Heads Up: Why Sensitivity Matters in Many‑Shot Multimodal ICL
A mechanism-first reading of STV, a task-vector method that makes many-shot multimodal adaptation less about longer prompts and more about knowing which attention heads to touch.
A mechanism-first reading of STV, a task-vector method that makes many-shot multimodal adaptation less about longer prompts and more about knowing which attention heads to touch.
JobSphere shows how multilingual RAG can make government employment portals more usable, cheaper to operate, and still far from magically solved.
A mechanism-first reading of why refusal in language models may behave less like a switch and more like a structured manifold.
Spark shows why the next leap in coding agents may come less from bigger models than from shared, curated experience.
A mechanism-first reading of MetaCUB, a bi-level contextual bandit framework for allocating scarce resources when outcomes arrive late, populations churn, and fairness cannot be bolted on afterwards.
A mechanism-first look at how MACHOP learns user-specific explanation preferences for constraint systems, and why the shortest explanation is not always the clearest one.
A practical reading of how graph neural networks, reinforcement learning, probabilistic topic models, and game theory can diagnose the real failure modes of strategic multi-agent AI.
A formal logic paper shows how default reasoning can keep conflicting viewpoints separate without turning every decision system into a semantic food fight.
A mechanism-first reading of how AI is turning scientific work from a sequence of tasks into a governed, mixed-initiative research operating system.
Regular Games reframes general game playing as a compiler problem: describe rules once, optimise them as automata, and generate fast forward models for agents.