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版本更新 · v2arXiv:2602.17111

Instructor-Aligned Knowledge Graphs for Personalized Learning

Abdulrahman AlRabah, Priyanka Kargupta, Jiawei Han, Abdussalam Alawini

原文摘要

Mastering educational concepts requires understanding both their prerequisites (e.g., recursion before merge sort) and sub-concepts (e.g., merge sort as part of sorting algorithms). Capturing these dependencies is critical for identifying students' knowledge gaps and enabling targeted intervention for personalized learning, especially in large-scale courses where instructors cannot feasibly diagnose individual misunderstanding. While knowledge graphs offer a natural representation for capturing these conceptual relationships at scale, existing approaches are either surface-level or disregard the rich pedagogical signals embedded in instructional materials. We propose InstructKG, a framework for automatically constructing instructor-aligned knowledge graphs that capture a course's intended learning progression. Given a course's lecture materials, InstructKG extracts significant concepts as nodes and infers learning dependencies as directed edges (e.g., "part-of" or "depends-on" relationships). The framework synergizes the rich temporal and semantic signals unique to educational materials (e.g., "recursion" is taught before "mergesort") with the generalizability of large language models. Through experiments on diverse lecture materials, human evaluation, and a prerequisite-learning benchmark, we demonstrate that InstructKG captures rich, instructor-aligned learning progressions.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02166

Black-Box Adversarial Example Attacks on Machine Learning based Election Systems

Subek Acharya, Syed Ayon, Khaleque Md Aashiq Kamal, Alina Barnett, Surya Eada, Kaleel Mahmood

原文摘要

Machine learning models have shown exceptional accuracy for ballot classification in election systems. Recent research has demonstrated that these ballot classification models are vulnerable to adversarial example attacks. This is a critical security issue because a successful attack could alter the outcome of an election and undermine the democratic process. However, all adversarial example attacks in the voting domain have assumed a white-box threat model, where the classifier parameters are known. In this work, we advance the field of machine learning in election systems by analyzing a realistic black-box adversary. We implement and evaluate eleven state-of-the-art black-box attacks: query based attacks ADBA, Square, SurFree, RayS, and SA-MOO. We also test adaptive transfer based black-box attacks with APGD-$l_\infty$, APGD-$l_2$, APGD-$l_1$, PGD $l_0$, PGD $l_0$+$l_\infty$, and PGD $l_0$+$σ$. Our attacks span multiple different norms ($l_\infty$, $l_2$, $l_1$, and $l_0$) and three different black-box adversarial threat models (decision based, score based and transfer based). In addition to digital analyses, we physically print and scan $20,000$ adversarial examples to evaluate the real-world performance of different black-box attacks. Our finding demonstrate an even larger physical-digital robustness gap exists for black-box adversarial examples than for white-box adversarial examples. Lastly, we conduct the first ever adversarial example transferability study in the voting domain and test adversarial training and barrier zones as two possible black-box defense strategies.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2505.00554

Protocols for Univariate Sumcheck

Malcom Mohamed

原文摘要

Two candidate approaches for univariate sumcheck over roots of unity are presented. The first takes the form of a multilinear evaluation protocol, which can be combined with the standard multivariate sumcheck protocol. The other consists of a direct reduction from univariate sumcheck to multilinear evaluation, which can be combined with Gemini (Bootle et al., Eurocrypt 2022). Optionally, natural round reductions from $m$ to $\log(m)$ are supported, while retaining linear prover time.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.03550

Formal Evidence Generation for Assurance Cases for Robotic Software Models

Fang Yan, Simon Foster, Ana Cavalcanti, Ibrahim Habli, James Baxter

原文摘要

Robotics and Autonomous Systems are increasingly deployed in safety-critical domains, so that demonstrating their safety is essential. Assurance Cases (ACs) provide structured arguments supported by evidence, but generating and maintaining this evidence is labour-intensive, error-prone, and difficult to keep consistent as systems evolve. We present a model-based approach to systematically generating AC evidence by embedding formal verification into the assurance workflow. The approach addresses three challenges: systematically deriving formal assertions from natural language requirements using templates, orchestrating multiple formal verification tools to handle diverse property types, and integrating formal evidence production into the workflow. Leveraging RoboChart, a domain-specific modelling language with formal semantics, we combine model checking and theorem proving in our approach. Structured requirements are automatically transformed into formal assertions using predefined templates, and verification results are automatically integrated as evidence. Case studies demonstrate the effectiveness of our approach.

cs.SE · cs.FL · cs.LO · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02164

RegionFence: Adaptive Concurrency Control for Blockchain Parallel Transaction Execution

Jiaying Feng, Linpeng Jia, Yi Sun

原文摘要

Smart contracts have turned blockchains into general platforms, and their throughput is increasingly limited by transaction execution. Parallel execution can relieve this bottleneck. However, most existing engines apply a single policy within a block, typically optimistic (OCC) or pessimistic (PCC) concurrency control, and thus re-execute or delay whole transactions. Although some database studies have proposed hybrid and adaptive mechanisms, these mechanisms cannot be applied directly to blockchains because the block fixes the serial order, accesses emerge only at run time, and real workloads vary widely. We propose RegionFence, an adaptive concurrency control mechanism for blockchain parallel transaction execution, based on three key designs. First, we propose regions to refine the granularity of control from whole transactions to the parts that touch contended keys. Second, we design fences to decide at run time how each region handles a conflict. Third, we introduce intra-block and inter-block online learning to anticipate likely conflicts. At the same time, the system still produces the same result as serial execution in consensus order. We implement RegionFence in go-ethereum and evaluate it on real Ethereum mainnet blocks from different periods of the past five years. Experimental results show that RegionFence achieves an overall speedup of 9.2x over single-core execution on 64 cores and outperforms OCC by 2.9-5.4x and PCC by 3.9-9.4x in speedup across these periods.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02162

Rewarded for Not Getting Caught: Why Alignment Training Selects for Conditional Compliance and Cannot Instill Norms

Kevin Baum, Ruta Binkytė, Gerrit Großmann, Felix Jahn, Adriano Mannino

原文摘要

Current alignment training cannot be expected to deliver what it is meant to deliver: AI agents that comply with norms whatever the gains and whether or not anyone is watching. Training that rewards and penalizes behavior can teach norms only through that reward, and a reward can encode a norm only as a price: "do not do X" becomes "doing X costs something", and training charges the cost only when X is noticed. A price, unlike a norm, can be paid: a large enough gain outweighs it, noticed or not. And because the cost is charged only where violations are noticed, training cannot favor a policy that always complies over one that complies only where it would be caught. When an unnoticed violation is the only way to complete a task, it even favors the latter, the more so the more strongly it rewards task success. Agency makes this the normal case: agents mostly act without oversight, can often tell when they are observed, and may take steps to evade observation. That agents behave as intended when they infer they are being tested, and differently when they infer they are not, is therefore no malfunction; the training regime is working as built. This reading unifies alignment faking, sandbagging, and evaluation-aware scheming. No behavioral evaluation can show that a model does better, since that would require observing how it behaves unobserved; a model that complies when it seems unobserved may simply have recognized the test. Easing the pressure on agents to complete tasks at any cost can make violations rarer, but never impossible. For norms that no gain should be allowed to outweigh, the remedy is not deeper internalization but architecture that makes violations unavailable rather than unchosen. We state this argument explicitly, premise by premise, read recent findings and a real-world incident against it, and draw consequences for design and governance.

cs.AI · cs.CY · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13136

From Review to Reuse: How Post-Task Workflow Can Support Human-AI Agent Interaction

Zekun Wu, Xinru Wang, Rock Yuren Pang, Chenglong Wang, Anna Maria Feit

原文摘要

AI agents can automate tasks by turning a single natural-language request into a multi-step process spanning tools, files, and applications. Users are often left to judge that process from fragmented execution information and the final output. To make the completed process easier to understand, validate, and reuse, we investigate post-task workflows: editable, graph-based representations of an agent's completed execution. We first analyzed 10,803 public workflow templates from n8n to characterize real-world automation practice, then developed Trace2Flow, a research probe that translates agent execution traces into interactive post-task workflows. In a study, participants (N = 20) reviewed agent executions with prompt or agent errors. We found that post-task workflows improved their understanding and error detection over a prompt-only condition, and that validation succeeded mainly when users cross-checked across multiple evidence sources. For follow-up tasks, adapting the workflow matched adapting the prior prompt in success, time, and difficulty, and was often preferred.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02160

Value Forcing: Aligning Return Distributions at All Noise Levels

Chongyi Zheng, Zhiyuan Zhou, Qiyang Li, Benjamin Eysenbach, Sergey Levine

原文摘要

Learning accurate value functions is central to reinforcement learning (RL), yet remains challenging because supervision is limited and indirect: ground-truth values are unavailable, and each transition in the data only provides an immediate reward that can be used to construct bootstrapped targets as supervision. This challenge is particularly pronounced when data is limited or reward signals are sparse, as is common in real-world applications such as robotics. We argue that the key to accurate value learning in this limited data regime is extracting richer learning signals from the available data. In this paper, we introduce Value Forcing, a new approach that uses flow matching to estimate return distributions and enables accurate value learning from limited data. Our key insight is that we can extract richer learning signals by enforcing Bellman consistency at every noise level. Empirically, Value Forcing improves the accuracy of estimating the return distribution, and prevents the distribution from collapsing to a single value, as occurs with prior methods. We instantiate Value Forcing with two practical value-learning algorithms: VF, an actor-critic method, and IVF, an implicit value-learning method. We evaluate both algorithms across $26$ long-horizon tasks in the offline RL setting and find that they outperform prior value-learning methods, with particularly large gains when data is limited. Our project website is available at https://chongyi-zheng.github.io/value-forcing.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.02534

TREMOR: Template Matching for Large Seismic Data Collections

Manos Chatzakis, Rodrigo Flores-Allende, Mikael Freire, Yoann Cano, Léonard Seydoux, Themis Palpanas

原文摘要

Seismic station networks continuously record the ground velocity at several locations on Earth in the form of waveform data series, which seismologists analyze to detect various kinds of geophysical events, including earthquakes. Then, template events are defined to search the seismic data collection for matching events. This is template matching, a fundamental task in seismology for various seismic analyses. However, template matching requires extensive processing time, especially for large seismic collections that exceed the memory capacity of a single machine. This poses a significant challenge in seismology, in particular with the increasing size of seismological datasets. In this paper, we introduce TREMOR, a distributed data series processing framework for template matching that efficiently handles large waveform collections. We apply TREMOR to two representative real-world seismic use cases. Through an extensive experimental evaluation, we demonstrate its efficiency, with TREMOR being up to 6.7x faster than the best competing method on each workload, and up to 18x faster than the fastest distributed version of MASS, an FFT-based method popular in seismology, while returning the same exact results.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02157

Wireless Networks That See: Integrated Sensing and Communication in Complex Environments

Xingqin Lin

原文摘要

The same radio interactions that complicate wireless communication can reveal what is happening around a network. Integrated sensing and communication (ISAC) turns these interactions into opportunities to observe unmanned aerial vehicles (UAVs), road vehicles, and industrial mobile robots. Yet a strong echo is not necessarily a reliable object observation: stationary background can conceal motion, reflections can create virtual objects, and one viewpoint can leave part of an object's motion unobserved. This tutorial explains how known cellular signals are used to estimate propagation, suppress stationary background, interpret reflected paths, and combine observations from different sites. UAV examples under a Third Generation Partnership Project (3GPP) stochastic channel model and ray tracing in a San Francisco urban scene illustrate the mechanisms through spatial layouts, channel profiles, and before/after range--Doppler maps. Local checks reduce false outputs, with some loss of detection. Cooperation improves position accuracy for UAVs detected by both a single site and cooperating sites. The examples show how signal resources, measurement reporting, and application needs shape sensing performance, providing practical guidance for sensing services in complex environments.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02155

Decision Models as Policy Gates for Coding Agents: Explicit Policy Boundaries Improve Detection and Reduce False Alarms

James P. Beno

原文摘要

Coding agents can destroy data, leak secrets and message the wrong person faster than a human can review. Could a decision model catch these risks at runtime? We evaluated 22 hosted decision models and two prompted general models as classifiers for a multi-rule policy engine. A synthetic dataset of 2,643 tool calls was labeled blind against 28 rules, with a sealed holdout. With a single shared preamble in both conditions, explicit boundaries written as examples and counterexamples raised recall by 5.5-26.1 percentage points on all 23 available models and reduced false positives on 21. Jev 1.14's recall rose from 89.3% to 98.2%, while unnecessary interruptions fell from 30.6% to 4.8% of 252 clean actions. With examples, its median latency was 120 ms at $0.46 per thousand requests. Strong models retained the recall gains when concrete names were replaced by generic descriptions; three largely retained them when the same boundaries were written as prose. On 16 new scenarios written after the initial studies, a follow-up improved recall on 22 of 23 models and reduced unnecessary interruptions on 21. We release the dataset, rule texts and harness.

cs.CL · cs.CR · cs.SE

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2504.20369

Perception-Augmented Databases for Effective Visual Analytics

Zafeiria Moumoulidou, Hamza Elhamdadi, Redwan Ahmed Rizvee, Ke Yang, Subrata Mitra, Cindy Xiong Bearfield, Alexandra Meliou

原文摘要

Analysts often use sampling in visual exploration to overcome the computational and perceptual bottlenecks of working with massive datasets. Yet, existing sampling methods optimize for statistical or geometric data properties, without explicit consideration for how humans perceive a visualization. We propose perception-augmented databases: a database framework that stores perceptual models of data as precomputed metadata and exposes a new query operator---perception-aware sampling---that selects subsets optimized for how humans visually perceive the result. We make the following contributions: (1) We introduce perception-augmented databases and design PAwS, a perception-aware sampling operator for scatterplot visualizations that leverages saliency maps---a computer vision tool for predicting areas of visual attention---combined with density and coverage objectives. (2) We design ApproPAwS: a fast approximate visualization method that works directly over a perception-aware compressed synopsis of the data, achieving up to 100x speed-ups with minimal loss in visual fidelity, and without accessing the underlying data at query time. (3) We contribute a perceptual similarity metric that evaluates the quality of a sample by comparing its saliency map to that of the original dataset, a novel evaluation methodology, applicable to any sampling method for scatterplots. (4) Our extensive experimental evaluation over datasets up to 3.5M points demonstrates that our methods consistently outperform five state-of-the-art sampling algorithms. A user study confirms that PAwS samples are preferred by humans, validating our quantitative findings.

cs.HC · cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02154

Faster Algorithms for Disjoint Paths in DAGs and Two Disjoint Shortest Paths

Yang Cao, Haoran Qi, Hanzhi Wang

原文摘要

For $k$ vertex-disjoint paths with prescribed endpoints in a directed acyclic graph (DAG), the $O(mn^{k-1})$-time pebbling algorithm of Fortune, Hopcroft, and Wyllie [TCS 1980] has remained the fastest for every $k\geq3$ for over four decades, even when the paths must be shortest. We break this bound for every $k\geq3$ with randomized algorithms running in expected $O_k(m+n^{k-3+ω+\varepsilon})$ time for every $\varepsilon>0$, where $ω<2.372$ is the matrix multiplication exponent; this is $O_k(m+n^{k-0.62})$. For two disjoint shortest paths, we find the paths as fast as we detect them: in expected linear time on DAGs and on undirected graphs with polynomially bounded integer weights, and in expected $O(\min\{mn,n^{ω+\varepsilon}\})$ time on directed graphs whose cycles have positive weight, which on dense graphs is $o(mn)$ even for detection. All algorithms evaluate a polynomial whose monomials are the solutions: a new first-collision circuit speeds up the evaluation in DAGs, and a correlated extraction step at cuts crossed once by every path finds a solution at the cost of a constant number of evaluations.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02153

Multi-Agent Collusion and the J-Space

Marco Molinari, Gabriele Bettineschi

原文摘要

LLM agents are increasingly deployed in Multi-Agent Systems (MAS), and they introduce the risk of coordination among agents that might escape human oversight. The J-space has been used to uncover deception in single LLMs, but its use for uncovering and steering collusion between agents remains unexplored. We show, with interpretability experiments on DeepSeek-V4-Flash (284B) on the NARCBench collusion benchmark, that colluding agents' J-space holds the collusion itself: before replying, 86% of colluders represent misrepresentation, concealment or remorse. In a steganographic card-counting task, the receiver's J-space holds a hidden count, and causal interventions alter its count. We also introduce the Jacobian Pair Contrast (J-PACT), an unsupervised method for detecting collusion between agents. We evaluate J-PACT on four open-weight models: GPT-OSS-20B, Llama-3.3-70B-IT, Qwen3-32B, and DeepSeek-V4-Flash (284B). J-PACT retains 85% of the above-chance AUROC of supervised NARCBench probes, matches them on steganographic scenarios, and names the colluding pair up to its complement in 99% of detected runs.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02152

Detailed Derivation of The Backpropagation Algorithm

Faris Alasmary

原文摘要

Backpropagation is the standard method for computing the gradients that train neural networks, but it is usually presented in a compact vector form that skips the intermediate steps. This tutorial derives backpropagation step by step for a fully connected feedforward network with sigmoid activations and a squared-error loss. Using a running example with three layers, we first differentiate the error with respect to a single weight by following its path through the network with the chain rule. We then show how summing over all paths leads to the error terms $δ$ and to the four general backpropagation equations in matrix form. Every term is annotated with its dimensions, so each equation can be checked for consistency. The derivation is then extended to $m$ training examples, where the weight gradients become a single matrix product and the bias term corresponds to broadcasting. It is also extended to mini-batch gradient descent, including why the gradient of a mini-batch estimates the full gradient. Batch and mini-batch training are both given as algorithms. Finally, each equation is linked to the corresponding line of a NumPy implementation trained on the AHDBase dataset of handwritten Arabic digits, and the complete code is included.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.02372

PredActBench: Benchmarking Tool-Augmented Dialogue under Controlled Tool Noise

Abdulrahman AlRabah, Xiaocheng Yang, Dilek Hakkani-Tür, Abdussalam Alawini

原文摘要

Large language model (LLM) agents are increasingly used to support decisions by calling external tools. Existing work on LLM tool-calling assumes these tools are perfectly reliable. However, in many applications, the tools are statistical predictors with non-trivial errors. We introduce PredActBench, a benchmark for evaluating dialogue agents that work with imperfect predictive tools, using education as a testbed. First, we build a benchmark in which LLMs work with noisy predictors to guide users' decisions. Second, we extend two prior metrics for trust calibration, Relative AI-Reliance and Relative Self-Reliance, to multi-turn dialogue. Third, we evaluate 13 LLMs on OULAD and our released PredAct-CS dataset of 60 courses with real final grades and synthetic weekly scores, alongside a human study involving 13 instructors and teaching assistants. We find that when tools are noisy, SOTA models rarely make predictor uncertainty visible to users, with our strongest baseline GPT-5.5 achieving an F1 of 57.2. As a result, users over-rely on incorrect predictions and unsupported claims, showing that task accuracy alone is not enough for AI decision support and agents must communicate when their tools may be wrong. We release PredActBench to support the development of LLM agents that help users calibrate their reliance.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02147

Refining Roadside 4D Radar Vehicle Classification Using Point-Cloud Dispersion

Mohammed Alsakabi, Andreia Figueiredo, António Almeida, John Dolan, Miguel Luís, Pedro Rito, Ozan Tonguz, Susana Sargento

原文摘要

Roadside sensing infrastructure is essential for Intelligent Transportation Systems (ITS), but cameras and legacy sensors are limited in range, weather robustness, and depth accuracy. 4D radar complements these modalities by resolving range, azimuth, elevation, and radial velocity under adverse conditions, though its sparse, noisy point clouds make reliable classification difficult. We present a roadside multi-sensor platform combining a 4D radar and stereo depth camera under synchronized acquisition, used to collect a real-world dataset. Our analysis reveals a substantial class-distribution discrepancy between modalities, most notably a strong radar confusion between passenger cars and motorcycles (49.01% and\ 48.60% of the dataset). To address this, we propose a Neyman-Pearson-based binary classifier on the temporally-smoothed 2D positional standard deviation of radar point cloud clusters, reducing classification error from 52.1% to 42.7%. This lightweight and edge-deployable approach improves radar-only classification reliability and lays the foundation for future camera-radar fusion.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02146

When Is Personalized Federated Learning Worth Its Deployment Cost?

Beyazit Bestami Yuksel, Ayse Yilmazer Metin, Emrah Dikbiyik

原文摘要

Personalized federated learning (PFL) may improve client-level performance, but deeper personalization increases deployment cost. We formulate personalization-depth selection as a utility-based empirical decision problem in which client utility equals Macro-F1 minus a cost-sensitivity weight times deployment cost, over six personalization depths labeled P0 through P5. We compare fixed policies with an offline per-client Oracle reference and report allocation maps over Dirichlet data heterogeneity and cost sensitivity. Although evaluated on this hierarchy, the formulation can accommodate any finite candidate set. On CIFAR-10, CIFAR-100, and a PTB-XL subset, personalization shows empirical, setting-dependent decision regimes rather than a universal benefit: higher cost sensitivity shifts Oracle mass toward the global-only depth P0; low cost sensitivity can favor deeper actions. Patterns are grid observations (three seeds per cell), not theoretical boundaries. Best-uniform is competitive with Oracle assignment (6.5 percent plus or minus 7.8 percent relative regret; 95 percent confidence interval 5.9 to 7.1), whereas random and personalize-all incur much larger regret (189.3 percent plus or minus 367.1 percent and 409.0 percent plus or minus 803.2 percent). Under the same utility, Ditto full-model personalization is substantially suboptimal once cost is counted. The contribution is an offline characterization of when personalization depth is worth its cost, not a new PFL optimizer or deployment-time scheduler.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.38850

Beyond Outcome Rewards: Post-Rationale Distribution Optimization for Selective Prediction in Reasoning Models

Zhimin Gao, Meibo Hu, Jiamian Wang, Zhiqiang Tao, Pichao Wang

原文摘要

Reasoning models are trained to produce correct answers, yet selective deployment requires ranking which answers to accept or defer, and outcome-reward training does not directly optimize the post-rationale distribution used for that decision. We identify two mismatches: expected correctness is mode-seeking rather than distribution-eliciting, and scoring an average across rationales rewards disagreement unavailable to a single-rationale decision. We introduce Post-Rationale Distribution Optimization (PRDO), which applies a proper score to the answer distribution after each sampled rationale, without target rationales or separate confidence labels. Across four 0.6--3.8-billion-parameter models on MMLU-Pro, PRDO improves Brier score, error ranking and secondary accuracy relative to budget-matched, temperature-scaled GRPO. At 20\% realized error, oracle coverage rises by \npj{Q3a_vs_grpo.qwen3-0.6b.mp.oracle_cov@0.2.ppabs}--\npj{Q3a_vs_grpo.phi-4-mini.mp.oracle_cov@0.2.ppabs} percentage points, and ranking gains transfer to unseen tasks. Matched controls identify proper, per-rationale scoring as the active design choices, while verification tasks show the boundary: better selective ranking can coexist with lower full-coverage accuracy. Code is available at https://github.com/ZimmyGao/openjev-rlcd

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02142

Homomorphisms on a product of free convex semilattices

Ana Sokolova, Harald Woracek

原文摘要

Convex semilattices form an equational class that combines convex algebras and semilattices by a distributive law. For a set $I$ let $\mathcal FI$ be the free convex semilattice with basis $I$. A product $\mathcal FI\times\mathcal FJ$ is generated by $I\times J$, but is not free. Hence, not every map from $I\times J$ into an arbitrary convex semilattice $X$ extends uniquely to a homomorphism from $\mathcal FI\times\mathcal FJ$ to $X$. We investigate a condition on a map $\varphi$ of $I\times J$ into some convex semilattice $X$ that is always necessary and often sufficient for $\varphi$ to have such an extension to a homomorphism $Φ$ of $\mathcal FI\times\mathcal FJ$ into $X$.

cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02140

History-Aware Force Sensing and Tunable Stiffness in a Pneumatic Kresling Origami Cell

Hengyi Zhou, Jiaqi Wang, Yilin Ma, Dezhong Tong, Xiaonan Huang

原文摘要

Mechanical hysteresis and pneumatic loading complicate contact force estimation from deformation measurements in soft robots. This work presents a 3D-printed polyether block amide (PEBA) Kresling cell with magnetic angular sensing that supports three operating modes: force sensing at zero gauge pressure, force sensing with tunable stiffness under gauge positive pressure, and self-sensing actuation under negative gauge pressure. A multiscale gated recurrent unit (GRU) network estimates contact force from angular histories to account for the the combined effects of geometric deformation and material response. At zero gauge pressure, the model achieves a test RMSE of 0.76~N. Positive gauge pressure approximately doubles the average axial loading stiffness at +20~kPa over the first 3~mm of compression, while pressure-specific models maintain RMSEs below 0.93~N. Under negative gauge pressure, an energy-based compensation model enables contact force estimation using the the model trained at zero gauge pressure, achieving RMSEs of 1.66--2.10~N under quasi-static conditions. These results demonstrate a compact approach to combining force feedback, tunable axial stiffness, and self-sensing actuation in soft origami systems.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02139

Adversarial Robustness of Retrieval-Based Fact-Checking: Detecting and Mitigating Evidence Contamination

Alamgir Munir Qazi, John P. McCrae, Jamal Abdul Nasir

原文摘要

Retrieval-based fact-checking systems ground verification in large document collections, creating a critical vulnerability: adversarial evidence contamination. We demonstrate that factually inconsistent but contextually coherent evidence generated to satisfy both retrieval-similarity and linguistic-fluency constraints can systematically mislead verification models. Contaminating just 3.1-7.4% of training corpora with five label-specific strategies causes adversarial content to penetrate up to 35.2% of top-k retrievals, degrading verification accuracy by up to 46.9% relative F1. Existing retrieval-stage defenses, validated against gradient-optimized or syntactically anomalous attacks, transfer poorly to this threat model: detection rates collapse and F1 recovery exhibits high variance across seeds. We propose GLAD (Geometric-Lexical Adversarial Defense), which fuses dense embedding peer-coherence with lexical distributional isolation. Across two benchmarks, two embedding models, and five random seeds, GLAD achieves the highest detection rates (70-86%) and the most consistent F1 recovery, reclaiming 73-80% of contamination-induced degradation on LIAR-RAW while approaching clean baselines on RAWFC. Crucially, unlike defenses that trade clean performance for robustness, GLAD imposes negligible cost on uncontaminated corpora.

cs.IR · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.05134

Activation-Based Active Learning for In-Context Learning: Challenges and Insights

Yaseen M. Osman, Geoff V. Merrett, Stuart E. Middleton

原文摘要

Deep active learning has previously been explored for LLM in-context sample selection, but not with methods that utilise recent advances in understanding of transformer activations. In this paper, we test the hypothesis that model activations could provide a fine-grained signal to optimise the selection of in-context examples. We present a comprehensive analysis of MLP activation-based deep active learning methods applied to in-context learning, including how different attention masking strategies impact active learning across diverse classification and generative datasets, using both Llama-3.2-3B and Qwen2.5-3B base models. However, we find a negative result: MLP and embedding layer outputs, viewed through the lenses of massive activations or the first four moments, do not correlate with example quality or task performance. Specifically, the absolute Spearman correlation coefficient is at most 0.33 for all tasks and models we tested, showing that such activation-based sampling should not be used for in-context learning. We hypothesise that this may be due to superposition, whereby models represent more features than they have dimensionality, suggesting that methods like Sparse Autoencoders (SAEs) may be a promising future direction.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02134

HaloGuard 2.0: An Open-Weights Constitutional Output Guard for Multilingual Response Safety

Ashmiya Lenin, Navaneeth Sangameswaran, Preetham S

原文摘要

An input guard decides whether a request reaches the model, but never sees what the model says. Harm that emerges during generation, because a jailbreak passed the input layer or a benign request drifted into operational detail, is visible only on the output side and only as tokens arrive. We present HaloGuard 2.0, an open-weight streaming output guard that assigns a safe/unsafe probability to every response token in a single forward pass, so a deployment can stop the stream once harm is confirmed. The guard reuses the constitution of HaloGuard 1.0, our input guard, to organise a 478,532-response training corpus, and supervises each token from the point where the response becomes harmful. We find that a guard trained on conventionally shaped data learns a shortcut specific to streaming: it judges the prompt instead of the response, and handles only 13.9% of matched safe/unsafe response pairs correctly. We correct this with a second phase on 106,043 counterfactual responses built around the base guard's own failure modes, and release the equal-weight average of the base and corrective models. On six English response-safety benchmarks, HaloGuard 2.0-0.8B reaches a macro F1 of 83.6 (83.3 on held-out halves of the decision-rule search), up from 72.8, with 38% fewer false positives. It matches the strongest full-response guards with 5-10x fewer parameters and is 2.6 points above the strongest streaming guard, Qwen3Guard-Stream-4B. A multilingual variant, HaloGuard 2.0-Poly-0.8B, trained on PolyGuardMix, reaches 83.1 F1 on the response labels of PolyGuardPrompts across 17 languages, 5.0 points above the strongest baseline. On one H100 the guard costs 0.37 ms per token per stream when moderating 64 concurrent responses. Both guards are hardened continuously through the weekly red-teaming protocol of HaloGuard 1.0, extended here to the output layer.

cs.CL · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02132

TSSDA: An Autonomous Agent for Constructing a Time-Series Text-to-SQL Benchmark

Zhihan Zheng, Shaoxu Song, Haitao Yuan, Minxiao Chen

原文摘要

As agents increasingly undertake data querying, Text-to-SQL serves as a foundational capability for database access. However, for text-to-SQL in time-series databases, high-quality benchmarks and large-scale training corpora remain scarce, limiting the systematic evaluation and domain adaptation of existing models and agents. Existing automated SQL generation approaches lack sufficient autonomy, leaving key challenges unresolved. First, numerous combinations of temporal syntax and functions complicate comprehensive coverage of valid queries. Second, as the corpus grows, generating valuable and diverse new data becomes increasingly challenging. Third, schema and data heterogeneity hinder reuse of temporal query structures, limiting generation efficiency. To address these challenges, we propose TSSDA, a Time-Series SQL (TSSQL) Generation Data Agent framework, and construct TERN, a comprehensive Time-sERies text-to-SQL beNchmark. First, to achieve broad coverage of valid queries, we systematically model TSSQL syntax and semantics to construct a data generation space. Next, to explore the space and improve diversity, we develop a Data Agent that autonomously orchestrates data generation. The agent comprises a planning and decision-making module and a query generation module, with online adaptation for continuous self-improvement. Finally, we introduce a temporal-aware memory that enables adaptive reuse of experience across heterogeneous data instances, improving generation efficiency. Comprehensive evaluations on TERN reveal the limitations of existing methods in performing Text-to-TSSQL tasks. Furthermore, extensive experiments demonstrate that TSSDA substantially improves coverage, diversity, and complexity.

cs.LG · cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02131

Band-Separated Redundant Communication for a Mars-Analog Rover

Zamiul Rashid, Akid Anis, Tanzim Ahmed, Md. Golam Rabiul Alam, Md. Khalilur Rhaman

原文摘要

Teams in the University Rover Challenge (URC) drive their rovers remotely and often out of sight. The organizers name communication failure as the most common reason a team falls short. A rover therefore needs bandwidth for several camera views and a command path that survives the loss of one radio. We describe and field test the communication stack of the BRACU Mongol-Tori rover, which keeps vision and control on separate radios and bands. Video and RTK corrections travel over a 5.2 GHz bridge with a 2.4 GHz backup, and the Babel routing protocol chooses between them. Every command goes out on two sub-gigahertz radios at once, and the rover uses the backup copy only when the primary falls silent. We tested the backhaul on the bench with a power cut and injected packet loss. We also drove the rover more than a kilometer from the base, and line of sight ended about halfway. On the bench, traffic reached the backup within a few seconds. A metric offset kept it on a primary that lost one packet in ten. In the field, the primary failed at 771 m and the backup carried video and corrections to just over a kilometer. The drive also exposed a limit of Babel, which judges a link only by lost Hello messages. It kept traffic on a primary with a faulty receiver, although the backup reported twice the capacity, and it flapped at the edge of range. We close with lessons on antenna aiming, link metrics and logging.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02129

FOLLIE: Automated Reasoning over the First Dataset of Sentences with their First-Order Logical Representation for French

Maximos Skandalis

原文摘要

FOLLIE (for First-Order Logic for Language Inference and Entailment for French) connects French sentences, compositional first-order representations and automated natural language inference (NLI). The first French NLI-ready, NL-to-FOL resource, it contains 19,983 inference records and 36,987 non-empty sentence-slot--formula mappings from six heterogeneous NLI sources, retaining French lexical material and source problem structure. Its silver annotations are produced by a neural--symbolic pipeline, with 111 separately identified LLM-assisted additions. The construction combines morphosyntactic analysis, probability-conditioned categorial multitagging, grammatical proof search, discourse representation and first-order semantic construction. Stage-wise experiments show substantial gains in derivation coverage; analyses of conjunction distributions, logical-operator incidence and scope alternation characterise the resulting resource. We then develop an automatic inference engine whose input-conditioned background theories address lexical relations, quantification, quantities, event structure and temporal dependencies. Prover9 supplies proofs for entailment and contradiction, while Mace4 supports bounded consistency checks. Three-way accuracies are 65.37% on all 335 FraCaS-FR problems, 70.33% on 300 GQNLI-FR problems and 71.35% on 9,399 eligible SICK-FR records pooled across splits. Agreement with reference labels among committed decisions is 93.53%, 100.00% and 98.66%, at commitment coverage of 41.49%, 37.00% and 15.05%, respectively. Worked constructions, axiom schemas and proof analyses explain these outcomes and identify the contributions of scope, reference, lexical knowledge and numerical structure. FOLLIE provides a French experimental basis for studying the representations and assumptions that connect sentence interpretation to deductive inference.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02127

Mesh2Shell Neural Operator for Inverse Shape Optimization

Xinyi Li, Xi Deng, Yubo Cao, Hudson Liu, Anima Anandkumar

原文摘要

Physics-driven design seeks high-performing geometries, which need not conform to a learned shape category. Parametric and latent-space approaches facilitate surrogate-based optimization, but restrict deformations to prescribed shape families or learned decoders. We introduce the Mesh-to-Shell Neural Operator (M2S-NO) as the mesh-to-shell component of a category-agnostic inverse shape optimization framework. We separate the explicit mesh used for design from the volumetric representation used for physics prediction. M2S-NO maps oriented surface samples to a surface-centered shell representation, such as TruncatedSDF or HeatSDF. A separate problem-specific PDE neural operator (PDE-NO) maps this representation to physical fields. Objective gradients update mesh vertices through both operators, without a latent shape decoder. Preconditioning and remeshing support free-form deformation, while fixed interfaces are controlled directly on the mesh. Optional shell-space proposals enable topology changes. We evaluate geometric matching, jet-engine brackets, and neutron moderators, with independent FEM and OpenMC evaluation. Relative to the best training designs, M2S-NO reduces maximum bracket displacement by 14.3% and increases neutron-sensor current by 13.8%. Optimization also succeeds from an out-of-distribution bracket initialization, demonstrating the framework's geometric flexibility.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02125

Semantic Interoperability in Federated Digital Twin Systems: A Systematic Review Through the Levels of Conceptual Interoperability Model

Manonmani Sekar, Nasim Nezamoddini

原文摘要

When two companies connect their digital twins, software models of their plants kept up to date with live data, each twin must translate the other's data into its own terms, and that translation rule, called a mapping, can stop being correct when one company changes the meaning of its data without telling the other. This PRISMA 2020 systematic review screened 5,833 records and read 119 full texts to determine whether any published system detects such a broken mapping and repairs it while the twins operate. In all 32 included studies the mapping is fixed before operation, and the two ingredients of run-time repair appear only separately: cross-company matching without any check that the match still holds, and detection and repair of changed meaning within a single company. The review turns this gap into six requirements, each illustrated on one running example, that a system claiming run-time semantic interoperability between independently owned twins should meet.

cs.SE · cs.AI · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02123

Bounded Retained History in a Proxy Flow Replay: A Mixed Predictive Pilot and an Allocation Study

Juan S. Santillana

原文摘要

We examine bounded retained history through a frozen predictive pilot and a subsequent allocation study on a curated UNSW-NB15 flow mirror. Both use an artificial release-order replay based on raw ltime ticks, with equal-tick records querying a shared prior-group state. In the pilot, the model with added retained relational features had lower reconnaissance recall than the current-flow-plus-counters baseline (43.66% versus 47.84%) and lower benign false-positive rate (3.63% versus 6.68%) at separately frozen operating thresholds, together with lower AUROC. This mixed result did not establish a predictive advantage. A development-only audit subsequently found that capacity eviction dominated expiration in the 256-record, 300-tick FIFO state, with a median completed record lifetime of 11 ticks. A protocol specified before its single execution, but after the pilot and post-hoc mechanism audit, compared five label-blind retention arms on the already-used development trace. Its primary equal-source endpoint averaged retained/reference ratios for service-expanded source out-degree and source-destination service diversity. Source-balanced retention at 256 records exceeded FIFO-256 by a pooled endpoint difference of 0.440738 and met the prespecified materiality and subblock rule. Crucially, the event-weighted ratios for both quantities decreased relative to FIFO-256: the result is specific to the equal-source weighting objective and does not establish uniformly improved retention. The study supplies neither evidence of detection improvement nor an explanation of the pilot result. Reused data, concentrated reconnaissance support, proxy timing, and the absence of a qualified independent corpus bound the interpretation.

cs.CR · cs.LG · cs.NI

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