arXiv AI
Jul 28
Let AI Agents Translate Networks, Not Reason About Them
arXiv:2607. 22947v1 Announce Type: new Abstract: A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change.
Self-evolving network verifiers
arXiv:2608. 11340v1 Announce Type: cross Abstract: Symbolic network verifiers can reason about correctness across vast spaces of routing inputs and failures, but only for the protocols and features an expert has encoded by hand.
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arXiv AI
Jun 2
AXIOM: A Trust-First Neuro-Symbolic Execution Architecture for Verifiable Mathematical Reasoning
arXiv:2606. 00671v1 Announce Type: new Abstract: We present AXIOM, a trust-first neuro-symbolic execution architecture for natural-language mathematical reasoning.
arXiv AI
Jun 2
Attested Tool-Server Admission: A Security Extension to the Model Context Protocol
arXiv:2605. 24248v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) standardizes how a large-language-model (LLM) agent and an external tool server exchange messages, but not trust: a host reads a server's self-declared tool list and dispatches calls, with no notion of which servers it may use, at what sensitivity, or which of a server's tools are in bounds.
arXiv AI
Jul 31
Pramana: A Composable, Domain-Specific Backend for Empirical Networking Research
arXiv:2607. 26352v1 Announce Type: cross Abstract: Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a hypothesis) and generating the data that tests it.
arXiv AI
Jun 30
Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing
arXiv:2606. 30555v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to execute complex workflows.
arXiv AI
Aug 10
Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On
arXiv:2605. 19035v2 Announce Type: replace Abstract: The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution.
Hugging Face Trending Papers
Jul 28
Pramana: A Composable, Domain-Specific Backend for Empirical Networking Research
Networking research advances by turning hypotheses into empirical evidence, so accelerating it means reducing the lag between ideation (synthesizing a hypothesis) and generating the data that tests it. Consider a concrete case: does a bulk BBR download fairly share its bottleneck with competing real-time Google Meet traffic?
arXiv AI
23h ago
Beyond the Trace: Coupling an Interpretable Reasoning-State Readout to Native MoE Routing
arXiv:2608. 17638v1 Announce Type: new Abstract: What a reasoning model writes is only a partial record of the process that produces it.
arXiv AI
Jun 16
Trust Without Trusting: A Recomputable Trust Protocol for Autonomous Agents
arXiv:2605. 06738v2 Announce Type: replace-cross Abstract: Autonomous AI agents already transact at production scale -- 69,000 bots, 165 million transactions, $50 million in volume on a single marketplace -- and any party can verify a signed credential without a central service.
arXiv AI
Jul 8
Proof of Execution: Runtime Verification for Governed AI Agent Actions
arXiv:2607. 05397v1 Announce Type: cross Abstract: Agent systems increasingly execute rather than advise.
arXiv AI
Jun 15
When the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer More
arXiv:2606. 14476v1 Announce Type: new Abstract: A growing line of work equips large language model (LLM) agents with graph neural networks (GNNs) as callable tools, assuming the agent exercises judgment over when and how much to rely on such a tool.
arXiv AI
Jun 30
When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
arXiv:2606. 29354v1 Announce Type: new Abstract: Chain-of-Thought (CoT) improves large language models (LLMs) on difficult reasoning tasks, but it often incurs long natural-language rationales that are poorly aligned with efficient machine reasoning.