Agentic loops are outperforming traditional RAG pipelines, and models like AREX-2 improve through propose-measure-reflect-revise cycles. New runtimes and controls for memory and world models make agents more efficient and reliable.
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...
A framework splits retrieval failures into two states: unanswerable and distracted. A lightweight module, Sieve, filters noisy documents before invoking the costly LLM, Sage. In tests, accuracy improves by up to 69.4 percentage points and Macro-F1 by...
KoboldCpp now includes an integrated agent harness, enabled with a checkbox or --agent flag. It offers nine built-in tools, a roughly 2,000-token system prompt, and aims to replace external tools like Opencode, Codex, and Claude Code. Minimums are 28...
