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新论文 · v1arXiv:2611.11228

Welfare in Envy-free Contracts: When Agents Have Sparse Interests

Junjie Chen, Cong Chen, Takuro Yamashita

原文摘要

We study welfare maximization under envy-free contracts, where a principal assigns tasks and offers success-contingent payments to heterogeneous agents with hidden effort. For unit-demand agents, we establish a sharp complexity boundary: the problem is polynomial-time solvable when each agent is interested in at most two tasks, but NP-hard with at most three interests. Our algorithm analyzes tree-structured dependencies among forced assignments and reduces optimization to maximum-weight independent set in a perfect graph. When agents may receive multiple tasks, no polynomial-time constant-factor approximation exists unless $\mathrm{P}=\mathrm{NP}$. Finally, with homogeneous success probabilities and rewards, we establish a welfare- and revenue-preserving equivalence between unit-demand contract design and envy-free pricing. This yields polynomial-time welfare maximization without restricting interests, while revenue maximization remains NP-hard.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11226

MCSA: Multi-Cutoff Spectral Aggregation in SO(3) Equivariant GNNs for Interatomic Potentials

Junfu Tan, Mengfan Wu, Yu Zhu, Yu Shi, Yifang Qin, Chang Liu, Bonan Zhu, Ziheng Lu

原文摘要

Machine Learning Interatomic Potentials (MLIPs) based on Equivariant Graph Neural Networks (EGNNs) have achieved remarkable progress in atomistic modeling. However, accurately representing atomic interactions across different spatial scales remains challenging. In this work, we investigate the radial spectral properties of SO(3)-equivariant message passing and establish that the cutoff radius controls the characteristic spatial-frequency scale of radial interactions. Based on this analysis, we propose Multi-Cutoff Spectral Aggregation (MCSA), a plug-and-play module that aggregates radial kernels with different cutoff radii to enrich multi-scale geometric representations. To achieve efficient implementation, we exploit the compact support of radial kernels to fuse cutoff-dependent radial weights before angular tensor-product operations, avoiding redundant angular computations while preserving rotational equivariance. Extensive experiments across representative EGNN backbones demonstrate consistent improvements in energy, force, and stress prediction, as well as enhanced accuracy in higher-order properties, including phonon dispersion and lattice thermal conductivity. Further evaluations on layered materials with distinct interaction scales demonstrate the effectiveness of physically motivated cutoff selection. Code is available at: https://github.com/JF-Tan/MCSA.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.06562

Mosaic: Unlocking Over 30$\times$ Context Length for Diffusion LLMs Inference via Global Memory Planning and Dynamic Peak Taming

Liang Zheng, Bowen Shi, Yitao Hu, Jiawei Zhang, Ruofan Li, Guotao Yang, Zhixin Zhao, Zhengchao Wang 等 13 位作者

原文摘要

Diffusion-based large language models (dLLMs) have emerged as a promising alternative to autoregressive models, leveraging simultaneous denoising to enable global planning and iterative refinement. These properties make dLLMs attractive for long-context generation. However, deploying dLLMs faces a prohibitive memory barrier, as existing inference systems are inefficient for the diffusion paradigm. We observe that current inference systems are misaligned with dLLMs. Unlike autoregressive models, whose memory footprint is dominated by the KV-Cache, dLLMs are bottlenecked by transient activations rematerialized per step. Moreover, generic memory reuse mechanisms lack the global visibility to handle dynamic memory peaks of dLLMs, which alternate between logits and feed-forward networks. To address these challenges, we present Mosaic, a memory-efficient inference system that shifts dLLM execution from local, static management to a global, dynamic paradigm. Mosaic integrates (i) a mask-only logits kernel eliminating redundant activation materialization, (ii) a lazy chunking optimizer using online heuristics to tame dynamic memory peaks, and (iii) a global memory manager leveraging virtual addressing to mitigate memory fragmentation. Evaluations show that Mosaic reduces the memory peak-to-average ratio by 2.71$\times$ on average and increases the maximum inference sequence length on identical hardware by 15.30-32.34$\times$. Crucially, Mosaic is training-free and preserves exact model outputs, while reducing end-to-end latency by 2.5%-55.4%. Our code is publicly available at https://github.com/flashserve/Mosaic.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11217

scBIG++: Learning Across Genes and Cells to Predict Single-Cell Perturbation Responses

Jiafa Ruan, Zican Qiao, Zongxin Yang, Ruijie Quan, Yi Yang

原文摘要

Predicting single-cell transcriptional responses to genetic and chemical interventions supports gene-function discovery and regulatory analysis. Existing studies often focus only on highly variable genes, restricting the expression changes available for downstream analysis. Cells exposed to the same intervention can also show heterogeneous expression responses, with coordinated gene programs active to different degrees. We propose scBIG++, a full-gene perturbation prediction framework that models this heterogeneity by connecting expression-adaptive gene modules with population-conditioned generation. To preserve shared gene structure while accommodating cellular variation, Gene-Relation Clustering organizes genes into common windows, within which Dynamic SoftSub learns expression-dependent submodule memberships. This shared organization enables Set-GCAE to exchange information about corresponding gene groups across control cells, placing individual representations in their source-population context. The cell representations initialize a latent flow, while their pooled summary and the perturbation representation condition response generation. Generated states are decoded into expression profiles covering all assayed protein-coding genes retained after preprocessing, supporting comprehensive analysis of perturbation-induced expression changes. Experiments across six genetic and chemical settings spanning 6,539--19,451 genes demonstrate competitive perturbation-effect recovery and full-gene expression reconstruction under unseen targets and combinations, cross-cell-line transfer, and held-out compounds. Our implementation is available at https://github.com/ttruan2426-dot/scBIG.

cs.LG · cs.AI · q-bio.GN

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08264

SplatAnything: Gaussian Spacing Normalization for Feed-forward 3DGS across Arbitrary Scenes

Hao Yu, Zhenwei Wang, Yongzhen Hu, Yuning Gong, Junta Wu, Xiaowei Zhou, Hujun Bao, Tengfei Wang 等 10 位作者

原文摘要

We identify a key limitation of existing feed-forward 3DGS methods: Gaussian attributes are typically predicted in a radial-distance-normalized space that canonicalizes a global radial statistic of the reconstructed geometry, while the spacing between neighboring Gaussian centers can still vary substantially across scene scales and camera configurations. This mismatch induces systematic shifts in Gaussian-scale targets, leading to ambiguous supervision. To address this, we propose SplatAnything with Gaussian Spacing Normalization (GSNorm), which canonicalizes a robust scene-level statistic of Gaussian spacing to provide a more consistent spatial reference for Gaussian prediction. To rigorously assess our method, we further establish a systematic benchmark spanning diverse datasets, input sparsity levels, and challenging extrapolated viewpoints. Extensive experiments demonstrate that SplatAnything achieves state-of-the-art performance, with particularly strong gains under substantial variations in scene scale and viewpoint. Project page: https://zju3dv.github.io/SplatAnything

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11212

Deterministic Narrative Gravity: Bridging Theory of Mind and Historical Simulation through ABM-LLM Architectures

Alicia Colmenero-Fernández, Lidia Ortega Alvarado

原文摘要

Historical social simulation faces a fundamental reproducibility challenge: the stochastic nature of large language models (LLMs) introduces a digital butterfly effect that prevents exact replication across multi-turn runs. Furthermore, the inability to control low-level server parameters in commercial LLM hosting environments severely limits reproducibility. To overcome this opacity, we present a deterministic multi-agent framework coupling LLMs to an Agent-Based Modelling (ABM) pipeline grounded in Theory of Mind (ToM) and Cognitive Archaeology, and validate exact artifact identity within local, pinned execution environments using SHA-256 hashing. Run locally at temperature zero with clean per-call model reloads, the system injects differentiated cognitive states per turn through dissonance-driven behavioural modes and gated internal voices. Evaluating seven model configurations spanning 4B to 31B parameters against an ablated baseline reveals clear operational boundaries. Small models ($\leq$8B) suffer from context capacity overload, producing repetitive, specular dialogue loops. However, at $\geq$12B parameters, the ABM scaffolding breaks the monotonous response patterns seen in ablation. Crucially, these scaffolded models exhibit spontaneous diegetic expansion: they coherently expand scenario lore through tactically motivated, anachronism-free details rather than escalating into aggression. By resisting the structural pull toward premature scene closure, scaffolded sessions sustain an open terminal Narrative Vector ($V_N = \mathrm{Open}$ in 12/21 overall; 9/9 in official $\geq$12B configurations) whereas unscaffolded ablation never does (0/21). This framework provides a transparent, auditable benchmark architecture for virtual archaeology, with the full pipeline released as open-source software to enable independent replication.

cs.MA · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04461

A Security Analysis of Verifiable Credential Verification Pipelines: Attack Surface Arising from JSON-LD/RDFC Canonicalization Compared with SD-JWT VC and mdoc

Naohiro Fujie, Shigeya Suzuki

原文摘要

We evaluate, at implementation level and under an explicit threat model, the verification pipelines of three Verifiable Credential (VC) formats: SD-JWT VC; the W3C Verifiable Credentials Data Model 2.0 with the Data Integrity cryptosuite eddsa-rdfc-2022 (W3C VCDM); and mdoc (ISO/IEC 18013-5). For W3C VCDM we identify and exercise three structural attack surfaces from remote context resolution and RDFC-1.0 canonicalization: denial of service (DoS) via poison graphs, context injection through @context, and server-side request forgery (SSRF) reachable via malicious URLs, all at default settings. A poison graph, whose blank-node references are symmetric enough to force RDFC-1.0 into deep recursion, raises it to 278x the baseline from five blank nodes in 527 bytes, and to 13.5 s from eight. Every experiment ran in four environments sharing runtime and library versions; the multiple moves with the machine (251--424x for one input), not with its speed. Cost tracks that symmetry rather than the node count: an acyclic input with 1,500 blank nodes stays at 462--714x. A call limit separates the two: every attack family aborts at 16 or below, every legitimate credential completes at 1, but a shallow, wide recursion, such as the acyclic control, passes whatever the bound. DoS does not presuppose adversarial input: with production education schemas, canonicalization costs 23--34x a simple credential in one environment, 16--49x across four. Effective mitigations are a static document loader, which removes the retrieval latency and both remote-context surfaces; a context allowlist; and a call limit with a size bound. SD-JWT VC and mdoc need neither canonicalization nor external references, and our verifiers resisted every vector evaluated. These are not format-specific defects but properties of the pipeline and its configuration.

cs.CR · cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03493

An Empirical Evaluation of Signing and Verification Performance of Verifiable Credential Formats: A Cross-Language Benchmark of SD-JWT VC, W3C VCDM, and mdoc

Naohiro Fujie, Shigeya Suzuki

原文摘要

We compare the signing and verification performance of three Verifiable Credential (VC) formats--SD-JWT VC; the W3C Verifiable Credentials Data Model 2.0 with a Data Integrity cryptosuite (W3C VCDM); and mdoc (ISO/IEC 18013-5)--over one scope: issue, present with selective disclosure, verify. Node, Go and Python implementations ran in four environments, one a cloud VM (N=2,000 across five runs; medians of each statistic and the spread). For an end-to-end exchange disclosing 5 of 20 attributes, the total (issue, present, verify) on Node in the cloud VM is 0.265 ms for mdoc, 0.298 ms for SD-JWT VC and 0.534 ms for W3C VCDM. The four span a factor of five in speed; across them W3C VCDM takes 1.73--2.04x mdoc and 1.57--1.79x SD-JWT VC--at least five times our 10% band--and the ordering survives unifying the algorithm on Ed25519 and P-256. The mdoc--SD-JWT VC difference is not robust: the totals differ by 12%, 28%, 19% and 10% in the four environments, and at verification mdoc is the slower in one of them. RDFC-1.0 canonicalization produces the gap: the cryptosuite canonicalizes twice per operation, so one exchange canonicalizes six times. We attribute that cost to the specification rather than to a library mainly by replacing RDFC-1.0 with JCS, which brings W3C VCDM verification to within 5% of SD-JWT VC in three environments and within 12% in the fourth; three implementations in three languages agree, though they share a lineage. Because eddsa-rdfc-2022 has no derivation step, presenting a subset re-signs it; ecdsa-sd-2023, which preserves the issuer signature, costs 3.4 ms to issue and 1.5--2.4 ms to derive and verify there. With production education schemas, canonicalization costs 31--49x a simple credential across the four, and blank node labeling is not the driver. The measured path ends at the issuer signature check.

cs.CR · cs.PF

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.14698

Lights, Camera, Malfunction: When Illumination Robustness Leaves VLA Models Blind to Color

Marino Watanabe, Takami Sato, Kentaro Yoshioka

原文摘要

Vision-Language-Action (VLA) models have emerged as a powerful paradigm for general-purpose robot manipulation; however, their transition to real-world environments reveals vulnerabilities to minor environmental perturbations. We propose FLARE, an optimization framework that exploits these vulnerabilities via targeted illuminations, causing critical disruption of baseline tasks. While adversarial training is the standard countermeasure, we identify a critical and previously underestimated defensive pitfall: naive data augmentations condition VLA models to largely discard color as noise, biasing their visual perception toward shape cues. We expose this degradation through a diagnostic grayscale evaluation, in which a naively defended model maintains high success rates on grayscale inputs, while its success rate on benign, color-dependent real-world tasks drops to at most 48.8%, compared with 78.8% for the undefended SmolVLA baseline. To address this, we propose ChromaGuard, a chroma-preserving adversarial training method. On a physical 6-DoF robotic platform using SmolVLA, we demonstrate that ChromaGuard achieves 95.0% and 88.8% success rates in benign and attacked color-dependent tasks, respectively. Demo videos are available at https://keio-csg.github.io/ChromaGuard/.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04371

Embodied AI for Scientific Discovery: From Physical Action to Scientific Evidence

Wenhao Xu, Changwei Wang, Shunpeng Chen, Yukun Song, Sifan Zhou, Kaize Luo, Shibiao Xu, Yuehua Zhao 等 9 位作者

原文摘要

Scientific experiments are increasingly selected, executed and interpreted by learned systems that act through physical interfaces. Robot learning builds physical competence, whereas AI for science chooses which experiment to run, each reporting different endpoints. This review treats their interface as one problem, embodied AI for science, where the experiment itself is partially observed: specimen state, apparatus state and measurement processes are latent, actions are costly and irreversible, and missingness depends on execution and outcome. We organize the literature by three closures operating at distinct levels: operational closure (the experiment realized is the one intended), epistemic closure (the result updates beliefs or selects subsequent experiments) and evidential closure (the record supports a claim). We analyze workflows through a record-linked evidence cycle - Record, Diagnose, Calibrate, Confirm - as analytic functions with feedback. A taxonomy crosses these closures with six delegated decisions and five embodiment classes. Our formulation separates commanded, realized and observed conditions and models diagnosis with explicit resource, risk and sample-change costs. We compare experiment-selection methods by terminal decision, review perception, protocol compilation, visuomotor policies, world models, recovery and transfer by evidence emission requirements, and compare chemistry, materials, biology, microscopy and field workflows by what their records establish. An appendix provides an evidence map of 28 workflows (26 physical, two software-boundary), characterizing validation pathways and claim confirmation requirements. We propose evaluation that separates actions, valid experiments, campaigns and confirmed claims, framing open challenges as controlled comparisons with measurable failure conditions.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11179

Guidance at Pure Noise Erodes Speaker Similarity in Few-Step Token-Conditioned Flow-Matching TTS

Jaeho Lee, Han-Lim Choi

原文摘要

Text-to-speech systems built from a language model and a flow-matching mel decoder now run the decoder with two to four Euler steps instead of the official ten or more. At two steps the word error rate (WER) is unchanged, but speaker similarity (SIM) drops by one to six points. This paper shows that most of this loss comes from classifier-free guidance on the first step. At pure noise the network can only return the conditional mean, so guidance shifts every trajectory by nearly the same vector. Later steps do not remove this shift, and its size grows with the length of the first step, so it is negligible with ten or more steps and accounts for most of the loss with two. Two training-free samplers are derived from this analysis. First-step unguided sampling (FSU) leaves the first step unguided and recovers $84$--$91\%$ of the two-step SIM loss on CosyVoice~2/3 with no significant WER increase. FSU-Mid replaces the two Euler steps by one midpoint step with an unguided first evaluation, recovers $77$--$85\%$ on IndexTTS-2 and matches the ten-step teacher on CosyVoice~2 and GLM-TTS. Both use three network function evaluations, one fewer than the official two-step sampler, and FSU-Mid gives the highest mean recovery among eight published guidance rules re-run at two steps on four decoders.

cs.SD · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12541

PPG2ECG: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation

Xiaoda Wang, Minxiao Wang, Kaiqiao Han, Defu Cao, Ching Chang, Yidan Shi, Runze Yan, Xiao Luo 等 13 位作者

原文摘要

Electrocardiography (ECG) is the clinical standard for cardiac assessment but requires dedicated hardware that does not scale to daily-life monitoring. Photoplethysmography (PPG) is ubiquitous in wearables but lacks ECG-specific diagnostic morphology and is corrupted by motion and sensor noise. PPG-to-ECG generation aims to bridge this gap by recovering electrical morphology and timing from peripheral pulse signals. However, existing methods largely rely on statistical alignment and data-driven generation. They fail to explicitly structure the latent space around physiology-aware electro-hemodynamic factors and lack constraints from forward physiological dynamics. To address these challenges, we propose PPG2ECG, a physiology-guided latent rectified flow framework. PPG2ECG introduces an electro-hemodynamic simulator that co-models ECG and PPG through shared cardiac phase dynamics. Guided by this simulator, a Physiology-Aware AutoEncoder learns a structured electro-hemodynamic latent space. Then we integrate this simulator guidance into a PPG-conditioned latent rectified flow, enforcing ECG-side morphology consistency and ECG-to-PPG forward hemodynamic consistency during generative transport. Experiments on the large-scale MC-MED dataset demonstrate that PPG2ECG significantly improves PPG-to-ECG generation and downstream cardiovascular disease classification, proving its ability to generate ECGs that are both signal-faithful and physiologically plausible under the ECG-to-PPG hemodynamic pathway

eess.SP · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12492

Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation

Kexuan Shi, Hanxuan Li, Zeju Qiu, Yandong Wen, Simon Buchholz, Weiyang Liu

原文摘要

We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additive optimizers such as Adam and Muon, Pion updates each weight matrix through left and right orthogonal transformations, preserving its singular values throughout training. This yields an optimization mechanism that modulates the geometry of weight matrices while keeping their spectral norm fixed. We derive the Pion update rule, systematically examine its design choices, and analyze its convergence behavior along with several key properties. Empirical results show that Pion offers a stable and competitive alternative to standard optimizers for both LLM pretraining and finetuning.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.21160

FISolver: Data-Driven Discovery of First Integrals via Backward Generation and Guided Reinforcement Learning

Jingfeng Zhong, Zhengxiang Liu, Zhijie Wang, Shuai Li

原文摘要

The discovery of first integrals is of profound scientific significance for understanding physical conservation laws in dynamical systems. However, existing symbolic computation systems and Large Language Models (LLMs) struggle with this task due to the scarcity of high-quality training data and the reliance on mathematical intuition for finding solutions. This paper introduces FISolver, an LLM-based solver designed to bridge this gap. First, we propose a novel "Backward Generation" algorithm that systematically constructs large-scale datasets of (differential equations, first integrals) pairs by deriving equations from sampled integrals, effectively overcoming the data scarcity bottleneck. Second, we perform supervised fine-tuning on a compact mathematical model and further enhance its capabilities using reinforcement learning with a Levenshtein Distance-based shaped reward function. Additionally, we introduce data synthesis and blending techniques that enable effective adaptation to specific hard instances with sparse examples. Experiments demonstrate that FISolver, with significantly lower computational cost, substantially outperforms larger mathematical LLMs and commercial solvers like Mathematica on complex benchmarks, establishing a new data-driven paradigm for automated mathematical discovery.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12190

Information-Theoretic Generalization Bounds for Sequential Decision Making

Futoshi Futami, Masahiro Fujisawa

原文摘要

Information-theoretic generalization bounds based on the supersample construction are a central tool for algorithm-dependent generalization analysis in the batch i.i.d. setting. However, existing supersample conditional mutual information (CMI) bounds do not directly apply to sequential decision-making problems such as online learning, streaming active learning, and bandits, where data are revealed adaptively and the learner evolves along a causal trajectory. To address this limitation, we develop a sequential supersample framework that separates the learner filtration from a proof-side enlargement used for ghost-coordinate comparisons. Under a row-wise exchangeability assumption, the sequential generalization gap is controlled by sequential CMI, a sum of roundwise selector--loss information terms. We also establish a Bernstein-type refinement that yields faster rates under suitable variance conditions. The resulting bounds specialize to online learning, streaming active learning with importance weighting, and stochastic multi-armed bandits.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11172

Do Benign-Trained Hidden-State Probes Detect Tool Errors Under Adversarial Conditions?

Bibek Bhandari, Kshitij Lingthep

原文摘要

Although hidden-state probes efficiently monitor model operation, it remains unclear whether probes trained on benign tool use remain useful for detecting incorrect actions under adversarial conditions. In this research, we assess this problem from the perspective of operational tool-action correct- ness. we use four AgentDojo suites, four conditions, and three models: Qwen2.5-7B-Instruct, Qwen2.5-14B-Instruct, and GLM- 4.6V-Flash (9B). We perform a five-fold, task-grouped out-of-fold (OOF) evaluation for each model. We train step-specific logistic- regression probes only on benign and non-defended trajectories and evaluate them on held-out trajectories for benign, prompt- injection, system-override, and tool-poisoning conditions. We compare our hidden-state probe to mean tool-call-token entropy, negative mean tool-call-token log-probability, and self-consistency uncertainty. The paired tests highlight six significant differences after Holm correction. All of the tests involve Qwen2.5-14B- Instruct under the system-override condition, with all three scalar uncertainty metrics obtaining higher area under the receiver operating characteristic curve (AUROC) and average precision (AP) than the hidden-state probe. These results suggest that benign-trained hidden-state probes should not be assumed to reliably detect operational tool-action errors under adversarial conditions.

cs.CR · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11165

The Race Is Decided Early! A Kernel Law Predicts Lock-In in Majority-Vote Test-Time RL

Jaeho Hwang, Sehong Park, Daejin Choi, Uiwon Hwang

原文摘要

Test-time reinforcement learning with majority-vote rewards trains a language model on unlabeled questions by rewarding the samples that agree with its most frequent answer. During training, each question locks into one answer, on hard questions often a wrong one, but which answer it locks into is known only after training. In this paper, we show that this outcome is decided early, in a short race driven by sampling noise between the answers that are close at the start, and we derive the probability of each outcome. With a separate logit for each answer, the runner-up wins with a normal tail probability of the initial gap, scaled by an explicit noise constant. In a network, however, the answer logits depend on shared parameters, and through the kernel of these parameters the updates of the trailing answers also change the gap of the two leaders. This spillover shifts the boundary between the starts won by each leader, and a closed form computes the shifted probability from a kernel probe and the start logits. In LoRA experiments with three models from two families, predictions made before training with the kernel frozen at its start value forecast which answer each question locks into better than a logistic model refitted on the outcomes, although they use no fitted constants. The closed form matches these predictions on the runs in which a check before training finds that the two leaders stay above the other answers, and it explains why a law that ignores the spillover needs a strongly inflated step.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2401.07174

On the (In)Compatibility between Group Fairness and Individual Fairness

Shizhou Xu, Thomas Strohmer

原文摘要

We study the compatibility between utility-optimized statistical parity and individual fairness. While individual fairness seeks to treat similar individuals similarly, optimal statistical parity aims to provide similar treatment to individuals who share relative similarity within their respective sensitive groups. Although both fairness perspectives are desirable, they often conflict in practical applications. Our goal in this work is to analyze the existence of this conflict and its potential solution. In particular, we establish sufficient (sharp) conditions for the compatibility between the optimal (post-processing) statistical parity $L^2$ learning and the ($K$-Lipschitz or $(ε,δ)$) individual fairness requirements. When a conflict arises, we first relax the optimal statistical parity solution to the Pareto frontier, representing the optimal trade-off between $L^2$ error and statistical disparity, and then examine its compatibility with individual fairness. Our analysis identifies regions along the Pareto frontier that satisfy individual fairness constraints. Additionally, we provide individual fairness guarantees for the composition of a trained model with the optimal post-processing step, enabling practitioners to assess the compatibility of post-processed models. This provides practitioners with a principled approach to achieving Pareto-optimal statistical parity while maintaining individual fairness constraints.

math.ST · cs.CY · cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.01472

Causal Optimal-Transport Attention: Positional Priors and Length Extrapolation

Ming Lian

原文摘要

Causal masking obstructs the column balancing used by optimal-transport (OT) attention. We study an unbalanced OT operator that uses only current and earlier rows and characterize its induced positional prior. For bounded scores and any fixed balancing strength below one, its fixed point assigns vanishing mass to every fixed window as context grows. With constant scores, the prior has an exact scale-invariant form. We classify positional encodings by whether they prevent this dilution. We also derive finite-length window-mass bounds for log-distance priors with exponents near one. Preregistered experiments use 25M- and 100M-parameter language models. The OT operator improves loss extrapolation over softmax, while exploratory comparisons favor an explicit distance bias at a fraction of the cost. Changing only the bias beyond the training length moves long-context loss in the predicted direction on all 30 tested checkpoints. During training, learned exponents approach one even when constrained above it. A margin chosen from prior calculations controls distant-mass growth in the tested setting.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11156

Federated learning for remote IoT over LPWAN: Preliminary insights from structural health monitoring

Lakshmiprasanna Bandaru, Anshika Singh, Tarutal Ghosh Mondal, T. Jothi Saravanan, Siddhartha S. Borkotoky

原文摘要

Realizing federated learning (FL) in remote regions lacking reliable cellular infrastructure is challenging, as it requires dependable communication among participating devices. Low-power wide-area networks (LPWANs) offer an attractive communication solution for such scenarios. However, their limited data rates necessitate careful investigation of the communication-learning trade-offs and mechanisms to reduce communication overhead. This paper presents an investigation of the Long Range (LoRa) LPWAN technology for FL-based structural health monitoring (SHM), a representative remote IoT application. We consider a SCAFFOLD-based FL framework for multi-class anomaly detection and analyze the latency-accuracy trade-off. We also examine strategies for allocating the LoRa spreading factor (SF) during model-update exchange. Numerical results show that, with appropriate SF allocation, LoRa-supported FL-SHM can achieve competitive learning accuracy. In particular, jointly accounting for link distances and clients' dataset sizes when selecting the SF provides a favorable balance between convergence performance and communication latency. These preliminary findings offer useful insights into the design of FL systems over LPWANs for remote IoT deployments.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.03014

MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency

Saptarshi Mitra, Yifan Zhang, Rachid Karami, Sreetama Sarkar, Phyo Pyae Moe Aung, Nazmul Takbir, Souvik Kundu, Sitao Huang

原文摘要

Mixture-of-Agents (MoA) systems improve reasoning accuracy by routing each query to multiple expert LLMs and aggregating their outputs. Efficiently executing this workload on limited GPU resources has bottlenecks. Skill-based routing creates skewed expert demand, and combining instruction-tuned LLMs with long-reasoning models results in extreme variability in generation lengths. Consequently, traditional scheduling strategies suffer from significant GPU idling and throughput collapse due to load imbalances. We present MOSAIC, a scheduling framework for accelerating batch-oriented MoA workloads. MOSAIC uses two-stage workload-aware scheduling: an expert placement stage, where an integer linear program (ILP) decides expert placement, followed by a query-sequencing stage that schedules requests based on predicted output lengths. Additionally, MOSAIC uses confidence-aware adaptive aggregation, leveraging inter-expert agreement to bypass the heavy aggregator LLM for consensus queries. On a 4-GPU system, MOSAIC achieves 2.7-3.4x end-to-end speedups over round-robin scheduling and reduces the mean time-to-answer by 3.9-7.8x, while matching accuracy within +/-1pp.

cs.LG · cs.AR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30946

Tracing Hallucinations in LLM Reasoning for UML Class Diagram Generation: An Empirical Taxonomy and Mitigation Study

Mojdeh Rahmanian, Foutse Khomh, Yanchao Yu, Ashkan Sami

原文摘要

Large Language Models (LLMs) can generate UML class diagrams from natural language requirements, but hallucinations may compromise the correctness and completeness of the resulting diagrams. Existing evaluations mostly assess the final diagram, without examining the intermediate traces of reasoning-capable LLMs, which can reveal where these hallucinations arise. We develop an empirical taxonomy of eleven hallucination sub-types organised into three dimensions: Prompt Faithfulness (deviations between requirements and reasoning), Code Faithfulness (discrepancies between reasoning and generated PlantUML code), and Reasoning Quality (problems within the reasoning). The taxonomy was derived from 60 manually annotated DeepSeek-R1 traces, applied to 120 further DeepSeek-R1 traces through a dual-LLM annotation pipeline, and examined on 20 DeepSeek-V4-Pro traces. Code Faithfulness was the most frequent dimension for DeepSeek-R1 in both datasets, whereas Prompt Faithfulness dominated for DeepSeek-V4-Pro, suggesting model-dependent failure patterns. In the manually annotated traces, reasoning-trace length showed no significant monotonic association with hallucination frequency, whereas requirement complexity was negatively associated with it, particularly for Prompt Faithfulness errors. In a mitigation experiment on 13 Golden UML ModelSet cases with three Qwen3-based models, adding Prompt Faithfulness and Reasoning Quality constraints to the generation prompt raised the pooled mean F1 score by 4.0 percentage points, whereas a post-hoc Code Faithfulness review provided no consistent further benefit. Relationship modelling remained the lowest-scoring element throughout. These results suggest that localising hallucinations within the generation process can inform targeted, prompt-based mitigation. Whether these findings generalise beyond the DeepSeek and Qwen3 model families remains to be tested.

cs.SE · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11145

DISCOVER: Online Variance-Guided Data Discovery for Budgeted Multimodal GRPO

Peyman Gholami, Shivam Chandhok, Saber Malekmohammadi, Shayan Shams, Robert Xiao, Leonid Sigal

原文摘要

Recent advances in reinforcement learning have made verifiable rewards a central post-training paradigm for improving multimodal reasoning. However, methods such as GRPO remain data- and compute-inefficient, spending rollout and annotation budget on prompts that provide little learning signal. We propose DISCOVER, an online data-discovery framework for budgeted multimodal GRPO that enables vision-language models to dynamically identify prompts that are most useful for the current policy. DISCOVER first organizes the multimodal sample pool into semantic clusters and queries a small set of density-diverse representative samples to obtain initial reward observations. During training, it estimates the utility of unannotated samples by transferring sparse reward feedback from queried samples. Since sample utility changes as the policy evolves, DISCOVER dynamically updates these estimates with recency-weighted rollout observations. It then selects only high-utility prompts for annotation using a policy-conditioned score that accounts for reward variance and difficulty. Once the discovery budget is exhausted, DISCOVER continues with utility-based replay. Experiments on ViRL39K and VLAA-Thinking with Qwen3-VL-2B show that DISCOVER substantially improves multimodal mathematical reasoning under a strict 10% unique-sample budget, outperforming budget-matched random selection, hard-example sampling, full-pool GRPO under the same training-step budget, and online selection baselines. Ablations further validate the importance of semantic coverage, online reward transfer, and stable discovery cadence, establishing DISCOVER as a practical framework for data-efficient multimodal RL post-training.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.11140

SurgWorld: Learning Surgical Representations Beyond Observation via Latent Prediction

Jingkun Yue, Pengxiang Wang, Siqi Zhang, Zinan Jia, Yiyao Zhang, Huihuan Xu, Yichang Hao, Xiaohong Liu 等 10 位作者

原文摘要

Surgical procedures are inherently partially observable, requiring surgeons to continuously infer evolving anatomical dynamics and future surgical states beyond the current observation. However, existing self-supervised learning methods primarily learn from observed surgical videos through masked reconstruction or predicting representations within the camera's current field of view, lacking the capability to explicitly model the out-of-sight surgical dynamics. Inspired by latent predictive world models, we introduce SurgWorld, a video-native, self-supervised framework for surgical representation learning, which treats a specified spatial or temporal shift as a pseudo-action and learns to predict the target latent representation from a partial observation. Specifically, we propose Out-of-Sight Dynamics Modeling (OSDM) as the pretraining objective for predicting representations conditioned on relative spatio-temporal shifts. We further regularize the learned latent dynamics from OSDM with a Geometric Visual Similarity (GVS) objective by aligning patch-level relational structures between predicted and target representations, preserving spatial geometry and local visual coherence for dense perception. These two objectives are integrated through an agglomerative training framework that consolidates complementary supervisory signals from multiple teachers to learn a unified latent representation of surgical dynamics and visual geometry. Extensive transfer learning experiments on 17 surgical benchmarks show that SurgWorld consistently outperforms existing vision foundation models across surgical workflow recognition, fine-grained action understanding, and dense visual perception. We further conduct tailored qualitative and quantitative analyses, demonstrating that SurgWorld effectively captures surgical dynamics beyond the directly observed context through latent prediction.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.24093

The Power of Menus in Dynamic Pricing: Optimal Regret and Equivalence Results

Shiliang Zuo

原文摘要

We study dynamic pricing where a seller repeatedly interacts with a strategic, non-myopic buyer who has a fixed private valuation and discounts future utility. Prior work focused exclusively on posted-price mechanisms, where the seller gives a take-it-or-leave-it offer. For our first result, under geometric discounting $γ_t = γ^{t-1}$, we show that menu mechanisms consisting of allocation-payment contracts achieve $O(T_γ)$ regret, where $T_γ$ is the buyer's effective discounted time horizon. We also establish a $Ω(T_γ)$ lower bound, demonstrating the bound is tight. Considering the geometric discounting buyer with a constant discount factor, our bound is $O(1)$, while prior bounds using posted-price mechanisms incur an unavoidable $Ω(\log\log T)$ factor in regret. Our second contribution is more conceptual in nature. The problem of dynamic pricing sits at the intersection of two paradigms: learning with strategic agents in computer science / machine learning and revelation-principle-based mechanism design in economics, yet their relationship has remained unclear. We establish an equivalence between these two classes of mechanisms in our single-buyer setting: any adaptive indirect menu mechanism can be converted into an incentive-compatible direct mechanism with no less revenue, and conversely, any such direct mechanism can be implemented by an adaptive indirect menu mechanism with no less revenue. Consequently, the two classes achieve identical optimal regret. The adaptive, data-driven algorithms of online learning and explicit type elicitation from direct revelation type mechanisms are two languages towards solving the same problem.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.07555

You Don't Need To Stay in The Loop: An Agentic Robotics Loop for Robot-Policy Improvement

Hang Yu

原文摘要

Coding agents close the software loop for training a robot policy without having a human in the loop: a main agent manages the training, subagents analyze and execute, treat robot policies and hardware as tools and do the work. We port this architecture to robot-policy improvement, where one difference dominates the design: robotic tools such as trained policies, training pipelines, and data collection fail routinely, so a tool's quality must be measured, recorded at every call, and expired when the artifact behind it changes. AgenticRobotics is a backend-independent control plane in which an LLM controller drives disposable workers through durable train--evaluate--improve transactions: an immutable objective, controller-owned measurement, commit-keyed crash recovery, an evidence-graded skill library, and a tool registry with a standardized, recorded call surface. The title is an operational claim, not a selection claim: the operator can leave because promotion is evidence-gated, state is recoverable, and capability quality is derived from records, not because the loop picks better checkpoints than a human. On the one lineage we measured, it does not. The gates provide measured false-promotion control (0.001 false promotions per run with the hardened margin, against 0.005--0.021 with the shipped rule), anytime-valid decisions under optional stopping, zero lost or duplicate effects under kill injection, and a signed verifier that caught six of six artifact-tampering classes.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.00896

Seamless Reconfiguration for DAG-BFT (Technical Report)

Michael Yiqing Hu, Leander Jehl, Paul Franke-Bergmann, Jialin Li, Christian Berger

原文摘要

Byzantine Atomic Broadcast in Asynchronous networks has been studied extensively for decades. In a fully asynchronous system with even one possible crash failure, the FLP impossibility result rules out deterministic consensus, motivating randomized protocols that achieve termination with probability one. More recently, DAG-Rider popularized a new abstraction in which processes continuously reliably broadcast blocks to construct a Directed Acyclic Graph (DAG) and then locally derive a total order using a randomized perfect coin. This separation of dissemination from ordering has renewed interest in asynchronous DAG-based Byzantine Fault Tolerance (BFT) and inspired numerous protocols that improve expected latency or throughput. Reconfiguration, however, remains largely unexplored. Existing DAG-based BFT protocols generally assume a static set of processes, whereas deployed systems must occasionally add or remove nodes in response to operational demands. Existing solutions are not seamless; they either pause the system or delay membership changes until a predetermined epoch boundary. This may be impractical and can introduce downtime. A single agreed switching round is difficult to use seamlessly: by the time processes learn it, the dissemination layer may already have advanced beyond it. In this work, we present a seamless reconfiguration protocol for Asynchronous DAG-based Byzantine Atomic Broadcast. Our protocol allows the DAG to continue growing while the membership and corresponding quorum thresholds change, without halting the system. To the best of our knowledge, ours is the first protocol to provide seamless reconfiguration for DAG-based Byzantine atomic broadcast in a fully asynchronous network.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11125

LongCap: Hour-Scale Timestamped Audio Captioning via Chunk-wise Processing

Jinchuan Tian, Sen Chen, Wenbin Huang, Chao Weng, Zhengwei Huang, Jiatong Shi, Yichen Lu, Haoran Wang 等 10 位作者

原文摘要

Describing hours of audio requires accurate event timing and sustained descriptive detail throughout the recording. We present LongCap, a framework for timestamped audio captioning (TAC), with the human-annotated LongCapBench and three diarization-inspired metrics: Timeline Error Rate (TER), Granularity Error Rate (GER), and Caption Similarity (tcpCapSim). LongCap interleaves audio chunks with structured event captions, using relative timestamps and retained context to support length extrapolation within a 1M-token inference context window. Event-level reannotation restores caption detail, while systematic studies examine model scaling, dependency supervision, and self-distillation. LongCap-Diar achieves strong benchmark results and extrapolates to 16 hours of concatenated audio despite training on recordings no longer than 40 minutes. Our TAC models are competitive with Qwen3.5-Omni-Plus on short clips, outperform evaluated baselines in caption similarity and timeline accuracy in segment-level comparisons on LongCapBench-TAC-Long, and caption full recordings approaching two hours. On LongCapBench-TAC, 9B and 35B-A3B offer favorable size-performance tradeoffs, while 122B-A10B substantially improves event-dependency recovery. These findings support long-horizon general audio understanding and provide a foundation for always-on voice agents.

cs.CL · cs.SD

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.04590

Dual-Part Multi-Lateral Branched Network for Multi-Class Segmentation in Cardiovascular Catheterization Angiograms

Olatunji Omisore, Ahmed Elazab, Ali Shahidinejad, Fariza Sabrina

原文摘要

Catheterisation image processing requires segmentation models that are fast, accurate and explainable. While most of the existing studies usually focus on binary segmentation, there is a recent demand for simultaneous segmentation of multiple structures found in catheterization scenes. In this study, a dual-part MLBNet architecture is designed with multi-lateral encoder blocks and multi-head decoder branches for class-aware segmentation in cardiovascular catheterization scenes. Lateral branches in the encoder enable repeated feature extraction to learn diverse shared representations, while multiple decoder heads are used to introduce class-skewed branches that specialize in different structural properties in catheterization scenes. To analyze the performances of the dual-part MLBNet architecture, several multi-class segmentation angiogram data obtained during cardiovascular catheterization in phantom models, synthetic human-simulated aorta, and animal model are used for model training and evaluation. Results obtained showed the dual-part models could effectively separate guidewire, catheter, vessels and background pixels to their classes of memberships with high probability. The results demonstrate that all models were able to distinguish the dominant background class from foreground structures with high overall accuracy.

cs.CV · cs.AI · cs.RO

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新论文 · v1arXiv:2611.11123

HARP-Bench: Benchmarking Robot Manipulation Around Active Humans

Qinhong Zhou, Riley Zong, Chunru Lin, Diyi Hu, Shivraj Singh Bhatti, Hongxin Zhang, Lin Qiu, Hanqing Zeng 等 9 位作者

原文摘要

Recent robot manipulation benchmarks have enabled systematic evaluation of general-purpose robot policies across diverse objects, scenes, instructions, and embodiments. However, most existing benchmarks still assume that the robot is the only active agent and that task semantics are determined primarily by the instruction and object state. This assumption overlooks a common source of real-world variation: humans actively sharing the workspace. In such settings, humans may reveal an implicit goal, complete part of the task, temporarily change object availability, or constrain safe robot motion while working nearby. We introduce HARP-Bench, a benchmark for evaluating robot manipulation policies in workspaces shared with active humans. HARP-Bench treats human activity as part of the manipulation problem, rather than as background scene variation. It organizes human-present manipulation into four conditions: Cueing, Contribution, Interruption, and Co-presence. These conditions are instantiated through everyday manipulation tasks, including paired human-present variants for controlled comparison with standard manipulation. HARP-Bench also provides a demonstration-generation pipeline for dynamic human-present settings. We evaluate VLA models, imitation-learning methods, and heuristic policies on HARP-Bench. Human-present variations challenge current policies, and higher task success does not consistently improve safety. Quantitative failure analysis identifies manipulation and recovery as the dominant remaining challenges. Training with HARP-Bench demonstrations improves human-present performance in simulation. A real-world study on one task family shows improved performance under human contribution, but not interruption, providing initial evidence of the data's utility and identifying limits of transfer. Code is available at https://github.com/UMass-Embodied-AGI/harp.

cs.RO · cs.CV

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