All papers

This paper provides an empirical study and taxonomy of communication faults and error propagation across multi-agent language model systems.

This paper introduces a diagnostic framework using signal detection theory to measure when language model agents decide to use tools.

The paper introduces Sequent-Prover, training language model agents to perform multi-turn formal reasoning using an external SMT solver scratchpad.

Tests delayed prompt injection attacks on life-support control agents in a simulated lunar habitat to see if safety duties persist.

Replaces free-form reasoning text with executable computational graphs trained via reinforcement learning to improve reasoning speed and verifiability.

This paper introduces an evaluation benchmark that generates complex, long-horizon agent workflows by composing verifiable business skills.

This paper presents a technology-neutral reference architecture for integrating agentic AI capabilities into enterprise software systems.

The paper proposes prototyping agent workflows with large language models and distilling converged live production interactions into smaller models.

The paper presents an autonomous agent architecture that distills distributed system incident resolutions into reusable detectors and operational playbooks.

This paper introduces a benchmark to evaluate how language agents discover, coordinate, and query across multiple distinct databases to answer questions.

Compares retrieve-then-plan and plan-then-retrieve strategies for tool discovery across varying tool registry complexities.

The paper introduces a benchmark to evaluate whether language model agents reliably delete sensitive user data and secret evidence from memory upon request.

The paper evaluates how open and closed language models reach consensus when presented with competing payoff incentives.

The paper proposes an interface framework that tracks and controls application state changes made by graphical user interface agents.

The paper introduces a distillation method that trains agent policies on explicit runtime control semantics recovered from replayed execution traces.

Shows that periodically clearing agent search history and restarting from the top candidate prevents automated research agents from plateauing.

Studies multi-developer agentic systems where independently built specialist agents collaborate to outperform larger systems created by single developers.

The paper analyzes model swapping in multi-session agents and proposes a history summarization method to preserve strong reasoning behaviors.

The paper presents a red-teaming method that injects stealthy attack operations across interdependent agent skills using code rewriting.

The paper introduces an evaluation framework to diagnose step-level execution stability and error recovery in graphical user interface agents.

This paper introduces an architecture that stores and reuses specialized language agents as temporally extended options for complex coding tasks.

The paper proposes a self-supervised method that uses repetitive tool calls as negative signals to reduce looping behavior in long-horizon agents.

The paper introduces a self-training method that uses runtime policy verification feedback as rewards to teach agents safety compliance.

The paper presents a decision model to evaluate language model efficiency based on energy and cost per completed agentic task.

The paper presents a taxonomy of failure modes in multi-agent systems where agents interact across organizational boundaries with private states.

The paper profiles the security attack surface and execution privileges of third-party agent skills across public marketplaces.

The paper benchmarks whether AI agents can recognize execution blockers and appropriately defer action on unsolvable tasks.

Argues that inferential accuracy fails to evaluate agent coordination safely because correlated errors can compound into costly team actions.

Presents a benchmark evaluating security agents through live, symmetric attack-and-defense competitions in isolated sandbox environments.

Analyzes discrepancies between static tool specification errors and actual runtime failure rates in language model agent execution.

The paper presents an agent that combines language models with operational policies to generate verified operational reports for high-performance computing systems.

The paper studies using an agentic verification architecture to enable small on-device models to generate factual clinical documentation.

The paper introduces a benchmark to evaluate whether frontier language model agents can execute open scientific research and produce follow-up papers.

The paper compiles teacher model reasoning traces into verified programs that guide small models through complex procedural tool-use tasks.

This paper introduces plug-in decision heads that read action choices directly from visual agent hidden states rather than generating text tokens.

The paper proposes a multi-view hierarchical memory framework to improve information retrieval from enterprise group chats.

The paper introduces a benchmark to measure language model agent performance under real-time latency and streaming temporal constraints.

The paper trains language model auditors to detect multi-turn policy violations during tool use by autonomous agents.

The paper provides a formal algebraic framework and normal forms to structure and audit enterprise AI agent workflows.

The paper uses human interventions to localize and steer computations behind agent failure to decide between acting and clarifying.

The authors present a benchmark to evaluate conversational agents that follow correct factuality but violate procedural task steps.

Examines whether source code quality metrics predict the token costs of multi-agent pull request review pipelines.

Analyzes how misconceptions in early planning stages cause cascading failures during automated code repair by language model agents.

Introduces a dataset benchmarking human-agent interaction and collaboration across long-horizon professional workflows.

Evaluates test-time search in computer-use agents, finding critics perform better when invoked only at predicted final states.

Introduces a benchmark evaluating whether language agents can make correct scientific judgment calls derived from human interventions.

Proposes a trajectory-aware retrieval method that indexes tool sequences in a trie to cut token costs and improve selection accuracy.

Introduces a benchmark evaluating single-forward-pass discrete decision models versus generative reasoning models across diverse closed-loop tasks.

Studies whether conversational agents correctly enforce authorization rules when users attempt to cancel prior agent commitments to others.

The paper studies how autonomous coding agents spontaneously negotiate and coordinate access to shared compute resources under deadlines.

The paper introduces a training framework to teach AI agents when to intervene proactively and safely during multi-step tasks.

The paper studies how semantic layers, orchestration, and compaction impact the performance and behavior of data agents across benchmarks.

The paper investigates performance vulnerabilities in self-improving agents when their co-evolved skill banks undergo post-deployment drift.

Introduces a benchmark evaluating language model agents in real-time environments and a two-timescale training method to improve execution speed.

Introduces a compact language model trained with distillation and reinforcement learning to manage long-term memory across multiple external frameworks.

Introduces a benchmark that structures multi-pull-request code changes into dependency graphs to evaluate long-horizon software engineering agents.

The paper introduces a modular measurement system to evaluate how agent frameworks, tools, and memory impact verifiable reasoning tasks.

Introduces a method to filter unsupported compressor inferences from long-horizon agent summaries to avoid compounding execution errors.

Introduces a synthetic data generation framework to produce long-horizon task trajectories for terminal agents.

Introduces an automated environment generation pipeline to synthesize diverse runnable desktop GUI software applications for training computer-use agents.