中 EN
Sign In Register

Academic Intelligence · Curated Daily

Explore the Frontier of Global Academia

AcademicHub aggregates real-time literature from top journals and preprint platforms. Build your personal research radar and let large language models compile cross-disciplinary analysis briefings automatically.

Sign In Register
01.
arXiv (CS.CV) 2026-06-24

ABACUS: Adapting Unified Foundation Model for Bridging Image Count Understanding and Generation

Authors:
Anindya Mondal↗Sauradip Nag↗Anjan Dutta↗

ABACUS is a unified vision-language model that handles object counting, crowd counting, referring-expression counting, and count-faithful image generation without any benchmark-specific training required. Our model is built on existing 3B-parameter unified foundation model and is adapted for object localization tasks using three key innovations: density-aware adaptive zooming with objectness maps for spatial grounding; a boundary-aware count policy via GRPO to eliminate crop-boundary errors; and a cycle-consistent GRPO strategy where the understanding branch self-critiques generated outputs, closing the understanding-generation gap without any external annotations. ABACUS achieves state-of-the-art results across seven benchmarks, outperforming both task-specific specialists and larger generalist models.

Read & Discuss → View Source →
02.
arXiv (CS.AI) 2026-06-25

Learning Non-Vacuous Generalization Bounds from Optimization

Authors:
Chengli Tan↗Jiangshe Zhang↗Junmin Liu↗Yihong Gong↗

arXiv:2206.04359v3 Announce Type: replace-cross Abstract: One of the fundamental challenges in the deep learning community is to theoretically understand how well a deep neural network generalizes to unseen data. However, current approaches often yield generalization bounds that are either too loose to be informative of the true generalization error or only valid to the compressed nets. In this study, we present a simple yet non-vacuous generalization bound from the optimization perspective. We achieve this goal by leveraging that the hypothesis set accessed by stochastic gradient algorithms is essentially fractal-like and thus can derive a tighter bound over the algorithm-dependent Rademacher complexity. The main argument rests on modeling the discrete-time recursion process via a continuous-time stochastic differential equation driven by fractional Brownian motion. Numerical studies demonstrate that our approach is able to yield plausible generalization guarantees for modern neural networks such as ResNet and Vision Transformer, even when they are trained on a large-scale dataset (e.g. ImageNet-1K).

Read & Discuss → View Source →
03.
arXiv (quant-ph) 2026-06-19

Ricci flow for the Bures–Helstrom qubit metric

Authors:
Andrew Lesniewski↗

arXiv:2606.19493v1 Announce Type: cross Abstract: The Bures–Helstrom metric is the minimal monotone Riemannian metric on the state space of a qubit. With the quantum Fisher normalization used here, it identifies the Bloch ball with a geodesic hemisphere of the unit round three–sphere. We describe its Ricci flow explicitly. In a general rotationally symmetric gauge the flow is a coupled system for the radial lapse and warping factor; a single scalar equation appears only after a Hamilton–DeTurck gauge choice. In the corresponding moving DeTurck frame the squared warping function $\Psi=\Phi^2$ satisfies the linear forced heat equation \begin{equation*} D_t\Psi=\Psi_{ss}-2, \end{equation*} while the fixed-lapse coordinate form contains the associated transport term. Since the Bures–Helstrom metric is Einstein, the geometric flow itself is the homothetic shrinker \begin{equation*} g(t)=(1-4t)g_{\mathrm{BH}}, \end{equation*} with scalar curvature $6/(1-4t)$ and extinction time $T=1/4$. Thus the metric remains inside the monotone cone for all $t

Read & Discuss → View Source →
04.
arXiv (CS.CV) 2026-06-24

PointVG-R: Internalizing Geometric Reasoning in MLLMs for Precise Pointing Localization via Visual Chain of Thought

Authors:
Ling Li↗Bowen Liu↗Zinuo Zhan↗Jianhui Zhong↗Ziyu Zhu↗Bingcai Wei↗Kenglun Chang↗Zhidong Deng↗

Pointing-based visual grounding requires models to precisely locate target objects by deciphering complex spatial relationships between the visual scene and pointing gestures. Traditional methods typically encode input images into static feature representations and perform reasoning primarily within the linguistic domain, often overlooking the rich perceptual cues and explicit spatial geometry inherent in images. In this study, we aim to mitigate the cognitive vulnerability of models in interpreting gestural spatial relations by proposing PointVG-R, a reasoning-guided Multi-modal Large Language Model (MLLM). PointVG-R introduces geometric-aware reasoning for pointing-based grounding, enabling the model to think with images through the strategic integration of Reinforcement Learning (RL) and cold-start data. Specifically, we design a novel geometric reasoning pipeline that simulates the iterative cognitive process humans employ when interpreting pointing gestures. Furthermore, we construct EgoPoint-CoT, a high-quality visual Chain-of-Thought (CoT) dataset featuring detailed reasoning trajectories to guide the model via Supervised Fine-Tuning (SFT) and RL. To address the varying quality of learning signals encountered during training, we further propose an Adaptive Importance Weighting strategy based on Group Variance, which dynamically adjusts reward signals to optimize the learning process. Experimental results demonstrate that PointVG-R achieves SOTA performance, outperforming the baseline by $15.86$ points in mIoU. Extensive ablation studies further validate the efficacy of our proposed modules. Code: https://github.com/lingli1724/PointVG-R.

Read & Discuss → View Source →
05.
arXiv (math.PR) 2026-06-25

On the jump of the cover time in random geometric graphs

Authors:
Carlos Martinez-Arevalo↗Dieter Mitsche↗

arXiv:2501.02433v4 Announce Type: replace Abstract: In this paper we study the cover time of the simple random walk on the giant component of supercritical $d$-dimensional random geometric graphs on $\mathrm{Poi}(n)$ vertices. We show that the cover time undergoes a jump at the connectivity threshold radius $r_c$: with $r_g$ denoting the threshold for having a giant component, we show that if the radius $r$ satisfies $(1+\varepsilon)r_g \le r \le (1-\varepsilon)r_c$ for $\varepsilon > 0$ arbitrarily small, the cover time of the giant component is asymptotically almost surely $\Theta(n \log^2 n$). On the other hand, we show that for $r \ge (1+\varepsilon)r_c$, the cover time of the graph is asymptotically almost surely $\Theta(n \log n)$ (which was known for $d=2$ only for a radius larger by a constant factor). Our proofs also shed some light onto the behavior around $r_c$.

Read & Discuss → View Source →
06.
arXiv (CS.CL) 2026-06-15

Poker Arena: Multi-Axis Profiling of Strategic Reasoning and Memory in LLMs

Authors:
Pratham Singla↗Shivank Garg↗Vihan Singh↗

Strategic reasoning under uncertainty underpins consequential decisions in negotiation, finance, and policy, but prevailing game-play benchmarks collapse heterogeneous reasoning dimensions into a single scalar, leaving the capability structure of frontier LLMs unexamined. We introduce Poker Arena, a no-limit Texas Hold'em tournament platform that couples a three-layer memory architecture (within-hand, session, and cross-session) with a nine-axis cognitive profile decomposing strategic reasoning into interpretable dimensions such as bet-sizing calibration and positional awareness. We evaluate seven frontier models across 50 sessions of 1,000 hands and a controlled memory ablation; tournament chips and aggregate axis score order the field differently: Claude Opus 4.6 wins +$15,730 chips with 14 first-place finishes, yet ranks only fifth of seven on mean axis score, while persistent memory helps some models and hurts others. These findings show that multi-axis evaluation surfaces capability structure that scalar leaderboards systematically misrank, with cross-dimensional consistency outweighing peak performance on any single axis.

Read & Discuss → View Source →
07.
arXiv (CS.AI) 2026-06-25

Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing

Authors:
Liwei Yu↗Shuo Li↗Ming Zhou↗Ge Chu↗Yan Guo↗

arXiv:2606.25332v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown promise for automated penetration testing, yet existing end-to-end black-box evaluations are highly susceptible to error cascading: failures in early reconnaissance can mask an agent's actual ability to exploit vulnerabilities. To more accurately characterize these capabilities, we propose a two-stage decoupled evaluation framework that separates exploit execution from reconnaissance. Using ground-truth injection and knowledge-driven ablation across 70 high-fidelity web vulnerability testbeds, our framework isolates exploitation performance from reconnaissance noise. We empirically evaluate five open-source penetration-testing agents, covering multiagent, monolithic, and graph-driven architectures, on a strictly aligned subset of 50 representative vulnerabilities. The results reveal a substantial capability gap. With accurate vulnerability context, agents achieve a functional success rate of up to 90.0%, whereas autonomous reconnaissance, measured by targeted vulnerability recall, plateaus at approximately 50.0%, primarily due to failures in parsing unstructured telemetry. Cross-architectural analysis further reveals distinct capability niches: multi-agent isolation is more effective for long-sequence interactions such as de-serialization, while monolithic and graph-driven designs perform better on short-chain injections and cross-session access-control vulnerabilities, respectively. This decoupled evaluation work provides a fine-grained benchmarking protocol and an empirical basis for designing next-generation automated offensive security agents.

Read & Discuss → View Source →
08.
bioRxiv (Bioinfo) 2026-06-18

Looking beyond stereotyped neuron structures reveals links between beading and morphological rearrangements in aging phenotypes.

Authors:
Gomez↗Nguyen↗Lagergren↗Flores↗San Miguel↗

Understanding how neuronal morphology changes during aging and acute stress is essential for elucidating mechanisms of neurodegeneration. The highly branched PVD neuron of Caenorhabditis elegans provides a powerful model for studying dendritic remodeling and degeneration-associated phenotypes such as dendritic beading. However, the complexity of this arbor presents substantial challenges for automated segmentation and quantitative analysis. In this study, we adapted a convolutional neural network (CNN)-guided region growing framework for automated dendrite tracing, coupled with two topology-based algorithms for categorizing dendritic segments by branching degree. The segmentation algorithm achieved high accuracy relative to manual tracing, with a median Dice coefficient of 0.82, while reducing analysis time by approximately tenfold. Automated dendrite categorization demonstrated strong agreement with manual annotations across branching orders, though position-based mapping performance declined with age due to progressive morphological distortion. Leveraging this platform, we investigated mechanistic differences in dendritic beading patterns observed during aging and cold shock. Consistent with prior work, aging was associated with decreased inter-bead spacing, whereas cold shock produced increased bead dispersion with stress severity. Structural analysis revealed that these trends were not driven by dendritic pruning or reduced arbor complexity. Instead, while a traditional anatomically unflexible paradigm falsely implicated lower-degree dendrites as highly vulnerable, our branching-informed framework revealed that age-dependent beading is fundamentally dictated by a segments history of successive branching events. Conversely, acute cold shock triggered systemic beading that expanded across all dendritic orders in a severity-dependent manner. Together, these findings demonstrate that chronic aging and acute stress engage distinct degenerative pathways (compartment-specific lineage vulnerability versus global architectural collapse) rather than gross morphological loss, as well as highlighting the need for paradigms that enable reliable analysis of changing morphologies.

Read & Discuss → View Source →
09.
arXiv (CS.CV) 2026-06-16

IGLU: The Integrated Gaussian Linear Unit Activation Function

Authors:
Mingi Kang↗Zai Yang↗Jeova Farias Sales Rocha Neto↗

Activation functions are fundamental to deep neural networks, governing gradient flow, optimization stability, and representational capacity. Within historic deep architectures, while ReLU has been the dominant choice for the activation function, modern transformer-based models increasingly are adopting smoother alternatives such as GELU and other self-gated alternatives. Despite their empirical success, the mathematical relationships among these functions and the principles underlying their effectiveness remains only partially understood. We introduce IGLU, a parametric activation function derived as a scale mixture of GELU gates under a half-normal mixing distribution. This derivation yields a closed-form expression whose gating component is exactly the Cauchy CDF, providing a principled one-parameter family that continuously interpolates between identity-like and ReLU-like behavior via a single sharpness parameter $\sigma$. Unlike GELU's Gaussian gate, IGLU's heavy-tailed Cauchy gate decays polynomially in the negative tail, guaranteeing non-zero gradients for all finite inputs and offering greater robustness to vanishing gradients. We further introduce IGLU-Approx, a computationally efficient rational approximation of IGLU expressed entirely in terms of ReLU operations that eliminates transcendental function evaluation. Through evaluations on CIFAR-10, CIFAR-100, and WikiText-103 across ResNet-20, ViT-Tiny, and GPT-2 Small, IGLU achieves competitive or superior performance on both vision and language datasets against ReLU and GELU baselines, with IGLU-Approx recovering this performance at substantially reduced computational cost. In particular, we show that employing a heavy-tailed gate leads to considerable performance gains in heavily imbalanced classification datasets.

Read & Discuss → View Source →
10.
arXiv (CS.AI) 2026-06-16

An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response

Authors:
Joseph Walusimbi↗Joshua Benjamin Ssentongo↗

arXiv:2606.08270v2 Announce Type: replace-cross Abstract: University Academic Management Information Systems (ACMIS) are high-value targets for a wide spectrum of security threats including brute-force login attacks, payment fraud, privilege escalation, insider data theft, and academic integrity violations. Traditional rule-based intrusion detection systems are inadequate because many malicious activities are structurally indistinguishable from normal operations. This paper presents an AI-based security agent for ACMIS that combines supervised anomaly detection, behavioural analytics, and a natural language processing chatbot for secure password recovery. The agent monitors five operational layers: authentication, authorisation, financial transactions, user behaviour, and system health, and responds through a four-tier risk escalation framework. A modular architecture allows the core engine to be extended to other institutional systems. Experiments on a simulated ACMIS event log dataset of 147,922 sessions demonstrate a threat detection macro-average F1 of 0.966, compared to 0.156 for a rule-based baseline and 0.836 for a sequence-only (LSTM) baseline, with end-to-end critical-tier automated response latency under 1 ms on a single-node prototype. The integrated recovery chatbot achieves 97.1 percent identity verification accuracy and an 87.3 percent mass-reset attack detection rate with zero false positives on legitimate high volume recovery periods.

Read & Discuss → View Source →
11.
arXiv (CS.CL) 2026-06-24

Self-Recognition Finetuning can Prevent and Reverse Emergent Misalignment

Authors:
Arush Tagade↗Shaoheng Zhou↗Jiaxin Wen↗Shi Feng↗

Emergent misalignment (EM) has been linked to the activation of misaligned persona vectors and evil character traits, suggesting that EM operates through disruption of the model's aligned character rather than direct learning of harmful content. Motivated by this connection, we study self-generated text recognition (SGTR) finetuning as a character-targeted intervention that is distinct from existing in-training defenses. We conduct two-stage finetuning experiments across three models (GPT-4.1, Qwen2.5-32B-Instruct, Seed-OSS-36B-Instruct) and multiple EM datasets to compare SGTR finetuning against benign finetuning baselines (correct domain-specific data, general knowledge, and word counting) to find it an effective defense in both reversal and prevention settings. We find that all interventions produce comparable EM reversal, but only when restoring capabilities that EM had degraded. For prevention, only SGTR finetuning consistently reduces misalignment without exacerbating any individual metric, suggesting that character fortification specifically drives prevention. We provide further evidence for EM's relation to the LLM's default character by showing that EM finetuning induces diversity into the LLM's identity self-reports, artificially corrupting self-recognition exacerbates misalignment caused by EM finetuning, and that removing the model's identity-bearing system prompt substantially reduces the effect of EM finetuning. Together, these findings reframe EM not as the adoption of a coherent misaligned persona but as the destabilization of aligned character.

Read & Discuss → View Source →
12.
arXiv (CS.LG) 2026-06-11

APEX: A Network-Native Time-Series Foundation Model for Forecasting and Anomaly Detection for Wireless Edge Operations

Authors:
Swadhin Pradhan↗Niloo Bahadori↗Peiman Amini↗

arXiv:2606.11553v1 Announce Type: new Abstract: Generic time-series foundation models transfer poorly to wireless network telemetry whose signals are bursty, zero-inflated, and coupled across protocol layers. We present APEX, a network-native, decoder-only transformer for forecasting enterprise AP telemetry, and evaluate it on DHCP degradation as a representative network task. APEX is pre-trained on 10-channel multivariate telemetry from ~4,500 production wireless networks (~100K AP time series, 34 metrics per AP), and is available as APEX-Large (269M, cloud) and APEX-Edge (10.5M, edge). On a 192-step (4-day) DHCP degradation benchmark, APEX-Large reduces MAE by 18% over the strongest foundation-model baseline (Toto) and 38% over SARIMA, with anomaly-detection F1 = 0.93, while APEX-Edge enables sub-second, privacy-preserving inference on AP-class edge hardware. These results suggest network-native pre-training is a practical foundation for proactive wireless operations.

Read & Discuss → View Source →
13.
arXiv (CS.LG) 2026-06-17

Expanding SPHERE-JEPA: A Family of Statistical Regularizers for the Hypersphere

Authors:
L\'eo Nicollier↗Enric Meinhardt-Llopis↗Max Dunitz↗Marc Pic↗Pablo Mus\'e↗Gabriele Facciolo↗

arXiv:2606.17603v1 Announce Type: new Abstract: In Self-Supervised Learning (SSL), preventing representation collapse by explicitly enforcing a uniform distribution on the unit hypersphere has proven to be effective. However, current frameworks typically rely on sliced statistical regularizers such as SIGReg (used in LeJEPA) and SUSReg (used in SPHERE-JEPA), which approximate this continuous objective via Monte Carlo sampling along random 1D directions. This stochasticity injects projection variance into the training gradients, destabilizing optimization, and hindering convergence. In this work, we first show that analytically integrating out these random projections natively yields a deterministic Maximum Mean Discrepancy (MMD), bypassing the variance of sliced methods. Motivated by this equivalence, we formulate full-dimensional objectives for MMD, Kernel Stein Discrepancy (KSD), and Kullback-Leibler (KL) divergence directly on the sphere to enforce a uniform distribution. To prevent spatial bias, we equip these tests with rotationally invariant kernels constructed via spectral theory, systematically evaluating two canonical families: smooth exponential decay (Heat) and strict frequency cutoff (Bandlimited) filters. Empirically, removing projection-induced noise results in more stable optimization, faster convergence, and consistent improvements over stochastic sliced regularizers on ImageNet and Galaxy10. Furthermore, we reveal that the choice of the statistical test shapes the geometry of the learned latent space: MMD and KSD favor locally clustered organization suitable for object-centric domains, whereas the continuous KDE-based KL divergence promotes fine-grained instance separation, yielding the strongest results on unclustered procedural texture retrieval.

Read & Discuss → View Source →
14.
arXiv (CS.AI) 2026-06-17

Optimism Stabilizes Thompson Sampling for Adaptive Inference

Authors:
Shunxing Yan↗Han Zhong↗

arXiv:2602.06014v2 Announce Type: replace-cross Abstract: Thompson sampling (TS) is widely used for stochastic multi-armed bandits, yet its inferential properties under adaptive data collection are subtle. Classical asymptotic theory for sample means can fail because arm-specific sample sizes are random and coupled with the rewards through the action-selection rule. We study adaptive inference for Thompson sampling with Gaussian randomized indices in $K$-armed stochastic bandits with independent sub-Gaussian reward noises, and identify optimism as a key mechanism for restoring stability, meaning that each arm's pull count concentrates around a deterministic scale. This stability yields asymptotically valid Wald inference despite adaptive sampling. First, we prove that variance-inflated TS is stable for any $K \ge 2$, including the challenging regime where multiple arms are optimal, with asymptotically uniform allocation over optimal arms and sharp logarithmic pull-count asymptotics for suboptimal arms. This resolves the $K$-armed extension question raised by \citet{halder2025stable}, using new winner-map and Lyapunov-drift techniques to control allocation among multiple optimal arms. Second, we analyze an alternative optimistic modification that keeps the Gaussian index variance unchanged but adds an explicit mean bonus to the index center, and establish a similar stability conclusion. In summary, suitably implemented optimism stabilizes Thompson sampling and enables asymptotically valid Wald inference in multi-armed bandits, while incurring only a mild additional regret cost.

Read & Discuss → View Source →
15.
arXiv (CS.AI) 2026-06-16

Distilling Drifting Transformers with Representation Autoencoders

Authors:
Jiawei Zhang↗Mengfei Xia↗Gen Li↗Yuantao Gu↗

arXiv:2606.15553v1 Announce Type: cross Abstract: Representation Autoencoders (RAEs) have improved diffusion and flow models by semantically richer latent space owing to the strongly label-wise clustered DINO features in the pretrained encoders. Yet in the distillation stage, the severe anisotropy and large curvatures caused by the rich semantic representations would hinder the convergence and performance, making the trajectory-based distillation unstable. In this work, we argue that the RAE latent space is compatible with distillation via the newly proposed Drifting Models. We first quantitatively study the curvatures and isotropy statistics across different autoencoders, and theoretically reveal that Drifting Model itself is highly likely to fail on extremely scattered spaces like reconstruction-based VAEs. These motivate us to apply the drifting paradigm directly to representation autoencoders. Our proposed method, Drift-RAE, distills pretrained flow models in RAE latent spaces using Drifting, together with insightful modifications that improve training stability by thereotically aligning drifting fields with other frameworks. Regarding the experimental evidences, we achieve 1.77 FID on ImageNet 256 dataset using only 10k distillation steps, surpassing state-of-the-art RAE distillation methods and appearing comparative with the original Drifting Model without requiring an auxiliary MAE feature extractor. The code will be made publicly available.

Read & Discuss → View Source →
16.
arXiv (CS.CL) 2026-06-17

ART: Attention Run-time Termination for Efficient Large Language Model Decoding

Authors:
Chen Qiu↗Guozhong Li↗Cristian McGee↗Aritra Dutta↗Panos Kalnis↗

Long-context decoding in Large Language Models (LLMs) is constrained by the cost of accessing and processing the Key-Value (KV) cache. Despite evidence that attention outputs depend jointly on keys and values, most existing KV management methods rely on key-only pruning, since incorporating values incurs prohibitive overhead. In this paper, we propose Attention Run-time Termination (ART), a lightweight run-time mechanism that tracks accumulated attention outputs during kernel execution and terminates subsequent KV block accesses once further contributions become negligible. Rather than replacing KV selection, ART dynamically terminates redundant KV traversal on top of existing dense or sparse attention policies. We introduce a stability-based criterion that monitors both magnitude and directional changes of intermediate attention outputs and provideds a theoretical characterization of the resulting truncation error. Experiments on the LongBench and RULER Needle-in-a-Haystack tasks show that ART increases the generation throughput of existing KV-cache methods by up to 20%, without compromising the result quality.

Read & Discuss → View Source →
17.
arXiv (CS.AI) 2026-06-16

Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks

Authors:
Yudou Tian↗Neeraj Mohan Sushma↗Harshvardhan Mestha↗Nicolo Colombo↗David Kappel↗Anand Subramoney↗

arXiv:2410.11687v3 Announce Type: replace-cross Abstract: Linear recurrent networks (LRNNs) offer linear-time sequence modeling, but standard recurrent updates do not directly expose the supervised products needed for in-context gradient descent. We propose a sufficient constructive inductive bias for LRNNs: equip a diagonal recurrent state with multiplicative readout and a short sliding-window cross-product self-attention update. The resulting architecture, Gradient-based Recurrent In-context Learner (GRIL), can implement minibatch gradient descent on a task-specific linear predictor during a single forward pass. The same design extends to multi-step updates and cross-entropy classification, with a limited MLP-based extension to non-linear regression. Empirically, trained GRILs recover the behavior and parameters predicted by the construction on synthetic ICL tasks, and the same architectural bias yields useful performance on Long Range Arena and language modelling. These results present windowed cross-product self-attention as a practical, testable inductive bias for LRNNs that learn in context through gradient-descent-like updates.

Read & Discuss → View Source →
18.
arXiv (CS.CV) 2026-06-15

FEMOT: Multi-Object Tracking using Frame and Event Cameras

Authors:
Shiao Wang↗Xiao Wang↗Chao Wang↗Yitao Li↗Menghao Liu↗Bo Jiang↗Yaowei Wang↗Yonghong Tian↗Jin Tang↗

Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information. However, their performance is often degraded under complex real-world challenges, such as motion blur, low illumination, and overexposure. Bio-inspired event cameras offer high temporal resolution and high dynamic range, providing complementary cues under extreme scenarios. Nevertheless, RGB-event multi-object tracking remains underexplored due to the lack of large-scale and well-annotated datasets. To address this issue, we propose FEMOT, a large-scale RGB-event multi-object tracking dataset that covers diverse real-world scenarios and 14 challenging attributes. With both RGB and event data as well as high-quality annotations, FEMOT provides a reliable platform for systematically evaluating RGB-event multi-object tracking methods. Based on FEMOT, we retrain and evaluate over ten strong trackers, thereby establishing a comprehensive benchmark for future research. Furthermore, we propose FEMOTR, a multimodal tracking framework that decouples RGB and event features and fuses them in the frequency domain, thereby effectively exploiting their complementary characteristics for robust object localization and identity association. Extensive experiments on FEMOT and DSEC-MOT datasets demonstrate the effectiveness of the proposed method. The source code and benchmark dataset have been released on https://github.com/Event-AHU/FEMOT.

Read & Discuss → View Source →
19.
medRxiv (Medicine) 2026-06-18

Cost-effectiveness of a virtual fracture clinic versus traditional in-person fracture clinic care for adults with acute simple fractures: a protocol for a health economic evaluation within the RECITAL trial

Authors:
Charteris↗Traeger↗A. C↗Maher↗C. G↗Copp↗Pickles↗Teng↗M. J↗Khoudair↗Warnock↗Shaw↗…

ABSTRACT Introduction Traditional in-person fracture clinics are often overcrowded and inconvenient for patients. Virtual fracture clinics aim to address some of these concerns by improving the efficiency of the orthopaedic service and reducing unnecessary interventions while maintaining safety and quality of care. The RECITAL trial is a non-inferiority randomised controlled trial comparing follow-up care provided at a virtual fracture clinic for people with acute simple fractures to follow-up care provided at an in-person fracture clinic. This study describes the protocol for an economic evaluation of RECITAL where the primary aim is to investigate the cost-effectiveness of a virtual fracture clinic compared with traditional in-person fracture clinic care from a health system perspective. Methods and analysis The RECITAL trial recruited 312 participants with acute simple fractures and randomised them to receive follow-up care provided at a virtual fracture clinic or follow-up care provided at an in-person fracture clinic. We will conduct a within-trial analysis from a health system perspective (primary analysis), as well as a health service, patient and societal perspective. The economic evaluation will estimate the difference in the cost of resource inputs on an intention to treat basis used by participants in the two arms of the trial, allowing comparisons to be made between the in-person and virtual fracture clinics. Data for intervention costs and healthcare utilisation will be collected from trial records, hospital electronic medical records and district performance units. The results of the economic evaluation will be expressed in terms of incremental cost per utility weight gained at 12 weeks and will be plotted on a cost-effectiveness plane. Bootstrapping by resampling will be used to estimate 95% confidence intervals around costs and outcomes, and to calculate the confidence intervals around the incremental cost-effectiveness ratio. A cost-effectiveness acceptability curve (CEAC) will be plotted, which will provide information about the probability that an intervention is cost-effective, given the level of a decision makers willingness to pay for each additional outcome. Ethics and Dissemination The trail was approved by the SLHD Ethics Review Committee (RPAH Zone) (X23-0200 and 2023/ETH01038). The findings will be disseminated through a peer-reviewed journal and conference presentations. Trial registration number The trial was prospectively registered on the Australian New Zealand Clinical Trials Registry (ANZCTR; 12623000934640)

Read & Discuss → View Source →
20.
arXiv (CS.LG) 2026-06-16

Sobolev Approximation by Fixed-Size Neural Networks with Arbitrary Accuracy

Authors:
Baicheng Li↗Haizhao Yang↗Shijun Zhang↗

arXiv:2606.16975v1 Announce Type: cross Abstract: In this work, we investigate new activation functions for achieving arbitrary-accuracy Sobolev approximation by fixed-size neural networks. We first show that any function in $W^{2,\infty}((a,b)^d)$ can be approximated with arbitrary accuracy, measured in the $W^{1,\infty}$-norm, by a fixed-size neural network using the Elementary Universal Activation Function ($\mathrm{EUAF}$). To extend this result to $W^{s,\infty}((a,b)^d)$ for $s\in\mathbb{N}$, we introduce a smooth activation $\mathrm{DUAF}_{\infty}$ from the family of Differentiable Universal Activation Functions ($\mathrm{DUAF}_n$). We prove that any function in $W^{s,\infty}((a,b)^d)$ can be approximated with arbitrary accuracy in the $W^{s-1,\infty}$-norm by a fixed-size $\mathrm{DUAF}_{\infty}$-activated network. We further construct sigmoidal variants $\widetilde{\mathrm{DUAF}}_n$ and show that, for every $1\leq s\leq n$, fixed-size $\widetilde{\mathrm{DUAF}}_n$-activated networks still approximate any $f\in W^{s,\infty}((a,b)^d)$ with arbitrary accuracy in the $W^{s-1,\infty}$-norm. In all these results, the width and depth bounds are computed explicitly, and the proposed activations are elementary.

Read & Discuss → View Source →
21.
PLOS Computational Biology 2026-06-09

Evolution of phenocopying in a dynamical model of developmental trajectories

Authors:
Yuuki Matsushita↗

by Yuuki Matsushita, Archishman Raju Developmental trajectories are known to be canalized, or robust to both environmental and genetic perturbations. However, even when these trajectories are decanalized by an environmental perturbation outside the range of conditions to which they are robust, they often produce phenotypes similar to known mutants, called phenocopies. This correspondence between the effects of environmental and genetic perturbations has received little theoretical attention. Here, we study an abstract regulatory model that is evolved to follow a specific trajectory. We then study the effects of small and large perturbations to the trajectory, both by changing parameters and by perturbing the state at specific times. We find that the phenomenon of phenocopying emerges in evolved trajectories and is not present in a null model of randomly sampled trajectories. Our results suggest that, in this class of dynamic models, evolution can allow high-dimensional phenotypic landscapes to simultaneously exhibit robustness and phenocopying.

Read & Discuss → View Source →
22.
arXiv (CS.LG) 2026-06-24

KLip-PPO: A per-sample KL perspective on PPO-Clip

Authors:
Riccardo Colletti↗Robin Holzinger↗

arXiv:2606.23932v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) is the standard policy-gradient algorithm for on-policy reinforcement learning. The literature presents it in two forms, a clipped surrogate that bounds the importance ratio between successive policies and a Kullback-Leibler penalty between them. These forms are treated as separate algorithms with their own gradients, their own hyperparameters, and their own reference implementations, and a sizeable body of empirical work compares them. We show that the gradient of the clipped surrogate is reproduced exactly by a Kullback-Leibler surrogate whose coefficient varies per sample, with closed-form dependence on the importance ratio and the advantage. The identity holds at every minibatch step and across the entire inner loop, and on five MuJoCo continuous-control benchmarks the two losses produce indistinguishable training curves. The reformulation exposes a structural feature of the clipped surrogate that the min notation hides. PPO-Clip's implicit per-sample penalty is a step function at the boundary of the trust region, and the shape of this coefficient is the natural design axis for generalising the algorithm. We sketch the resulting follow-up directions in the discussion.

Read & Discuss → View Source →
23.
Nature (Science) 2026-06-18

Daily briefing: The brain builds a sentence neuron by neuron

Authors:
Jacob Smith↗

Researchers have tracked the electrical activity of individual brain cells during conversation in real time. Plus, the history of GPS and a cross-species transplant that could reveal clues about the origin of animals. Researchers have tracked the electrical activity of individual brain cells during conversation in real time. Plus, the history of GPS and a cross-species transplant that could reveal clues about the origin of animals.

Read & Discuss → View Source →
24.
arXiv (CS.AI) 2026-06-16

Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning

Authors:
Kuo-Chung Peng↗Samuel Yen-Chi Chen↗Jiun-Cheng Jiang↗Chen-Yu Liu↗En-Jui Kuo↗Yun-Yuan Wang↗Prayag Tiwari↗Andrea Ceschini↗Chi-Sheng Chen↗Yu-Chao Hsu↗Chun-Hua Lin↗Tai-Yue Li↗…

arXiv:2605.06734v2 Announce Type: replace-cross Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with variational quantum circuits (VQCs), but existing implementations rely on multi-qubit architectures that are difficult to scale on noisy intermediate-scale quantum (NISQ) devices and expensive to simulate classically. We propose gated QKAN-FWP, a fast-weight framework that integrates FWP with Quantum-inspired Kolmogorov-Arnold Network (QKAN) using single-qubit data re-uploading circuits as learnable nonlinear activation, known as DatA Re-Uploading ActivatioN (DARUAN). We further introduce a scalar-gated fast-weight update rule that stabilizes parameter evolution, supported by a theoretical analysis of its adaptive memory kernel, geometric boundedness, and parallelizable gradient paths. We evaluate the framework across time-series benchmarks, MiniGrid reinforcement learning, and highlight real-world solar cycle forecasting as our main practical result. In the long-horizon setting with 528-month input window and 132-month forecast horizon, our 12.5k-parameter model achieves lower scaled Mean Square Error (MSE), peak amplitude error, and peak timing error than a suite of classical recurrent baselines with up to 13x more parameters, including Long Short-Term Memory (LSTM) networks (25.9k-89.1k parameters), WaveNet-LSTM (167k), Vanilla recurrent neural network (11.5k), and a Modified Echo State Network (132k). To validate NISQ compatibility, we further deploy the trained fast programmer on IonQ and IBM Quantum processors, recovering forecasting accuracy within 0.1% relative MSE of the noiseless simulator at 1024 shots. These results position gated QKAN-FWP as a scalable, parameter-efficient, and NISQ-compatible approach to quantum-inspired sequence modeling.

Read & Discuss → View Source →
25.
arXiv (quant-ph) 2026-06-24

Quantum Correlations of Neutrinos in the Kerr-Newman Space-time

Authors:
Ze-Wen Li↗Shu-Jun Rong↗

arXiv:2605.10424v2 Announce Type: replace-cross Abstract: Quantum phases provide a connection between gravitation and quantum information, which proposes a novel avenue to explore the properties of space-time. In this paper, we investigate the quantum correlations (QCs) of neutrinos in the Kerr–Newman space-time. Both radial and non-radial propagations are considered under the weak-field approximation. The results show that, for inward propagations, the oscillation probabilities and QCs differ significantly from those obtained in the Schwarzschild metric. In the case of radial outward propagation, the larger angular momentum $a$ increases the oscillation period of the survival probability $P_{ee}$, entanglement, and monogamy of nonlocality, whereas the larger charge $Q$ decreases the corresponding periods. For non-radial propagations, $M$ and $a$ can noticeably modulate the amplitudes of the considered QCs, which is not observed in the case of radial propagations. Furthermore, we find that, despite differences in their variation ranges, entanglement and coherence exhibit highly consistent oscillation behaviors in both radial and non-radial propagation cases. These findings provide a comprehensive understanding for the neutrinos-based relativistic quantum information.

Read & Discuss → View Source →