Search papers, labs, and topics across Lattice.
11 papers published across 5 labs.
Admissibility in predictive inference isn't a single concept, but four distinct, non-overlapping geometries, each with its own optimality certificate.
You can now detect whether an AI *really* wants to stay on, or is just pretending.
Hypergraph observers minimizing prediction error must maintain internal models, satisfying the Good Regulator Theorem and uniquely admitting natural gradient descent as a learning rule.
LLM reasoning research is inadvertently paving a dangerous path towards AI situational awareness and strategic deception, demanding a re-evaluation of current safety measures.
Intrinsic reward signals in unsupervised RL for LLMs inevitably collapse due to sharpening of the model's prior, but external rewards grounded in computational asymmetries offer a path to sustained scaling.
You can now detect whether an AI *really* wants to stay on, or is just pretending.
Hypergraph observers minimizing prediction error must maintain internal models, satisfying the Good Regulator Theorem and uniquely admitting natural gradient descent as a learning rule.
LLM reasoning research is inadvertently paving a dangerous path towards AI situational awareness and strategic deception, demanding a re-evaluation of current safety measures.
Intrinsic reward signals in unsupervised RL for LLMs inevitably collapse due to sharpening of the model's prior, but external rewards grounded in computational asymmetries offer a path to sustained scaling.
Alignment doesn't guarantee smooth collaboration: this framework reveals how similar alignment can lead to wildly different collaboration trajectories and outcomes in human-AI teams.
Forget "trustworthiness" – the key to AI trust is verifiable "conviction," or the likelihood a model's claims will be independently validated.
Recursive self-improvement can boost performance by 18% in code and 17% in reasoning, but only if you can keep it from going off the rails – SAHOO provides the guardrails.
RLHF's reliance on gradient-based alignment inherently limits its depth, causing it to focus on early tokens and neglect later, potentially harmful, contextual dependencies.
Admissibility in predictive inference isn't a single concept, but four distinct, non-overlapping geometries, each with its own optimality certificate.
Debate between AI models hits a phase transition: it's useless when they know the same things, but becomes essential as their knowledge diverges.
Current AI benchmarks miss the crucial effects of AI R&D automation, so here are the metrics we should be tracking instead.
