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新论文 · v1arXiv:2610.38112

From the Gettier Machine to Justified True Output: A Three-Step Account of Rational Trust in Generative AI

Tevfik Uyar

原文摘要

As generative artificial intelligence (AI) settles into everyday practices of inquiry, an old epistemological question returns in a new guise: when does a proposition a user takes from an AI output become something the user knows, and when is it rational to trust it as such? This paper argues that the characteristic failures of AI-mediated inquiry are precisely those that the justified-true-belief (JTB) tradition and its post-Gettier anti-luck refinements were built to diagnose, and it advances three claims. First, AI outputs inherit neither the presumptive warrant of testimony nor the reliabilist warrant of deterministic tools: unaudited trust in a calculator rests on a type-level reliability record, and for general-purpose generative models there is no type-stable process to which such a record could attach -- a practical form of the generality problem. Second, use that stops at obtaining an output turns generative AI into a Gettier machine: the user's fluency-based seeming of justification is disconnected from the truth of what is believed, and, as a clock running ten times too fast illustrates, sheer speed of production multiplies coincidental hits, inflating the appearance of reliability without improving the standing of any single hit. Third, the remedy is a three-step structure of use -- acquisition, understanding, independent verification -- whose first two steps answer to the classical belief and justification conditions and whose third answers to the post-Gettier sensitivity condition. The result is a stakes-indexed criterion of rational trust, justified true output (JTO), with an everyday test: "Had this been false, would I have noticed?" The criterion is defended against objections from over-demandingness, the extended mind, model improvement, and redundancy.

cs.CY · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38110

SPARSEBLEED: Bypassing Attribute-Inference Defenses via Sparsity Optimizations

Darsh Asher, Farshad Dizani, Joshua Kalyanapu, Ro Cammarota, Aydin Aysu, Samira Mirbagher Ajorpaz

原文摘要

Deep neural networks (DNNs) are widely deployed in safety- and privacy-critical domains such as healthcare and finance, where models are trained on confidential data. This paper presents the first hardware-level attribute-stealing attack that bypasses state-of-the-art privacy defenses, revealing that accelerator optimizations intended for performance can directly undermine data confidentiality. SparseBleed requires no access to gradients, logits, model weights, ground-truth labels, power traces, DVFS manipulation, shared caches, or other channels. Instead, we show that sparsity-driven execution paths encode stable timing signatures that act as a surrogate for model confidence, enabling high-fidelity attribute inference from a single query. SparseBleed generalizes across heterogeneous backends and hardware engines, including Intel AVX2/AVX-512/AMX, NVIDIA Ampere Tensor Cores with 2:4 sparsity, and nine well-known sparse and dense libraries such as Intel oneDNN, oneMKL SparseBLAS, and NVIDIA cuSPARSE. We evaluate SparseBleed to infer sensitive input attributes and true output labels. On Texas-100X, SparseBleed infers sensitive input attributes such as Sex with 66.39% accuracy and recovers the true output label, Surgical Procedure, with 99.8% accuracy. Finally, we evaluate mitigations. Constant-time execution by disabling sparsity optimizations is expensive (e.g., up to $2\times$ higher latency on NVIDIA Sparse Tensor Cores and $\approx 25\%$ higher power in AMX microbenchmarks). By contrast, a padding defense that equalizes responses to the worst-case time masks timing leakage with only 7.24% average performance overhead across nine libraries on Texas-100X/Adult and near-zero extra energy.

cs.CR · cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38109

Who Called? V33DA: A Physically Verified Multimodal Benchmark for Vocal Attribution in Zebra Finch Groups

Maris Basha, Yuhang Wang, Xiaoran Chen, Longbiao Cheng, Luca Yapura, Anja T. Zai, Mathieu Salzmann, Richard Hahnloser

原文摘要

Deciding which member of an animal group produced a vocalization and where that vocalization was produced are central to studying social communication but are rarely evaluated with physically verified ground truth. Existing resources either localize sounds without identifying the caller, or recognize individuals from isolated vocalizations without reliance on the 3D candidate geometry. We close this gap with V33DA, a dataset-centered multimodal benchmark for spatial vocal attribution in social zebra finches: it comprises 33,625 verified events coupling 5-channel audio with three camera views, calibrated 3D pose for every visible candidate, and per-bird radio telemetry across 10 individuals and three experiments. Caller identity is verified from an on-body accelerometer-derived vibration signal; this channel is withheld from benchmark models and used only by an on-body reference ceiling. Given a detected vocalization and the visible candidate birds, the task is to select the caller. We evaluate it under three regimes: session-disjoint testing on familiar birds from held-out recording dates, held-out-experiment transfer to a new cohort, and leave-one-individual-out evaluation on a bird whose vocalizations are held out from training. Reference methods expose a consistent gap between in-domain accuracy and transfer to unseen individuals: methods that can exploit familiar vocal identity perform well on known callers but degrade sharply on unseen ones, whereas candidate-aware methods preserving explicit spatial reasoning transfer substantially better. The on-body reference stays near-perfect across regimes, confirming the internal consistency of the accelerometer-verified labels. We release V33DA with fixed protocols and code for the evaluated methods, and V33DA++, an unevaluated companion set of verified overlapping-call events and +/-2 s context windows.

cs.SD · cs.CV · cs.LG · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38106

TGTV: An Interpretable Framework for UAV Fault Detection and Traceability via Spatiotemporal Graph Learning and Large Language Models

Zixuan Wang, Changbo Shi, Ji Liu

原文摘要

Fixed-wing unmanned aerial vehicles experience operating-condition transitions, asynchronous sensor sampling, and control-system compensation that can obscure fault signatures. Existing temporal detection methods have limited capacity to represent coupled faults across subsystems, track their evolution, and constrain language-model diagnoses with quantitative evidence. This paper proposes TGTV, an interpretable fault detection and traceability framework that integrates spatiotemporal graph learning with a large language model. A temporal convolutional network and a graph attention network with weighted fused adjacency extract multiscale temporal patterns and spatial dependencies among sensors. A hybrid temporal Transformer and variational autoencoder then evaluates window-level anomalies using prediction errors and reconstruction probabilities. Continuous-time Shapley attribution identifies critical sensors and high-contribution intervals while preserving the temporal continuity of fault evolution. A structured evidence package combines detection results, graph relationships, temporal attribution, sensor attribution, and operating context. An evidence-constrained diagnosis protocol uses this package to generate flight-level conclusions with traceable evidence identifiers. Cross-flight holdout experiments on the public ALFA fixed-wing flight dataset yield a window-level area under the precision-recall curve of 0.976, accuracy of 0.973, recall of 0.902, and F1 score of 0.920. Flight-level fault diagnosis achieves recall of 0.943 and an F1 score of 0.944. These results demonstrate accurate anomaly detection and quantitatively supported fault-tracing cues, providing a basis for interpretable post-flight fault review and analysis.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38104

Same Label, Different Interface: Beyond Action-Space Labels in Robot Learning

Arshia Sangwan, Zhongyang Zhang

原文摘要

Robot-learning studies often compare position and velocity actions as though each label identified a single experimental treatment, yet the label alone does not determine what the robot executes. Before affecting robot motion, bounded policy output passes through filtering, physical scaling, reference construction, feedback, timing, and effort limits. We study how these omitted properties affect attribution under one shared learner and training budget. Across three contact-rich Factory tasks (PegInsert, GearMesh, and NutThread), we compare five joint-space interfaces, with ten independently trained policies per condition. We first remain within the same position-feedback law and vary the executed interface: substituting only the physical action scale as the directly modified component changes sample-median evaluation success by 0.645-1.000 across tasks, while removing an exponential moving average produces task-dependent changes. We then move to a cross-law comparison and choose a velocity scale that exactly matches the position interface's local raw-torque command gain. This calibration raises the sample median on GearMesh and NutThread, with no median change on PegInsert. Yet clear residual sample-median gaps remain on PegInsert and GearMesh, while the NutThread interval includes zero. Thus, even this explicit equality leaves feedback and held-action dynamics unmatched and does not establish position-velocity trained-outcome equivalence. Action-interface comparisons should therefore report the executed pathway and state the physical quantity they match.

cs.RO · cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38103

The Prestige: Benchmarking Cognitive Visual Reasoning using Magic Tricks

Shuo Wen, Beixi Du, Edwin Meriaux, Junming Shi, Chloe Si, David Meger, Doina Precup, Gregory Dudek

原文摘要

We introduce The Prestige, a video understanding benchmark that probes cognitive visual reasoning through professional magic performances, where deliberate misdirection challenges models to track events and explain unexpected outcomes. The benchmark comprises open-ended questions ranging from temporal and causal understanding to robustness against adversarial questions. Evaluation of leading open-source and proprietary VLMs reveals a substantial gap between even the strongest model and human performance. We also find systematic discrepancies between human and automated grading and identify recurring model failure modes. Ablations further show that models can select correct multiple-choice answers even when they fail to generate correct explanations independently, motivating further investigations into question formats. Together, these findings highlight limitations in both VLM reasoning and its evaluation, underscoring the need for more reliable video understanding models and more rigorous benchmarks.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.26844

AnyGoal: On World Model Generalization Beyond Expert Rollouts

Heejeong Nam, Divyat Mahajan, Quentin Le Lidec, Randall Balestriero, David Klindt

原文摘要

The goal of a world model is to capture the dynamics of an environment. This has the great appeal that it allows agents to plan and simulate a course of actions with the help of a world model. For these uses to be reliable, however, the learned dynamics must behave well beyond the specific state-action combinations observed in expert rollouts. Despite this, the relationship between data coverage, dynamics generalization, and downstream planning performance remains underexplored. We study this relationship through the lens of compositional generalization: can a world model recombine familiar state and action factors into valid dynamics? To answer this, we introduce AnyGoal, a benchmark of goal-shifted task variants built from widely used world-model environments, together with an open-source library. Each task is designed to be within reach of a world model that has learned the underlying dynamics: individual state factors and actions remain within their training support, but their combinations are underrepresented in expert trajectories. Across multiple environments and world model families, we find that current models consistently exhibit failures under these recombinations. By examining dynamics and representation failures across model families, interaction regimes, and evaluation metrics, we study where current world models generalize and where challenges remain for goal-shifted prediction and planning. Together, this framework, benchmark, and analysis provide a basis for evaluating progress toward world models that generalize to new planning tasks.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38101

AI-Driven Digital Twin Framework for Warehouse Logistics Automation Optimization

Md Mahinur Alam, Kanita Jerin Tanha, Md Tanjin Alam, Shadman Sakib, Min Gyu Kim

原文摘要

In recent years, warehouse logistics optimization has increasingly focused on intelligent, data-driven systems to enhance operational efficiency. Conventional static product placement strategies are being progressively replaced by dynamic slotting approaches that can adapt to real-time variations in warehouse conditions. In this work, we present a novel framework that employs a reinforcement learning agent integrated within a FlexSim-based Digital Twin environment for real-time product placement optimization. The proposed agent continuously learns from streaming operational data, enabling it to reduce picking time while improving overall system throughput. The simulation environment models a four-zone warehouse layout with forklift-based material handling and leverages the Asset Administration Shell framework to ensure seamless interaction between the learning agent, physical asset, and the digital twin. Experimental evaluation demonstrates substantial performance gains, with the proposed approach achieving a 29.66\% reduction in average picking time, a 35.42\% increase in throughput, and an 18.54\% decrease in travel distance.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38100

Evaluating Causal Reasoning in LLMs: A Case Study in the Challenges of Procedural Generation

Justin Clarke, Isha Karn, Patrick Murray, KJ Hill, David Jensen

原文摘要

Large language models (LLMs) appear to represent and reason about a large range of phenomena. In particular, recent studies have examined whether LLMs can accurately reason about causation. One of the most commonly used benchmarks for evaluating formal causal reasoning in LLMs is CLadder, a framework for procedural generation of benchmark data sets that was released in 2023. Experiments with CLadder appear to indicate that LLMs can perform formal causal reasoning in a large percentage of generated examples. In this paper, we examine what can be reliably inferred when LLMs perform well on test sets produced by CLadder. We identify several examples of superficial and highly predictive patterns that the CLadder generator embeds in data sets it generates. We find that even simple models such as random forests perform well on CLadder-generated data sets after training on a sample of generated data. We also find that the performance of many commercial LLMs on CLadder test sets is extremely brittle and that removing large sections of the text that are critical for formal causal reasoning has almost no effect on performance. Given these results, the most likely hypothesis for the strong performance of state-of-the-art LLMs on CLadder is that they are exploiting superficial patterns learned from exposure to training data from the generator. Any inference that LLMs are exhibiting more complex causal reasoning must rule out this hypothesis.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38096

Calculation and rejection diverge in language models tested at known mechanical limits

Shaoliang Yang, Jun Wang

原文摘要

When a language model returns engineering quantities with the status "solved", software that trusts it passes them on, even when the requested design is physically impossible. Such failures could reflect poor calculation, how the answer is requested, or a failure to judge feasibility. We set beam loads and disk speeds at known percentages above or below plastic collapse or burst, measured each model's limit calculation separately and tested 12 models in 4,860 calls, including follow-up experiments on new designs. We find that a model can recognize an impossible design when asked only whether it is feasible, yet accept the same design, reporting "solved", when asked for a full answer. Three models accepted beams 10 to 30% beyond collapse despite precise limit calculations in separate questions, and many accepted replies stated the violation. Asked only about feasibility, Opus 5 and Gemini 3.8 Flash rejected every impossible new design they were given, including those accepted in full answers, without false alarms. Rewording when to decline or changing the answer format raised rejection on the original beams, but not consistently on new designs. Software and benchmarks using a model's status should test feasibility judgment and full-answer rejection separately, and report the exact prompt.

cs.CE · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38095

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

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

原文摘要

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

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38094

PyPSA-Libya: An Open-Source Energy System Model for National Decarbonization and Hydrogen Export Pathways

Mohamed Abuella

原文摘要

Libya possesses significant renewable energy potential that remains largely untapped. This paper presents PyPSA-Libya, a national-scale model built on the PyPSA-Earth framework, designed to explore optimal transition pathways for the Libyan power sector. We present two primary studies: (1) an electricity-only capacity expansion analysis comparing a zero-emission constraint against an unconstrained baseline at 36-cluster resolution, and (2) a sector-coupled hydrogen export study evaluating the levelized cost of exporting 70 TWh/year of hydrogen to Europe under three technological scenarios and a 100 EUR/tonne CO2 carbon tax regime. Results indicate that while solar PV dominates the domestic capacity mix, integration of hydrogen production significantly alters grid storage requirements. Furthermore, under carbon pricing, steam methane reforming with carbon capture (SMR-CC) frequently out-competes pure green electrolysis, establishing a lower-cost compliance pathway for cross-border export. The model, data, and configuration files are released under an open-source license to support transparent energy planning in Libya.

cs.CE · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.12801

Evaluating open-weight LLMs for scoring L2 essays: Accuracy, prompt transfer, and deployment of AiAWE

John Maurice Gayed

原文摘要

This study presents AiAWE, an open-source automated writing evaluation system that scores argumentative essays with a LoRA-adapted large language model deployed on local hardware. Using 480 TOEFL Independent Writing essays from a restricted-access Educational Testing Service (ETS) dataset, we fine-tuned Gemma-3-27B and LLaMA-3.3-70B under identical LoRA configurations on 120 essays from one prompt, with all hyperparameters fixed in advance, and evaluated them on the remaining 360 essays. Gemma achieved a root mean square error (RMSE) of 0.474 and a quadratic weighted kappa (QWK) of 0.828 on the half-point grid against the ETS scores. In paired comparisons, it significantly outperformed a fine-tuned GPT-3.5 baseline from prior work on every metric tested, showing that a locally deployable open-weight model can achieve competitive scoring performance in this setting. It also had significantly lower error than the larger LLaMA configuration, although, because the two models differ in family as well as size, this comparison does not isolate the effect of scale. Neither open-weight model showed a statistically detectable increase in error on the 240 essays written to a second, unseen prompt from the same task family. On a single consumer-grade GPU, the scoring model returned a complete response in a median of 14.2 s, demonstrating technical feasibility under the tested conditions. Only the scoring component was evaluated, not the generated feedback. The system is publicly accessible at https://app.awade.gec.waseda.ac.jp, and its code, LoRA adapters, and training configurations are openly released.

cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.16912

PepEVOLVE: Position-Aware Dynamic Peptide Optimization via Group-Relative Advantage

Trieu Nguyen, Hao-Wei Pang, Shasha Feng

原文摘要

Macrocyclic peptides are an emerging modality that combines biologics-like affinity with small-molecule-like developability, yet their vast combinatorial space and multi-parameter objectives make lead optimization slow and challenging. Prior generative approaches for peptide lead optimization (e.g., PepINVENT) require chemists to pre-specify mutable positions for optimization, choices that are not always known a priori, and rely on static pretraining and optimization algorithms that limit the model's ability to generalize and effectively optimize peptide sequences. We introduce PepEVOLVE, a position-aware, dynamic framework that learns both where to edit and how to dynamically optimize peptides for multi-parameter improvement. PepEVOLVE (i) augments pretraining with dynamic masking and CHUCKLES shifting to improve generalization, (ii) uses a context-free multi-armed-bandit router that discovers high-reward residues, and (iii) couples a novel evolving optimization algorithm with group-relative advantage to stabilize reinforcement updates. During in silico evaluations, the router policy reliably learns and concentrates probability on chemically tractable sites that influence the desired peptide's properties. On a therapeutically motivated Rev-binding macrocycle benchmark, PepEVOLVE outperformed PepINVENT by reaching higher averaged permeability-weighted optimization scores of approximately 0.8 vs. 0.6, achieving best candidates with score of 0.97 (vs. 0.89), and converging in fewer steps under the task of optimizing permeability and lipophilicity with structure constraints. Overall, PepEVOLVE offers a practical, reproducible path to linear and macrocyclic peptide lead optimization when optimal edit sites are unknown, enabling more efficient chemical space exploration and improving design quality across multiple objectives to accelerate the design cycles.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36219

Bringing Hardware Security Education to All: A Seamless Curriculum Integration Approach

Tanvir Hossain, Mahmudul Hasan, Kareem El-Araby, Lorenzo R. Godinez, Swarup Bhunia, Md Tauhidur Rahman, Tamzidul Hoque

原文摘要

Cybersecurity education has predominantly focused on software and network security, under the assumption that the underlying hardware is trustworthy. Threats introduced through the global semiconductor supply chain, together with vulnerabilities arising during design and integration, challenge the assumption that hardware can serve as an unquestioned root of trust. Sensor-enabled systems and machine-learning accelerators further expand the hardware attack surface. The undergraduate curriculum of electrical and computer engineering and computer science programs has not evolved accordingly. Core courses cover the design and the functionality of hardware while the security aspects remain overlooked, and dedicated hardware security (HS) courses are elective, scarce, and often restricted to graduate students. At the University of Florida, fewer than 7% of the undergraduate electrical and computer engineering students register for the introductory HS course each year. We present an integrated and economical approach that disseminates fundamental HS concepts within existing core courses through contextual learning. Nine hands-on modules were developed such that each security concept exercises an associated design concept of the core course, as a component of that course and not as a course of their own. Doing the security work is how the student exercises the design topic, so the modules are built to strengthen the host course and not only to add to it. Three were assessed across three offerings of the embedded systems course through one course survey and two conducted by an external evaluator, with 25 respondents. We report what those instruments measured and the limitations of the approach.

cs.CR · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38091

Jailbreak Connectivity: Towards Diverse, Transferable, and Universal Multimodal Large Language Model Jailbreak

Xu Zhang, Ziqing Hu, Shuo Han, Ren Wang

原文摘要

Multimodal large language models (MLLMs) are vulnerable to visual jailbreak attacks that bypass safety safeguards. Existing methods typically optimize isolated adversarial examples, limiting attack diversity, transferability, and generalization. We propose Jailbreak Connectivity (JC), a framework based on the observation that effective adversarial inputs can be connected through low-loss regions in image space. JC optimizes continuous adversarial paths: path sampling yields diverse jailbreak images, lightweight surrogate classifiers guide cross-model transfer, and a distribution-level objective enables universal attacks across harmful queries. On MM-SafetyBench, JC achieves average attack success rates of 84.01%, 85.77%, and 97.95% on Qwen3-VL-8B-Instruct, InternVL3.5-8B, and Gemma-3-12B-IT, respectively, exceeding the strongest baselines by up to 68.36 percentage points. In black-box evaluations, JC improves attack success rates over FigStep by 24.69 and 27.34 percentage points on GPT-5.6 and Gemini 3.1 Pro, respectively. These findings reveal structured vulnerabilities in MLLM safety alignment and inform more comprehensive security evaluation.

cs.LG · cs.AI · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38090

Predicting What Answer Engines Cite: Open Decision Models, Rerankers and a Distilled 4B Student on Ten Engines under Controlled Retrieval

Elisha Bajemon, Andre-Louis Rochet

原文摘要

Generative engine optimization needs a scorer that ranks candidate pages by the share of an answer they will receive as citations. We measure pretrained rerankers, a hosted typed decision model (Jev), eight open decision models and distilled students on GEO-Bench queries answered by ten engines that receive the same candidate sources in their prompt. Where several engines answer the same query, they agree with one another at a within-group Spearman correlation of 0.704. Jev and two open 27B decision models, used without training, are the strongest scorers at 0.575 to 0.578. A Qwen3-Reranker-4B fine-tuned with low-rank adapters on teacher labels for other GEO-Bench queries, released as GEO Citation Ranker 4B, reaches 0.542 to 0.549 across three seeds, and its differences with the 27B models and with Jev are not distinguishable from zero on 229 queries. Most of the remaining distance between the student and Jev disappears when Jev scores one passage at a time, as the student does. Students trained on teacher labels and on observed engine citations reach the same level, and labels from Jev alone match the two-teacher target at 0.6B. We release the model, the benchmark, the predictions of every reported scorer and the code.

cs.IR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.15742

Collective Kernel EFT for Pre-activation ResNets

Hidetoshi Kawase, Toshihiro Ota

原文摘要

In finite-width deep neural networks, the empirical kernel $G$ evolves stochastically across layers. We develop a collective kernel effective field theory (EFT) for a simplified pre-activation residual model. Exact conditional Gaussianity of the residual increment gives finite-width discrete identities for $G$ and its sigma-kernel source. A projected $G$-only closure and a continuous-depth approximation define reference equations for the mean kernel $K_0$, the kernel covariance $V_4$, and the next-to-leading mean correction $K_1$. Numerical tests show that this closure captures the leading mean-kernel evolution, whereas its covariance and next-to-leading accuracy depend on sigma-kernel modes omitted from the $G$-only state. For the scalar ($N=1$) model with $\tanh$ activation, a Hermite--Galerkin extension agrees with the extrapolated $K_1$ value and reduces the $V_4$ endpoint error by $85$--$93\%$ relative to the $G$-only reference. These results clarify the range of validity of the equal-time Markov $G$-only description and identify the additional collective information required beyond it.

cs.LG · hep-th · stat.ML

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.14681

Generalized Hierarchical Bayesian Segmentation with Irregular Designs, Multi-Sequence Hierarchies, and Grouped/Latent-Group Designs

Omid Shams Solari

原文摘要

Bayesian change-point and segmentation models provide uncertainty-aware piecewise-constant representations of ordered data, but exact inference is often limited to narrow likelihood classes, single sequences, or index-uniform designs. We present BayesBreak, a modular offline Bayesian segmentation framework that separates local block scoring from global inference: each candidate block supplies a marginal likelihood and any needed moment numerators, while a dynamic program combines these scores to compute posteriors over segment counts, boundaries, and latent signals. For weighted exponential-family likelihoods with conjugate priors, block evidences and posterior moments are available in closed form from cumulative sufficient statistics, enabling exact sum-product inference for p(y|k), p(k|y), boundary marginals, and Bayes regression curves. We distinguish these summaries from the joint MAP segmentation, recovered by a separate max-sum recursion. BayesBreak supports design-aware partition priors for irregular observations, exact pooling across replicates with shared boundaries, and latent-template mixtures with exact EM updates. For non-conjugate GLM blocks, the same DP layer can use deterministic local approximations such as Laplace, variational methods, EP, or quadrature. We prove a posterior-odds stability bound: uniform per-block log-evidence error ε perturbs k-odds and boundary-odds by at most (k+k')ε and 2kε. Validation includes synthetic recovery, calibration, and scaling experiments, plus four real-data illustrations: well-log geology, array-CGH copy number, equity-return volatility, and CpG-atlas methylation.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38088

What Can Firms Do in the Face of Model Distillation?

Diptangshu Sen, Jibang Wu, Juba Ziani

原文摘要

Model distillation has become a major concern in the rapidly growing generative AI discourse. In its simplest form, distillation is the phenomenon where smaller firms can use data distilled from transcripts of conversations with powerful proprietary closed-weight models to train their own models, thus achieving reasonably high model quality but at much lower costs. Besides the legitimate safety and security concerns associated with distillation, another major issue is how it can also distort marketplace outcomes. This motivates us to initiate an exploration of the problem from an economic theory lens. We consider a setting with two players: an incumbent and a distilling competitor and characterize how the incumbent's model characteristics drive the incentives around how much the competitor should distill. We also consider the impact of distillation on key market outcomes like user-side surplus and the incumbent's utility. Our key contribution, however, is to study interventions for preventing or limiting distillation attacks. In this context, we study two settings: i) when it is possible, with a certain probability (which grows in the extent of distillation conducted), to detect an attack on the incumbent's model; and ii) when a distillation attack cannot be reliably detected. In the first setting, we show that penalty-based regulations can be very effective in completely deterring distillation, although it can have potentially adverse effects on market competition and user surplus. In the latter setting, we show that the incumbent can use self-distillation as a mitigation strategy. Importantly, the effectiveness of self-distillation depends on how cost-efficiently the incumbent can deploy a new self-distilled model on the market. On the positive side, self-distillation can preserve market competition thus protecting user surplus to some extent.

cs.GT · cs.AI

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版本更新 · v2arXiv:2603.19134

Introducing M: A Modular, Modifiable Social Robot

Victor Nikhil Antony, Zhili Gong, Yoonjae Kim, Chien-Ming Huang

原文摘要

We present M, an open-source, low-cost tabletop social robot platform aimed at lowering barriers to social robotics research by making robots easier to reproduce, modify, and deploy in real-world settings. M combines a modular mechanical design, multimodal sensing, and expressive yet mechanically simple actuation architecture with a ROS2-native software package that cleanly separates perception, expression control, and data management. The platform includes a simulation environment with interface equivalence to hardware to support easy sim-to-real transfer of interaction behaviors. We demonstrate extensibility through additional sensing/actuation modules and provide example interaction templates for storytelling and two-way conversational coaching. Lastly, we report real-world use of M in interaction design and week-long in-home deployments, showing how M can serve as a practical platform for longitudinal, reproducible social robotics research.

cs.RO · cs.HC

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版本更新 · v3arXiv:2510.11547

Sublinear Algorithms for Estimating Single-Linkage Clustering Costs

Pan Peng, Christian Sohler, Yi Xu

原文摘要

Single-linkage clustering (SLC) is a fundamental method for hierarchical data analysis. In the distance setting, a $k$-clustering produced by SLC can be obtained by computing a minimum spanning tree (MST) and deleting its $k-1$ heaviest edges. This naturally induces a cost profile for the SLC hierarchy: for each $k\in[n]$, we define $\mathrm{cost}_k$ to be the weight of the resulting $k$-component spanning forest, equivalently, the minimum total weight of any spanning forest with exactly $k$ connected components. The corresponding \emph{SLC cost profile} is $(\mathrm{cost}_1,\ldots,\mathrm{cost}_n)$, and the scalar quantity $\mathrm{cost}(G)=\sum_{k=1}^{n}\mathrm{cost}_k$ is the area under this profile. We study the problem of approximating these quantities in sublinear time. We assume that the input is a weighted graph $G$ of average degree $d$ with edge weights in $\{1,\dots,W\}$, accessed through adjacency-list queries; missing edges are treated as having infinite distance. Our main result is a sampling-based algorithm that outputs a succinct sketch of the entire SLC cost profile in the distance setting. The algorithm runs in $\widetilde{O}(d\sqrt{W}/\varepsilon^3)$ time and returns a sketch from which one can derive estimates $(\widehat{\mathrm{cost}}_1,\ldots,\widehat{\mathrm{cost}}_n)$ satisfying $\sum_{k=1}^{n}\bigl|\widehat{\mathrm{cost}}_k-\mathrm{cost}_k\bigr| \le \varepsilon\,\mathrm{cost}(G)$. Thus, we obtain an $\ell_1$ approximation to the full profile whose error is at most an $\varepsilon$-fraction of the area under the true profile. In particular, this yields a $(1\pm\varepsilon)$-approximation to $\mathrm{cost}(G)$ within the same running time. We also prove a nearly matching lower bound of $Ω(d\sqrt{W}/\varepsilon^2)$ queries for estimating $\mathrm{cost}(G)$.

cs.DS

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版本更新 · v3arXiv:2608.30076

Budget-Aware Compression Pipeline for Single-GPU LLM Inference: Methods, Trade-offs, and Coupling Effects

Hongyu Yu, Yifei Shen

原文摘要

Single-GPU deployment of 70B-parameter language models on an NVIDIA GPU is constrained by device memory, long-context throughput, and engineering integration cost. We cast single-GPU inference as a budget-aware design problem over these three axes and study how pruning, quantization, and KV-cache compression interact under realistic execution. Controlled ablations show that layer-wise pruning makes weight quantization more robust. KV-cache sparsification complements INT8 KV quantization by reducing memory without hurting decoding speed, while static vector quantizers often conflict with dynamic caching. Guided by these coupling results and explicit budget tracking, we assembled a practical pipeline and compressed a 70B model to about 33 GB, sustained about 57 tokens/s on 10k token prompts on a single A40, and kept absolute accuracy within 5% on common and reasoning benchmarks. We contribute design rules and a reproducible evaluation protocol that jointly report quality, memory, and end-to-end speed, and we provide a foundation for automated pipeline search under realistic single-GPU constraints.

cs.CL

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新论文 · v1arXiv:2610.38086

From Next Day Wildfire to Next Days Wildfire: A New Multi-Continent Dataset of Active-Fire Spread for Sequence-to-Sequence Forecasting

Jose Guillen, Renzo Zanca, Valentin Barriere

原文摘要

Benchmarks such as WildfireSpreadTS (WSTS) have enabled data-driven forecasting of wildfire spread from multimodal satellite time series, but they suffer from limitations: size restricts them to large fires that burn for weeks, and they are from similar environments. We introduce AFSeq, a multimodal dataset of 1,194 recent fire events (2023 to 2026) from 8 different countries across three regions, dominated by the short, small fires that existing benchmarks underrepresent. Our dataset is the largest and most diverse to date for time-series-based wildfire spread prediction. We also introduce UTAE-AR, an extension of a classical U-Net with Temporal Auto Encoder, enhanced with recurrent modules before the decoder. This allows the model to not only predict the next day but the whole sequence in an autoregressive way. Moreover, our model can feed its own predicted fire mask back one day at a time, producing the day-by-day forecasts that single-step models cannot. Our results show that UTAE-AR surpasses state-of-the-art on both WSTS, WSTS+ and AFSeq for next-day accuracy while having 21.5% of their parameters, and being able to predict confidently at a longer horizon.

cs.CV

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首次观测 · 记录 #1软件学报:jos:7669软件学报

在轨环境预测驱动的卫星计算调度方法

黄天泽, 张其阳, 徐梦炜, 李晴, 王尚广, 黄罡, 刘譞哲

原文摘要

星地传输限制已成为当前制约卫星应用时效性的主要瓶颈, 导致海量遥感数据无法及时回传. 卫星计算通过在轨预处理与智能分析将原始数据压缩为轻量化的语义结果, 从而缓解下行压力. 然而, 通过在两颗在轨卫星上进行长期实测发现, 传统的地面静态调度难以适应高动态的太空环境, 导致卫星计算的实际部署面临以下核心矛盾: 卫星在阳照区的太阳能利用率极低, 而激进提升计算负载又易触发系统热失控. 为此, 提出一种环境预测驱动的卫星计算调度方法. 该方法通过模型预测控制, 利用轻量级的环境预测模型(太阳能采集功率、电池及温度模型)在轨构建实时约束边界, 并通过卫星数据处理流水线解耦与主动计算分载策略实现资源的动态优化. 基于在轨实测数据的模拟实验结果表明, 该方法在严格满足卫星能源与温度约束的前提下, 可显著提升在轨计算的端到端数据交付时效性与太阳能利用率.

cs.*

软件学报 · 2026 · 正式发表

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首次观测 · 记录 #1软件学报:jos:7670软件学报

基于比特切片的SM4-BGV转密技术

谭清泉, 彭聪, 李小红, 何德彪

原文摘要

随着数据安全需求的提升与云计算场景的广泛应用, 如何在不解密的前提下对加密数据进行高效计算已成为密码学研究的重要方向. 全同态加密(fully homomorphic encryption, FHE)能够支持密态计算, 但较高的计算与存储开销限制了其在高吞吐或实时场景中的应用. 为此, 业界提出转密(transciphering)的技术, 即利用同态加密对已有对称加密体系(如AES、SM4)的密文进行同态解密和再计算, 从而有效结合对称密码的紧凑性和同态加密的密态计算能力. 相较于已较为成熟的AES转密研究, 现有SM4转密方案普遍面临乘法深度大、噪声增长快和整体性能受限的问题. 针对上述挑战, 提出一种基于比特切片(bit-slicing)的高效SM4转密实现方案, 并在BGV同态加密框架下完成设计与实现. 该方案通过对SM4 S盒进行布尔建模与算子层级的结构化优化, 显著提升S盒性能; 同时利用比特切片的特性高效实现线性变换, 避免额外的代价较高的同态操作. 最后, 为应对32轮轮函数迭代所需的高乘法深度和噪声积累问题, 设计动态噪声管理模块, 可在噪声预算不足时自动实现密文批量自举操作, 从而保证计算的正确性和可扩展性. 实验结果表明, 所提方案较当前公开的SM4转密实现均取得更优性能: 相较于基于BGV的有限域实现, 吞吐量提升2.35倍; 相较于基于CGGI的布尔电路实现, 吞吐量提升7.04倍.

cs.*

软件学报 · 2026 · 正式发表

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首次观测 · 记录 #1软件学报:jos:7671软件学报

融合多智能体系统与检索增强的芯片分区布局框架

孙可钦, 杨秋松, 李明树

原文摘要

在超大规模集成电路物理设计流程中, 芯片分区直接决定后续布局布线的效率与质量. 构建融合多智能体与检索增强的芯片分区布局框架ChipMASRAO, 将多智能体强化学习(multi-agent reinforcement learning, MARL)与检索增强(retrieval-augmented optimization, RAO)技术深度融合. 该系统利用RAO机制从知识库检索历史分区结果作为高质量初始化, 通过MARL进行增量优化和多智能体协同评估, 最终驱动OpenROAD完成布局. 与传统方法每次从零计算不同, ChipMASRAO通过复用历史方案避免重复计算. 在ISPD 2015测试集8个设计上的实验表明: 相较于Baseline和ChipDRAG中性能较好的方法, 半周线长(half-perimeter wirelength, HPWL)总平均改善21.6%; 多智能体评估机制使测试集设计负载平衡度平均提升3.47%. 消融实验证实了RAO的关键作用: 引入RAO后平均超边切割数(Cutsize)降低86.5%, HPWL改进30.4%. 提出“检索-复用-修正”的新型范式, 验证了知识驱动方法在芯片分区问题上的有效性.

cs.* · cs.AI · cs.MA

软件学报 · 2026 · 正式发表

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首次观测 · 记录 #1软件学报:jos:7743软件学报

人工智能保险箍: 发展现状与未来展望

李金海, 李若涵, 胥迤潇, 李默涵, 孙彦斌, 田志宏, 方滨兴

原文摘要

近年来, 以大语言模型和具身智能为代表的面向通用人工智能发展的相关技术快速演进, 其广泛应用在带来巨大潜力的同时, 也引入了复杂的安全风险. 这些风险总体可分为两类: 一类是源于模型缺陷、系统设计或环境不确定性的自发性风险, 另一类是由外部对抗攻击或恶意诱导触发的诱发性风险. 为保障人工智能系统在开放环境中的安全可靠运行, 人工智能保险箍通过风险感知、风险量化与风险熔断这3类核心能力, 为人工智能行为体构建独立的安全保障机制, 使系统具备在行为偏离安全边界时及时识别、风险评估与干预控制的能力. 以人工智能保险箍为研究对象, 系统梳理相关研究进展与关键技术. 首先界定人工智能行为体的基本概念及其潜在失控风险类型. 随后围绕风险感知、风险量化与风险熔断这3个技术维度, 对相关研究进行系统分类与综述, 并分析国内外代表性工作的研究现状. 区别于以威胁检测或单点防御技术为核心的综述, 从构建面向人工智能行为体的底线安全机制这一视角出发, 对人工智能保险箍的技术体系进行整体分析. 最后讨论该领域面临的关键挑战与未来发展方向, 为构建安全可控的人工智能系统提供参考.

cs.* · cs.CL · cs.AI

软件学报 · 2026 · 正式发表

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新论文 · v1arXiv:2610.38084

CADENCE: Distributional Next-Transaction Forecasting for Business Cash-Flow Underwriting

Alexander Wu, Jingchao Huang

原文摘要

Recurring cash flows are the backbone of cash-flow underwriting: they separate durable revenue from one-off deposits, expose standing obligations, and ultimately determine ability to pay. For small businesses, recurrence is both indispensable and hard to estimate, because much of their income arrives as ambiguous transfers, most payment streams are short, and schedules follow calendar conventions that frequency-domain methods treat as noise. We present CADENCE, Calendar-Aware Distributional Event Network for Cash-Flow Estimation, a compact causal Transformer that treats each transaction as a token and predicts the amount and a full, nonparametric timing distribution of every cash flow's next payment. Inheriting transaction semantics from an external labeler and a frozen text encoder keeps CADENCE compact yet general across the long tail of small-business counterparties, while predicting a calibrated distribution rather than an opaque embedding makes its outputs interpretable enough to feed existing underwriting models and power credit decisions. Ablations confirm the role of each design choice, with counterparty semantics driving recall on short histories, calendar inputs driving timing accuracy, and no-event supervision driving precision. Against the production FFT baseline, CADENCE improves one-day timing accuracy by 93.3% and next-event F1 by 6.7%, while producing calibrated distributions that serve directly as a confidence gate for downstream decisions. In an end-to-end credit evaluation on production applications, replacing FFT recurrence features with CADENCE features raises KS by 4.8%, raises the approval rate at a fixed loss rate by 18.2%, and lowers the loss rate at a fixed approval rate by 13.1%.

cs.LG · q-fin.RM

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新论文 · v1arXiv:2610.38082

Interpretable Feature Discovery from Unstructured Data Using LLMs and Statistical Choice Models

Baptiste Arnaudo, Amine Bennouna, Sébastien Martin

原文摘要

Feature extraction from unstructured data typically relies on opaque embeddings unsuited to interpretable decision-making. Interpretable features, on the other hand, typically require costly, and often infeasible, hand-engineering. Large language models (LLMs) offer a natural bridge, as they can read raw data and describe it in interpretable terms. One could simply ask an LLM to extract features from a large sample of the dataset, but context limitations and diluted attention can lead to unsatisfactory results. We introduce a method that automatically discovers interpretable, decision-relevant features from unstructured data. Our key idea is to perform pairwise comparisons between items, asking an LLM to articulate, in interpretable terms, the features distinguishing each pair. We treat these explanations, or rationales, as data: we develop a statistical choice model that combines these rationales across the comparisons to identify relevant features and estimate per-item, per-feature scores. We first test our method on a synthetic hiring dataset of cover letters with known ground-truth features and show that it significantly outperforms direct LLM feature extraction in feature recovery. We then apply it to the ChangeMyView (Reddit) debate dataset, where it discovers several interpretable features associated with persuasion while reaching predictive accuracy comparable to established baselines. We also show how the same rationales can be used to audit LLM decisions, yielding a holistic score whose decomposition over interpretable features reveals what the LLM rewards.

stat.ML · cs.AI · cs.LG

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