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4 results for “LLM reasoning”
What Drives LLM Self-Reflection? A Controlled Ablation of Uncertainty Routing in Armed Conflict Forecasting
arXiv:2608.12322v1 Announce Type: new Abstract: Self-reflection is widely assumed to improve LLM reasoning, yet which component drives the gain remains poorly understood. We present a controlled six-condition ablation isolating four components of LLM self-reflection: evidence exposure, diagnostic scaffolding, taxonomy vocabulary, and action routing. Two precise null results converge on a single mechanism. First, structured diagnostic questions add no measurable value over unstructured reflection
Aug 14, 2026
CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
CoEvoT is a new prompting framework that dynamically updates graph token representations during Chain-of-Thought reasoning, enabling step-wise structural evidence refinement for Graph-LLMs under distribution shift.
Jul 17, 2026
Is current most of the agent/multi agents solution are deterministic, predictable, is anyone accept this or not what you find in those agents (llm) creative
A Reddit user questions whether current enterprise AI agent systems prioritize predictability and auditability over creativity, observing that most solutions use finite state machines or rule engines to constrain LLM behavior.
Jul 15, 2026
MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning
MILES is a new research framework that enables large language models to improve reasoning at test time by dynamically building and selecting from modular, step-wise memory units under realistic constraints.
Jul 10, 2026