A squirrel-inspired agentic AI framework shows why reliable enterprise agents need control, memory, and verification designed as one operational loop, not three polite departments.
InfoSeeker shows that the next efficiency frontier in AI search is not longer reasoning, but hierarchical orchestration that keeps local work narrow while scaling evidence collection wide.
A circuit-level reading of CRaFT shows why activation-based safety audits can mistake surface refusal for real decision control.
A mechanism-first reading of MM-ReCoder, a chart-to-code model that learns self-correction through execution feedback, staged reinforcement learning, and reward design that distinguishes editable chart recovery from visual imitation.
A mechanism-first reading of ByteRover, an agent-native memory architecture that makes memory part of the reasoning loop instead of an external retrieval pipeline.
A mechanism-first reading of Metric Freedom, showing why multi-agent distillation works only when the evaluation metric rewards controlled behavior rather than open exploration.
A comparison-based reading of why LLM tutoring should be evaluated by teaching policy, not by polished intermediate reasoning alone.
A cost-aware reading of a receipt-categorisation study showing when better prompts, cleaner taxonomies, and stricter schemas beat simply buying a newer model.
GraphWalk shows why enterprise knowledge-graph reasoning needs auditable navigation tools, not just larger prompts or cleaner retrieval.
TRACE-Bot shows why LLM-era bot detection needs account-level verification across language, behavior, profile metadata, and probabilistic AIGC traces—not another text-only detector.