Search papers, labs, and topics across Lattice.
10 papers published across 2 labs.
Multi-head attention applied to network depth unlocks substantial performance gains and compute efficiency in large MoE models.
Irrelevant text distractors cripple mixed-modality retrieval performance more than images, revealing a text modality preference that degrades performance in a V-shaped curve.
Standard two-level softmax sampling is demonstrably biased; corrected methods achieve near-exact softmax approximations with negligible overhead.
Adversarial shifts in bandit arm means, not just gaps, break standard best-arm identification algorithms, requiring a novel importance-weighting approach for robust performance.
Stored patch vectors from visual document retrieval systems can be inverted to reconstruct original pages, revealing sensitive information and enabling source page identification with high accuracy.
Multi-head attention applied to network depth unlocks substantial performance gains and compute efficiency in large MoE models.
Irrelevant text distractors cripple mixed-modality retrieval performance more than images, revealing a text modality preference that degrades performance in a V-shaped curve.
Standard two-level softmax sampling is demonstrably biased; corrected methods achieve near-exact softmax approximations with negligible overhead.
Adversarial shifts in bandit arm means, not just gaps, break standard best-arm identification algorithms, requiring a novel importance-weighting approach for robust performance.
Stored patch vectors from visual document retrieval systems can be inverted to reconstruct original pages, revealing sensitive information and enabling source page identification with high accuracy.
Generative recommender training strategies that append missed targets can actively harm ranking performance by introducing detrimental probability competition.
Black-box MIPS oracles can approximate attention with $O(1/\varepsilon^2)$ keys by augmenting queries and keys, bypassing $\sqrt{n}$ and $\log n$ dependencies.
Intermediate retrieval signals, not just final outcomes, unlock efficient credit assignment for training LLM search agents.
AI agents can now build and reuse persistent, artifact-specific knowledge, dramatically improving their ability to find relevant information for complex tasks and reducing operational costs.
LLMs can learn to actively compute evidence by routing through retrieval and computation, dramatically improving performance on complex, multi-source reasoning tasks.
