Enterprise agent deployments are held back less by model quality than by fragmented knowledge, legacy systems, and context. Security research on MCP trust boundaries and optimization work on memory and tool execution are becoming central to production viability.
Google and four other organizations have acknowledged vulnerabilities in which a compromised AI agent spreads malicious instructions to other internal agents by exploiting trust gaps in the Model Context Protocol. The technique is a form of prompt in...
TinyDecide is a 10-million-parameter model with a roughly 6 MB footprint, smaller than every entry on the Decision Index leaderboard. It runs in the browser, Node.js, Python, Rust, and on an ESP32. For edge and on-premise deployments, the signal is n...
MIT Technology Review Insights surveyed 300 executives and found that only 34% of agentic AI projects reach production. The root cause is not model capacity but fragmented data, legacy systems, and weak semantic context. Production leaders show stron...
Microsoft has released a 4-billion-parameter model derived from Qwen3.5-4B, trained with reinforcement learning on synthetic SWE environments for agentic software engineering. The approach without behavioral distillation aims to reduce dependence on ...
Observational world models are not enough for modular LLM agents: a sequence does not say whether payment authorizes shipment or a hidden factor explains both. The FedCausalCompose framework shows that interventional error decreases with intervention...
An audit shows that LLM agents concentrate retrieval on a small core and leave rare states in a long tail, where prediction errors build up. The CTWM controller allocates prompt budget with a single exponent and reduces tokens by 24.48% on LongMemEva...
A benchmark on 824 multi-hop questions from FRAMES compares 18 RAG pipelines with an agent loop. The best pipeline reaches 78.9%, the agent 92.7%. The most awkward result concerns reranking: a small model drops accuracy by 9 points, a large one barel...
A runtime executes LLM agent tool calls out of trajectory order but commits only after validation. Speedups up to 1.35x on benchmarks with slow tools, zero false accepts across 4,010 records. A structural shift for self-hosted deployments focused on ...
AREX-2 explores self-improvement in LLM agents through reflection and long-horizon iterations. Trained on synthetic trajectories from machine learning and algorithmic programming tasks, built on Qwen3.8-27B, it achieves 81.8 on MLE-bench Lite and 70....
BAAI has released AREX-2, a 27B parameter agent model compatible with Qwen3.8. Its propose-measure-reflect-revise loop uses scores, logs, and errors to refine answers. With a 262,144 token context and GGUF files, it matters for self-hosted deployment...
