AI · Math · Science

Textual Features Alone Are Not Reliable Indicators of AI or Human Authorship

Tue, 01 Sep 2026 00:00:00 +0000

1.1. The Problem of Inferring Authorship from Writing Characteristics

The increasing use of Large Language Models (LLMs) in academic and professional writing has generated considerable interest in determining whether a document was written by a human, generated by AI, or produced through some form of human–AI collaboration. Such judgments are often made from observable features of the completed text. Highly organized structures, consistent terminology, systematic argumentation, balanced explanations, regular paragraph patterns, explicit qualifications, and clearly formulated conclusions, for example, are sometimes interpreted as indications of LLM generation.

Understanding Agentic AI: Large-scale Asynchronous Training

Fri, 28 Aug 2026 00:00:00 +0000

Definition of Large-scale Asynchronous Training

Large-scale asynchronous training is a machine-learning training approach in which a large amount of computational work is distributed across many workers, devices, machines, or training processes that can perform and contribute training operations without requiring every participant to remain synchronized at each step. In conventional tightly synchronized training, participating workers commonly perform computation and then wait at coordination points so that their results can be aggregated before the next shared training step proceeds. In an asynchronous approach, different workers may instead generate training data, compute updates, evaluate model behavior, process experience, or perform other parts of the training workflow at different rates, while their outputs are collected and incorporated according to the design of the training system. This can be particularly valuable for large and heterogeneous training workloads—including reinforcement learning, agentic training, distributed model optimization, and workloads involving environments or tools with variable response times—because faster workers do not necessarily have to remain idle while waiting for slower ones. At large scale, asynchronous training can therefore improve computational utilization, throughput, scalability, and tolerance of variable workloads, although it also introduces challenges such as stale training information, uneven worker progress, coordination complexity, inconsistent model versions, and the need to determine how asynchronously generated data or updates should influence the continuously evolving model.

Understanding Agentic AI: LLM Vulnerability Discovery

Fri, 28 Aug 2026 00:00:00 +0000

Definition of LLM Vulnerability Discovery

LLM vulnerability discovery is the systematic process of identifying, examining, and validating weaknesses, failure conditions, exploitable behaviors, or insufficient protections in a large language model (LLM) $M$ that may reduce the reliability, safety, robustness, or effectiveness of the model when it is used as the reasoning and decision-making foundation of an AI agent $A$. The process may examine how the LLM responds to different inputs $x$, operates under different execution conditions $c$, and produces model outputs $y=M(x\mid c)$ in order to identify circumstances under which its behavior becomes incorrect, inconsistent, unreliable, insufficiently protected, or otherwise undesirable. Such vulnerabilities may become particularly important in agentic applications because individual model weaknesses can affect planning, reasoning, coding, tool use, information processing, evaluation, and subsequent decisions across multiple execution steps. Systematically discovering and understanding these vulnerabilities can therefore provide evidence for improving the model, its surrounding safeguards, and the design of the agentic system, thereby increasing the efficiency and reliability of AI agents when performing both simple and long-horizon tasks $\tau$, including coding, professional work, and learning tasks.

Understanding Agentic AI: Long-Horizon Tasks

Fri, 28 Aug 2026 00:00:00 +0000

Definition of Long-Horizon Tasks

A long-horizon task $\tau_H$ for agentic AI $A$ is a task whose successful completion requires an AI agent to sustain goal-directed reasoning and action across an extended execution trajectory $H_{\tau}=\{a_1,a_2,\ldots,a_T\}$, where each action $a_t$ represents an operation performed at execution step $t$, and where intermediate actions, results, and decisions can influence subsequent parts of the task. Rather than being completed through a single model response or a small number of largely independent operations, $\tau_H$ typically involves multiple interdependent subtasks $S_{\tau}=\{s_1,s_2,\ldots,s_n\}$ that may require planning, information gathering, reasoning, tool use, execution, intermediate evaluation, revision, and integration of partial results. During execution, the agent maintains a task state $x_t$ representing relevant information about progress at step $t$, including completed work, intermediate outputs, available information, unresolved requirements, environmental conditions, and other context needed to determine subsequent actions. The execution trajectory may be sequential, parallel, conditional, iterative, or recursive and may change as new information, intermediate results, errors, or environmental conditions emerge. Examples include developing and testing a substantial software system, conducting a multi-stage research project, analyzing and integrating information from many sources, or completing a professional workflow involving multiple tools and intermediate deliverables. The defining characteristic of $\tau_H$ is therefore the requirement for sustained and coherent agentic execution across an extended sequence of interdependent states and actions, placing continuing demands on goal maintenance, planning, contextual continuity, state management, tool use, intermediate evaluation, adaptation, and error recovery.

Understanding Agentic AI: Training Environment Scaling for End-to-End Expert Work

Fri, 28 Aug 2026 00:00:00 +0000

Definition of Training Environment Scaling for End-to-End Expert Work

Training environment scaling for end-to-end expert work refers to systematically expanding the number, diversity, realism, complexity, and operational capacity of training environments $E$ in which a large language model $M$, intended for agentic applications $A$, learns to perform complete professional tasks that require sustained expert-level work across extended execution trajectories. Given an expert task $\tau$ and its sequence of interdependent subtasks $S\_{\tau}=\{s\_1,s\_2,\ldots,s\_n\}$, an environment $E$ provides the task context, information sources, tools, resources, constraints, feedback mechanisms, and executable conditions through which $M$ can learn to progress from the initial objective to a completed and evaluated outcome. For long-horizon coding tasks $\tau\_C$, such environments may include software repositories, codebases, development tools, documentation, dependency systems, execution sandboxes, test suites, debugging facilities, and evaluation mechanisms, allowing the model to perform activities such as understanding requirements, inspecting existing code, planning modifications, implementing changes, executing and testing software, diagnosing failures, revising solutions, and validating the final implementation. For long-horizon research tasks $\tau\_R$, the environments may provide research questions, literature collections, databases, search and retrieval capabilities, analytical tools, computational resources, intermediate evidence, and evaluation mechanisms, allowing the model to progressively formulate research directions, gather and examine information, compare evidence, conduct analyses, revise interpretations, synthesize findings, and construct a final research output. Scaling $G(E)$ expands these environments across task volume, task diversity, difficulty, execution length, domain coverage, tool availability, environmental variation, and feedback opportunities so that $M$ gains experience with a broader range of realistic end-to-end trajectories rather than learning primarily from isolated responses or narrowly bounded subtasks. The overall purpose is to develop the model’s capacity to maintain objectives, manage intermediate states and dependencies, use tools appropriately, respond to changing information, recover from failures, evaluate its own progress, and integrate many connected operations into coherent expert work suitable for agentic execution.

Understanding Agentic AI: Agent Member

Wed, 26 Aug 2026 00:00:00 +0000

Definition of Agent Member

An agent member $(A\_i)$ is an individual agent instance that belongs to an agent team $(\mathcal{A}\_{\tau})$, an agent network $(\mathcal{N})$, or another multi-agent organizational structure and contributes to the execution of its assigned or dynamically determined work. Depending on the organizational structure and current execution requirements, an agent member may work individually or collaboratively with other agent members on a single subtask $(\tau\_j)$, multiple subtasks $(\mathcal{T}\_i)$, a complete task $(\tau)$, or, where its scope of responsibility is sufficiently broad, parts or the entirety of a project $(P)$. An agent member may also be assigned or dynamically assume a particular role $(r\_i)$ and corresponding responsibilities $(q\_i)$, such as planning, information retrieval, analysis, execution, coordination, review, evaluation, supervision, or integration. Its participation does not need to remain fixed throughout execution: the work, role, responsibilities, relationships, and actions $(a\_{i,t})$ of an agent member may change according to the current task or project state, available information, intermediate results, environmental conditions, interactions with other members, and relevant contextual information. When operating collaboratively, an agent member may communicate with other members, exchange intermediate results and contextual information, request or provide feedback, review and critique other members’ work, propose or compare alternative approaches, debate decisions, coordinate subsequent actions, and jointly refine intermediate or final results.

Understanding Agentic AI: Agent Network

Wed, 26 Aug 2026 00:00:00 +0000

Definition of Agent Network

An agent network $(\mathcal{N})$ is a large-scale organization of multiple agent instances that collectively support the execution of a complex project $(P)$ composed of many tasks $(\mathcal{T}\_{P}=\{\tau\_1,\tau\_2,\ldots,\tau\_m\})$. The participating agents $(A\_i\in\mathcal{N})$ may differ in their roles $(r\_i)$, capabilities $(c\_i)$, responsibilities $(q\_i)$, available tools or resources, and relationships with other agents. Unlike a task-scale agent team, an agent network does not require all agents to participate in the same task or to communicate continuously with one another. Instead, different subsets of agents $(\mathcal{N}\_{\tau,t}\subseteq\mathcal{N})$ may become relevant to different tasks at different execution steps $(t)$, and their participation, coordination, and interaction patterns may change as the project progresses. Some agents may perform specialized tasks independently, some may coordinate across task boundaries, and some may temporarily form smaller collaborative configurations for particular tasks. The network therefore provides a broader organizational structure through which many agents, tasks, capabilities, and inter-agent relationships can be dynamically arranged to support the overall project objective.

Understanding Agentic AI: Agent Team

Wed, 26 Aug 2026 00:00:00 +0000

Definition of Agent Team

An agent team $(\mathcal{A}\_{\tau})$ is a task-scale configuration of multiple agent instances that collaboratively perform a single task $(\tau)$ comprising multiple subtasks $(\mathcal{T}\_{\tau}=\{\tau\_1,\tau\_2,\ldots,\tau\_m\})$. Each agent instance $(A\_i\in\mathcal{A}\_{\tau})$ can participate in the execution of one or more subtasks $(\tau\_j\in\mathcal{T}\_{\tau})$ or assume a particular role $(r\_i)$ that contributes to performing the target task. The relationship between agent members and subtasks does not need to follow a fixed one-agent–one-subtask assignment: different agent members may perform different subtasks, multiple agent members may perform the same subtask, one agent member may participate in several subtasks, and these responsibilities may change as execution progresses. During task execution, agent members can take different actions $(a\_{i,t}\in\mathcal{U}\_{\tau,t})$ at execution step $(t)$, where $\mathcal{U}\_{\tau,t}$ represents the actions available to the team at that step. Such actions may include performing a subtask, communicating with other members, sharing contextual information or intermediate results, proposing an approach, requesting feedback, reviewing or critiquing another member’s work, comparing competing results, debating a decision, revising an intermediate result, coordinating subsequent actions, or contributing to the final task result. The agent team therefore represents an interaction-intensive form of task execution in which multiple agent members contribute through potentially different, overlapping, or changing responsibilities while collaboratively pursuing completion of the same target task.

Understanding Agentic AI: Dynamically Engineered Skill

Tue, 25 Aug 2026 00:00:00 +0000

Definition of Dynamically Engineered Skill

A dynamically engineered skill is a new reusable skill constructed during the execution or preparation of an agentic task when a required subtask cannot be adequately fulfilled by any compatible reusable skill already available in the agent’s skill database. For an agentic task $\tau$ with a subtask $t_i$, let $\mathcal{S}={s_1,s_2,\ldots,s_m}$ denote the existing skill database, where each $s_j$ (existing reusable skill) represents an available skill that can potentially be applied to different tasks and subtasks. Before engineering a new skill, the agent determines whether at least one $s_j\in\mathcal{S}$ is sufficiently compatible with the requirements of $t_i$. This compatibility does not require the skill and subtask to use identical wording; rather, the skill should provide the capabilities, instructions, procedures, tools, input-output handling, and other relevant characteristics needed to perform the subtask as expected. If no existing skill satisfies the required compatibility conditions, a new skill $s_{\mathrm{new}}$ (dynamically engineered skill) can be constructed specifically to fulfill $t_i$. Although $s_{\mathrm{new}}$ originates from the requirements of a particular subtask, it should, where appropriate, be defined at a sufficiently general and modular level so that it can subsequently be stored in the skill database and reused for compatible subtasks in other agentic tasks rather than being unnecessarily reconstructed each time a similar requirement occurs.

Understanding Agentic AI: Skill Repository

Tue, 25 Aug 2026 00:00:00 +0000

Definition of Skill Repository

A skill repository is a managed collection of reusable skills available to an AI agent for performing different tasks and subtasks. It can contain both pre-installed skills supplied as part of the agent’s default capabilities and custom skills that have been subsequently developed, tested, validated, and approved for reuse. Rather than requiring the agent to consider every available skill directly during every task, the repository provides an organized source from which a task-relevant subset of skills can be retrieved. For a skill repository $\mathcal{S}={s\_1,s\_2,\ldots,s\_n}$, where $\mathcal{S}$ (skill repository) represents the complete collection of available skills and each $s\_i$ (reusable skill) represents an individual skill, a new task $\tau$ (target task) can be used as the basis for task-aware skill retrieval. Retrieval may use skill metadata, semantic matching, keyword or metadata search, capability matching, constraint filtering, ranking, or combinations of these methods to derive $\mathcal{S}\_{\tau}\subseteq\mathcal{S}$, where $\mathcal{S}\_{\tau}$ (task-relevant skill set) contains the skills considered sufficiently relevant or potentially useful for the particular task. For more complex repositories or tasks, this retrieval and filtering process may be delegated to a specialized skill-selection subagent operating within its own context window, allowing it to examine the task requirements and available skill descriptions without unnecessarily occupying the main agent’s working context. The resulting task-relevant skill set can then be made available to the main agent, which dynamically determines which skill to invoke for a particular subtask or execution state as the task progresses. The repository therefore serves not merely as storage, but as an organized and extensible skill-management layer supporting skill registration, discovery, retrieval, selection, reuse, and continued expansion across different agentic tasks.

Understanding Agentic AI: Agentic Task

Mon, 24 Aug 2026 00:00:00 +0000

Definition of Agentic Task

An agentic task is a composite task performed by an AI agent through a structured combination of multiple subtasks, where each subtask represents a distinct part of what needs to be accomplished and is fulfilled by an appropriate reusable skill. An agentic task can be represented as $\tau=({T}\_{\tau},{S}\_{\tau},{R}\_{\tau},{M}\_{\tau})$, where $\tau$ (agentic task) denotes the complete task to be performed, ${T}\_{\tau}={t_1,t_2,\ldots,t_n}$ (subtask set) contains the subtasks that collectively constitute the task, ${S}\_{\tau}={s_1,s_2,\ldots,s_m}$ (skill set) contains the reusable skills available for fulfilling those subtasks, ${R}\_{\tau}$ (execution relations) specifies how the subtasks and their corresponding skills are organized and performed sequentially, iteratively, recursively, or through an appropriate combination of these patterns, and ${M}\_{\tau}$ (evaluation metrics) contains the task-specific measures used to determine whether intermediate results and the overall task outcome satisfy the expected requirements. For each subtask $t_i$ (subtask), an appropriate skill $s_j$ (reusable skill) may be selected from predefined skills or dynamically engineered during task execution when an existing skill does not adequately address the subtask.

Understanding Agentic AI: Callable Skills

Mon, 24 Aug 2026 00:00:00 +0000

Definition of Callable Skills

Callable skills in Agentic AI are skills that an AI agent can actively invoke during task execution when their capabilities, procedures, or instructions are relevant to the requirements of the current task. A callable skill typically represents a defined unit of task-supporting capability, such as retrieving information, analyzing content, processing data, generating or modifying an artifact, validating an output, interacting with a tool, or carrying out a specialized workflow. Describing a skill as callable emphasizes that the skill is not merely defined, documented, or known to the agent, but is available through an invocation mechanism that allows the agent to activate it and use its prescribed functionality as part of the ongoing workflow. Callable skills may be selected from a broader runtime skill catalog according to the task instructions, detected requirements, current context, intermediate results, and execution conditions. Multiple callable skills may therefore be invoked sequentially, conditionally, or in combination to support different subtasks, while skills that are available but irrelevant to the current task can remain unused.

Understanding Agentic AI: Instruction Space

Mon, 24 Aug 2026 00:00:00 +0000

Definition of Instruction Space

An instruction space is the organized set of instructions that may govern, constrain, guide, or otherwise influence the behavior of an AI agent. It can be represented as $I = \{i_1, i_2, \ldots, i_n\}$, where $I$ denotes the instruction space and each $i_j \in I$ denotes a unit instruction expressing an individual rule, requirement, constraint, preference, procedure, condition, or behavioral direction that the agent can interpret and apply. The instruction space may contain instructions originating from different authorized sources or functional levels, such as system instructions $I^{S}$, user instructions $I^{U}$, task instructions $I_{\tau}$, and skill or workflow instructions $I^{K}$, with these instruction sets forming relevant parts of the broader space, for example $I^{S}, I^{U}, I_{\tau}, I^{K} \subseteq I$. The elements of $I$ do not necessarily have equal authority, applicability, or effect: some instructions may apply generally, others only to a particular task $\tau$, state $z_t$, or runtime condition, and some may be overridden, disabled, superseded, or rendered inapplicable according to the governing instruction hierarchy and applicable conditions. Thus, $I$ represents the broader space of instructions potentially relevant to agent behavior, while an active instruction set $I^{*}_{\tau,t} \subseteq I$ can represent the instructions that actually govern or guide the agent for task $\tau$ at execution step $t$.

Understanding Agentic AI: Runtime Skill Catalog

Mon, 24 Aug 2026 00:00:00 +0000

Definition of Runtime Skill Catalog

A runtime skill catalog in Agentic AI is the collection of skills that are available to an AI agent for consideration and use while it is actively performing a task. Each skill represents a defined capability, procedure, workflow, or set of instructions for handling a particular type of requirement or subtask, such as information retrieval, document analysis, data processing, content generation, validation, or interaction with an external tool. The catalog provides the agent with an organized set of possible skills from which relevant ones can be identified and selected according to the current task, rather than requiring every available skill to be applied. It may contain skills that are permanently available in the agent’s environment as well as skills that become available dynamically because of the current configuration, connected tools, user permissions, task context, or other runtime conditions. In this sense, the runtime skill catalog describes the agent’s currently accessible skill space: it indicates what skills the agent can potentially use at a particular point during execution, while the actual skills triggered for a task form only a task-relevant subset of that catalog.

Understanding Agentic AI: Agentic AI

Sun, 23 Aug 2026 00:00:00 +0000

Definition of Agentic AI

Agentic AI refers to an approach to artificial intelligence in which an AI system is designed not only to generate responses but also to pursue defined objectives by determining and performing actions within an operational environment. An agentic AI system may interpret a task, reason about what needs to be accomplished, plan or select appropriate actions, use available tools and information sources, execute individual steps, evaluate intermediate results, and determine subsequent actions according to the current state of the task. Depending on its design, these activities may include searching for information, analyzing data, interacting with software, generating or modifying files, calling external tools, coordinating multiple operations, or requesting additional information when necessary. The defining characteristic of agentic AI is therefore not simply the use of a large language model, but the organization of AI capabilities into a goal-directed process in which reasoning and action can be connected across multiple steps. The degree of autonomy can vary substantially: some agentic AI systems operate within tightly predefined workflows, while others are given greater flexibility to determine how an objective should be achieved.

Understanding Agentic AI: Fallback Instructions

Sun, 23 Aug 2026 00:00:00 +0000

Definition of Fallback Instructions

Fallback instructions in the context of agentic AI are predefined alternative instructions that specify how an AI agent should proceed when its primary instructions, preferred method, or intended workflow cannot be successfully performed. They provide the agent with an alternative path toward the same task objective rather than allowing the process to stop unnecessarily when the preferred approach encounters a limitation or failure. The fallback instructions may simplify the original procedure, relax nonessential requirements, use a different available tool or information source, substitute an alternative method, or produce a more limited but still useful result. For example, if the primary instructions require an agent to retrieve information from a specialized external tool that is unavailable, fallback instructions might direct the agent to use another approved information source or complete only the parts of the task that can be reliably performed. Fallback instructions therefore serve as a form of predefined contingency guidance: the primary instructions describe how the task should preferably be performed, while the fallback instructions describe an acceptable alternative when that preferred approach is not feasible. Importantly, fallback instructions do not necessarily represent an inferior method; they represent a secondary method selected because the conditions required for the primary method are not satisfied.

Projects

Tue, 26 May 2026 00:00:00 +0000

Active Project

🚀 Dual Encoder for Semantic Search

Status: 🔵 Active - In Development

Description: A neural architecture designed for efficient semantic similarity comparison between queries and documents. This project implements a dual encoder model with in-batch negative sampling for information retrieval tasks.

Technologies: PyTorch, Transformers, FAISS, FastAPI

Features:

Query and document encoding into shared embedding space
Cosine similarity scoring for relevance ranking
Scalable vector search with FAISS indexing
REST API for deployment

Current Milestones:

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