Search papers, labs, and topics across Lattice.
29 papers published across 3 labs.
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.
Supervised learning is fundamentally flawed: models *must* retain sensitivity to irrelevant features, opening the door to adversarial attacks and other vulnerabilities.
AI's assumption that users always know what they want leads to "Fantasia interactions," where systems provide superficially helpful but ultimately misaligned assistance, demanding a new approach to alignment research.
Forget about perfectly aligned AI; the real challenge is navigating whose values count, how information is shared, and what trade-offs are acceptable in a world of competing interests.
Conditional risk calibration reveals a unique perspective on uncertainty quantification that could transform how we approach decision-making in machine learning.
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.
Supervised learning is fundamentally flawed: models *must* retain sensitivity to irrelevant features, opening the door to adversarial attacks and other vulnerabilities.
AI's assumption that users always know what they want leads to "Fantasia interactions," where systems provide superficially helpful but ultimately misaligned assistance, demanding a new approach to alignment research.
Forget about perfectly aligned AI; the real challenge is navigating whose values count, how information is shared, and what trade-offs are acceptable in a world of competing interests.
Conditional risk calibration reveals a unique perspective on uncertainty quantification that could transform how we approach decision-making in machine learning.
LLMs can guide their own self-play, leading to superhuman performance with smaller models and less compute.
Correcting errors in long-video understanding doesn't have to be a nightmare: IMPACT-CYCLE slashes human arbitration costs by 4.8x while boosting VQA accuracy by intelligently decomposing the task and focusing human effort where it matters most.
Guaranteeing uncertainty quantification in dynamic environments is now possible even when feedback is strategically withheld by an adversary.
Representational alignment in AI and biology may stem from shared ecological constraints, not a universal optimal model.
A multi-domain curriculum can enhance AI agents' performance, yielding significant improvements in both security and social reasoning capabilities.
MADDPG-K scales multi-agent learning by ditching the all-seeing critic for a neighborhood watch, achieving faster training and better performance without the quadratic cost of full observation.
Predicting steerability with near-perfect accuracy while detecting drift more effectively than existing methods could transform how we monitor and control language models in real-world applications.
The dream of universal representations across modalities may be just that: scaling up datasets and relaxing constraints reveals that models trained on different modalities learn rich, but fundamentally different, representations of the world.
Query probabilities can stabilize and improve mean estimation accuracy by balancing uncertainty with a constant probability, revealing a surprising optimal weight configuration.
LLM protocols can actively *harm* accuracy through "corruption," and this paper provides a way to measure and mitigate this effect, turning opaque pipelines into auditable modules.
Symmetry in your model might be the secret weapon guaranteeing accurate statistic recovery in variational inference, even when your model is wrong.
Forget trajectory forecasting – TacticGen generates *adaptable* football tactics, bridging the gap between predicting what *will* happen and prescribing what *should* happen to win.
Executable visual transformations enable MLLMs to achieve continuous self-evolution without the pitfalls of pseudo-labels, leading to superior performance in dynamic VQA tasks.
Targeted prompt interventions can drastically alter AI trading behaviors, amplifying or suppressing market bubbles in ways that mirror human financial psychology.
Foundation models are poised to revolutionize multi-agent systems by enabling semantic-level reasoning and flexible coordination that surpasses the limitations of classical approaches.
Multi-agent LLM systems for idea generation can backfire, with smarter models and more communication leading to *less* diverse ideas due to structural coupling.
Routing decisions in MoEs can create distinct semantic paths for tokens, revealing that interpretability hinges on trajectories rather than individual experts.
You can now detect governance evidence degradation in risk decision systems *without* labels, but be warned: pure concept drift remains undetectable.
Current AI-assisted coding's "vibe coding" approach, while fast, creates unmaintainable codebases because it collapses complex system topology into un-auditable chat logs.
Bridging the gap between trust region methods and PPO, this new framework guarantees performance improvements while outperforming existing algorithms in stability and effectiveness.
LLMs can now automatically translate messy, real-world requirements into formal specifications with surprising accuracy, opening the door to AI-driven verification of safety-critical systems.
Enforcement mechanisms in agent systems can miss significant behavioral drift, but the Invariant Measurement Layer can detect these deviations in real-time, revealing a hidden vulnerability in current governance approaches.
Guaranteeing safe autonomous system behavior demands a fundamental shift: admissibility must be a property of execution itself, not pre- or post-hoc evaluation.
Coordination errors in LLM-based multi-agent systems can be systematically avoided with a new language that guarantees deadlock-free interactions.
