All papers

This paper introduces a multimodal meme dataset and a pipeline to extract entities and their semantic relationships.

This paper presents a framework and a dataset to generate holistic full-body co-speech gestures from audio and masked movement priors.

The paper introduces an open-source library to construct validation suites that stress test the reliability of LLM judges.

The paper evaluates twenty-one language models used as judges across three benchmarks, finding widespread discrepancies between agreement metrics and underlying validity.

This paper presents a benchmark to evaluate how adversarial attacks affect reasoning security in multi-agent debate systems.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

The paper introduces a controlled benchmark evaluating how accurately LLMs determine whether software components match vulnerability advisory criteria.

The paper proposes a metamorphic testing method with mutation operators to evaluate how retrieval-augmented generation systems handle changing document corpora.

This paper presents a mid-training method, corpus, and benchmark to help language models understand and reason about time series data.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

The paper introduces an evaluation benchmark comprising four clinical datasets to assess large language model performance in the Swedish medical domain.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

This paper presents a benchmark to evaluate how adversarial attacks affect reasoning security in multi-agent debate systems.

The paper analyzes 33,000 GitHub pull requests to examine why AI coding agent contributions succeed or fail.

Proposes Agentless, an agent-free approach to software development issues that compares favorably against autonomous software engineering agents.

The paper introduces a system layer that separates control flow from untrusted data to protect language model agents against prompt injection attacks.

This paper introduces specification templates to define operational boundaries, authority, and oversight for autonomous software agents.

The paper provides a conceptual taxonomy and review distinguishing task-specific AI agents from collaborative agentic AI systems.

Maps Erlang and OTP fault-tolerance principles onto multi-agent LLM systems to evaluate supervisory reliability architectures.

Introduces AgentChaosBench, a benchmark for detecting and localizing injected runtime boundary faults in LLM-based multi-agent applications.

The paper shares eight lessons from building an agent pipeline that ports legacy C/C++ code to memory-safe CHERI hardware architectures.

The paper introduces an interactive web-based tool for analyzing and visualizing execution trajectories of software engineering agents.

Compares four multi-agent workflow architectures to determine how coordination structure affects filtering of static analysis alerts.

Proposes an agent memory architecture that uses biologically inspired adaptive decay functions to selectively retain and forget context.

This paper introduces a multi-agent system that evaluates code extensibility and detects SOLID design violations through automated code extension.

The paper proposes a graph-based framework that integrates multi-agent decisions, rationales, and memory transitions to improve assistant explainability.

The paper presents a multi-agent framework that dynamically generates execution context to enable partial code execution.

Proposes a conceptual framework to evaluate the resilience of LLM-based multi-agent systems in software engineering tasks.

The paper reviews architectures, reasoning methods, and multi-agent collaboration strategies for agentic language models.

Introduces READ-MAS, a multi-agent system using retrieval-augmented generation to decompose requirements engineering and software design.

Presents MOSAICO, an orchestration infrastructure for coordinating specialized collaborative LLM agents across software engineering tasks.

This paper presents a multi-agent framework where a central model coordinates specialized agents and tools for business workflow automation.

This paper presents a multi-agent architecture using structured states and human checkpoints to manage software requirements engineering workflows.

The paper evaluates five planning algorithms for breaking down complex goals into executable workflows in agentic AI systems.

The paper evaluates token usage, runtime, and energy consumption across four different memory setups for single-model multi-agent educational systems.

The paper introduces an open-source library to construct validation suites that stress test the reliability of LLM judges.

This paper presents a benchmark to evaluate how adversarial attacks affect reasoning security in multi-agent debate systems.

The paper analyzes 33,000 GitHub pull requests to examine why AI coding agent contributions succeed or fail.

The paper introduces a system layer that separates control flow from untrusted data to protect language model agents against prompt injection attacks.

This paper introduces specification templates to define operational boundaries, authority, and oversight for autonomous software agents.

This survey paper reviews attacks, defenses, and evaluation methods across 135 studies on secure retrieval-augmented generation systems.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Studies how chain-of-thought reasoning affects social bias and code quality during LLM code generation.

Proposes DataFilter, a model-agnostic test-time defense that strips prompt injection instructions from data before reaching an LLM.

The paper proposes a metamorphic testing method with mutation operators to evaluate how retrieval-augmented generation systems handle changing document corpora.

Proposes a seven-layer architecture and runtime control plane to enforce human oversight across planning, retrieval, and execution in AI agents.

This paper proposes a non-interactive secure inference framework for Transformer models using fully homomorphic encryption to reduce user overhead.

This paper presents a hierarchical classification method using layer hidden states to detect prompt injection attacks with lower latency.

The paper presents an autonomous framework using multiagent reinforcement learning to monitor, diagnose, and recover from failures in production AI systems.

Discusses artificial intelligence governance frameworks and risk assessment methods for cybersecurity and IT environments.

Presents a taxonomy and lifecycle analysis of hallucination causes in large language models and multimodal systems.

The paper introduces a zero-shot LLM pipeline to generate counterfactual examples for stress-testing NLP models.

Surveys hallucination in language models through a mechanistic framework that maps internal causes to observable errors.

Detects prompt injection attacks using a hybrid pipeline that combines supervised models, zero-shot classification, and rule-based heuristics.

This survey provides an algorithmic overview and failure mode analysis of reinforcement learning techniques applied to language models.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.