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版本更新 · v4arXiv:2604.18216

EFX Need Not Exist

Hannaneh Akrami, Simon Mackenzie, Alexander Mayorov, Kurt Mehlhorn, Shreyas Srinivas, Mashbat Suzuki, Christoph Weidenbach

原文摘要

The existence of envy-free up to any good (EFX) allocations has long been a central open problem in discrete fair division. We resolve it in the negative by constructing a counterexample with three agents and eight goods. The instance is invariant under every simultaneous permutation of the agents and their associated good types, and this symmetry yields a short, self-contained proof of nonexistence. The counterexample can be realized with weighted-coverage, and hence submodular, valuations, and it extends to $n$ agents and $m$ goods for all $n\ge3$ and $m\ge n+5$. For three agents, eight goods is the minimum: every instance with three agents and at most seven goods admits an EFX allocation under arbitrary monotone valuations. The proof relies on a new analysis, valid for any number of goods, of when the invariant-based technique of [AACGMM25] produces an EFX allocation. This analysis strictly extends the class of MMS-feasible valuations. We also give explicit bounds for approximate EFX. For subadditive valuations, $α$-EFX allocations need not exist for any $α>2^{-1/6}\approx0.891$, and for submodular valuations for any $α>0.958$. On the positive side, if two of three agents have arbitrary monotone valuations and the third has a subadditive valuation, there is always an allocation that is EFX for the first two and $2^{-1/3}$-EFX ($\approx0.794$) for the third. A first version of the counterexample and of the seven-goods result was obtained using SAT/SMT solving, and the insights it provided guided our classical, pen-and-paper proofs. We also describe the encoding, which we believe is a valuable tool in its own right: to our knowledge, this is the first use of SAT/SMT solving for EFX.

cs.GT · cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2507.22953

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography

Murong Xu, Fernando Navarro, Maksym Fritsak, Ibrahim Ethem Hamamci, Suprosanna Shit, Bastian Wittmann, Sezgin Er, Sebastian M. Christ 等 33 位作者

原文摘要

Volumetric computed tomography (CT) segmentation supports quantitative image analysis, but most AI-based methods remain organ-specific, with fragmented models, inconsistent label definitions, and heterogeneous training and evaluation protocols. Here we present CADS, a unified data-model framework for scalable and generalizable whole-body CT segmentation. We construct a SNOMED-CT-referenced anatomical label space harmonizing heterogeneous cohorts from over 100 imaging centers, yielding complete annotations of 167 structures across 22,022 CT volumes and markedly expanding anatomical coverage and scale beyond existing resources. To complete labels on sparsely annotated CTs, we combine self-training with shape-informed quality control based on neural implicit functions, curating high-quality pseudo-labels. Trained on this resource, CADS shows robust whole-body segmentation across distribution shifts and improved performance relative to state-of-the-art approaches across 18 public datasets. We further validate it in an independent, expert-reviewed real-world oncology cohort of 2,864 CTs. The curated dataset, pretrained models, and user-friendly software together provide an accessible, reproducible foundation for large-scale anatomical segmentation and computational anatomy research.

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.12497

Allocating Human Oversight in AI-Enabled Analytics

Zikun Ye, Jiameng Lyu, Rui Tao

原文摘要

Artificial intelligence provides low-cost predictions for many analytics tasks, but their reliability and the value of another human label vary across tasks. We study how to allocate a shared human-validation budget when task-level uncertainty is unknown and each acquired label informs both estimation and subsequent allocation. For known difficulty levels, the benchmark follows a cost-adjusted Neyman rule that accounts for task importance and label cost. Within our Tuned Rectification-Difficulty framework, we develop an upper-confidence-bound policy that jointly learns the correction coefficients of tuned prediction-powered inference and the resulting difficulties from paired human labels and predictions, and establish an allocation-regret guarantee for the confidence-radius policy. In synthetic experiments and a real digital-twin survey with 68 tasks and over 2,000 respondents, our method closes most of the gap to the oracle on the allocation objective. It matches the estimation error of uniform allocation with up to 22.7% and 4.5% fewer human labels, respectively. The same allocation structure extends beyond scalar means to vector-valued decision models, including regression and choice/conjoint estimation, by replacing the scalar residual variance with a scalarized sandwich covariance. The value of AI depends not only on model accuracy but also on the operational policy that targets human oversight where it matters most.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.09672

ODEAction: From Predicting Chunks to Evolving Actions

Qishen Wang, Pengxiang Ding, Shifeng Bao, Mingyang Sun, Hengtao Li, Yi Gao, Baohua Yin, Yang Liu 等 17 位作者

原文摘要

Vision-language-action (VLA) models typically generate actions in chunks, dividing continuous robot execution into separately predicted action chunks, which can introduce discontinuities at chunk boundaries. We propose ODEAction, a chunk-free action generation paradigm that replaces action chunk prediction with Continuous Action Evolution. We use an ordinary differential equation (ODE) to describe the continuous evolution of actions, and define the vector field of this ODE as the Action Transition Field to characterize how actions evolve with respect to evolution time. During inference, the model takes the current action as the initial condition and numerically solves the ODE defined by the learned Action Transition Field, thereby recursively generating subsequent actions with explicit dependencies. ODEAction is implemented as a modular action head that can be integrated with different vision-language backbones. Experiments on LIBERO, EBench, RoboTwin, and real-robot tasks show that, compared with action-chunk-based baselines, ODEAction improves both task performance and trajectory quality, with particularly pronounced gains on fine manipulation tasks.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2509.05956

Pandora's Knapsack via Simple Policies

Zohar Barak, Asnat Berlin, Ilan Reuven Cohen, Alon Eden, Omri Porat, Inbal Talgam-Cohen

原文摘要

We introduce Pandora's Knapsack: a hybrid of the classic stochastic knapsack [Dean, Goemans, and Vondr'ak, 2008] and Pandora's box [Weitzman, 1979]. As in stochastic knapsack, items have sizes drawn from known distributions, and items that fit within a knapsack contribute to the total value. As in Pandora, every item $i$ comes in a box that costs $c_i$ to open. What distinguishes our problem is that the size is revealed only after opening the box and paying the cost. A motivating application from contract design is knapsack contracts, where a principal sequentially delegates jobs with stochastic processing times to agents before a deadline, and must pay each agent to incentivize effort, even if the job ends up not completing in time. We study the power of simple decision-making policies to approximate Pandora's Knapsack, along two complexity axes: (i)~knowledge of size distributions, and (ii)~adaptivity. We show that, compared to the classic stochastic knapsack and Pandora's box, policies in our model must be significantly more complex along both axes to be approximately optimal. Conversely, we show that with full distributional information and by allowing adaptive skipping of items, a constant approximation can be recovered. Our analysis reveals an economic quantity, namely ROI (return-on-investment, defined as the jobs' minimum utility over cost), which smoothly characterizes the performance of simple policies. Across a hierarchy of increasingly adaptive policies, we establish tight adaptivity gaps all governed by ROI. To demonstrate the importance of the ROI parameter in characterizing simple policies, we revisit Pandora's box, and show that return-on-investment exactly captures the adaptivity gap in this classic problem as well.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.16382

Rotated Robustness: A Training-Free Defense against Bit-Flip Attacks on Large Language Models

Deng Liu, Song Chen

原文摘要

Bit-flip corruption of quantized weights poses a serious reliability threat to Large Language Models (LLMs), as even a small number of weight faults can trigger catastrophic model degradation. We show that such failures can be strongly amplified when corrupted weights are coupled to high-sensitivity activation channels. Based on this observation, we propose Rotated Robustness (RoR), a training-free sensitivity-aware hierarchical rotation that applies matched orthogonal transformations to activations and weights, redistributing sensitive channel contributions across feature dimensions while preserving the underlying linear mapping in exact arithmetic. Across eight evaluated LLMs, RoR matches or exceeds all compared methods in the number of cumulative PBS flips sustained before PPL exceeds 100 within the 100-flip evaluation horizon. Under BER-based random bit-flip injection, no RoR failures are observed in the evaluated 1,000-trial experiments for the tested models. Under defense-aware SPoF reconstruction, RoR increases the cost of reproducing specified one-bit catastrophic failures to thousands of deployed INT8 bit changes. RoR also preserves downstream-task utility under PBS perturbations. On NVIDIA H200 at batch size 8, RoR incurs 0.097\%--0.584\% persistent storage overhead, 5.2\%--14.1\% Prefill overhead, and 0.5\%--9.8\% Decode overhead across four representative models. These results show that sensitivity-aware hierarchical rotation provides a practical approach to improving the bit-flip robustness of quantized LLM Linear weights.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.05202

Optimizing Bit-Labeling of Voronoi Constellations

Carilyn Rumrill, David Muzzey, Connor Davis, Stephen Mackes, Dan Chew

原文摘要

We optimize the bit-to-symbol map of the $D_4$ and $E_8$ root lattices in reference to bit error rate, by implementing two searches on a performance metric, Hamming Density. The first search is a traditional gradient descent and the second is an efficient search using twisted theta functions. We hold other sources of lattice gain constant by fixing the lattice constellation, and consider basis changes that permute the integer labelings of the lattice points. After searching the possible basis matrices for $D_4$ and $E_8$, we found up to 0.09 dB of gain in $D_4$ bit error rate curves, and up to 0.36 dB of gain in $E_8$ compared to the standard bases commonly used in literature at a BER of $10^{-4}$, which is on the scale of gain from Gray coding uncoded QAMs.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2403.13441

Robustness Verification in Neural Networks

Adrian Wurm

原文摘要

In this paper we investigate formal verification problems for Neural Network computations. Of central importance will be various robustness and minimization problems such as: Given symbolic specifications of allowed inputs and outputs in form of Linear Programming instances, one question is whether there do exist valid inputs such that the network computes a valid output? And does this property hold for all valid inputs? Do two given networks compute the same function? Is there a smaller network computing the same function? The complexity of these questions have been investigated recently from a practical point of view and approximated by heuristic algorithms. We complement these achievements by giving a theoretical framework that enables us to interchange security and efficiency questions in neural networks and analyze their computational complexities. We show that the problems are conquerable in a semi-linear setting, meaning that for piecewise linear activation functions and when the sum- or maximum metric is used, most of them are in P or in NP at most.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.14521

A Generative Data Framework with Reliable Supervision for Underwater Image Restoration and Enhancement

Yufeng Tian, Yifan Chen, Zhe Sun, Libang Chen, Mingyu Dou, Jijun Lu, Ye Zheng, Xuelong Li

原文摘要

Learning-based underwater image restoration and enhancement rely heavily on high-quality paired training data. However, obtaining clear reference images for real underwater scenes remains challenging. Existing datasets often use the outputs of enhancement algorithms as references, which may introduce uncertainty into supervision targets. To address this issue, we propose a generative data framework that preserves clear source images as restoration references and synthesizes corresponding underwater inputs through unpaired image-to-image translation. Using representative underwater appearances to guide the translation, we construct UWNature from natural photographs. We systematically examine the reliability of the generated supervision through its agreement with real underwater appearances, structural consistency, and depth-related degradation characteristics. Experiments across 6 restoration architectures and 3 test sets evaluate its effectiveness through objective metrics, human perceptual assessment, and the correspondence between synthetic degradation and learned restoration behavior. The results demonstrate competitive restoration performance and favorable perceptual quality on real underwater images. Further analysis reveals that greater spatial plausibility of synthetic degradation does not necessarily improve restoration performance, highlighting the importance of evaluating both the intrinsic properties of synthetic data and their effectiveness for model learning. These findings support a scalable framework for constructing and evaluating reliable paired supervision for underwater image restoration and enhancement. The generated datasets are available at: https://github.com/tianyufengcn/SynUIEDatasets.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.02157

RamanPFN: A physics-guided spectral foundation framework for in-context learning across diverse Raman applications

Xingyu Pan, Huan Wang, Jinjia Guo, Zhenlin Zhao, Siming Dong, Jixi Lu

原文摘要

Raman spectroscopy enables label-free molecular characterization across materials science, analytical chemistry, biomedicine, and industrial process monitoring. However, machine learning for high-dimensional spectroscopy remains constrained by limited labeled data and a mismatch between the physical organization of spectra and feature-agnostic models. Tabular foundation models such as TabPFN predict in context without parameter updates, but on wide spectra feature subsampling preserves channel coverage while losing joint visibility: in the five widest tasks, every channel was covered, yet only 4.4% of random channel pairs co-occurred in any forward pass. We present RamanPFN, a general-purpose spectral foundation framework for unified in-context inference through physics-guided spectral learning, which encodes the additive mixing of constituent spectra and the locality of vibrational bands. Global Compositional Unmixing decomposes distributed, multi-band mixture signatures into shared non-negative bases to couple covarying peaks across the full spectrum, while Local Vibrational Subspace Encoding extracts orthogonal modes within contiguous spectral neighborhoods to preserve peak morphology, intensity fluctuations, and peak shifts. Posterior marginalization over admissible unmixings unifies both scales into in-context prediction without updating the model. Across 74 public Raman datasets exceeding 330,000 spectra, RamanPFN outperformed 26 methods spanning chemometrics, spectral neural networks, deep tabular learners, and tabular foundation models on 129 regression targets, reducing backbone RMSE by 13.2%. We demonstrate across TabPFN-3, TabICLv2, and Kumo-Tabular that this advantage generalizes to both regression and classification tasks, reducing regression error by 12.3%-25.7% and multiclass log loss by 42.4%-63.5% to enable data-efficient quantitative analysis and sample identification.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2308.14527

MDS Array Codes With Small Sub-packetization Levels and Small Repair Degrees

Jie Li, Yi Liu, Xiaohu Tang, Yunghsiang S. Han, Bo Bai, Gong Zhang

原文摘要

High-rate minimum storage regenerating (MSR) codes are known to require a large sub-packetization level, which can make meta-data management difficult and hinder implementation in practical systems. A few maximum distance separable (MDS) array code constructions have been proposed to attain a much smaller sub-packetization level by sacrificing a bit of repair bandwidth. However, to the best of our knowledge, only one construction by Guruswami et al. can support the repair of a failed node without contacting all the surviving nodes. This construction is certainly of theoretical interest but not yet practical due to its requirement for very large code parameters. In this paper, we propose a generic transformation that can convert any $(\overline{n}, \overline{k})$ MSR code with a repair degree of $\overline{d}<\overline{n}-1$ into another $(n=s\overline{n},k)$ MDS array code that supports $d<n-1$ with a small sub-packetization level and $(1+ε)$-optimal repair bandwidth (i.e., $1+ε$ times the optimal value) under a specific condition. We obtain three MDS array codes with small sub-packetization levels and $(1+ε)$-optimal repair bandwidth by applying this transformation to three known MSR codes. All the new MDS array codes have a small repair degree of $d<n-1$ and work for both small and large code parameters.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2409.18745

A study on the effects of mixed explicit and implicit communications in human-artificial-agent interactions

Ana Christina Almada Campos, Bruno Vilhena Adorno

原文摘要

Communication between humans and artificial agents is essential for their interaction. This is often inspired by human communication, which uses gestures, facial expressions, gaze direction, and other explicit and implicit means. This work presents interaction experiments where humans and artificial agents interact through explicit and implicit communication to evaluate the effect of mixed explicit-implicit communication against purely explicit communication and the impact of the task difficulty in this evaluation. Results obtained using Bayesian parameter estimation show that the task execution time did not significantly change when mixed explicit and implicit communications were used in neither of our experiments, which varied in the type of artificial agent (virtual agent and humanoid robot) used and task difficulty. The number of errors was affected by the communication only when the human was executing a more difficult task, and an impact on the perceived efficiency of the interaction was only observed in the interaction with the robot, for both easy and difficult tasks. In contrast, acceptance, sociability, and transparency of the artificial agent increased when using mixed communication modalities in both our experiments and task difficulty levels. This suggests that task-related measures, such as time, number of errors, and perceived efficiency of the interaction, as well as the impact of the communication on them, are more sensitive to the type of task and the difficulty level, whereas the combination of explicit and implicit communications more consistently improves human perceptions about artificial agents.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.02274

Awomo-SimDataEngine: Agentic Simulation-ReadyWorld Generation

Awomo-PhysicalRSI Team, Danjiao Ma, Enhui Ma, Haohan Liu, Heng Jia, Hui Shan, Jianhua Xu, Jiahuan Zhang 等 24 位作者

原文摘要

Generating useful robot-training data requires more than visually plausiblescenes: objects must support interaction, placements must remain physicallyvalid, and tasks must admit repeatable execution. We present\textbf{Awomo-SimDataEngine}, an agentic system that connects asset and scenegeneration to robot demonstration synthesis. Shared asset services providerigid and articulated objects, including structure-grounded part and jointgeneration with ISArt. Scene generation supports two complementary routes:Unravel reconstructs editable scenes from images, while SimForge buildssingle-room and multi-room environments from text. A graph-native harnesscoordinates construction, validation, andbounded repair, routing failures to the responsible module while retainingunaffected scene state. PolicyForge binds validated worlds to tasks and robotembodiments to produce replayable demonstrations. Evaluations cover assetgeometry, scene quality, and downstream policy learning. On MuJoCo-basedLIBERO-Plus, co-training with Isaac Sim demonstrations improves the overallsuccess rate of a World-Action Model (WAM) from $77.17\%$ to $89.43\%$. Goal and spatialsuccess improve by $31.66$ and $6.25$ percentage points, respectively . These results support the utility of the generated data for cross-simulatorpolicy training, with more limited gains on long-horizon tasks.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.18556

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling

Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan, Michael Helcig, Eldar Kurtic, Maximilian Kleinegger, Dan Alistarh

原文摘要

Quantization has become a standard tool for efficient LLM deployment, especially for local inference, where models are now routinely served at 2-3 bits per parameter. The state of the art is currently split into simple scalar quantization techniques, such as GPTQ or AWQ, which are widely deployed but plateau in accuracy at 3-4 bits per parameter (bpp), and "second-generation" vector- or trellis-quantized methods, such as QTIP, GPTVQ and AQLM, which push the accuracy frontier but are notoriously hard to implement and to scale. In this paper, we ask whether this gap is fundamental, or whether a carefully optimized $\textit{scalar}$ quantizer can recover most of it. We answer in the affirmative, by introducing GSQ (Gumbel-Softmax Quantization), a post-training scalar quantization method which jointly learns the per-coordinate grid assignments and the per-group scales using a Gumbel-Softmax relaxation of the discrete grid. GSQ matches the cardinality of the relaxation to the small number of levels available in the target bit-width regime (e.g., 3-8 levels for ternary and 3 bpp, respectively), making optimization tractable. Practically, on the standard Llama-3.1-8B/70B-Instruct models, GSQ closes most of the gap between scalar quantization and the QTIP frontier at 2 and 3 bits, while using a symmetric scalar grid with group-wise quantization, and thus remains compatible with existing scalar inference kernels. We further show that the same discrete-assignment optimization can be applied to practical GGUF K-Quant checkpoints: starting from publicly released GGUF models, GSQ improves accuracy while projecting the result back into the same deployment format. Finally, GSQ scales to trillion-scale Mixture-of-Experts models such as Kimi-K2.5, where vector-quantized methods are difficult to apply. The source code is publicly available at https://github.com/IST-DASLab/GSQ.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.18585

RECURSUM: Automated Code Generation for Recurrence Relations Exceeds Expert Optimization via LayeredCodegen

Rubén Darío Guerrero

原文摘要

Automated code generation can systematically exceed expert hand-optimization for recurrence relations--computational primitives ubiquitous in orthogonal polynomials, special functions, numerical integration, and molecular integral evaluation. We present RECURSUM, a Python DSL generating optimized C++ for arbitrary recurrence relations via three backends--template metaprogramming, a novel LayeredCodegen backend, and runtime loop-based evaluation--specifying recurrences in 10--30 lines of Python that generate 650+ lines of production C++. LayeredCodegen achieves 9.8$\times$ speedup over expert hand-written code and 1.9$\times$ over template metaprogramming for McMurchie-Davidson Hermite coefficients, through zero-copy output parameters, guaranteed inlining, and exact-sized stack buffers. A DAG-topological generalization of this architecture--built to express the four-center electron-repulsion integral that LayeredCodegen's tower model cannot--beats libint2, a mature, two-decade-refined production library, on 12 of 14 Cartesian integral classes, with $\sim$2$\times$ faster contracted Coulomb/exchange (J/K) matrix construction (alkanes and 3-D DNA fragments alike) and 1.1--2.7$\times$ faster one-electron integrals, completing the full Hartree-Fock integral set from one DAG framework. We validate 24 recurrence types spanning pure mathematics, numerical analysis, and quantum chemistry; production benchmarks confirm these speedups propagate to complete algorithms, matching expert baselines within 3.3\%. By eliminating the dual expertise barrier of domain knowledge and C++ metaprogramming, RECURSUM establishes systematic code generation--not hand-tuning--as the performance ceiling for recurrence algorithms.

cs.PL

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2510.05107

Structured Cognitive Loop for Behavioral Intelligence in Large Language Model Agents (Extended Revision: From Behavioral Architecture to Epistemic Accountability)

Myung Ho Kim

原文摘要

The central challenge for AI agents is not performance but accountability. An agent that produces correct outputs through an opaque process offers no basis for verifying why a decision was permitted, attributing errors to identifiable causes, or delegating authority with justified confidence. We introduce the Structured Cognitive Loop (SCL), an architecture that separates cognition, control, action, and memory: the language model proposes, a lightweight controller mediates every proposal before action, and memory is kept externally as a structured store of verified facts. Against ReAct and common LangChain agents on three tasks under matched models, tools, and decoding, SCL achieves 86.3% task success versus 70.5-76.8% across 360 episodes, with higher goal fidelity, fewer redundant calls, more reliable reuse of intermediate states, and fewer unsupported assertions; ablations show that control and external memory contribute independently. We then situate SCL within a broader accountability architecture comprising context-aware human-in-the-loop control, Pool-Gated Retrieval, and the Horizon-Warrant-Commitment framework, and outline the current direction of development, in which instructions are compiled into programs validated before execution so that execution authority derives from a verified program rather than per-cycle model interpretation. For the kind of accountability it pursues, SCL adopts neither multi-agent consensus as a proxy for truth nor the imitation of human cognition as a design goal: it institutionalizes cognition rather than imitating it.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.17561

Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports

Mahmut Furkan Gon, Emre Dinc, Tevfik Emre Sungur, Eray Tuzun

原文摘要

Issues faced when using software are reported in the form of bug reports. However, many bug reports are invalid, meaning they do not require code changes, and are resolved with a no-code fix. Manually determining the root cause of the invalid bug reports and providing actionable resolutions by the customer support causes a serious waste of resources. Our goal is to introduce a standardized taxonomy for root-cause oriented invalid bug report subclassification, and perform experiments to test the accuracy of various approaches on invalid subclassification and no-code fix generation. We study how different configurations perform on a gold-standard benchmark we have created. Using a manually curated benchmark for higher quality analysis, we experimented with vanilla LLMs, Retrieval Augmented Generation, and agentic web search to identify invalid subclasses and generate no-code fixes. We evaluated the results against manually labeled ground truth data that includes the invalid subclass and no-code fixes from the original bug reports. We measured subclass detection performance with weighted F1-Score, and assessed no-code fix suggestions using BERTScore and Judge LLM success rates. For subclassification, retrieval augmented generation achieves the highest overall performance with 0.66 weighted F1, slightly outperforming vanilla LLMs at 0.65 and agentic web search at 0.64. At the subclass level, performance peaks at 0.85 F1 for Non-reproducibility and 0.79 for Feature Request and Question, while Wrong Version remains the most challenging with scores between 0.00 and 0.29. For no-code fix generation, agentic web search achieves the highest overall Judge LLM success rate at 68.9%, compared to 64.4% for RAG applications and 64.9% for vanilla LLMs, with subclass-level peaks of 87.4% for Working as Designed and 72.2% for Question.

cs.SE · cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.10201

GAGR-Lab: Evaluating Joint Spatial-Geometric and Analytic Function Reasoning

Jingyao Zhang, Yun Li, Lu Han

原文摘要

Joint spatial-geometric and analytic function reasoning requires translating a perceived spatial configuration into a symbolic function whose executed curve satisfies geometric constraints. We present GAGR-Lab, a framework for measuring this capability through Cartesian game scenes, explicit function semantics, and authoritative Rust trajectory execution. It distinguishes spatial perception, metric grounding, geometric relations, function interpretation, function construction, and constrained synthesis. We specify four configurable scene-difficulty presets and a prospective 24-cell diagnostic design, while reporting only the subset actually evaluated. A bounded pilot of one hosted model (Llama 3.2 11B Vision Instruct) using two API credentials as execution replicas yields 72 balanced games with 432 attempts, 429 valid provider responses, and no target hits; exploratory ordinary-function prompt variants also fail to hit, while the structured localization interface yields no scoreable outputs. A privileged analytic search control independently succeeds on 600 directional cases from 300 generated scenes, with exact repeatability and 1,200 successful vertical-reflection or translation checks. The framework separates serving reliability, symbolic compliance, and geometric success, and preserves exact model-visible inputs and realized paths. A staged protocol outlines diagnostic calibration, held-out replication, multi-model comparison, and paired robustness tests. The contribution is an operational research framework with an executed pilot and a clearly identified prospective study plan; the full difficulty matrix and comparative model results remain untested.

cs.AI · cs.CL · cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.24337

Meet UD_Czech-PDTC: A Large and Genre-Rich Treebank in Universal Dependencies

Marie Mikulová, Barbora Štěpánková, Daniel Zeman, Jan Štěpánek, Milan Straka, Jan Hajič

原文摘要

Czech has been part of Universal Dependencies since its first release in 2015. It has also been one of the best represented languages, with the Prague Dependency Treebank being order of magnitude larger than most other UD treebanks. More recently, three other datasets from the Prague family were added and the annotations thoroughly revisited, forming the "Prague Dependency Treebank-Consolidated" (PDT-C). In comparison to the original PDT, PDT-C is more than twice as large, but it is also much more diverse in terms of genres and domains. In this paper, we describe the conversion of the new resource to Universal Dependencies. While the two annotation schemes are relatively similar at the first sight, there are numerous small differences in topology of the dependency structures and in granularity of the POS and relation type inventories. We demonstrate a selection of such differences on examples, discuss the diverging motivations, as well as ways to overcome the differences during conversion. We argue that while PDT is less "universal" and more tightly bound to one language, its multi-layer annotation is rich and provides all information needed for basic UD trees, and much more.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2509.25136

BALF: Budgeted Activation-Aware Low-Rank Factorization for Fine-Tuning-Free Model Compression

David González-Martínez

原文摘要

Activation-aware low-rank factorization techniques yield strong compression results, but they are generally confined to linear layers, and existing whitening-based theory typically makes an implicit full-rank assumption on layer activations. We introduce a layer representation framework that allows us to understand and extend low-rank factorization beyond linear layers, including standard and grouped convolutions. Within this framework, we provide a broader characterization of whitening-based activation-aware factorization that naturally covers rank-deficient activations, and show that the resulting projection is optimal in terms of layer output distortion. We then use output distortion as a proxy to allocate per-layer ranks under explicit FLOP or parameter count budgets, with negligible overhead via a Lagrangian relaxation of the allocation problem. Together, these components form BALF, an end-to-end, fine-tuning-free pipeline for model compression. Across CNNs and vision transformers on CIFAR-10 and ImageNet-1K, BALF generally achieves higher accuracy than SVD-based factorization baselines at matched FLOP or parameter count targets, and remains competitive with other fine-tuning-free compression techniques.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2407.04898

Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning

Joon Suk Huh, Kirthevasan Kandasamy

原文摘要

We study a multi-round mechanism design problem, where we interact with a set of agents over a sequence of rounds. We wish to design an incentive-compatible (IC) online learning scheme to maximize an application-specific objective within a given class of mechanisms, without prior knowledge of the agents' type distributions. Even if each mechanism in this class is IC in a single round, if an algorithm naively chooses from this class on each round, the entire learning process may not be IC against non-myopic buyers who appear over multiple rounds. On each round, our method randomly chooses between the recommendation of a weakly differentially private online learning algorithm (e.g., Hedge), and a commitment mechanism which penalizes non-truthful behavior. Our method is IC and achieves $O(T^{\frac{1+h}{2}})$ regret for the application-specific objective in an adversarial setting, where $h$ quantifies the long-sightedness of the agents. When compared to prior work, our approach is conceptually simpler,it applies to general mechanism design problems (beyond auctions), and its regret scales gracefully with the size of the mechanism class.

cs.GT · cs.CR · cs.DS · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.25379

StateRAG: Typed State Contracts for Complex Retrieval-Augmented Generation

Miaohe Niu, Xinyu Liu, Runsong Zhao, Pengxiang Li, Jingbo Zhu, Tong Xiao

原文摘要

Multi-step retrieval-augmented generation requires tracking evolving information needs, selected evidence, and unresolved gaps. However, free-form trajectories conflate this history, forcing each step to implicitly reconstruct the current state, while existing explicit-state methods remain vulnerable to inconsistent state overwrites. To address these limitations, we introduce StateRAG, a framework that coordinates a Planner, Navigator, Retriever, and Verifier through an explicit retrieval state with checked updates. These roles propose updates across four state fields: the plan, retrieval trace, evidence, and sufficiency assessment. A dedicated controller validates each proposal against the accepted state, rejecting inconsistent updates and guiding revisions within the query budget. A final Reader then answers strictly from the validated evidence. Ablation experiments on a shared graph backend show that StateRAG outperforms free-form trajectory and unverified explicit-state baselines on LongBench by 7.7 and 4.1 Macro F1 points, respectively. These gains consistently extend to document QA and flat passage retrieval. Furthermore, StateRAG surpasses the strongest external baselines by 5.3, 5.0, and 4.3 points on LongBench, QASPER, and DocVQA.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2403.01361

Bandit Profit-maximization for Targeted Marketing

Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy

原文摘要

We study a sequential profit-maximization problem, optimizing for both price and ancillary variables like marketing expenditures. Specifically, we aim to maximize profit over an arbitrary sequence of multiple demand curves, each dependent on a distinct ancillary variable, but sharing the same price. A prototypical example is targeted marketing, where a firm (seller) wishes to sell a product over multiple markets. The firm may invest different marketing expenditures for different markets to optimize customer acquisition, but must maintain the same price across all markets. Moreover, markets may have heterogeneous demand curves, each responding to prices and marketing expenditures differently. The firm's objective is to maximize its gross profit, the total revenue minus marketing costs. Our results are near-optimal algorithms for this class of problems in an adversarial bandit setting, where demand curves are arbitrary non-adaptive sequences, and the firm observes only noisy evaluations of chosen points on the demand curves. For $n$ demand curves (markets), we prove a regret upper bound of $\tilde{O}(nT^{3/4})$ and a lower bound of $Ω((nT)^{3/4})$ for monotonic demand curves, and a regret bound of $\tildeΘ(nT^{2/3})$ for demand curves that are monotonic in price and concave in the ancillary variables.

cs.LG · cs.GT · econ.GN · q-fin.GN

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.02126

Remote Sensing Change Detection via Weak Temporal Supervision

Xavier Bou, Elliot Vincent, Gabriele Facciolo, Rafael Grompone von Gioi, Jean-Michel Morel, Thibaud Ehret

原文摘要

Semantic change detection in remote sensing aims to identify land cover changes between bi-temporal image pairs. Progress in this area has been limited by the scarcity of annotated datasets, as pixel-level annotation is costly and time-consuming. To address this, recent methods leverage synthetic data or generate artificial change pairs, but transferring change detection models across datasets and acquisition conditions remains challenging. In this work, we introduce a weak temporal supervision strategy that leverages additional temporal observations of existing single-temporal datasets, without requiring any new annotations. Specifically, we extend single-date remote sensing datasets with new observations acquired at different times and train a change detection model by assuming that real bi-temporal pairs mostly contain no change, while pairing images from different locations to generate change examples. To handle the inherent noise in these weak labels, we employ an object-aware change map generation and an iterative refinement process. We validate our approach on extended versions of the FLAIR and IAILD aerial datasets, showing competitive zero-shot and low-data regime performance. Lastly, we showcase results over large areas in France, highlighting the scalability potential of our method.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.09109

TransferBreaker: Breaking Adversarial Transferability in Fine-Tuned Speech Recognition

Mojtaba Nafez, Aref Mousavi, Mohammad Ebrahim Mahdavi, Mobina Poulaei, Kiarash Kiani Feriz, Mohammad Hossein Rohban

原文摘要

Many organizations fine-tune publicly available pretrained Automatic Speech Recognition (ASR) models and deploy them in black-box settings, assuming limited access provides protection. We show this assumption is fragile: adversarial perturbations crafted on the public base model transfer effectively to fine-tuned target models, severely degrading performance and posing concerns for safety-critical applications. We propose TransferBreaker, a unified fine-tuning framework that suppresses adversarial transfer by integrating Base Adversarial Fine-Tuning, which restricts adversarial training to base-effective perturbations; Latent Jacobian Regularization, which enforces latent-space invariance by suppressing adversarially sensitive directions; and HybridGrad-AFT, which improves robustness against adaptive attacks by interpolating transferable perturbations from base and target gradients. We theoretically justify all components and evaluate TransferBreaker across three languages and four large ASR models, reducing adversarial WER from 92.6 to 27.8. Our code is publicly available at https://github.com/rohban-lab/TransferBreaker.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.17991

DORIS: Polarization Aware Multimodal Degradation Perception for Underwater Image Restoration

Wenshuo Zhang, Xiangyue Zhang, Junlin Li, Wenjie Tian, Yubo Zheng, Zhixin Dong, Hongli Xu, Jingyu Ru 等 9 位作者

原文摘要

Recovering high-quality images from degraded underwater observations is crucial for underwater exploration. Polarization observations show promise as an effective source of complementary physical cues for recovering target radiance under clear-water conditions. However, existing methods have yet to jointly model raw intensity information and polarization cues to enable image restoration driven by an understanding of mixed underwater degradations. To overcome these limitations, we propose DORIS, a unified multimodal language-driven framework that jointly uses polarization and RGB information to generate textual descriptions of degradation types and severity levels through structured semantic instructions and enhances joint restoration representations under the guidance of degradation-aware semantic features to improve restoration robustness under mixed degradations. We further construct a new benchmark named MixPolar, which contains underwater images exhibiting real degradations and the corresponding annotations. It supports systematic evaluation of degradation perception, image restoration, and downstream task performance. Extensive experiments demonstrate that our method effectively integrates polarization and RGB information, achieving competitive performance in degradation perception and image restoration.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2507.01835

Filter-Enhanced Smartphone Hyperspectral Imaging

Daniil Reutsky, Daniil Vladimirov, Yasin Mamedov, Georgy Perevozchikov, Nancy Mehta, Egor Ershov, Radu Timofte

原文摘要

Hyperspectral reconstruction from RGB imagery offers an affordable alternative to costly hyperspectral acquisition devices. However, its accuracy is fundamentally constrained by the limited spectral resolution of standard RGB cameras. In this paper, we propose a new approach that leverages the multi-camera systems integrated into most modern smartphones and enhances their capabilities by modulating their spectral response functions using carefully selected spectral filters, enabling a smartphone to capture richer spectral information. We propose a neural network able to exploit auxiliary RGB images with diverse spectral characteristics, enabling more accurate hyperspectral reconstruction compared to conventional single-image methods. Training is supported by a newly collected dataset, Doomer, which consists of multiple misaligned RGB images alongside corresponding hyperspectral data. We also propose a device-adaptation strategy that conditions the multi-image hyperspectral reconstruction network on camera color correction matrices available in standard DNG metadata, requiring no real images from the target device. We evaluate it on three physical smartphones, two of them unseen during training.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.24324

Prague Dependency Treebank -- Consolidated 2.0: Enriching a Complex Annotation Scheme

Marie Mikulová, Jiří Mírovský, Milan Straka, Pavlína Synková, Jan Štěpánek, Barbora Štěpánková, Jan Hajič

原文摘要

The Prague Dependency Treebank framework is unique in its attempt to systematically include and link different layers of language, including a meaning representation with several types of inter-sentential phenomena, especially coreference and discourse relations. We present its second consolidated version (PDT-C 2.0), which concludes almost 30-years long project of sustained development of the resource to a uniformly and coherently annotated, genre-diversified, almost 4 million token language resource of Czech language, with accompanying fully compatible lexicons. In addition to continuous linguistic research, the richly linguistically annotated corpus is also widely used in international comparisons of the development of traditional and novel NLP tools as well as in conversions into other formalisms. The corpus and the trained parsers are available under the CC BY-NC-SA licence.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.02212

3A-VLA: Abstraction-Aligned Action Learning for Vision-Language Agents in 3D Game Worlds

Zheyuan Zhou, Liang Du, Zixun Sun, Xiaoyu Zhou, Ruimin Ye, Qihao Chen, Yinda Chen, Lemiao Qiu

原文摘要

Despite advances in vision-language-action (VLA) models, they remain limited in highly dynamic game settings such as 3D open worlds and competitive player versus player (PvP) settings, where agents must efficiently extract sparse, actionable signals from dense visual input while integrating multimodal cues to track off-screen high-value targets in real time. To address this, we introduce 3A-VLA (Abstraction-Aligned Action VLA), a framework that grounds action learning in explicit intention and environment abstractions rather than superficial pattern matching. We introduce dual task-agnostic abstractions: the intention abstraction (IA), which condenses verbose instructions and reasoning into explicit semantic primitives, and the environment semantics abstraction (ESA), which structures dense visual streams into a spatial-functional affordance representation to guide grounded actions. We further propose an abstraction alignment reweighting (AAR) module that adaptively reweights the action imitation loss. A continuous intention-environment alignment signal is used to emphasize reliable action supervision when abstractions agree and reduce the influence of ambiguous demonstrations when they diverge, thereby learning a policy that balances fine-grained control and high-level reasoning without the need for manual rules. Extensive experiments show that 3A-VLA performs competitively among the evaluated baselines in both open-world (Minecraft) and competitive PvP (Game for Peace) settings. It also demonstrates zero-shot combat transfer from Game for Peace to high-fidelity games, including Valorant, CS2, GTA V, Elden Ring, and Mount & Blade.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.22715

AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild

Baiyu Chen, Zechen Li, Wilson Wongso, Lihuan Li, Xiachong Lin, Hao Xue, Benjamin Tag, Flora Salim

原文摘要

As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highly dependent on the sensing setup, including body location, mounting position, sensor orientation, device hardware, and sampling protocol. This setup dependence makes it difficult to learn motion representations that transfer across devices and datasets, and limits the broader use of wearable IMUs beyond closed-set recognition. We introduce AnyMo, a geometry-aware framework for setup-agnostic human motion modeling. AnyMo uses physics-grounded IMU simulation over dense body-surface placements to generate diverse and plausible synthetic signals, pre-trains a graph encoder from paired synthetic placement views and masked partial observations, tokenizes multi-position IMU into full-body motion tokens, and aligns these tokens with an LLM for motion-language understanding. We evaluate AnyMo on three complementary tasks: zero-shot activity recognition across 14 unseen downstream datasets, cross-modal retrieval, and wearable IMU motion captioning, where it improves average Accuracy/F1/R@2 by 11.7\%/11.6\%/22.6\% on HAR, increases zero-shot IMU-to-text and text-to-IMU retrieval MRR by 15.9\% and 28.6\%, respectively, and improves zero-shot captioning BERT-F1 by 7.5\%. These results support AnyMo as a generalist model for wearable motion understanding in the wild. Project page: https://anymo-project.github.io.

cs.CV · cs.AI · cs.CL · cs.HC

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arXiv ↗PDF ↗