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新论文 · v1arXiv:2611.16889

Control-Flow Profiling for Workload Representativeness

Mahesh Madhav

原文摘要

Workload characterization in performance engineering often replaces expensive target executions with lower-cost proxies, including reduced datasets, miniapps, and training inputs. Such substitutions require empirical verification that the proxy faithfully exercises the target's dynamic execution behavior. This paper presents an address-keyed control-flow profiling methodology and tool suite designed to evaluate workload representativeness directly from compiled, unmodified binaries. We pair time-ordered basic-block vector (BBV) recurrence analysis with exp-branchprof, an experimental Valgrind tool that captures five orthogonal structural profile families spanning branch direction bias, four-bit global history contexts, dynamic indirect targets, function call transitions, and intraprocedural CFG edges. Our method compares weighted event distributions from two executions of the same binary. It reports support coverage, reference event-mass coverage, distribution distance, entropy-derived quantities, and phase structure, rather than claiming that a single similarity score certifies a proxy. We validate the tool with exact-oracle microbenchmarks across AArch64 and x86-64, covering conditional and inverted exits, calls, returns, direct and indirect jumps, and forked processes. We then apply the workflow to SPEC CPU2017 and CPU2026 train/reference pairs and seven refrate/refspeed pairs. The corpus contains both close matches and cases where a shorter execution reaches much of the same code but misses substantial target, callgraph, CFG, or BBV event mass. The accompanying artifact supports the same analysis for proprietary workload/proxy pairs without requiring raw PC-level profiles to leave the collection environment.

cs.PF · cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16886

LLM-Assisted Verification in Dafny

Wieger Wesselink, Kees Huizing, Huub van de Wetering

原文摘要

Formal verification of complex algorithms requires expertise in program specification, invariant construction, mathematical proof, and the management of automated theorem provers. Although large language models (LLMs) can generate program code and proof fragments, reliably developing complete machine-checked verifications remains challenging: it requires an iterative process of constructing and validating specifications and invariants, building proofs, diagnosing solver failures, and optimizing verification performance. This paper presents a systematic ten-step workflow for LLM-assisted verification of scientific algorithms using the Dafny verification system. The workflow integrates algorithm formalization, contract development, empirical invariant validation, proof construction, bottleneck profiling, and resource-driven proof optimization. We applied the workflow using Antrhopic's Claude Opus 5 and Opus 5.5 models, combining LLM-driven development with automated testing and verification feedback, while retaining human oversight for correctness-critical decisions and strategic interventions. We evaluate the approach on 37 complex algorithms from graph theory and labelled transition systems. Notably, we verified the Hopfcroft-Tarjan triconnectivity test, demonstrating that even highly challenging algorithmic verification tasks can be carried out through a structured LLM-assisted workflow. Many of our machine-checked verifications were obtained within hours to a few days of interactive development. Our results suggest that LLM-assisted workflows can shift the role of the human verifier from detailed proof construction toward supervision, specification review, and strategic guidance.

cs.LO · cs.AI · cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.23628

How Hard Is It to Rig a Benchmark? A Social Choice Analysis of Leaderboard Robustness

Polina Gordienko, Georg Schollmeyer, Frauke Kreuter, Christoph Jansen

原文摘要

Multi-task benchmarks have become a central pillar of machine learning research, yet their growing influence has incentivised benchmark gaming -- strategic actions taken to improve the leaderboard rank of a specific model. Treating datasets as voters and models as candidates, we consider benchmark-specific training -- the inclusion of benchmark data in training -- as a form of election manipulation. For any ordinal benchmark, the problem of choosing datasets to train on so that a target model becomes top-ranked corresponds to shift bribery, a class of manipulation problems from computational social choice. Leveraging this identification, we show that the benchmark-specific training problem is NP-hard under Borda count and mean win rate. Complementing this worst-case perspective, we introduce the instance-level robustness, the minimum number of datasets a model developer must include in training to top a given leaderboard, and derive expressions for it under arithmetic mean, median, mean win rate and pairwise majority. We evaluate these expressions on MMLU under HELM and on BIG-Bench Hard (BBH) under the Open LLM Leaderboard. Across both suites, mean win rate is hardest to manipulate: this gap is clear on BBH (24 tasks, 4507 models), where its median robustness is 22 tasks (92%), compared with 13 (54%) under arithmetic mean and 12 (50%) under median and pairwise majority.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16884

Collision-Resilient All-to-All Broadcast Scheduling in Underwater Acoustic Networks

Guoliang Weng, Enqi Zhang, Lizhao You, Deqing Wang, Liqun Fu

原文摘要

This paper studies all-to-all broadcast scheduling for underwater swarms with collision-resilient physical layers that can decode asynchronously overlapping packets. Propagation-aware collision-free schedulers coordinate packet arrivals but do not account for this reception capability. We first design a data-driven neural reception model that captures per-interferer power and timing effects. The model improves prediction accuracy over SINR baselines while admitting an exact mixed-integer linear programming (MILP) formulation. The resulting neural-network-embedded MILP (NN-MILP) optimizes transmission times while explicitly encoding schedule-dependent overlaps and the predictor's inference, enabling globally optimal scheduling for small-scale instances. For larger networks, a two-stage ant colony optimization (ACO) heuristic combines low-cost transmission-order search with neural-aware timing refinement. Simulation results show that ACO shortens broadcast rounds compared with existing schedulers. It approaches the NN-MILP optimum on small-scale instances while supporting larger networks at substantially lower computational cost.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16881

Protein Language Models Fail To Capture Contact Predictions Under Invariant Relabeling

Paarth Batra, Aryan Keluskar

原文摘要

Protein language models (pLMs) produce highly accurate contact maps from single-sequence inputs. People often take this accuracy as proof that these models implicitly learn invariant coevolutionary statistics, extracting the same signals as classical alignment-based methods like Potts. In this work, we introduce a diagnostic framework around a global bijective relabeling of the amino-acid alphabet. This transformation permutes residue letters while leaving all alignment-level information, column entropies, and pairwise edit distances unchanged. Pseudolikelihood Potts models and inverse-covariance estimators remain invariant under this transformation, but pLMs fail the test. Across multiple architectures (ESM-2, MSA Transformer, AMPLIFY) and model scales (8M to 3B parameters), pLM contact maps collapse under arbitrary relabeling. Using Hamming distance controls and biochemical grouping interventions, we attempt to isolate what likely causes this degradation and find that these models rely on explicit, canonical residue identities (``alphabet anchoring'') rather than pure coevolutionary statistics. Increasing scale makes models more responsive to biochemical classes, but their performance still degrades under relabeling. Our findings show a gap between how pLMs and classical coevolutionary models compute contacts. More broadly, our work shows how diagnostic perturbations that leave classical models provably unchanged can expose hidden representation biases and prevent over-reliance on neural black boxes in biological modeling.

q-bio.QM · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.20335

Read, Parse, Describe: Unified Document Parsing with Visual Element Description Generation

Yuheng Chen, Yufan Chen, Zhuojun Cai, Ruiping Liu, Junwei Zheng, Jiale Wei, Di Wen, Weijia Fan 等 11 位作者

原文摘要

Document understanding systems are typically evaluated as separate components via layout analysis, optical text recognition, reading-order prediction, and image captioning. This separation leaves scientific figures, captions, and visual semantics weakly connected, and fails to measure whether a model can produce a single page-level parse that is both structurally faithful and semantically accessible. We introduce Read-Parse-Describe, a unified dataset for document parsing with visual element description generation. Given a page image, models are required to output an ordered list of layout elements containing bounding boxes, semantic labels, reading-order indices, recognized textual content, and alternative text descriptions for visual elements. The benchmark is built from 2,402 documents, comprising 18,163 continuous pages, 125,218 layout elements, and 11,338 visual instances. Besides, we further propose a plug-and-play space-awareness enhancer that reuses multi-layer visual features and injects grid-aware spatial guidance into vision-language tokens for layout-aware generation. Experiments across end-to-end VLMs and pipeline parsers show that existing systems still struggle to jointly recover reading order and visual semantics, while our enhanced model achieves strong joint performance. These results highlight the need to move beyond isolated document parsing modules toward unified models that jointly read, parse, and describe complex documents. The new dataset and code will be made publicly available at https://github.com/yuheng98chen/RPD-Unidoc

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16880

Tight Bounds for Downward-Closed Prophet and Secretary Problems

Hanrui Zhang

原文摘要

We study online selection problems subject to arbitrary downward-closed feasibility constraints, under different arrival orders, with different distributional assumptions. We establish the following bounds: (1) $Θ(\log^2 n / \log \log n)$ for the standard (adversarial-order) prophet problem, (2) $Ω(\log n)$ for the (Bayesian, random-order) prophet secretary problem, and by extension, for the standard (prior-free, random-order) secretary problem, and (3) $O(\log n / \log \log n)$ for the free-order prophet problem. Together with complementary bounds established in prior work, these results pin down the asymptotically tight competitive ratios for all these variants of prophet and secretary problems subject to downward-closed constraints. Our results refine and reinforce the big-decisions-first principle by Rubinstein and Singla [2026], establishing a superconstant separation between the random-order and free-order models.

cs.GT · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16879

Context Graph: Explainable Multi-Agent Decision Chains in AI-RAN

Xijun Wang, Zhaoyang Liu, Xiang Chen, Howard H. Yang, Chenyuan Feng

原文摘要

AI-RAN distributes network control across multiple autonomous agents, namely xApps and rApps operating over shared radio resources. When an alert or degradation appears, operators currently lack a structured way to trace which decision caused it, what context triggered that decision, and how other agents interacted. Existing explainability tools focus on single-model feature attribution or linear event logging, neither of which captures cross-agent decision chains. We propose the Context Graph, a heterogeneous directed graph that organizes policies, measurements, decisions, and alerts into a queryable explanation layer. Its foundational primitive is the Decision-Event node acting as a causal junction, where incoming edges capture triggering context and outgoing edges track downstream consequences that are progressively upgraded through counterfactual verification. Instantiated over existing E2, A1, and O1 data flows, our proof-of-concept demonstrates that compliance and diagnosis queries are resolved at sub-millisecond latency over graphs of tens of thousands of nodes, while reliably distinguishing true causal impacts from spurious correlations.

cs.NI · cs.AI · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16878

Topology-Aware Handover Management for Service Continuity in LEO Satellite Networks

Jui-Che Han, Nasir Khan, Siva Satya Sri Ganesh Seeram, Cicek Cavdar

原文摘要

Low Earth orbit (LEO) satellites are expected to play a key role in sixth-generation (6G) non-terrestrial networks. The high mobility of LEO satellites necessitates frequent handovers (HOs), posing challenges to service continuity and availability for user equipment. Conventional methods based on handover margin (HOM) and time-to-trigger (TTT) have limited adaptability to time-varying channel conditions and do not explicitly account for the impact of feeder-link and ground station (GS) connectivity on HO interruption time. To address these limitations, this paper develops a topology-aware HO framework based on proximal policy optimization (PPO). The framework uses reference signal received power (RSRP), satellite--GS associations, remaining visibility time, and elevation angles to determine when to initiate an HO and which satellite to select. Simulation results demonstrate that the proposed policy achieves a service availability of 99.32% at a minimum elevation angle of 40 degrees, exceeding that of optimized HOM--TTT (O-HT) and least-delay (LD) satellite selection by 1.67 and 1.41 percentage points, respectively. The policy maintains service availability above 97.52% as the minimum elevation angle increases to 55 degrees, demonstrating its ability to sustain high service availability under stricter elevation constraints.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16877

Informative Path Planning with Gaussian Processes: Error Bounds, Algorithms, and Benchmarks

Kalvik Jakkala, Jennifer Wakulicz, Donald G. Dansereau, Srinivas Akella, Lantao Liu, Jason M. O'Kane

原文摘要

Accurate environmental monitoring relies on Informative Path Planning (IPP) to deploy autonomous systems that maximize information gain. While Gaussian Processes (GPs) provide a robust mathematical foundation for modeling the environment, contemporary IPP approaches remain hindered by heuristic operational constraints and the lack of simultaneous theoretical guarantees for worst-case estimation error and approximation quality. Rather than introducing yet another heuristic, this paper presents a rigorous theoretical analysis of existing error bounds and algorithms to further develop the mathematical foundation for IPP. By evaluating probabilistic and deterministic bounds on reconstruction error, we establish that predictive variance is the theoretically ideal optimization objective for GP-based IPP. Furthermore, we analyze GreedyCover, a recent approach for optimizing predictive variance, and demonstrate that its near-optimal approximation guarantee matches the fundamental hardness-of-approximation limit for the problem. Comprehensive benchmarking confirms that this approach delivers superior estimation accuracy and computational efficiency compared to prominent information-theoretic objectives and optimization methods used in IPP. Finally, this theoretical analysis establishes a rigorous, near-optimal foundation for discrete offline IPP under bounded uncertainty constraints, offering clear and principled guidance for future work in continuous-space and online formulations.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16874

Stochastic Kernel Mode Decomposition: Information, Uncertainty, and Conditional Regime Inference

Boumediene Hamzi, Houman Owhadi

原文摘要

When do mode energies preserve regime information as measurement quality changes? Stochastic Kernel Mode Decomposition (sKMD) fits joint regime laws on signed KMD mode coordinates and transfers the reference mode posterior to regime probabilities and conditional reconstructions. For centered Gaussian regimes with independently varying known noise variances, fixed coordinate energies retain the full regime posterior precisely when all classes share their cross-covariances and class variances can differ only at isolated vertices of the covariance graph. We extend the characterization to nonzero means and show that freely varying correlated noise forces a fixed linear log-odds decoder to retain the full quadratic feature span even for two distinct centered regimes. At fixed quality, a three-regime example has no sufficient two-energy basis. Its smallest Bayes log-loss gap over bases of condition number at most $κ$ has sharp order $κ^{-1}$. Exact Bayes energy posteriors supply numerical gap estimates for specified bases. Additional bounds relate feature compression, independently learned mode spans and sensor-noise error to log loss. Two controlled experiments with 64 independent panels each show that signed information can improve prediction over energy summaries, while integration gives small gains over strong signed controls and depends on how the competing point rule is calibrated and how accurately observation noise is measured.

stat.ML · cs.CE · cs.LG · math.DS · nlin.PS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31996

Multi-Material Topology Optimization for Targeted Structural Dynamic Vibration Attenuation

Bir Bahadur Chaudhary, Bidhan Pandey, Bin Ouyang, Shiguang Deng

原文摘要

Resonance-induced structural vibrations can compromise the performance, reliability, and service life of engineering structures. Multi-material topology optimization (MMTO) offers a promising approach to vibration attenuation by tailoring the spatial distributions of material constituents to optimize structural stiffness, mass, and natural frequencies. However, existing eigenfrequency-based MMTO methods face several challenges, including the need for flexible interpolation of multiple material properties, non-smooth optimization objectives associated with mode switching and repeated eigenvalues, and numerical difficulties in imposing differentiable constraints to exclude natural frequencies from prescribed operating frequency ranges. To address these challenges, we propose a novel MMTO framework for structural vibration attenuation based on several innovations: (1) a novel multivariate weight-based multi-material interpolation scheme to simultaneously represent material stiffness and mass density, (2) a bound-based optimization formulation to improve the smoothness and stability of gradient-based optimization by addressing mode switching, and (3) a smooth frequency-band formulation to smoothly approximate the discontinuous Heaviside function while enabling differentiable frequency-band constraints. Additionally, we systematically investigate and compare three different topology optimization formulations for dynamic vibration attenuation, including maximizing the fundamental eigenfrequency, maximizing the gap between adjacent eigenfrequencies, and excluding natural frequencies from prescribed frequency bands. Furthermore, we present the detailed derivation of analytical design sensitivities for the three formulations.

cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16872

ACE-CaP-0: Real-World Code-as-Policies in the Era of Experience

ACE-CaP Team, Changxu Wei, Yunpeng Qing, Guozheng Ma, Bowen Liu, Zexin He, Yilun Kong, Haoming Gu 等 28 位作者

原文摘要

Recent advances in frontier models have pushed the boundaries of agentic robot control, enabling agents to perform complex robotic tasks through reasoning and interaction. However, repeated calls to these models during execution can substantially increase task completion time. Code as Policies (CaP) offers a route for coding agents to acquire physical skills through experience and retain them in executable programs, allowing the reasoning invested in development to be reused across runs. Developing reliable code policies while limiting the cost of physical experimentation remains a central challenge. We investigate human guidance as a source of priors for feasible actions and simulation as an auxiliary environment for exploring and testing candidate policies. Our findings favor robot demonstrations available on request and simulation used selectively during real-world policy refinement. These findings inform ACE-CaP-0, an agentic recipe that couples policy revision with experience acquisition through Demonstrations on Request and Simulation on Demand. Across eight real-world manipulation tasks spanning alignment, multi-object handling, and bimanual coordination, the resulting frozen programs achieve 92.1\% success (221/240 trials). Mean execution times across tasks range from 0.97 to 2.27 minutes. The programs execute with runtime perception and control, without a human or coding agent in the loop. These results highlight CaP's potential to turn experience into reliable, reusable physical skills.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16871

DPA: Decentralized Primal Averaging with Quasi-Global Momentum for Highly Heterogeneous Data

Diyako Ghaderyan, Stefan Werner

原文摘要

Decentralized training of deep learning models keeps training data local and supports on-device learning over networks. In realistic scenarios, heterogeneity across agents' local data poses an optimization challenge and can severely degrade test accuracy, especially on sparse communication graphs. Building on recent advances in centralized primal averaging, we propose Decentralized Primal Averaging (DPA), a decentralized learning algorithm that combines primal averaging with a local quasi-global momentum buffer. DPA maintains three coupled sequences for optimization, gradient evaluation, and output averaging, while using consecutive mixed iterates to construct a network-aware momentum direction without transmitting an auxiliary tracking variable. For smooth nonconvex objectives, we establish a convergence bound for DPA and identify conditions for linear speedup in the number of agents for sufficiently many iterations. We also introduce DPA-1G, a variant that uses one communication round per iteration and matches the per-iteration communication cost of DSGD-style baselines. Experiments following the GUT decentralized image-classification benchmark show that DPA compares favorably with DSGD, gradient tracking, QG-DSGDm, momentum tracking, and QG-GUTm across datasets, architectures, topologies, and heterogeneity levels, with the largest gains under high heterogeneity on sparse rings. At the same total communication cost, both DPA variants achieve higher mean test accuracy than the best baseline on CIFAR-10 and CIFAR-100 with ResNet-20. Our code is publicly available.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16869

Raysemble: Metric 3D Bone Reconstruction from a Single X-ray

Xiaoyang Li, Xinghao Yan, Yuan Chai

原文摘要

Raysemble reconstructs eight lumbar and pelvic bones from a single X-ray by separating shape and placement. Each 3D query samples regional and global features at its image projection. A shared decoder predicts normalized bone shapes. Predicted depth and camera geometry determine 3D centers and scales for resampling these shapes onto a common grid in millimeters. Evaluation uses 23 held-out patients with 43 CT scans, each generating one simulated anteroposterior radiograph. Without rigid alignment to CT,Raysemble leads 15 evaluated methods, averaging 65.24% Dice, 9.09 mm HD95, and 3.32 mm ASSD over three random seeds. With identical inputs, it improves Dice over VolumeNeRF by 5.86 percentage points.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16861

Visual Cipher Bench: Benchmarking LLM Agents on Long-Horizon Visual Reasoning

Deyao Hong, Jiayi Jiang, Yuchun Chen, Yixuan Shi, Wenyi Li, Zheng Jiang, Chengxing Xie, Mingyu Su 等 15 位作者

原文摘要

While language-model agents excel at long-horizon coding and terminal tasks, this success relies on two conditions: explicit text specifications and immediate execution feedback. However, neither condition holds in real-world problems where critical information exists only in images, leaving it unclear whether long-horizon capability transfers from text to visual reasoning. In such visual settings, because rules and clues resist text transcription and lack test-case feedback, agents must repeatedly inspect images, simulate spatial transformations, and autonomously cross-check clues. Despite this challenge, existing evaluations fail to capture such capability: visual reasoning benchmarks largely reduce tasks to single-turn, short-chain question answering, whereas agent benchmarks focus on text-based environments or interactive states that permit trial-and-error. To bridge this gap, we introduce Visual Cipher Bench, a collection of 40 original visual puzzles across four categories: spatial manipulation and simulation, logical constraints and planning, rule induction, and knowledge-based decoding. Each puzzle provides only one to three black-and-white line drawings and a minimal submission instruction, requiring agents to discover the underlying solving mechanism entirely from the images. Across 29 reasoning-effort configurations of eight frontier models, even the strongest model, GPT-6 Astra (max), achieves only 26.7% accuracy, while five of the eight models score below 1%. Yet on a ten-task subset, providing a single non-spoiler mechanism hint boosts accuracy from 7.8% to 55.6%. This sharp contrast shows that the primary bottleneck of current agents lies not in executing multi-step plans, but in autonomously discovering rules from visual evidence. Visual Cipher Bench thus provides a rigorous benchmark for measuring long-horizon visual reasoning in agents.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.25832

MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources

Ke Zhao, Zixiang Di, Hong Qian, Xiang Shu, Yaolin Wen, Qitao Shi, Bingdong Li, Xingyu Lu 等 12 位作者

原文摘要

Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented large language models (LLMs). Existing learning-based methods, however, typically require large-scale supervised data, expensive intermediate step verification, and extensive computational resources, while exhibiting limited optimization generalization across diverse optimization structures. In this paper, we propose \textsc{MiniOpt}, a lightweight optimization-specialized LLM trained through a reasoning-to-model-and-solve paradigm and a two-stage training framework with reinforcement learning with verifiable rewards (RLVR). MiniOpt encourages structure-oriented optimization learning by reinforcing the completeness of optimization formulations and optimization outcomes. Extensive experiments on eight optimization benchmarks demonstrate that MiniOpt consistently outperforms existing learning-based optimization models while requiring substantially fewer parameters and training data. To further understand optimization generalization, we introduce a complementary parameter-decoupled evaluation protocol that disentangles optimization structure understanding from numerical parameter processing. The results reveal that MiniOpt preserves optimization capability under parameter-decoupled settings significantly better than previous optimization-specialized models, suggesting that its performance gains primarily originate from stronger optimization structure understanding. These findings demonstrate an effective pathway toward efficient and generalizable optimization-specialized LLMs. The code is available at https://github.com/Hsiang-1/MiniOpt.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.14433

New linear codes over GF(7), GF(8) and GF(9) from quasi-cyclic codes and Construction X

Sebastian Cochinescu

原文摘要

We present linear codes with parameters [88,8,64]_7, [90,8,65]_7, [98,7,75]_8, [97,8,72]_8, [44,7,31]_9, [97,8,71]_7, [85,6,67]_8 and [78,8,56]_7. Each has a minimum distance one larger than the lower bound listed in Grassl's tables of best known linear codes; codes with parameters [89,8,64]_7, [96,8,71]_8, [95,8,70]_8, [94,8,69]_8, [77,8,55]_7 and [86,6,68]_8 follow. Seven of the eight codes are obtained from a quasi-cyclic code and a vector that is constant on each block, by Construction X with a repetition code. The remaining one, the [90,8,65]_7 code, is obtained from the first by appending two coordinates. The parameters and all weight counts were obtained by exhaustive enumeration, and a short program that repeats these computations is included.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16849

Constrained intersection representations and forbidden patterns

Laurent Feuilloley, Michel Habib, Amalia Sorondo

原文摘要

We study the interplay between two types of graph classes: the ones defined by intersections of geometric objects and the ones defined by the absence of a substructure. More precisely, we study intersection graphs that are grounded, that is, the objects lie above a horizontal line that they touch, and ordered graphs with forbidden patterns, that is, ordered graphs that avoid a set of ordered induced subgraphs. These two approaches are fundamentally different, but it is known that several interesting graph classes have both types of characterizations, and we aim to understand whether this is a general phenomenon. Our contributions are the following. First, we identify a family of patterns on four vertices and a natural hierarchy of intersection graphs of strings whose classes are nicely interleaved. This raises hopes to prove connections between the two perspectives. Second, we dampen these hopes by proving that a class of (ordered) intersection graphs that we study cannot be characterized by a finite set of forbidden patterns. Third, we show a dual impossibility result: there exists a pattern-defined class of our hierarchy that admits no intersection representation, if we require this representation to be closed under natural operations such as translation and scaling. However, it turns out that if we allow slightly more exotic intersection representations, then we can find one for this pattern-defined class.

cs.DM · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16844

Locality Meets Interference: Structure and Scheduling in Radio Multicasting

Qin Xin

原文摘要

We study multipoint-to-multipoint multicasting in synchronous radio networks with unknown participant locations and unrestricted message aggregation. Let $n$ be the network size, $k$ the number of participants, and $d$ their hop diameter. A supplied distance-two coloring with $C$ colors yields completion within $(C-1)d+1$ slots and at most $nk$ transmissions. In unit disk graphs, coordinates, one-hop neighbor data, and a common degree bound $Δ_0$ suffice for a $15(Δ_0+1)$-color schedule. A known fixed separation bound permits optimal deterministic $Θ(d)$ completion from coordinates alone. Without geometric data, strong selectivity gives $O(d\min\{n,(Δ_0+1)^2\log n\})$ slots when $n$ is known. Independent random transmission gives $O((Δ_0+1)(d+\log(k/η)))$ slots with failure probability at most $η$; for fixed $Δ_0$, this simplifies to $O(d+\log(1/η))$. A star construction shows that the logarithmic term is necessary for this random rule when the degree grows. We extend color schedules to fixed deletions and subgraphs connected over bounded windows of cycles. Separate conditional reductions preserve additive costs under diameter guessing and control parameter-table and cluster preprocessing. Finally, a counterexample shows that pruning and reranking a breadth-first tree can destroy interference-free delivery. For one-hop designated delivery with receiver degree at most $b$, we prove that the optimal schedule length equals the chromatic number of an associated conflict graph and is at most $2b-1$, with matching examples.

cs.DC · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16843

Shortcuts or Memorisation? Revisiting Data Corruption in Natural Language Understanding with Large Language Models

Aarne Talman, Stergios Chatzikyriakidis

原文摘要

Pre-trained encoders fine-tuned on natural language inference (NLI) and other language understanding tasks keep much of their accuracy after the input is perturbed, e.g. by removing all nouns or shuffling the words. Does the same hold for large language models (LLMs), and if so, is it because they have seen the benchmarks during pre-training? In this paper we revisit these perturbation experiments with eight LLMs alongside fine-tuned encoders on GLUE and the three rounds of ANLI. Regardless of model size, every system that answers directly retains 36-72% of its above-chance performance without nouns on MNLI compared to only 14-29% on ANLI. Only a model that reasons before answering departs from this pattern. We also check contamination indicators (including exact $n$-gram counts in the public pre-training data of two of the models) and find no link between exposure and robustness. Our strongest evidence comes from 368 new, human-validated items that none of the models can have seen. All eight LLMs keep more of their performance without nouns on items written in MNLI's style than on items written without its cues. When the perturbation instead removes the function words carrying such cues, this advantage reverses. In light of these results, we argue that robustness to perturbation on familiar NLI benchmarks reflects dataset shortcuts rather than memorisation, and tells us more about the benchmark than about the model.

cs.CL

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新论文 · v1arXiv:2611.16840

Kosmo-GFM: A Production-Ready Geometry Foundation Model

Sheng Tang, Shengzhe Wei, Tiancheng Chen, Wenhua Jin, Xinyang Lu, Tinghao Jiang, Shuoxuan Feng, Yupeng Liu 等 32 位作者

原文摘要

We present Kosmo-GFM, a scene reconstruction system that integrates a geometry foundation model (GFM), a pose optimization engine (PoseEngine), and reconstruction-derived supervision from KosmoWorld. GFM predicts camera parameters, depth, cross-view correspondences, and dynamic 3D points from images and videos. PoseEngine combines these predictions with geometric optimization to recover scene-level camera poses and geometry for downstream 3D and 4D Gaussian Splatting. KosmoWorld generates images and geometric supervision from reconstructed real-world scenes to improve GFM's geometric predictions. On the Sparse and Medium subsets of SpatialBench, GFM achieves the lowest absolute relative depth error and the highest relative-pose AUC at 5 degrees among the compared methods. It also achieves the lowest mean endpoint errors for dynamic points and scene flow across the five evaluated datasets. Across 34 scenes completed by all compared reconstruction systems, PoseEngine achieves the highest scene-averaged pose AUC at thresholds of 1, 3, and 5 degrees, with a cumulative runtime of 14.07 hours, approximately 20% of GLUEMAP's 69.67 hours. PoseEngine Flash provides a further accuracy-efficiency trade-off, with a measured mixed-precision runtime of 2.62 hours, approximately 4% of GLUEMAP's runtime. Separate scaling experiments show generally favorable trends with increased data, model computation, and training budgets. These results support the use of learned geometric priors within a scene-level reconstruction pipeline.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.01712

ActiNet: An Open-Source Tool for Activity Intensity Classification of Wrist-Worn Accelerometry Using Self-Supervised Deep Learning

Aidan Acquah, Shing Chan, Aiden Doherty

原文摘要

The use of accurate and reliable open-source human activity recognition (HAR) models on passively collected wrist-accelerometer data is essential in large-scale epidemiological studies that investigate the association between physical activity and health outcomes. While self-supervised learning has generated considerable excitement in improving HAR, the extent to which these models, coupled with hidden Markov models (HMMs), would make a tangible improvement to classification performance and the effect this may have on the predicted daily activity intensity compositions is unknown. Using up to 24 hours of wrist-worn accelerometer data from 151 CAPTURE-24 participants (aged 18-91, mean age 42, 66% female), we trained the ActiNet model, consisting of HARNet, a self-supervised, 18-layer, modified ResNet-V2 model, followed by hidden Markov model (HMM) smoothing, to classify labels of activity intensity. The performance of this model, evaluated using 5-fold stratified group cross-validation, was then compared to a baseline random forest (RF) + HMM, established in existing literature. Differences in performance and classification outputs were compared with different subgroups of age and sex within the CAPTURE-24 population. The ActiNet model was able to distinguish labels of activity intensity with a mean macro F1 score of 0.80 and a mean Cohen's kappa score of 0.85. This exceeded the performance of the RF + HMM, trained and validated on the same dataset, with mean scores of 0.76 and 0.80, respectively. The improvements in performance were consistent across subgroups of age and sex. These findings encourage the use of ActiNet for the extraction of activity intensity labels from wrist-accelerometer data in future epidemiological studies.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.04325

The microscope is the mask: privileged views and labels from a cryo-ET forward model

Bogdan Toader, Kiarash Jamali, Tanmay A. M. Bharat, Sjors H. W. Scheres

原文摘要

We explore the use of simulated data for training a model for protein annotation in crowded cryo-electron tomography volumes reconstructed from images collected at limited tilt angles and severely corrupted by the measurement operator. Firstly, we leverage the corruptions imposed by the forward model to generate domain-specific augmented paired views of the exact same scene for an invariance objective integrated into the LeJEPA self-supervised training framework. Secondly, we use additional information from the simulation pipeline such as the positions and identity of proteins in the simulated volumes to inform the architecture of the model and the loss function, so that semantic information is localised at protein positions in the resulting dense feature volume. The resulting model, CARNIVAL, is evaluated without finetuning on classification and detection tasks in real tomograms, using a benchmark dataset containing multiple protein types and two tomogram processing types. We show that CARNIVAL outperforms a state-of-the-art model trained using a contrastive objective on simulated data but without forward model-based paired views or privileged information.

cs.CV · cs.LG · math.OC

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新论文 · v1arXiv:2611.16835

Explicit Edit Codes via Multicoloring

Huiling Xie, Zitan Chen

原文摘要

For any fixed integers $q\ge2$ and $t\ge1$, we construct explicit $q$-ary codes of length $n$ that correct any combination of at most $t$ insertions, deletions, and substitutions, with redundancy at most $2t\log_q n+O_{q,t}(\log\log n)$ $q$-ary symbols. This matches the leading redundancy term of Levenshtein's classical existential bound. Building on the standard graph-coloring approach, we use a prime-based multicoloring of the edit-confusion graph to assign multiple colors to each $q$-ary word of length $m<n$. Appending a short, edit-protected representation of an assigned color to the length-$m$ word yields a length-$n$ codeword. The choice among the assigned colors carries additional message information, thereby reducing the redundancy. The construction admits deterministic encoding and decoding in $\widetilde O_{q,t}(n^{6t+2})$ and $\widetilde O_{q,t}(n^{7t+2})$ time, respectively.

cs.IT · cs.DM · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16833

Earlier citation practices are linked to signs of artificial intelligence use in scientific writing

Hongkan Chen, Yongyuan He, Qingshan Zhou, Yi Bu

原文摘要

As artificial intelligence (AI) becomes more common in scientific writing, some authors show fewer signs of AI assistance than expected across successive periods. We analysed OpenAlex publication histories for 71,133 authors with at least one eligible first-authored English-language publication each year from 2023 through September 2026. Using abstract-based writing scores, we defined resistance as the predicted minus observed share of abstracts classified as AI-assisted. Positive scores indicate fewer such abstracts than expected. Early resistance was positively associated with later resistance, although the share with positive scores fell. Environmental characteristics contributed more than individual characteristics to predicting later scores, although overall predictive power was low. Having a collaborator with a non-positive early score was associated with lower subsequent resistance. Authors with positive mean scores in both 2023-2024 and 2025-2026 cited older work, drew on fewer subfields and more often cited sources they had used before than authors with non-positive mean scores in both periods. These differences were evident before 2023. The adjusted gap in reference reuse rose from 3.24 percentage points before 2023 to 7.95 in 2025-2026. The findings link variation in signs of AI assistance to earlier differences in how scientists used prior work.

cs.DL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16831

A Modular Framework for ESG Prediction through Domain-Specific Language Modeling and Graph Learning

Sofia Baiocco, Gianluca Bonifazi, Christopher Buratti, Michele Marchetti, Camilla Mazzoli, Federica Parlapiano, Giulia Quaglieri, Davide Traini 等 10 位作者

原文摘要

Environmental, Social and Governance (ESG) scores are increasingly used to evaluate corporate sustainability, supporting investment decisions, ensuring regulatory compliance and assessing risk. However, current ESG evaluations largely rely on proprietary methodologies that often produce inconsistent ratings. This motivates the development of data-driven prediction frameworks. In this paper, we present ESGPred, a modular framework for predicting ESG scores by combining domain-specific language modeling with graph-based learning over a network that models the relationships among the companies being evaluated. In addition to presenting the rationale behind ESGPred and detailing its ESG score prediction process, we show that ESGPred can effectively predict both overall ESG ratings and their individual Environmental, Social, and Governance components. We evaluated ESGPred using a dataset of 5,266 sustainability reports published by 2,134 publicly listed companies between 2020 and 2023. In this evaluation, we used ESG ratings provided by LSEG Data & Analytics as ground truth. Our experimental results demonstrated that integrating graph-based relational learning improves prediction performance over purely text-based baselines.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.25735

Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale

Peichun Hua, Danyang Chen, Junan Zhang, Haifeng Sun, Jingyu Wang, Diwen Xue, Mingyu Li, Yunming Xiao

原文摘要

Hosted retrieval-augmented generation (RAG) and semantic search allow users to query valuable provider-held corpora, raising two competing demands: to hide each query and chosen result, yet reveal only the documents that the user is authorized to receive. Existing cryptographic approaches either make this costly by processing the entire corpus for every query, or sacrifice quality for efficiency by scanning a few clusters. We repurpose learned deep hashing as a private filter: a randomized binary code points the provider to a short candidate list, while encrypted reranking and oblivious key transfer protect the precise query and final selection. This shortlist short-circuits full-corpus cryptographic search without sacrificing retrieval quality: with 200-500 candidates, it closely matches full-corpus retrieval across five zero-shot corpora spanning 25K to 5.4M documents. On the full 2.68M-passage NQ corpus over a 10-Gbps link, our protocol only adds 0.38 seconds, or 6.5%, to a 128-token Qwen3-32B RAG pipeline. The released code satisfies directional metric differential privacy (DP) and substantially reduces embedding-inversion and property-inference leakage, demonstrating that a carefully learned shortlist can make private dense retrieval both accurate and practical.

cs.CR · cs.AI · cs.IR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16822

A Class of Bayesian Conjugate Distributions on $SE_n(3)$ for Motion Estimation

Shijie Wang, Haichao Gui, Rui Zhong

原文摘要

This paper proposes a novel class of Bayesian Conjugate Distributions (BCDs) on extended special Euclidean groups SE_n(3) for global motion uncertainty representation and estimation. BCDs are constructed by restricting matrix Gaussian distributions on ambient Euclidean space to SE_n(3), avoiding singularity of local coordinates. The unique feature of BCDs is their conjugacy to a broad class of sampling distributions arising in motion estimation, enabling exact closed-form Bayesian inference. Optimal estimation with respect to BCDs is further investigated for implementing motion estimation. Two means are extracted as random state estimates, which maximize probability and minimize mean squared error, respectively. Moreover, within the BCD class, moment matching is shown to minimize the Kullback-Leibler divergence, and maximum likelihood estimation is also derived, which provide analytical methods to approximate underlying distributions with the optimality guarantee. As an application, a Bayesian estimator for pose and velocity estimation of an Unmanned Aerial Vehicle (UAV) is developed within an assumed density filtering architecture. The propagation and estimate extraction leverage the optimal estimation related to BCDs, and the landmark measurements are exactly fused with the BCD prior in closed form due to the conjugacy. Numerical simulations of a UAV flight indicate the advantages of BCD-based estimators over two classic Kalman filters on Lie groups with various initial uncertainties and measurement noise.

stat.ME · cs.RO

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新论文 · v1arXiv:2611.16820

Canonical Truncation for Mixed-Integer and Combinatorial n-Fold Optimization

Klaus Jansen, Felix Ohnesorge, Björn Schumacher

原文摘要

We consider a special block-structured integer program with $n$ blocks. Every block has a prescribed number of vectors that need to be selected while minimizing the objective $c^{\intercal}x + g(Ax)$, where $c$ is a rational cost vector, $A$ is an integral matrix with $d$ rows, and $g \colon \mathbb{Z}^d \to \mathbb{Q}\cup \{+\infty\}$ is an arbitrary function. We access $g$ via an oracle that minimizes $c^{\intercal}x + g(Ax)$ when all but $O(d)$ variables are fixed. Our algorithm runs in time $(ndΔ)^{O(d)}(Q + \text{poly}(|I|))$ where $Δ$ is the largest absolute value of an entry in $A$, $Q$ is the necessary time to evaluate the oracle for $g$ and $|I|$ is the size of the input. This model is a variation of the model studied by Dadush et al.~[IPCO 23]. Their model has only a single block but individual upper and lower bounds on the variables instead of prescribed block sums. For their model they present an algorithm with running time $t^d(dΔ)^{O(d^2)}Q$ where $t$ is the number of variables. We use our algorithm to solve mixed-integer linear programs with no upper bounds on the variables, a linear objective, and arbitrarily many constraints involving only fractional variables in time $(mΔ)^{O(m)}\text{poly}(|I|)$, where $m$ is the number of constraints involving integral variables and $Δ$ is the largest absolute value of coefficients of integral variables. This improves the dependence on $m$ in the exponent to asymptotically match the dependency in the integer setting. Further, we use our algorithm to generalize the parameterized algorithm for scheduling on uniformly related machines by Rohwedder~[ICALP 25] to obtain an algorithm for multiple knapsack with running time $w_{\max}^{O(d)}\text{poly}(|I|)$ where $w_{\max}$ is the maximum weight of an item and $d$ the number of different weights.

cs.DS

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