A user reports that OpenCode enters an infinite 'thinking loop' when using local models, prompting itself continuously without ending. The issue occurs across multiple models and configurations, including Qwen and GPT-OSS, and persists in both llama.cpp and LMStudio environments, though the chat window in LMStudio functions normally.
Thinking Loop Bug in OpenCode with Local Model
OpenAI releases Jalapeño inference chip benchmarks; Perplexity launches local Portable Computer
OpenAI published benchmark details for its custom inference chip, Jalapeño, claiming superior efficiency and latency compared to NVIDIA GB200/GB300 systems. Simultaneously, Perplexity launched the Portable Computer on NVIDIA DGX Spark hardware, enabling fully local agent orchestration without cloud dependency.
Neurosymbolic Alignment improves clinical LLM safety via physiological grounding
Researchers propose Neurosymbolic Alignment, a training-time framework that couples a 7B clinical LLM with an HGNN-based Physiological World Model to improve safety in generative clinical reasoning. The method uses structured physiological knowledge from an 847K-node biomedical knowledge graph to score candidate responses and drive iterative on-policy ORPO updates.
Researchers introduce AtlasNav to solve Evidence Blindness in direct corpus interaction
Researchers have introduced AtlasNav, a persistent multi-view corpus-navigation framework designed to address "Evidence Blindness" in large language model agents interacting with external corpora. This condition occurs when required evidence fails to surface or remain usable within finite interaction budgets.
Recuris improves long-horizon agent success by 17.8 points on tau-bench
Researchers introduce Recuris, a recursive Experiential-Working Memory architecture designed to address the challenges of recursive self-improvement in long-horizon tasks. The system uses Working Memory to track progress and guide skill selection from Experiential Memory, creating a bounded loop that localizes failures and updates skills based on current needs.
Recuris introduces recursive Experiential-Working Memory architecture for long-horizon agent harnesses
Researchers introduce Recuris, a recursive Experiential-Working Memory architecture designed to address the challenges of recursive self-improvement in long-horizon tasks. The system uses Working Memory to track task progress and guide skill selection from Experiential Memory, grounding decisions in current needs rather than full history.