Science & Mathematics

Multi-turn agents are commonly evaluated with LLM-based simulated users, which can be overly cooperative and can therefore overestimate an agent's performance. We here introduce StressSim, an intent-preserving adversarial user simulator framework for stress-testing agents with challenging yet valid user interactions. StressSim optimises the simulator's high-level adversarial plan for each task instance. Candidate planner prompts are evolved with a genetic prompt-optimisation loop that uses rollout traces, granular task-completion signals, and validity feedback to favour conversations that reduce task success without changing the user's underlying goal. We evaluate the approach on the MultiWOZ dataset and the retail and airline domains from tau-bench with three target LLM agents, comparing against standard simulators and hand-designed non-collaborative user behaviours. We demonstrate the effectiveness of StressSim in exposing robustness gaps across frontier LLMs, with tau-bench pass^4 scores decreasing by up to 69% on gemini-3.8-flash and 62% on gpt-5.6-sol. StressSim maintains goal preservation performance (which decreases by at most 10%), using an optimisation budget of 20 rollouts per task. Sensitivity analysis suggests that increasing the budget further decreases task success. Overall our results demonstrate that StressSim exposes robustness gaps missed by standard and hand-designed user simulators while maintaining conversation realism. This is achieved through lightweight prompt optimisation alone, without model fine-tuning.

Structured sparse learning is a leading paradigm for high-dimensional data modeling, as it enables rigorous integration of domain-validated structural priors into the learning process. Among these priors, pre-defined grouped structure and piecewise smoothness are ubiquitous in real-world applications. However, most existing structured regularization methods are built on squared loss and convex penalties, rarely jointly model these two complementary structural priors, and suffer from two key limitations. Their optimal tuning parameters are sensitive to the unknown noise level $σ$ due to the squared loss. Meanwhile, convex penalties induce inherent estimation bias and severe over-shrinkage of large magnitudes. To address these issues, we propose a knowledge-driven Nonconvex Structured Smooth Adjustment for Square-Root Regularization (NS$^3$R), which unifies the $σ$-free square-root loss with a joint nonconvex $\ell_{2,q}$ penalty ($0 < q < 1$) to simultaneously encode group sparsity and structured smoothness priors. Theoretically, we establish rigorous non-asymptotic error bounds for the NS$^3$R estimator under a mild $(2,q)$-group restricted compatibility condition. Computationally, we develop an efficient double-screened IRLS with thresholding (DS-IRLS-th) algorithm, which adopts a nested double-screening strategy to reduce computational cost and improve numerical stability, with provable global convergence for the resulting nonconvex optimization problem. Extensive experiments on synthetic and real-world data demonstrate that NS$^3$R consistently outperforms state-of-the-art methods in finite-sample settings.

Multi-robot teams executing predefined missions must adapt to runtime changes and sustain mission management despite robot failures. This paper proposes MATE-BT (Mission, Allocation, and Task Execution with Behavior Trees), a hierarchically decentralized framework that separates mission execution, allocation, and task execution. An active onboard leader manages mission progression and team-level task assignment, with authority transferable to another leader-capable robot after failure. Interchangeable decentralized methods resolve target assignments, while each robot adapts its local behavior through planning and acting with behavior trees. Webots simulations and real-robot experiments demonstrate mission continuity despite new targets and robot failures, including active-leader failure. In the physical experiments, all MATE-BT computation runs onboard. Hardware-in-the-loop resource measurements further support onboard execution on Jetson Orin Nano and Raspberry Pi 5. By updating only the affected layers without full-plan regeneration, MATE-BT maintained a response time of approximately 130 ms with 30 robots and 120 targets. Comparisons of four target-assignment plugins reveal scenario-dependent trade-offs between mission completion time and total travel distance.

Large language models are increasingly used to turn the feature attributions of multimodal prognostic models into narratives for clinicians, yet costly modalities such as amyloid PET and cerebrospinal fluid biomarkers are often missing, and whether these explanations stay faithful under missing evidence has not been tested. We separate two failure points: phantom attribution, in which the model credits modalities that were never observed, and unfaithful narration, in which the report omits, cites or misjudges missing modalities. Using 307 participants of the Alzheimer's Disease Neuroimaging Initiative with complete data, we masked every subset of four costly modalities, predicted 36-month conversion from mild cognitive impairment to Alzheimer's disease, and restored the masked data to obtain interventional ground truth. All five missing-data strategies assigned 12% to 23% of attribution to unobserved modalities, and up to 45% with four absent; missingness-aware strategies minimised it at no cost in accuracy. Across 51,200 reports from four language models and four prompting regimes, naming absent modalities in the prompt raised acknowledgement from 48% to 88% and cut phantom evidence twenty-fold, but advice on whether acquiring a missing test would change the prediction was correct in only 27% to 41% of cases until checked against executed restorations, which raised accuracy to 69% for open-weight narrators and 99.9% for a proprietary model. Higher phantom attribution was associated with more phantom evidence and fewer faithful direction claims, and direction fidelity, measured by a calibrated language-model judge, depended on narrator scale rather than prompting. Narrators told to name absent modalities sometimes declared observed ones absent, an error that a structured template eliminated. We release the masking protocol and evaluation code.

Neural audio codecs are increasingly used to encode speech for integration with large language models. This paper investigates the phonetic representations in the first codebook (CB0) of the Mimi codec. ABX style evaluation is poorly suited to Mimi fixed 80 ms tokenization window, which often spans multiple phonemes; we instead use forced alignment, entropy analysis, and Phone-Normalized Mutual Information (PNMI). Using TIMIT speech (58/55 speakers, SA1/SA2), individual tokens are strongly associated with specific phonemes (PNMI = 0.84, above random), yet phonemes are realized by multiple competing tokens rather than a one-to-one mapping.

Recursive self-improvement (RSI), in which a system also improves the way it improves, is a long-standing aspiration on the path to AGI. Language agents increasingly store procedural knowledge in reusable skills. Optimizing skills offers a direct way to achieve RSI, because the optimization procedure itself can be encoded as a skill. Existing skill optimizers, however, follow a hand-designed procedure. Even methods that evolve a meta-skill confine it to a fixed pipeline and relearn it for each task. We present Skill$^{\infty}$, which makes skill optimization recursively self-improving: a meta agent follows a meta-skill to optimize task skills and optimizes the meta-skill itself using the same procedure. The optimization is agent-led. The meta agent diagnoses execution traces, proposes candidates, requests independent reviews, and allocates the search budget. The meta-skill defines these steps, allowing self-improvement to rewrite them. This meta-skill is also decoupled from task skills, refined on multiple skill optimization tasks and then reused to optimize task skills across diverse domains. As in deployment, skills at both levels are standard packages loaded through native skill discovery. On 83 SkillsBench tasks, Skill$^{\infty}$ reaches a pass rate of 60.7%, compared with 56.4% for human-curated skills and 54.5% for SkillEvolver.

Defect engineering of memristive materials offers a powerful strategy for regulating ionic transport and analog resistive switching required for neuromorphic computing. Here, we report a wet chemical synthesis route for chloride-vacancy (VCl) engineering in lead-free all-inorganic KCuCl3-x thin films and establish a direct correlation between defect concentration, electronic structure, transport dynamics, and neuromorphic performance. Controlled precursor-ratio variation systematically tunes the VCl concentration in KCuCl3-x thin films, driving preferential vacancy formation at the terminal chloride sites, band-gap narrowing, and enhanced electronic disorder. W/KCuCl3-x/FTO memristors exhibit stable bipolar VCM-type resistive switching, with increasing VCl concentration promoting forming-free operation. An optimal VCl concentration (~ 1.3*10^21 cm^-3) provides a balance between ionic mobility and defect controllability, resulting in highly linear potentiation/depression characteristics and superior pulse endurance (>104 cycles). When the experimentally measured conductance characteristics of the optimized memristor are implemented as synaptic weights in artificial neural network simulations, recognition accuracies of 98.96% and 91.82% are achieved on the MNIST and Fashion-MNIST datasets, respectively. These findings establish precursor ratio-controlled VCl engineering as an effective strategy for simultaneously optimizing ionic transport, analog switching, and neuromorphic learning in lead-free halide perovskite memristors, providing a general materials design principle for energy-efficient neuromorphic electronics.

Anti-jamming capability is of paramount importance in modern wireless communications, yet conventional receivers are severely constrained by their fixed array geometries, which limit the available spatial degrees of freedom and leave performance highly susceptible to strong interference. By contrast, movable antennas (MAs) provide additional spatial degrees of freedom through antenna-position optimization. This paper investigates MA-enabled anti-jamming by jointly optimizing the antenna-position vector (APV) and receive beamformer to maximize the output signal-to-interference-plus-noise ratio (SINR). For the canonical setting with a single jammer and a single line-of-sight (LoS) propagation path on the legitimate link, we develop a damped projected-gradient descent (DPGD) algorithm with low computational complexity, which scales linearly with the number of receive MAs. We establish a generic active strict-saddle landscape for the resulting steering-coherence minimization problem and prove that DPGD converges almost surely to a local minimizer rather than merely to a Karush-Kuhn-Tucker (KKT) point. For the general setting with multiple channel paths on the legitimate link and/or multiple jammers, we derive an equivalent ridge least-squares formulation and develop a low-complexity soft-active projected-gradient (SAPG) algorithm, which also scales linearly with the number of receive MAs. We further show that the proposed antenna position optimization framework applies to both minimum variance distortionless response (MVDR) and zero-forcing (ZF) reception. Numerical results validate the proposed algorithms, demonstrating substantial gains in both anti-jamming performance and computational efficiency.

Omnimodal large language models (Omni-LLMs) are increasingly expected to assist users while observing continuous audiovisual streams. However, existing evaluations provide limited insight into how reliably these models connect stream evidence and user needs to timely, sustained interventions. We introduce \textbf{Omni-InteractScope}, a benchmark of 1,479 human-annotated, cross-reviewed interactions with 1,790 temporal targets across eight task groups. It links input modalities, event triggers, intervention types, and user needs, covering event-timed tool invocation and query-free assistance alongside notifications, warnings, state tracking, and continuous responses. Our experimental results on the proposed benchmark reveal three critical limitations of current Omni-LLMs: (1) \textbf{Timely tool invocation remains unreliable}: all 12 evaluated models produce an on-time first response for at most 40\% of tool-invocation targets, and joint timing-and-tool accuracy reaches at most 32.0\%. (2) \textbf{Query-free reminders remain poorly covered}: all evaluated models achieve lower timely reference-target recall on query-free reminders than on explicit questions, measured on their respective target sets. Reminder recall never exceeds 23.0\%. (3) \textbf{End-to-end guidance coverage remains limited}: nine models cover none of the 26 guidance sequences in full, and the remaining three cover only one to three sequences each. Targeted controls further show that tool-invocation scores alone do not establish event grounding and that reminder coverage depends on response history and frequency. These findings motivate developing and evaluating streaming assistants for timely action, initiative under general user needs, and coverage of entire response sequences.

Accurately simulating high-order harmonic generation (HHG) for attosecond science requires connecting the microscopic single-atom quantum dynamics and the macroscopic propagation of intense driving laser fields. HarP is an open-source Python code designed to address this multiscale problem. It solves the wave equations for both the driving laser field and the generated harmonics, using precomputed dipole tables for the nonlinear polarization density. This tabulation enables the direct integration of accurate microscopic models while avoiding costly calculations during the propagation. The methodology builds upon the approach of [L'Huillier et al. (1992) PRA 46, 2778] and has already facilitated multiple studies on phase matching, propagation effects and HHG optimization. This paper presents the underlying physical model, its numerical implementation, and comparison against alternative simulation frameworks.

Neural posterior estimation (NPE) enables Bayesian inference of the posterior parameter in agent-based models (ABMs) with intractable likelihoods, but simulating training data can be computationally expensive. We propose Double-Flow Neural Posterior Estimation (DF-NPE), which combines learned time-series summaries with two conditional normalizing flows for synthetic-data generation and posterior estimation. The framework supplements direct simulations with synthetic training pairs and supports both pooled and ordered training strategies. Experiments in two settings of the Brock-Hommes asset-pricing model show that DF-NPE can reduce direct simulations by 90 percent while achieving lower mean posterior discrepancies and runtime than standard NPE. Performance varies with the allocation of directly simulated and synthetic data and the training strategy. These results establish learned summary augmentation as a practical approach to reducing simulation costs for Bayesian posterior inference of parameters in ABMs.

Dealing with climate change requires that all countries engage in costly efforts to reduce their emissions. Reaching this objective has so far been elusive because it is in the best interest of each country to let the others do the effort and benefit itself from a better environment. The presence of negative externalities and strategic behavior have made game theory a natural paradigm to design an international environmental agreement (IEA) that codifies what countries should do. Considering that countries are sovereign and no supranational entity can impose an agreement, a stream of literature adopted a noncooperative mode of play to the formation of an environmental coalition. On the other hand, as joint optimization of all countries' payoff leads to the best outcome, cooperative games approach has also been proposed to share the cost of climate change. Both approaches have their pros and cons.

End-to-end driving models perform well on standard benchmarks but collide frequently in safety-critical scenarios. We present a failure-mode analysis on NeuroNCAP which shows that 95.9% of DiffusionDrive's collisions are planning failures despite reliable detection, pinpointing the bottleneck in planning rather than perception. We attribute this to a fundamental data imbalance, as safety-critical interactions are rare in real driving logs, so planners are hardly ever trained on them. We close this gap with a novel model-aware framework that generates safety-critical training scenes against a specific planner. Each planner is attacked at the bird's-eye-view (BEV) level through a differentiable surrogate distilled from its own planning head. This keeps the expensive video renderer out of the attack loop. We extract 50 scenes per planner, each pairing an attack with a safe escape trajectory that serves as the fine-tuning label. A fully automated quality-diversity search generates both the attacks and the escapes, so that their variety is not specified by hand. Only these scenes are rendered to fine-tune the planner, which reduces the computational cost significantly. Fine-tuning on these scenes roughly halves the closed-loop collision rate of two planners on NeuroNCAP (SparseDrive 28.0% to 12.2%, DiffusionDrive 37.5% to 19.2%) while largely preserving normal driving. A cross-planner transfer experiment shows that the gain comes from targeting the specific planner. Each planner improves most on the scenes generated against itself, and DiffusionDrive fine-tuned on SparseDrive's scenes retains only 6.7 of its 18.3-point collision-rate reduction.

Referring expression grounding in overhead imagery requires oriented bounding boxes rather than horizontal ones, since aerial objects appear at arbitrary orientations. Most grounding models emit box coordinates sequentially; Parallel Box Decoding (PBD) instead predicts all geometry slots in one forward pass, but only for four-coordinate horizontal boxes. We extend this block-aligned interface to a six-slot oriented representation using a double-angle parameterization. Rather than assuming correctness from a decreasing loss curve, we verify the extension with image/prompt-shuffle controls and visibility/leakage probes, which show that the corrected model depends on both the image and the prompt rather than on prompt statistics alone. On the aerial vehicle visual grounding (AVVG) benchmark, whose targets are genuinely oriented, the extended model improves rotated intersection over union (rotIoU) from 0.1253 for the zero-shot baseline to 0.303 +/- 0.012 (mean +/- standard deviation over three training seeds) across the complete 6139-record test split spanning all 120 images. Learning oriented boxes rather than their axis-aligned envelopes adds 0.055 +/- 0.008 rotIoU beyond a matched control (per-seed 95% confidence intervals all excluding zero), whereas block expansion alone yields no rotIoU gain distinguishable from zero.

AI-powered personal assistant frameworks such as OpenClaw combine LLM reasoning, persistent memory, tool invocation, local execution, and web-based workflows. These capabilities enable useful automation, but they also create security risks when untrusted inputs, model reasoning, tools, memory, and deployment configuration interact without explicit trust boundaries. This paper presents an empirical study of reported security concerns in OpenClaw using two complementary data sources: Reddit discussions and security-tagged GitHub issues. We analyze more than 25K Reddit posts from 13 OpenClaw-related subreddits and identify 162 security-relevant posts with 1,787 associated comments. We further collect 358 security-tagged GitHub issues and retain 180 issues describing concrete security concerns. Using qualitative analysis and evidence-grounded CWE mapping, we identify 521 CWE instances across 59 distinct weakness types, concentrated in access-control failures, sensitive-data exposure, injection, and unsafe execution. We also extract 543 practitioner mitigation suggestions and consolidate them into seven recurring strategies. To complement the empirical analysis, we present an evidence-grounded illustrative reconstruction of a representative sensitive-data exposure pathway using dummy secret values. Our findings show that OpenClaw security issues are not isolated implementation bugs; they reflect recurring trust-boundary failures across the agent execution pipeline. We discuss how these weaknesses map to both traditional software-security categories and agent-specific failure mechanisms, and derive implications for safer agentic systems, including explicit trust boundaries, least-privilege tool access, execution containment, runtime oversight, and defense-in-depth deployment.