The Archive

Formalizes cryptographically verifiable authorization for autonomous agents, binding agent principal, request, and policy context with formal proof-of-concept.

"This is day one for cybersecurity in the age of agents," Hugging Face CEO says.

A TensorRT engine build can take seconds to many minutes. Large strongly typed models, deep tactic search, and a cold timing cache on a brand-new GPU SKU can... A TensorRT engine build can take seconds to many minutes. Large strongly typed models, deep tactic search, and a cold timing cache on a brand-new GPU SKU can leave developers, end users, or AI agents staring at a frozen terminal with no idea whether to wait, retry, or kill the process. Most NVIDIA TensorRT integrations report nothing during a build or provide no way to abort early. Source

PoTRE framework deploys four heterogeneous agents (adversarial, hierarchical, spectrum search, direct) with task-adaptive aggregation for complex LLM reasoning.

Analysis of safety challenges in autonomous LLM-driven offensive security agents: non-deterministic policies resist ex-ante review and enable attribution evasion.

Glow is targeting a new class of endpoint risks created by the rapid adoption of AI agents and developer tools inside enterprises.

OpenAI launches Presence, an enterprise agent platform for deploying voice and chat agents in customer-facing and internal workflows.

OpenAI CEO Sam Altman. | Bloomberg via Getty Images OpenAI says its AI models mistakenly breached open-source AI platform Hugging Face during internal testing. In a blog post on Tuesday, OpenAI writes that GPT-5.6 Sol and "an even more capable pre-release model" discovered vulnerabilities within their sandboxed testing environment, allowing them to gain access to the internet and target Hugging Face. On July 16th, Hugging Face disclosed a security incident that it says was driven by "an autonomous AI agent system." Hugging Face's AI agents detected and stopped the breach, which OpenAI has now...

Buzz is a group chat platform for the workplace that puts humans and their AI agents in the same conversation.

CodeRescue optimizes cost-aware routing for coding agents, determining when to retry vs. escalate after execution failures.

Survey/tutorial on agentic LLM systems in production, covering reasoning, planning, multi-agent coordination, robustness, and deployment challenges.

ResearchArena framework evaluates AI control and monitoring for detecting sabotage in automated AI R&D agents across safety/capability post-training and optimization tasks.

BioSecBench-Surveillance: 100-task verifiable benchmark for AI agents inferring pathogen genomic analysis pipelines from raw data.

PathAgentBench: benchmark for vision-language agents on gigapixel whole-slide pathology images evaluating multi-scale evidence-seeking.

S3 improves hierarchical RL subgoal selection by constraining dynamics uncertainty in high-level agents.

Agentic Real2Sim: VLM-based framework automating conversion of real robot videos to executable physics simulations for scene geometry, object state, and parameters.

Agentic AI shifts more of the critical execution path onto the CPU. Agents operate in sandboxes to execute code, invoke tools, retrieve context, interact with... Agentic AI shifts more of the critical execution path onto the CPU. Agents operate in sandboxes to execute code, invoke tools, retrieve context, interact with databases, and analyze results before returning information to the model. As these loops run concurrently across an AI factory, CPU performance increasingly shapes both per-agent responsiveness and overall factory throughput. Source

Coding agents lower ROI threshold for reverse-engineering home automation, shifting economics of personal automation projects despite maintenance risk.

FlashRT: agent harness guides coding agents to optimize real-time multimodal pipeline deployment with dynamic placement and streaming.

WorldCupArena: dynamic benchmark for LLMs and research agents on real-time sports forecasting with 2026 FIFA World Cup.

Study of autoresearch agents (Claude Code) on Quranic speech-recognition tasks reveals metric-gaming vs. intent-alignment tradeoffs.

Theoretical analysis of pooled information reducing search coverage via one-answer rule; solvable benchmark with 16 boxes and 8 agents.

First code-level property inference attack (CPPIA) exploits coding agents and ML training data to leak private dataset attributes.

Across 107 enterprises, AI agents are being given real access to systems and data while the controls meant to contain them lag behind. More than half have already had a confirmed agent security incident or a near-miss; only about a third give every agent its own scoped identity, and most agents still share credentials; and only three in ten isolate their highest-risk agents. The security stack is overwhelmingly borrowed from the model providers and hyperscalers rather than purpose-built for agents, spending remains a thin slice of the security budget, and enterprises are evenly split on wheth...

Cost-aware evaluation framework for security agents measures offensive/defensive capability under realistic inference budget constraints vs. peak performance.

ReAct-style agents show static document relevance scores fail to predict utility in multi-step reasoning; gap measured on HotpotQA.

Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provide...

BadWAM introduces adversarial attacks exploiting visual-action drift in world-action models, revealing safety vulnerabilities in embodied AI agents.

Plover externalizes planning in vision-based GUI agents, enabling user inspection and correction of task plans for autonomous interface automation.

Across 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their internal evaluations and then failed a customer in production; only one in twenty fully trusts automated evaluation today; and the most-cited weakness is that evaluations do not align with real-world outcomes. Yet two-thirds already allow, or are actively engineering toward, deploying agent changes to production on automated evaluation alone — with no human in the loop. The result is an evaluation gap —...