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版本更新 · v3arXiv:2508.20729

Re4: Scientific Computing Agent with Rewriting, Resolution, Review and Revision

Ao Cheng, Lei Zhang, Guowei He

原文摘要

Large language models (LLMs) have demonstrated abilities to perform complex tasks in multiple domains, including mathematical and scientific reasoning. In this work, we construct a novel agent framework for solving representative problems in scientific computing. The proposed agent incorporates a "rewriting-resolution-review-revision" logical chain via three reasoning LLMs (the Consultant, Reviewer, and Programmer) in a collaborative and interactive manner. The Consultant module enables knowledge transfer to link problems to professional domain insights, thereby rewriting problem descriptions through text augmentation. The Programmer module generates and executes well-structured code to solve problems. The Reviewer module enables self-debugging and self-refinement through interactive feedback with code runtime outputs. Through the end-to-end review mechanism, the Programmer's executable code undergoes iterative revision. We comprehensively evaluate the framework on typical partial differential equations (PDEs), engineering-oriented flow past a cylinder, ill-conditioned linear systems, and data-driven physical analysis problems. Compared with a single-model approach, this collaborative framework significantly improves the bug-free code generation rate and reduces non-physical solutions, establishing a reliable framework for autonomous code generation from natural language descriptions. The review mechanism improved the average execution success (bug-free code and non-NaN solutions) rate of DeepSeek R1 from 59% to 82%, while ChatGPT 4.1-mini improved from 66% to 87% and Gemini-2.5 improved from 60% to 84%. Automatic code generation and review provide a promising workflow for scientific computing; analysis of solver-development trajectories further reveals recurring implementation difficulties in boundary treatment, array indexing, and the preservation of numerical constraints.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.14627

Algebraic Operations on Tensor Trains

Petr Tichavsky

原文摘要

The tensor train (TT) model is widely used to approximate high-dimensional tensors, enabling efficient handling of data that may exceed available memory. TT helps address the curse of dimensionality in applications such as system identification and dynamic programming. In some applications, TT is known as a ``matrix product state" (MPS). The last product is also known under the acronym MPO--MPS. The proposed algorithms achieve an improved trade-off between computational efficiency and accuracy compared to state-of-the-art methods.

cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37350

Generalizable Target-Less Joint Intrinsic-Extrinsic Camera-LiDAR Calibration using Deep Pixel-Point Correspondences

Simon Bultmann, Daniele Cattaneo, Abhinav Valada

原文摘要

Accurate camera-LiDAR calibration is an essential prerequisite for robust multi-modal perception in robotics. Recent target-less methods based on learned pixel-point correspondences achieve high extrinsic calibration accuracy and generalize across environments. However, they operate on rectified images with known camera intrinsics, restricting their applicability to systems whose cameras have already been individually calibrated. We present a fully automatic, target-less framework that jointly estimates camera intrinsics, radial-tangential distortion, and camera-LiDAR extrinsics from raw images and LiDAR scans. Our method generalizes deep correspondence-based calibration from rectified images to raw cameras through intrinsic- and distortion-aware correspondence learning via synthetic augmentations. During calibration, we iteratively couple correspondence prediction and nonlinear optimization to jointly refine the camera parameters and relative sensor transformation. We evaluate our approach on an unseen camera-LiDAR configuration from the KITTI dataset and on three real robotic platforms in a zero-shot manner. The results demonstrate that joint calibration with our generalized pixel-point correspondence network improves extrinsic accuracy over correspondence-based methods using pre-calibrated cameras, while additionally recovering accurate intrinsics.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37349

TimeLineXR: An Interactive Exploration of Time in Vision-Language Models

Nidham Tekaya, Markus Passecker, Matthias Zeppelzauer

原文摘要

Recently, it has been shown that vision-language models (VLMs) have a certain intrinsic understanding of time. They can estimate the temporal origin of objects and encode time along a low-dimensional, non-linear manifold inside their embedding space~\cite{tekaya2025matter}. That result is quantitative and reproducible, but the structure itself stays out of reach for end users: they cannot see it, query it with their own images, or inspect where a query lands relative to other years on the time axis. We present TimeLineXR, an interactive demonstration that turns this time axis into an explorable 3D scene. A web demo lets users upload or select images and see each placed on a color-coded 3D time axis; a Three.js WebXR scene renders the same time axis as a room-scale representation that can be grabbed, scaled, inspected, and queried by throwing image cards into it. Both clients run the same prediction pipeline against the same cached temporal index: encode the query with CLIP, project it through cosine-kernel KPCA, and snap it to the Bézier time axis. The system turns each image into a point on the time axis, so users can compare where different objects land and read off their estimated first year of appearance.Code source and reproduction steps can be found in: https://github.com/sterngefeuert/timeline-xr.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37348

Uneven Use, Similar Trust Generative AI in Denmark and Romania

Iulian Lucau, Adelin-George Voicu, Simon Munk Christensen

原文摘要

Generative AI (GenAI) has spread unevenly across Europe, and it is unclear whether higher use brings higher trust. This paper compares Denmark and Romania, the EU's highest and lowest GenAI users, and asks whether the difference in public and institutional trust between them corresponds to the difference in trust in AI. Drawing on trust, technology-acceptance and digital-divide theory, it is a descriptive, contrasting-case analysis of harmonised data: GenAI usage comes from Eurostat, trust in AI more generally from the KPMG/Melbourne global study and public and institutional trust from the Standard Eurobarometer. In 2025, 48.4% of Danes aged 16-74 had used GenAI in the past three months, against 17.8% of Romanians. Use falls with age and rises with education in both countries, and the relative gaps are far larger in Romania; the patterns remain visible within the published age-by-education categories. Trust in AI does not follow usage: Romanians trust AI as much as Danes or slightly more, although Denmark leads by 28 to 45 percentage points on trust in government, parliament and the legal system. The Denmark-Romania comparison thus does not support a simple country-level correspondence between general institutional trust and trust in AI; in KPMG's pooled model, trust in AI is tied most closely to confidence in AI governance. The observed usage difference is more consistent with disparities in general digital skills and opportunities for work- and education-related use than with differences in trust. However, the available data cannot identify the independent effects of these factors. The results rest on two cases and non-identical constructs, and call for comparable microdata and disclosure experiments.

cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.38208

CraftSPH: A composable framework for differentiable smoothed particle hydrodynamics implemented in PyTorch

Gen Matono, Shujiro Fujioka, Mayuko Nishio

原文摘要

Smoothed particle hydrodynamics (SPH) is well suited to large-deformation fluid problems, and the growing use of automatic differentiation in engineering applications has motivated interest in differentiable SPH solvers. In this study, a composable differentiable SPH framework, CraftSPH, has been developed. In CraftSPH, major computational operations are implemented as independent modules that users can combine to construct solvers for different purposes. This design supports high-accuracy discretization and both explicit and implicit computation within a common framework, while allowing individual components to be maintained and extended. Selected numerical operations also support automatic differentiation (AD) for physical-parameter estimation and potential coupling with deep learning models. These capabilities were demonstrated through forward and inverse analyses. Forward analyses using an implicit SPH scheme with high-accuracy discretization showed good agreement with analytical and reference solutions for Poiseuille flow and a rising bubble, while capturing the key experimental trends in two- and three-dimensional dam-break problems. In inverse analyses, explicit and implicit SPH schemes were used to estimate kinematic viscosity in Taylor-Green vortex flow and Reynolds number in lid-driven cavity flow. The results also showed that the choice of operations included in the computational graph affects the computed gradients. Together, these findings demonstrate that CraftSPH enables users to assemble problem-specific solvers for forward and inverse analyses within a common framework.

cs.LG · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37343

HIDE: Hierarchical Isolation via Documentation and Evidence for Rustc Bugs

Tianrong Zhang, Yakun Zhang, Dan Hao

原文摘要

Rustc bugs can prevent valid programs from compiling or cause incorrect compilation results, making the identification of responsible source files essential for repair. Using large language models (LLMs) for rustc bug isolation raises three challenges. First, distributed compiler functionality requires understanding both what individual files do and how they work together. Second, a large source file search space makes exhaustive analysis of all files costly. Third, including documentation and source code for many candidate files in LLM prompts leads to context overload, which can reduce bug isolation accuracy. We propose HIDE, a hierarchical LLM-based approach that combines compiler documentation with bug evidence to rank suspicious rustc source files. Given a rustc revision and a bug context, HIDE consists of two stages that generate compiler documentation and narrow the search space. To understand distributed compiler functionality, the Layered Documentation Aggregation stage summarizes what individual files do and how files with similar functions work together. To reduce the large source file search space, the Hierarchical Bug Isolation stage uses hierarchical candidate selection to reduce the number of files requiring detailed analysis. To mitigate context overload, this stage uses Scoped Candidate Competition and Evidence-Guided Code Slicing to limit the number of candidates and the amount of source code included in each prompt, respectively. We evaluate HIDE on 160 reproducible rustc bugs and compare it with Agentless, SoapFL, and GraphLocator. With GPT-5.4, HIDE achieves 42.08% Accuracy@1 and 80.42% Accuracy@10, exceeding the strongest baseline results by 5.83% and 6.04%, respectively. These results demonstrate that generating compiler documentation, narrowing candidates hierarchically, and limiting analysis context enable effective rustc bug isolation.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37342

Aligning Institutional Power and Operational Ability

Andrea Marino, Giovanni Sileno, Thomas van Binsbergen, Tom van Engers

原文摘要

To be socially sustainable, Agentic AI systems require normative designs that align responsibilities with the ability to fulfil them. Identifying incongruences such as unsatisfiable expectations or privilege escalation is crucial in avoiding normative pitfalls. In this study, we discuss design dimensions suitable for describing multi-agent systems in light of this norm-ability alignment requirement. We propose a formal framework centred on process-oriented modelling and institutional power for the analysis of a given normatively relevant process, discuss strategies for repairing it, and elaborate on how the symmetrical relation between operational process and institutional state affects the lifecycle of normative positions.

cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37341HF 社区推荐

CodeCap-Bench: Can Code Serve as a Good Video Caption?

Jiafu Tang, Yifan Xu, Eric Tang, Junjie Zhao, Shipeng Luo, Te Sun, Haoxuan Hu, Yiyan Ji 等 14 位作者

原文摘要

Video captioning aims to preserve visual information through compact descriptions, predominantly using natural language. However, precisely expressing fine-grained spatial structures and temporal dynamics in natural language often leads to lengthy and potentially ambiguous descriptions. In this work, we explore Code as Video Caption, a paradigm where executable programs serve as a complementary representation of visual content through graphical primitives, layout rules, and functions of time. To examine whether code captions effectively preserve video information, we compare them with natural language captions on downstream question answering and video reconstruction tasks. Our results show that code captions achieve superior reasoning accuracy and reconstruction fidelity. Despite these advantages, realizing the potential of code captioning depends on whether current multimodal large language models (MLLMs) can generate faithful code captions. To systematically assess this capability, we introduce CodeCap-Bench, which comprises 323 videos spanning five categories of structured 2D content, along with fine-grained annotations and a multi-level evaluation framework. Extensive experiments reveal that while leading proprietary MLLMs demonstrate promising code captioning capabilities, they still struggle to faithfully capture complex temporal dynamics, with open-source models lagging substantially behind.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37340

Mind the Intent Gap: A Methodology for Collaborative Agent-Driven Software Engineering

Nicolas Tacheny, Tom Mens, Saïd Mahmoudi

原文摘要

AI agents have entered software teams, first as tools and now as team members. They produce in minutes what used to take days, yet the expected gain in delivery often fails to appear. We argue that they are entering processes designed for people, which quietly relied on people to cover three of their weaknesses. The team's intent was never fully written down, because colleagues filled the gaps. An AI agent fills them too, though by guessing, and drifts from what the team had in mind. The reasoning behind each deliverable was already lost from one step to the next, and AI agents now inflate and deflate the documents passed along. The reasoning people did alone was partly private and now moves into AI tools that keep it private by nature. We present CAIA, for Collaborative Agentic Intent Alignment, a methodology for software teams that brings agents into the way they work. A knowledge base describes the product that a project maintains. For each Change, a micro-team aligns on its intent before agents evolve the product and propose updates to the Knowledge Base. The proposed CAIA methodology emerged from an industrial need and is already in use at Ni2, with a platform built to support it. We present the methodology and outline our plans to evaluate it further.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.08984

SoftMCC: An MCC-Brier Calibration Bridge for Threshold-Free Model Selection under Class Imbalance

Özkan Canay

原文摘要

Model selection for imbalanced binary classification often uses the Matthews correlation coefficient (MCC), but its thresholding step makes validation rankings depend on the chosen cutoff. SoftMCC is a post-training MCC validation framework built on established probability-valued confusion counts, coupling an MCC-specific calibrated identity with a tie-aware, shared-pool selection protocol. Its core score is a covariance-normalized probability-label association, reduces exactly to MCC for hard predictions, and is Pearson-bounded. Under perfect population calibration it equals the Brier skill score with identical candidate ordering, which is the calibrated case of Murphy's partition of the Brier score. Outside that regime the gap does not identify calibration error. Across 18 settings with 12 duplicate-safe grouped repeats, SoftMCC attains the best stability mean rank (2.31) and highest mean tie-corrected Kendall's W (0.659), with a significant Friedman test (p=0.004). Nemenyi analysis separates it from area under the precision-recall curve (AUPRC) and MCC@0.5, and 14-source-family sensitivity retains both separations. For selected-model utility, none of six prespecified paired comparisons is significant before or after Holm correction (smallest raw p=0.093), and the dataset-level test is not significant (p=0.746). Label permutation lowers mean W to 0.092. Temperature scaling shifts SoftMCC rankings (mean Spearman 0.851) whereas rank-based and threshold-optimized metrics remain invariant. SoftMCC is a calibration-sensitive MCC-family selector with bounded stability evidence and inconclusive utility differences.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.13401

SQuID: A Benchmark for Quantitative Spatial Reasoning on Satellite Imagery

Peter A. Massih, Eric Cosatto, Martin Renqiang Min

原文摘要

Vision-Language Models (VLMs) describe satellite images fluently but struggle with the quantitative spatial reasoning that remote sensing (RS) analysts perform routinely, such as counting buildings, measuring forest area in hectares and computing distances in meters. We introduce SQuID (Satellite Quantitative Intelligence Dataset), a benchmark of 2,000 questions over 587 images from four sources at 0.3 to 1.0 m ground sampling distance, covering 24 question types in three difficulty tiers. Answers are computed from curated segmentation masks or, for 50 questions, from human consensus, and numeric answers are scored against tolerance ranges informed by human annotations. SQuID also releases the reference masks behind its answers, so that modular systems can be evaluated using reference rather than predicted masks, and 75,687 training question-answer pairs on disjoint images (SQuID-Train). Among 15 evaluated systems, the best zero-shot end-to-end VLM reaches 28.1%, and RS-specialized VLMs score lower. As a baseline, we propose QASR (Quantitative Agentic Spatial Reasoning), in which an LLM writes a program that segments the image and measures the masks with geometric operators. QASR reaches 42.0%. On the 1,950 mask-derived questions, running its programs on the reference masks instead of predicted masks raises accuracy from 42.2% to 82.6%, so segmentation is its main source of error. Fine-tuning Qwen2.5-VL-7B on SQuID-Train raises its accuracy from 16.1% to 40.7%, close to QASR. Multi-condition questions (Tier 3) remain the hardest in every setting, at 62.0% for QASR with reference masks and 13.2% after fine-tuning. These results leave clear room for end-to-end models that learn metric spatial reasoning, and SQuID provides the data to train and evaluate them.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.06126

AffectGPT-RL: Revealing Roles of Reinforcement Learning in Open-Vocabulary Emotion Recognition

Zheng Lian, Fan Zhang, Lan Chen, Yazhou Zhang, Rui Liu, Jinyang Wu, Haoyu Chen, Xiaobai Li 等 11 位作者

原文摘要

Open-Vocabulary Multimodal Emotion Recognition (OV-MER) aims to predict emotions without being constrained by predefined label spaces, thereby enabling fine-grained emotion understanding. Unlike traditional discriminative methods, OV-MER leverages generative models to capture the full spectrum of emotions and employs emotion wheels (EWs) for metric calculation. Previous approaches primarily rely on token-level loss during training. However, this objective is misaligned with the metrics used in OV-MER, and these metrics cannot be directly optimized via gradient backpropagation. To address this limitation, we turn our attention to reinforcement learning, as this strategy can optimize non-differentiable objectives. We term this framework AffectGPT-RL. Furthermore, we conduct extensive experiments to elucidate the role of reinforcement learning in this task, revealing the necessity of the reasoning process, the impact of different rewards, and the generalizability to other emotion tasks such as sentiment analysis and basic emotion recognition. Experimental results demonstrate that AffectGPT-RL yields significant performance improvements on OV-MER. Beyond this task, we also achieve remarkable performance gains on basic emotion recognition, attaining state-of-the-art results on MER-UniBench. To the best of our knowledge, this is the pioneering work exploring the role of reinforcement learning in OV-MER, providing valuable guidance for subsequent researchers. Our code is provided at: https://github.com/zeroQiaoba/AffectGPT/tree/master/AffectGPT-RL.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2603.18740

When AI Agrees Too Much: Manipulating Automated Code Review via Bias Injection

Georgios Alexopoulos, Nikolaos Alexopoulos, Diomidis Spinellis, Dimitris Mitropoulos

原文摘要

Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, ranging from interactive assistants to autonomous agents in CI/CD pipelines. In this paper, we study how LLM-based vulnerability detection in ACR is affected by the framing effect: the tendency to let the presentation of information override its semantic content in forming judgments. We examine whether adversaries can exploit this through contextual-bias injection (crafting PR metadata to bias ACR security judgments) as a supply-chain attack vector against real-world ACR pipelines. To this end, we first conduct a large-scale exploratory study across 6 LLMs under five framing conditions, establishing the framing effect as a systematic and widespread phenomenon in LLM-based vulnerability detection. We then design a realistic and controlled experimental environment, evaluating 33 CVEs across 20 real-world projects and two popular ACR pipelines (Claude Code and CodeRabbit), to assess the susceptibility of real-world ACR pipelines to vulnerability re-introduction attacks. We employ two attack strategies: a template-based attack inspired by prior related work, and a novel LLM-assisted refinement attack. We find that template-based attacks are ineffective and may even backfire, as direct biasing attempts raise suspicions. Our refinement attack, on the other hand, is successful in 32/33 (97%) cases, exploiting a fundamental asymmetry: attackers can iteratively refine attacks against a local clone of the review pipeline, while defenders have only one chance to detect them. Overall, our findings highlight the dangers of over-relying on ACR and stress the importance of human oversight and contributor trust in the development process.

cs.SE · cs.AI · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37338

Normative Deviation Modeling for EEG-based Depression Detection

Wan Jiang, Jingqi Cheng, Hui Zhang, Xu Zhao, Xiaojing Chen, Shengeng Tang, Jingjing Wu, Richang Hong

原文摘要

Web-enabled digital health services are expanding access to physiological monitoring and mental health screening. However, electroencephalography (EEG)-based major depressive disorder (MDD) detection remains challenging across heterogeneous data sources, where individual variability and acquisition differences may affect disease-related representations. In this work, we investigate the use of healthy normative references for EEG-based MDD detection. Specifically, structural and distributional reference models are independently trained using healthy control (HC) recordings to characterize departures from healthy EEG patterns. We develop two diagnostic approaches that incorporate normative deviations at different representation levels. Scalar-level Normative Deviation-guided Diagnosis (S-NDD) summarizes structural and distributional deviations into scalar scores and integrates them with deep EEG representations. Feature-level Normative Deviation-guided Diagnosis (F-NDD) retains dimension-wise residuals and combines them with deep EEG representations through attention-based aggregation and auxiliary supervision. We also employ target HC-assisted score calibration for cross-source evaluation without updating the trained models. Experiments on two public EEG datasets examine both approaches under subject-independent within-source and bidirectional cross-source settings. The results demonstrate the effectiveness of normative deviation-guided diagnosis across different evaluation settings.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.32963

More of the same? Are scientific papers losing originality?

Ilan Doron-Arad, Elchanan Mossel

原文摘要

Scientific output is growing rapidly, but it is unclear whether the expanding literature remains original or is increasingly repeating itself. Originality has many dimensions. One dimension can now be measured directly: how textually distinct a paper is from the work that came before it. Using semantic language models, we screened more than 26,000 full-text papers across eight fields in the CORE database (2015--2025) for passages resembling recent literature. We compared every paper against a fixed-size sample of earlier work, so increases in resemblance are not a by-product of literature growth. We find that the share of a paper's text resembling recent work was stable before 2021, then rose sharply, more than tripling by 2025. The increase came not from a few papers borrowing more heavily but from resemblance spreading across the literature: more papers now contain passages that echo recent work, and almost none of this growth is explained by cited work. We also found an analogous increase in passages expressing the same scientific idea. The pattern held across robustness checks and replicated in a second, independent database. Along this one measurable axis of originality, scientific writing has become less distinct from the work preceding it.

cs.DL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37336

CoS-VQ: Coordinated Shared-Scale Vector Quantization for Large Language Models

Zukang Xu, Zhixiong Zhao, Xing Hu, Zhe Jiang, Xin Si, Dawei Yang

原文摘要

Large language models (LLMs) require substantial memory, motivating low-bit weight quantization for efficient deployment. Existing vector quantization (VQ) methods often improve accuracy by increasing the effective coding dimension, which can complicate independent lookup and local codeword updates. We propose CoS-VQ (Coordinated Shared-Scale Vector Quantization), which preserves low-dimensional codewords while jointly optimizing hard indices and their shared scale across positions. The key challenge is scale-induced coupling: the optimal scale depends on the entire codeword combination and, in turn, changes candidate rankings across positions, so pointwise updates can stall even after scale re-optimization. CoS-VQ collects candidates at multiple probe scales and constructs a tangent separable upper bound for the residual-aware quadratic objective. By reducing candidate comparisons to lower envelopes of lines over positive scales, it identifies finitely many representative intervals for joint index--scale updates without exhaustively enumerating codeword combinations. On Qwen3.8-27B, CoS-VQ outperforms QTIP and LLVQ on the evaluated language modeling, coding, and reasoning benchmarks. Ablations from the same initialization further show larger gains from joint updates than from scale-only refinement, highlighting joint selection as an alternative to increasing codeword dimensionality. The code is available at https://github.com/xzktx003/Cos_VQ.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.14406

Friction in AI-Assisted Clinical Decision-Making: A Case Study on The Role of Questions and 'What-if' Scenarios

Simon WS Fischer, Hanna Schraffenberger, Miranda L. van Hooff, Serge Thill, Pim Haselager

原文摘要

A major challenge in AI-supported decision-making is achieving appropriate reliance on AI. To reduce overreliance on AI, approaches for cognitive friction have been proposed that promote reflection and critical thinking. In this paper, we investigate how two such approaches are perceived by medical experts in a real-world decision task. We replicated an existing decision support system used in clinical practice and extended it with outputting reflective questions and what-if scenarios. We gathered clinicians' (n=7) feedback on this prototype through in-situ interviews. While the questions were perceived as unhelpful for reflective thinking in itself, their potential was seen in serving as reminders to consider relevant information, e.g., when attention wanes. Inspecting what-if scenarios was found useful for considering alternative outcomes and informing discussions with patients. Our participants regarded the prototype as a promising training tool for novice clinicians. From the clinicians' feedback, we make recommendations for designing friction in human-AI interactions.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37333

diff-soup: GPU-native Differentiable Simulation and Inference for Large-scale Stiff Dynamical Systems

Samuel Degnan-Morgenstern, Martin Z. Bazant

原文摘要

Learning physical models from spatiotemporal data requires simulating systems of partial differential equations (PDEs) and differentiating through their solution. Upon spatial discretization, these models give rise to stiff dynamical systems that require repeatedly solving large, sparse linear systems. Traditional scientific computing frameworks are largely CPU-based and require problem-specific tuning to accelerate, scale, and differentiate. In this work, we introduce diff-soup, a differentiable, sparse-oriented, unified PDE suite for GPU-native simulation and inference of large, stiff dynamical systems. Built in JAX, diff-soup provides 1) a fully differentiable design for problems with sparse structure, 2) scaling to large problems through the NVIDIA cuDSS sparse direct solver with model and data multi-GPU parallelism, and 3) end-to-end PDE-constrained optimization, from classical statistical inference to embedded deep learning. On stiff PDE benchmarks, diff-soup outperforms established CPU- and GPU-based frameworks by one to two orders of magnitude in both forward solves and gradients. We demonstrate both a classical Bayesian inference workflow, identifying constitutive relationships, and a modern deep learning workflow, training a neural field for the catalyst activity of a three-dimensional packed bed reactor. We provide diff-soup as an open-source repository to facilitate research in computational science and engineering.

cs.MS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35715

Quantitative Generalization Bounds for Multi-Layer Transformers: A Few Long Strings can Beat Many Short Ones

Marius Henry, Michael Rizvi-Martel, Michael Hahn

原文摘要

Transformer-based language models exhibit an astonishing capacity to generalize to longer and more difficult problem instances than those seen in training. However, theoretical understanding of this phenomenon still lags behind. Unlike most machine learning models, the generalisation abilities of transformers inherently require considering two axes. On one hand, one must examine in-distribution statistical generalisation, which concerns the ability of a model to broaden a function from training samples to novel samples of the same distribution. On the other hand, given the sequential nature of language data, one must also consider length generalisation, which reflects whether a model trained on inputs of a given length can extrapolate to longer ones. These two notions have mostly been studied separately, leaving open how they interact. In this work, we give a joint treatment of both axes for fixed-precision transformers. We give generalisation guarantees in terms of the parameter sizes of the involved models, as well as the quantization space and the alphabet size that supply the sequences. Finally, we illustrate how the requirements in the sample count and training length trade off. In particular, we find that increasing training length can be exponentially more efficient than increasing the sample size. Experiments support predictions of the theory.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.17306

Calibrating LLM Explanations to Human Decisions with Reinforcement Learning

Dong Shu, Ziyang Guo, Negar Kamali, Matthew Groh, Jessica Hullman

原文摘要

Explanations of LLM outputs are considered an important way to make model behavior legible, helping human users make good decisions on downstream tasks. However, default training approaches for improving explanations target model task performance or human preference rather than human decision quality. To bridge this gap, we introduce \textbf{HD-RL}, a reinforcement learning framework for optimizing LLM explanations for human decision making. Our approach calibrates a human decision simulator on observed human decisions, applying a proxy reward model to optimize the LLM to generate explanations that help people to make good decisions. We apply this framework to AI-generated text detection. In our initial data collection phase, we observed that while LLMs possess strong autonomous detection capabilities, the explanations they generate for users yield near-random human accuracy (51.3\%). By applying the HD-RL framework to the open-source Qwen 3.6 model, we generate optimized explanations that improve human performance, achieving a GLMM-estimated human accuracy of 63.7\% in a randomized between-subjects experiment with repeated measures and outperforming RLVR, RLAIF, and other baselines by over 8\%.

cs.HC · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37332

Hidden in Plain Sight: an Empirical Study about The Instruction Stacks of Coding Agents

Felipe de Sant'Anna Paixão, Dhyego Tavares, Eduardo Almeida, Iftekhar Ahmed

原文摘要

Coding agents send their models more than the user's prompt: they add instructions, tool definitions, and environmental context that form an instruction stack. This stack guides execution and changes across releases, yet remains largely unexposed to users and insufficiently characterized in research. We present a longitudinal, cross-agent study of the instruction stacks captured in the first model requests of 1,886 releases of Claude Code, Codex, Gemini CLI, Qwen Code, and OpenCode. Under controlled conditions, we analyze their composition and evolution and trace changes in the four open-source agents to documented motivations. Our findings show why examining the system prompt or reporting the model alone provides an incomplete account of agent inputs. Tool definitions dominate three agents' latest requests, while occasional concentrated revisions alter instructions governing tool use, attribution, and coding style. Such revisions can complicate how researchers identify agent-authored contributions and interpret generated code. We also found that only 48% of examined change units directly explain the motivation for stack revisions. Across agents, shared capabilities emerge despite increasingly distinct wording. These findings expose an evolving, incompletely documented layer of agent design. The findings motivate documenting captured instruction stacks alongside agent releases, models, and user prompts, and preserving the rationale behind their revisions.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.17123

LineageMark: Multi-user White-box Watermarking for Contribution Tracing in Model Derivation Chains

Bingxue Zhang, Xiaofeng Xu, Feida Zhu

原文摘要

In open large language model (LLM) ecosystems, models are frequently adapted across multiple domains and applications, forming multi-stage derivation chains. Consequently, tracking and verifying historical contributions is essential for model provenance and intellectual property protection. However, existing watermarking methods are mainly designed for single-user, one-time embeddings, often fail under repeated model derivation and incremental updates. To address this problem, we propose LineageMark, a multi-user white-box watermarking framework for model derivation chains. The framework encodes watermarks in model parameters using a projection-based approach. Stable carriers are first selected to reduce sensitivity to model changes, each watermark bit is then represented as a projection statistic over these carriers. Additional watermark insertions introduce only bounded perturbations in the projection space, and margin constraints are used to maintain signal integrity. We evaluate the effectiveness of LineageMark in multi-stage model derivation chains. Experimental results show that LineageMark preserves contributor watermarks across multi-stage derivation and supports incremental multi-user watermark insertion. Furthermore, it exhibits robustness against perturbations such as re-watermarking, fine-tuning, quantization, and pruning.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.21638

Geometric Characterisation and Structured Trajectory Surrogates for Clinical Dataset Condensation

Pafue Christy Nganjimi, Andrew Soltan, Danielle Belgrave, Lei Clifton, David Clifton, Anshul Thakur

原文摘要

Dataset condensation constructs compact synthetic datasets that retain the training utility of large real-world datasets, enabling efficient model development and potentially supporting downstream research in governed domains such as healthcare. Trajectory matching is a widely used condensation approach that supervises synthetic data using changes in model parameters observed during training on real data, yet the structure of this supervision signal remains poorly understood. In this paper, we provide a geometric characterisation of trajectory matching, showing that a fixed synthetic dataset can only reproduce a limited span of such training-induced parameter changes. When the resulting supervision signal is spectrally broad, this creates a conditional representability bottleneck. Motivated by this mismatch, we propose Bezier Trajectory Matching (BTM), which replaces stochastic gradient descent (SGD) trajectories with quadratic Bezier trajectory surrogates between initial and final model states. These surrogates are optimised to reduce average loss along the path while replacing broad SGD-derived supervision with a more structured, lower-rank signal that is better aligned with the optimisation constraints of a fixed synthetic dataset, and they substantially reduce trajectory storage. Experiments on five clinical datasets demonstrate that BTM consistently matches or improves upon standard trajectory matching, with the largest gains in low-prevalence and low-synthetic-budget settings. These results indicate that effective trajectory matching depends on structuring the supervision signal rather than reproducing stochastic optimisation paths.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37330

Tokens of Motion: Primitive Motion Learning for Human-Humanoid Skill Transfer

Hanwen Ren, Meng Zheng, Wenzhe Cui, Benjamin Planche, Zhongpai Gao, Anwesa Choudhuri, Terrence Chen, Ziyan Wu

原文摘要

Vision-language-action (VLA) models provide a promising paradigm for scalable robotic manipulation, yet their reliance on success-only behavioral cloning leaves them brittle; lacking corrective training signals, minor execution errors rapidly compound into unrecoverable, out-of-distribution failures. To address this limitation, we propose Adaptive Failure-Informed Learning (AFIL), an end-to-end framework that leverages failure trajectories as adaptive negative guidance for diffusion- and flow-based VLA policies. AFIL uses a pretrained VLA to generate failure rollouts online, avoiding the need for handcrafted failure-mode design or human-in-the-loop recovery. It then jointly trains Dual Action Generators (DAGs) for successful and failed behaviors while sharing a common vision-language backbone, enabling efficient failure-aware policy learning with limited parameter overhead. During sampling, the failure generator adaptively steers action generation away from failure-prone regions and toward more reliable success modes, with guidance strength determined by the per-diffusion-step distance between success and failure distributions. Experiments across in-domain and out-of-domain robotic manipulation tasks, covering both short- and long-horizon settings, show that AFIL consistently improves task success rates and robustness over existing VLA baselines, demonstrating its effectiveness, efficiency, and generality.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37329

WCC: A Learning-Based Framework for Deciding When to Apply Cooperative Coevolution in Large-Scale Optimization

Wenjie Qiu, Hepei Li, Jianfeng Wang, Jun Zhang, Yue-Jiao Gong

原文摘要

Cooperative Coevolution (CC) and Non-Decomposition Algorithms (NDAs) are two primary approaches for large-scale global optimization (LSGO). Although both have achieved success, they exhibit markedly different performance across problem types and each faces approach-specific limitations, leaving the question of "when to use CC" unresolved. We address this gap with WCC, a novel learning-based framework that dynamically decides when to deploy CC in LSGO. WCC employs a streamlined design for efficient adaptive learning. We formulate the decision as a Markov decision process, designing a targeted feature set, a robust reward, and a tailored network architecture to provide the agent with rich information about the problem state and optimization progress. Our auto-regressive policy enables WCC to perform fine-grained, dynamic switching between CC and NDAs while conditionally allocating the computational budget without expert specifications. WCC shows strong competitiveness and superior generalization across problems, optimization horizons, and baseline optimizers. It realizes a "train-once and reuse" paradigm under a low budget, significantly curbing LSGO cost. Additionally, in-depth analyses yield insights not captured by existing theory, offering practical guidance on when and how to effectively deploy CC.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37326

ScIntBench: Physical Interaction Plausibility in 3D Scene Generation

Wendi Ying, Daochang Liu, Leonid Sigal, Ajmal Saeed Mian

原文摘要

Existing evaluations of text-conditioned 3D indoor scene generation largely emphasize semantic fidelity and physical validity at rest, leaving interaction-time physical reliability insufficiently assessed. To address this gap, we introduce ScIntBench, a unified benchmark centered on interaction readiness: it executes prescribed manipulation actions (e.g., pushing, opening, and surface probing) in physics simulation and measures task success, capability and progress, post-interaction integrity, and dynamic plausibility. Multi-view semantic and static physical evaluations complement these metrics and help explain interaction outcomes. We systematically evaluate 12 state-of-the-art scene generation systems across 10 room types. Our results reveal that strong semantic and static physical performance does not necessarily translate into interaction readiness: a system with near-perfect static plausibility completes only 11.54% of interactions, 7 of the 12 systems produce scenes that cannot support any interaction, and even the best system succeeds in only 36.92% of prescribed tasks. These findings highlight two fundamental challenges in generating interaction-ready scenes: providing the necessary physical interaction capabilities and ensuring reliable physical behavior under active manipulation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37323

UCVE: Graph Structured Retrieval for Compliance Validation

Emily Chen, Michelle Menachery, Himanshu Kumar, Rahul Nenavath, Amirfarrokh Iranitalab

原文摘要

Structured compliance validation requires determining whether every obligation in a predefined requirement space is satisfied, violated, or unaddressed by a given document. This can be described as a coverage problem that existing retrieval and classification systems, by treating requirements as independent, are not designed to solve. We introduce \textbf{\ac{UCVE}}, a domain-agnostic framework that represents compliance requirement spaces as a \ac{CRG} and performs retrieval via spectral reranking over its reinforcement learning-weighted Laplacian. Edges between requirement nodes are learned via reinforcement learning with a traversal reward grounded in violation detection, reshaping the graph's community structure such that low-pass spectral filtering distinguishes relevant from irrelevant obligation clusters. Evaluating across three compliance domains, \ac{UCVE} averages 4.3\% matched-$K$ recall lift over flat cosine retrieval and consistently outperforms strong retrieval and graph-based baselines.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.13624

From Language to Navigation Goals: A Vision-Language Approach for Semantic Navigation of Mobile Robots Using RGB-D Perception

Jose Martínez-Fajardo, Pablo Pueyo, Fernando Caballero, Luis Merino

原文摘要

Natural language interaction provides an intuitive way for non-expert users to communicate with robotic platforms. However, transforming user requests into executable navigation actions remains a challenging task, requiring the integration of language understanding, environment perception, and autonomous navigation. This work presents a language-driven navigation framework that enables mobile robots to interpret user requests in natural language to move the robot to a destination and autonomously navigate towards it. The framework is composed of modular ROS 2 components that cooperate to transform natural language instructions into navigation actions. Given a natural language request referring to a target in the environment (e.g., "go to the mail box"), the system identifies the referenced object, estimates its position using RGB-D data, and generates a navigation goal, which is then executed through the ROS 2 Nav2 navigation stack. The ROS 2-based implementation facilitates portability across different robotic platforms, requiring only the configuration of the corresponding topics and services. The system is evaluated in both simulation and real-world scenarios using a TurtleBot3 Waffle and a Unitree Go2 robot with a RealSense camera. Experimental results show that the framework successfully interprets both direct commands and contextual requests, generates meaningful natural-language feedback, and navigates towards the desired target. These results demonstrate the feasibility of combining semantic perception and autonomous navigation to provide an intuitive human-robot interaction paradigm. Code will be released as open source upon acceptance.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37321

Hierarchical Sparse Graph for Multi-dimensional Assignment in Event-based Tracking

Muxi Zha, Yang Shang, Wenlong Zhang, Qifeng Yu, Banglei Guan

原文摘要

Existing event-based tracking methods typically operate in an online manner, associating tracklets across adjacent batches. However, this limited temporal window often leads to failures in challenging scenarios, such as long-term occlusions or missed detections, where broader temporal context should be incorporated for high-precision tracking. To achieve robust and accurate tracking, this paper concentrates on the event-based multi-dimensional assignment that associates tracklets across multiple batches simultaneously. Unlike image-based detectors, event-based detectors can generate multiple correct yet spatially distinct tracklets for a single object per batch. This violates the one-to-one rule in classic data association, making event-based association an underdetermined many-to-many matching problem. To the best of our knowledge, this is the first work to tackle this NP-hard problem. To address its underdetermined nature, we formulate the assignment problem via fuzzy logic, where pairwise tracklet correlations are quantified through multiple long-range kinematic metrics. This fuzzy design eliminates the need for hard tracking thresholds and enables multiple heterogeneous metrics to be integrated into a unified correlation. By treating tracklets as nodes and their fuzzy correlations as edges, we construct a Hierarchical Sparse Graph (HSG). Building upon the HSG, we pose assignment as a graph partition task, and employ a heuristic solution that measures vertex affinity for partitioning adaptively. The entire pipeline is training-free. Comprehensive experiments on two challenging event datasets, eTram and Ev-UAV, show that our method outperforms existing event-based and most image-based association models. Particularly, it achieves over 2$\times$ the performance of OC-SORT across all metrics in the many-to-many association scenario.

cs.CV

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