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版本更新 · v3arXiv:2607.08407

Who Needs DRAM? We Have Fiber

Hannah Atmer, Yuan Yao, Thiemo Voigt, Stefanos Kaxiras

原文摘要

The rising pressure on DRAM availability and contract pricing reflects generative AI's massive high-performance memory requirements. This pressure is heavily compounded by hyperscale data center expansion, which now consumes a significant portion of global DRAM output. In this work, we propose a new architecture, Fiber Memory, which reimagines the role of optical fiber in a hyperscale data center, deploying it as an active, recirculating delay-line memory for immutable data, such as large language model weights. We present a hybrid data- and pipeline-parallel optical broadcast delay-line memory architecture that accounts for fiber's physical realities. By incorporating space-division multiplexed multi-core fibers, passive optical tap-and-amplify interfaces, co-packaged optics, and centralized in-ring 3R (re-amplify, reshape, retime) regeneration, our case study evaluation suggests that Fiber Memory can eliminate redundant weight storage across tens of thousands of AI accelerators and reduce weight-delivery power by over 75% compared to traditional HBM3e configurations.

cs.AR · cs.DC · cs.ET · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06299

STEK: Structural-Terrain-Enhanced Koopman-Inspired Cross-Channel Regularization for Retrospective Battery Multi-Target Estimation

Jing Wang, Shiyuan Piao, Fugee Tsung

原文摘要

Retrospective battery characterization across heterogeneous cells is challenging because cycling records vary substantially with chemistry, protocol, cell format, capacity, and operating conditions. An endpoint charge--discharge curve captures the local condition of an evaluated cycle, while a cell-level lifecycle terrain summarizes structural evolution across the archived trajectory. We propose STEK, a structural-terrain-enhanced framework for retrospective battery multi-target estimation. STEK combines a cell-level lifecycle (dQ/dV) terrain with an endpoint-derived structural proxy through quality-proxy-conditioned gating. Bounded FiLM and a validity-gated residual head refine the endpoint-curve representation while preserving a curve-based prediction pathway. Battery-type and optional material-text representations further condition an identity-centered low-rank linear map, providing a Koopman-inspired cross-channel consistency objective without modeling temporal dynamics or performing rollout. Under the fixed seven-output MIX-battery protocol, the full retrospective setting improves aggregate performance over endpoint-only learned baselines across multiple temporal backbones; this comparison reflects both the additional archived context and the modeling design. Under matched auxiliary inputs, STEK outperforms direct concatenation and quality-unaware FiLM, while descriptor-removal tests show graceful fallback to the endpoint pathway. Deterministic rules remain preferable for directly recoverable capacity summaries. The method targets retrospective archive harmonization rather than online estimation from cycle t alone.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06297

Speaker-Consistent Grounding for Confusion-Resilient LLM-Based Target Speech Extraction

Hanyu Meng, Hao Shi, Zhanhong He, Qiquan Zhang, Yang Xiao, Sungwoo Cho, Haowei Lou, Haizhou Li

原文摘要

Deterministic target-speaker extraction (TSE) tends to preserve observed linguistic content but may extract the interfering speaker. In contrast, autoregressive generative TSE improves perceptual quality, but is prone to content hallucination and speaker identity drift, which can be mitigated by model grounding with the evidence from the deterministic TSE. However, grounding such a generative TSE model with speaker-confused evidence may therefore reinforce the error. To address this gap, we proposed a repair-before-grounding framework with a training-free evidence-repair stage. Conditioning-diverse candidate selection (CDCS) selects target speaker-consistent evidence from complementary frozen TSE outputs. The selected waveform can then be used by code-space grounding (CSG) to constrain LLM-based TSE decoding. On Libri2Mix, CDCS reduces wrong-speaker extraction errors from 6.87% to 0.75% on clean mixtures and from 18.95% to 10.46% under noise. With the LLM-based TSE checkpoint unchanged, CSG further reduces noisy content- and acoustic-switch rates from 12.79% and 1.68% to 9.10% and 0.51%, respectively. Despite this stronger grounding, generative TSE retains its predicted perceptual-quality advantage over direct deterministic extraction. These results show that repairing evidence identity before grounding improves the reliability of generative TSE. Audio demos and code are available at https://hanyu-meng.github.io/LLM-TSE-Grounding-Demo/.

eess.AS · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06296

Multi-Resident Data Association in Smart Homes via MDP Modeling and Behavior Cloning

Xi Chen, Julien Cumin, Fano Ramparany, Dominique Vaufreydaz

原文摘要

Ambient sensors offer a privacy-preserving solution for activity recognition in smart homes, yet they face a critical challenge in multi-resident environments known as Data Association, which aims to identify the resident triggering a specific anonymous sensor event. Existing approaches fundamentally fail to maintain global consistency by often yielding conflicting associations for silent residents. Additionally, these methods frequently suffer from severe trajectory fragmentation caused by the ontological mismatch of applying continuous physical motion models to discrete logical behaviors. To address these limitations, we propose a novel framework that reformulates data association as a sequential decision-making task within a Markov Decision Process (MDP). We solve the defined MDP via Behavior Cloning (BC) to avoid complex reward engineering by directly learning from expert demonstrations. We instantiate the policy using two complementary architectures, including a lightweight Transformer that treats association as a multi-label classification task over linearized event histories, and a fine-tuned Large Language Model (LLM) that reformulates the problem as conditional text generation to leverage pre-trained semantic knowledge. Extensive evaluations on three diverse datasets demonstrate that our approach significantly outperforms state-of-the-art baselines, particularly in resolving concurrent multi-resident events. Furthermore, the LLM-based policy shows preliminary cross-environment transfer potential under benchmark settings, while its robustness in real deployments still requires further validation.

cs.AI · cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06294

A.S.E 2.0: Understanding How Repository Context Impacts the Security of LLM-Generated Code

Jiongchi Yu, Keke Lian, Junqing Wang, Zhikang Fan, Zhaoxuan Li, Yongheng Huang, Ziming Zhao, Dong Zhang

原文摘要

Large language model (LLM) coding assistants and agents construct a repository context before generating code. For security-sensitive code, this context decides whether the model sees the project's safeguards (e.g., authorization checks) or insecure code to imitate. However, context construction is designed almost exclusively for functional correctness, and secure-code benchmarks treat the context as fixed, so how repository context affects the security of generated code remains unclear. We construct CASE-Bench, a benchmark of 145 repository-level tasks from real CVEs in 51 projects. Each task runs in Docker, has functional tests and a proof-of-concept exploit validated in both directions, and is annotated with the repository code that enforces or undermines its security. Using CASE-Bench, we compare 13 context construction methods and six controls with six LLMs, edit single context elements, and analyze coding-agent trajectories. We find that (1) functionality-oriented context raises the functional pass rate by up to 8.2 percentage points (pp) but the secure pass rate by at most 3.7 pp, (2) withholding a safeguard lowers the secure pass rate by up to 8.1 pp, while one inserted insecure exemplar raises correct-but-exploitable code by 6.3 pp, and (3) coding agents observe the relevant safeguard in only 27-36% of trajectories. These findings inform security-aware context construction and secure-code evaluation.

cs.CR · cs.AI · cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.34249

Evolving Support Priorities in Empathetic Reinforcement Learning

Pengyu Huang, Zhiyuan Han, Wenwen Tong, Hewei Guo, Jiangnan Chen, Sirui Chen, Lewei Lu, Beier Zhu 等 9 位作者

原文摘要

We identify a fundamental mismatch in empathetic reinforcement learning: support priorities evolve with the dialogue state, yet existing methods typically optimize predefined reward specifications that remain fixed across turns. To model these evolving support priorities, we organize empathetic support along cognitive, affective, and proactive empathy, and propose Context-Adaptive Rubric Evolution (CARE). At each turn, CARE generates a context-adaptive rubric by adjusting both the weights of these three empathy dimensions and their fine-grained evaluation criteria. The rubric generator is trained with turn-level rubric supervision and human preference data through supervised fine-tuning followed by preference-based reinforcement learning, and then serves as an adaptive reward interface for online empathetic RL. Integrated with both RLVER and MICA, CARE achieves state-of-the-art performance across SentientBench, EQBench3, and EMPA under three independent LLM judges. Notably, on EMPA, CARE improves EPM-Idx over the strongest baseline by at least 13 points under all three judges, including an increase from 28.11 to 83.54 under Gemini-2.5-Pro. Further analyses show that learned rubric priorities systematically vary across dialogue stages and user emotions, demonstrating that CARE adapts what is rewarded as support needs evolve. Code is available at https://asa13opp.github.io/CARE-project-page.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06965

When Can Self-Play Fix a False Belief? Answer Keys, Pass Bands and Replacement Rules in an Execution-Grounded Loop

Nirdesh Chauhan, Dushyant Rajput, Siddharth Kosaraju

原文摘要

Self-play loops in which a model writes tasks, derives an answer key and trains on its own matching solutions are often called grounded when the key comes from executing code. We ask which false beliefs such a loop removes. We run expert-iteration self-play with Qwen3-4B-Base on output-prediction tasks, vary the source of the answer key, and track natural beliefs about Python semantics and two misreadings of task specifications planted by fine-tuning ("at most n" read as < n; "the k-th smallest" read as 0-indexed). Executing the generator's own code gave a key that encoded the "at most" misreading on every audited task, and training on it never raised the truth rate above 0.03. A key from an implementation by the unplanted model, a teacher without the error, raised it to 0.23-0.58 after three rounds in four runs. A correct key is not sufficient: the pass band, which keeps tasks solved 1-7 times of 8, admits almost no corrective task while a belief is held on nearly every sample. Execution keys sharply reduced both natural runtime beliefs on diagnostic items in both seeds. Corrections came with replacement rules that fail on neighbouring items the old belief got right. The robust case, present in all four spec runs, is that after the "at most" fix the model increasingly counts the nearest value above a limit that is absent from the list (overcount 0.18-0.38 of control samples at round 3), a case our planted generators almost never wrote (4 of 10,924 tasks); diagnostic-only metrics miss it. Asked for those cases, the planted generator wrote none in 2,061 checkable tasks, against 27% for the untrained unplanted model in one round. Both generators often moved the request into the specification instead ("the cap itself does not count"), and a decorrelated key follows the specification.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.06385

Data-Driven and Cycle-Agnostic Covariate Selection for Nonparametric Causal Effect Estimation

Ana Leticia Garcez Vicente, Gijs van Seeventer, Saber Salehkaleybar

原文摘要

Estimating causal effects from observational data requires identifying valid adjustment sets. This task is especially challenging in realistic settings where latent confounding and feedback loops are present. Existing approaches typically assume acyclicity or rely on global causal structure learning, limiting applicability and computational efficiency. In this work, we study a local, data-driven method for covariate selection based on conditional independence information under a pre-treatment assumption. While this method is known to be sound and complete in acyclic causal models, its validity in the presence of cycles has remained unclear. Our main contribution is to show that these guarantees extend to cyclic causal models. In particular, our result relies on the invariance of conditional independence assertions under $σ$-acyclification. These findings establish a unified, cycle-agnostic perspective on covariate selection, which allows for causal effect estimation using any consistent estimator. Therefore, showing that the method applies across cyclic and acyclic settings without modification. Empirically, we validate this on extensive synthetic data, showing reliable performance in cyclic causal models.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.18086

Gap-Induced Sidelobes in Multiband Wi-Fi CSI and Their Impact on Multipath Delay Estimation

Ana Jeknić, Mihael Mohorčič, Carolina Fortuna, Aleš Švigelj, Tomaž Javornik, Andrej Hrovat

原文摘要

Leveraging channel state information from multiple Wi-Fi bands has the potential to improve delay resolution for ranging and sensing when wide contiguous spectrum is unavailable. However, non-contiguous spectrum access reshapes the spectral window, producing sidelobes and secondary peaks that can mask closely spaced multipath components. Prior work either designs the bands, which fixed Wi-Fi channelization does not allow, or addresses only the first multipath component. To characterize these secondary peaks, multipath delay estimation is studied in Wi-Fi-compliant multiband settings using channel state information. For a two-path model with unknown complex gains and delays, the Cramér-Rao lower bound for delay separation is derived and analyzed, confirming the benefit of larger frequency aperture at every separation considered, while revealing pronounced, separation-dependent oscillations driven by gap geometry and coupling of the two multipath components. Increasing aperture sharpens the mainlobe but also intensifies sidelobes and multipath interference, yielding competing peaks and a dominant peak shifted from the true delay. For the single-path matched filter, this shift is a deterministic offset governed by the slope of the autocorrelation function (ACF) of the allocation at the separation. A closed-form expression is derived for this offset, predicting it to within 21 ps for the analyzed channel combinations. Since the spectrum allocation enters both the bound and the offset, the impact of its gaps can be assessed for any legal aggregation before a measurement is taken.

eess.SP · cs.ET · cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06290

The Split Protocol Decides the Result: Re-Evaluating Encrypted Traffic Classification

Mayan Sharma, Kriti Saini, Devansh Behl

原文摘要

Encrypted traffic classifiers are routinely reported at 95-99% accuracy. We show that on one of two public corpora, the same model and code score macro-F1 0.992 under the evaluation protocol standard in the literature and 0.567 under a protocol that keeps flows from one packet capture on one side of the split. Nothing changes but the partition (gap 0.426 over eight seeds, p = 0.008 after Holm-Bonferroni correction). The inflation is not a property of random splitting as such. On a month of backbone QUIC traffic, we evaluate five split protocols at eight seeds each: random, temporal and client-disjoint splitting are statistically indistinguishable from session-disjoint, while server-disjoint splitting costs 0.192 macro-F1 (d = 17.0, p = 0.008). Which axis matters depends on how the corpus was collected, in a way invisible from its label space. We give a diagnostic that identifies the right axis before any model is trained: single-identifier probes. A decision tree on the capture identifier alone reaches 0.983 under a random split and exactly 0.000 under session-disjoint splitting. Under this evaluation, a metric-trained flow embedding leads four deep baselines but does not beat gradient-boosted trees on twenty packet sizes. Five of our own pre-registered architectural claims fail: distance-based rejection of unknown applications beats softmax thresholding, but ablating every metric objective makes rejection better, because class separation correlates at r = -0.88 with rejection quality. Few-shot enrollment is flat within a corpus but, across corpora, five labelled flows per class recover 91% of the deficit left by failed zero-shot transfer. All splits, results, code and negative findings are released.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06287

Corridor-Based Monitoring and Emergency Braking Decision for Automated Vehicle Safety

Junnan Pan, Vladislav Nenchev, Mohak Mansharamani, Prodromos Sotiriadis, Ferdinand Englberger

原文摘要

This paper addresses emergency-braking decisions for a redundant safety channel in automated vehicles. The channel is responsible for identifying hazards from sensor data and determining whether braking is necessary, given the constrained computational resources of embedded hardware. We present an emergency-braking approach that uses a predicted motion corridor to filter \ac{lidar} measurements and then decide whether the ego vehicle must brake. \ac{pmcm} constructs a curvature-aware boundary from the planned path and vehicle geometry so that the monitored region contains the full swept body on both straight and curved paths. \ac{gdeta} then decides whether the ego vehicle must command an emergency brake for objects inside this boundary, scoring geometric occupancy and temporal urgency independently. Hard gates permit braking when either source is conclusive; soft-OR arbitration combines partial evidence when neither gate is active. Consecutive-scan confirmation reduces sensitivity to transient measurement noise. Corridor coverage is evaluated in controlled collision simulations and recorded campus driving. Braking decisions are evaluated in Monte Carlo simulations of lead-vehicle braking and transverse-crossing encounters.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06286

Clear Human Web: Where a Person Vouches for Every Client

Lorenzo Porcelli

原文摘要

The web cannot tell whether a request comes from a person or from a program. Sites have checked the network address and puzzles in its place, and both have given way: automated traffic exceeds human traffic, and puzzles are solved for pay and by agents. This paper proposes the Clear Human Web, the part of the public web that sites open only to clients a person vouches for. A person's wallet presents their person identification data (PID) to one issuer, the only party that registers as a relying party, and receives a bounded number of blind-signed tokens, proofs of personhood that carry no attribute. The browser spends one token at each site that runs the gate, and the site learns that a valid token of a trusted issuer was spent, and nothing else. No field is added to the PID, no protocol is introduced, no site registers, and the publisher of a site holds the same proof as its readers. We define the Clear Human Web through five properties and seven adversaries, and state which properties the construction satisfies against each adversary. The identities a person can hold on one site in an epoch are bounded to k. The bound holds once the wallet limits a person to one pseudonym at the issuer. In the optimum k = 1, and a zero-knowledge proof of PID possession and a pseudonym per site replace the tokens.

cs.CR · cs.CY · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06282

MonoLaDN: Language-Guided DeNoising for Monocular 3D Object Detection

Kiet Dang Vu, Cao Le Minh Khoa, Nguyen Viet Tri Pham, An Nguyen Gia, Tam Le Thuc Ho, Duc Dung Nguyen

原文摘要

Monocular 3D object detection offers a low-cost route to 3D perception, but it remains fundamentally ill-posed, since depth must be inferred from a single view. Recent transformer-based detectors learn geometry purely from image features and 3D box annotations, lacking the high-level semantic priors that vision-language models (VLMs) acquire from large-scale pretraining. VLMs offer such priors, but deploying one directly as a detector is impractical due to its heavy inference cost. We propose MonoLaDN, a language-guided denoising framework that transfers the geometric reasoning of a VLM into a monocular 3D detector while keeping the deployed model purely vision-based. During training, a frozen VLM produces structured reasoning about each object's occlusion, depth, dimension, and angle from the image and its ground-truth boxes. We encode this reasoning into queries, perturb them with noise, and train the detector to reconstruct the clean reasoning. Reconstructing the corrupted reasoning forces the detector's depth-guided features to internalize the VLM's geometric priors, so this knowledge remains available even after the reasoning path is discarded at inference, adding no latency or parameters to the deployed model. Experiments on KITTI 3D and nuScenes show that MonoLaDN consistently outperforms state-of-the-art detectors and generalizes well to new domains.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06281

Building a Method: Challenges and Lessons from Developing an Impact Evaluation Framework for Public Investment in Open Source Software

Laia Domenech Burin

原文摘要

Modern societies and institutions depend on an invisible layer of open digital infrastructure: foundational, openly accessible digital technologies that support a broad range of social and economic activities. Open source software is a core component of this infrastructure, underpinning everyday technologies and critical systems in various critical sectors. As open source software becomes increasingly recognised, funded, and governed as open digital infrastructure, policymakers face growing demands for accountability, transparency, and credible evidence of impact. However, methods for evaluating public investment in open source software remain underdeveloped, with no widely accepted methodological standard. This paper describes the Sovereign Tech Agency's transition from qualitative reporting and funding-recipient self-assessment towards a mixed-methods approach that incorporates quantitative, goal-oriented evaluation to inform funding decisions. Drawing on the development of this framework, it examines its initial design, the practical and structural challenges encountered, and the responses adopted. The work identifies three central challenges: evaluation indicators must reflect heterogeneous project goals and contexts; quantitative assessment relies on fragmented and fragile data infrastructure; and observed changes before, during, and after investment do not establish causal attribution without additional research designs. The paper argues that evaluating investment in open source software is not only a methodological task but also a governance practice, dependent on sustainable open research infrastructure, community knowledge, and ongoing methodological refinement.

cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2403.20132

A formal specification of the jq language

Michael Färber

原文摘要

jq is a widely used tool that provides a programming language to manipulate JSON data. However, the jq language is currently only specified by its implementation, making it difficult to reason about its behaviour. To this end, we provide a formal syntax and denotational semantics for a large subset of the jq language. In particular, we give a translation from jq programs to lambda terms. Our most significant contribution is to provide a new way to interpret updates that allows for more performant execution. We implement our semantics in two interpreters and evaluate the performance.

cs.LO · cs.PL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.06004

Do Tabular Foundation Models Agree with Themselves?

Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme, Tom Hanika

原文摘要

Tabular Foundation Models (TFMs) are the state of the art for many tabular prediction tasks. They are transformers that approximate the Bayesian posterior predictive distribution under a pretraining prior. TFMs become multivariate predictors autoregressively, by sampling one target and appending it to the features. This paper is the first to evaluate whether this setup induces a valid stochastic process. Informally, we ask: *Do we get the same answer to the same question asked in two different ways?* The standard Kolmogorov consistency conditions cannot be checked on TFMs directly, since TFMs are unable to model certain conditional distributions. We therefore propose two adapted, testable requirements. Marginalization consistency demands that marginalized conditionals equal directly predicted marginals, and factorization consistency that both factorization orders yield the same joint. Since the data-generating distribution satisfies both, a perfect TFM would as well, and every violation lower-bounds how far the model's predictions are from the ground truth. Every TFM we evaluate violates both requirements, for classification and regression, on all datasets.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06280

DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation

Yizhou Zhang, Panqi Chen, Lei Cheng, Ting Zhang, Jianlong Li, Jianfeng Shi, Shikai Fang

原文摘要

Physics sensing in evolving environments requires reconstructing dense spatiotemporal fields from sparse observations while adapting where future observations should be collected. Existing reconstruction and sensor-placement methods have made substantial progress, but they typically rely on static sensing assumptions: they either use a single layout across all frames or optimize placements independently at each frame, which limits their ability to produce temporally coherent sensing trajectories. We introduce DyPSI, a dynamic physics sensing framework that formulates this problem as the joint generation of future fields and sensor trajectories conditioned on historical observations. DyPSI lifts discrete sensor coordinates into continuous Sensor Position Fields, encodes fields and sensing configurations with a shared Functional Tucker representation, and trains a joint diffusion model to generate future fields and sensing trajectories simultaneously in the resulting latent space. A Gaussian-process temporal kernel correlates the perturbation injected during diffusion training, biasing the denoiser toward temporally smooth recoveries, and we further provide an analysis linking the EDM training objective to a sequence-aggregated A-optimality criterion and a kernel-induced bound on discrete trajectory variation. Experiments on turbulent flow, GLORYS12 sea-surface temperature, and 3D car aerodynamics show that DyPSI consistently outperforms static and frame-wise placement strategies, with substantial error reduction under tight sensor budgets. The code is available at https://github.com/mengqi132/DyPSI.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.06896

RCOMPSs: A Scalable Runtime System for R Code Execution on Manycore Systems

Xiran Zhang, Javier Conejero, Sameh Abdulah, Jorge Ejarque, Ying Sun, Rosa M. Badia, David E. Keyes, Marc G. Genton

原文摘要

R has become a cornerstone of scientific and statistical computing due to its extensive package ecosystem, expressive syntax, and strong support for reproducible analysis. However, as data sizes and computational demands grow, native R parallelism support remains limited. This paper presents RCOMPSs, a scalable runtime system that enables efficient parallel execution of R applications on multicore and manycore systems. RCOMPSs adopts a dynamic, task-based programming model, allowing users to write code in a sequential style, while the runtime automatically handles asynchronous task execution, dependency tracking, and scheduling across available resources. We present RCOMPSs using three representative data analysis algorithms, i.e., K-nearest neighbors (KNN) classification, K-means clustering, and linear regression and evaluate their performance on two modern HPC systems: KAUST Shaheen-III and Barcelona Supercomputing Center (BSC) MareNostrum 5. Experimental results reveal that RCOMPSs demonstrates both strong and weak scalability on up to 128 cores per node and across 32 nodes. For KNN and K-means, parallel efficiency remains above 70\% in most settings, while linear regression maintains acceptable performance under shared and distributed memory configurations despite its deeper task dependencies. Overall, RCOMPSs significantly enhances the parallel capabilities of R with minimal, automated, and runtime-aware user intervention, making it a practical solution for large-scale data analytics in high-performance environments.

cs.DC · stat.CO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.08462

Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck

Fabio Valerio Massoli, Andrey Kuzmin, Arash Behboodi

原文摘要

Chain-of-Thought (CoT) prompting improves LLM accuracy on complex tasks but often increases token usage and inference cost. Existing ``Budget Forcing'' methods reduce cost via fine-tuning with heuristic length penalties, suppressing both essential reasoning and redundant filler. We recast efficient reasoning as a lossy compression problem under the Information Bottleneck (IB) principle, and identify a key theoretical gap when applying naive IB to transformers: attention violates the Markov property between prompt, reasoning trace, and response. To resolve this issue, we model CoT generation under the Conditional Information Bottleneck (CIB) principle, where the reasoning trace $Z$ acts as a computational bridge that contains only the information about the response $Y$ that is not directly accessible from the prompt $X$. This yields a general Reinforcement Learning objective: maximize task reward while compressing completions under a prior over reasoning traces, subsuming common heuristics (e.g., length penalties) as special cases (e.g., uniform priors). In contrast to naive token-counting approaches, we introduce a semantic prior that measures token cost by surprisal under a language model. Crucially, the prior is queried only for token-level log-probabilities, adding negligible overhead to the training loop. Empirically, our CIB objective prunes reasoning redundancy while preserving fluency and logic, improving accuracy at moderate compression and enabling aggressive compression with minimal accuracy drop. These gains generalize across model families and task domains, confirming CIB as a domain-agnostic CoT compression framework.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06273

Quo Vadis, Driving Scene Reconstruction? Occlusion-Aware Gaussian Densification

Dung Do Tien, Kien Nguyen Do Trung, Kiet Dang Vu, Trung Thai Tran, Duc Dung Nguyen

原文摘要

While recent feed-forward 3D Gaussian Splatting methods enable efficient driving-scene reconstruction from surround-view images, their reliance on a single observation fundamentally hinders novel-view synthesis. Regions occluded in the input can become visible from novel viewpoints, leaving the reconstructed Gaussians under-constrained and incomplete. Moving beyond image-space inpainting for rendered views, we introduce OccSplat, a feed-forward framework whose core contribution is Occlusion-Aware Gaussian Densification, explicitly targeting missing scene geometry rather than missing pixels. By predicting new Gaussian primitives for unsupported regions, OccSplat completes the scene directly in the 3D representation. Specifically, OccSplat first identifies occluded regions that lack Gaussian support through Gaussian Occlusion Mapping. Gaussian Occlusion Completion then generates new primitives for these regions. The predicted primitives are integrated into the original representation to improve novel-view synthesis while retaining feed-forward inference without per-scene optimization. Experiments on the nuScenes benchmark demonstrate that OccSplat improves on the compared methods and yields particularly large gains in occluded regions, leading to more accurate and visually coherent driving-scene reconstruction.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06272

Evaluating Human-LLM Alignment on Visualization Preference

Tanmay Bakshi, Chenglong Wang, Simra Shahid

原文摘要

There are often multiple valid visualizations, spanning different chart types, visual encodings, and aesthetic choices, that can all support a user's goal of analyzing a dataset. Determining which of these candidate visualizations is most appropriate, however, remains challenging. Prior work has leveraged expert-curated taxonomies to guide visualization design and subsequent quality evaluation, a practical evaluation model must also account for human-centric factors. Latest visualization tools integrate Large Language Models (LLMs) to generate and refine visualizations, using LLM-as-a-judge to provide design feedback. Without a deeper understanding of whether, and in what ways, human and LLM perceptions of visualizations align, there is a real risk of deploying low-quality agents that do not reflect human judgment. We study this question through a pilot experiment - we formulate the problem as a preference task over curated chart pairs and compare the judgments of state-of-the-art vision models with those of a panel of human annotators. We establish a cautionary tale: such judges risk filtering out informative, compelling displays. We find that frontier models collapse human subjectivity, a rich signal that reveals contrasting design choices, into near-deterministic verdicts.

cs.HC

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新论文 · v1arXiv:2611.06271

Consistency Learning and Distillation for One-Step MIMO-OFDM Channel Estimation

Fredrik Nguyen, Qiwen Wang

原文摘要

Iterative neural channel estimators can improve recovery under pilot contamination, but repeated network evaluations increase inference cost. This paper develops observation-anchored consistency and Consistency Mid-Training (CMT) estimators for one-step channel estimation in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Both estimators refine pilot-derived least-squares channel estimates in the delay-beam domain. Consistency learning couples endpoint predictions along noisy-to-clean paths. CMT then refines a compact consistency-trained student to predict a flow-matching teacher's endpoint from intermediate trajectory states. Across the evaluated SNR range, the compact CMT student reduces uncoded symbol error rate (SER) by 9.6-20.2% relative to the one-step flow-matching baseline (0.44-0.98 dB in logarithmic SER reduction), while using 74.5% fewer parameters and 73.0% fewer network multiply-accumulates.

cs.LG · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06270

NextGAN: Towards Simple and Scalable GAN Training for Image and Video Generation

Xinhao Lou, Dongye Xie, Peng Sun, Chenyi Zhuang, Kaicheng Yu, Tao Lin

原文摘要

Adversarial training enables one- and few-step image and video generation, but conventional adversarial losses supervise critic values without directly specifying the gradients used to update the generator. We present NEXTGAN, which trains a scalar critic by regressing its input gradient onto displacements between independently paired reference and generated features at interpolated locations in frozen representation spaces. For a fixed generator, this regression is equivalent to an adversarial objective with a gradient-energy penalty, while the generator retains the standard potential-based GAN update. The same objective adapts pre-trained image and video generators with up to 14B parameters, without auxiliary losses, online teacher scores, or trajectories. On ImageNet $256 \times 256$, NEXTGAN achieves one-step FIDs of 2.87 with pixel-space JiT-B and 1.54 with latent-space DiT-XL/1; its adversarial form further reaches 1.36. For text-to-video generation, a single trained Wan2.1-1.3B model supports one-, two-, and four-step sampling, achieving the highest VBench total scores among the compared 1.3B methods at one and two steps. At two steps, NEXTGAN scores 83.13, exceeding rCM and AnyFlow by 1.25 and 0.47 points, respectively. Code is available at https://github.com/LINs-lab/NextGAN.git.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.06179

Diffusion Models Observe Only Gradients: A Geometric Perspective on Score Matching Errors

Naïl B. Khelifa, Richard E. Turner, Ramji Venkataramanan

原文摘要

Score-based diffusion models are typically trained by minimizing the $L^2$ score matching error, and standard theoretical analyses rely on this quantity to bound the sampling discrepancy between the learned and target distributions. Recent work has shown that the score admits an orthogonal decomposition into gradient and solenoidal components in which only the gradient part matters for exact sampling. We apply this decomposition to the score error of trained models and study its quantitative consequences. Since only the gradient component of the error enters the marginal Fokker-Planck dynamics, a model can incur arbitrarily large $L^2$ score error while perfectly matching the target. Starting from this observation, we present three results. First, we derive an upper bound on the Kullback-Leibler divergence that depends only on the observable gradient component of the error. It tightens the standard Girsanov bound for generic score networks and identifies that bound's looseness as the cost of working with path-space rather than marginal-space dynamics. Second, we prove that no non-trivial function of the $L^2$ score error can uniformly lower bound any divergence between the learned and target distributions. Third, we give an estimator of the gradient component, computable from denoising score matching residuals via a dual Sobolev identity, which empirically correlates substantially better with sample quality than the full $L^2$ error.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.04311

How the Misuse of a Dataset Harmed Semantic Clone Detection

Jens Krinke, Chaiyong Ragkhitwetsagul

原文摘要

BigCloneBench is widely used to evaluate traditional clone detection and, more recently, machine learning approaches to semantic clone detection and functional similarity. This paper demonstrates that BigCloneBench's Weakly Type-3/Type-4 (WT3/T4) subset is problematic as ground truth for learning or evaluating functional similarity. Its tagged-tagged WT3/T4 clone pairs combine two methods labelled as true positives for the same functionality and do not contain exemplar functions. A population-weighted analysis of a stratified probability sample of 406 such pairs estimated that 93.9% of pairs in the eligible tagged-tagged WT3/T4 population are not functionally similar under our operational definition and are therefore mislabelled (conservative 95% confidence interval: 91.5%-96.2%). From a literature review of 179 papers, we estimate that 128 used BigCloneBench to evaluate semantic clone detection in a way that their results could be affected by the mislabelling. High F1 scores on BigCloneBench do not establish whether an approach has learned functional similarity or relied on dataset-specific shortcuts. Our study does not assess BigCloneBench's validity for evaluating syntactic or textual clone detection of Type-1, Type-2, and Type-3 clones. We acknowledge its important contributions to traditional clone detection research. However, using its WT3/T4 subset as ground truth for same-main-function similarity without validating its labels for that task constitutes misuse. By threatening the validity of a substantial body of reported results, this practice has harmed the evidence base of semantic clone detection. We encourage more critical and responsible use of benchmarks and datasets and more rigorous validation during dataset creation and use.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.16133

The Dangers of Non-Self-Fixed Architecture Technical Debt and Its Impact on Time-to-Fix

Edi Sutoyo, Paris Avgeriou, Andrea Capiluppi

原文摘要

Technical Debt (TD) refers to the long-term costs incurred when developers prioritize short-term delivery over quality-improving work. Architectural Technical Debt (ATD) arises when architectural decisions (e.g., technology choices, patterns, or decomposition) prioritize near-term progress over future maintainability and evolvability. Because ATD affects a system's core structure and propagates through architectural dependencies, it is often more expensive and disruptive to remediate than localized code-level debt. Although ATD has been widely studied, an important but underexplored aspect of repayment is who performs it. Prior work provides limited empirical evidence on repayment responsibility in ATD and its relationship to time-to-fix. We empirically study self-fixed ATD, where the introducer also repays the debt, and contrast it with non--self-fixed ATD in large Apache open-source projects. We reconstruct ATD lifecycles by tracing Jira artifacts to version-control history to identify introduction and repayment points and attribute developer roles. We address three research questions on the prevalence of self-fixed ATD, time-to-fix differences between self-fixed and non--self-fixed items, and how factors related to code change and collaboration metrics relate to repayment speed. Using descriptive statistics, non-parametric tests, and survival analysis, we show that self-fixed and non--self-fixed ATD exhibit distinct repayment dynamics and differences in how changes are shared on ATD-affected files. In particular, non--self-fixed ATD is more likely to remain unresolved longer when changes are spread across many developers. These results provide actionable guidance for maintainers to identify potentially high-risk ATD items and support knowledge transfer by maintaining introducer involvement when feasible and documenting the design rationale during repayment.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.40571

On the Quantum Chromatic Gap II

Lorenzo Ciardo, Antoine Mottet

原文摘要

We confirm a conjecture of the first author [SODA'26] on a strong form of pseudo-telepathy for Khot's $d$-to-$1$ games: for every $d\geq 4$, every $ε>0$, and every $k\in\mathbb N$, there is a $d$-to-$1$ label-cover game of classical value less than $ε$ that admits a perfect quantum strategy using finite-dimensional shared entanglement with measurement contexts of radius at least $k$. As a consequence, there exist graphs whose quantum chromatic number $χ_q$ is $3$ and whose classical chromatic number $χ$ is arbitrarily large. This is the first super-exponential $χ_q$ vs. $χ$ gap, and it improves on the exponential lower bounds of Brassard, Cleve, and Tapp [Phys. Rev. Lett. '99], who produced graphs for which $χ\geq 1.1397^{χ_q}$, and Lalonde, who recently achieved $χ\geq 1.1546^{χ_q}$. To prove the conjecture, we proceed as follows. We start with a ``stretched'' version of the Mermin-Peres magic square, obtained by spacing out its equations and linking them with additional long equation wires. This operation increases the radius of the initial measurement contexts of a perfect quantum strategy for the game, while preserving classical unsatisfiability. We then apply parallel repetition to make the classical value small, and we turn the system into a pseudo-telepathic multi-layered Kneser hypergraph via a PCP construction of Dinur, Guruswami, Khot, and Regev [STOC'03]. Finally, we feed the resulting hypergraph into the reduction of Fei, Minzer, and Wang from hypergraph colouring to $4$-to-$1$ games, we establish quantum completeness of this reduction, and we show that it preserves context radius.

quant-ph · cs.CC · cs.LO · math.CO · math.OA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06268

Beyond Identity Creation: Bilinear Sybil Cost in Identities and Time

Homayoun Maleki, Nekane Sainz, Jon Legarda, Igor Santos-Grueiro

原文摘要

Sybil defenses price the creation of an identity, yet influence goes, again and again, to active identities. We study the cost of maintaining influence. A defense has a bilinear Sybil cost with ratio $A$ if keeping $s$ identities active for $T$ periods costs at least $sT/A$ honest units: the average cost per identity-window is at least $1/A$ times an honest participant's cost. Recurrence alone does not guarantee this cost. The cost can be carried over from earlier periods, shared among many identities, or be negligible when fast hardware does the work of thousands of honest devices. We give four sufficient conditions for a defense that checks every identity each period to achieve bilinear Sybil cost: identity binding, freshness, a shared deadline, and sequential, non-batchable work. They yield two guarantees: a bilinear total cost, with $A$ the speed advantage of the adversary's fastest hardware, and, because the work must be fresh and finished by a common deadline, a bound on the identities active in a period by the processors running at that time. We prove that none of the four follows from the other three. A verifiable delay function on an identity-bound input meets all four without payments, with $A$ of 2 to 8 for CPUs and about 28 for a hypothetical ASIC, against $10^3$ to $10^7$ for hash puzzles. We measure one active identity per core per period against about 80,000 under a signed heartbeat, no gain from batching up to 1,000 identities, and rejection of all tested attacks.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06265

Learning Pseudo-Riemannian Manifolds for Heterophilic Graphs via Graph Signature

Yun Young Choi, Asung Kil, Sun Woo Park, Minho Lee, Seokhwan Kim

原文摘要

Several widely used families of Graph Neural Networks carry a positive-definite geometric prior. Graph convolution read as heat diffusion needs a positive semi-definite operator, hyperbolic models place features on positive-definite Riemannian manifolds, and dot-product and GAT-style attention scores use positive-definite inner products. On heterophilic graphs, where edges often signify dissimilarity or structural repulsion, that prior is a mismatch: no positive-definite form can assign a negative squared distance to a direction the labels separate. To measure the mismatch, we introduce the Graph Signature Index ($s_{\mathcal{G}}$), the inertia of a class-compatibility matrix built from label topology alone, and we prove that no value of the edge or node homophily index in $(0,1)$ rules out a negative eigenvalue. We then propose the Pseudo-Riemannian Attention Network (PRAT), whose attention logit is a signed squared pseudo-distance under a learnable diagonal metric initialized from $s_{\mathcal{G}}$. The design is diagnose-then-learn: the signature supplies a sign structure that unconstrained training keeps outside near-zero directions, and the projection learns the rest of the metric around it. Across fifteen node-classification benchmarks PRAT outperforms or matches the strongest heterophily-adapted models. A controlled ablation ties that performance to the sign structure itself: with architecture and protocol fixed, forbidding the metric to be indefinite costs accuracy on the graphs $s_{\mathcal{G}}$ marks as indefinite and leaves the others unchanged.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06263

CLEAR: Structured Specifications for Controllable Long-Horizon ML Research Engineering

Xin Liu, Hongru Song, Ruqing Zhang, Jiafeng Guo, Xueqi Cheng

原文摘要

Large language model (LLM) agents are increasingly being used to automate machine learning (ML) research workflows, ranging from code development and experiment execution to research reproduction and scientific discovery. Recent systems demonstrate that LLMs can perform substantial portions of ML research engineering with limited human intervention. These advances suggest a shift from automating individual research tasks toward automating long-horizon workflows that transform research artifacts into executable ML systems. However, ML research engineering remains substantially more difficult than isolated code generation. A research artifact typically specifies a system only implicitly through a mixture of natural-language descriptions, equations, pseudo-code, implementation details, datasets, and evaluation protocols. An agent must first infer the intended computational structure, translate it into executable components, coordinate their dependencies, and repeatedly diagnose and repair failures during execution. Consequently, the quality of the final system depends not only on code generation, but also on whether the agent can maintain a consistent representation of what should be implemented throughout this long-horizon process.

cs.SE

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arXiv ↗PDF ↗