Emerging Technologies
ASTRA: Toward Agentic AI for Intelligent Device-Network-Cloud Synergy in Next-Generation Mobile Communication
Yalong Guo, Jinbo Tan, Ying Wang et al. (+3 more)
The
evolution toward next-generation mobile communication systems demands intelligence-native networks capable of autonomously adapting to user intent, yet the prevailing 3GPP protocol-driven device-network-cloud (DNC) architecture imposes three structural bottlenecks: protocol-constrained decision spaces confining optimization to predefined parameter subsets, cascaded information asymmetry from lossy interface compression that strips semantic context and causes intent miscalibration, and inherently reactive coordination mechanisms that trigger actions only after performance degradation. This paper proposes an autonomous agentic AI paradigm named Agentic Synergy for Telecommunication Resource Autonomy (ASTRA), which introduces a three-tier
agent layer, including device
agent, network
agent, and cloud
agent, decoupling network intelligence from the underlying hardware infrastructure. These
agents collaborate through bidirectional semantic channels, including semantic intent messages, capability abstraction messages, global directives, and peer coordination, executing a six-phase cycle of perceive, reason and predict, communicate, decide, act, and learn that transforms reactive protocol-driven operations into proactive, intent-calibrated optimization over the full decision space. Validated through system-level simulations in two representative scenarios, ASTRA achieves a 13.1% average throughput gain in dense-crowd
cell selection by redistributing UEs from congested
cells via semantic load exchange, and an 18.2% passive handover reduction in high-speed mobility through predictive trajectory-aware coordination, providing initial evidence that the proposed agentic framework accesses solution regions structurally inaccessible under protocol-constrained architectures.
Read the full abstract ▾
All authors Yalong Guo, Jinbo Tan, Ying Wang, Fan Zhang, Jintao Wang, Changyong Pan
Machine Learning
FairLMs: A Turnkey Library for Fairness in Language Models
Jiale Zhang, Michael Larionov, Zichong Wang et al. (+2 more)
Fairness research on
language models involves measuring bias, applying mitigation methods, and examining the evidence on which an evaluation rests. Existing tools offer complementary functionality through different interfaces, so combining them requires reconciling model interfaces, evidence formats, access constraints, and result types before applicability can be checked or methods compared. We introduce FairLMs, a Python library that connects these activities through explicit declarations of model capabilities and input requirements. It provides 33 intrinsic and extrinsic metrics, 14 mitigation components spanning four intervention categories, 14
dataset and scoring-instrument diagnostics, adapters for the three
Transformer architectures and supported hosted completion APIs, and
benchmark loaders. Declarations are checked before execution and results carry the configuration under which they were obtained, so that compatible components can be combined, methods compared under a common protocol, and workflows extended to new models and
datasets. The source code is available at: https://github.com/FairLMs/FairLMs.
Read the full abstract ▾
All authors Jiale Zhang, Michael Larionov, Zichong Wang, Zhipeng Yin, Wenbin Zhang
Numerical Analysis
Kernel-free Boundary Integral Methods for Allen-Cahn and Cahn-Hilliard Equations on Irregular Domains
Xinru Liu, Pensong Yin, Wenjun Ying et al. (+1 more)
A unified kernel-free boundary integral (KFBI) framework is proposed for the Allen-Cahn and Cahn Hilliard equations with homogeneous no-flux boundary conditions on two- and three-dimensional irregular domains. With a stabilized first-order implici-explicit (IMEX) discretization, the Allen-Cahn update reduces to a Neumann modified Helmholtz problem. An auxiliary-variable reformulation reduces the Cahn-Hilliard update to subproblems of the same type without evaluating the
Laplacian of the nonlinear source. The Cahn-Hilliard update requires two sequential solves for real shifts, while complex-conjugate shifts allow the real-valued solution to be reconstructed from a single complex solve. All subproblems are handled by the same KFBI solver that indirectly evaluates potentials through equivalent interface problems. To correct discrete mass defects in the Cahn-Hilliard update, a geometry-weighted discrete $L\v{s}$ mass projection is constructed tailored to the KFBI treatment of irregular boundaries. Exterior overlap weights are redistributed to physical nodes, excluding auxiliary KFBI extension values from the mass calculation. Numerical experiments demonstrate second-order spatial convergence, first order convergence of paired temporal-error indicators, and preservation of the prescribed discrete mass. The reported source-free simulations exhibit energy decay, with cumulative projection-induced energy perturbations remaining small relative to the observed dissipation.
Read the full abstract ▾
All authors Xinru Liu, Pensong Yin, Wenjun Ying, Yulin Zhang
Numerical Analysis · Cross-listed from math.AP
A variational model of nonlinear poroelasticity
James H. Adler, Xiaozhe Hu and Arkadz Kirshtein
We derive a thermodynamically-consistent model of fluid flow through a poroelastic medium. Starting from elastic and fluid free-energy densities, an energy-dissipation rate, and a kinematic constraint, the force-balance equations are derived using variational principles, with the pressure–density constitutive relation emerging as a direct consequence of the variational structure; the same kinematic constraint also supplies the total-flux transport structure. In the ideal-gas limit, the model linearization recovers the classical linear Biot equations. For power-law fluid energies, it yields isentropic pressure–density relations. A key advantage of the variational formulation is that extensions to richer physics, such as thermal effects, chemical reactions, or multi-component fluids, can be incorporated systematically by augmenting the energy and dissipation functionals without redesigning the force-balance or transport closure. We support the model with an energy-compatible two-field discretization and study consolidation under a surface load with three lateral-boundary treatments and three fluid-compressibility exponents.
Read the full abstract ▾
Artificial Intelligence
GameASG-Bench: Benchmarking Autonomous Software Generation for Game Development
Xiuhui Zhang, Yi Chen, Shusheng Xu et al. (+4 more)
Autonomous software generation (ASG) aims to turn human requirements into executable applications, but delivering these applications does not necessarily establish that their interacting components satisfy the specified behavioral requirements. We introduce GameASG-Bench, a
benchmark that makes behavioral testability part of the generation task for game development. Our design declares an evaluation interface specification before generation, fixing legal starting scenarios, player-level actions, stable snapshots, rejection behavior, and invariants while leaving private implementations open. Concretely, we include: (i) static L1 checks that assess source-level compliance; and (ii) browser-executed L2 checks that combine semantic observations with real input and runtime evidence. We implement this protocol as 47 browser-native game-generation tasks spanning 12 primary genres and both 2D and 3D interaction, each with executable checks and an independently verified reference implementation. Our experiments answer four key questions about end-to-end
agent performance, tool access and nominal turn budget,
reasoning effort, and harness choice. Across nine
agent stacks, the highest observed mean L2 check pass rate is 93.2%, yet the highest observed strict task success rate, requiring all L1 and applicable L2 prerequisite and core requirement checks, is only 55.3% (26/47 tasks). For DeepSeek-V4-Flash, full tool access and larger nominal turn budgets yield more strict task successes, while the strict task success rate is not monotonic in
reasoning effort. Both tested harnesses achieve 18 strict task successes, but only ten tasks succeed under both. These results expose task-level compliance gaps that high average check pass rates actually obscure.
Read the full abstract ▾
All authors Xiuhui Zhang, Yi Chen, Shusheng Xu, Fan Li, Huan Wang, Tongkai Yang, Binhang Yuan
Networking
Locating and Enumerating Anycast: a Comparison of Two Approaches
Remi Hendriks, Tim Betzer, Ben Du et al. (+3 more)
Anycast allows for providing services from multiple, geographically distant Points of Presence (PoPs), using a single IP address, to, e.g., improve resilience. Due to its opaqueness, it is often unknown which addresses are provisioned using anycast and, if so, where the PoPs are located. As anycast is widely used for critical Internet infrastructures (e.g., the DNS) efforts have been made to map anycast deployments. The current state-of-the-art mapping technique, iGreedy, relies on latency-based measurements, and is adversely affected by noise caused by, e.g., network processing delays. Previous work has shown that traceroute can alternatively be used to detect anycast. As traceroute reveals the hops a packet traverses, it may also be used to locate sites using geolocation data for hops near the anycast PoPs. This paper is the first to assess the performance of the traceroute-based approach at scale, by targeting 14k prefixes from an anycast census. Using ground truth we show traceroute achieves a slight increase in enumeration and geolocation precision over iGreedy. However, it suffers from overestimating the number of PoPs and incurs a 4 times increase in probing cost, making it unattractive for anycast censuses.
Read the full abstract ▾
All authors Remi Hendriks, Tim Betzer, Ben Du, Raffaele Sommese, Mattijs Jonker, Roland van Rijswijk-Deij
Machine Learning
Multi-Subject Pretraining Enables Short-Calibration Personalization for Closed-Corpus Surface EMG Speech Decoding
Chenqian Le, Beatrice Fumagalli, Yasamin Esmaeili et al. (+5 more)
Surface electromyography (sEMG)-based silent
speech interfaces are limited by cross-user variability and calibration burden. We study a limited-data setting in which each of 27
speech-typical participants contributed less than 0.5 h of data (21.3 min on average) across Aloud and Mimed
speech. Within a closed 50-sentence corpus, we used leave-one-subject-out evaluation, initializing from a released single-subject checkpoint, pretraining on non-held-out participants, and fine-tuning on the target participant. This pipeline achieved 21.7% character error rate (CER) and 31.9% word error rate (WER), compared with 49.3% CER without target-subject calibration and 68.0% CER for direct checkpoint fine-tuning. Multi-subject pretraining from random initialization followed by fine-tuning reached 44.9% CER and did not converge under the fixed schedule in 5 of 27 folds, indicating substantial optimization and accuracy benefits from checkpoint initialization. Macro-averaged CER declined from 74.4% with one pretraining participant to 21.7% with 26. Three minutes of target-subject calibration achieved 20.5% CER and 31.7% WER, with no statistically significant difference from the full approximately 13-min pool (21.7% CER and 31.9% WER). A subject-specific adapter provided no detectable benefit. Excluding the five evaluation sentences from all sEMG model-training data increased CER and WER to 78.6% and 99.9%. These results support short-calibration personalization in a standardized-montage, closed-corpus setting.
Read the full abstract ▾
All authors Chenqian Le, Beatrice Fumagalli, Yasamin Esmaeili, Xupeng Chen, Tianyu He, Nikasadat Emami, Adeen Flinker, Yao Wang
Human-Computer Interaction
GUIDE: Designer-in-the-loop Authoring of Conformant Generative User Interfaces
Hyewon Lee, Ziying Wang, Aiden Moy et al. (+2 more)
Generative User Interfaces (GenUIs) enable applications to generate interfaces on demand from user needs and context. Like conventional UIs, they must still reflect designers' intent and conform to requirements such as brand identity. Unlike conventional UIs, designers cannot directly specify or see every interface a GenUI may produce, making design intent harder to enforce. We introduce GUIDE (GenUI Development Environment), a system that lets designers continuously inspect and refine GenUI behavior as they create and edit interfaces. GUIDE uses designers' modifications and interactions to adapt GenUIs through
prompt optimization and a novel adaptive conformance scoring model. We validate GUIDE's scoring model and system. The scoring model matched or outperformed proprietary LLM baselines on held-out comparisons of real and synthetic application screens. In a study with 12 UI/UX practitioners, participants found GUIDE effective and usable and significantly preferred aligned outputs over a strong baseline using exemplars and a model-generated design.md.
Read the full abstract ▾
All authors Hyewon Lee, Ziying Wang, Aiden Moy, Saran Nagubandi, Jason Wu
Programming Languages
Authorization Revocation for Long-Running AI Agents: Root-Scoped Quiescence under Delegation and Asynchronous Execution
Genliang Zhu and Chu Wang
Long-running AI
agents outlive initiating processes through credentials, delegated tasks, queues, callbacks, reservations, and provider-side operations. Cancellation, process exit, and credential revocation neither close every pre-cut carrier nor distinguish independently authorized shared work. We define root-scoped authorization quiescence: for each manifested sink, a certificate accounts for every cut-relevant acceptance under the retired root-epoch atom that precedes its local fence and excludes protected acceptance under that atom after the fence, while permitting exact rebind to a current, independently sufficient support. The root-scoped quiescence protocol linearizes a root cut, fences old-root expansion and protected sinks, represents alternative and conjunctive authority as antichains of minimal sufficient root sets, and composes provider-frontier certificates into a cutset over registered old-root paths. Exact channel-
token accounting reconciles transfers; missing or conflicting evidence remains indeterminate. Under stated assumptions, we prove post-cut issuer non-expansion, support-sound projection, compositional soundness under exact channel conservation, independent-support preservation, merge-order independence, and crash/replay stability. A provider-free late-effect test suite matches 17/17 registered outcomes. Two cancellation-only and one cut-only execution accept the same class of already scheduled late effect; two cut-plus-fence executions, one restart, and one stale-process execution reject it. A separately implemented checker verifies 17/17 traces and rejects 44/44 consistently rehashed semantic regressions. The certificate establishes root-relative authorization quiescence within its bound manifest and configuration, not global idleness, rollback, or business completion.
Read the full abstract ▾
Numerical Analysis
A Low-rank ADI Algorithm for the Numerical Solution of Large Discrete-time Non-symmetric Algebraic Riccati Equations
Umair Zulfiqar
Discrete-time non-symmetric algebraic Riccati equations (DTNAREs) arise in game-theoretic computations of Nash
equilibria. Solving such equations at large scale is often computationally prohibitive. This paper develops a numerical approach for large-scale DTNAREs whose solutions are low rank. A low-rank alternating direction implicit (ADI) method is introduced that recursively constructs a low-rank stabilizing solution without explicitly solving any projected DTNARE. Through the pole-placement property of the low-rank ADI iteration, the method ensures that the implicitly solved projected DTNARE always admits a stabilizing solution. An automatic shift-generation strategy is also developed for the ADI iterations. Once an initial shift is provided, the
algorithm computes the low-rank solution without further user intervention. Numerical experiments on DTNAREs with dimensions between \(10^6\) and \(10^7\) demonstrate the accuracy and efficiency of the method. The results confirm that the proposed low-rank ADI
algorithm is an effective solver for large-scale DTNAREs that would otherwise be computationally prohibitive.
Read the full abstract ▾
Information Retrieval
Hybrid GPU-CPU Retrieval for Personalized Search at Ultra-Large Scale
Hao Fu, Jichao Sun, Baiting Zhu et al. (+15 more)
Embedding-based
retrieval on user-generated content at the trillion-document scale exposes a sharp conflict between two production demands: deep, expressive personalization for queries with rich user intent, and broad coverage of a massive inventory under fixed latency and resource budgets. We characterize this as the personalization-scale paradox: hosting the full serving inventory in GPU
memory is too resource intensive, while CPU compute cannot execute the same interaction-heavy model on the latency-critical path. We present a hybrid GPU-CPU co-serving system that resolves the paradox through orchestration rather than a new model class. A high-depth GPU pathway fuses
retrieval and interaction pre-ranking over a curated online pool on the order of a billion documents, while a high-breadth CPU pathway searches an independently selected online inventory roughly twenty times larger with lightweight personalized scoring. Either or both pathways can run per request; candidates are deduplicated before shared downstream ranking. The system is deployed in production. A full-system A/B test against the legacy CPU-only configuration improves model-scored relevance and substantive engagement, while separate pathway experiments show positive value at their own deployment scopes.
Retrieval logs show that the pathways contribute structurally distinct candidates, production serving measurements characterize their latency, and a matched capacity plan quantifies the economic rationale for assigning modeling depth to GPUs and inventory breadth to CPUs. Together, these results validate a practical, independently evolvable depth-breadth architecture for ultra-large-scale personalized search.
Read the full abstract ▾
All authors Hao Fu, Jichao Sun, Baiting Zhu, Qiaoling Liu, Yan Shi, Cheng Lu, Liu Liu, Yubo Wang, Xin Yao, Xiangyu Niu, Xu Dong, Wenhan Lyu, Chiyao Shen, Yinjie Huang, Minglei Chen, Shuai Ding, Li Fan, Xiao Kong
Machine Learning
MIRCID: Inferred Hub-miRNAs Drive Cross-Task Improvements in Drug Mechanistic Modeling
Xin Cao, Yigang Chen, Jiatong Xu et al. (+11 more)
Drug mechanism-of-action (MoA) modeling commonly relies on perturbational transcriptomes, but matched microRNA (miRNA) measurements are often unavailable. Inferred regulatory features offer a scalable way to reuse these data. Here, we present MIRCID, a framework comparing gene expression with inferred transcription factor (TF) activity and miRNA expression across pathway classification and similarity-based MoA
retrieval. HubmiRNet infers 414 pan-cancer hub miRNAs (HubmiRs) from 977 L1000 landmark genes, achieving a Pearson correlation coefficient of 87.72%; its 1,298-output variant also outperformed SiCmiR on the full-miRNA task (71.21% versus 67.30%). In the evaluated comparisons, miRNA augmentation provided more consistent gains than TF activity. Generic
embedding controls showed model-dependent utility, while complementarity analyses identified a distinct, partially linearly recoverable representation that retained gene-derived structure. Illustrative rescue cases linked improved classification to biologically plausible miRNA patterns in samples with weak transcriptional signatures. These findings support inferred HubmiRs as a biologically informed recoding of transcriptomic data for perturbational drug modeling, while leaving recovery of measured perturbational miRNA responses to further validation.
Read the full abstract ▾
All authors Xin Cao, Yigang Chen, Jiatong Xu, Ziyue Zhang, Xiang Cheng, Shenyu Wang, Yangyi Zhang, Xiaoxuan Cai, Shidong Cui, Zihao Zhu, Xiang Ji, Hsi-Yuan Huang, Yang-Chi-Dung Lin, Hsien-Da Huang
Data Structures and Algorithms
A Walk From Free Probability to Matrix Discrepancy III: Higher Rank Kadison-Singer and Spectrally Thin Trees
Tarun Kathuria
Let $A_1,\ldots,A_N$ be positive semidefinite
matrices of rank at most $r$, with $\sum_i A_i=I$ and $\norm{A_i}\le\varepsilon$. We prove that one sign can be assigned to each original
matrix with discrepancy $O(\sqrt{\varepsilon\log(2r)})$, independently of their dimension and number, which is known to be optimal upto constants. We give a separate existence proof and a deterministic
algorithm with
polynomial work in a real-arithmetic model with semidefinite-value and exact spectral primitives. Separate Lean formalizations verify the existence proof and the
algorithm in this arithmetic model. The proof extends the variational approach to discrepancy developed in the companion papers, motivated by operator-valued free interpolation, Lehner's formula, and spectral Tsallis regularization. A concave
matrix power interpolates between a trace source and a sandwich source. Its concavity controls the response of the optimizing density. The walk maintains independent source reserves and a second
matrix recording reserve expenditure. This
matrix certifies contraction of the unfinished input mass between epochs; within an epoch, preparation and a negative-
curvature step control the spectral potential while the coefficients advance toward signs. As applications, one spanning tree can be chosen simultaneously $O(\varepsilon\log(2s))$-spectrally thin for $s$ positive weightings of a common graph whose edge leverages are at most $\varepsilon$. The diagonal specialization gives discrepancy $O(\sqrt{L\log(2k)})$ for
matrices with row sums at most $L$ and at most $k$ nonzero entries per column, through a walk of fractional colorings.
Read the full abstract ▾
Computation and Language
How Many Humans Are 32 LLM Judges Worth?
Chao Li, Yingying Yu and Yunfeng Li
A panel's human-equivalent size is target-specific. Matching a fixed 32-judge panel to empirical human label distributions on three ChaosNLI tasks yields two distinct effective sizes: distributional-error matching gives $\nu_{\mathrm{MSE}}=2.304$, $3.750$, and $3.445$, whereas spectral matching gives $\nu_H=4.242$, $6.459$, and $6.499$, a gap of $1.72$–$1.89\times$; a binary-error diagnostic credits the same panels with only $1.971$–$2.227$ effective votes. Extrapolating the distributional-error curve at fixed squared mean residual, mean member variance, and normalized mean covariance gives asymptotes of $2.392$, $3.990$, and $3.655$, with 32 judges already reaching $94.0$–$96.3\%$. An exact spectral identity explains the gap: error depends on member energy and on the orientation of residual variation relative to averaging, information that the participation ratio (PR) discards. A realizable hard-label construction confirms that higher spectral diversity can coexist with worse distribution recovery even under equal member energies and nonnegative correlations, and the consensus direction retains $\gamma_{\mathrm{co}}=43.8\%$, $33.7\%$, and $35.9\%$ of centered residual variance. An external check on CC-1000, a 1,000-item Civil Comments subset with a different panel, gives $\nu_H=2.84$. For panel choice, we establish an existence result and one feasible path: exhaustive enumeration at $k\in\{5,7\}$ shows that panels beating the accuracy-top-$k$ baseline on both accuracy and $\nu_H$ always exist, and greedily swapping at most two members reaches $24.8$–$56.0\%$ higher $\nu_H$ at $0.10$–$1.10$ percentage points higher accuracy. Our
dataset and code are available at https://github.com/Chao1208/32judges-votes.
Read the full abstract ▾
Computer Vision
Beyond Exact Match: Task-Aware GRPO for Cross-Domain PCBA Visual Question Answering
Jia Li, Li Dai, Peng Jia et al. (+4 more)
In automated Printed
Circuit Board Assembly (PCBA) inspection, standards-guided decisions require systems to jointly reason over fine-grained visual cues, component semantics, and manufacturing knowledge. Although large
vision-
language models (VLMs) provide a promising foundation, their deployment is hindered by the domain shift between standards-derived samples and real-world production-line imagery, together with heterogeneous output spaces spanning choice-based and numerical counting tasks. To address these challenges, we propose a
multimodal reasoning framework for cross-domain PCBA visual question answering. The framework converts standards-derived, real-world, and auxiliary PCB-domain data into a unified instruction format and constructs verified
reasoning traces aligned with visual evidence, question semantics, candidate options, and ground-truth answers. We further introduce Task-Aware Group Relative Policy Optimization (GRPO), which moves beyond exact-match supervision by integrating multi-component semantic rewards for choice-based questions, distance-aware rewards for counting questions, and an auxiliary format reward for valid outputs. During inference, answer-option semantic consistency correction, self-consistency voting, and multi-model arbitration are combined to improve prediction robustness. The proposed system achieves an Overall Score of 83.24 on the official PCBA Standard-to-Real Grand Challenge leaderboard, demonstrating the effectiveness of task-aware reward design and robust inference for cross-domain PCBA visual question answering.
Read the full abstract ▾
All authors Jia Li, Li Dai, Peng Jia, Zhenzhen Hu, Chee Seng Chan, Bingkun Bao, Richang Hong
Robotics
LOInK: Learned Optimal Inverse Kinematics via Structured Neural Surrogate Models
Michael Somerfield, Damian Abood, Ruigang Wang et al. (+1 more)
We introduce Learned Optimal Inverse Kinematics (LOInK), a method to generate approximately optimal solutions to an inverse kinematics problem. When trained on data consisting of sampled configurations and associated task variables and a given cost function, LOInK learns a bi-Lipschitz invertible mapping from configuration space to a decoupled task/latent space, and moreover, the latent space is structured so as to place cost-minimizing solutions at the origin. This enables efficient sampling of cost-minimizing solutions via a network-inversion
algorithm based on operator splitting. We demonstrate the proposed approach on three problems: an illustrative three degree-of-freedom manipulator problem; a quadrupedal climbing
robot for which LOInK can generate near-optimal solutions on average 31 times faster and up to 100 times faster than a constrained optimization approach; and a simulated soft actuator as a purely data-driven example, in which LOInK can explicitly generate high-quality solutions, unlike existing generative approaches that require diverse sampling and evaluation of candidate solutions.
Read the full abstract ▾
All authors Michael Somerfield, Damian Abood, Ruigang Wang, Ian R. Manchester
Cryptography and Security
Transcript-Bound Combiners for Downgrade-Resilient Hybrid Post-Quantum Key Establishment: Definition, Proof, and Embedded-Device Cost
Bhanwar Gupta and Sanjeev Rana
Hybrid key establishment runs a post-
quantum key-encapsulation mechanism (KEM) alongside a classical Diffie-Hellman primitive, so that the session key stays secure while either component resists attack. This design is now standardized in the Transport Layer Security protocol, Secure Shell, and the Internet Key Exchange, with the standardized module-
lattice KEM (ML-KEM) as the post-
quantum component. A hybrid KEM secures the derived key, but not the integrity of the negotiation that selects which primitives are used. Full protocols authenticate that negotiation through a handshake transcript; a hybrid KEM deployed as a standalone drop-in primitive, or inside a minimal handshake without transcript authentication, inherits no such guarantee, and an active attacker can strip the post-
quantum option. We ask what the key schedule alone must contain to make downgrade resilience a local property of the combiner. We give a game-based definition at the combiner layer and prove a two-sided separation: a combiner that ignores the transcript is downgraded with certainty, whereas one that binds the session key and the confirmation tag to a hash of the transcript blocks every such attempt, up to a term negligible for a 256-bit transcript hash. We also give an explicit strongest-link security bound. Using a calibrated cost model composed from published Cortex-M4 measurements, transcript binding adds one hash per party - about 11.8% of handshake computation but only 1.5% of
radio-inclusive energy - and adds no messages or bytes on the wire. Every reported number is produced by a released harness that passes a 30-check validation gate.
Read the full abstract ▾
Systems and Control
Rate-cost Trade-offs in H-infinity Control with Initial State Uncertainty
Vikrant Malik, Victoria Kostina and Babak Hassibi
We consider the problem of H-infinity control in the presence of a digital communication channel between the observer and the controller and investigate the fundamental trade-off between the required communication data rate and H-infinity performance. The system follows linear dynamics and has no additive internal noise, so that the uncertainty is confined to the initial state. For the scalar system, using a deterministic time-zero covering argument, we establish a lower bound on the required data rate for a given H-infinity cost and demonstrate why logarithmic quantization (Elia, 2000) is a natural choice. We further develop an achievability scheme and show that, for the scalar system, its rate matches the converse asymptotically in the high-data-rate limit.
Read the full abstract ▾
Computer Vision
Edit-VAR: Taming Visual Autoregressive Model for Precise Video Editing
Chongbo Zhao, Jiangming Wang, Xilai Wang et al. (+5 more)
Text-guided
video editing modifies target content while preserving the appearance and temporal coherence of unedited regions. Training-based approaches provide strong control but demand substantial data and computation. Training-free methods fall into inversion-free and inversion-based paradigms. Inversion-free approaches avoid trajectory recovery, but their source-preserving guidance can limit editing strength and leave semantic changes incomplete. Inversion-based approaches recover a latent trajectory before regeneration, where approximation errors can accumulate and cause source-content drift and temporal inconsistency. We introduce Edit-VAR, the first training-free and inversion-free framework for text-guided
video editing with a pretrained visual autoregressive
video model. Edit-VAR directly encodes the source
video into multi-scale discrete
tokens and performs probability-guided conditional
token replacement for source preservation.
Attention-guided
token-wise and scale-aware modulation selectively relaxes source constraints over edit-relevant positions and generation stages. Scale-Decoupled Generation, implemented as late-scale constraint release, regenerates motion-consistent details and reduces texture fragmentation. Residual-guided
token pruning further exploits redundancy at the final two high-resolution scales to reduce inference cost. Extensive experiments and a blind user study demonstrate that Edit-VAR outperforms existing training-free
video editing methods overall in editing fidelity, source preservation, temporal coherence, and inference efficiency.
Read the full abstract ▾
All authors Chongbo Zhao, Jiangming Wang, Xilai Wang, Xinyu Wang, Jingyi Tang, Chunjie Hao, Pengjie Song, Yue Ma
Artificial Intelligence
Efficient Benchmarking in Production: A Study of an Evolving LLM Agent
Yining She and Lei Lin
Production LLM
agents are evaluated repeatedly as they evolve, but full
agent benchmarks are costly to rerun. We study efficient recurring evaluation for a production analytics
agent serving tens of thousands of monthly active users and report first-hand deployment experience. Using 574 historical runs of the production
benchmark, split chronologically into calibration and held-out periods, we compare random sampling, historical caching, fixed representative subsets, and IRT-based adaptive testing. The results show that multidimensional 2PL adaptive testing achieves the best overall score fidelity: executing 200 questions, 38.5% of a full run, yields 1.03 pp of MAE. We nevertheless deployed difficulty-stratified fixed subsets because of their operational simplicity, and show they transfer without recalibration to five other
agent families and remain stable across calibration windows as short as one day. Drawing on this deployment experience, we report practical recommendations for recurring production-
agent evaluation.
Read the full abstract ▾
Information Theory · Cross-listed from eess.SP
Fronthaul Compression for Uplink Cloud-RAN with Finite-Alphabet Inputs: A Reverse Mercury/Waterfilling Approach
Subin Shin, Jaehoon Lee, Seok-Hwan Park et al. (+1 more)
The cloud
radio access network (C-RAN) mitigates inter-
cell interference by jointly processing the observations of distributed remote units (RUs) at a centralized unit (CU), but limited fronthaul capacity forces each RU to compress its received signal. Under transform-compress-forward, an RU transforms its signal and quantizes the resulting coefficients, with bit allocation distributing a finite bit budget across them. Classical reverse waterfilling assumes Gaussian sources, yet practical finite-alphabet symbols carry mutual information that saturates at $\log_2 M$, leaving bit allocation for such inputs unresolved. We address this by formulating bit allocation as maximizing the finite-alphabet generalized mutual information (GMI) achieved after linear MMSE (LMMSE) detection at the CU. Via the I-MMSE relation, this yields a fixed-point update whose converged solution decomposes into a vessel height, a shared water level, and a finite-alphabet mercury level; we term it {reverse mercury/waterfilling} (RMWF). Numerical results show that RMWF sustains end-to-end rate under tight fronthaul budgets and remains robust under
antenna scaling, which is increasingly consequential as
antenna counts outpace fronthaul capacity in modern C-RAN.
Read the full abstract ▾
All authors Subin Shin, Jaehoon Lee, Seok-Hwan Park, Jeonghun Park
Hardware Architecture
Programming AMD XDNA NPUs with Open-source Compiler Tools: A FlashAttention Case Study
Erwei Wang, Ephrem Wu, Victor J. B. Jung et al. (+4 more)
Spatial NPUs such as AMD XDNA place compute tiles beside small local
memories and leave data movement between them to software. Mapping a multi-stage workload onto such a device is largely a question of where the intermediate tensors live. We report what we learned making those choices for FlashAttention with the open-source IRON and MLIR-AIR flows. We compare four reference designs on XDNA 1 and XDNA 2: one runs each operator separately, two stream between operators on chip, and one fuses all three
attention stages into a single kernel. The fused kernel holds the $\boldsymbol{QK}^{\mathsf T}$ scores in compute-tile local
memory and reduces partial results over the cascade interconnect, so the scores never return to shared MemTile
memory. On XDNA 2, it reaches 3.62 TFLOP/s over complete end-to-end execution, twice the IRON design, with 5.3 to 7.2 times the energy efficiency of the integrated GPU on the same chip at 2K
tokens and above. It covers twelve LLM configurations, from BERT to DeepSeek, up to 128K
tokens. Roofline analysis at each
memory level explains this result and shows when to stop. XDNA 1 has lower ridge points, so streaming on chip already reaches the compute-bound regime: the same fusion that doubles throughput on XDNA 2 is nearly wasted on XDNA 1. Comparing a mapping's operational intensity against each level's ridge point predicts which case applies before writing any code. Fuse until the mapping clears that ridge point, then stop. We release the reference designs as maintained open source.
Read the full abstract ▾
All authors Erwei Wang, Ephrem Wu, Victor J. B. Jung, Jiajie Li, Andre Rosti, Joseph Melber, Samuel Bayliss
Artificial Intelligence
PlaceReasoner-Beta: Reasoning-Driven Macro Placement and Benchmarking
Qiufeng Li, Chengxuan Wang, Rongqian Chen et al. (+6 more)
Automated macro placement remains a fundamental challenge in VLSI physical design. Despite decades of research, existing approaches predominantly optimize hand-crafted proxy objectives, such as estimated wirelength, and typically produce placements through one-shot numerical optimization, limiting their ability to incorporate visual layout context, codified design expertise, and downstream physical-design feedback in a unified loop. We present PlaceReasoner-Beta, a verifier-guided multi-
agent framework that reformulates macro placement as a closed-loop
reasoning problem rather than black-box optimization. A
vision-
language model (VLM) planner generates candidate placements from the floorplan image, macro specifications, and connectivity structure; a
geometric verifier enforces physical legality and expert placement principles; a physical verifier refines candidates using early implementation feedback; and a post-route optimizer further improves promising layouts using final PPA. To enable reproducible evaluation, we introduce PlaceReasoner-Bench, a fully open end-to-end
benchmark built from open RTL designs, EDA tools, and technology libraries. It comprises 8 designs at two aspect ratios, yielding 16 tasks with fixed floorplans and I/O assignments, so methods differ only in macro positions and orientations and are evaluated using routed PPA and DRC rather than pre-route proxies. Across the
benchmark, PlaceReasoner-Beta achieves the best timing among DRC-clean methods on all square tasks, reducing post-route TNS by 61.2% at 1:1 and 53.0% at 2:1 relative to the classical baseline field. It also shortens routed wirelength on most designs despite never explicitly optimizing it, demonstrating that
reasoning over spatial structure under physical-design feedback can improve end-to-end layout quality beyond proxy-objective optimization.
Read the full abstract ▾
All authors Qiufeng Li, Chengxuan Wang, Rongqian Chen, Quan Cheng, Yihui Ren, Chia-Tung Ho, David Z. Pan, Tian Lan, Weidong Cao
Systems and Control
Optimal Regret for Online Storage Control via Cumulative Policies
Kamiar Asgari and Michael J. Neely
We study online control of a scalar storage system with
adversarial nonnegative arrivals, known retention coefficient, and convex costs depending on both state and action. Each action must respect current resource availability and is chosen before the current arrival and cost function are revealed. For the existing simplex disturbance-action policy class, we give an exact reparameterization by cumulative allocation fractions and a decay-weighted projected subgradient update. The resulting regret bound is independent of policy
memory length. For fixed retention coefficient and cost constants, the controller achieves $O(\sqrt T)$ regret against the best fixed infinite-
memory policy in this class, using $O(\log T)$
memory and arithmetic operations per round and one cost-subgradient query. A storage-specific block construction gives a matching lower bound against every causal feasible controller, including randomized controllers. Writing $\tau=(1-\alpha)^{-1}$, the minimax expected regret is $\Theta(\sqrt T\min\{T,\tau\}^{3/2})$ for every finite-
memory simplex policy class and its infinite-
memory extension, when $\alpha\in[1/2,1)$, $T\ge4$, and the positive cost constants are fixed. This identifies the joint horizon and retention-time dependence for these policy
benchmarks.
Read the full abstract ▾
Artificial Intelligence
CogGym: Towards Large-Scale Comparative Evaluation of Human and Machine Cognition
Lance Ying, Jinzhou Wu, Yingshan Susan Wang et al. (+53 more)
Understanding and modeling human intelligence are parallel goals shared by artificial intelligence (AI) and cognitive science. As AI systems grow increasingly capable, in what ways do model responses resemble human responses, and where do they systematically diverge? The sheer breadth and diversity of the tasks humans can perform and think about pose a challenge for scalable and rigorous comparison between humans and models. We introduce CogGym, a scalable, unified framework grounded in cognitive science for systematically comparing model and human behavior on matched experimental trials. CogGym uses a semi-automated, human-in-the-loop pipeline to standardize diverse experimental paradigms into a task-agnostic Experiment Markup
Language (EML), enabling reproducible and faithful comparison at scale. For initial release, we curate and standardize 258 cognitive experiments from 100 papers that focuses on human commonsense
reasoning, and evaluate 50 large
language models against human responses. We find a clear scaling trend where larger and more recent AI models better reproduce human judgments. Yet AI models' improvement on such common
reasoning tasks is considerably slower than the gains observed on formal-
reasoning benchmarks like math and coding, and model–human fit remains well below human splithalf reliability ($R^2 = 0.93$ on text, $0.95$ on image, and $0.92$ on
video) with the best models achieving $R^2 = 0.59$ on text, $0.58$ on image, and $0.43$ on
video experiments. We intend for CogGym to provide a living evaluation framework that continually incorporates new cognitive science experiments to characterize where model behavior resembles human behavior, where it systematically diverges, and how those patterns change as models and experiments evolve.
Read the full abstract ▾
All authors Lance Ying, Jinzhou Wu, Yingshan Susan Wang, Shivam Aarya, Luca M. Schulze Buschoff, Harry Chen, Katherine M. Collins, Andrea de Varda, Shuhao Fu, Sean Dae Houlihan, Akshay K. Jagadish, Guangyuan Jiang, Samuel Kiegeland, Tetsu Kurumisawa, Rongzhi Liu, Ryan Liu, Ningshan Ma, Kathryn McGregor, Younes Strittmatter, Polina Tsvilodub, Jacob Hoover Vigly, Sarah Wu, Enjie Xu, Yiling Yun, Kelsey Allen, Tyler Brooke-Wilson, Brian Christian, Evelina Fedorenko, Michael C. Frank, Michael Franke, Tao Gao, Samuel J. Gershman, Robert D. Hawkins, Jennifer Hu, Julian Jara-Ettinger, Max Kleiman-Weiner, Sydney Levine, Tal Linzen, Hongjing Lu, Timothy O'Donnell, Desmond C. Ong, Steven T. Piantadosi, Rebecca Saxe, Eric Schulz, Tianmin Shu, Felix A. Sosa, Ilia Sucholutsky, Tan Zhi-Xuan, Tomer Ullman, Fei Xu, Ilker Yildirim, Jian-Qiao Zhu, Thomas L. Griffiths, Tobias Gerstenberg, Kevin Smith, Joshua B. Tenenbaum
Information Retrieval
Verify, Don't Trust: Agentic Model Development for Video Discovery Retrieval at Scale
Hao Fu, Baiting Zhu, Minglei Chen et al. (+2 more)
Large
language model (LLM)
agents can propose, implement, and evaluate model changes. Autoresearch loops demonstrate this capability through minutes-scale iterations on a self-contained program. Online autoresearch instead spans asynchronous systems, hours-long variants, and weeks-long campaigns that can influence a product. A completed run can still support an invalid conclusion when a code change is a no-op, data windows leak, evaluator semantics drift, or the two arms traverse different serving funnels. We present EvoPilot, a human-gated method for long-horizon online autoresearch. Role-specific
agents execute each round through a versioned domain skill and typed adapter. Durable records preserve experiments and failures; deterministic checks enforce recorded lessons. We study a 37-day campaign for the
retrieval system that powers
Video Deep Dive (VDD), an online experience for discovering follow-on
videos after a user opens a seed
video. The campaign covered seven directions and used an hourly refreshed index of hundreds of millions of
videos. Earlier manual experiments had not established a benefit from an interaction head. A primitive autoresearch attempt revisited the direction but incorrectly attributed an offline hit-rate decline of 22 percentage points to the head. We then introduced EvoPilot. Its human-gated verification traced the drop to a pre-existing evaluation defect that produced output depths of 3,000 and 600. After repair, a matched comparison measured an offline improvement of 3.20 percentage points. Post-study replay and mutation tests rejected invalid comparisons while admitting valid counterparts. Durable state recovered an interrupted round, and artifact reuse avoided approximately five GPU-hours. Separately, a seven-day randomized online evaluation estimated a 0.66% relative increase in the VDD slice of Good Search Result Rate for Retention (GSRR).
Read the full abstract ▾
All authors Hao Fu, Baiting Zhu, Minglei Chen, Yinjie Huang, Shuai Ding
Human-Computer Interaction
Two's a Crowd: Human and AI-Based Copresence for Developers with ADHD
Veronica Pimenova, Seth Bernstein, Shalini Madan et al. (+2 more)
Effective collaboration and communication are vital to developer productivity and well-being, yet remain constrained by human factors such as
attention, intrinsic motivation, and interpersonal accountability. These constraints are particularly vital for developers identifying with
Attention Deficit Hyperactivity Disorder (ADHD), who navigate persistent environmental barriers in modern hybrid workplace settings. While developers with ADHD frequently rely on collaborative copresence practices (such as body doubling or pair programming) to support executive function, the recent emergence of agentic AI coding assistants has begun reshaping these collaborative dynamics. To investigate how developers with ADHD engage in human and AI-based copresence practices, we conducted semi-structured interviews with 14 software engineers with ADHD. Our findings reveal that while traditional human-human copresence provides critical social support and onboarding structure, it forces developers to constantly manage professional reputation and sacrifice personal
privacy. Conversely, developers leverage emerging human-AI copresence to maintain accountability and cognitive flow without the social anxiety, performance judgment, or surveillance associated with human observation. Based on these empirical insights, we map developer copresence practices onto core dimensions of Goffman's copresence theory and Forsgren et al.'s SPACE framework of developer productivity, and provide design recommendations for AI-based tools that promote inclusive collaboration for developers with ADHD.
Read the full abstract ▾
All authors Veronica Pimenova, Seth Bernstein, Shalini Madan, Dhruv Jain, Venkatesh Potluri
Computer Vision
Geometry-Aware Diffusion Guidance via Curvature-Adaptive Tubular Correction
Enze Jiang, Jinwei He and Zheng Ma
Gradient-guided
diffusion samplers provide flexible priors for inverse problems and conditional generation, but strong guidance can move the sampling trajectory into regions where the learned score is poorly supported. Existing tangent-projection strategies limit first-order departure from an iso-density surface, yet discard potentially useful normal motion and overlook the second-order departure induced by tangent motion on a curved surface. We introduce
curvature-adaptive tubular correction (CAT), a training-free plugin that regulates both effects within a shared, noise-dependent
geometric budget. CAT decomposes the guidance gradient into normal and tangent components, charges normal displacement at first order and tangent displacement according to directional
curvature, and obtains their jointly optimal magnitudes from a one-dimensional dual equation. Armijo backtracking calibrates the resulting finite step against the actual guidance objective, while
matrix-free directional derivatives avoid constructing the full score Jacobian. We establish local guarantees for the tubular approximation, uniqueness of the correction, and sufficient objective decrease. Across seven inverse problems on FFHQ and ImageNet, CAT improves the evaluated pixel- and latent-space host samplers, with particularly consistent gains in perceptual metrics. It also improves black hole reconstruction on InverseBench and yields the lowest FID among the compared methods at every tested classifier-free guidance scale, while maintaining stable saturation and contrast. These results support
curvature-aware tubular control as a reusable mechanism for stabilizing
diffusion guidance.
Read the full abstract ▾
Computation and Language
When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces
Yuxiang Liu, Jiaming Luo, Eleftheria Briakou et al. (+1 more)
Large
Reasoning Models increasingly use intermediate traces for machine translation, but it remains unclear when such
reasoning helps or hurts. We analyze
reasoning traces across models,
languages, domains, and
datasets, focusing on
reasoning language, length, and structure. We find that the best
reasoning language is model-specific,
reasoning length has a non-monotonic relationship with quality, and traces exhibit recurring functional patterns. To uncover these patterns, we introduce Hierarchical Meta-Summarization (HMS), a scalable framework that induces coarse- and fine-grained
reasoning structures without predefined taxonomies. HMS reveals a shared organization–understanding/planning, translating/drafting, and refining/verifying–alongside domain-specific variation. Our results suggest that MT
reasoning should be controlled in a model-aware, length-aware, and pattern-aware manner rather than uniformly encouraged.
Read the full abstract ▾
All authors Yuxiang Liu, Jiaming Luo, Eleftheria Briakou, Colin Cherry
Robotics
VLA-Scope: Shift-Aware Failure Prediction for Vision-Language-Action Models
Kaiwen Zhu, Dongfang Liu and Liangkai Liu
Vision-
language-action (VLA) models map visual observations and natural-
language instructions to robotic actions, but distribution shifts can compromise their reliability. Because these models may still succeed under out-of-distribution (OOD) conditions, detecting OOD inputs alone is insufficient to predict execution failure. In this paper, we introduce VLA-Scope, a two-stage framework that combines input-shift characterization with execution history to predict failure during OOD rollouts. The first stage uses pooled image and
language representations to detect OOD inputs and classify their shift categories. For inputs flagged as OOD, the second stage combines the predicted category, action-prefix features, and execution progress features. A logistic regression model shared across shift categories updates failure risk as execution proceeds. We evaluate the framework with OpenVLA on ten LIBERO-Spatial tasks using leave-one-group-out cross-validation. OOD detection achieves a ROC-AUC of 0.9454, and shift classification achieves 91% accuracy. Evaluated independently of the OOD gate on all 1,400 OOD rollouts, the failure predictor achieves a ROC-AUC of 0.8497 after 60 executed actions, compared with 0.7906 without execution progress features. It also achieves a higher ROC-AUC than the evaluated ActProbe and SAFE-MLP baselines. These results suggest that combining action features with temporally aggregated execution step representations improves failure prediction under input shifts.
Read the full abstract ▾