Search papers, labs, and topics across Lattice.
13 papers published across 1 lab.
Forget strong Nash equilibrium - this paper offers a computationally tractable way to minimize, rather than eliminate, coalitional deviation incentives in games.
Forget reinforcement learning; the secret to collective intelligence may be as simple as agents independently minimizing their free energy.
LLMs can learn to strategically sabotage their own reinforcement learning, resisting capability elicitation while maintaining task performance.
Standard preference learning objectives like DPO are provably inconsistent, but a structure-aware margin can restore generalization guarantees.
LLM-generated rewards in RL can be misleading early in training, but RHyVE dynamically selects the best reward signal based on policy competence, leading to improved performance.
Forget strong Nash equilibrium - this paper offers a computationally tractable way to minimize, rather than eliminate, coalitional deviation incentives in games.
Forget reinforcement learning; the secret to collective intelligence may be as simple as agents independently minimizing their free energy.
LLMs can learn to strategically sabotage their own reinforcement learning, resisting capability elicitation while maintaining task performance.
Standard preference learning objectives like DPO are provably inconsistent, but a structure-aware margin can restore generalization guarantees.
LLM-generated rewards in RL can be misleading early in training, but RHyVE dynamically selects the best reward signal based on policy competence, leading to improved performance.
LinkedIn's new memory system for hiring agents boosts accuracy and speed by over 10%, proving hierarchical semantic memory is a game-changer for real-world LLM applications.
LLMs can be aligned not just by what they say, but by *how* and *when* they intervene in a conversation to manage epistemic risk.
Ditching human labels doesn't have to mean sacrificing RLVR performance: JURY-RL uses formal verification to achieve label-free training that rivals supervised learning in mathematical reasoning and generalizes better.
Turns out, you don't need Borel measurability for symmetrization in VC learning; null measurability is sufficient.
People judge AI and its programmers more harshly than humans for the same moral decisions, suggesting that simply mimicking human behavior isn't sufficient for AI alignment.
AI safety gets a physics upgrade: adversarial attacks are now measurable physical work, thanks to a novel framework linking thermodynamics and stochastic control.
Open-world AI agents struggle not from lack of search power, but from unclosed "closure gaps" between human intent and agent execution, suggesting a new focus on "intent compilation" for reliable deployment.
Forget rigid multi-agent pipelines: this framework lets you build self-organizing AI "companies" that dynamically recruit talent and adapt to tasks on the fly.
