Beyond Context Sharing: A Unified Agent Communication Protocol (ACP) for Secure, Federated, and Autonomous Agent-to-Agent (A2A) Orchestration
arXiv:2602.15055v1 Announce Type: cross Abstract: In the artificial intelligence space, as we transition from isolated large language models to autonomous agents capable of complex reasoning and tool use. While foundational architectures and local context management protocols have been established, the...
The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
arXiv:2602.15382v1 Announce Type: new Abstract: Multi-Agent Systems (MAS) powered by Large Language Models have unlocked advanced collaborative reasoning, yet they remain shackled by the inefficiency of discrete text communication, which imposes significant runtime overhead and information quantization loss. While latent...
Perspectives - Interactive Document Clustering in the Discourse Analysis Tool Suite
arXiv:2602.15540v1 Announce Type: new Abstract: This paper introduces Perspectives, an interactive extension of the Discourse Analysis Tool Suite designed to empower Digital Humanities (DH) scholars to explore and organize large, unstructured document collections. Perspectives implements a flexible, aspect-focused document clustering...
Multi-agent cooperation through in-context co-player inference
arXiv:2602.16301v1 Announce Type: new Abstract: Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Recent work showed that mutual cooperation can be induced between "learning-aware" agents that account for and shape the learning dynamics of their...
Agent Skill Framework: Perspectives on the Potential of Small Language Models in Industrial Environments
arXiv:2602.16653v1 Announce Type: new Abstract: Agent Skill framework, now widely and officially supported by major players such as GitHub Copilot, LangChain, and OpenAI, performs especially well with proprietary models by improving context engineering, reducing hallucinations, and boosting task accuracy. Based...
Towards a Science of AI Agent Reliability
arXiv:2602.16666v1 Announce Type: new Abstract: AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many agents still continue to fail in practice. This discrepancy highlights a fundamental limitation of current...
The Perplexity Paradox: Why Code Compresses Better Than Math in LLM Prompts
arXiv:2602.15843v1 Announce Type: cross Abstract: In "Compress or Route?" (Johnson, 2026), we found that code generation tolerates aggressive prompt compression (r >= 0.6) while chain-of-thought reasoning degrades gradually. That study was limited to HumanEval (164 problems), left the "perplexity paradox"...
State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models
arXiv:2602.15858v1 Announce Type: cross Abstract: As large language models (LLMs) move from static reasoning tasks toward dynamic environments, their success depends on the ability to navigate and respond to an environment that changes as they interact at inference time. An...
Improved Upper Bounds for Slicing the Hypercube
arXiv:2602.16807v1 Announce Type: new Abstract: A collection of hyperplanes $\mathcal{H}$ slices all edges of the $n$-dimensional hypercube $Q_n$ with vertex set $\{-1,1\}^n$ if, for every edge $e$ in the hypercube, there exists a hyperplane in $\mathcal{H}$ intersecting $e$ in its...
AgentLAB: Benchmarking LLM Agents against Long-Horizon Attacks
arXiv:2602.16901v1 Announce Type: new Abstract: LLM agents are increasingly deployed in long-horizon, complex environments to solve challenging problems, but this expansion exposes them to long-horizon attacks that exploit multi-turn user-agent-environment interactions to achieve objectives infeasible in single-turn settings. To measure...
HQFS: Hybrid Quantum Classical Financial Security with VQC Forecasting, QUBO Annealing, and Audit-Ready Post-Quantum Signing
arXiv:2602.16976v1 Announce Type: new Abstract: Here's the corrected paragraph with all punctuation and formatting issues fixed: Financial risk systems usually follow a two-step routine: a model predicts return or risk, and then an optimizer makes a decision such as a...
M2F: Automated Formalization of Mathematical Literature at Scale
arXiv:2602.17016v1 Announce Type: new Abstract: Automated formalization of mathematics enables mechanical verification but remains limited to isolated theorems and short snippets. Scaling to textbooks and research papers is largely unaddressed, as it requires managing cross-file dependencies, resolving imports, and ensuring...
Continual learning and refinement of causal models through dynamic predicate invention
arXiv:2602.17217v1 Announce Type: new Abstract: Efficiently navigating complex environments requires agents to internalize the underlying logic of their world, yet standard world modelling methods often struggle with sample inefficiency, lack of transparency, and poor scalability. We propose a framework for...
All Leaks Count, Some Count More: Interpretable Temporal Contamination Detection in LLM Backtesting
arXiv:2602.17234v1 Announce Type: new Abstract: To evaluate whether LLMs can accurately predict future events, we need the ability to \textit{backtest} them on events that have already resolved. This requires models to reason only with information available at a specified past...
One-step Language Modeling via Continuous Denoising
arXiv:2602.16813v1 Announce Type: new Abstract: Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. In practice, however, they exhibit a sharp degradation of sample quality in the few-step regime,...
BankMathBench: A Benchmark for Numerical Reasoning in Banking Scenarios
arXiv:2602.17072v1 Announce Type: new Abstract: Large language models (LLMs)-based chatbots are increasingly being adopted in the financial domain, particularly in digital banking, to handle customer inquiries about products such as deposits, savings, and loans. However, these models still exhibit low...
Real-time Secondary Crash Likelihood Prediction Excluding Post Primary Crash Features
arXiv:2602.16739v1 Announce Type: new Abstract: Secondary crash likelihood prediction is a critical component of an active traffic management system to mitigate congestion and adverse impacts caused by secondary crashes. However, existing approaches mainly rely on post-crash features (e.g., crash type...
A Residual-Aware Theory of Position Bias in Transformers
arXiv:2602.16837v1 Announce Type: new Abstract: Transformer models systematically favor certain token positions, yet the architectural origins of this position bias remain poorly understood. Under causal masking at infinite depth, prior theoretical analyses of attention rollout predict an inevitable collapse of...
Construction of a classification model for dementia among Brazilian adults aged 50 and over
arXiv:2602.16887v1 Announce Type: new Abstract: To build a dementia classification model for middle-aged and elderly Brazilians, implemented in Python, combining variable selection and multivariable analysis, using low-cost variables with modification potential. Observational study with a predictive modeling approach using a...
Malliavin Calculus as Stochastic Backpropogation
arXiv:2602.17013v1 Announce Type: new Abstract: We establish a rigorous connection between pathwise (reparameterization) and score-function (Malliavin) gradient estimators by showing that both arise from the Malliavin integration-by-parts identity. Building on this equivalence, we introduce a unified and variance-aware hybrid estimator...
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
arXiv:2602.17028v1 Announce Type: new Abstract: Detecting anomalies in time-series data is critical in domains such as industrial operations, finance, and cybersecurity, where early identification of abnormal patterns is essential for ensuring system reliability and enabling preventive maintenance. However, most existing...
FLoRG: Federated Fine-tuning with Low-rank Gram Matrices and Procrustes Alignment
arXiv:2602.17095v1 Announce Type: new Abstract: Parameter-efficient fine-tuning techniques such as low-rank adaptation (LoRA) enable large language models (LLMs) to adapt to downstream tasks efficiently. Federated learning (FL) further facilitates this process by enabling collaborative fine-tuning across distributed clients without sharing...
FCC asks stations for "pro-America" programming, like daily Pledge of Allegiance
Brendan Carr wants "patriotic" shows for Trump's yearlong America 250 celebration.
Supreme Court blocks Trump's emergency tariffs, billions in refunds may be owed
Economists estimated more than $175 billion may need to be refunded.
UAE’s G42 teams up with Cerebras to deploy 8 exaflops of compute in India
Abu Dhabi-based tech company G42 has partnered with U.S.-based chipmaker Cerebras to deploy 8 exaflops of compute through a new system in India.
CheckIfExist: Detecting Citation Hallucinations in the Era of AI-Generated Content
arXiv:2602.15871v1 Announce Type: new Abstract: The proliferation of large language models (LLMs) in academic workflows has introduced unprecedented challenges to bibliographic integrity, particularly through reference hallucination -- the generation of plausible but non-existent citations. Recent investigations have documented the presence...
Omni-iEEG: A Large-Scale, Comprehensive iEEG Dataset and Benchmark for Epilepsy Research
arXiv:2602.16072v1 Announce Type: new Abstract: Epilepsy affects over 50 million people worldwide, and one-third of patients suffer drug-resistant seizures where surgery offers the best chance of seizure freedom. Accurate localization of the epileptogenic zone (EZ) relies on intracranial EEG (iEEG)....
HiPER: Hierarchical Reinforcement Learning with Explicit Credit Assignment for Large Language Model Agents
arXiv:2602.16165v1 Announce Type: new Abstract: Training LLMs as interactive agents for multi-turn decision-making remains challenging, particularly in long-horizon tasks with sparse and delayed rewards, where agents must execute extended sequences of actions before receiving meaningful feedback. Most existing reinforcement learning...
What the Justice Department overlooks in its historical argument to end birthright citizenship
Immigration Matters is a recurring series by César Cuauhtémoc García Hernández that analyzes the court’s immigration docket, highlighting emerging legal questions about new policy and enforcement practices. In my last […]The postWhat the Justice Department overlooks in its historical argument...
“Open & Close Strategy”: How Japanese Tech Companies with Niche Technologies Can Leverage IP for Competitive Advantage
Tomotaka Hosokawa, LL.M. Class of 2026 The Strategy The “Open & Close Strategy” refers to a business and intellectual property strategy where a Japanese technology company intentionally “opens” specific technologies to expand the market while simultaneously “closing” other technologies to...