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.
A mechanism-first reading of SEAL, a proposed framework that turns synthetic 6G data generation into an auditable, fairness-aware, and federated calibration loop.