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02.
Nature (Science) 2026-06-10

‘Hidden hero’ peptides guard crops against sudden cold

作者: 未知作者

A protein signal remains silent under normal conditions but is activated under cold stress to protect developing pollen. This ‘on-demand’ resilience mechanism could enable the development of ‘climate smart’ crops that maintain high yields in good years and food security under climate stress. A peptide signal ensures that, in cold conditions, developing pollen receives nutrients at the right time.

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03.
arXiv (CS.CV) 2026-06-17

EventDrive: Event Cameras for Vision-Language Driving Intelligence

作者:
Dongyue Lu↗Rong Li↗Ao Liang↗Lingdong Kong↗Wei Yin↗Lai Xing Ng↗Benoit R. Cottereau↗Camille Simon Chane↗Wei Tsang Ooi↗

Event cameras sense the world through asynchronous brightness changes with microsecond latency and high dynamic range, offering motion fidelity far beyond frame-based sensors and capturing temporal structure that conventional exposures often miss. These properties make events a powerful complement to RGB in autonomous driving, especially under blur, glare, and rapid motion, where frame-based perception can become unreliable. However, existing event-aware vision-language models remain limited to generic perception and do not reveal how event sensing contributes to reasoning and decision-making across the full driving loop. We present EventDrive, a large-scale benchmark and model suite that unifies event streams, RGB frames, and language supervision across four core dimensions: Perception, Understanding, Prediction, and Planning, covering captions, structured QA, grounding, motion-state recognition, trajectory forecasting, and planning tasks. Building on this foundation, EventDrive-VLM introduces a multi-horizon event pyramid and a temporal-horizon mixture-of-experts module to adaptively encode and fuse asynchronous and frame-based information for downstream reasoning. Comprehensive evaluation across diverse tasks shows that event streams provide substantial gains in temporal precision, motion awareness, and robustness, bringing event sensing into the center of driving intelligence.

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04.
arXiv (CS.CV) 2026-06-15

SED:Lightweight Saliency prediction for Event-based data via Distillation

作者:
Romaric Mazna↗Jean Martinet↗Michele Magno↗

Event-based saliency prediction has gained attention recently, as combining event cameras with saliency estimation can act as an upstream stage that naturally improves the efficiency of downstream eventbased perception at the edge. However, current approaches are either neuromorphic, underperforming on event-based saliency benchmarks, or too heavy for resource-constrained edge applications due to their reliance on transformers or 3D convolutions. Drawing inspiration from efficient convolutional modules, SED and aiming to exploit the temporal information in event data, we propose a lightweight network, trained through knowledge distillation, built on a Depthwise Spatio-Temporal Block (DSTconv) – a factorization of the 3D depthwise separable convolution. Relative to its teacher, our model reduces the model size from 180 MB to 0.32 MB (562x) and the parameter count from 45M to 81k (554x), while matching or outperforming it on the N-DHF1K and N-UCF Sports datasets. Moreover, it generalizes strongly beyond its training distribution, transferring from synthetic to real event data where a model trained from scratch fails.

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05.
medRxiv (Medicine) 2026-06-11

Assessment of occupational aerosol exposure for laboratory technicians: A quantitative study using {Phi}X174 phage as a substitute virus

作者:
Liu↗Zhang↗

This study aimed to clarify aerosol exposure risks throughout the workflow of a Biosafety Level 2 (BSL-2) polymerase chain reaction (PCR) laboratory, validate the suitability of the {Phi}X174 bacteriophage as an indicator virus, and provide evidence for biosafety control measures. The {Phi}X174 bacteriophage was used to simulate viral samples, and a concentration-bacteriophage plaque standard curve was constructed (R2=0.998). Five operational steps in a simulated PCR laboratory were quantitatively monitored for aerosol concentration using double-layer agar plates, with blank controls used to eliminate interference. Statistical analysis was employed to identify risk differences. Sample homogenization ((5.67 {+/-} 1.23) x 104 plaque-forming units (PFU)/m3) and nucleic acid extraction ((3.45 {+/-} 0.89) x 104 PFU/m3) were identified as high-/very high-risk steps. The viral load in the samples was strongly positively correlated with the aerosol concentration (r = 0.926, P

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06.
arXiv (CS.CV) 2026-06-12

From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning

作者:
Honglin He↗Yukai Ma↗Brad Squicciarini↗Wayne Wu↗Bolei Zhou↗

Navigation foundation models trained on massive web-scale data enable agents to generalize across diverse environments and embodiments. However, these models, which are trained solely on offline data, often lack the capacity to reason about the consequences of their actions or adapt through counterfactual understanding. They thus face significant limitations in real-world urban navigation, where interactive and safe behaviors, such as avoiding obstacles and moving pedestrians, are critical. To tackle these challenges, we introduce the Seeing-to-Experiencing (S2E) learning framework to scale the capability of navigation foundation models with reinforcement learning. S2E combines the strengths of pretraining on offline videos and post-training through reinforcement learning. It maintains the model's generalizability acquired from large-scale real-world videos while enhancing its interactivity through reinforcement learning in simulation environments. Specifically, we introduce two innovations: (1) an Anchor-Guided Distribution Matching strategy for offline pretraining, which stabilizes learning and models diverse motion patterns through anchor-based supervision; and (2) a Residual-Attention Module for reinforcement learning, which obtains reactive behaviors from simulation environments without erasing the model's pretrained knowledge. Moreover, we establish a comprehensive end-to-end evaluation benchmark, NavBench-GS, built on photorealistic 3D Gaussian Splatting reconstructions of real-world scenes that incorporate physical interactions. It can systematically assess the generalizability and safety of navigation foundation models.

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07.
arXiv (quant-ph) 2026-06-16

Quantum vortex in a fluid flow: negative effective mass and a novel mechanism for turbulence formation

作者:
S. V. Talalov↗

arXiv:2606.15803v1 Announce Type: cross Abstract: We explore the movement of a thin, circular quantum vortex filament within an infinite cylindrical pipe. The fluid surrounding the vortex ring moves through the pipe at a non-zero velocity denoted by $v$. Our study examines the energy spectrum $E = E(p)$, where $p$ represents the total momentum of a vortex ring. We have demonstrated that the function $E(p)$ significantly depends on the velocity $v$. The discovered spectrum $E(p)$ reveals the existence of states with both negative and extremely large effective masses. We also explored the hypothesis regarding the existence of coupled vortex pairs possessing finite summary effective masses. Every pair consists of vortices that possess both positive and negative masses, with the magnitude of these masses being unrestricted. In our model, the criterion for the appearance of these states is based on comparing two numbers. The first is seen as a quantum counterpart to the Reynolds number, while the second represents its critical value for a flow with a single vortex. We also explore how this studied effect might contribute to the emergence of quantum turbulence. This study discusses a method for determining the critical Reynolds number in quantum turbulence, using the proposed model as a framework. Here, we use a new quantization technique for classical closed vortex filaments developed by the author earlier.

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08.
arXiv (CS.AI) 2026-06-16

MADAR: An Address-Free Processor

作者:
Mohamed Amine Bergach↗

arXiv:2606.15535v1 Announce Type: cross Abstract: In a modern processor, computing is the cheap part. Most of its area and energy go to addressing – moving operands to and from a register file and cache, and running the tags, ports, miss queues, and bypass networks that find a value where it was left. MADAR deletes that machinery by abolishing the address. All state circulates in rings of slots that advance one position per clock; instructions and data ride in the same slots; a value is named by its place in an orbit – a \rp{} coordinate – not by an address; a fixed station computes when a circulating instruction sweeps past its operands, on a schedule set at compile time; and a hierarchy of rings of increasing period replaces the cache hierarchy, movement between them scheduled rather than triggered by a miss. No prior circulating-store, dataflow, or statically scheduled machine combines all four of these. We define the execution model, validate it in a cycle-accurate register-transfer-level implementation, show it compilable – a constructive scheduler emits programs cross-checked against the implementation – and price it with a first-order energy model. The payoff is clearest for AI acceleration: the multiply-accumulate at the heart of every matmul and convolution compiles to a streaming form whose energy per operation stays flat as the reduction grows, and the operand reuse that makes matrix multiplication efficient is carried by the ring-period hierarchy – the memory hierarchy doing by rotation what a cache does by tags. MADAR is a new design point for any computation whose data movement is known before the program runs.

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09.
arXiv (CS.AI) 2026-06-17

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

作者:
Umer Siddique↗Peilang Li↗Yongcan Cao↗

arXiv:2606.18111v1 Announce Type: cross Abstract: Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentially conflicting objectives. While single-policy MORL methods can learn fair policies for fixed user preferences using welfare functions such as the generalized Gini welfare function (GGF), they fail to provide the diverse set of policies necessary for dynamic or unknown user preferences. To address this limitation, we formalize the fair optimization problem in multi-policy MORL, where the goal is to learn a set of Pareto-optimal policies that ensure fairness across all possible user preferences. Our key technical contributions are threefold: (1) We show that for concave, piecewise-linear welfare functions (e.g., GGF), fair policies remain in the convex coverage set (CCS), which is an approximated Pareto front for linear scalarization. (2) We demonstrate that non-stationary policies, augmented with accrued reward histories, and stochastic policies improve fairness by dynamically adapting to historical inequities. (3) We propose three novel algorithms, which include integrating GGF with multi-policy multi-objective Q-Learning (MOQL), state-augmented multi-policy MOQL for learning non-statoinary policies, and its novel extension for learning stochastic policies. We evaluate our algorithms across various domains and compare our methods against the state-of-the-art MORL baselines. The empirical results show that our methods learn a set of fair policies that accommodate different user preferences.

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10.
bioRxiv (Bioinfo) 2026-06-16

FlowBench: separating planning, fault recovery and interpretation in agentic bioinformatics

作者:
Kurjan↗Cribbs↗A. P↗

Agentic large language model (LLM) systems are being deployed in bioinformatics faster than they are understood, and single-metric evaluations conflate capabilities that fail independently. We introduce FlowBench, a benchmark that decomposes agentic bioinformatics performance into planning, fault recovery, biological interpretation, and end-to-end output-fidelity. Existing systems achieve high plan completeness, but their closed, single-provider designs prevent attribution of performance to scaffolding versus the underlying model. We therefore built FlowAgent, a modular, provider-agnostic framework whose components can be selectively disabled and whose backbone model can be swapped across providers on a shared harness, and used it to evaluate 23 models from three main providers. Three findings emerge. First, generating a valid workflow plan from a named toolchain is largely solved, whereas inferring an appropriate toolchain from biological intent alone is uniformly difficult regardless of model tier, compressing all models into a narrow 44-57% pass-rate band. Second, ablation shows that the dependency-structured plan and a completeness-reflection step drive performance, while adding a same-context validator-driven retry makes structural quality worse. Third, fault recovery and data-grounded interpretation remain unsolved. Models frequently propose fixes that force a clean exit while leaving the underlying data invalid, and data-grounded interpretation lags internal-knowledge recall by a consistent margin. Safety does not emerge from capability, and reasoning-tier models were among the least reliable at recognising unrecoverable faults. Once planning saturates, agent architecture and refusal calibration, not model scale, are the productive frontier.

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11.
arXiv (math.PR) 2026-06-11

Stochastic Reaction Networks Within Interacting Compartments with Content-Dependent Fragmentation

作者:
David F. Anderson↗Aidan S. Howells↗Diego Rojas La Luz↗

arXiv:2511.10223v4 Announce Type: replace Abstract: Stochastic reaction networks with mass-action kinetics provide a useful framework for understanding processes – biochemical and otherwise – in homogeneous environments. However, cellular reactions are often compartmentalized, either at the cell level or within cells, and hence non-homogeneous. We investigate a model of compartmentalization in which the rate of fragmentation of a compartment depends on the abundance of some designated species inside that compartment. The particular model of study is part of a general framework for compartmentalized chemistry with dynamic compartments that was proposed in (Duso and Zechner, PNAS, 2020). This paper builds on (Anderson and Howells, Bull. Math. Biol., 2023) where the special case where the compartment dynamics do not depend on their contents was studied mathematically. In particular, we demonstrate that the explosivity characterization from (Anderson and Howells, Bull. Math. Biol., 2023) fails in this setting and provide new sufficient conditions for non-explosivity and positive recurrence, under the assumption that the underlying CRN admits a linear Lyapunov function. These results extend the theoretical foundation for modeling content-mediated compartment dynamics, with implications for systems such as cell division and intracellular transport.

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12.
arXiv (CS.CV) 2026-06-19

SSD: Spatially Speculative Decoding Accelerates Autoregressive Image Generation

作者:
Shilong Xiang↗Zirui Zhang↗Lijun Yu↗Chengzhi Mao↗

Autoregressive models excel in visual generation by treating images as 1D sequences of discrete tokens, mirroring language modeling. However, this flattening discards the intrinsic 2D spatial locality of visual signals, creating severe computational bottlenecks during inference. We introduce Spatially Speculative Decoding (SSD), a framework that aligns the predictive objective with the natural geometry of images. Rather than predicting only the immediate next token in a 1D sequence, our model simultaneously predicts the adjacent horizontal token and the token directly below it. By capitalizing on this 2D spatial correlation, spatially speculative decoding overcomes the memory wall in visual inference. Our approach accelerates autoregressive image generation by up to 13.3x while maintaining high fidelity on DPG-Bench and GenEval. Our results suggest that respecting the underlying geometry of vision unlocks massive computational efficiencies, paving the way for real-time, high-resolution autoregressive generative models.

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13.
Nature Medicine 2026-06-15

Activity-dependent adaptive deep brain stimulation improves gait in Parkinson’s disease

作者:
Stefano Scafa↗

Parkinson’s disease leads to a spectrum of locomotor deficits that vary in severity with the nature of daily activities and the fluctuating physiology of patients. Many of these deficits remain inadequately addressed by existing deep brain stimulation therapies that rely on activity-agnostic parameters optimized for cardinal motor symptoms. By contrast, therapies embedding activity-specific parameters have the potential to better address the entire range of symptoms. Here we expose physiological principles that enable real-time decoding of ongoing locomotor activities across motor fluctuations from the neural dynamics of the subthalamic nucleus. This decoding steered activity-dependent adaptations of deep brain stimulation therapies that improved locomotor deficits while preserving efficacy for cardinal motor symptoms across activities of daily living. Our activity-dependent framework provides a blueprint for next-generation neuromodulation therapies that continuously select parameters optimized to the behavioral context and fluctuating physiology of each patient. ClinicalTrials.gov registration NCT06791902 . Neural decoding algorithms that leverage physiological principles of locomotor encoding support activity-dependent deep brain stimulation therapies that improve locomotor deficits in people with Parkinson’s disease.

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14.
medRxiv (Medicine) 2026-06-15

Anti-Platelet Factor 4 Antibody Clonal Heterogeneity and MGUS Status in HIT

作者:
Kanack↗Mauch↗Kohlhagen↗Coker↗Murray↗Padmanabhan↗

Background Monoclonal gammopathy of thrombotic significance (MGTS) is a recently described chronic prothrombotic condition characterized by monoclonal anti-PF4 antibodies that are detected above the polyclonal antibody background in patient sera (i.e. present as monoclonal gammopathy of undetermined significance, MGUS). Due to conflicting data in the published literature on antibody clonality in heparin-induced thrombocytopenia (HIT), we evaluated clonality and abundance of anti-PF4 antibodies in HIT, including investigating whether an MGUS, if present in HIT, represents the causative anti-PF4 antibody. Methods Blood samples from 15 patients with HIT were subject to Platelet Factor 4-dependent antigen-based and functional tests. The unmanipulated serum antibody repertoire and isolated anti-PF4 antibodies were subjected to mass spectrometric evaluation. Results Two of the 15 HIT patients had an IgG MGUS. Notably, anti-PF4 antibodies were not synonymous with the MGUS antibody in either of the two patients. Eight of the 15 patients demonstrated monoclonal anti-PF4 antibodies, however, none of the anti-PF4 antibodies were detectable as an MGUS upon evaluation of the entire serum antibody repertoire, reflecting their low abundance. In the seven patients with multiple anti-PF4 antibodies, non-monoclonality was confirmed by analysis of deglycosylated antibody heavy chains. Conclusions Anti-PF4 HIT antibodies are monoclonal in approximately 50% of HIT patients, however, antibody abundance is low such that they are not detectable over the polyclonal IgG background (i.e. are MGUS-negative), differentiating HIT from MGTS. This observation helps explain the transient nature of HIT relative to the persistent prothrombotic state seen in MGTS.

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15.
arXiv (CS.CL) 2026-06-17

Unintended Effects of Geographic Conditioning in Large Language Models

作者:
Naz Col↗David M. Chan↗

Modern conversational AI systems frequently rely on user metadata to localize responses, yet the unintended regional biases introduced by this hidden context remain poorly understood. In this work, we evaluate location leakage: the phenomenon where a model generates geographic references despite receiving a geographically neutral user prompt. Across both creative writing and open-ended Q&A prompts, even state-of-the-art LLMs systematically favor region-specific outputs when exposed to location metadata, with leakage spiking by up to 793 times above baseline (e.g., from 0.04% to 31.7% for Llama 3.1-8B, and 21.3% and 8.8% for Qwen3-8B and Claude Sonnet 4.6, respectively). Our analysis further shows a novel structural conditioning effect: replacing the injected location with the placeholder "Unknown" still elevates leakage by up to 72 times above baseline, demonstrating that the user profile frame itself, independent of any geographic content, acts as a generative conditioning signal.

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16.
arXiv (CS.AI) 2026-06-16

TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate

作者:
Erica Zhang↗Fangzhao Zhang↗Aneesh Pappu↗Batu El↗Jose Blanchet↗Susan Athey↗Jiashuo Liu↗James Zou↗

arXiv:2605.13909v2 Announce Type: replace-cross Abstract: Negotiation is a central mechanism of economic exchange, shaping markets, procurement, labor agreements, and resource allocation. It is also a canonical testbed for agentic language models, requiring multi-turn interaction under hidden preferences, strategic communication, and binding constraints. These properties make negotiation hard to evaluate: unlike math or code, it has no intrinsic verifier. Existing LLM negotiation evaluations rely on LLM-vs.-LLM interaction or aggregate outcomes such as deal rate, leaving failures opaque. We introduce Terms-Bench, short for Testbed for Economic Reasoning in Multi-turn Strategy, a Bayesian-game framework that makes the environment itself the verifier by specifying the counterpart's latent type, policy, and payoff structure. We instantiate it in bilateral price negotiation, where the counterpart's private state and simulator policy are hidden from the agent but observable to the evaluator. This turns the counterpart from a black-box opponent into a diagnostic instrument, enabling agent-attributable failure analysis and oracle-reference optimality gaps. Evaluating 13 LLM agents spanning frontier systems from major providers, Terms-Bench turns negotiation evaluation from aggregate ranking into actionable diagnosis: where agents fail, why they fail, and what to strengthen. Empirically, frontier models saturate deal rate yet diverge in surplus extraction, cue use, belief calibration, and compliance, revealing agent-specific bargaining bottlenecks masked by prior benchmarks.

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17.
arXiv (CS.LG) 2026-06-17

Clarify Before You Draw: Proactive Agents for Robust Text-to-CAD Generation

作者:
Bo Yuan↗Zelin Zhao↗Petr Molodyk↗Bin Hu↗Yongxin Chen↗

arXiv:2602.03045v2 Announce Type: replace Abstract: Large language models have recently enabled text-to-CAD systems that synthesize parametric CAD programs (e.g., CadQuery) from natural-language prompts. In practice, however, geometric descriptions can be under-specified or internally inconsistent: critical dimensions may be missing and constraints may conflict. However, existing fine-tuned models tend to reactively follow the user instructions and hallucinate dimensions when the text is ambiguous. To address this, we propose a proactive agentic framework for text-to-CadQuery generation, named as ProCAD, that resolves specification issues before code synthesis. Our framework pairs a proactive clarifying agent, which audits the prompt and asks targeted clarification questions only when necessary to produce a self-consistent specification, with a CAD coding agent that translates the specification into an executable CadQuery program. We fine-tune the coding agent based on a curated high-quality text-to-CadQuery dataset and train the clarifying agent via agentic SFT on clarification trajectories. Experiments show that proactive clarification significantly improves robustness to ambiguous prompts while keeping interaction overhead low. ProCAD outperforms frontier closed-source models, including Claude Sonnet 4.5, reducing the mean Chamfer distance by 79.9% and lowering the invalidity ratio from 4.8% to 0.9%. Our code and datasets are made publicly available on https://github.com/BoYuanVisionary/Pro-CAD.

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19.
arXiv (quant-ph) 2026-06-17

Induced Resource Theories and Harvesting via Quantum Probes

作者:
Ron Nystr\"om↗Simone Cepollaro↗Nicola Pranzini↗Stefano Cusumano↗Alioscia Hamma↗Esko Keski-Vakkuri↗

arXiv:2606.17287v1 Announce Type: new Abstract: We consider scenarios in which a quantum system with a well-defined resource theory is used as a probe to interact with an environment, such as a quantum field, for which a resource-theoretic description is absent or incomplete. We clarify if and how the harvesting of a resource in the probe can tell us about the state of the environment. This is particularly ambiguous when the probe-environment interaction is not a free operation, or the concept of such free operations cannot be defined altogether. We propose a framework and precise conditions under which it becomes possible to interpret resource generation on the probe as evidence of resources in the environment, thereby introducing an effective notion of resources for the latter. Our results clarify in which sense resources can be said to be harvested from the environment and provide a systematic way to analyse such processes beyond fully controlled resource-theoretic settings. More generally, this work may provide a step towards a more general understanding of the interplay of different quantum resources.

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20.
arXiv (CS.AI) 2026-06-15

FAConformer: Frequency-Aware Convolutional Transformer for Auditory Attention Decoding

作者:
Ziwei Wang↗Xingyi He↗Tianwang Jia↗Hongbin Wang↗Dongrui Wu↗

arXiv:2606.14120v1 Announce Type: cross Abstract: Auditory attention decoding (AAD) aims to infer the attended speaker from neural responses in multi-speaker acoustic environments and is a key problem for neuro-steered hearing systems. Although recent studies have achieved encouraging progress, existing AAD models still do not fully exploit frequency domain electroencephalography (EEG) information. In particular, most approaches introduce multi-band information through handcrafted feature extraction or direct cross-band feature concatenation, which mainly exploit frequency information at a shallow level and may overlook band-specific patterns and cross-band interactions. To address these limitations, this paper proposes FAConformer, a frequency-aware CNN-Transformer framework for AAD that explicitly integrates band-specific encoding and adaptive cross-band interaction. Specifically, FAConformer first decomposes EEG signals into multiple frequency bands and assigns each band to an independent CNN-Transformer encoder for band-specific modeling. The resulting band-wise features are then adaptively fused by a carefully designed frequency-aware attention (FAA) module that models cross-band dependencies by treating band-wise features as tokens. Further, band-wise auxiliary supervision (BAS) is introduced to prevent weakly contributing branches from being under-optimized during joint training. In this way, FAConformer performs frequency-aware modeling that more effectively exploits frequency domain information. Extensive experiments on two public AAD datasets with three decision-window lengths demonstrated that FAConformer consistently outperformed 12 competitive baselines, surpassing the current state-of-the-art model by 4.9%. Further analyses of band importance, ablation, and parameter sensitivity verify the effectiveness, robustness, and interpretability of the proposed framework. Code is available at https://github.com/wzwvv/FAConformer.

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21.
arXiv (CS.AI) 2026-06-12

A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction

作者:
Phan Nguyen↗Dat Cao↗Hien Chu↗Khue Hoang↗

arXiv:2606.12673v1 Announce Type: cross Abstract: Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data. However, existing methods often depend on dataset-specific feature semantics and structural patterns, which limits their ability to generalize across different domains. To address this challenge, we propose AlignGAD, a zero-shot generalized graph anomaly detection framework. Our framework is built upon three key components: a Global Unification Module that aligns heterogeneous node features and normalizes graph signals in the spectral domain; a Clustering Module that constructs cluster-aware graph views to capture group-level abnormal patterns; and a Node Discrepancy Scoring Module that measures reconstruction discrepancy and aggregates anomaly evidence from different graph views. Experiments on multiple real-world datasets demonstrate the effectiveness of AlignGAD under the zero-shot GAD setting.

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22.
arXiv (CS.AI) 2026-06-11

Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning

作者:
Chuanke Pang↗Junyi Huang↗Zhijun Zhao↗Yaobing Wang↗Kun Xu↗Xilun Ding↗

arXiv:2606.12109v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable zero-shot generalization in robotic manipulation, yet the vast majority of pre-trained pipelines remain strictly confined to low-DoF parallel grippers. Adapting these rich semantic priors to high-DoF dexterous hands introduces a severe morphology gap, direct end-to-end joint fine-tuning inherently causes catastrophic forgetting of spatial reasoning and acute action manifold collapse due to data scarcity. In this paper, we present InDex, a novel, data-efficient adaptation framework rooted in cross-morphology semantic inheritance. Rather than discarding the pre-trained 1-DoF parallel grasp output, we repurpose it as a continuous, macroscopic virtual grasp intent proxy to sequentialize the control topology. We implement a two-stage decoupled learning architecture: the first stage parameter-efficiently aligns the VLA backbone to predict continuous arm trajectories and the scalar grasp intent; the second stage freezes this spatial backbone and leverages an intent-conditioned denoising diffusion head to decode fine-grained joint articulations for multi-fingered end-effectors. Extensive simulation benchmarks across a suite of multi-stage, contact-rich dexterous manipulation tasks demonstrate that InDex effectively masters intricate skills with minimal demonstration data, substantially outperforming monolithic baselines while preserving the robust spatial generalizability of the original VLA prior.

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23.
arXiv (CS.AI) 2026-06-17

Belief-Space Control for Personalized Cancer Treatment via Active Inference

作者:
Deniz Sargun↗H. Bugra Tulay↗C. Emre Koksal↗

arXiv:2606.10376v2 Announce Type: replace Abstract: Cancer treatment is at the core a sequential decision-making problem with partial observability, latent patient heterogeneity, and explicit constraints on the budget for medical measurements. Unlike standard Reinforcement Learning (RL) approaches that control state trajectories, cancer treatments permanently modify patients' transition dynamics, changing how states evolve over time. We model cancer treatment as a belief-space planning problem using active inference, deriving an expected free-energy objective that unifies goal-directed control and information acquisition under measurement budgets without. We implement this framework using real clinical cancer data from the AACR Project GENIE Biopharma Collaborative dataset. Results on clinical data demonstrate a simultaneous patient categorization and high treatment efficacy, under real measurement and treatment constraints.

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24.
Nature (Science) 2026-06-16

Mathematicians are developing rules for AI use — other fields should follow

作者: 未知作者

The mathematics community is right to call for transparency, integrity and fairness to be protected when AI tools are used. Researchers in other disciplines could learn from this approach. The mathematics community is right to call for transparency, integrity and fairness to be protected when AI tools are used. Researchers in other disciplines could learn from this approach.

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25.
arXiv (CS.CV) 2026-06-19

VFACamou: View-Fused Adversarial Camouflage for Environment-Adaptive Physical Evasion

作者:
Shihui Yan↗Hu Liu↗Junyu Shi↗Zihui Zhu↗Ziqi Zhou↗Yufei Song↗Youming Geng↗Minghui Li↗Shengshan Hu↗

Adversarial camouflage in the physical world remains highly challenging, particularly under UAV reconnaissance where targets undergo continuous geometric changes and extreme illumination variations. Existing methods either optimize 2D digital perturbations that fail to generalize to dynamic viewpoints or produce visually unnatural textures that cannot be deployed in real scenarios. Therefore, we propose an end-to-end framework for adversarial camouflage generation that automatically produces wearable adversarial patterns and maintains stable attack performance in real physical environments with changing viewpoints, poses, and lighting conditions. Our method integrates UV-volume rendering with a diffusion-based texture generator, enabling consistent appearance under varying scales, poses, and lighting conditions. To ensure environmental realism, we propose an illumination color consistency estimator that extracts dominant background attributes and guides a natural texture loss to align the generated UV texture with the surrounding environment. A multi-scale dynamic training strategy further enhances robustness against viewpoint shifts and body deformation. Extensive experiments across multiple mainstream detectors demonstrate that our method achieves strong and stable physical attack performance while maintaining high perceptual naturalness, reducing human detection rates without introducing unnatural artifacts.

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