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版本更新 · v2arXiv:2608.30768

CORAL: A Benchmark for Structure-aware and Brain-wide Neuron Reconstruction in Light Microscopy

Zekang Yang, Jiamin Li, Zhenghua Li, Jiaqi Fan, Zengcai Guo, Xiaolin Hu

原文摘要

Automatic neuron reconstruction from light microscopy images is a central problem in computational neuroanatomy. While recent methods have achieved encouraging results on local image blocks, it remains unclear whether such progress translates to reconstruction that is both structurally accurate and scalable to the whole-brain scale. We present CORAL, the first benchmark for structure-aware evaluation of automatic neuron reconstruction from light microscopy images at both local and whole-brain scales. Built on a high-quality whole-brain fMOST dataset with carefully curated annotations, CORAL establishes two progressive tasks: block-level reconstruction, which evaluates reconstruction methods under limited spatial context, and brain-wide reconstruction, which assesses complete neuron reconstruction at the whole-brain scale. To account for topological correctness beyond geometric distance similarity, we introduce a structure-aware metric based on fiber prediction. To further achieve complete neuron reconstruction across the entire brain, we develop a brain-wide neuron tracing framework that extends arbitrary local reconstruction methods to the whole-brain scale through an iterative local-to-global process. Using this benchmark, we provide the first structure-aware comparison of mainstream methods for local neuron reconstruction and further evaluate their performance in brain-wide reconstruction. Our results underscore the importance of structure-aware evaluation and the need for more robust methods for complete neuron reconstruction.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16376

SPEC CPU2026 Under the Microscope: Programs, Phases, and Samples

Peijun Li, Pei-Hung Lin, Tianming Cui, Caiwen Ding, Yang Katie Zhao, Maya Gokhale

原文摘要

SPEC CPU2026, the latest generation of the SPEC CPU benchmarks, is set to become the standard benchmark suite in computer architecture research, where new designs are often evaluated through sampled simulation with tools such as SimPoint. Existing characterizations of the suite focus on whole-run averages, which reveal neither how behavior changes within a run nor how well sampling captures it. We characterize CPU2026 on real hardware at three levels: programs, phases, and samples. At the program level, we report whole-run metrics and top-down bottleneck classes for all four suites and examine how they change with workload size, compiler version, and platform, across machines with three different instruction set architectures: x86, Arm, and RISC-V. Notably, changing only the compiler version can halve or nearly double the IPC of individual benchmarks. At the phase level, we track hardware counters every 10 ms over all 90 SPECrate inputs, identify recurring patterns of time-varying behavior, and divide the inputs into three groups: steady, multi-phase, and bursty. More than half of the inputs show time-varying behavior, and IPC within a single input can vary by more than an order of magnitude. At the sample level, we validate SimPoint against hardware measurements of every interval it can select. SimPoint is accurate on steady inputs, but its error reaches 43% on the others, and changing only the random seed spreads the error of one input over a range of 58 percentage points. Keeping SimPoint's clusters but choosing each representative by measured CPI brings every input within 2.6%, so the error comes from the choice of representatives, not from the clustering. We conclude with practical recommendations on sampling strategy for CPU2026, which give researchers a deeper understanding of the suite and show where additional simulation effort is needed to keep sampled results accurate.

cs.AR · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16375

Recursive State-Augmented Language Models: Persistent Latent-State Adaptation of Pretrained Language Models

Omer Haq

原文摘要

Language models rely on finite token contexts, limiting access to earlier interactions. We introduce Recursive State-Augmented Language Models (Recursive SALM), a framework in which a persistent latent state evolves with each observation and generates adaptations of a pretrained language model. A modular read-transition-write architecture separates observation encoding, latent dynamics, and predictor adaptation. We develop deterministic and stochastic formulations. The experiments evaluate only deterministic SALM with frozen GPT-2 small and exclude earlier evidence from its decoder context. On a four-turn hypothesis-inference task, latent-gated LoRA reaches 89.1% accuracy, comparable to 88.0% for a trained full-context LoRA baseline, with earlier evidence available solely through a 64-dimensional state. Zeroing or shuffling the state substantially reduces accuracy. In a two-turn task with codewords unseen in training, several interfaces improve candidate ranking over chance, but rarely generate the exact codeword under the evaluated training schedule. These experiments show that persistent latent-conditioned adaptation can support sequential inference when earlier evidence is absent from the decoder context.

cs.LG · cs.AI · cs.CL · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16374

ManiLadder: Benchmarking Robot Manipulation Through a Categorized and Multi-Difficulty Level Task Ladder

Chongkai Gao, Jieao Shi, Zhaoyu Mu, Yudi Lin, Xuanwei Liu, Wei Ken Ng, Baiye Cheng, Anqi Chen 等 17 位作者

原文摘要

Recent advances in robotic manipulation have enabled increasingly complex and dexterous behaviors, yet existing benchmarks still focus on simple pick-and-place tasks and lack a systematic framework for measuring progress in robot learning along the axis of task difficulty. We present ManiLadder, a large-scale and organized simulation benchmark for robotic manipulation. ManiLadder contains 112 carefully designed tasks stratified into 4 difficulty levels, with 8 categories in each level, spanning rigid-body and deformable-object manipulation, single-arm and dual-arm settings, and gripper and dexterous-hand end-effectors. To calibrate difficulty, we fix the data amount the same for every task and use a unified flow- matching imitation learning policy under the same training and evaluation protocols, then use its evaluation success rate as an operational empirical difficulty signal. All tasks are iteratively refined until they fall into target difficulty ranges, which are then verified across different random seeds, policies, and agentic learning systems. We further analyze the task properties, compare different policy performances and training settings, analyze task transfer and data scaling behaviors, and conduct real-world experiments showing that the designed tasks preserve their difficulty ordering beyond simulation. ManiLad- der provides a calibrated ladder for measuring, comparing, and advancing robotic manipulation capabilities. More results are available at https://maniladder-benchmark.github.io/.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.03327

TIDE: Token-Level Credit Assignment via Entropy-Gated Bounded Deviation

Qiaoman Zhang, Shunhua Min, Xiang He, Zhenghan Zhang, Yuting Zhang, Guangxian Li, Yunzhu Kuang, Rui Jiang 等 11 位作者

原文摘要

Reinforcement learning (RL) is central for aligning large language models (LLMs) with complex reasoning, yet the leading critic-free algorithm, Group Relative Policy Optimization (GRPO), broadcasts a single sequence-level advantage uniformly across long Chain-of-Thought (CoT) generations and constrains exploration with an unbounded Kullback-Leibler (KL) penalty, which dilutes token-level supervision and induces gradient instability and mode-seeking conservatism. We propose TIDE (Token-level Importance via Deviation and Entropy), a critic-free RL framework that reinterprets distribution deviation as a guiding signal rather than a rigid penalty. TIDE replaces the volatile reverse KL divergence with the bounded Hellinger distance, safely quantifying token-level exploration without risk of gradient explosion. To separate genuine reasoning breakthroughs from confident hallucinations, an entropy gating mechanism scales this deviation by the policy's predictive uncertainty, and the gated scores redistribute the sequence-level advantage across tokens. The resulting updates amplify exploratory steps while suppressing low-entropy deviations, without a value network and with negligible overhead. Across in-domain, held-out, and out-of-domain benchmarks, TIDE outperforms strong critic-free baselines: on the Qwen2.5-32B backbone it reaches 60.0% and 46.0% Avg@32 accuracy on AIME 2024 and AIME 2025, respectively, above competitive baselines such as DAPO. Token-level attribution, causal perturbation, and exploration analyses further support the interpretation that the entropy-gated deviation identifies pivotal reasoning steps rather than merely perturbing optimization dynamics.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.08439

Label-Wording Augmentation in a Typed-Decision Encoder: Gains on Unfamiliar Words, Not on Abbreviations or Codes

Vladimir Beskorovainyi

原文摘要

Typed-decision models score options that the caller supplies at request time, so their accuracy depends on label wording that the model's developer does not control. We compare two releases of a 307M-parameter multilingual cross-encoder, Ru-Jev, trained on thirteen Russian-language tasks; the later release adds an augmentation that presents labels in several surface forms. Holding questions fixed and varying only how eighteen routing labels are worded, we evaluate ten label vocabularies at five and eighteen candidates, checked for overlap with the training data. On held-out vocabularies made of ordinary or borrowed words (jargon, office slang, department names), the augmented release has higher point accuracy in all fourteen cells, by up to 0.24. Several of these gains rest partly on one label that the earlier release almost never chose; fitting one bias per option removes part of them, while the jargon gain largely survives. On abbreviations and opaque codes the augmented release gives no usable gain: its loss on 17 abbreviations unrelated to training (-0.060) is one label and disappears after the same correction. The comparison is between two trained releases, not a controlled ablation, and the earlier recipe varies across seeds by up to 0.098 on the strictest slice. On two datasets neither release saw, accuracy moves by -8.4 and +5.6 points, narrowed to -3.4 and +1.4 by the per-option biases; on a third, scientific rubrics, the augmented release is ahead of both runs of the earlier recipe after the correction. Rankings of ten training checkpoints disagree across the three unseen datasets, so none of them should choose the checkpoint. We also report four evaluation hazards from comparing the two releases.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16373

Description-Guided CLIP Adaptation with Local Evidence Calibration for Few-Shot Plant Disease Recognition

Zhenkun Ye, Qian Zhou, Hua Zou

原文摘要

Accurate plant disease recognition is essential for intelligent agriculture, but few-shot settings provide limited labeled samples that may not capture diagnostic symptoms and intra-class variations. Pretrained vision-language models (VLMs), such as CLIP, offer transferable visual-semantic knowledge. However, existing CLIP-based methods often rely on coarse class semantics and global visual representations, overlooking disease-specific diagnostic information and fine-grained local cues. Excessive few-shot adaptation may also cause over-specialization and weaken the generalization ability of large-scale pre-training. We propose a description-guided, two-stage CLIP adaptation method to address these issues. Symptom and comparative descriptions enrich class semantics with typical visual symptoms and discriminative cues between similar diseases. The enhanced semantics guide the aggregation of class-relevant patch evidence to calibrate global image-text matching with fine-grained cues. For adaptation, we first update the vision-language encoders with lightweight parameters, then freeze them and train a linear classifier. At inference, the classifier is used for in-domain prediction, while class prototypes from the adapted text encoder support cross-domain prediction and retain vision-language transferability. Experiments with two backbones show that our method achieves the highest mean accuracy in most few-shot settings on PlantVillage, Cassava, and Pear, reaching 93.16%, 79.30%, and 96.56%, respectively, with ViT-L/14 under 16-shot. On class-aligned cross-domain evaluation across 21 shared PlantDoc classes, it achieves 56.96% accuracy, outperforming all compared methods. These results demonstrate its effectiveness in fine-grained few-shot plant disease recognition and cross-domain generalization.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.05688

Square-Root Regret for Adversarial Multiplayer Bandits without Collision Information or Shared Randomness

Chenyu Gan

原文摘要

We study cooperative multiplayer bandits with adversarial rewards and no collision information. In each of $T$ rounds, each of $m$ labeled players picks one of $K>m$ arms. A player alone on its arm receives that arm's reward in $[0,1]$; players who share an arm receive $0$. Each player observes only its own reward, so it cannot distinguish a collision from an arm with zero reward, and there is neither shared randomness nor a communication channel. Bubeck, Li, Peres and Sellke (2020) obtained regret $\widetilde O(KT^{3/4})$ for two players and $\widetilde O(mK^{3/2}T^{1-1/(2m)})$ for $m$ players, and conjectured that regret $Ω(T^{2/3})$ is unavoidable already for two players. We refute this conjecture. We give a strategy whose expected regret against every oblivious adversary is $O(m^{3/2}\sqrt{KT}\log^2(KmT))$, which is optimal in $K$ and $T$ up to logarithmic factors. Three ideas make this possible. (i) Bits are sent through deliberate collisions as random parity checks, and the regret spent while a message is delayed is charged to the number of arms with positive reward rather than to elapsed time: an adversary that jams the channel with zero rewards forfeits the regret it could inflict. (ii) An exponential-weights learner over sets of arms combines a frequently resampled coordinator arm with a follower assignment that changes only $O(\sqrt{mT/K})$ times in expectation; delayed updates are reconstructed exactly. (iii) We teach the followers a privately chosen row index in a fixed public table using reward observations alone, allowing them to coordinate without shared randomness. The bound holds simultaneously for all reward matrices, even ones chosen with knowledge of the table, and a random table works with probability $1-O(1/(KmT))$.

cs.LG · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.21666

A Contradiction Judge Counted Reported Teammate Errors as Hallucinations: An Assertion-Aware Re-Score of One Multi-Agent LLM Harness

Carson Rodrigues

原文摘要

LLM judges that score hallucination flag any statement that contradicts a reference fact. Agents in multi-agent LLM systems quote and dispute each other's claims, so such a judge can count a correct report of a teammate's error as a hallucination. An earlier version of this study, a preprint (arXiv:2606.21666v1), reported that broadcasting raised a hallucination rate from 0.492 to 0.658 (+34%) and called it contamination; this version withdraws that claim, and the preprint's runs cannot be re-scored. An unpublished revision of that harness reported 0.289 to 0.554 (+0.265, 95% CI [0.225, 0.305]), and its saved outputs are re-scored here. Under an analysis plan fixed before any judge call, three judges from three vendors labeled each of six facts in 1,832 outputs as asserted wrong, attributed to another source, asserted correct or absent. On values that outputs assert as their own, the broadcast effect was -0.0208 (95% CI [-0.0458, +0.0041]) on 10 Full-Broadcast trials: not distinguishable from zero, and equivalent to zero only within a post hoc margin of +/-0.0663 and under the plan's prompt. On these LLM-judge labels, reports of a teammate's wrong value accounted for nearly all of the Haiku travel effect. Scored for mentions, the judges reproduced the effect (+0.1996). With Llama agents, 2 of 40 teammate outputs asserted the wrong destination. One of twelve Claude judge-prompt combinations gave an asserted increase (Sonnet 4.6, one paraphrase: +0.0246, 95% CI [+0.0055, +0.0436]). In 40 new trials per arm, judged by Claude models like the agents, the asserted difference was -0.0505 when both judges agreed and +0.0161 (95% CI [+0.0024, +0.0299]) when either did. Main limit: the labels have no human validation; human labels for 120 outputs are pending. The outputs come from one harness and 13 hand-written scenarios, so the shares do not transfer.

cs.AI · cs.CL · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16369

Bait: Mitigating Harmful Agentic Reinforcement Learning via Probing Reward Function

Tiansheng Huang, Ananth Balashankar, Virat Shejwalkar, Santosh Pande, Ling Liu

原文摘要

We expose a safety vulnerability on nascent agentic reinforcement-learning-as-a-service. Specifically, with a cost as low as \$20, we show that safety alignment of a large language model (e.g., Inkling) can be completely subverted through rounds of harmful agentic reinforcement learning (agentic RL) with a toxic environment from its users. We also demonstrate that upon a successful harmful agentic RL attack via the service API, the attackers can potentially use the safety-degraded model to assist with real-world harmful activities, e.g., exploit software vulnerabilities and launch code re-use attacks. To mitigate harmful agentic RL, we propose Bait, a defense that mitigates the attack via proactively probing the reward function. Experimental results show that such a simple defense can sufficiently mitigate learning from a harmful reward function and can still learn from a benign reward function that promotes benign agentic tasks. Further analysis on both empirical and theoretical aspects shows that an adaptive attacker who assumes the knowledge of Bait defense cannot perfectly subvert it due to a systematic challenge. Source code is available at https://github.com/git-disl/Bait.

cs.LG · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16368

ReliaGuard: Reconstruction-Aware Watermarking for Source Attribution and Blind Edit Localization

Hailong Wang, Wuzheng Tan, Zixin Zhang, Xujun Fang, Fengjuan Gong

原文摘要

Generative image editing facilitates content creation but complicates source attribution and edit verification. Existing watermarking methods combine copyright protection with tamper localization, yet background reconstruction can weaken spatial evidence even when source identifiers remain recoverable. We propose ReliaGuard, a reconstruction-aware watermarking framework for source attribution and blind edit localization. The writer optimizes keyed feature responses in both the protected image and its variational autoencoder reconstruction under image-distortion and source-preservation constraints. Using only the received image and a secret key, the reader combines watermark responses with local image context through residual evidence refinement and predicts an edit mask through native-resolution-guided multiscale reading. It is trained on complete editing outputs, locally spliced counterparts, and processed versions of both. On an independent test set of 300 source images and 900 editing cases from three image domains, our method achieves edit-region intersection-over-union scores of 85.61% for complete outputs and 94.90% for locally spliced outputs at a threshold calibrated to a nominal 1% background false-positive rate on complete outputs. The complete-output result exceeds the strongest baseline by 45.02 percentage points, while decoded identifier-bit accuracy reaches 100% in both conditions. Across seven families of image processing, the corresponding localization scores are 81.01% and 86.49%, with identifier-bit accuracies of 92.76% and 92.79%, respectively. These results support source recovery and blind edit localization under background reconstruction and subsequent image processing.

cs.CV · cs.AI · cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16366

CURATOR: Scalable Fine-grained Evidence Selection for Retrieval-Augmented Generation using Decision Models

Shib Dasgupta, Bandish Parikh, Rashmi Gangadharaiah

原文摘要

Retrieval-augmented generation (RAG) provides entire retrieved passages to the LLM, even though only a handful of spans within them contain the evidence pertaining to the question. While precise selection of these spans can improve context utilization, identifying them in isolation is challenging. A span may appear irrelevant by itself, and deciding whether to retain it may require examining other retrieved information. One can evaluate each span's candidacy with the full retrieved context, but this is computationally inefficient as the same text must be processed repeatedly. Pretrained decision model can be used to efficiently evaluate multiple spans simultaneously, allowing each decision to consider the surrounding spans as well. However, our experiments show that their ability to identify relevant evidence deteriorates as the number of simultaneous decisions increases. To address this, we introduce CURATOR, a fine-tuned decision model trained to make reliable evidence-selection decisions over large candidate sets. By training a decision model to select varying numbers of supporting spans from large candidate pools, CURATOR develops the capability to select evidence for RAG while retaining the computational efficiency of joint decision-making. We observe that CURATOR maintains a strong evidence discrimination capability as the number of candidates increases, generalizes across different tasks and document domains, and improves downstream RAG performance, particularly when the available context is limited.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16363

Vector-Space Projection and Unified Complex-Valued Acceleration for Scaling Matrix-Based Brain-Inspired Representations

William Youngwoo Chung, Hyunwoo Oh, Calvin Yeung, Hansen Jin Lillemark, Hamza Errahmouni Barkam, Mohsen Imani

原文摘要

Symbolic reasoning at scale requires representations that are both expressive and hardware-efficient. Hyperdimensional Computing (HDC) provides a promising brain-inspired framework, encoding concepts as high-dimensional vectors composed through efficient algebraic operations. However, the widely used Fourier Holographic Reduced Representation (FHRR) relies on commutative binding, which cannot encode order or directionality. Prior FPGA/ASIC accelerators recover this through costly permutation logic, incurring irregular memory access and high control overhead. Generalized Holographic Reduced Representations (GHRR) instead redefine binding as non-commutative matrix multiplication, naturally encoding sequences, graphs, and hierarchies without permutations-but at the cost of turning every element into a matrix, introducing compute and storage overhead that limits scalability. In this work, we propose a flattening methodology that preserves GHRR's matrix-based encoding while executing training and inference directly in vector space, functionally equivalent to FHRR inference yet free of permutation logic. We further design a custom 28 nm ASIC that fuses binding and similarity into a unified complex-valued datapath, with dual-DMA streaming and runtime normalization for accurate inference. Against a PyTorch baseline on an NVIDIA RTX 4090 GPU, the prototype delivers 1.36x-1.56x higher throughput and 16.2x-18.6x better energy efficiency.

cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16361

MutAtlas: A PDB-Wide Energy-Guided Atlas of Protein Mutation Effects

Ruihan Guo, Chaoran Cheng, Zhanghan Ni, Neil He, Bangji Yang, Ge Liu

原文摘要

Protein mutation effect prediction is fundamental to protein engineering and disease variant interpretation, yet experimentally measured mutation data remain accurate but extremely sparse. To provide scalable supplementary mutation signals, we construct a PDB-wide mutation augmentation dataset that exhaustively enumerates single-site substitutions on experimentally resolved protein structures and aligns mutation signals from physics-based energy models, protein language models, and inverse folding models. Large-scale analysis under a unified mutation preference representation reveals substantial differences in the consistency, concentration, and substitution patterns of mutation distributions across models, indicating that disagreement is pervasive and reflects conflicting inductive biases rather than random noise. Motivated by these observations, we propose an unsupervised multi-source mutation preference distillation framework that learns from relative mutation preferences while explicitly modeling cross-source disagreement. Without using any experimental mutation labels during training, our approach achieves the best overall performance among the evaluated zero-shot baselines and naive multi-source fusion strategies on ProteinGym. We release the dataset and evaluation pipeline to support reproducible studies of protein mutation effects.

cs.LG · q-bio.GN

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16359

IsharaBART: An efficient BART-Based Framework for Low-Resource Bangla Sign Language Translation

Aonmoy Das, Ananna Dev Aishi, Md. Fazlul Kader

原文摘要

Millions of Bangla Sign Language (BdSL) users lack reliable automatic translation tools. Existing systems are limited by data scarcity, unidirectional design, and computationally expensive architectures that are not suitable for low-resource deployment. We introduce IsharaBART, a bidirectional generative framework for gloss-sentence translation in BdSL. IsharaBART uses Bidirectional and Auto-Regressive Transformers (BART)-based conditional language modeling with denoising learning objectives. We developed a parallel dataset of 1,000 professionally annotated gloss-sentence pairs, balanced across tenses and structures. We expanded the dataset to 4,831 pairs through four augmentation techniques: rule-based tense transformations, masked token substitutions, back-translation, and one-shot prompting. A compact denoising sequence-to-sequence model was pre-trained from scratch on monolingual Bangla text from six different corpora, then fine-tuned on the annotated and augmented datasets. We compare IsharaBART against four larger baseline models under identical conditions, where it achieves comparable or superior bidirectional performance. On the gloss-to-sentence (G2S) task, it attains Bilingual Evaluation Understudy (BLEU-4) score up to 51.7, and on the sentence-to-gloss (S2G) task, it achieves 61.40, outperforming multilingual models despite having fewer than 10% of the parameters. Ablation studies demonstrate that pre-training provides 62.5-90.4% faster convergence and 11.5-118% higher BLEU-4 than scratch. Low-rank adaptation (LoRA) reduces trainable parameters by 99.3%, but with 28.0-52.2% loss of BLEU-4 compared to full-parameter fine-tuning.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.20345

Corrected Integrated Laplace Approximation for Bayesian Inference in Latent Gaussian Models

Jinlin Lai, Charles C. Margossian, Daniel Sheldon

原文摘要

Latent Gaussian models (LGMs) are a popular class of Bayesian hierarchical models that include Gaussian processes, as well as certain spatial models and mixed-effect models. Efficient Bayesian inference of LGMs often requires marginalizing out the latent variables. For LGMs with a non-Gaussian likelihood, exact marginalization is not possible and a popular approach is to do approximate marginalization with an integrated Laplace approximation (ILA). Using ILA produces an approximate posterior which, in some settings, can differ significantly from the correct posterior, which impacts downstream applications. We propose an importance sampling scheme to correct the error introduced by ILA. By increasing the number of samples in importance sampling, the posterior with ILA converges to the correct posterior. This idea is realized with various techniques, including pseudo-marginalization, quasi-Monte Carlo and randomized quasi-Monte Carlo. We implement our methods in an automatic differentiation framework to support gradient-based algorithms when doing inference on the hyperparameters. For the latter, we specifically consider the use of Hamiltonian Monte Carlo. We demonstrate the benefits of reduced error in various applied models.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.31272

The Decomposition Is the Fingerprint: Per-Component Identity for Agent Skills

Hongliang Liu, Yuhao Wu, Tung-Ling Li

原文摘要

AI agents increasingly acquire and execute skills at runtime: bundles of prompt instructions, executable code, and tool declarations fetched from marketplaces and other agents. Governing them needs a stable notion of skill identity, yet cryptographic hashing is engineered to destroy the very similarity we need, as a one-character edit scrambles the digest. We present a compact, locality-sensitive fingerprint that embeds each component of a skill and projects it to bits with a multi-bank SimHash, giving a fixed 120-byte signature compared in constant time by Hamming distance. Our central claim is that keeping the fingerprint as a per-component triple (prompt, code, tools), rather than a single score, is what makes it useful: the triple recovers skill-family identity through paraphrase, renaming, refactoring, and controlled code translation when another component remains shared, while independent multilingual reimplementation is not recovered; it also localizes which component carries the reuse. We claim lineage, not behavioral equivalence: identity supplies the structural axis of a registry and leaves safety to behavioral verification. The fingerprint reaches an area under the ROC curve (AUC) of 0.974 (95% CI [0.956, 0.994]) over 4,950 pairwise comparisons while using 77x fewer bits than the embedding it approximates, with ranking preserved in expectation and finite-bit concentration; the per-component split turns one number into relationship classification, families, novelty, and a portable "SkillBOM" for a skill registry. On a 906-skill injection benchmark the fingerprint recognizes injected skills as tampered copies of a known base and localizes the change, but recognition is not trust: it remains, by design, an identity signal complementary to behavioral verification rather than a safety verdict.

cs.CR · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2508.02001

Versatile yet Efficient Network Traffic Analysis: Offloading Network Foundation Model to SmartNIC

Chungang Lin, Xuying Meng, Tianyu Zuo, Weiyao Zhang, Meng Shen, Ruijie Zhao, Guanming Che, Ruiqi Meng 等 12 位作者

原文摘要

Pervasive encryption makes large-scale labeling infeasible for traffic analysis, while security operations demand edge analysis to avert service degradation and vulnerabilities. These pressures have produced two research lines that advance 1) versatile analysis, via network foundation models for low label dependency, and 2) efficient analysis, via hardware offloading for low analysis latency. However, co-achieving versatility and efficiency via such lines has appeared incompatible, with prior work advancing one while compromising the other, yet we show that this incompatibility is a consequence of polarized design choices across the three components of traffic analysis systems, i.e., traffic processing, model architecture, and analysis execution. In response, we present Nepco, a versatile yet efficient network traffic analysis system that offloads network foundation models to SmartNIC. Our key observation is that discriminative traffic information often exhibits localized patterns within byte sequences, motivating versatile yet efficient localized byte-sequence modeling. To exploit this without incurring the latency bottlenecks of complex encoding steps, we employ a hardware-friendly processing strategy that directly embeds raw byte sequences. To maintain versatility across diverse tasks characterized by spatially shifting features, we explore an observation-driven convolution architecture equipped with hardware-friendly scoring and gating designs. By exploiting translation invariance, this design dynamically locates and extracts salient semantic signatures irrespective of their byte-level offsets. We prototype Nepco on the NVIDIA BlueField-3 SmartNIC with multi-engine collaborative analysis execution. The experimental results demonstrate that Nepco achieves macro F1 competitive with the best performances achieved by 8 state-of-the-art network foundation models...

cs.NI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16356

DIAGNO: Diagonal Spherical Neural Operators for Heterogeneous Earth Dynamics Modeling

Herui Li, Bin Lu, Haonan Qi, Lei Zhou, Luoyi Fu, Xinbing Wang, Meng Jin

原文摘要

Accurate forecasting of Earth's dynamic systems is vital in geophysics, where neural operators offer a powerful data-driven paradigm. Standard operators often treat the Earth as a flat Euclidean plane, introducing geometric artifacts. In contrast, Spherical Neural Operators (SNOs) resolve this mismatch by operating within the spherical spectral space, naturally adapting to spherical geometries. However, for computational efficiency, existing SNOs typically rely on the rotation equivariance assumption. This assumption simplifies the transitions of physical states into isolated spectral modal evolutions, thereby over-idealizing the Earth as a homogeneous system. To solve this problem, we no longer rely on the rotation equivariance assumption that isolates spectral modal evolution, but directly derive an explicit modal interaction kernel and propose Diagonal Spherical Neural Operators named DIAGNO. DIAGNO explicitly parameterizes cross-modal interactions through spectral modal decomposition, thereby preserving the inherent heterogeneity of Earth dynamics. Furthermore, leveraging the unique diagonal structure of the spherical spectrum, we design inter- and intra-diagonal interaction mechanisms to natively capture essential zonal and meridional dynamics, respecting intrinsic geophysical regularities. Extensive experiments across simulated fluid dynamics and real-world Earth system reanalysis (including atmosphere and ocean) datasets demonstrate DIAGNO's superior capability, consistently achieving state-of-the-art performance in multi-step forecasting. Beyond this, we validate DIAGNO's practical analytical value in professional geophysical domains by assessing its fidelity in capturing climatological anomalies and maintaining physical consistency.

cs.AI · cs.LG

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新论文 · v1arXiv:2611.16357

Optimal Seat Movement Strategies Among Modular Bus Units

Shiyue Wang, Yuhao Liu, Zhibin Chen, Mingyang Pei, Changhong Lu

原文摘要

Movable seats enable passengers to transfer between coupled modular bus units while remaining seated, but require physical reconfiguration that existing modular transit models largely abstract away. We model seat reconfiguration as a colored generalized sliding-tile puzzle on the physical track graph, capturing simultaneous movements, seat indistinguishability within destination groups, and vertex, edge, and corner-following conflicts. A lexicographic objective minimizes makespan first and then the sum of costs (SoC), which penalizes movements and waiting at non-target positions. We develop an exact time-expanded mixed-integer linear programming formulation and a Boundary-Sequence Dynamic Programming (BSDP) algorithm. BSDP decomposes the problem at single-edge module interfaces and coordinates local schedules through compressed boundary-crossing sequences, provably preserving feasibility and optimal costs. On two- and three-module benchmarks, BSDP achieves mean per-instance speedups of 3.24x and 6.30x over Gurobi, respectively. In fixed-horizon tests with four to seven modules, both methods start from identical greedy schedules and receive a 300 s budget. BSDP obtains lower final SoC than Gurobi on 197 of 200 instances and ties on three, reducing mean final SoC by 17.93%-69.07% across the four size classes. Geometry sensitivity and ablation studies further clarify the contribution of boundary compression to computational performance. The framework links seat reconfiguration to the timing and operational cost of modular bus decoupling.

cs.CE · math.OC

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新论文 · v1arXiv:2611.16351

HyBridge: Bridge-Guided Self-Supervised Hyperbolic Hypergraph Representation Learning

So-Bin Jung, David Yoon Suk, Sang-Wook Kim

原文摘要

Hypergraphs capture groupwise relationships among multiple nodes and often exhibit tree-like structures. Although hyperbolic space is well suited for modeling such structures, simply extending existing hypergraph representation learning (HRL) methods to hyperbolic space does not consistently improve performance. In this paper, we investigate the relationship between node structural properties and hyperbolic embedding radii and find that bridge nodes tend to lie closer to the center in graphs, whereas this relationship is not consistently observed in hypergraphs. We further find that conventional bridge score estimation may fail to capture the groupwise connectivity of hypergraphs. Motivated by these findings, we propose HyBridge, a self-supervised hyperbolic HRL framework that (1) estimates node bridge scores by considering the external connectivity of individual hyperedges, (2) learns node and hyperedge embeddings through hyperbolic hypergraph encoding, (3) guides node radial positions according to their bridge scores, and (4) employs contrastive learning to preserve node discriminability. Extensive experiments on 10 real-world hypergraphs demonstrate that HyBridge outperforms 22 competing methods, achieving performance improvements of up to 9.82% in node classification. For reproducibility, we provide the code at https://anonymous.4open.science/r/HyBridge/.

cs.SI · cs.AI

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版本更新 · v3arXiv:2510.19278

D2D: Detector-to-Differentiable Critic for Improved Numeracy in Text-to-Image Generation

Nobline Yoo, Olga Russakovsky, Ye Zhu

原文摘要

Text-to-image (T2I) diffusion models have achieved strong performance in semantic alignment, yet they still struggle with generating the correct number of objects specified in prompts. Existing approaches typically incorporate auxiliary counting networks as external critics to enhance numeracy. However, since these critics must provide gradient guidance during generation, they are restricted to regression-based models that are inherently differentiable, thus excluding detector-based models with superior counting ability, whose count-via-enumeration nature is non-differentiable. To overcome this, we propose Detector-to-Differentiable (D2D), a novel framework that transforms non-differentiable detection models into differentiable critics, leveraging their superior counting ability to guide numeracy generation. Specifically, we design custom activation functions to convert detector logits into soft binary indicators, which are then used to optimize the noise prior at inference time with pre-trained T2I models. Our extensive experiments on SDXL-Turbo, SD-Turbo, and Pixart-DMD across four benchmarks of varying complexity (low-density, high-density, and multi-object scenarios) demonstrate consistent and substantial improvements in object counting accuracy (e.g., boosting up to 13.7% on D2D-Small, a 400-prompt, low-density benchmark), with minimal degradation in overall image quality and computational overhead. Project page: https://nobliney.github.io/d2d/.

cs.CV

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新论文 · v1arXiv:2611.16346

MotionSkill: From Bipedal Motion Video to Control across Embodiments

Philip Niu, Xiaofeng Han, Sylvie Pan, Yang Wang

原文摘要

Reusing a demonstrated gait can reduce the work of specifying the same motion for each biped. A video captures the timing of a step, but each robot must execute it with its own limbs and actuators. MotionSkill recovers a timed reference once from monocular video and adapts it to each target in simulation. It aligns root motion and body landmarks in a common frame and records which landmarks are available at each instant. At each source timestamp, the mapper preserves limb directions, uses the target segment lengths, and solves inverse kinematics under joint limits and closure constraints. In MuJoCo, we train a separate residual policy for each of ten bipeds on its mapped reference from one four-second walking clip. Each policy uses the measured robot state to correct joint commands during contact. From initial states held out during training, MotionSkill completed 98% of trials on the original video and 26% with the upper body occluded for one second.

cs.RO

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新论文 · v1arXiv:2611.16345

DRAMA: Dissecting Attention Redundancy for Accelerating Multimodal Diffusion Large Language Models

Yi-Hsin Hung, Fangfu Liu, Shao-Yuan Lo, Chu-Song Chen

原文摘要

Multimodal diffusion large language models (dLLMs) generate text through iterative masked denoising with bidirectional attention, offering a compelling alternative to autoregressive vision-language modeling. However, each denoising step requires a full forward pass over long multimodal sequences in which visual tokens dominate the sequence length, and a fixed step budget further compounds this quadratic per-step cost. Existing inference acceleration approaches for multimodal dLLMs characterize attention redundancy implicitly, motivating designs from isolated attention visualizations rather than from measurement. We close this gap with DRAMA, a training-free acceleration framework grounded in a systematic dissection of attention redundancy in multimodal dLLMs. By probing attention tensors along spatial and temporal axes, we reveal two persistent redundancy structures: (i) attention mass consistently concentrates on a sparse subset of visual tokens, and (ii) attention distributions rapidly stabilize across denoising steps after an initial formation phase. These findings motivate two complementary mechanisms: Spatial-Redundancy-Aware Visual Token Pruning (SVTP) and Sufficiency-Guided Adaptive Stopping (SGAS). Together, DRAMA achieves an average 18.67 times inference speedup across six vision-language benchmarks without training, architectural modification, or attention caching, while maintaining competitive accuracy. Our results establish a strong, measurement-grounded acceleration baseline for multimodal dLLM inference.

cs.LG · cs.AI

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版本更新 · v2arXiv:2611.12356

DForkTree: Accelerating Speculative Decoding with Fork-Aware Block Diffusion Draft Trees

Zhicheng Zheng, Cheng Zhang, Chenkai Tang, Tianxiang Zhao

原文摘要

Speculative decoding speeds up language models by having a lightweight drafter propose future tokens that the target model verifies in parallel. Block diffusion drafters such as DFlash predict an entire block in one forward pass, and DDTree builds draft trees directly from their per-position distributions. Each distribution implicitly assumes that the drafter's own greedy continuation is accepted, whereas a draft tree improves over a chain only on branches that leave this greedy path. Once a path forks, the drafter's top-1 accuracy drops from 89% to 18% and its top candidates often miss the true token. Re-running the drafter from the fork token largely restores its accuracy, showing that the errors stem from conditioning on an outdated prefix rather than from limited model capacity. In this paper, we introduce DForkTree, a training-free method that re-anchors each branch on its own tokens. It adjusts the drafter's scores with pointwise mutual information from target-generated n-grams, weighted more heavily after a fork, and a layer-wise GPU search recovers the exact optimal tree under these scores. On Qwen3.5-4B and 9B, DForkTree outperforms all baselines on every benchmark, by up to 41.5% over DFlash and 15.1% over DDTree, with average speedups of up to 4.75x over autoregressive decoding.

cs.AI

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新论文 · v1arXiv:2611.16341

SUMNO: Spatially Unrolled Multigrid Neural Operator for Fluid Flow Surrogates

Yingqi Hu, Nan Deng, Changxiao Shao, Xiaozhou He

原文摘要

Data-driven surrogate modeling has emerged as a powerful paradigm for accelerating computational fluid dynamics, and hierarchical U-shaped networks, which resemble the structure of classical Geometric Multigrid (GMG) methods, have become prevalent designs. Modern surrogates built on these designs achieve competitive accuracy at a fraction of the cost of numerical solvers; however, accurate prediction of complex flows remains challenging, and no existing architecture leads across them. In such networks, the multigrid analogy mainly concerns the multi-resolution topology, while their components largely follow common vision practice. To address this, this study introduces SUMNO, a multigrid neural operator for fluid flow fields on regular grids that spatially unrolls a V-cycle into a feed-forward U-shaped network. Parameter-free down- and upsampling stand in for restriction and prolongation, and additive skip connections for coarse-grid correction, with normalization-free transfer and skip pathways. Within this macro-architecture, convolutions are used at the fine and intermediate resolutions and shifted-window attention at the coarsest, whose window partitioning is generalized to handle periodicity independently per axis. Evaluated on unseen test configurations across four unsteady and steady flow benchmarks against five representative baselines, SUMNO is best in 5 of 7 settings and ranks second in the remaining two. It reduces the 10-step rollout MSE by about 30% relative to the strongest baseline, and outperforms three baselines with over 130 million parameters using 9.4 million parameters.

physics.flu-dyn · cs.AI

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新论文 · v1arXiv:2611.16339

Hand in Silhouette: Synthetic-to-Real Multi-View Hand Reconstruction via Binary Masks

Leiyao Cui, Lecheng Ruan, Zihang Zhao

原文摘要

Calibrated multi-view hand reconstruction has advanced rapidly, yet the effectiveness of existing methods depends strongly on real captured data, which is costly to acquire, limited in hand-geometry diversity, and often affected by imperfect annotations. Synthetic data offers a scalable alternative with accurate supervision, but a substantial synthetic-to-real appearance gap hinders its use. To address this, we introduce Hand in Silhouette (SilHand), a calibrated multi-view hand reconstruction framework that operates on binary hand masks, obtainable at low cost either automatically or manually, to reduce the synthetic-to-real observation gap. Rather than relying on RGB features for 3D lifting, SilHand projects metric 3D hypotheses into each view to query and fuse mask-ray evidence for hand reconstruction. This mask-centric formulation enables training on a large-scale synthetic dataset comprising 46.8k grasping poses and 60k free-hand samples, providing broad coverage of hand geometry while directly transferring to real captured hand geometry. Across five real-world benchmarks, SilHand improves relative reconstruction accuracy from 49.2% to 63.4% using masks rendered from reference hands. Moreover, reconstruction accuracy improves consistently as the synthetic training set grows, indicating that broader geometric coverage translates into better real-world performance. While achieving such diversity is difficult with real captured data, it becomes practical under our mask-centric formulation. Finally, SilHand remains robust under mask corruption, with reconstruction error increasing by at most 1.63 mm. Together, these results support binary masks as an effective interface for learning hand reconstruction from synthetic geometry.

cs.CV

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新论文 · v1arXiv:2611.16337

Memory-Efficient Parallel Policy Learning for Collapse-Reactive Retreat via Hybrid Terrain Modeling

Yuki Kadokawa, Ritsuki Matsunaga, Yuqi Yang, Edgar Anarossi, Takamitsu Matsubara

原文摘要

At disaster sites and on unstable slopes, robot contact can collapse uninspected terrain, requiring retreat upon support loss. Massively parallel reinforcement learning of collapse-reactive retreat requires independent state--action experiences. However, on shared rigid-body collapsible terrain, one robot's collapse alters others' contacts without direct interaction. We call this indirect interference. Duplicating terrain objects prevents this but is memory-intensive, limiting parallel sampling. Our key insight is that policy learning does not necessarily require rigid-body collapse modeling, which is feasible but computationally expensive; collapse-induced contact responses can suffice. We therefore propose a memory-efficient parallel policy-learning framework for collapse-reactive retreat via hybrid terrain modeling. Hybrid terrain modeling combines rigid-body simulation of non-collapsible terrain with virtual contact forces for collapsible regions. A State-Multiplexed Contact Model shares terrain geometry and a contact-response law while maintaining environment-specific collapse states, enabling independent experiences without terrain objects for collapsible regions. The framework trains a proprioceptive policy for both normal locomotion and collapse-reactive retreat. It supports over 100 times as many parallel environments as fine-resolution rigid-block terrain within the same GPU memory limit. This scalability and improved sample efficiency enable over 50 times faster policy convergence with comparable retreat performance. Sim-to-sim and real-robot experiments demonstrate retreat under multiple collapse conditions. A supplementary video is available at: https://youtu.be/_RrhwjzEPE0

cs.RO

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新论文 · v1arXiv:2611.16336

Visibility graphs of irregularly sampled two-dimensional fields via Voronoi tessellations

Francisco J. Muñoz, Juan Carlos Nuño

原文摘要

Visibility graphs map a time series into a network by linking two data whenever the straight line between them is not obstructed by intermediate data. Their extension to two-dimensional fields has so far been restricted to regular lattices, where visibility is checked along a finite number of lattice directions. We propose a definition for scalar data sampled at arbitrary, irregularly scattered points of the plane. Each point is assigned its Voronoi cell, so that the data become a landscape of prisms, and two points are linked when the line of sight between them clears the cells crossed by the connecting segment. This yields horizontal ($\HVGV$) and natural ($\NVGV$) Voronoi visibility graphs that reduce exactly to the classical horizontal and natural visibility graphs when the points are collinear, contain the Gabriel graph of the point set, and can be built by an exact walk through the Delaunay triangulation. For uncorrelated heights, the horizontal graph depends only on the ranks of the data, so its statistics are independent of the marginal distribution. The probability that two points see each other is $2/[(M+1)(M+2)]$, where $M$ is the number of Voronoi cells crossed, which reduces the problem to the geometry of the tessellation. For Poisson points this gives a degree distribution with a scale-free tail $P(k)\simeq(π^3/4)\,k^{-2}$ and a mean degree that grows as $(π^3/8)\ln N$, in contrast with the finite value $\langle k\rangle=4$ of one-dimensional series. Two dimensions are therefore the marginal case in which isotropic horizontal visibility ceases to be local. Numerical simulations with up to $N=1.28\times10^5$ points support these predictions.

math.DS · cond-mat.stat-mech · cs.CG · physics.data-an

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新论文 · v1arXiv:2611.16335

From More to Better: Causal Inference and Digital Experimentation in the Age of AI

Winston Chou, Mårten Schultzberg, Lo-Hua Yuan, Wenjing Zheng, Avi Feller, David Holtz, Widad Machmouchi, Sven Schmit 等 21 位作者

原文摘要

Artificial intelligence (AI) is quickly expanding the capacity of organizations to produce and analyze data. We argue that this expansion shifts the leverage of data scientists from performing individual analyses to assessing the credibility and importance of findings and determining when evidence generalizes across contexts. Drawing on a roundtable of academic researchers and industry practitioners, we examine three ways in which data scientists who work on causal inference and experimentation can help organizations turn more analytical capacity into better decisions: preserving and accumulating the learning value of experimentation beyond individual tests; choosing and combining experimental, observational, and synthetic evidence; and ensuring evaluation keeps pace with AI as its capabilities and uses evolve. In each area, we distinguish established practice from open questions. We conclude with implications for the training and practice of data science and for organizations.

cs.AI · stat.AP

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