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Notes on computer science
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Inducing Task Models from Computer-Use Traces
Researchers present Task Model Induction (TMI), a method that extracts structured task models from naturalistic computer-use traces. These traces are screenshots and mouse or keyboard actions recorded during work. TMI…
Inducing Task Models from Computer-Use Traces
Researchers present a new method called Task Model Induction (TMI) to extract detailed and structured task models from computer use traces, such as screenshots and mouse or keyboard actions. These task models can help…
MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection
The paper presents MaliciousSkillBench, a comprehensive benchmark for detecting malicious Agent Skills in large language models (LLMs). The benchmark consolidates various sources of malicious artifacts and reduces them…
Repo0: Design-Driven Zero-to-All Code Generation
The paper introduces Repo0, a framework for zero-to-all code generation that maintains an explicit architectural state using a Dual-Directed Acyclic Graph (Dual-DAG). It iteratively evolves component boundaries guided…
DreamFly: Causal Memory and Receding-Horizon Diffusion Planning for Aerial Vision-Language Navigation
In this paper, the authors introduce DreamFly, a new framework for aerial vision-language navigation that addresses the challenges of limited historical context, short planning horizons, and unreliable implicit…
VICBench: A Multi-Language Benchmark for Code Vulnerability Detection
The paper introduces VICBench, a multi-language benchmark for code vulnerability detection. It was created by human experts and an agentic workflow, featuring 100 verified vulnerabilities across 88 projects in Python…
A Gateway Architecture for Enterprise MCP Authentication: Unifying Heterogeneous Auth, Identity Delegation, and the User / Non-User Persona Problem
The paper discusses a solution to a governance crisis caused by the rapid adoption of Model Context Protocol (MCP) for connecting LLM agents with enterprise tools. The solution is a centralized MCP gateway that…
ChemWorld: Programmable Chemical Worlds for Controlled and Replayable Agent Experimentation
The paper introduces ChemWorld, a programmable chemical environment that allows researchers to create executable worlds with reusable components. This environment separates the experimental contract available to an…
Conversational Orchestration for Organic 6G
The authors propose a lightweight, decentralized conversational orchestration framework using Large Language Model (LLM)-driven domain agents for the future network of networks, Organic 6G. The aim is to provide…
Defining Decentralization: An Ontological Perspective
This research paper addresses the lack of a universal definition for decentralization in computer science and its related fields like security, AI, cloud infrastructure, IoT, etc. The authors propose a graph-based…
Defining Decentralization: An Ontological Perspective
The authors address the lack of a universally accepted definition of decentralization in computer science, particularly in emerging fields like decentralized AI and machine learning. They propose a graph-based ontology…
MicroEvo: Knowledge-Guided LLM Sampling for Efficient Microarchitecture Design Space Exploration
A new method called MicroEvo is proposed for efficient exploration of microarchitecture design spaces. This approach utilizes a combination of large language models (LLMs) and Monte Carlo Tree Search (MCTS) to optimize…
Agentic Method for Deterministic Validation of Legacy Code Migration
The paper proposes a new method called 'Locksmith Loop' for testing the migration of COBOL programs to Java. This method is designed to improve coverage beyond input-search plateaus, reaching near complete coverage in…
AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching
Researchers have introduced a new framework for ontology matching called AgentMap. This framework can simultaneously discover both equivalent and subsuming mappings in ontologies, which existing systems cannot do. The…
MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
MemSecBench is a new benchmark for testing the lifecycle security of agent memory systems. The benchmark contains 310 cases drawn from various contexts, and it follows a controlled protocol to test the persistence…
SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context
Researchers have created SciFigQual-Bench, a new benchmark for evaluating the quality of scientific figures in computer science papers. The benchmark assesses five dimensions: clarity, layout, caption fit, context…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose a new framework called Agent-Guided Concept Discovery to improve the clinical adoption of deep learning models for surgical margin assessment using Rapid Evaporative Ionization Mass Spectrometry…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose a new method called Agent-Guided Concept Discovery to help deep learning models assess surgical margins more effectively, particularly in noisy and unlabeled data acquired during surgery. The…
From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs
The paper proposes a methodology using Petri nets to generate tests for concurrent stateful Rust APIs, aiming to address issues with large language models (LLMs) in generating executable tests that may violate API…
From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs
The authors present a methodology using Petri nets to generate tests for concurrent stateful Rust APIs, aiming to bridge the gap between formal scenario design and low-cost test concretization.
From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs
The paper presents a methodology using Petri nets for test generation in concurrent stateful Rust APIs, aiming to bridge the gap between formal scenario design and low-cost test concretization. It allows large language…
PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling
The researchers propose Physics-Informed Environmental Retrieval (PIER), a model-agnostic framework to improve the accuracy of environmental system modeling. The framework is designed to address challenges in limited…
SelectInfer: Selective Neuron Loading and Computation for On-Device LLMs
Researchers present SelectInfer, a neuron-level optimization framework for Large Language Models (LLMs) that can run efficiently on resource-constrained edge devices. It reduces memory footprint and computation by…
In-Place Tokenizer Expansion for Pre-trained LLMs
Researchers have developed an in-place method called Tokenizer Expansion to upgrade a pre-trained model's tokenizer when language priorities shift, focusing on improving efficiency for users of languages added later…
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