arXiv Machine Learning
Jul 3
Certified World Models: Predictability Across Configuration, Horizon, and Resolution
arXiv:2606. 13092v3 Announce Type: replace Abstract: Scale buys interpolation; structure buys certifiable transfer.
Conformal Orbit-Valid Trust Horizons for Equivariant World Models
arXiv:2606. 24946v1 Announce Type: new Abstract: Learned world models are useful only over horizons on which their rollout error remains controlled.
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arXiv Machine Learning
Jul 27
Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not
arXiv:2607. 21645v1 Announce Type: new Abstract: Multi-horizon latent consistency is a common training knob in video predictors and world models, but practitioners rarely know what it does to transition geometry.
arXiv Machine Learning
Jun 2
Measuring the Symmetry--Data Exchange Rate
arXiv:2606. 01090v1 Announce Type: cross Abstract: Equivariance theory predicts that an architectural symmetry prior reduces sample complexity by a factor of |G|; this is widely cited but rarely measured as a scaling law with controls that separate the prior from its confounds.
arXiv Machine Learning
Jun 30
When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
arXiv:2606. 29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by 7.
arXiv AI
Jun 3
Exact equivariance, kept through training, buys zero-shot generalisation across the symmetry group
arXiv:2606. 03003v1 Announce Type: cross Abstract: A latent world model built from an equivariant encoder $E$ and an equivariant predictor $f$ inherits a provable symmetry of its training loss: when the world's dynamics genuinely carries a group $G$ acting on latents by an orthogonal representation $\rho(g)$, the one-step prediction relMSE is exactly invariant across the whole group, so fitting the dynamics on a restricted slice of orientations mathematically determines it on the entire orbit (j\v{u} y\=i f\v{a}n s\=an).
arXiv Machine Learning
Jun 25
When Do Conservation Laws Survive Learned Representations? Certified Horizons for Latent World Models
arXiv:2606. 24945v1 Announce Type: new Abstract: We ask a representation-learning question about physical world models: when does a conservation law remain certifiable after a model learns a latent representation?
arXiv Machine Learning
Jul 3
Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception
arXiv:2607. 01537v1 Announce Type: new Abstract: Certified world models estimate how long their predictions remain valid.
arXiv Machine Learning
Aug 11
RouteGuard: Certifying Routing Gain in LLM Multi-Agent Systems When Complementarity Is Not Enough
arXiv:2608. 07583v1 Announce Type: cross Abstract: Multi-agent LLM systems route among model-backed advisors, yet a deployer rarely knows before shipping whether routing will help at all.
arXiv AI
1d ago
Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusion
arXiv:2608. 14617v1 Announce Type: cross Abstract: A recurring proposal in legal AI is to improve case-outcome prediction by fusing uncertainty tools (evidence graphs with belief propagation, sequential Bayesian odds updating, Dempster-Shafer combination, and conformal prediction) into one pipeline.
arXiv AI
23h ago
Beyond Suspicious Steps: Ontological Trust in Long-Horizon Agents
arXiv:2608. 17718v1 Announce Type: new Abstract: Long-horizon agents increasingly operate across many steps, tools, and observa- tions.
arXiv AI
5d ago
Agent Behavioral Contracts II: Certifying Compositional Reliability Without Assuming Independence
arXiv:2608. 12895v1 Announce Type: new Abstract: Compositional reliability bounds for multi-agent systems multiply component reliabilities, a step licensed by a conditional-independence assumption that is routinely stated and rarely tested.
arXiv AI
Jul 2
Testing Frontier Large Language Models' Physics Literacy in Parallel Physical Worlds
arXiv:2607. 00276v1 Announce Type: cross Abstract: Current large-language-model (LLM) physics benchmarks are usually scored by answer accuracy, which cannot distinguish genuine reasoning from recall of familiar problem patterns and reveals little about where a model's reasoning breaks down.