All papers

Formulates valid calibrations to safely replace global sensitivity with local sensitivity in differential privacy exponential mechanisms.

Proposes a cryptographic framework uniting aggregate authorization, identity-based authentication, and bitstream encryption for cloud FPGAs.

Systematizes 22 protocol families for privacy-preserving order matching across traditional financial markets and decentralized exchanges.

Formalizes the organizational context gap in enterprise security and outlines challenges in connecting human policies to machine state.

Introduces a moving target defense to detect and mitigate low-rate denial-of-service attacks in software-defined EV charging networks.

Designs a sign-less hybrid authenticated key exchange using ML-KEM and RSA to secure constrained IoT devices against quantum threats.

Establishes information bounds and query complexity frontiers for verifying answers when transferring hints from related source tasks.

Proves modeling limits and incentive mechanisms for separating validation from attention in optimistic blockchain rollups.

Analyzes local leakage resilience in Shamir secret sharing and bounds worst-case optimal polynomial intersection over finite fields.

The paper demonstrates microarchitectural attacks that extract DRAM mapping and memory contents using low-resolution on-chip power rail measurements without timers.

The paper designs a monitor architecture leveraging AMD SEV-SNP privilege levels to run secure process enclaves inside confidential virtual machines.

The paper presents a two-server federated learning protocol using secret sharing and public verification to defend against poisoning attacks in industrial networks.

The paper describes ongoing approaches to decentralize identity trust by linking human-readable handles with self-authenticating cryptographic identifiers.

The paper benchmarks the power, area, and performance tradeoffs of implementing hardware-performance-counter malware detectors on FPGAs and ASICs.

The paper builds a game-theoretic model to evaluate the trade-offs between system transparency and security by obscurity against strategic adversaries.

The paper introduces a sample-preserving search-to-decision reduction for noisy linear equations over large moduli with polynomial runtime.

The paper presents a trust-network protocol using smart contracts to bound financial exposure and mitigate Sybil attacks in permissionless AI agent markets.

The paper quantifies message overhead and transport limitations when integrating post-quantum cryptography into the Matter over Thread IoT protocol.

The paper proves subexponential time bounds for solving bounded-error Learning with Errors given very few samples using an adapted Wagner-style algorithm.

The paper proposes a context-aware prioritization engine that incorporates system operational details to improve enterprise vulnerability scoring and remediation decisions.

The paper introduces a formal modeling framework combining attack graphs and fault trees to analyze cyber attacks triggered by industrial safety responses.

The paper provides an empirical study of cross-chain bridge verification vulnerabilities and categorizes common flaws in cross-chain smart contract validation logic.

The paper proposes a deep convolutional, quantization-free watermarking framework to embed metadata into electrocardiogram signals without hurting diagnostic details.

The paper introduces a method to embed hidden data inside Wi-Fi legacy short training fields without breaking packet detection.

The paper evaluates security enforcement across Android inter-component communication boundaries, identifying recurring enforcement gaps.

The paper introduces a decision-focused framework that aligns power grid cyber intrusion detection directly with operational cost minimization.

The paper models structured sentiment analysis as dependency graph parsing implemented through sequence labeling across five languages.

The paper proposes a four-tensor attention architecture that jointly normalizes attention over semantic and temporal fibers for scene prediction.

The paper introduces a dual-path parametric memory method that combines pooled history representations with sequential adapter updates.

The paper proposes a sparse factor framework to map multimodal behavioral data to interpretable concepts for depression assessment.

The paper demonstrates that entropy-based byte patching misses predictable calculation points and proposes a boundary-dependence signal to fix it.

The paper develops an LLM-based pipeline to automatically detect outcome switching and presentation spin in clinical trial reports.

The paper benchmarks a specialized decision model against general-purpose LLMs on English and Chinese stance detection tasks.

The paper proposes an event-centric memory framework that converts dialogue history into query-relevant graph working memory for conversational agents.

The paper describes training a competitive decoder-only search reranker from scratch using modest computational resources and common datasets.

The paper introduces a 7,000-task benchmark to evaluate how multimodal models selectively use or ignore conversational context.

The report presents an omni-modal model family trained by scaling reinforcement learning throughput, environments, and agentic reward compute.

The paper benchmarks a specialized multiple-choice decision model against 19 general-purpose LLMs across knowledge, reasoning, and multilingual tasks.

The paper reviews LLM-based first-order logic autoformalization, distinguishing ontology extraction from logical translation and outlining key benchmarking challenges.

The paper introduces an educational tool combining Arabic knowledge graphs and passage retrieval to generate narrative-based questions.

The paper shows that whole-context KV cache rehearsal harms compression under tight budgets, proposing targeted rehearsal to preserve answer-relevant entries.

The paper trains 123 models across 54 tokenizers to show tokenizer choice matters more for languages with less training data.

Releases BERT models pre-trained with yearly chronological cutoffs to reduce lookahead bias in scientific and patent text mining.

Presents a rolling masked diffusion method that accelerates full-duplex spoken dialog models by forecasting multiple future interaction frames.

Evaluates whether language models can act as world models to predict empirical experimental outcomes across AI research workflows.

Designs an inference serving system optimized for dynamic, token-level routing across multiple language models.

Proposes a scope-then-rank retrieval method using document topology and relational structure rather than pure semantic similarity.

Develops a framework for egocentric multimodal assistants to decide both when to speak and what guidance to provide.

Introduces a post-training framework that trains text-to-SQL agents directly inside interactive database execution environments.

Identifies distribution shift during autoregressive decoding and proposes a decoding-aware pruning method to accelerate model inference.

Presents a symmetry-aware value cache merging method to compress key-value memory during long-context decoding without architectural changes.

Proposes a method to select a compact subset of procedural skills that provide effective training signals during model distillation.

Introduces a multilingual tokenizer that uses structural discovery and linguistic constraints to improve downstream model representations.

Presents a framework that uses language models offline to generate executable feature-engineering code for tabular risk models.

Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions.

Proposes a framework that encodes visual trajectories into reusable skill cards to reinforce vision-language agent planning and rewards.

Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models.

Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams.

Presents an arena benchmark where pairs of coding agents dynamically generate, test, and debug code against each other.

Introduces a lightweight correction mechanism to restore token dependencies during parallel draft generation in speculative decoding.