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新论文 · v1arXiv:2610.34529

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

Tansel Uyar

原文摘要

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

eess.IV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.17384

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

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

原文摘要

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

cs.RO · cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.03473

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

Nicklas Nidhögg

原文摘要

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

cs.FL · cs.DC · cs.PL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29802

Prefix Visibility Ratio: Quantifying Partial Observability in Carpet Bombing Detection

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

原文摘要

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

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.29109

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

Yucheng Du, Xiyang Hu

原文摘要

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

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34523

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

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

原文摘要

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

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34521

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

Sam Uwejan, Erdogan Dogdu

原文摘要

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

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34520

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

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

原文摘要

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

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34519

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

Sooyoung Kwon, Carlee Joe-Wong, Kyunghan Lee

原文摘要

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

cs.RO · cs.AI · cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2501.02624

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

Pierre C Bellec

原文摘要

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

math.ST · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34516

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

Reijo Jaakkola

原文摘要

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

cs.CC · cs.LO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2411.08550

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

Azmine Toushik Wasi, MD Shafikul Islam

原文摘要

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

cs.LG · cs.CE · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34514

Ambiguous Fits: The Rashomon Effect and Challenges in Scaling Law Estimation

Bruno Mlodozeniec, Spencer Frei

原文摘要

Estimating neural scaling laws involves an end-to-end pipeline: designing a scaling recipe, training models across scales, evaluating models on a target metric, and deploying an algorithm to fit a scaling law to the resulting data. This final fitting stage is often treated as routine: standard procedures minimize a non-convex fitting loss using local optimization from a grid of initializations, and return the best solution found. Yet such heuristic searches can fail in two distinct ways: they offer no guarantee of returning a nearly optimal solution, and they obscure whether fundamentally different parameter choices fit the data nearly as well. Indeed, we show that standard scaling law fitting is subject to a Rashomon effect (Breiman, 2001): the sublevel sets of near-minimizers correspond to broad parameter regions whose extrapolations diverge substantially at scale. We resolve these issues with a deterministic branch-and-bound algorithm that guarantees nearly optimal fitting loss and exhaustively maps the near-minimizers to the implied range of scaling curves.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.13174

TellOnce: Correcting Coding Agents Once, Improving Their Future Actions

Yujun Zhou, Kehan Guo, Haomin Zhuang, Xiangqi Wang, Yue Huang, Zhenwen Liang, Pin-Yu Chen, Tian Gao 等 11 位作者

原文摘要

Interactive LLM agents increasingly work with users across tasks, yet remembering a previous correction does not ensure that the agent changes its behavior in the future. In a diagnostic constructed from anonymized real-world agent interactions, agents using Mem0 still violate 57.5% of applicable preference checks. We introduce TellOnce, a drop-in personalization layer that turns conversational corrections into persistent, scoped, and revisable behavioral rules without updating the underlying agent model. TellOnce identifies which requirements should persist, determines when they apply, reconciles subsequent feedback with existing rules, and prepares reusable checks when reliable verification is possible. We evaluate whether learned corrections improve future behavior by freezing the learned state and testing on subsequent tasks without additional user corrections. Across three models on ClawArena and MemoryArena, TellOnce achieves the lowest preference violation rate in all six settings, reducing violations relative to PGMem by 19.3-42.3 percentage points on ClawArena and 3.0-22.9 points on MemoryArena, while maintaining broadly comparable task performance. On SWE-Together, TellOnce further improves task quality on new coding tasks by learning from feedback received during earlier tasks. Experiment code is available at https://github.com/YujunZhou/TellOnce_exp, and the deployable skill at https://github.com/YujunZhou/tellonce.

cs.LG · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.16063

ViT-AdaLA: Adapting Vision Transformers with Linear Attention

Yifan Li, Seunghyun Yoon, Viet Dac Lai, Franck Dernoncourt, Jason Kuen, Yu Kong, Trung Bui

原文摘要

Vision Transformers (ViTs) based vision foundation models (VFMs) have achieved remarkable performance across diverse vision tasks, but suffer from quadratic complexity that limits scalability to long sequences. Existing linear attention approaches for ViTs are typically trained from scratch, requiring substantial computational resources, while linearization-based methods developed for large language model decoders do not transfer well to ViTs. To address these challenges, we propose ViT-AdaLA, a novel framework for effectively adapting and transferring prior knowledge from VFMs to linear attention ViTs. ViT-AdaLA consists of three stages: attention alignment, feature alignment, and supervised fine-tuning. In the attention alignment stage, we align vanilla linear attention with the original softmax-based attention in each block to approximate the behavior of softmax attention. However, residual approximation errors inevitably accumulate across layers. We mitigate this by fine-tuning the linearized ViT to align its final-layer features with a frozen softmax VFM teacher. Finally, the adapted prior knowledge is transferred to downstream tasks through supervised fine-tuning. Extensive experiments on classification and segmentation tasks demonstrate the effectiveness and generality of ViT-AdaLA over various state-of-the-art linear attention counterpart.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13148

When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels

Robson Tigre, Hugo Gobato Souto

原文摘要

Large language models are increasingly used to simulate consumers, yet agreement with aggregate survey outcomes can conceal errors in the subgroup estimates that guide decisions. We develop a framework for deciding when to trust, correct, or abandon a synthetic consumer panel by distinguishing population mismatch from differences in conditional responses. The framework combines diagnostics for covariate overlap, conditional calibration, and stability across models with an augmented inverse probability weighting estimator that treats LLM predictions as auxiliary information. Correction requires a complete invitation frame, response indicators for everyone invited, and human outcomes for respondents, together with assumptions of missing at random and positivity. We evaluate the framework using simulations and controlled nonresponse experiments constructed from paired human and LLM responses in ANES and Twin-2K-500. In identified simulation regimes, point bias remains small when either the response model or the outcome model is correct, but nominal 95\% confidence intervals cover only 88--94\% in some regimes. When nonresponse depends on missing human outcomes, substantial bias persists and coverage approaches zero. Controlled masking of the paired datasets reproduces this contrast between successful point correction under imposed missing at random and failure under nonignorable nonresponse. These findings show why validation must address the intended population and task, and why the scope for correcting synthetic estimates depends on the available human data and the assumptions that identify the target.

cs.HC · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34511

Insdel Decoding Algorithms for Maximum Distance Separable Codes

Bencheng Fan, Margreta Kuijper, Jingge Zhu

原文摘要

In this paper, we focus on decoding MDS linear block codes for insdel errors. Building on our recent work for two-dimensional Reed-Solomon codes, we present a decoding algorithm for bounded distance decoding for the general insertion-and-deletion scenario that can be used for any MDS code of rate at most half. Our algorithm employs a sliding window and has a time complexity smaller than brute-force decoding. In particular, for optimal Reed-Solomon codes, it corrects insdel errors up to the half-Singleton bound, which exceeds the scope of existing methods. Additionally, for the special cases of insertion-only and deletion-only insdel error patterns, we present two different algorithms with a time complexity smaller than our general algorithm.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34509

Symbolic Direct Construction of Augmented Systems for Levenberg-Marquardt Power Flow

Tianshi Cheng, Venkata Dinavahi

原文摘要

Levenberg-Marquardt power flow (LMPF) addresses ill-conditioned equations and provides least-squares stationary points for infeasible operating requests. Conventional implementations form damped normal equations, where J^T J introduces fill, squares the spectral condition number of the undamped part, and adds sparse-product cost. We propose an augmented LMPF formulation with a symbolic-permutation-based Jacobian operator that fills the augmented blocks directly. We prove that the matrix is symmetric quasi-definite for positive damping, enabling fixed-order, pivot-free QDLDL factorization. This property also motivates the solver choice: KLU value-only refactorization can reuse an unsuitable pivot order, forcing fresh full factorizations on difficult LMPF trajectories. We further introduce one-probe adaptive trust-region initialization for the radius and damping. Direct assembly is 7.5-11.9x faster than the fastest coordinate-list path and 18.0-58.8x faster than cached normal-equation sparse-product formation; QDLDL reduces numerical refactorization and triangular-solve time by up to 3.40x on identical matrices. Adaptive initialization solves flat-started RTE cases in 19-25 factorizations, 7.5-11.6x faster than reproduced three-step LM. On case2383wp, our 84-iteration run performs 84 factorizations and 168 resolves in 237 ms, versus published times of 200.780-441.72 s with fewer outer iterations, corresponding to speedups of 847-1860x. These results require complete factorization and resolve accounting and reveal initialization and legacy-parameter effects. DC-initialized NR exhibits fast quadratic convergence throughout every tested solvable loading range, motivating clearer, reproducible definitions of ill-conditioned power-flow benchmarks.

eess.SY · cs.PF · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34503

Multimodal Requirements Traceability Link Recovery in Industrial Settings

Haoming Huang, Shinpei Hayashi, Kazuya Kohata, Myeongki Kim, Yuki Kurihara

原文摘要

Requirements traceability links support activities such as change-impact analysis, coverage checking, and validation. Industrial requirements can include traceable images, which text-only approaches cannot process directly. For a fixed classifier configuration, binary decisions alone offer no direct way to adjust the balance between missed and incorrect links. We extend a retrieval-augmented generation (RAG) approach for inter-requirements traceability link recovery (TLR). Our extension retrieves text and image candidates separately, classifies each retrieved pair with a vision-language model, and filters the predicted links by verbalized confidence. We evaluate our approach on three non-overlapping industrial requirement sets and six public text-only datasets. On the industrial test sets, our approach achieves an F1 score of 0.310, compared with 0.114 for our replication of the baseline approach by Hey et al. Our approach recovers links in all four modality combinations while improving text-only F1 on the industrial data and maintaining similar performance on the public datasets, but recovery remains limited by failures to retrieve related text requirements for image queries. Confidence filtering produces link sets with different precision and recall, removing false positives and some correctly recovered links from the unfiltered link set. This study provides industrial evidence on the potential and limitations of a current RAG-based TLR approach for multimodal requirements and identifies cross-modal retrieval as the component to prioritize in further optimization.

cs.SE · cs.AI · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34501

Best Policy Tracking in Reinforcement Learning

Judith Echevarrieta, Etor Arza, Aritz Pérez, Josu Ceberio

原文摘要

Reinforcement learning commonly exhibits non-monotonic performance dynamics in single-task settings. During learning, a sequence of policies is generated whose true performance is unknown and may oscillate significantly across iterations. Despite this well-known phenomenon, standard experimental practice typically reports either the final visited policy or the best among periodically evaluated ones, implicitly assuming that these selection criteria reliably approximate the best-performing policy visited throughout the entire sequence. In this work, we present an empirical study on the implications of non-monotonicity for policy selection in single-task policy-based reinforcement learning. We introduce post-hoc diagnostic metrics to characterize performance degradation and assess the accuracy and efficiency of different selection criteria. These metrics allow us to evaluate commonly used selection strategies across standard continuous-control benchmarks using a well-established on-policy actor-critic method. Our results show that, in this representative experimental setting, widely adopted selection practices can deviate considerably from the best policy encountered during learning, leading to underperforming estimates and suboptimal use of computational resources. As an alternative, we recommend a validation-based policy selection strategy constrained by computational cost, which provides a more consistent and accurate approximation of the best visited policy while improving selection efficiency. Overall, our findings highlight policy selection as an underexplored factor in the evaluation protocols of reinforcement learning and motivate further study on explicit and standardized validation-based selection procedures.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34500

CaliMem: Search-Efficient Data Agent via Calibrated Memory

Hanze Li, Athan Z. Li, Feiyang Kang, Jiachen T. Wang, Yuhan Chen, Ruishan Liu, Ruoxi Jia

原文摘要

Data curation is a first-order design choice in machine learning, yet tailoring training data to a downstream model and task remains costly in labor, expertise, and compute. Datasets are therefore often processed through fixed pipelines and reused across applications. We show that this assumption is fragile: across 55 dataset--model tasks spanning biological domains, the same curation recipe can help one model while hurting another. Effective curation must adapt to the downstream setting, motivating automated search over data recipes. We first build an agentic system that searches for curation policies at deployment, improving data utility via \textit{test-time curation (TTC)}. Although achieving performance gains, its search is driven largely by recent feedback and remains local. Adding persistent memory helps the agent retain evidence across iterations and reveals a deeper problem: it forms claims that extend beyond the conditions in which they were observed, sometimes abandoning an entire strategy after a single unsuccessful trial. To address this problem, we introduce CaliMem, a calibrated-memory agentic loop that requires the agent to predict the outcome of each proposed policy before executing it. Comparing this prediction with the observed result gives the agent a \textit{reality check} on the claims behind its proposal. It uses the prediction error to retain supported claims, revise mistaken ones, and specify the conditions under which a conclusion holds. This makes the agent's beliefs testable, reduces overgeneralization, and supports more effective long-horizon exploration. Across biological and LLM post-training tasks, CaliMem consistently outperforms fixed curation baselines and published methods, and exceeds full-data training by up to 19.4\%. Code is open-sourced.

cs.AI

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版本更新 · v2arXiv:2605.00246

Fidelity-Guaranteed Entanglement Routing with Distributed Purification Planning

Anthony Gatti, Anoosha Fayyaz, Prashant Krishnamurthy, Kaushik P. Seshadreesan, Amy Babay

原文摘要

Many quantum-network applications require end-to-end Bell pairs whose fidelity exceeds a request-specific threshold, but existing entanglement routing algorithms either optimize only throughput without regard for fidelity or enforce fidelity guarantees using centralized controllers with global link-state knowledge. We present Q-GUARD, an online entanglement routing algorithm that enforces per-request fidelity thresholds within a distributed protocol model in which nodes exchange link-state information only with their $k$-hop neighbors. After link outcomes are realized in each slot, Q-GUARD builds per-link purification cost tables from realized Bell pairs, allocates per-hop fidelity targets using a Werner-state equal-split rule, and selects between candidate path segments using a segment-local expected-goodput (EXG) metric that jointly accounts for swap success, purification overhead, and resource availability. We also introduce Q-GUARD-WS, an extension that exploits per-link hardware quality estimates to allocate purification effort non-uniformly across hops. On synthetic 100-node topologies with heterogeneous link fidelity and stochastic BBPSSW purification, Q-GUARD raises the qualified success rate from under 20\% to over 85\% on 4-hop paths and nearly doubles the qualified service radius in Euclidean distance relative to throughput-only and naive-purification baselines, while Q-GUARD-WS provides additional throughput gains under high hardware heterogeneity.

cs.NI · quant-ph

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新论文 · v1arXiv:2610.34495

Approximate Unlearning in Prototype-based-classifiers

Sreejita Ghosh, Mohammad Mohammadi

原文摘要

The recent data and AI regulations and governance needs have highlighted the need for removing specific data even from models trained on or with them. While retraining the entire model is a theoretically trivial solution, it is computationally expensive, thereby motivating research in Machine Unlearning. This paper introduces a robust approximate unlearning technique for the family of prototype-based classifiers called Generalized Learning Vector Quantization, aimed at removal of effects of specified samples, either due to deletion request or due to them being identified as noisy or corrupt. We first show the impact of the different sizes of the forget set, as well as the characteristics of the data in this set, on the unlearned model. To address the sources of instabilities detected in this unlearning method we propose an adapted version of this technique, such that the unlearning is robust to the characteristics of the forget set. Thereafter using the intrinsic interpretability property of GLVQ and appropriate metrics defined in unlearning literature, we show the efficacy (approximately 0 deviation of parameters and performance of unlearned model relative to retrained model) and efficiency (6X for smallest dataset to 10^(5)X in the largest dataset) of the proposed unlearning techniques compared to naive retrained, on six benchmark datasets for tabular data recommended by literature. Although in this work we investigated only binary class problems, the proposed robust unlearning strategy can be readily applied for removing sample effects from any trained GLVQ model, i.e., it does not require any modification in the training scheme of GLVQ.

cs.LG

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版本更新 · v2arXiv:2606.02552

Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation

Siyuan Bian, Congrong Xu, Jun Gao

原文摘要

Despite advances in depth estimation, flying points remain a persistent failure mode: near object boundaries, depth estimators often predict spurious 3D points in the empty space between foreground and background surfaces. We trace this artifact to a standard modeling choice: assigning each pixel a single depth hypothesis. At boundaries, a pixel can straddle a foreground and a background surface, so its true depth is ambiguous between the two. A model that predicts a single depth cannot keep both possibilities, so training instead pulls the prediction toward an intermediate depth that lies on neither surface. We address this with MDA, a mixture-density representation that lets the model predict multiple depth hypotheses and their associated probabilities for each pixel. Near boundaries, different hypotheses can align with different surfaces, and the decoded depth is selected from one of these hypotheses rather than placed in the empty space between them. Across different architectures, MDA substantially improves boundary reconstruction over each base model and largely removes flying-point artifacts even under severe input blur, while adding negligible runtime overhead. The same mixture-density framework naturally extends to transparent objects, where it predicts multiple depth layers at transparent pixels, and to sky regions, where a dedicated component separates the unbounded sky from finite-depth regions, producing flying-point-free skylines.

cs.CV · cs.AI

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新论文 · v1arXiv:2610.34493

Certifying Distributionally Robust Decisions from Projection Observations

Pouya Ahadi, Kamran Paynabar

原文摘要

A scalar measurement can determine a robust decision without identifying the latent covariance or every decision risk. We study finite Gaussian projection experiments with a fixed mean-plus-standard-deviation target. The observation laws define a set of indistinguishable moment models. We exhibit decisions that agree throughout that set despite unidentified risk values, and give a confidence-set certificate that returns a decision or an unresolved outcome. We prove soundness, necessary abstention for indistinguishable models with incompatible optima, and a fixed-checkpoint certification guarantee. A scalar profile reduces nonlinear risk comparisons to convex inner problems. Across seven synthetic instances, jointly calibrated moment regions give sufficient budgets 21 to 52 times explicit expected-sample information lower bounds. Separately measured stopping times are lower, with zero decision errors in 1,900 simulated streams. These comparisons do not constitute matching bounds, and the guarantees require Gaussian replication.

stat.ML · cs.AI · cs.LG · math.OC

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新论文 · v1arXiv:2610.34492

Wave Is All You Need: A Quantum Language Model Based on Fully Connected Quantum Boltzmann Machine

Jun Liu

原文摘要

This work proposes a self-attention mechanism based on the fully connected quantum Boltzmann machine (FC-QBM), and further constructs a fully connected quantum Boltzmann machine language model (FC-QBM-LM) using the mechanism as the core engine. By performing unitary evolution on semantic quantum states, the mechanism implicitly implements positional encoding and self-attention computation, eliminating the need to decompose continuous semantic streams into discrete matrices for explicit matrix multiplication, with the measurement results naturally satisfying the normalization condition. Specifically, this work rigorously derives the computational principle of the self-attention mechanism based on FC-QBM, and demonstrates that this mechanism is mathematically equivalent to the classical self-attention mechanism. The mathematical principles governing the training and generation of FC-QBM-LM are thoroughly derived, and the two corresponding algorithms are subsequently developed. An 8-qubit FC-QBM-LM is finally constructed and trained on the corpus of Tang poetry. Under the 8-qubit setting, the quantum layer implements the entire positional encoding and self-attention computation pipeline with only 44 parameters. In the noiseless scenario, the model exhibits favorable convergence. Furthermore, the quantum circuit proposed in this work, which terminates with Y-axis rotation gates, achieves better convergence than the counterpart terminating with X-axis rotation gates. Under the small-sample regime and across eight distinct noise intensity levels, the model demonstrates strong noise robustness, retaining over 99% of its noiseless convergence performance. Experiments on five-character quatrain generation reveal that the model preliminarily exhibits three core capabilities: associative and divergent thinking, content generation across imagery and human-aligned word ordering.

quant-ph · cs.CL · cs.LG

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新论文 · v1arXiv:2610.34491HF 社区推荐

What Effort Level Should I Choose? On the Economics of Reasoning Effort in LLM Agents

Kaiser Sun, Yongchan Kwon, Zain Hasan, Shang Zhu, Kaitlyn Zhou, Federico Bianchi

原文摘要

Generative models increasingly expose a reasoning effort setting that lets users scale inference-time compute, trading higher cost for potentially improved performance. Users must choose an effort level before knowing whether extra reasoning will help, and even a successful run does not reveal whether the effort purchased was necessary. Reasoning effort is therefore a credence good: buyers observe the outcome but cannot assess the marginal value of the computation they paid for. In this work, we measure the counterfactual effect of effort levels on both proprietary and open-weight models on four benchmarks, spanning single-call reasoning and long-horizon agentic tasks. Effort, it turns out, does not consistently preserve tasks that lower effort solves, can overspend, and can change trajectory behavior. First, we find that although higher effort can raise accuracy in aggregate, it frequently fails on tasks that lower effort solved. Second, on the subset of easy tasks that every effort level solves, higher effort is generally more costly, with the highest effort level costing up to 42x as much as the lowest. Lastly, effort changes not only how many tokens are used, but also how agents solve tasks: at higher effort, agents verify their own work more heavily and more often search for solutions than work through problems directly, both of which lengthen trajectories without necessarily altering the outcome. These findings show that naively choosing the highest level is not only cost-inefficient but also offers no guarantee of the best instance-level performance. A simple alternative, starting at the lowest effort and escalating only after a verified failure, matches or improves on fixed top effort across all models and benchmarks at lower cost for most proprietary models.

cs.AI · cs.CL

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新论文 · v1arXiv:2610.34490

Efficient Proof Generation for Lean Statements with Multiple Candidate Answers

Pengzhen Lin, Ke Zhang, Maziar Raissi, Zhouxing Shi

原文摘要

In formal systems such as Lean 4, efficiently finding answers to mathematical problems and generating verifiable proofs is an important task in automated mathematical reasoning. In the traditional way where an upstream solver provides multiple candidate answers, the common workflow requires searching for proofs for each candidate separately, incurring redundant computational cost. To this end, we propose a joint proof framework that integrates candidate answer selection, answer supplementation, and formal proof generation into a unified reasoning process. At its core is a trained prover that selects given or proposes new answers and generates a proof for at least one answer in a single call. Joint proof can also reuse generated proofs to check for faithfulness errors. For problems with a unique answer, if two distinct answers both obtain verifiable proofs under the same formal statement, this confirms the statement does not faithfully preserve the original problem semantics. On MATH-500, AMC 2023, and AIME 2024/2025/2026, our framework achieves the same formal proof success rate as per-candidate proof generation with about one sixth of the token cost, and identifies at least 6.1% of the formalized statements as unfaithful without requiring additional judges. Our model and code are available at https://github.com/Linppz/Gobble-Prover.

cs.CL · cs.AI · cs.LG

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版本更新 · v2arXiv:2606.12019

MPPI-based Informative Trajectory Planning for Search and Capture of Drifting Targets with ASVs

Sanjeev Ramkumar Sudha, Marija Popović, Erlend M. Coates

原文摘要

Autonomous surface vehicles (ASVs) offer an efficient solution for environmental cleanup as well as search and rescue operations in open waters. Targets in these settings drift continuously, so efficient search must balance exploration of unobserved regions with tracking of known targets. However, most target tracking and pursuit scenarios consider simple guidance behaviours and short-term predictions for decision-making. In this letter, we address the problem of search and capture of multiple drifting targets, such as litter, in marine environments, using an adaptive informative planning approach. A key aspect of our strategy is a spatiotemporal informative planning method based on model predictive path integral (MPPI) control, a sampling-based model predictive control method. Our planner balances search and tracking objectives with a cost design that couples spatiotemporal target drift predictions and exploration objectives. The planner directly generates kinematic-level commands by optimising continuous trajectories over long horizons. In the interception stage, we use pure pursuit guidance for physical capture of the targets. Simulations show that our planner captures up to 18% more targets than the strongest baseline, proving its efficiency for environmental cleanup. Finally, we validate our approach in field trials with an ASV.

cs.RO

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新论文 · v1arXiv:2610.34489

Proceedings Twelfth International Conference on Non-Classical Logics. Theory and Applications

Andrzej Indrzejczak, Tomasz Jarmu{ż}ek, Mateusz Klonowski, Michał Zawidzki

原文摘要

Non-Classical Logics. Theory and Applications (NCL) is an international conference aimed at presenting novel results and survey papers in the broad field of non-classical logics and their applications. The first two editions were held in Ł{ó}d{ź}, Poland, in September 2008 and 2009. Subsequent editions were organised in Toru{ń} (2010, 2012, 2015, 2018) and Ł{ó}d{ź} (2011, 2013, 2016, 2022, 2024). The tenth edition of the conference, organised by the University of Ł{ó}d{ź} in 2022, was the first to have its proceedings published in EPTCS. We are honoured and pleased to continue this practice and, for the third time, include all accepted long papers in an EPTCS volume. Furthermore, for the first time, we have also included all accepted extended abstracts in the volume.

cs.LO · cs.SC

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