A major open problem in quantum communication complexity is whether quantum protocols can be exponentially more efficient than classical protocols for computing total Boolean functions; the prevailing conjecture is that they cannot be so. In a seminal work, Razborov (2002) resolved this question for AND-functions of the form $$ F(x,y) = f(x_1 \land y_1, \ldots, x_n \land y_n), $$ when the outer function $f$ is symmetric, by proving that their bounded-error quantum and classical communication complexities are polynomially related. Since then, extending this result to all AND-functions has remained open and has been posed by several authors. In this work, we settle this problem in a strong way. We show that for every Boolean function $f$, the bounded-error quantum and classical deterministic communication complexities of the function $f \circ \mathrm{AND}_2$ are polynomially related, up to polylogarithmic factors in $n$. We prove this by showing that both are characterized--up to polynomial loss--by the logarithm of the De Morgan sparsity of $f$. Our results build on the recent work of Chattopadhyay, Dahiya, and Lovett (2025) on structural characterizations of non-sparse Boolean functions, which we extend to resolve the conjecture for general AND-functions.

Large Language Model (LLMs)-assisted scholarly workflows introduce critical privacy and intellectual property risks. As a uniquely vulnerable cohort driven by publication pressure and a lack of institutional support, novice researchers rely heavily on public LLMs, compelling them to navigate high-stakes privacy-publication trade-offs. To investigate these concerns, we conducted semi-structured interviews with 44 researchers across diverse disciplines. Our findings reveal that the fear of idea leakage paradoxically accelerates, rather than deters, reliance on LLMs, as researchers utilize them to expedite publication. They also held misconceptions that their ideas lacked the unique value to attract targeted attacks, and that their inputs would be safely diluted within massive datasets, preventing reconstruction. From interviews, we identified five types of mitigations including input fragmentation and adversarial probing, though we found that participants largely perceived these measures as ineffective. We outline implications including implementing institution-level sandboxed isolation, scenario-based privacy pedagogy, and verifiable data-deletion audits for transparency.

Reverse k-nearest neighbor (RkNN) search returns all data points that regard a query vector as one of their k-nearest neighbors (kNNs). Existing RkNN methods typically follow a filter-and-verification framework: vectors near the query vector are first collected as candidates and then verified against their kNN-radius (i.e., the distance to their k-th nearest neighbor). However, existing methods face two key limitations in high-dimensional spaces. First, nearby vectors often do not belong to the query's true RkNN set, resulting in excessive candidate expansion overhead. Second, existing methods compute kNN-radius online during verification, incurring substantial query-processing cost. To address these limitations, we propose HRNN, a hybrid graph index for approximate RkNN search. (1) Rather than directly treating nearby vectors as RkNN candidates, HRNN uses them as proxy points based on the assumption that a query's RkNN results can often be discovered through the RkNN results of its nearby vectors. (2) To reduce verification cost, HRNN materializes high-fidelity kNN-radius offline, eliminating expensive online reconstruction while preserving accuracy. HRNN combines a navigation graph, a ranked KNN graph, and reverse-neighbor lists into a hybrid index that supports efficient proxy retrieval, candidate generation, and kNN-radius access. We also develop efficient index construction and append-only maintenance algorithms. Extensive experiments show that HRNN consistently outperforms existing methods, achieving up to one order of magnitude higher throughput. Moreover, HRNN scales to datasets containing up to 10 million high-dimensional vectors while supporting efficient dynamic index maintenance.

Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning, reflection, and tool utilization, unlocking new paradigms for automating complex engineering workflows. However, in the domain of sequential recommendation (SR), tuning models on new datasets still relies heavily on the manual trial-and-error of experienced machine learning engineers. To bridge this gap, we propose \textbf{VirtualMLE}, an LLM-agent framework that leverages the cognitive capabilities of LLMs to organize recommender optimizing into a closed loop of execution, reflection, and memory update. After each trial, the agent explicitly analyzes the observed outcomes and stores concise heuristic feedback in a hierarchical memory system. We evaluate VirtualMLE on three Amazon SR benchmarks with two representative backbones, SASRec and HSTU. VirtualMLE reaches competitive recommendation quality with substantially fewer trials. Furthermore, we observe that cognition summaries distilled from previous datasets can significantly accelerate the search process on unseen datasets, demonstrating the potential of transferring tuning heuristics. Overall, our results provide compelling evidence that LLM agents equipped with reflection and memory can serve as practical virtual engineers to automate and amortize heuristic learning in SR optimization. Our codes are available.

Despite the growing popularity of macOS among end users and enterprise systems, malware research has primarily focused on Windows and Android operating systems, leaving the problem of macOS malware detection relatively unexplored. Indeed, the specificity of the operating system and the unique characteristics of the Mach-O file format can play a fundamental role in the classification of unknown samples, drastically increasing the detection rate. In this work, for the first time in the literature, we employ new domain-specific features, i.e., static features specific to macOS binaries, such as embedded certificates, entitlements, persistence techniques and key system APIs, to train a machine learning malware detector. We perform a comprehensive experimental evaluation on a novel dataset of 41,129 samples, comprising 11,413 benign and 29,716 malicious executables, and demonstrate that our solution achieves state-of-the-art detection performance (98.50%), outperforming all existing approaches, with an average improvement of 16% in terms of detection rate. We also provide an in-depth analysis of the importance of the individual features, showing that our detector effectively leverages the new domain-specific features. Then, in order to evaluate the generalization capabilities of our detector over time, we perform a real-world evaluation on a new dataset of 9,000 fresh macOS executables. The results show that (i) our detector maintains a very high detection rate (99.50%), (ii) outperforms the state-of-the-art by 50%, and (iii) the domain-specific features are crucial for generalizing to novel malware samples, as their removal leads to a 15.92% drop in detection performance. Finally, we also release our dataset to the research community.

E-commerce dispute resolution typically relies on the security assumption that digital evidence truthfully reflects physical reality. Generative AI (GenAI) invalidates this threat model, enabling attackers to fabricate hyper-realistic evidence of product defects at negligible cost. Through semi-structured interviews with merchants (N=17) and platform workers (N=13) in the Chinese e-commerce market, we characterize this shift toward GenAI-enabled scalable fabrication. We outline a taxonomy of four GenAI-enabled threat vectors across the transaction, dispute, logistics and communication phases, highlighting how attackers exploit GenAI to synthesize physically plausible product defects at scale. To mitigate these threats, platforms and merchants are adapting verification strategies, relying on AI tools for automated screening and adversarial interrogation (e.g., requesting multi-angle videos) to increase attack complexity. However, we find several challenges that hinder the adoption of these defenses, including implementation hurdles like structural platform constraints and fundamental limitations regarding the technical sophistication of GenAI. We conclude by outlining design implications for privacy-preserving cross-platform fraud databases, and traceability mechanisms such as embedding verifiable material anchors into the product.

The Membership Problem for Pedigree Polytope (M3P) asks, given $X\in\mathbb{Q}^{\binom{n}{3}}$, whether $X\in\mathrm{conv}(P_n)$, where $P_n$ is the set of all pedigrees. A pedigree is a structured encoding of a Hamiltonian cycle construction in $K_n$. We establish that M3P is solvable in strongly polynomial time via a recursively constructed layered network $(N_k, R_k, μ)$ and a multicommodity flow problem MCF$(k)$. The necessary and sufficient condition for membership established is that the optimal total flow in MCF$(n-1)$ equals the maximum possible flow $z_{\max}$. The complexity analysis, grounded in Tardos's strongly polynomial algorithm for combinatorial linear programs (1986), shows that this condition can be checked in strongly polynomial time in the dimension of the matrix involved. By sufficiency, this implies M3P~$\in$~P. Since the Symmetric Travelling Salesman Problem (STSP) reduces to M3P via the Multistage Insertion (MI) formulation (Arthanari 1983), STSP is solvable in polynomial time, and the P vs.NP question is resolved. The proofs leading to this result are fully machine-verified in Lean~4/Mathlib4, with zero unresolved \texttt{sorry}s in the main proof chain. The main contribution is the Lean~4 machine verification of all proofs in the main chain, resulting in \texttt{theorem p\_equals\_np}: P = NP. The Lean~4 formal verification covers the sufficiency of MCF(n-1) for membership in $\mathrm{conv}(P_n)$, and the P = NP chain via Maurras (2002), Grötschel--Lovász--Schrijver (1988), Cook (1971), and Karp (1972). The complete lean project (36 Lean~4 files, 2968/2968 build targets clean) is available at https://github.com/TiruArt/Pedigree-Polytopes-Lean4.

The proliferation of AI agents empowers independent developers, defined as individual or small groups who self-initiate projects rather than fulfill client-based contracts, to create sophisticated autonomous systems, but also introduces novel security and privacy (S&P) challenges beyond traditional corporate structures. We conducted an interview study (N=28) with Chinese developers, whose extensive use of global LLM services offer valuable insights into this population. We investigate their understandings, practices and challenges of S&P challenges in their developed AI agent products. We revealed that independent developers frequently think and act from their users' perspective. They focused on user-facing safety risks such as harmful content while exhibiting low awareness of security vulnerabilities. Consequently, developers rely almost exclusively on ad-hoc, manually crafted safeguards and informal communication, with an absence of formal tools or processes for S&P practices. We found these actions are driven by various inhibitors, primarily a lack of formal training on S&P related skills, accessible security tools and actionable guidance from platforms. Our work contributed the first exploration of independent AI agent developers' S&P understanding, outlining opportunities for tailored security tooling.

Protein complexes are central units of cellular organization, yet their identification from protein-protein interaction (PPI) networks remains difficult because interactome maps are noisy, incomplete, context dependent, and unevenly annotated. This focused methodological review examines evidence-aware approaches that combine PPI topology with Gene Ontology (GO) annotations, expression profiles, subcellular localization, sequence or domain evidence, temporal information, and representation learning, with emphasis on post-2018 methods and selected historical baselines. The central synthesis is that transparent evidence-aware graph methods currently offer the strongest tradeoff between biological plausibility and reproducibility, while deep, hypergraph, and dynamic heterogeneous models expand biological realism but require stronger benchmark control. The central bottleneck is no longer only the lack of algorithms, but the lack of harmonized, overlap-aware, and reproducible evaluation protocols. We therefore recommend unified benchmark versions, explicit GO-circularity controls, overlap-aware metrics, uncertainty estimates, and executable software packages over isolated source-specific F-measure gains.

Artificial intelligence systems that model and support human cognition require reliable measures of cognitive state. We present the Focus Performance Score (FPS) from the Pulse Focus mobile Stroop application and evaluate whether it measures attentional control during color-word conflict resolution. We conduct behavioral, neural, and formula validation analyses. Behavioral results (N=466, 111,133 trials) show that FPS captures the Stroop interference effect, tracks individual differences in attentional control, and demonstrates strong test-retest reliability. Neural validation using the DMCC55B fMRI dataset (N=55) shows that the primary FPS component, mean incongruent reaction time, is significantly associated with anterior cingulate cortex activation, a key neural substrate of conflict monitoring. Formula validation identifies and resolves structural redundancy within the scoring framework and provides convergent support for the weighting design. Together, these findings establish FPS as a behaviorally valid, reliable, and neurally grounded measure of attentional control. FPS provides a defensible behavioral signal for evaluating human attentional state and supports future work on attention-aware human-AI interaction and physiological state modeling.

Classical query optimization searches over algebraically equivalent plans that differ only in cost. This assumption breaks once LLM-backed operators enter the picture: their placement, ordering, and granularity jointly determine both dollar cost and answer quality, and the right choice among the alternatives is often revealed only at runtime. We formalize this setting as agentic query execution, a query execution paradigm in which agent-based planning is interleaved with execution, and agent workflow optimization becomes the analogue of classical query optimization. We then present EnumGRPO, a self-improving optimizer for this setting. During a learning stage, EnumGRPO enumerates query plans over decisions such as execution paradigm, operator type, operator placement, selectivity scope, and projection width, then distills quality-cost feedback into reusable planning heuristics via in-context reinforcement learning. Across four databases in SWAN, EnumGRPO achieves 35.4% execution accuracy at $0.011 per query in LLM-operator cost, a ~317x cost reduction over the hybrid query baseline with an 18% relative improvement in answer accuracy.

Vector database search with frequent updates is increasingly critical in applications such as retrieval augmented generation, recommendation systems, and large-scale embedding retrieval. Existing solutions, such as graph-based and partition-based approximate nearest neighbor search (ANNS), suffer from frequent index rebuilding due to data distribution-dependent indexing that impacts continuous deployment and causes long rebuilding latency. This paper proposes an algorithm-hardware co-designed platform, ACRONYM, that addresses key problems with state of the art database search. Algorithmically, it leverages efficient encoding independent of data distribution and Hamming-distance based search for efficient hardware acceleration. Architecturally, we propose CAM-based in-memory parallel distance computation followed by time multiplexed approximated top-k selection to enable the exhaustive search. We propose two-stage search that includes coarse search followed by binary refinement to achieve high recall in CAM based search which is heavily limited to small vector dimension due to capacity and wordline parasitic. ACRONYM supports continuous update without stalling and integrates novel XOR-and-Accumulate (XAC) based systolic-array encoder for efficient on chip encoding during search. Across million-scale datasets, while serving dynamic database ACRONYM achieves >90% recall at a throughput of 8e6 queries per second, with a memory footprint of only 32MB and an average energy consumption of 2.56uJ per query, speedup over HNSW (CPU) of about 400x and FAISS-IVF (GPU) of about 80x.

The increasing penetration of electric vehicles (EVs) introduces new challenges for emergency evacuation planning due to limited driving range, long charging times, and constrained charging infrastructure, particularly under disaster induced disruptions. This paper proposes a novel optimization based evacuation framework for EVs using Equivalent Circuit Models (ECMs) to jointly address routing, charging, and congestion management. By leveraging electrical analogies, traffic flow is modeled as electrical current, travel time as resistance, and driving range as voltage, enabling the use of Kirchhoff laws to enforce flow balance and energy feasibility constraints. The proposed controllable ECM incorporates binary switches to regulate route selection and explicitly models charging delays and range replenishment at both Fixed Charging Stations (FCSs) and Mobile Charging Stations (MCSs). The resulting formulation leads to an integer programming problem that determines optimal evacuation routes, charging durations, and the placement and number of MCSs to minimize evacuation time. The framework is extended to multiple origin destination pairs using the principle of superposition and supports fairness aware performance metrics, including worst case, average, and variance based evacuation times. Simulation studies on large scale transportation networks in California demonstrate that the proposed approach significantly improves evacuation efficiency and robustness, particularly in scenarios with limited charging access, highlighting the critical role of MCSs in EV based emergency evacuations.

Logical database design has traditionally optimized database schemas, including tables, columns, keys, constraints, and views, for correctness, integrity, and human-written application queries. LLM-based Text-to-SQL changes the consumer: the schema is now often read as text by a language model, so design choices that preserve database semantics can still change SQL-generation accuracy. We argue that this creates a new design objective alongside the classical ones - LLM-friendly logical database design, the property that a schema is easy for a language model to map from natural language to correct SQL - and treat it as the optimization target of this paper. We instantiate this objective with three semantics-preserving schema transformations that re-purpose classical schema-design ideas: schema abstraction (+A: logical views that materialize recurring join paths), schema partitioning (+P: workload-aware logical partitions that prune irrelevant context), and schema renaming (+R: descriptive identifiers that improve downstream column linking and predicate construction). The three operators compose, and each preserves the underlying database semantics. When historical question-SQL pairs are available, they guide both partitioning and abstraction; in zero-shot settings, renaming applies directly, and abstraction falls back to an ad-hoc per-question variant. We evaluate the resulting schemas on BIRD-Union and Spider-Union across multiple Text-to-SQL pipelines and language model backbones, with gains of up to 4.2% in execution accuracy. The best transformation varies modestly across pipelines and models, with the full +A+P+R consistently improving; multiple operator combinations are competitive on each pipeline. These results show that LLM-friendly logical design is a practical and underexplored database-side optimization target, complementary to existing Text-to-SQL pipelines.

Finding truly analogous precedents requires capturing legal reasoning beyond surface word overlap. We present a two-stage, section-aware framework for legal case retrieval that first segments raw judgments into facts, issues, decision, and reasoning using a deterministic large language model (LLM) offline. In Stage 1, we combine parallel lexical (BM25) and semantic (dense ANN) whole-document searches via Reciprocal Rank Fusion (RRF) to form a high-recall candidate pool. In Stage 2, we perform fine-grained, like-for-like comparisons (e.g., query reasoning vs. candidate reasoning). To address the scale mismatch between unbounded lexical scores and cosine similarities, we apply query-wise Z-score normalization before aggregating signals with learned section weights. For the top results, the system returns the relevant section text with a concise, grounded rationale and party-stance labels. We evaluate on a jurisdiction-scale benchmark, demonstrating consistent gains over strong lexical and neural baselines while maintaining high candidate coverage

A thermomagnetic pendulum is introduced as a coupled thermo-magnetic-mechanical system consisting of a ferromagnetic bob under gravity and an offset permanent magnet. Heating drives the bob temperature above and below the Curie point, causing magnetic attraction to vanish and recover as the bob moves and cools. A multiphysics model is developed in which the magnetic torque depends nonlinearly on the bob temperature field and pendulum configuration. The formulation couples transient three-dimensional heat transfer, a temperature-dependent magnetization law, and pendulum dynamics. Simulations show angular torque asymmetry, rapid force reduction near the Curie point, and sustained oscillations.

Impact factor, H-index, citation index, and such other indices have been playing an increasing role in scientific assessment of institutions, researchers, allocation of research funds,... across the globe. These indicies do not have any statistical basis but lots of decisions such as ranking of institutions, ranking of departments, assessment of faculty members for hiring and for promotions as well as selection for various awards are being made by using these indices. Several experts across disciplines have been writing that these indicies should have a marginal role, if any, and judgements should be based on critical assessment of the content by experts. But the dependence on these s steadily increasing. This article cites various such documents being published across the globe and across the disciplies and urging decision makers to ignore or at best give a minimal weight to such indices.

Multi-agent AI systems show promise for automating software engineering tasks, yet existing approaches suffer from coordination overhead, quality control gaps, and limited human oversight. We introduce SPOQ (Specialist Orchestrated Queuing), a methodology combining three innovations: (1) wave-based topological dispatch that computes parallel execution waves from task dependency graphs; (2) dual validation gates applying quality metrics before execution (planning validation) and after (code validation) to reduce rework cycles; and (3) Human-as-an-Agent (HaaA) integration, where a human specialist participates in decomposition and can be consulted during execution. SPOQ uses a three-tier agent hierarchy (Opus workers, Sonnet reviewers, Haiku investigators) to optimize cost-quality tradeoffs. We evaluate SPOQ through four experiments. Experiment 1: wave dispatch approaches the critical-path lower bound (ratio 1.03--1.11, speedup up to 14.3x); on a 2-slot local backend it delivers a stable 1.4x speedup. Experiment 2: SPOQ improves planning coverage from 93.0 to 99.75, eliminates cyclic plans, and lifts parallelism from 31.0 to 75.25. Experiment 3: dual validation reduces defects from 0.34 to 0.20 per task and lifts test pass rate from 91.25% to 99.75%. Experiment 4: human review reduces residual defects from 0.47 to 0.03 per task. Results are replicated on a locally hosted open-weights model (Qwen3.6-35B-A3B), verifying gains are attributable to orchestration rather than any specific model. A longitudinal study across 17 repositories, 8,589 commits, 1,822 tasks, and 13,866 tests (99.87% pass rate) provides ecological validation.

This paper develops an impulse-supervised confined exploration framework for learning local optimal controller for a class of nonlinear systems. The proposed approach combines continuous-time approximate dynamic programming (ADP) with an impulsive supervisory layer, where impulsive braking confines the state within a prescribed region in which a local linear approximation of the nonlinear system is valid. This enables desired persistent excitation required for parameter convergence while preventing large state deviations that invalidate local optimality. The resulting hybrid closed-loop system enforces invariance of the exploration region through state-triggered braking inputs. Simulation results on a nonlinear mechanical system demonstrate effectiveness of the proposed approach.

With the growing integration of renewable energy sources into power grids, the risks of oscillation caused by interactions between grid-tied inverters and the grids are becoming increasingly prominent. Although existing studies have made significant progress in inverter modeling and oscillatory stability analysis, most of them do not sufficiently consider complex mirror frequency coupling effects (MFCE) under unbalanced operating conditions, leading to unreliable models and erroneous stability analysis results. To address this inadequacy, this work develops a novel sequence impedance modeling scheme that can be widely applied to unbalanced operating conditions. In particular, taking a representative type of grid-forming inverter for instance, i.e., droop-controlled inverter (DCI), a single-input single-output sequence impedance modeling method based on harmonic linearization (HL) is proposed to comprehensively model both a given DCI and the connected grid. By accounting for multi-frequency interactions within the DCI, this method captures MFCE and unbalanced factors, leading to a more accurate impedance model. Further, the dominant factors influencing system stability are identified with a combination of normalized sensitivity analysis and proportional weighting. Finally, the detailed impacts of these dominant factors on system stability margin under three typical unbalanced operating conditions are analyzed through the Bode criterion. The effectiveness and reliability of the whole scheme proposed in this work are validated on the constructed grid-connected droop-controlled experimental platform.

AI systems increasingly shape human workflows by generating intermediate artifacts that users can adopt, revise, or ignore. While prior work has shown that AI assistance can improve the efficiency and accuracy of required tasks, less is known about whether it can increase participation in discretionary but beneficial work that users often intend to perform but frequently skip. We study this question in the context of personalized feedback provision in higher education, a pedagogically valuable but often optional practice. We conduct a mixed-methods study combining a randomized field experiment and qualitative interviews in a 300-level machine learning course with n=11 teaching assistants (TAs) and n=88 students. Student submissions were randomly assigned to either (1) a treatment condition where TAs received AI-assisted feedback drafts after grading or (2) a control condition without drafts. TAs remained fully in control and could use, edit, or ignore drafts at their discretion. We find that AI-assisted feedback significantly increases feedback provision (+10.8 percentage points, SE=1.1, p<0.001) and feedback length (+39.8 chars, SE=3.45, p<0.001) without negatively affecting student usefulness ratings or reducing time per character. Qualitative findings suggest that AI-assisted drafts function as editable scaffolds that lower barriers to initiating feedback rather than reducing overall effort. Our findings highlight AI's promise for discretionary but beneficial tasks: increasing work that might otherwise go undone while preserving human control over final outcomes.

This paper investigates the stabilization of linear systems subject to simultaneous, mismatched time delays in both the control input and system output vectors. The proposed control framework is developed in two primary stages. First, an asymptotically stabilizing delayed state-feedback controller is synthesized by leveraging recent advancements in Linear Matrix Inequality (LMI) techniques. Second, this controller is realized using novel time-delay compensators \cite{trinhnam26}. This architecture successfully accommodates an output measurement delay $τ_y$ that is independent of the input delay $τ_u$, enabling direct estimation of the delayed state-feedback control law. The proposed methodology is then extended to target output controllers to account for simultaneous, mismatched time delays in both the control input and system output vectors.

Zero-knowledge proofs (ZKP) allows a prover to convince a verifier of computational correctness without revealing private data, ensuring both privacy and verifiability. However, proof generation is highly compute-intensive, dominated by polynomial (POLY) and elliptic-curve (EC) operations. These workloads pose two key challenges for hardware acceleration: (1) efficiently supporting diverse large-precision modular multiplications, and (2) maintaining high utilization across workloads that dynamically shift between POLY and EC stages. Existing reconfigurable accelerators address these issues only partially, remaining limited in precision scalability, algorithmic flexibility, and resource efficiency. To overcome these limitations, we propose ZK-Flex, a flexible and scalable software-hardware co-designed framework for accelerating ZKP proof generation. The software layer incorporates POLY and EC optimizers that reduce computation through hardware- and workload-aware algorithmic choices, while the hardware integrates TCore, a Toom-Cook-based multi-precision core with a flexible NoC and a linked-list memory mechanism that improves parallelism under limited memory capacity. Across representative ZKP benchmarks, ZK-Flex achieves 5 to 11 times speedup and up to 3.8 times higher area efficiency over the state of the art, establishing a new foundation for high-performance, reconfigurable ZKP acceleration.

This study examines whether standard matching mechanisms function as intended in LLM-agent markets, where LLM agents make allocation-related decisions as delegated decision-makers. We compare decentralized free-negotiation markets with centralized mechanism-based markets including several representative mechanisms. Across controlled one-to-one matching environments, mechanism-based markets generally outperform free negotiation in terms of stability and efficiency. We also find that LLM agents report preferences truthfully at substantially higher rates than human subjects in comparable DA and EADA environments. However, truth-telling is not uniformly aligned with formal strategy-proofness across all mechanisms: TTC, despite being strategy-proof, does not always elicit higher truth-telling than EADA. These results suggest that matching theory provides a useful but incomplete guide for designing institutions in LLM-agent markets.

Agent skills extend LLM agents with reusable instructions, scripts, tool bindings, and contextual dependencies. However, current skill ecosystems largely rely on trust-based loading and static inspection, leaving a gap between what a skill can inject into an agent's context and what it can cause the agent to do at runtime. This gap introduces new security and privacy risks, and existing defenses primarily inspect skill files statically or regulate individual tool calls, without systematically connecting a skill's declared intent with its runtime behavior. In this paper, we present SkillGuard, a skill-centric permission framework that treats skills as permission-bearing executable artifacts. SkillGuard introduces a dual-plane governance model that jointly regulates context influence and action side effects through skill manifests, runtime access control, user-mediated authorization, deny-by-default enforcement, capability inference, and behavior monitoring. We evaluate SkillGuard on 315 real-world skills and SkillInject. The permission taxonomy covers 99.76% of observed protected objects, and automated manifest generation reaches 91.0% F1. In adversarial evaluations, SkillGuard reduces attack success from 32.37% to 23.02% for contextual injections and from 25.56% to 16.67% for obvious injections, while maintaining benign task utility. These results suggest that SkillGuard, as a skill-centric permission framework, can provide a practical foundation for improving the privacy and security of agent skill ecosystems.

Drivers with peripheral visual field defects may fail to notice pedestrians in their peripheral visual field, leading to delayed hazard awareness and increased collision risk. This study explores hanger reflex cue (HRC) as a driving assistance method for drivers with peripheral visual field defects, in which mechanical pressure is applied to specific regions of the head to facilitate anticipatory orientation toward potentially risky pedestrians and support safer driving. In a driving simulator experiment with 15 participants, we compared driving behavior with and without HRC during pedestrian encounters under simulated peripheral visual field defect. The results showed that HRC significantly shifted drivers' modal head rotation angle toward the risky pedestrian and significantly increased gaze duration toward that pedestrian. Collision occurrence was lower in the w/ HRC condition than in the w/o HRC condition, although the direct effect of HRC on collision occurrence showed only a marginal trend. A piecewise structural equation modeling analysis further suggested that HRC may contribute to collision reduction through a sequential pathway from head rotation to gaze allocation and then to collision occurrence. These findings provide preliminary evidence that HRC can support anticipatory attention allocation toward peripheral hazards and may offer a promising driving assistance method for drivers with visual field impairment.

Alternative data availability (AltDA) systems provide Ethereum L2s with an external data publication layer for high throughput rollup designs. By moving bulk data publication outside of Ethereum, AltDA allows L2s to process more data than native DA. However, this replacement introduces a new consensus critical integration layer. Existing ecosystem frameworks identify high level risks, such as external DA trust assumptions and the presence or absence of a DA verifier, but do not provide a complete specification for how an L2 should integrate with AltDA. This gap can lead to L2 halts, inconsistent derivation across honest L2 nodes, invalid state assertions, or bridge attacks. This paper presents a canonical validation framework for secure AltDA integration. We model the boundary as a typed, deterministic, and total translation from L1 inbox bytes to an AltDA commitment, then to externally available data, and finally to the rollup payload consumed by the rest of core L2s logic. The central principle is that every adversarial input must lead to a defined unique outcome. We show how missing obligations lead to concrete failure modes, including underconstrained settlement, derivation halts, inconsistent honest node behavior, invalid state assertions, and bridge safety failures. We then apply the framework to representative AltDA integration architectures, including Celestia-Blobstream, EigenDA based designs, and Avail-ZKsync. Our evaluation shows that secure AltDA integration is not determined solely by the DA provider or bridge. The surrounding L2 integration must also enforce the full validation relation connecting L1 inbox inputs to accepted L2 state.

Graph indexes are widely used for high-recall approximate nearest neighbor search (ANNS), but many real-time applications require streaming ANNS. In these real-time applications, continuously arriving embeddings must search the existing graph for candidate neighbors before updating graph edges, which makes repeated index construction a bottleneck for streaming ingestion workloads. We propose Slipstream, a new method that significantly reduces the computational cost of frequent insertions in graph indexes for ANNS. The core idea of Slipstream is exploiting the continuity in vector streams: the newly arrived point starts from promising candidates found during the previous insertion rather than searching from the entry point. More technically, Slipstream evaluates distinct subsets of starting candidates followed by an adaptive controller that narrows or widens the range according to the stream's stability. We further show that Slipstream is beyond heuristic: We derive an abstract model to characterize Slipstream's performance and analyze its theoretical bounds. We have implemented Slipstream in two popular open-source libraries (Faiss, HNSWLib) and compared it with four baseline methods on five streaming vector datasets. Experimental results show that Slipstream achieves up to 30.8$\times$ higher end-to-end throughput than baselines while maintaining at least 0.95 recall@10.

Multimodal user interfaces increasingly combine speech, gesture, vision, gaze, touch, biosignals, and other sensor data. Recent toolkits from the past five years, such as Geno, Multisensor-Pipeline (MSP), ReactGenie, and EmoSync, aim to make it easier for developers to prototype such interfaces, while older work such as WAMI shows how early web-based multimodal systems were conceived. Yet the field still lacks a systematic and reusable way to compare what these toolkits actually support, how much implementation work they offload from developers, and which evaluation strategies are appropriate for them. This paper reframes an HCI seminar draft into a benchmarking framework paper for multimodal user interface toolkits. Rather than reporting completed empirical results, it proposes a structured benchmark based on document analysis, technical comparison, and a future developer-based evaluation. The framework is organized around three dimensions: modality coverage and interaction abstraction, developer experience and workflow, and experimental and integration support. The paper illustrates the framework through five representative toolkits: Geno, MSP, ReactGenie, WAMI, and EmoSync. The contribution is a reusable benchmark template that future researchers can instantiate with empirical measurements, developer studies, and additional multimodal toolkits.

Intelligent tutoring systems excel at generating explanations but rarely provide principled diagnosis of where and why a learner is wrong. We introduce a misstep-aware coaching capability for Ivy, a neurosymbolic AI coach, built on a two-model architecture that augments a Task-Method-Knowledge (TMK) model with a new Pedagogical Model (PM) in the context of an online graduate AI course at Georgia Tech. The PM makes instructor diagnostic knowledge explicit and machine-readable by encoding, for each quiz question and incorrect response, the learner's underlying belief(a brief statement of the incorrect idea or missing knowledge), a TMK locus(the source of the misunderstanding), a misconception type and targeted scaffolding derived from the instructor's Q\&A key. Using quiz questions from the course, we demonstrate a proof-of-concept pipeline that detects and classifies learner errors and generates diagnosis-grounded scaffolding, moving Ivy beyond knowledge retrieval toward diagnostic misstep awareness, and enabling more precise, actionable feedback that supports conceptual change and advances adaptive learning systems in AI in education and the learning sciences.