Tool-using large language models (LLMs) can make multiple attempts to complete a task, using execution feedback from failed tool calls to identify errors and revise subsequent actions. However, their recovery performance often plateaus in later attempts, as models may repeat similar tool-call errors instead of making effective corrections. To address this limitation, we introduce PRFC, a reinforcement learning method that models error recovery as a sequential decision-making process. PRFC uses a progress-aware reward that assigns credit only when a later attempt improves upon previous failures, encouraging steady improvement across the recovery trajectory. Across four benchmarks with a Qwen3-4B backbone, PRFC improves multi-turn recovery on BFCL-v3 by an absolute 6.4% and achieves the highest mean score across three single-step tasks (API-Bank, T-Eval, and Nestful). These results show that modeling progress across attempts provides an effective learning signal for multi-attempt tool-use recovery. These results suggest that explicitly modeling progress across attempts provides a useful learning signal for tool-use recovery.

Reliable multi-image geological reasoning is essential for automating expert tasks in remote-sensing mineral exploration, yet remains challenging for multimodal large language models (MLLMs) due to the need for locating target areas, accurate cross-image referencing, and consistency over long reasoning chains. We propose STA-CoT, a Structured Target-centric Agentic Chain-of-Thought framework that orchestrates planning, execution, and verification agents to decompose, ground, and iteratively refine reasoning steps over geological and hyperspectral image sets. By aligning each reasoning step to specific image target areas and enforcing consistency through agentic verification and majority voting, STA-CoT robustly mitigates tool errors, long-chain inconsistencies, and error propagation. We rigorously evaluate STA-CoT on MineBench, a dedicated benchmark for multi-image mineral exploration, demonstrating substantial improvements over existing multimodal chain-of-thought and agentic baselines. Our results establish STA-CoT as a reliable and robust solution for consistent multi-image geological reasoning, advancing automated scientific discovery in mineral exploration.

Mental health is increasingly critical in contemporary healthcare, with psychotherapy demanding dynamic, context-sensitive interactions that traditional NLP methods struggle to capture. Large Language Models (LLMs) offer significant potential for addressing this gap due to their ability to handle extensive context and multi-turn reasoning. This review introduces a conceptual taxonomy dividing psychotherapy into interconnected stages–assessment, diagnosis, and treatment–to systematically examine LLM advancements and challenges. Our comprehensive analysis reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. We identify critical challenges including capturing dynamic symptom fluctuations, overcoming linguistic and cultural biases, and ensuring diagnostic reliability. Highlighting future directions, we advocate for continuous multi-stage modeling, real-time adaptive systems grounded in psychological theory, and diversified research covering broader mental disorders and therapeutic approaches, aiming toward more holistic and clinically integrated psychotherapy LLMs systems.