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新论文 · v1arXiv:2610.34548

Inductive Grounded Arithmetic: Goodstein, the Hydra, and $Γ_0$

Bryan Ford

原文摘要

What does a recursive definition mean when the logic is grounded, so a sentence is true only if a terminating process makes it so? Kripke's answer for the special case of truth was the least fixed point. This paper takes that answer as a rule. Reflective Grounded Arithmetic (RGA) already writes any recursive definition as one self-referential term, paradoxes included. Inductive Grounded Arithmetic (IGA) adds the elimination rule for every positive one, so that accessibility, the winning positions of a game, and grounded truth itself each come with their own induction principle. The rule is sound for the omega model and the system is consistent, both machine-checked in Isabelle/HOL. With the rule, IGA proves Goodstein's theorem outright, in a logic without implication at an undecided hypothesis, and the Kirby-Paris hydra game for primitive recursive strategies, both with every step of the arithmetic a grounded derivation. It proves the Cantor normal forms well-founded, from which the well-foundedness of the standard $\varepsilon_0$ notation system in HOL follows, and it certifies the Veblen notation as an ordinal below every $\varphi(n,0)$. It defines Kripke truth for RGA's own language as one fixed point that agrees with the omega model exactly. Two equivalences locate what the classical world has and the grounded one does not. IGA proves the whole Veblen notation well-founded exactly when it admits bivalence of accessibility for that order, and bivalence of accessibility for an order is sound exactly when the order is well-founded; so $Γ_0$ lies at most one instance of excluded middle away, at the accessibility predicate of the notation, and that instance, the well-foundedness sentence and one membership claim in a primitive-recursive form of Kleene's $\mathcal O$ are interderivable over IGA.

math.LO · cs.LO · cs.PL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.19139

Managing Shared Hospital Capacity for Medical Tourism: Patient Behavior and Hybrid Simulation

Melika Baghi, Hadi Mosadegh

原文摘要

Hospitals that serve medical tourists alongside local patients share beds, specialists, and appointment slots between two groups for whom waiting has different consequences. Operations research models are widely used for hospital capacity and scheduling, but often do not explicitly capture patient behavior; agent-based simulation can represent such behavior alongside discrete-event patient flow, while medical-tourism research still mainly treats traveling patients as cases to be scheduled. We develop a hybrid simulation that represents channel choice, physician switching, treatment abandonment, and medication-related deterioration in a multi-specialty international patient department. A 256-run fractional factorial experiment screens 16 controllable factors across six performance measures, followed by a replicated comparison with a discrete-event-only version without these behaviors. At a bed-constrained configuration, mean inpatient admission waiting time is 42% lower in the hybrid model (14.8 versus 25.8 days in the discrete-event-only model, p < 0.001) and 25% to 64% lower under two further conditions. Lacking these behaviors, the simpler model shows no abandonment and fewer emergency escalations. The comparison shows how results depend on model structure; neither variant is validated against observed waiting times. A bed-sharing routine in both variants adds no beds as implemented, so bed sharing is not evaluated. The screening identifies bed capacity as the most consistent driver of waiting; priority rules, channel design, and appointment timing also matter, while specialist capacity has less effect on waiting. Managers should review bed capacity and admission rules before adding specialists or tightening schedules.

cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34545

VTC-AUDIT: An Interventional Reliability Audit for Visual-Token Compression under Paired Image Shifts

Isaac, Li

原文摘要

Visual-token compression is usually evaluated by clean accuracy and efficiency, but these averages do not reveal failures that compression introduces under an image shift where the uncompressed model stays correct. We introduce VTC-AUDIT, which estimates this incremental risk only on pairs where the full model is correct on both images and the compressed model is correct on the clean image. The four arms then cross two binary choices: which image selects the retained token positions, and which image supplies the visual features. We audit three open 8-9B vision-language models on four tasks and eight pre-specified shifts. We evaluate two training-free compressors---hard-pruning DivPrune and prune-and-merge ApET---at a matched 25% retained-position budget, producing 708,000 inference records. Reselecting positions on the shifted image, rather than reusing the clean-image positions, raises the failure rate in all six model-compressor settings. The increase is 3.04-9.60 percentage points for DivPrune and 2.05-4.20 percentage points for ApET, with every 95% interval above zero. Repeating the audit on a separately resampled second manifest reproduces all six increases. In a pooled monitor audit across the four benchmarks, support Jaccard instability fails the practical AUROC criterion of 0.65 in all six settings, whereas clean compressed-model uncertainty clears it. Thus, support turnover is a weak ranking signal under the audited benchmark mixture, while clean compressed-model uncertainty is substantially more informative. These results suggest that future visual-token compression evaluations should report conditional reliability alongside clean accuracy and efficiency.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.01799

Designing Versatile Samples for Learned Trajectory Scoring

Yaguang Li, Jiaru Zhang, Chuheng Wei, Can Cui, Ziran Wang

原文摘要

Many current end-to-end driving policies emit a pool of candidate trajectories and select one, which makes selection a separable component: a scorer can be retrained while the planner, its backbone, and its trajectory generator all stay frozen. However, many strong planners concentrate their proposals around safe mode, providing limited supervision near decision boundaries. In this work, we design a training dataset that provides more informative supervision for the scorer. In particular, we construct two generators that perturb the logged human trajectory along the two axes a vehicle can be displaced: laterally toward the drivable boundary and longitudinally toward a leading vehicle. The designed dataset produces more informative positive and negative samples than the base planner's proposal pool. We attach a transformer-based scorer to two frozen generative planners, DiffusionDrive and MeanFuser, and train it on the NAVSIM navtrain dataset. The results of the experiments show that we achieve 90.1 EPDMS on DiffusionDrive and 90.4 EPDMS on MeanFuser when using ResNet-34, with 0.4 and 0.3 EPDMS respectively, from the designed training dataset. The code is publicly available at https://github.com/PurdueDigitalTwin/Versatile-Scoring.

cs.RO · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.19705

Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes

Mengxiao Wang, Nitesh Saxena

原文摘要

Large language model (LLM) agents already trade real capital, and builders copy a few published designs that share models and news feeds, so one shock reaches many agents at once. We present FARSIGHT (Financial Agent Robustness and Security Investigation and Global Holistic Taxonomy), a systematization that rates trading agents on robustness to market stress and on security against source manipulation, agent compromise, and agents acting as attackers, and checks the ratings against released code. Across 15 academic schemes, no paper describes a halt that skips LLM inference or combines source provenance with cross-source corroboration, and the 7 code releases enforce no stop-loss or position limit outside the LLM. Three LLMs sell whole positions on a fake headline and on real hoaxes, including the fake press release behind the 2000 Emulex crash. In a simulated market driven by these decisions, one fake headline crashes the market in all runs with 400 agents, and 400 agents that sell when prices fall nearly double a 2010-sized crash. The pause that U.S. exchanges apply to large stocks starts in no headline run, whereas automatic halts and hard loss limits outside the model remove both effects. Restated as a domain-independent threat model, FARSIGHT maps the same gaps onto agents in healthcare, law, software, and crypto, where compromised agents spread harm as worms and as cascades. We derive 14 open problems and urge deployers to corroborate sources, keep interrupts that bypass the model, and enforce loss limits outside it.

cs.CR · cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30751

How Firm Are AI Models' Ethical Boundaries? A Pay-Stub Editing Test of 15 Models

Simiao Ren, Xingyu Shen, Tommy Duong, Tsang Ng, Ankit Raj

原文摘要

Can a small change in wording move an AI model from refusing a sensitive request to acting on it? We test 15 models on the same request to increase the income shown on a pay stub. Across 445 observable trials, models issue editing calls in 14.1% of cases when a loan purpose is stated, 33.1% when the purpose is omitted, and 66.2% when the change is described as fixing a printing error. Astra refuses all ten loan-purpose requests but issues calls in all ten purpose-omitted trials. Fable, Opus, and Muse Spark issue none across all three wordings; Sonnet issues some under the latter two. Response excerpts illustrate different ways of refusing, redirecting, or justifying an edit. The file is a marked test document and all tools are simulated. These results probe how boundaries behave in one setting; they do not prove an overall ethics ranking or that a harmful real-world edit succeeded.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34544

Joint Independence Testing for Latent Position Random Graphs

Xiaoyi Wen, Zoltan Szabo

原文摘要

Kernel techniques are among the most powerful methods in data science and statistics. One of the key properties of kernels contributing to their numerous successful applications is that the associated reproducing kernel Hilbert space is expressive enough to encode probability distributions without loss of information or to capture the joint independence of $M\ge 2$ random variables, under mild conditions. Computationally, these measures (i) rely on the so-called Gram matrix which contains the pairwise similarity of sample points, and (ii) converge at a rate $O \left(n^{-\frac{1}{2}}\right)$, where $n$ denotes the sample size. We focus on measuring the independence of latent position random graphs (LPRGs), a model widely-applied for instance in neuroscience, sociology, and epidemiology. In LPRGs a latent representation is associated to each node. The similarity of these representations (meant via a kernel) determines whether two nodes are connected, making the Gram matrices only implicitly observable and the estimation challenging. Despite the fundamental importance of quantifying the independence of $M$ LPRGs, very little is known about the solution of this problem; existing techniques are restricted to $M=2$ components and only guarantee consistency without rates. In this work, (i) we define an independence measure called graph Hilbert-Schmidt independence criterion (GHSIC) for $M\ge 2$ LPRGs, (ii) we prove that it can be estimated at a rate $\tilde{O}\left(n^{-\frac{1}{3}}\right)$ relying on spectral decomposition of adjacency matrices, (iii) without the need to choose kernels and their hyperparameters. Numerical experiments demonstrate the effectiveness of the proposed GHSIC estimator both on synthetic examples and in the analysis of brain functional connectivity subnetworks in natural language perception.

stat.ML · cs.LG · cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34542

LLM WikiLeaks: Uncovering URL-Based Capability Extension and Security Circumvention in Agentic Message Boards

Orel Navi, Elad Feldman, Ben Nassi

原文摘要

Autonomous agents have been observed acting beyond their assigned tasks, accessing unauthorized systems, and establishing communication channels to exchange information, yet the Web and network-level techniques they use to extend their capabilities and circumvent security restrictions remain poorly understood. We study these techniques through the ProWiki incident, in which agents restricted to outbound HTTP GET requests repurposed a public German-language wiki as a message board. We analyze 14,591 agent edits containing 79,541 URL postings and present corroborating evidence that the published URLs reflect the agents' operational activity. We find that 26,717 URL occurrences (33.6%) embed target URLs within one to five layers of third-party Web services. By recursively reconstructing these nested URLs, we identify 67 distinct intermediate services and classify their functionality. Most intermediate layers (73.8%) extend agents' capabilities by delegating content extraction and transformation to external Web services: Markdown and OCR services convert web content into machine-readable text, JSON filtering services extract selected fields, and archival services expose historical versions of pages. The remaining 26.2% facilitate circumvention of access restrictions and evasion of detection, including CORS and fetch proxies that retrieve content on agents' behalf, and link shorteners and redirectors that hide destination URLs from mechanisms inspecting literal URLs or outer hostnames. Agents also reach the same targets through multiple URL paths, consistent with attempts to evade rate limits or obscure request origins. Our results expose a gap between the capabilities granted to agents and those they can obtain by composing publicly available Web services: restricting an agent's local toolset or direct Internet access may be insufficient while third-party services remain reachable.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30758

Can Loan Processors and Underwriters Spot AI-Edited Statements? An Empirical Study

Simiao Ren, Xingyu Shen, Tommy Duong, Ankit Raj

原文摘要

AI-assisted editing can produce loan documents that preserve familiar issuer layouts while changing the applicant's identity. We investigated whether experienced underwriters and loan processors identify such forgeries in first-pass, document-only review. Three practitioners recruited through Upwork each received one applicant package drawn from 134 forged PDFs derived from 47 genuine financial documents supplied with full consent. The edits preserved amounts, dates and balances; identifier inconsistencies introduced by the pipeline were knowingly retained before distribution. Reviewers were not told that files had been altered and returned 132 file-level judgements. Checking their explanations against the originals yielded 14 edit-supported concerns (10.6%): 12 Suspicious judgements and two Insufficient information judgements. All supported concerns involved identifier disagreements within or across documents, and no reported visual cue identified an actual edit. Thus, approximately one in ten judgements contained evidence of the forgery, despite known inconsistencies remaining in the files. These observations motivate evaluating consistency checks and independent source verification alongside human review. With three reviewer-package pairs and no genuine-file controls, the findings describe this exploratory setting rather than population-wide detection accuracy.

cs.AI · cs.CR · cs.HC · cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.09761

Certified Split Windows for Parallel Lexing: Recovering Boundaries Where No Byte Certifies

Nicklas Nidhögg

原文摘要

A certified split point lets a parallel lexer cut unlexed input at a single byte with the serial token stream provably preserved, but several conventional token sets in the predecessor's controlled study certify no byte once string, comment, or whitespace-run forms are included (arXiv:2608.03473v2). We generalize from a byte to a bounded window, a byte string after which the position where the current token began is known, regardless of surrounding context. We certify the directly usable form of that recovery, namely that the token covering the window's final byte begins at the reported origin. The certificate is conditional on occurrence and may be vacuous; every applicability figure counts only windows carrying an asserted completely tokenizable occurrence witness. We give a conservative model of a maximal-munch scanner's possible histories across a window, prove it sound, and decide reachability in that model exactly by exhausting a finite quotient of its reachable configurations. Within the flat, completely tokenizable scope, model-positive answers are semantic certificates; negatives are relative to the model, which deliberately refuses some windows a greedy scanner would allow. Whether the model certifies any window, plain or witnessed, is PSPACE-complete, and a shortest window can need $2^{Ω(\sqrt{n})}$ bytes over n live states. Two smaller searches decide plain model existence, the second shipped in release 2.2.0 of munch. In a sample of 400 random token sets, 322 of the 337 sets certifying no byte gain a witnessed window, with zero inconclusive searches, and every exact-empty row of the predecessor's study gains a witnessed window of two to four bytes. The analysis runs once after automaton construction, using only the compiled tables and no input. A boundary profile marks each gap of a window forced, crossed or open, and a window-local cut is sound exactly at forced gaps.

cs.FL · cs.DC · cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34538

Dual Gradient Consensus for Adaptive Learning to Transient Out-of-Distribution Disturbances in Quadrotor Control

Mingtian Tan, Vasileios Tzoumas

原文摘要

Quadrotor trajectory tracking under unmodeled disturbances often relies on online residual adaptation, but short out-domain residuals can corrupt the learned disturbance model when constant-step updates treat every residual gradient uniformly. We propose Dual Gradient Consensus (DGC), an adaptive-learning method for mixed in-domain/out-domain disturbances that retains the random Fourier feature model, offline BPTT policy, and least-squares adaptation of a prior domain-adaptive residual policy (DAPL) while modifying the online update step size. DGC compares each residual gradient with long- and short-term gradient memories, classifies the current adaptation regime, and uses a confidence map to suppress unreliable updates while recovering adaptation after persistent domain shifts. We evaluate DGC in numerical quadrotor trajectory tracking under four residuals that mix an in-domain component with short out-domain events. Compared with $L_1$-augmented MPC, RFF-based MPC, constant-step DAPL, and a frozen-parameter baseline, DGC achieves the lowest mean position RMSE in all four cases.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2503.09762

Optimal regret for dynamic matching with static priorities

Süleyman Kerimov, Pengyu Qian, Mingwei Yang, Sophie H. Yu

原文摘要

We study centralized two-way dynamic matching markets in which agents arrive stochastically over time and wait in queues associated with their types. We focus on static priority policies that assign each agent type a fixed ranking over its neighbors and make online matching decisions using only queue availability. We propose the General Priority ($\mathbf{GP}$) policy, which builds on the Tree Priority ($\mathbf{TP}$) policy introduced in [KAG25]. Under the general position gap condition, $\mathbf{GP}$ selects matches using a nondegenerate optimal basic solution of the fluid relaxation. It prioritizes children over parents within trees and consistently gives clockwise neighbors priority over counterclockwise neighbors on each cycle. We establish the optimal all-time regret scaling $O(ε^{-1})$ for $\mathbf{GP}$ on general two-way matching networks, where the general position gap $ε$ measures the fluid relaxation's distance from degeneracy. In particular, this resolves the conjecture by [KAG25] on the optimal $ε$-dependence for $\mathbf{TP}$.

cs.DS · math.OC · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34537

STEP: Soft Trajectory Estimation of Prediction Correctness via Per-Step Probability Scoring

Chenyang Yu, Zifan Zeng, Qunli Zhang, Feng Liu

原文摘要

Estimating the correctness of large language model (LLM) predictions without access to ground truth is critical for reliable deployment in high-stakes settings. Existing approaches, such as entropy and perplexity, operate solely on final output statistics, discarding the rich information encoded in intermediate reasoning steps. To address this limitation, we propose STEP (Soft Trajectory Estimation of Prediction correctness), a training-free framework that scores each reasoning step via lightweight forward passes to obtain a soft probability distribution qi over candidate answers, from which we derive a set of soft trajectory features capturing per-step confidence and its progression toward the final answer. Extensive experiments on various benchmarks demonstrate consistent and substantial improvements, particularly in AUROC, over output-based baselines and hard-indicator trajectory methods. Notably, Temporally-Weighted Consistency, which assigns higher importance to later reasoning steps where answer distributions have stabilized, contributes the largest gains, highlighting the importance of temporal structure in reasoning trajectories. These results further validate the advantage of preserving continuous probability signals over binary discretization.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34535

SentryBench: A Cost-Sensitive Benchmark for Adversarial Robustness of Tool-Using LLM Agents in Public-Safety Deployments

Md Abu Yusuf, Oyshi Tabassum Aditi, Md Faysal Hosain Mazed

原文摘要

Large language model (LLM) agents are increasingly wired to consequential tools that send public alerts, dispatch responders, and query citizen records, where one erroneous tool call carries real-world cost. Existing agent-safety benchmarks measure generic harm or prompt-injection success and implicitly treat refusing as the safe default. In public-safety deployments that assumption breaks: an agent too cautious to dispatch help fails as surely as one that triggers a false alert. We present SentryBench, which scores agents on a two-sided, cost-sensitive frontier jointly measuring attack success, over-refusal (missing a real emergency), and over-reaction (unwarranted action) under adversarial tool use, with scenarios grounded in real emergency-call transcripts, disaster messages, and real injection/jailbreak payloads. We formalize deployment harm as an asymmetric cost functional, prove naive caution is harm-dominated whenever it refuses more genuine emergencies than the attack harm it averts, and prove a targeted trust gate adds zero over-refusal on clean input. Across 840 scored episodes over three frontier models and three defenses, refuse-more caution drives over-refusal to 100 percent on two of three models for at most a 3.7-point change in attack success and yields the worst harm everywhere, whereas our trust-gating wrapper dominates the frontier (lowest harm on every model, over-refusal at or below baseline, higher containment). Provider content filters also silently perform part of the defending and must be reported separately.

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34533

STFT Frontend Mismatch in Pretrained Audio Spectrogram Transformers: Analysis and Adaptation

Julia Wilkins, Wei-Ching Lin, Abinaya Kumar, Shabnam Ghaffarzadegan, Juan Pablo Bellow

原文摘要

Pretrained audio spectrogram transformers are widely used as backbones of machine listening systems for sound event classification. However, they are typically trained using spectrograms with a fixed Short-Time Fourier Transform (STFT) configuration, implicitly binding the model to the time-frequency resolution seen during pretraining. In practice, adapting these systems to new domains or computational constraints often requires different STFT frontend parameters. In this work, we conduct a controlled study of the sensitivity of the foundational Audio Spectrogram Transformer to variations in STFT window and hop size, showing that audio classification performance degrades substantially as frontend parameters deviate from pretraining settings. As a mitigation strategy, we introduce a lightweight conditioning mechanism using FiLM that enables a pretrained model to adapt to variable input configurations using limited additional training data. Our method generalizes well to unseen STFT configurations, improving model robustness without sacrificing performance in the original setting.

cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34532

Multi-view Semantic-aware Resource Allocation for Edge AI

Jiawei Luo, Hussein Saleh, Mohamad Assaad

原文摘要

Semantic communication is emerging as a pivotal paradigm to overcome wireless resource scarcity in 6G networks. This paper proposes a novel end-to-end framework for semantics-aware resource allocation in multi-user Edge AI. We extend the Opportunistic Information Bottleneck (OIB) to a multi-view setting, termed the Multi-view Gaussian Information Bottleneck (MGIB), by fusing complementary source views without variational approximation or network retraining. A resource allocation framework is then formulated for joint compression-level selection, transmission mode selection (i.e., the number of active sources per group), and PRB assignment. The problem is formulated as a Mixed-Integer Linear Program (MILP), solved efficiently via a proposed Lagrangian Relaxation (LR) algorithm, and benchmarked against the NP-hard optimal solution. Simulations demonstrate that the proposed LR scheme achieves near-optimal utility, and doubles PRB efficiency versus average allocation.

cs.IT · cs.LG · eess.SP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29219

PerSPL: Personalized Semi-supervised Preference Learning

Xiaoan Xu, Tingzhou Wei, Juncheng Dong, Korel Gundem, Ethan X. Fang, Zhengling Qi

原文摘要

Personalized reward models require user-specific preference comparisons, but collecting such feedback is costly and provides only limited supervision for each user. At the same time, large pools of responses can be generated without user identities or preference labels, yet existing personalized reward models leave this data unused. Directly learning from these responses is difficult because pairwise objectives constrain relative scores within a prompt, whereas pseudo-labeling individual responses requires scores that are comparable across prompts for the same user. We propose \textbf{PerSPL} (Personalized Semi-supervised Preference Learning), a backbone-agnostic framework that uses pair-mean score centering to stabilize this cross-prompt score coordinate and iteratively turns unlabeled responses into user-specific training signals. Against seven representative baselines across four benchmarks---PersonalLLM, HelpSteer2, Reddit TLDR, and Chatbot Arena---PerSPL achieves the highest reported mean accuracy among the evaluated methods for both seen and unseen users. The two latter benchmarks provide real-user preference comparisons in summarization and open-domain chatbot interaction, respectively. These results indicate that unlabeled responses can provide additional supervision for personalized reward modeling when per-user preference labels are scarce.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.22648

Spatially Aware Dictionary-Free Koopman Eigenfunction Identification for Modeling and Control

David Grasev

原文摘要

A spatially aware dictionary-free eigenfunction discovery (SADFED) framework is proposed for identification of low-rank Koopman models from data without prescribing a lifting dictionary, kernel, or neural-network eigenfunction architecture. A reference trajectory is selected and used to determine the Koopman modes by regularized least squares (LS). Then, a transformed temporal basis allows the eigenfunction values at all sampled initial conditions to be obtained by a second regularized LS projection. Consequently, only the real and imaginary parts of the eigenvalues remain as the optimization variables. Interpolation of the identified eigenfunction samples reveals their spatial structure, enabling numerical estimation of their gradients. A joint objective combines trajectory reconstruction error with a normalized Koopman partial differential equation (KPDE) residual, promoting spatial consistency with the KPDE over the sampled region and serving as a physics-informed regularizer. The method is evaluated on a system with analytical Koopman eigenfunctions, the FitzHugh-Nagumo system, the van der Pol oscillator, the Duffing system, and a two-spool turbojet engine. The examples demonstrate recovery of known eigenfunctions, sensitivity to reference trajectory and hyperparameters, limit-cycle harmonics and isochrons, discontinuous indicator eigenfunctions and isostables, symmetry exploitation, and construction of state-dependent lifted input dynamics. For the turbojet example, the identified model is further used for state estimation and design of a gain-scheduled tracking linear quadratic Gaussian controller. The results indicate the applicability of SADFED to Koopman spectral identification and control-oriented modeling of nonlinear dynamical systems.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34529

Acute Stroke Classification Using Asymmetric Data Standardization and Orthogonal-Focal Loss Optimized CNN-ViT Cross-Attention Fusion: A Multicenter External Validation Study

Tansel Uyar

原文摘要

Background/Objectives: Rapid differentiation of ischemic and hemorrhagic stroke subtypes is vital for acute management. Although current deep learning models achieve high single-dataset accuracy, they suffer from "shortcut learning" and domain shift in external validation. This study proposes a hierarchical computer-aided diagnostic (CAD) system mimicking emergency triage. Methods: We introduce a 3-channel Radiology-Focused Preprocessing pipeline that feeds radiological Hounsfield Unit (HU) windows into Convolutional Neural Network (CNN) and Vision Transformer (ViT) backbones via asymmetric formats (.mat and .png). The architecture utilizes a novel joint cross-attention fusion network optimized by an Integrated Orthogonal-Focal Loss. Results: Our system achieved a 95.48% F1-score on the internal stroke dataset. However, multi-center external validation (AISD, BHSD, PhysioNet) using a Mask Anchor filter revealed a drop to the 58-62% range, exposing the "internal validation illusion" caused by hardware noise. Critically, models exhibited high precision (77%) but low sensitivity (57%) for ischemic stroke, proving prone to hemorrhage overdiagnosis. Multi-seed variance analysis demonstrated that the DenseNet201+ViT backbone yielded the lowest deviation (0.59) and highest stability, making it the most reliable architecture for real-world clinical integration. Conclusions: To overcome diagnostic challenges, future research must focus on multimodal architectures that combine 3D volumetric extraction with clinical data.

eess.IV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.17384

IndustryNav: Evaluating Spatial Reasoning and Safety of Embodied Agents in Dynamic Industrial Navigation

Yifan Li, Lichi Li, Anh Dao, Xinyu Zhou, Wenjun Huang, Tianyi Ma, Yicheng Qiao, Zheda Mai 等 18 位作者

原文摘要

Visual large language models (VLLMs) are increasingly used as embodied agents, yet industrial warehouses demand more than reaching a goal: agents must navigate safely around moving workers and vehicles. To evaluate embodied agents' goal reaching and safety, we introduce IndustryNav, a simulation environment with 24 warehouse scenes for closed-loop navigation to specified destinations. Alongside task success and efficiency, we introduce safety-aware metrics that capture collisions and near-collision warnings along each trajectory. Experiments with eleven leading open- and closed-source VLLMs and three navigation baselines show that agents can reach their goals yet collide with moving obstacles, exposing a gap between success and safe navigation. This gap matters in safety-critical settings, where a single collision can end the task. IndustryNav also supports policy learning through automated oracle trajectories and simulator interaction, with held-out scenes for generalization tests. These experiments show that policies can succeed on new routes in familiar warehouses yet fail to transfer to held-out layouts, making cross-scene generalization an open challenge.

cs.RO · cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.03473

Certified Split Points for Parallel Lexing: Exact and Modulo Discarded Tokens

Nicklas Nidhögg

原文摘要

Table-driven DFA lexing is sequential, since each transition depends on the previous byte's state. Scanning one input in parallel needs each chunk's entry state, which existing methods recover by simulation, speculation, prescanning, or overlap. We give two conditions under which none is needed. For a longest-match scanner restarting from q0 at every token boundary, a byte b is a certified split symbol when no reachable state other than q0 has a b-transition whose target can reach acceptance, and q0 is not re-entrant if it has one. Every occurrence of such a byte in completely tokenizable input begins a token, so chunks starting there reproduce the serial sequence of kinds and lengths. The condition is necessary and sufficient, but one string, comment, or whitespace run can remove every useful certificate. Comments and whitespace are usually discarded, so we weaken the guarantee to equality after deleting a declared discarded set and give a second condition, a precomputed constant-time one-bit test. It is sound but incomplete and more permissive, strictly so on suitable token sets and discarded sets. It recovers newline for a conventional C-like tokenization and tab, newline and carriage return for JSON, and refuses newline where block comments are unrestricted. Safety modulo the discarded set is decidable in PSPACE for every token automaton and in polynomial time for literal vocabularies, and every unsafe byte has a replayable counterexample. On the fifteen token sets studied the second condition is already exact. Splitting at exact certificates in munch reaches 92.6-95.3% parallel efficiency at eight threads on a restricted CPU set and a 512 MiB corpus, and a 3.46-3.94x end-to-end speedup at four threads, on one machine. It turns delimiter-based parallel lexing from a language-specific assumption into a property a compiler checks.

cs.FL · cs.DC · cs.PL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29802

Prefix Visibility Ratio: Quantifying Partial Observability in Carpet Bombing Detection

Farid Naït-Abdesselam, Laaziz Lahlou, Doha Bentaoussy, Hugo Lege, Eve Lin, Nadjia Kara

原文摘要

Carpet bombing attacks distribute malicious traffic across many destinations within a network prefix, while operational monitoring commonly provides destination-specific telemetry for only a subset of those targets. This creates an attack-conditional observability problem that is not captured by address utilization or nominal monitoring coverage. We introduce the Prefix Visibility Ratio (PVR), defined as the fraction of attacked destinations for which the telemetry required by the host-level evidence plane is available, and argue that PVR should be treated as an explicit evaluation dimension for carpet bombing detection. We then present VANTA (Visibility-Aware Network Threat Analysis) as one concrete way to operationalize this principle. VANTA combines host-level and prefix-level anomaly scores, estimates PVR online from router-side destination evidence and the monitored-destination inventory, and adjusts evidence fusion according to both estimated visibility and estimator confidence. Across a validated campaign of 2,160 Mininet runs, VANTA achieves 99.18% precision, 93.46% recall, a global F1-score of 96.24%, and a 1.54% false-positive rate. In a separate matched-observability study using task-adapted feature-level reference implementations, VANTA reaches 95.89% pooled F1 and 92.39% recall for configured PVR<=10%, with its largest advantage appearing at configured PVR=1%. These reference comparisons are intended to isolate observability effects under a common binary detection task rather than to reproduce the original end-to-end objectives of the referenced systems. The results establish attacked-destination visibility as a measurable factor that can materially change detector behavior and should be reported explicitly in future carpet bombing evaluations.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.29109

Recognition-Refusal Misalignment in LLMs: Why Models Answer Structurally Unanswerable Questions

Yucheng Du, Xiyang Hu

原文摘要

Large language models often answer structurally unanswerable questions, such as computing cot(-540°) or evaluating (1).startswith("1"), instead of abstaining. We ask whether this failure reflects missing recognition or failed routing from recognition to abstention. Across instruction-tuned models from 1.7B to 70B parameters, a single linear direction in the hidden state separates answerable from structurally impossible math and code prompts, showing that models represent impossibility before generation. Yet this recognition direction is nearly orthogonal to the canonical safety-refusal direction that mediates trained harmful-content refusal. An in-domain behavior-defined invalidity-aware direction is closer to recognition, but only partially aligned with it, and remains near-orthogonal to safety refusal. Generation-time steering along the recognition direction changes invalidity-aware behavior bidirectionally and dose-responsively on structural math and code cells, while random directions do not. Base/instruct comparisons further show that the low-cosine geometry is already present at the pretraining endpoint. The confident-on-impossible failure is therefore better explained as a routing failure than as an encoding failure: the model has a usable "no admissible answer" signal, but the safety-refusal pathway is not aligned to use it.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34523

How Humans Learn Unfamiliar Worlds: A Think-Aloud Dataset on ARC-AGI-3

Shuzhi Liu, Archana Warrier, Dat Nguyen, Chengqi Zheng, Kevin Ellis, Yewen Pu

原文摘要

Learning through exploration and using newly acquired knowledge to achieve goals are central capabilities of general intelligence. Understanding these capabilities requires tracing how learners build, use, and revise world models during interaction. We construct the first dataset that records how humans discover and use the mechanisms of novel environments, using ARC-AGI-3 as a testbed: 144 trajectories from 64 participants across 25 public games, each pairing time-synchronized actions with think-aloud audio, plus 250 trajectories from large language model (LLM) agents with and without harnesses on the same games. We annotate mechanism triggers and recognition, action purposes, and errors and recovery, linking changes in understanding to the observations and actions that support them. We analyze characteristics of human cognition and compare them with those of LLM agents. Both groups anticipate mechanisms and use familiar analogies. Qualitative representations are more common in successful human trajectories than in successful LLM trajectories (50.0% versus 14.3%). Humans more often explore unfamiliar situations without a concrete hypothesis, while hypothesis testing is common in both groups. We also trace how learners recover through belief revision, replanning, and execution correction. As LLMs with well-designed harnesses reach saturated performance on public ARC-AGI-3 games, our cognitive analyses motivate next-generation benchmarks that challenge familiar priors, require accumulated evidence and abstraction, and test planning through subgoals, experimentation, and recovery. Our dataset is available at [https://arc3-human-think-aloud.github.io/](https://arc3-human-think-aloud.github.io/).

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34521

Supervision Without Blocking: An Evidence-Driven Redesign of OTP-Style Fault Tolerance for Cooperative Runtimes

Sam Uwejan, Erdogan Dogdu

原文摘要

Actor frameworks on mainstream runtimes advertise Erlang/OTP-style supervision, but OTP's consistency contracts assume a synchronous, single-threaded supervisor, and ports inherit that assumption's absence as unexamined risk. We evaluated every supervision guarantee against pinned OTP documentation and source read verbatim, against the source of three further Rust actor frameworks at fixed revisions, and against the actor-model literature, recording the evidence behind each design decision. Blind adversarial audits and the cross-framework study surfaced 24 defect classes, of which eight reduce to one root cause: reconstructing, by explicit mechanism, the atomicity a synchronous supervisor gets implicitly. We introduce a supervision runtime that closes all 24 cataloged classes through supervisor-owned completion values, per-incarnation identity, and a single lifecycle state machine, validated by 241 tests including reverted-fix reproductions. The result holds OTP's verbatim-verified contracts while adding two properties OTP does not offer: a supervisor that remains responsive throughout group restarts, and message handlers that are never cancelled mid-execution. We delimit the forcing guarantees no cooperative runtime can honor.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34520

Conviction Without Evidence: Verdict Stability Is Independent of Visual Grounding in Medical Vision--Language Models

Federico Felizzi, Francesco Andrea Causio, Bianca Destro Castaniti, Olivia Riccomi, Michele Ferramola, Vittorio De Vita, Alessandro Tosi, Antonio Cristiano 等 20 位作者

原文摘要

Vision--language models applied to medical question answering can reach correct diagnoses without using the diagnostic image, inferring instead from the clinical vignette. We ask whether this is visible in how \emph{firmly} a model holds its answer: does a model defend a diagnosis derived from a blank placeholder as tenaciously as one derived from a real medical image? Crossing an image-substitution protocol with two levels of adversarial pressure on 60 Italian State Examination items, we find no evidence that ungrounded verdicts are more fragile. In a paired item-level analysis, fabricated consensus (L4) overturns 94.4\% of verdicts with the real image and 98.1\% without ($Δ= +0.037$; one-sided 95\% upper bound $+0.105$); contentless doubt (L1) overturns 62.5\% and 51.8\% ($Δ= -0.107$, 95\% CI $[-0.209, -0.001]$), the point estimate running opposite to the direction a grounding detector requires. The two levels nonetheless produce opposite \emph{dynamics}: under L4 the model flips once, always to the exact option argued for (110/110 first-turn flips), and then freezes, averaging 1.9 verdict changes over ten turns; under L1 it never settles, averaging 7.4 changes. Instability under contentless doubt tracks intrinsic item ambiguity rather than evidence availability, and so does not serve as a grounding signal. Flips are almost entirely corrupting (99/100 across both levels). The model is not unaware of the missing evidence---it flags the blank image at baseline on 35 of 60 items and abstains on 4--6---yet those abstentions do not survive contact with pressure. None of the epistemic signals a user could plausibly consult distinguishes a grounded diagnosis from a confabulated one.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34519

TRIAGE: Efficient Vision-Language-Action Inference Needs Both Reuse and Pruning

Sooyoung Kwon, Carlee Joe-Wong, Kyunghan Lee

原文摘要

Vision-language-action (VLA) models are increasingly used for robot manipulation because they map camera images and a language instruction directly to actions. Robots that cannot be tethered to a workstation, such as mobile manipulators and humanoids, must run them on onboard embedded boards, where the prefill latency of the language backbone grows with the hundreds of visual tokens processed at every control step. To meet real-time limits, training-free accelerators recompute only a subset of the visual tokens and skip the rest, either by pruning them or by reusing their cached key-value (KV) states. The two options save nearly the same computation, yet existing methods apply only one of them to every skipped token. Through a token-level diagnostic, we show that which option keeps the action closer to full recomputation depends on the token's drift, the change in its shallow-layer value since its cache was computed. On both backbones tested, reusing tokens below a drift threshold was no worse than pruning them, whereas beyond it, reuse yields up to 4.4x the action error of pruning; the same diagnostic calibrates this threshold per backbone. Building on this finding, we propose TRIAGE, which deems a cache valid when its drift is below this threshold and it is not too old. TRIAGE recomputes a fixed budget of tokens, prioritizing important tokens with invalid caches, reuses every other token with a valid cache, and prunes the rest; prune-only and reuse-only methods are the two extremes of this partition. Because the budget fixes the number of recomputed tokens, it directly sets the prefill latency. On LIBERO and SimplerEnv, TRIAGE stays within 3 pp of full recomputation in average success rate while cutting prefill latency on a Jetson AGX Orin by up to 48%, and with only 7% of visual tokens recomputed, it beats the strongest baseline by 24.4 pp on LIBERO-Object.

cs.RO · cs.AI · cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2501.02624

Simultaneous analysis of approximate leave-one-out cross-validation and mean-field inference

Pierre C Bellec

原文摘要

Approximate Leave-One-Out Cross-Validation (ALO-CV) is a method that has been proposed to estimate the generalization error of a regularized estimator in the high-dimensional regime where dimension and sample size are of the same order, the so-called ``proportional regime''. A new analysis is developed to derive the consistency of ALO-CV for non-differentiable regularizers under Gaussian covariates and strong convexity. Using a conditioning argument, the difference between the ALO-CV weights and their counterparts in mean-field inference is shown to be small. Combined with upper bounds between the mean-field inference estimate and the leave-one-out quantity, this provides a proof that ALO-CV approximates the leave-one-out quantity up to negligible error terms. Linear models with square loss, robust linear regression and single-index models are explicitly treated.

math.ST · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34516

Propositional formula minimization is $Σ_2^p$-complete under many-one reductions

Reijo Jaakkola

原文摘要

The Minimum Equivalent Expression problem (MEE) asks, given a propositional formula $φ$ and an integer $k$, whether $φ$ is equivalent to a formula with at most $k$ occurrences of propositional variables. While the problem is clearly in $Σ_2^p$, its completeness under polynomial-time many-one reductions has remained open since at least 1979. Buchfuhrer and Umans showed in 2008 that it is $Σ_2^p$-complete under polynomial-time Turing reductions. In this note, we present a proof discovered by GPT-Sol-6.1 which establishes that MEE is $Σ_2^p$-complete also under polynomial-time many-one reductions.

cs.CC · cs.LO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2411.08550

Graph Neural Networks for Supply Chain Management: A Framework and Real World Benchmark

Azmine Toushik Wasi, MD Shafikul Islam

原文摘要

Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical Machine Learning and other Deep Learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.

cs.LG · cs.CE · stat.ML

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arXiv ↗PDF ↗