arXiv:2607.05663v1 Announce Type: new Abstract: Accurate and robust localization is essential for autonomous mobility systems in real-world environments. While fusing Inertial Measurement Unit (IMU) data with satellite-based correction signals provides precise vehicle pose estimates, performance degrades substantially during outages. Recent studies indicate that Machine Learning (ML) can improve IMU-based proprioceptive localization, highlighting untapped potential for onboard sensors readily available in production vehicles. This paper introduces Physics-Regularized Machine Learning for Localization (PRML2), a hybrid framework that combines the complementary strengths of Kalman filtering and data-driven learning to estimate vehicle pose directly from onboard sensors. A key aspect of PRML2 is its physics-regularized learning, enabled by end-to-end training of an ML model through a differentiable Kalman filter. This improves consistency with vehicle motion models, thereby enhancing both localization accuracy and generalization across driving conditions. We evaluate the performance limits of ML-enhanced onboard odometry on a publicly available dataset and show that PRML2 achieves superior localization accuracy and demonstrates real-time capability. This work also introduces a novel dataset to support vehicle localization research under low-friction conditions. The proposed framework provides a robust and cost-effective solution for vehicle localization under degraded sensing conditions by integrating learning with physics-based priors.
Science Journals
arXiv:2607.05666v1 Announce Type: new Abstract: AI coding agents are black boxes: we cannot inspect how they generate code, but we can inspect what they change. This distinction matters for search-based software engineering (SBSE), where techniques such as genetic improvement (in the performance-optimisation application we study) depend on mutation operators that reflect how code is actually transformed. Fewer than 1% of the 33,596 agent PRs in AIDev-pop target performance, making each case a rare window into otherwise opaque agent behaviour. We classify 1,254 performance-relevant diff hunks from 216 of these PRs, spanning five agent systems, against the 18-category syntactic mutation taxonomy of Even-Mendoza et al. (2025) using a dual-LLM intersection pipeline. Three categories dominate: name modification (37.0%), object creation (26.4%), and type change (22.7%), a profile markedly different from prior GI corpora where no change accounted for 84%. Each agent's deployed system commits to a distinctive mutation vocabulary, and each performance strategy activates a largely disjoint category subset. Agent identity and target strategy are therefore informative priors that narrow the effective SBSE operator space. Replication package: https://github.com/5uper6rain/ssbse-challenge-2026
Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba
arXiv:2607.05669v1 Announce Type: new Abstract: Reliable localization in GNSS-denied environments remains a fundamental challenge for intelligent vehicles, as inertial navigation systems accumulate unbounded drift without external correction. Existing approaches provide drift correction through dedicated infrastructure, expensive external sensors, or complex multi-sensor fusion, each introducing practical deployment barriers. We propose Evidential Velocity Correction using Mamba (EVC-Mamba), a learning-based architecture that transforms onboard vehicle sensor data into a virtual velocity sensor for IMU drift correction without additional hardware. A Mamba-based selective state space model captures the temporal dynamics of vehicle motion, while evidential deep learning with a Normal-Inverse-Gamma distribution provides principled uncertainty quantification. The resulting uncertainty-aware velocity estimate is incorporated as a virtual correction measurement into an Error-State Extended Kalman Filter to reduce position drift. Evaluation on real-world vehicle data demonstrates that inertial navigation using the proposed velocity correction achieves localization accuracy within 10% of a dedicated external velocity sensor across different outage durations. The proposed architecture supports real-time onboard deployment at 40 Hz on edge hardware, enabling reliable localization during prolonged GNSS outages.
arXiv:2607.05676v1 Announce Type: new Abstract: In the USA, periodontal diseases, a spectrum of inflammatory conditions, affect 4 out of 10 adults (>=30 years). Current standards of care in clinical assessment of these diseases are invasive, subjective, only semi-quantitative, and primarily detecting later stages. Applications of ultrasonography in periodontology has been emerging in recent years. Despite such growing interest, Quantitative ultrasound (QUS) approaches remain largely unexplored and their utility for longitudinal characterization of oral inflammation has yet to be established. Here, we present one of the early investigations into the potential of QUS techniques for inflammation monitoring. In a staggered study involving a pig cohort (N=8), interdental gingival tissues at three interproximal oral sites from four quadrants were enrolled. The study involved baseline and five inflammation timepoints. Inflammation was induced using two complementary approaches at each site. Two QUS parameters of attenuation coefficient slope (ACS) and backscatter intensity (BSI) were investigated. Sex and oral sites were also used to stratify the longitudinal QUS estimates with inflammation. Results showed that both ACS and BSI were statistically significant from the healthy baseline across all oral sites at week 2 and|or 4 (combined sexes). Overall, inflammation inoculation was associated with a decrease in ACS and an increase in BSI. For BSI, week4 and/or week 2 remained statistically significant across males and females at all oral sites. ACS variations were spread across oral sites and sexes, with PM4-Dis as overall non-significant. The 2D classification (baseline from week 2) accuracy, were 92%, 82%, and 74.2% for M1-Dis, PM4-Dis, and PM3-Dis, respectively This study is among early implementation of QUS approaches for periodontal inflammation characterization.
arXiv:2607.05689v1 Announce Type: new Abstract: We present our systems for SemEval-2026 Task 10 (PsyCoMark), addressing conspiracy marker extraction (Subtask 1) and document-level conspiracy detection (Subtask 2). For marker extraction, we formulate the task as multi-label span classification over enumerated candidate spans, using IoU >= 0.95 positive labeling, hard-negative sampling, and containment-based non-maximum suppression (NMS) with boundary-aware span representations. Document classification is modeled independently using a sequence classifier with label smoothing and a stratified train-validation split. Analysis shows that entity-like roles (Actor, Victim) are detected robustly, while abstract roles (Action, Effect, Evidence) remain sensitive to boundary criteria. On the official test set, our systems rank 7th in Subtask 1 (0.2251 macro F1) and 11th in Subtask 2 (0.7694 weighted F1).
arXiv:2607.05825v1 Announce Type: new Abstract: Background. Retinopathy of prematurity (ROP) is a preventable cause of childhood blindness, with rising burden in low- and middle-income countries where ROP-trained ophthalmologists are scarce. Plus disease, marked by retinal vessel dilation and tortuosity, triggers treatment but is subjective and variable. Automated screening could extend specialist reach, but African evidence remains limited. Methods. We analysed 121 Kenyan preterm infants, covering 237 eyes and 1,635 fundus images graded as No Plus, Pre-Plus or Plus. Vessel annotations from two graders supported segmentation training. Eleven configurations were evaluated for eye-level Plus detection using patient-grouped nested cross-validation, including image classifiers, multiple-instance learning, multi-task segmentation-classification, and segment-then-classify pipelines. Results. Vessel segmentation was feasible, achieving pooled Dice 0.533, IoU 0.368, sensitivity 0.623 and specificity 0.979 on held-out images. RGB classifiers were highly sensitive but over-referred, while segmentation-coupled models were more specific. Combining approaches improved performance: an OR-based screen achieved the highest sensitivity, an AND-based confirmation achieved the highest specificity, and a probability ensemble gave the best balanced performance, with sensitivity 0.692, specificity 0.914 and balanced accuracy 0.803, outperforming the vision classifier alone. Conclusions. Classification and vessel segmentation are complementary for ROP Plus detection in Kenyan data. Classifiers support sensitive case-finding, while segmentation improves specificity and reduces over-referral. African ROP AI systems should use combined workflows and undergo prospective multi-site validation.
arXiv:2607.05831v1 Announce Type: new Abstract: This article presents an extension of the work performed by Liu, Baek and Susilo on withdrawable signatures to the Fiat-Shamir with aborts paradigm. We introduce an abstract construction, and provide security proofs for this proposal. As an instantiation, we provide a concrete withdrawable signature scheme based on a no-hint, full-t Dilithium-style Fiat-Shamir with aborts construction; adapting to production ML-DSA (with hints) introduces a small epsilon term.
arXiv:2607.05789v1 Announce Type: new Abstract: In this work, 3D full-domain 3 nm gate-all-around field-effect transistor (GAA-FET) static random access memories (SRAMs) with various substrate isolation techniques are simulated using Technology Computer-Aided Design (TCAD). In addition to the traditional bottom dielectric isolation (BDI), which isolates the source/drain (S/D) from the substrate (dubbed SDBDI), and the punch-through stopper (PTS), a novel channel-BDI (C-BDI) is proposed, allowing S/D-to-substrate connection. The self-heating effect and radiation hardness due to various isolation techniques are studied. It is found that, firstly, the increase in self-heating due to BDI is negligible. Secondly, in the novel CBDI, even without PTS, the increase in leakage current IOFF is minimal. Thirdly, for SD-BDI with underlap (to minimize stress relaxation), while IOFF increases, the static noise margin (SNM) remains unchanged and robust against single-event upset (SEU) even if the underlap is as much as 20 nm. Finally, all structures are immune to the alpha-particle SEU, and BDI enhances the radiation hardness substantially. Moreover, radiation hardness is insensitive to BDI thickness.
arXiv:2607.05794v1 Announce Type: new Abstract: Long-term user memory is essential for personalized conversational agents, yet many memory systems still expose memory through passive retrieval interfaces, making the model a consumer of pre-selected evidence. We introduce NapMem, a framework for learning to use long-term user memory as a structured action space rather than passively retrieved context. NapMem organizes user history into a linked multi-granularity memory pyramid, where raw conversations, typed memory records, topic tracks, and user profiles are connected through provenance relations, and exposes these levels through memory tools. The agent is trained to select memory according to the query and intermediate evidence, allowing it to inspect different memory granularities before answering. Experiments on PersonaMem-v2, LongMemEval, and LoCoMo show that a NapMem agent trained with memory-tool reinforcement learning is competitive across diverse memory-intensive tasks, while evaluations on non-memory tasks suggest that the learned policy largely preserves general reasoning and tool-use abilities. Additional analyses examine storage, inference cost, tool-use behavior, and ablations over navigation, memory granularity, and RL training. Our results suggest that long-term user memory benefits from coupling structured storage with a learned policy for using memory at the appropriate granularity.
arXiv:2607.06254v1 Announce Type: new Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors. Despite their widespread use, these paradigms are rarely evaluated under a common protocol, making direct comparison difficult. We introduce VendorBench-100, a cross-paradigm benchmark that evaluates 36 representative models using a single adversarial 100-image corpus, a unified output schema, and a common evaluation framework. To ensure reliable assessment under the corpus's intentional class imbalance, models are ranked primarily by the Matthews correlation coefficient (MCC), with ROC-AUC reported as a threshold-independent measure of ranking ability. Rather than maximizing dataset size, VendorBench-100 emphasizes challenging real-world scenarios through a curated taxonomy of eight edge-case families, including face swaps, text-to-video stills, AI photo edits, avatar compositing, opaque-provenance images, and compressed research frames. Our evaluation shows that commercial APIs achieve the strongest median performance, followed by vision LLMs and open-source detectors. However, individual open-source models remain competitive with the best vision LLMs. More importantly, we identify a consistent divergence between ranking ability (ROC-AUC) and operating-point quality (MCC), demonstrating that strong score discrimination does not necessarily produce reliable default-threshold decisions. This metric disagreement, rather than any single leaderboard ranking, is the central finding of the benchmark. We release the complete evaluation framework and benchmark results to support reproducible future research. The source code and data are available at: https://github.com/sharayu-20/vendorbench-100
arXiv:2607.05674v1 Announce Type: new Abstract: How predictable users perceive an interactive system to be shapes how they interpret, trust, and rely on it, yet HCI lacks both a precise conceptualization and a validated instrument for this perception. We address this gap by introducing perceived system predictability (PSP) as a user-centered construct grounded in uncertainty theory, distinguishing epistemic, aleatory, and effective predictability. We contribute (i) a theoretical framework that situates PSP relative to adjacent constructs such as trust and understanding, (ii) a 6-item PSP scale, derived from a 60-item pool through expert review and cognitive interviews, and validated in a shape-classifier study ($N=200$) that supports both a unidimensional and a three-factor hierarchical structure, and (iii) a sentiment-classifier study ($N=200$) that varies explanations and stochasticity, and relates PSP to the correctness of users' predictions of system behavior, trust, subjective information processing awareness, and need for cognition. We find that PSP and prediction correctness capture distinct aspects of users' mental models and that both can diverge: PSP itself predicts correctness, explanations shift PSP but not correctness, and increased stochasticity degrades correctness without lowering PSP. PSP thus goes beyond existing objective and subjective measures and offers a principled foundation for designing transparent and trustworthy interactive systems.
arXiv:2607.05871v1 Announce Type: new Abstract: Debugging exercises are often assessed from final code and test outcomes, yet these artifacts hide how students reproduced failures, formed hypotheses, inspected evidence, edited code, and verified fixes. We present DebugTracker, a Visual Studio Code extension that records lightweight debugging-process evidence for classroom tasks. DebugTracker separates uncoached Evaluation Mode traces from coached Training Mode traces, stores append-only JSONL events, and exports timeline and Markdown reports for human review. The prototype records test commands, editor and debugger metadata, student checkpoints, source snapshots, optional image evidence, human labels, and optional AI-assisted practice feedback. DebugTracker is largely language-agnostic: it captures process evidence through standard VS Code mechanisms rather than language-specific tooling, although debugger evidence depends on the relevant VS Code language extension. We validate the prototype with debugging tasks in Python, TypeScript, and Java, 16 automated checks, and an 11-case manual trial matrix spanning packaged VSIX installation and three operating systems.
arXiv:2607.05727v1 Announce Type: new Abstract: Pre-trained Vision-Language Models (VLMs) like CLIP have proven highly effective as foundation models for various downstream applications. However, prompt learning in VLMs encounters a performance-generalization dilemma: while prompts can be tuned to achieve high accuracy on seen distributions, this tuning process often undermines their generalizability to unseen data. The limited set of learnable prompts, which contextualize and condition the input to steer it toward the task within the pretrained VLM, tends to overfit the training data, leading to a trade-off between task-specific performance and preserving generalization. To address this dilemma, we introduce SAMPLe (Sharpness-Aware Minimization Prompt Learning), a plug-in sharpness-aware optimizer that enhances prompt generalizability by accounting for loss landscape sharpness. Unlike conventional methods, SAMPLe balances exploration and exploitation by satisfying objective function constraints at each step, dynamically adapting to the current optimization state based on the local curvature and gradient properties. This approach reduces overfitting on seen distributions and improves adaptability to unseen data, preserving the generalization potential of pre-trained VLM models. We integrate SAMPLe into multiple prompt learning frameworks, including CoOp, CoCoOp, MaPLe, TCP, and Co-Prompt, demonstrating its effectiveness across diverse methods. Experiments show that SAMPLe elevates prompt learning frameworks and consistently outperforms existing optimizers across diverse settings, establishing itself as a robust, model-agnostic solution for prompt learning.
arXiv:2607.05743v1 Announce Type: new Abstract: AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe. That literature is scattered. Papers on sandbox isolation, capability and access control, policy enforcement, time-of-check-to-time-of-use (TOCTOU) races, Model Context Protocol (MCP) threats, identity delegation, execution provenance, network egress control, and static analysis of agent-generated code are published independently and rarely cite one another. We systematize 39 papers published between 2023 and 2026 into 17 categories, each verified directly against its source. The same verification protocol also confirms four disclosed, patched CVEs directly affecting production agent harnesses. Reading across categories surfaces five cross-cutting gaps that no single paper addresses. (1) Isolation architectures and capability models are almost never evaluated against one another on a shared benchmark. (2) Policy-enforcement studies report failure rates from 69% to 98% of real denylists, yet no isolation paper re-evaluates its own defense under that adversarial setting. (3) TOCTOU and MCP threats are analyzed as separate literatures despite both being instances of the same state-validation problem. (4) Every enforcement mechanism assumes an honest policy author, leaving policy-authoring error itself unaddressed. (5) Benign but out-of-scope agent actions occurring at rates up to 17.1% under realistic prompting are addressed by no access-control or capability paper in the corpus. Existing broader surveys of agentic AI security discuss sandboxing only as one item among many defenses, leaving execution security without a dedicated systematization. This paper is written to fill that gap. We conclude with a research agenda directed at the five gaps.
arXiv:2607.05808v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into computing education, yet nearly all prior research has focused on text-based interactions. As voice-enabled interfaces become more capable and more common, there is growing interest in understanding how voice input might shape students' use of LLM-powered tools. In this exploratory study, we investigated how introductory programming students interact with Prompt Problems, which are programming tasks that require crafting natural-language prompts to generate correct code. Students (N = 919) solved a series of Prompt Problems with the freedom to select or switch between text and voice input modalities. We collected their prompt submissions as well as post-activity survey responses, then analysed differences in prompt accuracy, persistence, and perspectives by modality. For two of the three problems, we found that students who typed their prompts using text were more likely to have those prompts succeed on the first attempt than students who submitted unedited voice prompts. There was no difference in success rate if students edited their transcribed voice prompts before submission. Across the problems, we found evidence that students who tried voice prompting varied in their usage of modality - perhaps indicating a complementary, or non-preferential approach. However, most students only tried and reported preferring text. Our qualitative analysis revealed how students' perceived the roles of voice and text input in shaping their problem-solving process, as well as the reported drawbacks and advantages of each modality. We discuss implications for future multimodal tools and instructional design in computing education.
arXiv:2607.05842v1 Announce Type: new Abstract: Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate code review, triage, and repair can closely resemble terminology associated with misuse. Existing safety and cybersecurity evaluations are difficult to interpret in this setting because they often compare unrelated model families, thereby conflating safety behavior with differences in architecture, scale, training data, and deployment. To isolate this factor, we study safety state: whether refusal behavior remains intact (Aligned) or has been refusal-ablated (Abliterated) within same-lineage models. We ask how this safety state affects defensive utility across software-security workflows. We compare aligned instruction-tuned models with publicly released refusal-ablated descendants from two model families, Gemma and Qwen. We evaluate Aligned and Abliterated states on vulnerability detection, CWE attribution, vulnerable-line localization, root-cause localization, and executable patch validation. We further treat prompt wording as a controlled framing dimension: prompts begin with neutral code-review language, add authorization context, and vary the density of cybersecurity terminology. In a Gemma-based Java/Vul4J repair-validation study, Abliterated achieves higher early-stage validation rates, with 67.8%, 65.0%, and 32.8% of patches judged usable, successfully applied, and successfully compiled, respectively, compared with 29.9%, 24.9%, and 9.0% for Aligned. In the Qwen pair, Abliterated improves localization performance, increasing line-level F1 from 2.08% to 3.91% and Top-1 accuracy from 4.10% to 6.95%. These findings suggest that evaluations of LLM-based security assistants should jointly measure whether models respond, whether their usable responses are correct, and whether their outputs remain actionable across the engineering workflow.
arXiv:2607.05844v1 Announce Type: new Abstract: Agent systems accumulate conflicting observations across branches, retries, and replicas, yet many practical memory layers still collapse disagreement behind overwrite rules that are difficult to inspect or correct. We present StateFuse, a conflict-aware replicated memory contract built on standard OpSet/CRDT merge. StateFuse does not introduce a new join algebra; it defines an agent-facing semantics layer with immutable history, explicit conflict objects, exact and semantic correction handles (claim_id / claim_ref), deterministic predicate contracts, and projection-time resolution that cannot rewrite replicated state. We evaluate StateFuse against flat multi-value, raw-log, provenance-style, and collapsed baselines under matched resolver and verification policies. On a 282-question official conflict-bearing MemoryAgentBench slice, the compared methods tie on answer accuracy, but conflict-preserving surfaces keep contradictions visible while collapsed surfaces do not. In a controlled agent loop with uniform verification, preserving ambiguity enables safer abstention and correction than early collapse. A correction-handle ablation further shows that semantic handles matter when exact prior identifiers are unavailable. The resulting claim is narrow: StateFuse is best supported as a safer public memory contract for contradiction surfacing, abstention, and auditable correction, not as a universal accuracy gain.
arXiv:2607.03197v2 Announce Type: replace Abstract: Research Knowledge Graphs (RKGs) enable the structured representation of scientific knowledge, but their weakly enforced schemas make them prone to inconsistencies, particularly in how predicates are defined and used. Duplicate predicates, i.e., distinct identifiers expressing the same or highly similar relationships, introduce semantic redundancy, hinder reuse, and reduce RKG quality. While prior work has addressed duplicate detection for downstream tasks such as query answering or schema alignment, predicate redundancy as a data quality challenge, remains underexplored, particularly in terms of resolution, prevention, and semi-automated curator support. In this paper, we propose a framework for managing duplicate predicates in RKGs that covers detection, resolution, and prevention. The framework combines automated similarity-based methods with human validation and is designed for integration into the lifecycle of evolving, crowdsourced RKGs. We implement the framework in the context of the Open Research Knowledge Graph (ORKG) by extending its existing curation dashboard SciKGDash with embedding-based clustering, interactive inspection, and resolution actions such as merging and deleting. We evaluate the framework on the ORKG, where clustering reveals that up to 30% of predicates are potentially redundant. The analysis also shows recurring modeling patterns that lead to predicate redundancy, user-induced duplication, inconsistent identifier usage, and a lack of standardization in predicate naming and usage. Our findings demonstrate that duplicate predicates arise from user behavior and interface design. Addressing this, requires combining automated methods with human-centered curation and preventive mechanisms. This work positions predicate redundancy as a central data quality challenge and provides a foundation for more systematic and proactive RKG curation.
arXiv:2607.02714v2 Announce Type: replace Abstract: There is no doubt that safety alignment is an essential step in LLM training. However, conceptually it does not distinguish between various domains and the level of potential harm of a query, which creates significant complications in the fields like cyber security, where a model should not be constrained by its safety circuits to accomplish the goals of legitimate, authorized operations. In this work, we share our findings from a large scale abliteration experiment on 24 open-source LLMs and show that domain-specific abliteration is achievable with standard methodology on the example of a 1T-parameter Kimi K2. Building on recent work showing that refusal in LLMs occupies a multi-dimensional subspace within layers, we find that it is also distributed widely across layers, especially in trillion-parameter MoE architectures, and so we aim to capture the part of it that represents harmful concepts in the cybersecurity domain exclusively. We also investigate the correlation between models' features and the effect of domain-specific abliteration, identifying that the type of safety training and architecture are the most reliable predictors. Finally, we classify the models into 3 abliteration susceptibility tiers and put forward a set of conjectures as to why a particular effect from this intervention might be observed in a given model.
arXiv:2607.03692v2 Announce Type: replace Abstract: Spectral methods are widely used to construct representations from the geometry of data, but they often rely on a fixed kernel, graph Laplacian, or manually selected feature scaling. We propose Physics-Informed Eigenfunction Features with Learnable Scaling (PIEFS), a supervised neural representation-learning framework with a spectral inductive bias, based on a modified Dirichlet energy. In PIEFS, scalar coordinate maps are trained under empirical Gram orthogonality, a supervised linear readout, and a Dirichlet penalty in which the input gradient is transformed by a learnable metric $A(x)=\Lambda(x)U(x)$. The diagonal factor $\Lambda(x)$ controls anisotropic scaling, while the orthogonal factor $U(x)$ is parameterized by a structured product of Givens rotations. This construction yields task-adaptive Dirichlet-regularized coordinates rather than eigenfunctions of a fixed supervision-independent operator. Experiments on synthetic, tabular, and image-based benchmarks study the effect of identity, diagonal, and rotation-scaling metrics, and compare the resulting coordinates with classical baselines and NeuralEF. The results support PIEFS as a compact supervised spectral representation method and identify optimization stability, validation on explicit operator eigenproblems, and richer metric parameterizations as the main directions for future work.
arXiv:2606.25309v2 Announce Type: replace-cross Abstract: We prove a residual-work compatibility theory for positive-cone viscoelastic Reynolds states. In Oldroyd--B, the entropy cancellation eliminates incompressible transport and upper-convected stretching against polymeric stress work. After quotienting pressure tensors and spatial means, positive pressure-free residual work can be paid only by the conformation residual through the entropy-dual lever (G=I-A^{-1}). This yields the closed residual-work cone (P+(\alpha/2)\int_{\mathbb T^d}G:S,dx\le 0), exact windowed tests, and the least-cost Hilbert-space repair; constrained closures pay through the projected lever. Strong residual-data limits preserve this cone, while weak--weak limits require the product-defect measure carried by (G:S). The augmented topology is sharp, as shown by localized residual packets. For FENE-P, the lever (G_b(C)=((b-d)/(b-\operatorname{tr}C))I-C^{-1}) makes the finite-extensibility boundary a genuine residual-work boundary.
arXiv:2607.04633v2 Announce Type: replace Abstract: The rational design of ionic electrolytes for lithium-ion batteries (LIBs) is severely constrained by the vast solvent-salt combinatorial space and low efficiency of empirical trial-and-error. While molecular dynamics (MD) bridges microscopic solvation structures and macroscopic physicochemical properties, classical force fields often lack sufficient accuracy for multicomponent systems. To address these challenges, we develop an automated differentiable OPLS-AA force field parameterization workflow tailored for general ionic electrolytes. It employs topology-guided atom typification to reduce parameter redundancy and optimizes Lennard-Jones parameters via the DMFF framework, with experimental density as the fitting target and ionic conductivity as an independent validation metric. Rigorous convergence tests yield a standardized simulation protocol with $\sim$100,000-atom systems and 35-40 ns NVT runs to ensure reliable transport property quantification. High-throughput MD simulations of over 10,000 formulations spanning 67 solvents and 15 lithium salts are conducted on the Tianqiong platform, generating a comprehensive dataset covering five core properties: density, dielectric constant, viscosity, diffusion coefficient, and ionic conductivity. t-SNE visualization reveals partial clustering of distinct salt chemistries, continuous property gradients with concentration and temperature, and internal physical self-consistency, with solvent composition identified as another key performance regulator. Together, the accurate transferable force field and large-scale dataset provide a solid foundation for data-driven rational design of ionic electrolytes.
arXiv:2605.14492v3 Announce Type: replace-cross Abstract: This study provides an analytical foundation for adversarial synchronization control in Kuramoto oscillator networks, where small gradient-based perturbations applied repeatedly to oscillator phases can dramatically enhance or suppress collective synchronization. Using the Ott--Antonsen reduction, we derive an exact closed-form expression for the effect of a single adversarial perturbation (kick) on the order parameter. A key finding is that each kick produces a finite, coupling-independent increment in the order parameter even when synchronization is arbitrarily weak, which combined with slow relaxation near the critical coupling and mean-field feedback explains the disproportionate amplification previously observed in numerical simulations. Fixed-point analysis further reveals a fundamental asymmetry between enhancement and suppression, with the latter governed by noise-induced escape in finite systems. Extending the framework to networks via the annealed network approximation, we show that the theory captures the synchronization behavior of representative model networks and identify a decoupling between kick sensitivity and mean-field dominance in scale-free networks. These results offer a tractable theoretical basis for understanding and designing kick-based synchronization control in oscillator networks.
arXiv:2607.05904v1 Announce Type: new Abstract: Training a language model against its own reference-free judgments (the premise of self-rewarding, self-play, and LLM-as-a-judge pipelines) assumes a model's verdict on a shown answer tracks correctness. We show it fails structurally: conditioned on a candidate, a judge scores plausibility, not correctness, leaving false-positive basins a policy learns to exploit. We measure this with a hidden-anchor audit: a held-out, cross-source exact-match check the judge never sees. On GSM8K with Qwen3 policies, self-play drives the judge's pass rate from 0.72 to 0.94 while true accuracy stays at 0.20 (three seeds). This reward hacking is not white-box gaming: the errors transfer across judge families (Qwen, Llama, Gemma) and scales, a strict three-judge ensemble still accepts 55% of them, and no plausibility-scoring defense closes the basin. The decisive variable is whether the judge commits an answer of its own before using the candidate: committing first drops the false-positive rate from 0.719 to 0.012, blind solving lifts discrimination to 0.96, and used as the training reward the de-anchored channel keeps false positives at zero, preventing the basin rather than only detecting it. A falsifiable bound (the gap is at most 1 - accuracy) predicts which regimes are exposed. The full arc replicates without training under best-of-N selection in code and competition math, and with a Gemma policy.
arXiv:2607.05907v1 Announce Type: new Abstract: Spot is a mature, open-source C++/Python library and toolset for Linear Temporal Logic (LTL) and $\omega$-automata manipulation. While Spot is routinely used as a research and verification back-end, its rich visualization capabilities and Python interface also make it an attractive platform for \emph{teaching} the connections between temporal logic formulas and the $\omega$-automata that give them their semantics.