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新论文 · v1arXiv:2611.04783

Who Is Doing the Thinking? Characterising Human Agency in First-Year Students' AI Interactions

Ali Shakiba, Thomas Chaffey

原文摘要

When students use generative AI to produce their work, the learning process becomes invisible. Established frameworks such as Bloom's taxonomy then classify the cognitive level of what the AI produced, not what the student contributed. We propose the Human Agency Depth Taxonomy (HADT), a preliminary six-tier taxonomy that classifies the engineering contribution a student makes in a prompt, independent of the AI's output. HADT combines three dimensions: contextual bandwidth, engineering abstraction and student agency. We applied HADT to 962 prompts from 105 students' structured AI Journals in an AI-permissive, first-year software engineering course of 145 students, and examined how agency changed over ten journalled weeks. Most prompts (84.2%) carried some student contribution beyond delegation. Agency tracked the development lifecycle. Architecture-discovery prompts dominated the requirements phase (48% of turns) and fell to 5-12% from week 9, while prompts analysing existing systems rose during build and verification (odds ratio 2.88, with turns clustered by student). Two-thirds of the variance in tier lay within students, so agency depended more on the phase of work than on the student. A turn-level rise in delegation in deadline weeks did not hold once turns were clustered by student. Explicitly specifying constraints on the AI's output was rare throughout, which we interpret as a knowledge bottleneck for novices rather than a gap in instruction. We discuss implications for course and assessment design, and for using HADT as a formative lens on how students engage with AI.

cs.SE · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04782

Reducing Logprob Without Causal Verification: Diagnosing SAE-Based PII Suppression in LLMs

Chaerin Lee, Wonho Lee, Sohee Park, Daeseon Choi

原文摘要

SAE-based feature-level interventions to suppress PII leakage in LLMs are primarily evaluated by log-probability reduction. We show that this criterion cannot distinguish feature specific suppression from a magnitude artifact: a direction-agnostic effect of sufficiently large activation perturbations. Across 36 (model, layer, PII type) cases, we compare selected SAE feature interventions with coherencematched random direction controls using logprob and rank-aware metrics. While 17 cases (47%) appear successful under logprob-only evaluation, only 1 case (2.8%) passes our multimetric framework. The rest reveal two failure modes: random directions outperform selected features, and feature selection collapse across PII types. These results indicate that existing evaluations cannot verify whether interventions target causal pathways of PII leakage. A 9,000 run causal mediation feasibility study corroborates this: the verifier separates candidates reliably, but correlation-selected candidates rarely pass it. We therefore propose a new research mission for NLP: causally grounded PII suppression through circuit-level localization and causal mediation analysis.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.27422

Approximation Algorithms for Directed Telephone Multicast

Shahin Kamali, Seyed-Mohammad Seyed-Javadi

原文摘要

Telephone multicast is a classical problem in information dissemination. In the directed setting, a message starts at a source in an $n$-vertex digraph, and each informed vertex may call one out-neighbour per round. The goal is to inform a given set of $k$ terminals in the fewest rounds. Despite previous work on telephone multicast, a substantial approximation gap remained in the directed setting: approximating the problem within a factor of $o(\log k)$ is NP-hard, while the best existing polynomial-time approximation guarantee depending only on $k$ was $\widetilde O(\sqrt{k})$. For every integer $\ell\ge2$, we give a deterministic combinatorial algorithm that runs in $n^{O(\ell)}$ time and returns a schedule of length $O(\ell^3\log k)(OPT+k^{1/\ell})$, where $OPT$ is the optimum number of rounds. This yields an $O(k^ε)$ approximation in time $n^{O(1/ε)}$ for every fixed $ε>0$, and an $O(\log^4 k)$ approximation in quasi-polynomial time. The same algorithm gives an $O(\log k)$ approximation whenever $OPT\ge k^ε$ for a fixed $ε\in(0,1)$, which is optimal up to a constant factor unless $\mathrm{P}=\mathrm{NP}$. On planar digraphs, we obtain a deterministic polynomial-time $O(\log^2 k)$ approximation. More generally, we give an $O(p\log^2 k)$ approximation for minor-closed graph classes with the following separator property. Given any rooted spanning tree, a balanced separator can be computed in polynomial time as the union of all vertices on at most $p$ tree paths from the root to chosen vertices. Classes of bounded treewidth satisfy this property. We also strengthen the known logarithmic approximation hardness for directed telephone multicast, extending it from instances with a polynomially large optimum to instances with $OPT=O(\log k)$.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2407.10780

Correlations Are Ruining Your Gradient Descent

Joshua Offergeld, Nasir Ahmad

原文摘要

Natural gradient descent, and the preconditioned optimizers which have followed from it, correct gradient descent by estimating curvature and applying its inverse to the gradient. For any single layer of a deep neural network, this correction can be shown to approximately separate into two factors: one which accounts for correlations in the gradient signal, and one which accounts for correlations in the data arriving at that layer. Herein we show that this second factor can be expressed as a change of basis rather than of curvature and, considered thus, it can be removed from the optimizer entirely and placed within the network itself. Specifically, we show that, once forward weights are corrected, right-sided natural gradient descent can be implemented in a form which has three exactly equivalent forms: a gradient preconditioner, a decorrelating linear transform in the forward pass, or a set of recurrent dynamics in which all updates are Hebbian and rely only upon locally available signals. The (recurrent) decorrelation method we discover aligns closely with proposed methods for inhibitory learning and homeostatic plasticity in the brain. Notably, our transform requires no matrix inverse, no matrix decomposition, and no information beyond that which is available at an individual synapse. It can therefore be applied in distributed and physically constrained systems, including neuromorphic hardware and nervous systems, where existing preconditioners cannot. We demonstrate its effectiveness empirically in convolutional and residual network models while significantly improving a range of approximate biologically plausible methods for credit assignment.

cs.LG · cs.NE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.20777

Nearly Optimal Submodular Sparsification, with and without Cardinality Constraints

Zhengting Bao, Dongrun Cai, Xue Chen, Yuchen He, Chihao Zhang

原文摘要

We study the sparsification of decomposable submodular functions: given a sum $F(S) = \sum_{i=1}^m f_i(S)$ of $m$ nonnegative submodular functions on an $n$-element ground set $[n]$ (without assuming monotonicity or symmetry), the goal is to find a sparse reweighting $\widetilde{F}(S) = \sum_{i=1}^m w_i f_i(S)$ with at most $s \ll m$ nonzero weights that preserves $F(S)$ within a $(1 \pm \varepsilon)$ factor across a target family of queries. For all subset queries $S \subseteq [n]$, we give a polynomial-time randomized algorithm that constructs a sparsifier of size $O(n^2 \log n / \varepsilon^2)$. This improves upon the previous best upper bound of $O(n^3 / \varepsilon^2)$ by Kenneth and Krauthgamer and resolves their open conjecture up to a logarithmic factor, matching the $Ω(n^2)$ lower bound from directed cuts. Motivated by capacity budgets in submodular applications and the Restricted Isometry Property (RIP) in compressed sensing, we further study submodular sparsification under a cardinality constraint $|S| \le k$, and obtain a polynomial-time algorithm that constructs a sparsifier of size $O(n k \log n / \varepsilon^2)$, nearly matching the $Ω(nk)$ lower bound. This shows that a cardinality constraint unconditionally reduces the size of general submodular sparsifiers by a factor of roughly $k/n$; we complement this with $Ω(n)$ lower bounds showing that even regular symmetric or almost-regular monotone submodular functions cannot be sparsified to $o(n)$ size for $k = 2$ and $k = 1$, respectively.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04780

LIPS-PC: Learning Illumination Patterns for Single-Pixel Classification

Tomer Porat, Talya Eden, Eliahu Cohen

原文摘要

In single-pixel (ghost) imaging, known illumination patterns probe an object and a detector with no spatial resolution returns one number per pattern. Each measurement costs acquisition time and photons and, for ionizing probes, contributes to delivered dose. Motivated by automated radiographic defect screening, where a class label suffices, we introduce Learning Illumination Patterns for Single-Pixel Classification (LIPS-PC). The patterns are learned end-to-end while the measurement operator remains one fixed linear matrix, identical for every input. We parameterize it as a residual correction to a two-dimensional discrete cosine transform (2D-DCT), $D=D_{\mathrm{base}}+Δ$, and jointly train the correction and a compact classifier using cross-entropy, without a reconstruction objective. This yields a task-driven linear embedding through optimization. We evaluate LIPS-PC numerically on MNIST and Fashion-MNIST under a controlled protocol, then on RIAWELC, a real industrial X-ray weld-radiograph dataset. Across five budgets, $M\in{8,16,32,64,128}$, corresponding to sampling ratios from below 1% to 12.5%, the learned operator outperforms the best tested fixed transform by 9-15 percentage points at $M=8$ on all three datasets. Its eight-measurement accuracy requires roughly 16-32 measurements with the best fixed transform, indicating a $2\times$ to $4\times$ saving at comparable accuracy. The advantage generally shrinks as $M$ increases, with a small reversal on RIAWELC at higher budgets. These results show that learning what to measure is especially valuable when measurements are scarce.

physics.optics · cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04779

Replica-Aware Split Inference for Edge IoT Systems

Zied Jenhani, Nasrin Razmi, Admela Jukan

原文摘要

Split Inference (SI) addresses the hardware resource limitations of Internet of Things (IoT) devices by distributing tiny Machine Learning (TinyML) model segments across multiple IoT devices. However, a device failure can interrupt the inference pipeline. This paper proposes a replica-aware recovery approach that deploys replicas of TinyML model segments originally hosted on failed devices onto surviving devices. We formulate the joint selection of replica hosts and communication protocols as a mixed-integer linear programming (MILP) problem that minimizes post-reconfiguration inference latency, accounting for computation and transmission latencies, under flash memory and runtime memory constraints. Simulations using MobileNetV2 on heterogeneous IoT networks compare the proposed approach with four baselines. We implement the model in a network of 15 IoT devices with a maximum link-rate reduction of 75%, and show that MILP approach achieves an average modeled end-to-end inference latency that is 6.8% lower than that of Computation Greedy and 18.3% lower than that of Random. Index Terms TinyML, split inference, Internet of Things, fault tolerance, replica placement.

cs.DC · cs.AI · cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.29110

Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps

RuiKang OuYang, Hanlin Yu, Xinyue Ai, Yutong He, Nicholas M. Boffi, Pradeep Ravikumar, Jose Miguel Hernandez-Lobato, Max Simchowitz 等 10 位作者

原文摘要

Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, there is a lack of similar advances in estimating the model likelihood. In particular, most existing methods either rely on restrictive architectures that enable exact calculations, or use stochastic approximations such as Hutchinson's trace estimator that introduce substantial variance. In this work, we introduce SCAlable LikeLihood distillation of flOw maPs (SCALLOP). SCALLOP builds on the recently proposed F2D2, a likelihood flow map model that can generate samples and their densities in a small number of function evaluations. While F2D2 uses Hutchinson's estimator during training, we introduce an alternative and more scalable likelihood distillation objective that is Hutchinson-free and admits a vectorized formulation. Empirically, we demonstrate the effectiveness of SCALLOP as a Boltzmann generator in molecular science, and further validate its benefit on image datasets. SCALLOP significantly reduces both training variance and training time while consistently improving performance compared to F2D2, and is competitive with the state-of-the-art while achieving up to 10x inference speedup over the fastest baseline.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.24707

Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts

Meruyert Aristombayeva, Jason S. Lucas, Chaewan Chun, Dongwon Lee

原文摘要

While text-based hallucination detection is well studied, reference-free detection of factual alterations in speech remains underexplored, especially for low-resource languages. Our spoken benchmark comprises 12,013 English, Russian, and Kazakh news samples with three synthetic alteration types and three severity levels, pairing source articles with rewrites as text, synthesized audio, and ASR transcripts. We add 290 fact-checked misinformation items collected in Russian (225) and Kazakh (65), translated into the other language and rendered through the same TTS-ASR pipeline. We evaluate fine-tuned multilingual encoders and zero-shot multimodal decoders on text, transcripts, and audio. Detectors receive only target inputs without source articles or external evidence; the task evaluates reference-free classification rather than evidence-grounded verification. Encoder degradation from source text to transcripts generally tracks per-language ASR error on the binary task. Among decoders, only Gemma-3n exceeds the binary majority-class baseline in macro-F1, on transcripts only; the other four fall below their respective baselines. Comparisons between audio and transcripts are confounded by differences in evaluation coverage and class balance. Synthetic-trained detectors achieve 0.82--0.88 macro-F1 on real-world misinformation source text; Russian provenance analysis reveals veracity-related and model-dependent machine-style signals, a key confound in synthetic hallucination benchmarks.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03260

The Cost of Mismatch: Noise Amplification in Zeroth-Order Reinforcement Learning

Lianmin Chen, Junbin Qiu, Chenxing Wei, Yao Shu, Kun He

原文摘要

Zeroth-order (ZO) optimization has emerged as a promising approach for memory-efficient large language model (LLM) post-training, yet its reliability in reinforcement learning with verifiable rewards (RLVR) remains largely unexplored. Motivated by the distinct data regimes of on-policy RL and fixed-data optimization, we develop a generic variance-theoretic framework to investigate what limits the reliability of ZO gradient estimation. Our analysis reveals that estimation reliability is structurally determined by whether the two finite-difference evaluations are performed on matched data. On-policy reinforcement learning (RL) under the mismatched-data regime produces a $1/μ^2$ oracle-noise amplification that sets an irreducible noise floor as the perturbation scale $μ$ decreases, whereas Boltzmann Targeted SFT (BOLT) under the matched-data regime eliminates this amplification via noise cancellation. We further derive convergence guarantees with explicit constants that cleanly separate the two regimes. These results motivate the ZO-BOLT algorithm, which builds on BOLT's matched-data advantage for reliable ZO optimization of an RLVR-aligned objective. Controlled perturbation-scale sweeps and matched-versus-mismatched end-to-end comparisons validate the predicted effect of data matching. Experiments across three models and two mathematical reasoning benchmarks further show that ZO-BOLT consistently improves over the pretrained baseline while retaining a near-inference memory footprint. The code is publicly available at https://github.com/lianmin-chen/the_cost_of_mismatch.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04776

Differentially Oblivious Priority Queues with Sublogarithmic Cost

Zimai Guo, Cheng Hong, Zhechen Li

原文摘要

In this paper, we study exact online integer priority queues under differential obliviousness in the balls-and-bins model. Given a public upper bound $T$ on the number of operations and assuming that the priorities are bounded by a polynomial in $T$, we construct an $(\eps,δ)$-differentially oblivious priority queue supporting insertion and minimum extraction, which requires $\polylog(T)$ private memory. For constant $\varepsilon>0$ and $δ=\exp(-Θ(\log^2 T))$, our construction incurs $O(\log T/\log\log T)$ expected amortized total work per operation, which breaks the $Ω(\log T)$ barrier for fully oblivious priority queues~[Jacob et al., SODA'2019]. Further, we prove a matching lower bound for $(\eps,δ)$-differentially oblivious priority queues with $\varepsilon=O(1)$ and $δ=o(1)$, which indicates that our construction is asymptotically optimal in terms of total work.

cs.DS · cs.CC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.10035

Failure-Driven Workflow Refinement

Jusheng Zhang, Kaitong Cai, Qinglin Zeng, Ningyuan Liu, Stephen Fan, Ziliang Chen, Keze Wang

原文摘要

Optimizing LLM-based workflows is typically formulated as a global search, where candidate workflows are evaluated based on a scalar metric. This paradigm, however, suffers from a critical flaw: information collapse. By reducing rich, multi-step execution traces to simple success/failure signals, existing methods are rendered blind to the underlying structure of failures, fundamentally preventing them from modeling the workflow's failure distribution. We reconceptualize this challenge as a distributional problem. We propose a new paradigm where the optimization goal is not to maximize a scalar score, but to directly minimize a workflow's Expected Failure Mass, i.e., the integral of its failure probability density function defined over a high-dimensional Failure Signature Space (FSS). This distributional lens allows us to move from inefficient, zero-order optimization to a principled, gradient-like descent on the failure landscape itself. We introduce CE-Graph, a framework that operationalizes this paradigm through a novel, failure-driven refinement process. CE-Graph approximates the failure distribution from a pool of counterexamples, identifies its densest regions as recurring failure modes, and applies targeted, operator-constrained graph edits via a Propose-and-Verify mechanism to greedily reduce the failure mass. On math, code, and QA benchmarks, our CE-Graph achieves higher robustness at a significantly lower cost than strong baselines. This suggests that a system's reliability emerges not from avoiding failures, but from systematically learning and reshaping the geometric structure of its failure distributions.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04775

Learning to Rank Explanations for Recommendation with Language Models

Ben Kabongo, Vincent Guigue

原文摘要

Recommender systems are ubiquitous on the Web, making understandable explanations essential for users. Although large language models (LLMs) increasingly generate natural-language justifications, evaluating their factual grounding and personalized relevance remains challenging. Statement-level ranking offers an alternative by selecting concise, review-derived explanations, enabling fine-grained and reproducible evaluation using established ranking metrics. However, existing statement ranking methods largely rely on collaborative representations, while semantic enhancements often fail to outperform simple user-history popularity baselines. Moreover, directly adapting language models for personalized statement ranking remains largely unexplored. In this work, we introduce LMStaR (Language Models for Statement Ranking), a fully text-based framework that formulates personalized explanation ranking as dense retrieval. A query encoder represents the user's interaction history and target-item metadata, while a statement encoder maps candidate explanations into a shared embedding space. Both encoders are jointly adapted through multi-positive contrastive learning to learn personalized relevance. Experiments on Amazon Beauty, Amazon Toys, and Yelp show that LMStaR achieves the highest NDCG@1 across all three domains, outperforming CLER by 1.06--8.25 points (+11.8--65.9%). It also leads top-five ranking on both Amazon datasets, although CLER remains stronger on Yelp. Task-specific adaptation improves all five evaluated backbones and transfers across domains without target-domain fine-tuning. Counterfactual analyses further reveal the importance of user-specific histories. These findings highlight the potential of task-adapted language models for personalized explanation ranking.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04774

Cooperative Multi-Agent Reinforcement Learning with Submodular Trajectory Rewards

Yasaman Zolfimoselo, Nikolas Geroliminis, Maryam Kamgarpour

原文摘要

We study cooperative multi-agent reinforcement learning with decoupled dynamics and a monotone submodular reward function over joint trajectories. Due to the non-additive reward, an optimal policy may depend on the history of all agents. This dependence makes exact optimization exponential in the horizon and the number of agents. To overcome this challenge while retaining theoretical guarantees, we introduce pre-window curvature $c_m$, which quantifies how states visited more than $m$ steps earlier reduce current marginal gains. Using this curvature to control the error from restricting the policy of each agent to depend only on its last $m+1$ states, we propose a greedy algorithm that exploits the decoupled dynamics to optimize local policies sequentially. For fixed $m$, the algorithm achieves a $\frac{1-c_m}{2-c_m}$ approximation guarantee up to estimation error, and its computation time is polynomial in the number of agents and the horizon. We recover the known $1/2$-factor approximation for time-additive rewards and obtain an approximation factor no smaller than the known total-curvature bound for a single agent. We formulate multi-agent traffic monitoring as an example of this framework and evaluate our algorithm on it.

cs.LG · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39715

NIF: Normal-Intersection Fields for Geometrically Consistent Oriented Box Refinement

Shaokai Zheng

原文摘要

Accurate oriented detection requires boxes to follow object boundaries, yet small errors in the coupled center, size, and angle estimates can displace long boundaries even when an object is correctly classified. We propose NIF, a plug-in refinement branch that represents box geometry with normal-intersection fields. Given a coarse oriented box, it samples local features at 20 boundary anchors and predicts signed distances along their outward normals. These observations have an exact affine representation in five rectangle parameters; a differentiable projection uses error-aware reliability weights to recover a geometrically consistent box. Tangential pooling preserves the normal-position profile, while the shared projection adds 0.132M parameters. On DOTA-v1.0, NIF obtains 77.98%, 79.03%, and 79.32% mAP with Oriented R-CNN, Strip R-CNN, and LSKNet-S, respectively; LSKNet-S gives the strongest single-scale result among the compared methods. NIF also reaches 68.19% mAP on DIOR-R and improves matched FAIR1M-v1.0 and DIOR-R baselines by 0.30% and 0.71%, respectively. On HRSC2016, it raises mean IoU from 0.867 to 0.888 while reducing field error by 24.5%. Finally, with four different detector frameworks frozen, NIF improves relative AP75 by 1.55-4.16% and COCO-style mAP by 0.37-3.76%, without detector retraining. Upon acceptance of this paper, we will publicly release the code at https://github.com/zsk466991125-collab/Normal-Intersection-Fields.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04772

TactiNavi: Serving and Revising an LLM Game Coach with Explicit Strategy Conditions

Qi Liu, Xiaoyang Yuan, Zhuomeng Zhang, Yubin Ruan, Xinyi Mou, Wenjin Wang, Hanying Jiao, Di Wu 等 12 位作者

原文摘要

A deployed LLM coaching agent answers requests whose correct response depends on player state and game version, so advice that is right for one player can be wrong for the next. We describe TactiNavi, the serving system behind the LLM coach of a commercial open-world game, which makes strategy applicability an explicit artifact shared by offline compilation, online execution, failure diagnosis, and release. An offline compiler turns versioned game evidence and expert guidance into strategy packages with explicit prerequisites, the serving plane evaluates those prerequisites as three-valued conditions over structured player state and records decision traces, and a control plane uses the resulting condition-rule dependencies to validate revisions on held-out traffic before release. In a retrospective evaluation of 1,122 logged coaching turns from three deployment periods, condition-bearing packages raise correctness from 57.9% to 82.0% over atomic facts and reduce strategy misapplication under changed player state from 18.1% to 10.4%, while trace-based claim verification reaches 86.4% precision at a median reply latency of 2.07 s. The deployment also showed that most apparent fixes are generation noise, because an unchanged-system rerun recovers 38.4% of failing turns and only one of 15 shipped revision units passes the release checks retrospectively. We distill these results into lessons on treating applicability as a first-class serving object, sizing traces for later diagnosis, and building the null model into the release path.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05439

Learning Implicit Solves for Dynamic Simulations

Chawit Kaewnuratchadasorn, Jiaji Wang, Xin Nie

原文摘要

High-fidelity simulation of dynamical systems is central to scientific discovery and engineering design, yet remains computationally demanding. Here we introduce the Implicit Neural Operator Solver (INOS), which learns the implicit solve and encodes a time integrator. INOS pretrains a neural operator from static equilibrium solutions and reuses it within Newmark-$β$ integration. At each time increment, the integrator forms an effective equilibrium problem, the pretrained neural operator infers the next-step solution field, and the Newmark relations advance the dynamic state. Numerical examples in linear and nonlinear elastodynamics demonstrate accurate full-field predictions without dynamic trajectory supervision. Comparative results show that INOS, evaluated zero-shot, achieves higher predictive accuracy than neural solver baselines trained on dynamic simulations. In addition, INOS delivers over 4,600$\times$ speedup in nonlinear simulations over sequential classical finite-element solvers. INOS natively accommodates changes in dynamic conditions and enables reuse of the pretrained operator across spatial and temporal discretizations. Further evaluation demonstrates a stable million-step rollout. These findings establish INOS as a reusable neural implicit solver for accurate, efficient, and robust dynamic modeling.

cs.LG · cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04769

RESTberus: Trace-Aware Benchmarking of Automated REST API Testing Tools Across Microservice Dependencies

John Toniutti, Massimiliano Baldo, Marino Miculan, Vincenzo Riccio

原文摘要

Existing REST API testing benchmarks focus on isolated services, overlooking failures arising from interactions with downstream dependencies. In microservice systems, response bodies and entry-point operations alone cannot reliably distinguish failures, compromising tool comparisons. We present RESTberus, a Kubernetes-based benchmarking platform for black-box REST API testing tools on deployed microservice systems. By complementing API-level evidence with distributed traces, RESTberus can distinguish failures manifesting in the target service from those manifesting in downstream dependencies. Across six tools on two microservice benchmarks, our trace-aware deduplication strategy achieves 87.1% accuracy, outperforming baselines from prior work by 10-13 percentage points. Our empirical study shows that all tools expose downstream failures, accounting for 11.7% of estimated unique failures on average, which isolated-service benchmarks cannot expose. On single-service benchmarks, RESTberus produces statistically equivalent results to RESTgym, demonstrating that integration-level assessments can preserve compatibility with existing tools and comparability with prior evaluations.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.40890

ChatGPT as a Surveillance Assistant? An Exploratory Case Study of AI-Assisted Surveillance Risks

Ayana Hussain, Nicholas Vincent

原文摘要

Large language models (LLMs) can generate detailed guidance, functional code, and persuasive text at scale. As one of the most widely used consumer language model products as of 2026, ChatGPT gives broad public access to these capabilities, motivating concerns about its potential use in surveillance-related tasks. We present an exploratory case study of what potentially harmful surveillance assistance looks like in ChatGPT's public web interface. We contribute a taxonomy of seven surveillance risk categories based on prior LLM risk research and reports of deployed surveillance systems, instantiated as 110 prompts, along with a manually reviewed analysis of ChatGPT's responses. Prompts were issued using two approaches: 55 jailbreak prompts drawing on five types of elicitation strategies, and 55 plain-language direct prompts, each run alone, and if unsuccessful, again with a short fixed prefix. We find that ChatGPT provides substantive surveillance guidance for 40 of the 55 jailbreak prompts and for 33 of the 55 direct prompts without any prefix. Compliant responses also rarely included policy mentions or suggestions for privacy-preserving alternatives. We manually review every response and find that even when ChatGPT states a refusal, it may still supply detailed instructions, working code, or architectural guidance alongside brief ethical caveats.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39528

PRIME W0: Physical Priors from World Models for Embodied Manipulation

Huabin Peng, Fagang Jin, Yancong Wei, Tengfei Ma, Chuhang Liu, Chao Yu, Liqing Chen

原文摘要

Vision-Language-Action (VLA) models excel at instruction following, yet most remain feed-forward: they condition actions only on the current observation and discard both long-horizon temporal context and the hierarchical structure of VLM representations. We present PRIME W0, a hierarchical conditional flow-matching framework that closes this gap through three synergistic designs: (1)per-layer KV injection from a pretrained VLM into an Adaptive MoE action expert; (2)dynamics-aware conditioning via asynchronous world-model subgoal-image prediction and a DINOv2 historical-frame semantic bridge; and (3)Action Dynamic Manifold (ADM) flow matching with covariance-structured noise that respects robot kinematic coupling. PRIME W0 is trained in four stages---VLM pre-training, DAT/NTP visuomotor pre-training, DiT flow-matching decoder assembly with WM injection, and end-to-end SFT---and achieves state-of-the-art results on LIBERO and real-robot benchmarks, with systematic ablations validating each architectural component.

cs.RO

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新论文 · v1arXiv:2611.04766

Testing for a Large Induced Expander: Sublinear Algorithms and Query Lower Bounds

Pan Peng, Ranran Shen

原文摘要

Testing expansion asks to distinguish graphs of large conductance from graphs that are far from having large conductance. We study a relaxation in which only part of the graph is required to expand: does an $n$-vertex graph of bounded degree contain an induced expander on a constant fraction of its vertices, when the rest of the graph, including the edges leaving the expander, is arbitrary? Let $P_{α,φ}$ denote the property of containing an induced subgraph on at least $αn$ vertices with inner conductance at least $φ$. For every constant $κ\in(0,1)$, we give a tester that accepts graphs in $P_{α,φ}$ and rejects graphs outside $P_{κα,μφ^2}$, where $μ=Θ_{α,κ}(1/\log n)$, using $O_{α,κ}(\sqrt n\,φ^{-2}\log n)$ queries. For fixed $α\in(0,1]$, $κ\in(0,1)$, and $0<φ\le9/20$, we further show that distinguishing these two classes requires $Ω_{α,κ,φ}(n^{1/2+c_{κ,φ}μ})$ queries for every sufficiently large $n$ and every $0<μ\le1$, where $c_{κ,φ}>0$. The bound holds for adaptive algorithms, uniformly in $μ$. Hence, for testers with $\sqrt n\,\mathrm{polylog}(n)$ queries, the logarithmic loss in conductance is necessary up to a $\log\log n$ factor. The same tester also permits distance-based rejection at a weaker conductance while retaining the original core size. Our tester compares the endpoint distributions of random walks from different vertices with each other, rather than with the uniform distribution. Its analysis uses the trace chain, which observes the walk only at its visits to the unknown expanding core. Our lower bound builds on the noisy-parity construction of [CKK+18], using vector-valued parity labels in $\mathbb F_2^r$ to create many hidden classes, each too small to contain a qualifying induced expander.

cs.DS

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新论文 · v1arXiv:2611.04764

Pixel-space video generation with U-JiT

Théophane Vallaeys, Jakob Verbeek, Matthieu Cord

原文摘要

Latent diffusion and flow matching models are ubiquitous in state-of-the-art generative video models. While highly effective, relying on pretrained autoencoders to compress the data into a lower-dimensional latent space also comes with drawbacks. First, training proceeds in two stages: autoencoder pretraining, with compression and reconstruction objectives, followed by latent generative model training, precluding fully end-to-end training. Second, autoencoders also lead to an inherent loss of signal, and the trade-off between reconstruction quality and generative performance in the latent space is still an active area of research. In this paper we explore the viability of generative video models directly in pixel space. We analyze recent approaches in pixel-space generative modeling of images that rely on basic transformer designs, and identify several shortcomings that limit their effectiveness for the high-dimensional pixel-space video generation. To overcome these limitations, we propose a UNet-like transformer architecture along with improvements to the training recipe and sampling strategy. We conduct controlled experiments, training latent and pixel-space models from scratch on Kinetics-600. For the standard frames-to-video task at 128 resolution, our U-JiT model improves generative FVD by 27% over prior state-of-the-art results, and by 14% against the JiT pixel-space baseline. Scaling to higher 256 resolution, we find the improvement over pixel-space and latent-based models grows as we increase the spatiotemporal compression rate up to $64 \times 64 \times 8$. Finally, we find that U-JiT improves per-frame FID by 27% over latent models and 61% over pixel-space models on class-to-video generation, with respectively 9% and 56% reduction in FVD.

cs.CV

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版本更新 · v2arXiv:2609.06512

A Near-Linear Element-Kernel for \(d\)-Hitting Set

Zimo Sheng, Mingyu Xiao

原文摘要

In \(d\)-Hitting Set, the input consists of a finite universe \(U\), a family \(\mathcal S\) of subsets of \(U\) with size at most \(d\), and an integer \(k\). The task is to decide whether at most \(k\) elements of \(U\) can intersect every set in \(\mathcal S\). Previously known kernels contain \(O(k^{d-1})\) elements and \(O(k^d)\) sets. Whether the number of elements can be reduced to \(O(k^{d-1-\varepsilon})\) for some constant \(\varepsilon>0\) is a longstanding open problem in kernelization. In this paper, we answer this question affirmatively by giving a near-linear randomized element-kernel and a subquadratic deterministic element-kernel through a re-encoding of the instance. For every fixed \(d\geq3\), we give a one-sided randomized kernel with \(O_d(k\log^3k)\) elements and a deterministic kernel with \(O_d(k^{7/4}\log^{3/2}k)\) elements. Our kernels may still contain \(k^{O(d)}\) sets and the parameter \(k\) may grow polynomially.

cs.DS · cs.CC

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版本更新 · v2arXiv:2610.28949

Online Open-World Wafer Defect Discovery using Hyperdimensional Computing

Quanling Zhao, Ava Emami, Tsuyoshi Idé, Rajiv Joshi, Steven Holmes, Michael Passow, Tajana Rosing

原文摘要

Conventional wafer defect analysis relies on offline, closed-world classifiers that fail to detect novel, unseen defect signatures emerging during continuous semiconductor manufacturing. To address this, we propose WaferHDC, an online open-world wafer defect discovery framework based on hyperdimensional computing (HDC). The memory-centric architecture of WaferHDC enables rapid anomaly detection and grouping of recurring failures into compact hypervector representations. This allows the memory space to dynamically scale with new defect modes without retraining a static classifier or archiving entire raw wafer history. WaferHDC first models normal spatial distributions, then dynamically groups recurring failures into persistent defect memory without labels or predefined class counts. It outperforms the strongest online baseline by 13.3 p.p. in adjusted Rand index and 10.5 p.p. in normalized mutual information on the industry-standard WM-811K dataset. These results demonstrate the effectiveness of WaferHDC for continuous and open-world wafer defect analysis.

cs.LG

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版本更新 · v2arXiv:2609.07803

Understanding the Impact of Model Pruning on Long-Tail Forgetting and Explanation Reliability in Medical Imaging

Nazish Khalid, Tausifa Jan Saleem, Amal Saqib, Donald C. Wunsch, Mohammad Yaqub

原文摘要

Model pruning is widely used to compress deep neural networks, reducing memory and computational requirements with minimal impact on aggregate performance. However, its effect on model behavior remains poorly understood, particularly for long-tailed medical datasets where rare but clinically important conditions are underrepresented. Furthermore, it remains unclear whether pruned models preserve reliable explanations of their predictions. To address this gap, we present a systematic study of long-tail forgetting and explanation reliability under model pruning. Across two long-tailed medical imaging datasets, two CNN architectures, four pruning methods, and sparsity levels up to 95\%, we evaluate predictive performance, explanation stability, and explanation faithfulness. Our results show that predictive performance exhibits a strong frequency-dependent trend, with lower-frequency classes generally experiencing earlier and larger degradation than higher-frequency classes. In contrast, explanation stability and faithfulness are influenced primarily by the pruning strategy, with gradient-informed methods preserving explanation reliability more effectively under aggressive compression. Qualitative and mechanistic analyses further indicate that explanation degradation is primarily associated with the collapse of class-discriminative gradients rather than the disappearance of feature activations. These findings suggest that model compression should be evaluated beyond aggregate performance. Incorporating class-aware and explanation-aware evaluation reveals failure modes that would otherwise remain hidden, while moderate sparsity levels provide a practical balance between compression, predictive performance, and explanation reliability.

cs.AI

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新论文 · v1arXiv:2611.04760

Koopman Generative Operators for Efficient Probabilistic Time-Series Forecasting

Raz Marshanski, Liran Nochumsohn, Mayank Jauhari, Boris N. Oreshkin, Omri Azencot

原文摘要

Probabilistic time-series forecasting requires models that simultaneously capture structured temporal dynamics, expressive uncertainty, and efficient inference, yet existing approaches fall short of this goal: latent dynamical models impose structure but rely on restrictive generation mechanisms, while modern generative methods such as diffusion and flow matching achieve flexibility at the cost of iterative and computationally expensive sampling. We introduce the Koopman Generative Operator (KGO), a probabilistic forecasting method that conceptualizes prediction as the evolution of structured uncertainty. KGO integrates three core components: (1) Koopman Patch Embedding (KoPE) for temporally consistent latent trajectory extrapolation; (2) Koopman Flow Matching (KoFM), which enables fast, single-step generation through a closed-form matrix exponential in a Koopman latent space, bypassing iterative sampling; and (3) an Adaptive Uncertainty Gate (AUG), which provides probabilistic forecasts by adapting uncertainty per-variable and per-horizon, corresponding to a learned decomposition of aleatoric uncertainty without manual tuning. By unifying these components, KGO achieves state-of-the-art accuracy on 13/17 ProbTS benchmarks in CRPS and 16/17 in NMAE. Further, by eliminating iterative sampling, KGO delivers at least a 25$\times$ reduction in inference time compared to iterative generative models. These results position KGO as a practical and principled framework that advances the accuracy-efficiency trade-off in probabilistic time-series forecasting, combining leading accuracy on the evaluated benchmarks with fast, single-step generation.

cs.LG

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新论文 · v1arXiv:2611.04759

Beyond Local Refinement: Wide-Area Precise Cross-View Localization

Qingguo Hu, Xinyu Ma, Yujie Lin, Yaoxiang Wang, Qi Shi, Zonghao Guo, Chi Chen, Jinsong Su

原文摘要

Fine-grained cross-view localization aims to precisely localize a ground camera against geo-referenced aerial imagery, but current methods typically assume a narrow geographic prior, substantially limiting their applicability when upstream localization remains coarse. To bridge this gap, we study this task over much larger aerial regions. However, this seemingly simple extension fundamentally changes the localization problem: the growing disparity in spatial extent between ground and aerial views makes reliable cross-view matching difficult to establish directly, while the increasing number of look-alike locations in larger regions introduces substantial visual ambiguity. To address these challenges, we propose RingMatch, a search-and-verification framework for wide-area cross-view localization. RingMatch first leverages predictive uncertainty to uncover plausible locations and progressively zooms into their surroundings to obtain increasingly fine-grained evidence, yielding a compact set of promising candidates. We then introduce the depth ring, a novel geometric representation that reveals subtle variations in the spatial layout around a location and serves as a view-shared spatial signature for an LVLM verifier to resolve residual ambiguity. To benchmark this setting, we construct VIGOR-Pro by composing VIGOR aerial tiles into contiguous regions with spatial extents of up to 400 m. Experiments show that RingMatch maintains strong localization performance as the aerial region expands, consistently outperforming representative end-to-end and retrieval-based methods.

cs.CV

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新论文 · v1arXiv:2611.04757

When Should an LLM Steering Edit Depend on the Input?

Rachael Harkness, Konrad Rawlik

原文摘要

An activation steering edit should depend on the input exactly when the destination varies \emph{inside} the class a query requests, and that is measurable before any rule is fitted or applied. Contrastive Activation Addition (CAA) adds one fixed direction to every input, the difference of the class means. Conditioning it on the requested class accounts for the between-class variation in the required displacement and leaves the within-class variation untouched, which no rule that reads only the source state can express. We study the impact of increasing input-dependence in the steering edit, moving from a difference of means to a destination retrieved per query --- the mean of the $k$ stored states nearest the query, inside the requested class --- in which the two baselines are endpoints: at $k$ equal to the class size the retrieved rule \emph{is} the class-conditional difference of means. We measure input-dependent steering on six substitution corpora that vary in whether the edit changes a word or a clause, and on an external benchmark, and find that input-dependent steering outperforms fixed-direction steering when the destination varies within the class. Retrieval wins at every layer of every substitution corpus in two models, and the level of input-dependence needed governs how to apply it: word-level edits are best made in the first tenth of the network from a few neighbours, clause-level edits at any depth from the class mean. Where the class already determines the answer the class mean suffices and retrieval only matches it, though a fixed direction still fails there. Four quantities computed from the states alone identify the regime in which the edit should be applied.

cs.AI

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版本更新 · v2arXiv:2610.40510

Zero-freeness for Random Regular Graphs up to the Replica Symmetry Breaking Threshold

Zejia Chen, Chunyang Wang, Xinyuan Zhang

原文摘要

We study antiferromagnetic spin systems on random regular graphs through the lens of the phase transitions characterized by the complex zeros of the partition function. Our motivating examples include the hardcore model and the antiferromagnetic Potts model (including proper $q$-colorings at $β=0$). For both models on random $d$-regular graphs, we establish, with high probability over the graph, zero-free regions throughout the interior of the replica-symmetric regime. Asymptotically, this regime extends up to tree occupation density $$α< α_{\mathrm{RSB}}(d) = (2\log d - 2\log\log d + 2 - 2\log 2 + o_d(1))/d$$ for the hardcore model and up to degree $$d < d_{\mathrm{RSB}}(q) = (2q-1)\log q - 2\log 2 + o_q(1)$$ for proper $q$-colorings. Leveraging the standard algorithmic implications of zero-freeness, we obtain deterministic polynomial-time algorithms that multiplicatively approximate the corresponding partition functions at fixed rational parameters throughout this regime, with high probability over the graph. Our algorithmic guarantees significantly improve upon prior work and extend beyond the parameter ranges reached by previously known search algorithms. As a key intermediate step, we establish the pointwise equivalence, away from the threshold, of three notions of replica symmetry: geometric (averaged pairwise correlation decay), thermodynamic (convergence to the Bethe free-energy prediction), and variational (nonpositivity of the Bethe functional). To establish zero-freeness, we develop reinforced small subgraph conditioning, lifting classical small subgraph conditioning to random holomorphic functions to control short-cycle fluctuations. We also use variational replica symmetry to obtain exponential planted overlap concentration. This enables a typical-graph truncation that controls complex second moments up to the replica symmetry breaking threshold.

cs.DS · math-ph · math.PR

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新论文 · v1arXiv:2611.04756

Non-Interfering Asymmetric AdamW Updates for Constrained Exploration of Equifinality in Hybrid Models

Ekaterina Gikalo, Zavud Baghirov, Christian Reimers, Markus Reichstein, Martin Jung, Marco Körner

原文摘要

Hybrid models let neural networks supply the unresolved parameters of a process-based simulator, and are fitted to observations that constrain aggregate fluxes and storages but not the internal partitioning that produces them. Such a fit is equifinal: configurations indistinguishable in the observed aggregates can disagree substantially about the unobserved internals, and therefore about the mechanism they attribute to the same data. We study evapotranspiration, whose total flux is observed while its split into transpiration and evaporation is not. A model ensemble built from random seeds alone spans only a narrow band of the partitionings the data admit. We produce the spread deliberately, training members under auxiliary repulsion on transpiration, subject to the fit to observations not degrading. Non-interference is usually arranged by making the auxiliary gradient orthogonal to the main one, but under AdamW the applied step is a preconditioned momentum direction, so orthogonality between raw gradients no longer implies it. We introduce Non-interfering Asymmetric Network Optimization (NANO), an update whose first-order effect on the main loss is exactly that of the main objective alone, whose auxiliary step is bounded, and which concentrates repulsion in the coordinates the main gradient leaves idle.

cs.LG

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