your Doctor's Priorities? Measuring Values from Clinical Decisions

Payal Chandak, Victoria Alkin, David Wu, Maya Dagan, Taposh Dutta Roy, Maria Clara Saad Menezes, Ayush Noori, Nirali Somia 等 14 位作者

原文摘要

Clinical decisions often require weighing autonomy, beneficence, nonmaleficence, and justice. Reasonable physicians may prioritize these values differently, yet the values that language models bring to medical advice remain poorly characterized. We introduce ValuePrism, an instrument for measuring value priorities from clinical decisions. ValuePrism combines a benchmark of 50 clinical dilemmas, each reviewed by two physicians, with an attribution method that estimates relative value priorities from choices between medically reasonable recommendations. Each case presents a clinical vignette and two mutually exclusive recommendations designed so that choosing one necessarily promotes certain values at the expense of others. Applying the instrument to frontier language models and a US-based physician panel, we find that models frequently acknowledge values favoring both recommendations but make highly consistent decisions across repeated queries, even on cases that sharply divide physicians. The ValuePrism attribution method shows that both physicians and language models express differentiated value priorities. Collectively, model values exhibit substantial diversity and most tend to fall within the physician-panel reference range. Nevertheless, a single model deployed unchecked would amplify its particular values to patients at scale. ValuePrism provides a common basis for measuring and comparing the clinical value priorities expressed by language models and physicians. Data and code are available at https://github.com/payalchandak/value-prism.

cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.09221

The Pokémon Theorem: Fairness Criteria Can't Catch 'Em All

Daniel Matsui Smola, Alex Smola

原文摘要

Debates around algorithmic fairness rarely end. Models considered "fair" by one criterion are soon shown to be unfair by another. We prove that this is unavoidable. Satisfying any finite checklist of fairness criteria never certifies fairness itself, since the model may always violate yet another criterion. We call this the Pokémon theorem: you can't catch them all. In fact, trying to catch all criteria recovers the classical impossibility theorems. Since these violations come from differences between the groups' data, a natural remedy would be to make the data fair by learning fair features. Unfortunately, this, too, is impossible: features that scrub out sensitive group information also scrub out all predictive signal. While exact fairness is out of reach, approximate fairness is not. We derive the Pareto frontier between fairness and predictive signal for approximately fair features, and show how to attain it.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.06874

Induced Cycles of Many Lengths

Maria Chudnovsky, Ilya Maier

原文摘要

Let $G$ be a graph and let $\mathrm{cl}(G)$ be the number of distinct induced cycle lengths in $G$. We show that for $c,t\in \mathbb N$, every graph $G$ that does not contain an induced subgraph isomorphic to $K_{t+1}$ or $K_{t,t}$ and satisfies $\mathrm{cl}(G) \le c$ has bounded treewidth. As a consequence, we obtain a polynomial-time algorithm for deciding whether a graph $G$ contains induced cycles of at least three distinct lengths.

math.CO · cs.DM · cs.DS

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11561

From Sketch to Design: A Cross-scale Workflow for Procedural Generative Urban Design

Jin Gao, Sayjel Vijay Patel

原文摘要

Urban designers use sketches and symbols to express spatial intentions, while many generative urban design tools rely on numeric parameters that are difficult to relate to this visual process. We propose a cross-scale workflow and a web-based prototype that translates symbolic sketches into controllable procedural urban generation. A vocabulary of shapes, informed by examples of urban design diagrams and sketching exercises with graduate urban design students, encodes attributes such as network structure, density, and program. The prototype uses the \$1 Unistroke Recognizer to map gestures to symbols and translates their locations and sizes into tensor-field attractors and weighted parameter grids. Grid and radial tensor fields guide street-network generation, while the parameter grids control building heights and programs. Implemented in JavaScript and TypeScript with React and Three.js, the prototype supports interactive sketching, visualization, and urban massing generation. We describe the methodology and illustrate prototype interactions, then discuss downstream integration with architectural-scale generative tools for further refinement and analysis. The work connects designers' symbolic input with an explicit procedural generation process.

cs.HC · cs.GR

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11068

Discrete Diffusion Playground: A 2D Benchmark for Discrete Generative Models

Justin Shao, Sihyun Park, David Ryan Koes, Maria Chikina

原文摘要

Discrete diffusion is a promising framework for categorical generative modeling, but current evaluation protocols often fail to reveal whether models have actually learned the target distribution. We introduce the Discrete Diffusion Playground, a diagnostic benchmark that converts known 2D continuous distributions into binary token sequences through an exact discretized bijection. This creates a rare setting in which models are trained on discrete sequences, but generated samples can be decoded back into 2D and compared directly against a known ground-truth distribution. The benchmark spans smooth Gaussian mixtures and tilted checkerboards with sharp boundaries, disconnected support, and coordinate-dependent structure, allowing it to expose failures that are invisible in standard proxy metrics. It supports exact grid divergences, Wasserstein distances, density visualizations, and nonparametric two-sample tests, providing both quantitative evaluation and interpretable failure diagnosis. Using this testbed, we show that masked diffusion models can learn meaningful adaptive reveal orders but remain less stable than autoregressive baselines on smooth targets and largely fail on tilted checkerboards, where autoregressive models preserve the target geometry. Control experiments with reversed and interleaved token orderings show that autoregressive performance is not explained by favorable bit order alone. The Playground provides a simple but stringent benchmark for stress-testing discrete generative models and for developing evaluation metrics that detect real distributional mismatch rather than proxy performance. Our code is available at https://github.com/shaojus/DiscreteDiffusionPlayground .

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10926

DeepFinance: Can AI Diligence a $100MM Deal?

Drew Hahn, Tyler Shibata, Jackson Grove, Rahul Sonwalkar

原文摘要

Existing evaluations of AI models on high-finance work focus on retrieving answers from financial documents and producing numerically correct outputs. Real private equity (PE) and investment banking (IB) work also depends on choosing between conflicting sources, making sound assumptions, and keeping every step auditable. We introduce DeepFinance, an ultra long-horizon benchmark of 21 end-to-end PE and IB tasks that take a human professional an average of 27.1 hours. Each task pairs a request with a data room averaging 19 files and is graded against an average of 84 expert-written rubric criteria covering output, workflow, and judgment. We evaluate 22 model configurations, each in its native agent harness. The best configuration, GPT-6 Astra with max reasoning, satisfies 57.6% of rubric criteria, and the top four configurations are not statistically distinguishable. Frontier models often calculate correctly from the wrong inputs and find decision-relevant evidence without acting on it.

cs.CL · cs.AI · q-fin.GN

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10412

TurnFlow: Benchmarking User-State Reasoning in Multi-Turn LLM Agents via Scene-Guided User Simulation

Zhixun Chen, Ming Li, Anjie Liu, Xiaoyuan Zhang, Quanyu Dai, Huangxun Chen

原文摘要

Large Language Models (LLMs) are increasingly deployed through conversational interfaces, yet strong single-turn performance does not imply reliability in multi-turn settings. Multi-turn assistance requires a model to maintain an evolving representation of user requirements and to reason over it before complying with later requests. Failures can therefore be subtle: models may fail to recognize a changed constraint or hidden conflict, or recognize it without enforcing it in the final response. Existing benchmarks primarily test fully specified prompts, long-context recall, or controlled domain environments, leaving open-domain requirement reconciliation undermeasured. We introduce TurnFlow, an interactive benchmark for evaluating whether models preserve, revise, and act on user requirements across turns. TurnFlow combines adaptive user simulation with verifiable judging, allowing conversations to unfold naturally while keeping hidden requirements and pass/fail criteria controlled. In our evaluation, nine LLMs achieve overall pass rates from 42.86% to 59.59% on 490 scenarios. Performance varies substantially across challenge types: models perform well on Stateful Multi-Constraint Following but remain considerably less reliable on Scope Creep and Goal Shift. These results reveal persistent limitations in reasoning over evolving user requirements across turns.

cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.19684

CoMPAS3D：交互式动作数据集与基准

CoMPAS3D: A Dataset and Benchmark for Interactive Motion

Bermet Burkanova, Yasaman Etesam, Payam Jome Yazdian, Trinity Evans, Chuxuan Zhang, Zoe Stanley, Paige Tuttösí, Angelica Lim

AI 速读 基于摘要

作者提出以萨尔萨双人舞作为具身交互评测域，发布含专家动作标注的动捕数据集 CoMPAS3D，并构建涵盖运动学质量、动作可读性、熟练度适配性及六项竞赛式主观维度的评测框架与三项基准任务。

cs.LG · cs.AI · cs.CL · cs.CV

arXiv ↗PDF ↗
查看原文摘要

Socially interactive humanoids aim to interact with humans through their bodies, adapting in real time to a partner's movement, intent, and abilities. This requires models that understand both how bodies move and what their movements mean. Yet, existing evaluation frameworks for interactive motion generation do not assess whether generated reactive motions are valid or appropriate within a shared movement vocabulary. Existing frameworks rely on kinematic metrics such as FID and beat alignment that cannot capture this semantic property, while improvised interaction datasets lack fine-grained move annotations. Inspired by computational evaluations of games such as Go and Chess, we propose Salsa as an embodied interaction evaluation domain. Salsa is interactive, dyadic, and governed by a move vocabulary and community-validated judging criteria covering timing, musicality, technique, difficulty, partnering, and originality. We present CoMPAS3D, a motion capture dataset of improvised partner salsa dance and an evaluation framework covering kinematic quality, two objective metrics (move legibility and proficiency appropriateness), and six competition-based subjective dimensions. The dataset comprises 3 hours of improvisation by 18 dancers spanning beginner, intermediate, and professional skill levels, with over 2,800 expert-annotated segments covering move types, execution errors, and stylistic elements. We define three benchmark tasks: move classification, proficiency estimation, and follower generation. Fine-tuned vision-language models perform well on ground-truth sequences and correlate with subjective expert judgments, suggesting their promise as measurement tools. Applied to Duolando and InterGen, our objective metrics reveal a large gap between ground-truth and generated dances, corroborated by expert human evaluations across six judging dimensions.

新论文 · v1arXiv:2611.07964

Time-Indexed Skill Success Models for Latency-Aware Skill Selection and Timing in Dynamic Manipulation

Mohsen Omidi, Maria Chiara Leva, Brian Vaughan

原文摘要

Robots that manipulate moving objects act on observations that are outdated when the chosen skill starts, and the best skill can change within a few hundred milliseconds. Skill competence models, however, predict success for a launch at the moment of observation. We propose time-indexed skill success models, which predict whole-task success when a skill is launched after a given delay. Trained on rollouts branched at a grid of launch delays, they let a receding-horizon planner select both the skill and its launch time under fixed or random latency. In a MuJoCo study in which a UR5e captures a sliding puck with three skills, the planner exceeded a static competence model by 2.8 to 6.2 percentage points at 100 to 300 ms of latency, including when that model was evaluated on point or sampled forecasts of the launch state. Across five complete runs (three training seeds and two distribution shifts), all 80 paired 95% intervals against four baselines excluded zero. On delayed launches, the time-indexed model was calibrated (expected calibration error 0.009 against 0.239), and pausing the world during latency overstated success by up to 23 points. On a real UR5e that captures a rolling ball after 0.2 s of injected latency, a static model trained in a hardware-matched simulator predicted 99% success on a prespecified subset of 35 delayed launches that it rated at least 0.8. Of these, 63% succeeded (95% interval 46 to 77%), and the time-indexed model predicted 56%. In this single-skill, predictable regime, a matched physics-based forecast was also well calibrated.

cs.RO

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07707

CAMP: Context-Aware Mapping and Placement for LLMs on 3D-IMC Arrays

Orhun Görkem, Athanasios Vasilopoulos, Georgios Pagonis, Victoria Clerico, Hadjer Benmeziane, Irem Boybat, Hsinyu Tsai, Abu Sebastian 等 9 位作者

原文摘要

Large Language Models (LLMs) incur severe memory-access overheads due to the cost of loading large weight matrices and key-value (KV) caches. Analog In-Memory Computing (AIMC) is an emerging solution that enables computation directly on static weights stored in memory, substantially reducing energy and latency. 3D-IMC further stacks memory arrays into vertical tiers, increasing capacity but revealing a new challenge: how weights are placed in memory determines which matrix-vector multiplications (MVMs) can execute concurrently, affecting latency and throughput. The concurrency aspect of 3D-IMC weight-mapping remains underexplored in the literature, leading to sub-optimal utilization of compute resources on 3D-IMC chips. In this context we propose CAMP (Context-Aware Mapping and Placement), an inference-context-aware weight mapping algorithm for 3D-IMC-based LLM accelerators. CAMP captures the potential resource contention resulting from the architecture, targeted network, and runtime context in a conflict matrix, enabling throughput-optimized weight mapping. This mapping is refined through a profiling-driven feedback loop that efficiently prunes the search space and returns optimized mappings within practical runtimes. CAMP is an offline, software-based solution requiring no hardware changes and improving 3D-IMC compute utilization from 42.4% to 60.3%, delivering up to 2.0x higher 3D-IMC throughput and 1.74x higher end-to-end throughput over state-of-the-art mapping algorithms.

cs.AR · cs.AI

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.19540

Continuous Adversarial MeanFlow for Few-Step Generative Adaptation from Any Parameterization

Yara Bahram, Zahra Dehghani, Mélodie Desbos, Eric Granger, Pablo Piantanida, Mohammadhadi Shateri

原文摘要

Training fast generators on new domains with limited data remains challenging for two reasons. First, adapting a pretrained diffusion or flow model leaves its costly multi-step sampling intact, and existing acceleration methods are tied to the source parameterization (x, epsilon, v, or u), so heterogeneous pretrained models share no common acceleration target. Second, while adversarial post-training sharpens generation quality, existing criteria each cover only one end of the step range. Discrete criteria compare jump endpoints and lose their signal as the jump shrinks, while continuous criteria score the instantaneous velocity and misjudge the large jumps of few-step sampling. Neither fits MeanFlow (MF) models, which predict average velocities over intervals of any length. We propose MeanFlow-Transfer (MF-T), which adapts a source domain model of any parameterization and compresses it into a few-step MF generator through a common velocity map. We then introduce Continuous Adversarial MeanFlow (CAMF), which scores the change of a learned potential along each predicted interval jump of MF. CAMF needs no gradient penalty or Jacobian-vector product (JVP). We also prove it stays well-conditioned for every interval length and recovers the instantaneous criterion as the interval vanishes. Adapting four ImageNet-based source models (DiT, SiT, JiT, iMF) to five target domains, MF-T + CAMF approaches or exceeds the fine-tuned many-step teacher in FID and FDD at up to 125x fewer Neural Function Evaluations (NFEs). Further, CAMF improves MF-T's few-step FID by about 30% on average, outperforming prior adversarial post-training on iMF at every NFE.

cs.LG · cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06513

HELIX: HEterogeneous LLM Inference with FleXible and Fine-Grained Partitioning

Victoria Clerico, William Simon, Corey Liam Lammie, Orhun Görkem, Athanasios Vasilopoulos, Hsinyu Tsai, Jeronimo Castrillon, Abu Sebastian 等 9 位作者

原文摘要

LLM inference is inherently heterogeneous, with performance bottlenecks shifting throughout inference phases down to individual operators. This has motivated heterogeneous serving systems that disaggregate execution across specialized accelerators, typically through fixed partitions such as prefill/decode or attention/FFN. However, these approaches commit to a partitioning strategy in advance, limiting their ability to adapt to different workloads and model architectures while forcing emerging accelerators into predefined roles. We present HELIX, a vLLM-based software stack that discovers, compiles and dispatches workload-aware partitions across heterogeneous accelerators at the operator granularity. At engine initialization, HELIX automatically determines hardware-specific operator placements that minimize execution latency, accounting for profiled execution costs, communication overheads, device capabilities, and memory constraints. It combines dynamic programming, piecewise compilation, and heterogeneous subgraph dispatch, enabling accelerators with partial operator coverage to participate in LLM inference without predefined model partitions. HELIX provides an open-source unified framework for exploring, evaluating, and integrating heterogeneous accelerators for LLM inference, lowering the barrier to emerging hardware by exposing them as subgraph executors. In systems combining H100 and B200 GPUs with emerging accelerators, HELIX resolves LLM partitions that multi-query decoding latency by up to 3$\times$ for dense and sparse transformer models and up to 3.5$\times$ for hybrid state-space models, relative to standard attention-FFN disaggregation.

cs.SE

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2502.04953

Automated Exploit and Security Test Generation: A Systematic Literature Review

Quang-Cuong Bui, Emanuele Iannone, Maria Camporese, Torge Hinrichs, Catherine Tony, László Tóth, Fabio Palomba, Péter Hegedűs 等 10 位作者

原文摘要

Static analysis techniques are often insufficient to determine whether a reported security vulnerability is exploitable and actually poses a real security risk to a software application. Dynamic analysis addresses this limitation by executing the target application with carefully crafted inputs that trigger or reproduce a given vulnerability. Over the past two decades, automatic exploit generation (AEG) and automatic security test generation (ASTG) have emerged as active research areas in cybersecurity and software engineering. This paper demystifies the field by establishing consistent terminology and presenting a systematic literature review (SLR) of research published in the last two decades. Starting from an initial set of 3,309 publications obtained from academic literature search engines, we selected and analyzed 71 papers comprising 67 distinct studies. We systematically compare existing AEG and ASTG approaches with respect to (i) the targeted software and vulnerability types, (ii) the generated artifacts, (iii) the underlying generation techniques and tools employed, (iv) the required inputs, (v) the evaluation methodologies, and (vi) implementation and tool availability.

cs.CR · cs.SE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06076

Breaking the One-Player Extension Barrier: History-Aware Repair for Multiplayer Approximate Nash Equilibria

Jinqi Tang, Shihong Ding, Cong Fang

原文摘要

Computation of Nash equilibria is a central problem in algorithmic game theory, yet exact equilibrium computation is $\mathsf{PPAD}$-complete even for two-player games. This hardness motivates the study of polynomial-time algorithms for approximate Nash equilibria. While substantial progress has been made for two-player and three-player games, existing algorithms for general $m$-player games have largely relied on repeatedly applying one-player extension starting from improved fixed-player algorithms. Such improvements can at best strengthen the classical $1-1/m$ guarantee to $1-1/(m-c)$, for some constant $c$ independent of $m$. We break this long-standing barrier by developing an iterative algorithm that directly tackles general $m$-player games and achieves a $(1-(σ_{\star}-ε)/m)$-approximation for fixed $ε> 0$ and sufficiently large $m$, where $σ_\star\approx1.5867$ is the unique root in $(1,2)$ of $σ_\star^3-19σ_\star^2+27σ_\star+1=0$. Our approach rests on a novel iterative descent-and-repair framework that allows multiple rounds of repair. At the heart of our analysis is a new potential function inspired by continuous-time dynamics, which certifies progress across iterations and ensures polynomial-time termination. Our results highlight the power of history-aware, iterative repair in breaking approximation barriers for multiplayer games.

cs.GT

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04742

Approximation Algorithms for Multi-Criteria Network Design Problems

Mehrshad Taziki, Ali Vakilian

原文摘要

Min-max multi-criteria optimization asks for a single feasible solution minimizing the maximum of its costs under $\ell$ nonnegative cost functions, a formulation that captures both fairness across groups and robustness across scenarios. It has proved hard to approximate even for basic problems: for robust shortest paths, polylogarithmic approximations are known only in quasi-polynomial time. For network design, the best known guarantee is an $\ell$-approximation, which is tight for the natural LP relaxation. We show that exponentially better and nearly optimal guarantees are achievable for two fundamental network design problems. For multi-criteria connectivity augmentation (MC-CAP), which asks to increase the edge connectivity of a given undirected graph by one, we give a randomized polynomial-time $O(\log \ell)$-approximation algorithm. Applying our augmentation procedure iteratively and analyzing its randomized choices jointly across iterations yields an $O(\log k \log \ell)$-approximation for the multi-criteria $k$-edge-connected spanning subgraph (MC-$k$-ECSS) problem, for every $k \geq 2$. We complement these algorithms with a nearly matching lower bound: for every fixed $\varepsilon > 0$, MC-CAP admits no polynomial-time $O(\log^{1-\varepsilon} \ell)$-approximation, even when the underlying graph is a star, unless $\mathsf{NP}\subseteq\mathsf{DTIME}\bigl(n^{\log^{O(1)}n}\bigr)$. The same hardness extends to the MC-$k$-ECSS problem.

cs.DS

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03024

DisC: Incentive Design for Green, Robust, and Fair Client Self-Participation in OTA Federated Learning

Driss Choukri, Essaid Sabir, Abdelkrim Haqiq

原文摘要

Over-the-Air Federated Learning (OTA-FL) improves communication efficiency by exploiting waveform superposition over wireless Multiple-Access Channels (MAC). However, client heterogeneity, fading, energy constraints, and participation imbalance can degrade both fairness and aggregation accuracy. Existing Pareto-oriented fair OTA-FL schemes may still suffer from large aggregation distortion when highly weighted clients experience poor channel conditions. This paper proposes DisC, a game-theoretic distributed client selection framework for fairness-aware, channel-aware, and energy-efficient OTA-FL. Each client selects its participation level by maximizing a utility that captures Pareto fairness importance, learning relevance, OTA distortion, energy consumption, and delay. We show that DisC admits a Nash equilibrium and can be formulated as an exact potential game, where stable equilibria correspond to locally optimal OTA-FL operating points. DisC therefore offers a distributed, incentive-compatible alternative to centralized client selection under wireless and resource constraints.

cs.NI · cs.GT

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01753

ReCoG: Relative Node Contexts for Multi-Domain Graph Pre-training

Yupeng Tong, Lianze Shan, Jitao Zhao, Dongxiao He

原文摘要

The success of foundation models in language and vision has motivated Graph Foundation Models (GFMs), which learn transferable knowledge through multi-domain graph pre-training. Existing methods advance GFMs through feature unification and structural learning, and in doing so they rely on the raw graph structure to organize node contexts. However, we observe that removing a large proportion of edges from source graphs during pre-training improves cross-domain transfer accuracy across four representative methods. Our theoretical analysis identifies the raw graph structure as a source of conflict in cross-graph model sharing. We therefore advocate defining node contexts using criteria consistent across graphs. Achieving this requires (1) constructing node contexts consistently across graphs without a shared positional structure, such as the token order of text or the pixel grid of images, and (2) retaining the structural and neighborhood information of the original graphs. To this end, we introduce Relative Contexts for Graphs (ReCoG) for multi-domain graph pre-training. Specifically, we first construct attribute contexts to provide a comparison rule independent of the raw graph structure. Furthermore, we construct structural contexts based on common neighbors and semantic contexts based on neighborhood attribute summaries to retain the topological and neighborhood information of the original graphs. Since all graphs share these construction rules, we pre-train a single graph encoder by reconstructing node attributes over these contexts, without domain-specific modules. We conduct extensive experiments on 18 target datasets, which demonstrate the effectiveness of ReCoG. In particular, ReCoG exceeds the strongest baseline by 7.61 percentage points in mean 1-shot cross-domain node classification accuracy and by 4.47 points in cross-type transfer to heterophilous graphs.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01493

TopoSteer: Topological Layer Selection for Activation Steering in LLMs

Tuc Nguyen, Marcos Gutiérrez-del-Pozo, Eduardo Paluzo-Hidalgo, Yifan Hu, Adaku Uchendu, Thai Le

原文摘要

Activation steering controls large language models at inference time by adding a vector to their internal representations. Most work asks how to build that vector, and far less attention has gone to where to add it, even though steering at a poorly chosen layer can be worse than not steering at all. Prior selectors choose a layer without ever steering the model to test it: they score how separable the target behavior is at that layer, and how consistently the individual examples point along the steering direction. We argue that what matters is not the separability of the two activation clouds, nor the alignment of individual displacements with their mean, but the extent of the cloud of per-example displacements that mean-difference steering averages. We propose a topology-based layer selection method, called TopoSteer, which scores a layer by the total H_0 persistence of its displacement cloud, computed as the length of the cloud's Euclidean minimum spanning tree. We show that the score focuses on the displacement cloud's relative geometry, while class separation affects it only through standardization. We also provide a theoretical explanation for our method by proving that, as example-level variation increases, TopoSteer and discriminability move in opposite directions, so the two criteria can rank layers in opposite ways. Across multiple language models and steering tasks, TopoSteer provides a principled alternative to discriminability- and separability-based layer selection and yields more effective steering without requiring held-out validation.

cs.CL · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01409

RAG-TRACE: Tracing Measurement Evidence and Diagnostic Completeness in AI-Generated ECG Reports

Elyas Irankhah, Shahed Salehzehi, Parisa Hosseini

原文摘要

Purpose: Retrieval-augmented generation (RAG) can introduce electrocardiogram (ECG) criteria that lack patient-specific measurement support. This study evaluates the reliability of retrieved diagnostic criteria in AI-generated ECG reports. Methods: We analyzed 1,083 human-validated PTB-XL patient records using a library of 47 criteria cards, including 33 without numeric verification rules. Language models generated structured reports with and without retrieval. Criterion applicability was evaluated using Marquette 12SL measurements, with independent assessment using Uni-G and comparison against expert-validated diagnostic labels. Results: Keyword retrieval introduced untestable or measurement-disconfirmed criteria in 46.8% of GPT-4.1-mini reports. A deterministic verification rule withdrew 647 criterion applications, including 639 without numeric checks, while preserving 268 of 269 applications independently supported by Uni-G. Restricting retrieval to 14 testable criteria reduced the initial unsupported-criterion rate to 13.8%. Under matched instructions and an identical output schema, retrieval increased unsupported-criterion reports from 17.4% to 31.1%, while hypertrophy recall remained unchanged at 62.6%. Conclusion: These findings identify a fundamental limitation of retrieval-based clinical report generation: retrieved criteria do not necessarily provide valid patient-specific evidence. Measurement-based verification can reduce unsupported criterion applications, but diagnostic validity requires independent assessment.

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01394

ERIA: An Instruction-Optimized Agent for Clinical EEG Reporting

Ahmed Sameh, Gayal Kuruppu, Neeraj Wagh, Bridger Brundy, Yogatheesan Varatharajah

原文摘要

Clinical EEG interpretation requires integrating technical quality, background and behavioral states, interictal and ictal abnormalities, and overall clinical context across long recordings. We introduce ERIA (EEG Reporting and Instruction-Optimized Agent), a locally deployable framework in which a single 27B language-model controller analyzes whole-recording EEGs by orchestrating specialized parametric and signal-processing tools. ERIA combines learned tools for background abnormality, sleep-wake state, seizure-like activity, and interictal epileptiform discharges with deterministic spectral, amplitude, symmetry, montage, and signal-quality analyses. Rather than fine-tuning the controller or using multi-agent orchestration, ERIA keeps the controller weights, specialist tools, and safety constraints fixed and optimizes only its natural-language analysis policy with GEPA (Genetic-Pareto), using feedback from expert-written EEG reports. GEPA improves physician-aligned whole-recording report quality over the raw policy, and the optimized policy transfers to external EEG benchmarks. On CerebraGloss-Bench and BrainBench Foundational Analysis, ERIA remains competitive with the models evaluated in the original benchmarks. These results show that specialist tools and language-level policy optimization can extend the clinical and general EEG capabilities of a single locally executable model while preserving local inference and traceable tool use.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01234

How Much Can Approximate Equilibria Change the Social Cost of Selfish Routing?

Yu Cheng, Tianle Jiang

原文摘要

We study approximate Nash equilibria in nonatomic congestion games, with the goal of understanding how much the social cost can change when the equilibrium condition is relaxed. We consider $ε$-approximate Nash equilibria ($ε$-NE), in which each player uses a strategy whose cost is at most $1 + ε$ times the minimum available cost. We compare the social costs of $ε$-NE and exact Nash equilibria. This lets us quantify, for example, how much a navigation app can reduce total travel time when drivers tolerate routes slightly slower than the fastest available route. For degree-$d$ polynomial latencies in both general nonatomic congestion games and single-commodity routing games on series-parallel networks, we give tight lower and upper bounds on the relative change in social cost from an exact Nash equilibrium to any $ε$-NE, with matching instances for every $ε$. For small $ε$, the largest possible relative reduction and increase in general congestion games are both $Θ(\sqrt{dε})$, independently of the number of resources and populations. In contrast, on series-parallel networks of depth $h$, the largest possible relative reduction is $Θ(hε)$, while the largest possible relative increase is known to be $ε$. For the classes of games we study, these results give a tight and quantitative characterization of the possible changes in social cost and establish a separation between general and series-parallel networks.

cs.GT · cs.AI · cs.DS · math.OC

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.40234

Unraveling the Efficacy of Neuron Coverage Criteria on Testing Large Language Models

Xiaoning Ren, Jiayang Song, Chongyang Liu, Jie Li, Lei Ma, Yinxing Xue

原文摘要

Neuron coverage criteria aim to assess test suite quality and guide test input generation, but their effectiveness for Transformer-based language models remains unclear. We evaluate six criteria, including four established and two recent criteria, across encoder-only, decoder-only, and encoder-decoder models. Our evaluation examines error-revealing capability, distribution diversity, sample fairness (category diversity), and guidance for test input generation. The results reveal uneven effectiveness. Erroneous samples provide only 0.32% additional coverage over noise samples, indicating limited sensitivity to adversarial errors. By contrast, coverage criteria effectively assess distribution diversity and sample fairness in 26 and 30 of 33 configurations, respectively. Incorporating neuron-wise coverage guidance into test input generation increases adversarial sample production by only 4.56%. These findings suggest that existing coverage criteria can serve as an antidote for assessing diversity but remain largely a placebo for revealing adversarial errors, particularly when used individually. Their practical value therefore depends on whether these limited benefits justify the computational overhead of testing large Transformer-based models. Our study highlights the need for more robust and efficient coverage criteria tailored to these models, with stronger error-revealing capability and greater value for test generation.

cs.SE

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.23943

QuechuaTok: Morphological Boundary Accuracy as a Necessary Metric for Tokenizer Evaluation in Agglutinative Low-Resource Languages

Maria Contreras

原文摘要

Tokenization is a foundational step in NLP pipelines, yet standard evaluation metrics such as fertility rate fail to capture morphological correctness for agglutinative languages. We present QuechuaTok, a systematic benchmark comparing four tokenization strategies - BPE, Unigram LM, WordPiece, and a morphology-aware PRPE tokenizer - for Southern Quechua (quz), a low-resource agglutinative language spoken by 8-10 million people in South America. Using a 200k-sentence corpus and the SQUOIA finite-state morphological analyzer (Rios, 2016) as silver standard, we evaluate three metrics: fertility rate, OOV rate, and morphological boundary accuracy (MorphAcc). Our results show that BPE achieves the lowest fertility rate (1.636 at 16k vocab) by memorizing surface word forms, while achieving only 6.67% MorphAcc. PRPE achieves 83.33% MorphAcc - the highest of all systems - demonstrating that fertility rate alone is insufficient to evaluate tokenizers for agglutinative languages. We further validate this finding extrinsically: on a Spanish-to-Quechua (Chanka) neural machine translation task, a model trained with PRPE outperforms an identical BPE-trained model by +68.7% BLEU and +17.0% chrF, showing that morphological boundary accuracy translates into measurable downstream gains. All code and models are publicly available at kaggle.com/code/macmaky/quechuatok

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38580

Structural Pattern Mining in Inka Khipus: A KFG Re-Analysis

Maria Contreras

原文摘要

This paper reports a methodological pivot from the Open Khipu Repository to the Khipu Field Guide (KFG) database, prompted by stalled progress on the OKR, and a full re-analysis of the resulting 650-khipu sample. Three findings recur across every analysis attempted. First, khipu size confounds nearly every derived structural count, explaining 42-70% of its variance; all subsequent models control for it. Second, the two-letter name prefix (AS, UR, HP, QU, JC, MM, KH) identifies the original cataloger, not a site or period, which turns documentation completeness into a second confound that must be modeled explicitly. Controlling for both, khipus cataloged by Hugo Pereyra's Proyecto Quipu (HP) show significantly fewer Ascher sum-relation types than Gary Urton's (UR) khipus (p = 0.044), while the Ascher-cataloged (AS) khipus are too collinear with their own near-total lack of side/direction documentation to assess (VIF = 5.0). Third, generic structural clustering (UMAP+HDBSCAN) does not separate the pooled 650-khipu sample from two independent null models (permutation and Gaussian), corroborating an independent conclusion already reached by the KFG itself; the same test restricted to a single cataloger (UR) proved not reproducible across runtime environments and is reported here as a methodological caveat rather than evidence of structure. A targeted replication of the KFG's own S/Z-knot spatial-block heuristic for calendar/astronomical khipus recovers 8 of 10 published candidates and proposes 34 additional ones under the same criterion.

cs.CL · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38095

Nemotron 3.5 Super and Lightning: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Models for Agentic AI

NVIDIA, :, Aastha Jhunjhunwala, Aayushi Ashokkumar Srivastava, Abhibha Gupta, Abhinav Garg, Abhinav Khattar, Achsah Wildish 等 559 位作者

原文摘要

We introduce Nemotron 3.5 Super and Nemotron 3.5 Lightning, open and efficient Mixture-of-Experts Hybrid Mamba-Transformer models designed for reasoning, long-context understanding, and agentic workloads. Super is a 120B-parameter, 12B-active multimodal model, while Lightning is a smaller 30B-parameter, 3B-active text model; both designed for efficient deployment. Both models combine large-scale distillation, long-context training, and native speculative decoding, while Super further incorporates Indic continued pretraining and agentic software-engineering mid-training. We develop a comprehensive post-training pipeline spanning supervised fine-tuning and reinforcement learning (RLVR and RLHF), complemented in Super by specialized teacher training and multi- teacher on-policy distillation (MOPD). This transforms Super from a text-only base model into a multimodal agent capable of reasoning over text, images, video, and interactive tool environments. We further explore test-time scaling through parallel agent execution and self-verification. Across training and deployment, we jointly optimize capability and efficiency. Indic continued pretraining improves Indian-language benchmarks by up to 9.82 percentage points, while agentic mid-training strengthens software-engineering capabilities and robustness across agent harnesses. The post-trained Super model achieves 51.78% on SWE-bench Pro, surpassing Nemotron 3 Ultra despite using approximately 4.6x fewer active parameters. Post-training MTP boosting improves speculative- decoding acceptance length by up to 21.8% under probabilistic draft sampling, while NVFP4 quantization achieves 99.62% median accuracy recovery relative to BF16. Inference optimizations enable up to 4,856 output tokens/s/GPU on GB200 while sustaining at least 50 tokens/s/user.

cs.CL · cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37599

MoI-Bench: A Benchmark for Image Generation and Editing on Mobile Devices

Kailai Feng, Haoxiang Rao, Yuxiang Wei, Bo Chen, Junsong Li, Yang Pan, Chengkun Cai, Jagmohan Chauhan

原文摘要

Developing on-device image models requires considering practical user needs, task types and device resource constraints. However, existing benchmarks provide limited guidance on identifying capability gaps between on-device models and server-side models. We introduce MoI-Bench, a benchmark grounded in mobile user needs, comprising 64 subtasks and 1150 samples. It defines 16 operations, such as removal and replacement, and eight visual target categories covering content such as portraits and text. These form two orthogonal axes whose applicable combinations define specific tasks, such as removing facial makeup. To better guide model improvement, we further propose MoI-Eval, which introduces task-specific criteria covering instruction following, visual quality and detail preservation. We further construct sample-level evaluation questions and aggregate their scores into overall quality and capability profiles along the operation, target and criteria axes. We evaluate representative on-device and server-side models and measure mobile deployment costs. Results show that this decoupled Operation x Target design provides finer-grained guidance for improving compact models. For example, it highlights the need to prioritize specific capability gaps (e.g., text-replacement) over broad application-level goals (e.g., improving text editing). Unlike conventional benchmarks that rank models using a single score, we introduce a two-dimensional Quality-Deployability capability map to assess models for practical mobile use. The map shows that current on-device models can achieve efficient generation or editing but expanding their task coverage deserves greater attention than further improving deployment efficiency. Further progress requires improving editing quality, particularly identity preservation and text rendering, while broadening task coverage.

cs.CV · cs.AI

查看论文
arXiv ↗PDF ↗