Agentic workflows are replacing static pipelines, with local harnesses, small agentic models, and long-horizon self-improvement loops. Techniques such as reflection, tool use, and heavy-tailed memory control make agents more capable on limited hardware.
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...
