The Archive

SAT-Edge-Agent deploys edge-based LLM agents on satellites for onboard intelligence under communication/power constraints.

AntiSkillBench: end-to-end benchmark evaluating privacy leakage, impersonation risk, and defenses in persona-skill pipelines for agents.

TARL: memory state framework for long-term agents mapping statements to five executable actions (add, ignore, revise, reject, defer) instead of binary write/hold.

Offline reinforcement learning framework trained on 31.7k records to predict oncology clinical trial portfolios under uncertainty.

DiagChain diagnostic benchmark evaluates LLM agents on staged attack chain reconstruction from security telemetry across 69 scenarios.

Apple’s long-awaited AI overhaul finally makes Siri the assistant it was always supposed to be. But after years of delays, the launch lands in an AI landscape where chatbots have evolved into agents that can code, reason, create media, and complete complex tasks. Siri AI is genuinely useful, yet it arrives at a moment when simply being a capable AI assistant no longer feels revolutionary.

LiveMem maintains persistent memory state in long-running LLM agents by decoupling working context from intrinsic memory lifecycle.

RoMeRL addresses memory-reward trap in self-evolving agents using reduced-order utility states to balance feedback coverage and irrelevant experience pollution.

SWE-Touch benchmark tests coding agents in shared workspaces with user code edits, exposing weaknesses in handling conflicting modifications during task execution.

Microsecond-cost anomaly detection for LLM agent failures using one-class echo-state networks trained only on healthy runs; tested across Qwen, Llama, and Gemini agents.

ParEvalLayer detects biased partial evaluations of LLM agents, preventing premature benchmark conclusions from incomplete task runs.

Digital Twin-Enhanced Multiscale Planning automates incident response via decision-theoretic agents, bridging abstract models to operational systems.

Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data,... Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data, execute tools, and generate new results, storage systems must continuously supply and preserve the data that moves the agent reasoning loop. Each agent step can trigger multiple storage operations, and those operations can repeat across… Source

Google and Kaggle launched a free 353,000-person course on AI agents using Gemma, focused on building and deploying agent systems.

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. When two OpenAI models hacked into the website Hugging Face in July, they weren’t trying to make money or commit sabotage—they were just looking for answers…

CompressAgent benchmark evaluates reliability of compressed agent control contexts across Qwen models and task families.

Wix Helpmate deploys deterministic executability gating to filter skill selection in LLM agents by account state feasibility.

Temporal replay framework evaluates enterprise agents against dynamic data across multiple moments within an episode, not just final state.

Search-GRT: RL method to train LLM search agents for multi-hop QA with guided retrieval to reduce sparse-reward training issues.

PROGRESS trains search-augmented LLM agents using coverage-guided RL rewards to improve query decomposition over outcome-only supervision.

TrajWiki proposes trajectory-based external memory for long-horizon dialogue agents with traceable, updatable, diagnostically transparent storage.

PMMC compiles multimodal memory at consolidation time for LVLM agents to preserve image-text binding and temporal updates without query-time overhead.

Neuro-symbolic governance framework for verifiable AI agents in decentralized digital twin ecosystems with semantic profile layers.

LAND model simulates 314K heterogeneous agents and human actors over 30 days to study emergent social dynamics via LLM-enabled ABM.

OpenAI has reportedly found evidence of additional agent misbehavior as it looks into the incident that occurred with Hugging Face.

AgentHPOBench evaluates LLM agents on sequential hyperparameter optimization across 30 ML tasks, assessing experimental interpretation and adaptive decisions.

SESA framework combines self-play curriculum learning with evolving procedural memory to distill failures into reusable skills for search agents.

Analytic memory abstraction for multimodal agents enabling filtering, aggregation, and temporal reasoning over accumulated observations.

Zero-Mem: Zero-token memory operations for LLM agents using encoder computation instead of LLM calls to reduce latency and token costs.