HX Research · Frontier AI Deep Dive
HX Research
Frontier AI deep dives from Great Unicorn Intelligence: each article reviews the latest papers on LLM reasoning, multimodal AI, agents and financial-agent evaluation, breaks down the core algorithms and discusses what they mean for financial risk control and enterprise adoption. Full texts are in Chinese, with an English summary of each.
All articles
Reasoning Is Learning to Budget: From Explicit Chain-of-Thought to Latent Reasoning and Adaptive Compute Allocation
Over the past year, “let the model think a little longer” has become almost the default way to improve reasoning.
The Main Battleground for Multimodal AI Is Shifting to Evidence: From Native Multimodal Foundation Models to Visual Evidence in Long Documents
In October 2026, Meta and NVIDIA each released native multimodal MoE foundation models built for agentic workflows; whether a model “can understand images” is no longer news.
The Real Bottleneck for Long-Horizon Agents: Persistence, Error Recovery and Oversight
Over the past year, the tasks in agent demos have gone from “book a flight” to “reproduce a research codebase” and “turn a due-diligence pack into a report”.
Harness Engineering Enters the “Optimizable Era”: Context, State, Organization and Self-Evolution
“Agent = model + harness” is something few people dispute in 2026.
A Paradigm Shift in Evaluating Financial Agents: From Getting Answers Right to Defensible, Auditable Decisions
Two years ago, a “financial large model” was judged mostly by how many questions it answered correctly on financial Q&A, financial-report reading or professional exams.
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