arXiv Machine Learning
Jun 11
Bootstrapped Monitoring: Leveraging Transparent Reasoning to Oversee Stronger AI Agents
arXiv:2606. 11998v1 Announce Type: new Abstract: Trusted monitoring is a cornerstone of AI control.
Trusting What You Cannot See: Auditable Fine-Tuning and Inference for Proprietary AI
arXiv:2603. 07466v2 Announce Type: replace-cross Abstract: Cloud-based infrastructure has become the dominant platform for deploying large models, particularly large language models (LLMs).
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arXiv Machine Learning
Jul 8
Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates
arXiv:2604. 04738v2 Announce Type: replace-cross Abstract: Fine-tuning is the dominant paradigm for adapting large machine learning models, yet current deployment pipelines provide no way to verify how a released model was updated.
arXiv AI
Jun 30
Proof-of-Guardrail in AI Agents and What (Not) to Trust from It
arXiv:2603. 05786v2 Announce Type: replace-cross Abstract: As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces a threat where safety measures are falsely advertised.
arXiv AI
Jun 16
The Proxy Knows Too Much: Sealing LLM API Routers with Attested TEEs
arXiv:2606. 16358v1 Announce Type: cross Abstract: Agents increasingly access large language models (LLMs) through API routers.
arXiv AI
Jun 9
Automated Framework to Evaluate and Harden LLM System Instructions against Encoding Attacks
arXiv:2604. 01039v2 Announce Type: replace-cross Abstract: System Instructions in Large Language Models (LLMs) are commonly used to enforce safety policies, define agent behavior, and protect sensitive operational context in agentic AI applications.
arXiv Machine Learning
Jun 4
Bypassing Prompt Guards in Production with Controlled-Release Prompting
arXiv:2510. 01529v3 Announce Type: replace Abstract: Ball et al.
arXiv AI
5d ago
Vero: Can AI Agents Build Formally Verified Software Repositories?
arXiv:2608. 13522v1 Announce Type: cross Abstract: AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code.
arXiv AI
Aug 12
Evaluation and Hardening of LLM System Instructions Against Extraction via Encoding Attacks
arXiv:2604. 01039v3 Announce Type: replace-cross Abstract: System Instructions in Large Language Models (LLMs) are commonly used to enforce safety policies, define agent behavior, and protect sensitive operational context in agentic AI applications.
arXiv Machine Learning
Jun 2
Bit-Exact AI Inference Verification Without Performance Tradeoffs
arXiv:2606. 00279v1 Announce Type: cross Abstract: Verifying claims about AI workloads is a pre- requisite for credible AI governance of covert adversaries (who comply with monitoring only when detection likelihood is high), yet the ap- parent non-determinism of GPU floating-point arithmetic forces auditors to accept approximate output matches.
arXiv AI
Jul 23
Integrity of peer-to-peer distributed LLM inference under malicious nodes
arXiv:2607. 19490v1 Announce Type: cross Abstract: Peer-to-peer distributed inference executes a Large Language Model (LLM) on pooled consumer hardware by spreading its layers across many nodes.
arXiv AI
Jul 2
Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique
arXiv:2407. 10887v4 Announce Type: replace-cross Abstract: Growing concerns over the theft and misuse of Large Language Models (LLMs) underscore the need for effective fingerprinting to link a model to its original version and detect misuse.
Hugging Face Trending Papers
6d ago
Vero: Can AI Agents Build Formally Verified Software Repositories?
AI agents are increasingly used for programming, but do not provide any guarantee on the correctness of generated code. Verified code generation, in which an agent produces both an implementation and a machine-checked proof of its specification, offers a stronger path toward trustworthy AI-generated software.