Search papers, labs, and topics across Lattice.
100 papers published across 7 labs.
Decision-oriented LLMs like Jev offer a novel quality-latency operating regime for recommendation reranking, outperforming pointwise LLMs in scaling while maintaining strong effectiveness.
LLMs can overcome knowledge gaps in evolutionary search by dynamically co-evolving solutions and targeted web queries, leading to substantial gains in scientific discovery.
A listwise agent inspecting top candidates holistically boosts patch retrieval accuracy by over 27% compared to treating them independently.
Standard information retrieval methods can match LLM-mediated skill selection for agents, slashing per-task costs from $51 to $27.
Parallel search agents can now achieve higher accuracy than generative aggregators by leveraging TRACE to intelligently select the best trajectory from aggregated cross-rollout evidence.
Decision-oriented LLMs like Jev offer a novel quality-latency operating regime for recommendation reranking, outperforming pointwise LLMs in scaling while maintaining strong effectiveness.
LLMs can overcome knowledge gaps in evolutionary search by dynamically co-evolving solutions and targeted web queries, leading to substantial gains in scientific discovery.
A listwise agent inspecting top candidates holistically boosts patch retrieval accuracy by over 27% compared to treating them independently.
Standard information retrieval methods can match LLM-mediated skill selection for agents, slashing per-task costs from $51 to $27.
Parallel search agents can now achieve higher accuracy than generative aggregators by leveraging TRACE to intelligently select the best trajectory from aggregated cross-rollout evidence.
Spherical interpolation between old and new model embeddings recovers backward-compatibility for vision-language retrieval, outperforming orthogonal alignment by finding optimal query directions in the interstitial space.
LLM agents waste immense token budgets repeatedly rediscovering identical cross-document links on every query—pre-indexing corpora into an offline entity-document graph solves fragmented multi-hop retrieval while actually lowering token costs.
Multimodal continual learning does not require rigid parameter isolation or massive replay buffers—external reasoning retrieval can dynamically compose modular capability paths while subspace recycling safely prevents cross-task interference.
Pumping capacity into feature cross-networks yields diminishing returns, but harvesting non-shared estimator variance via recursive weight-sharing unlocks state-of-the-art CTR accuracy without standard ensemble deployment overhead.
Transferring latent group priors across cohorts eliminates the standard bandit cold-start penalty while actively preventing the premature catalog exhaustion that plagues real-world deployments.
Turning text into images cuts reranking token overhead by up to 50% and yields a 1.70× throughput boost—all while outperforming standard sub-4B text rerankers across BEIR.
Distilling multi-billion-parameter multi-vector retrievers no longer requires caching terabytes of document tokens: entropic optimal transport allows a lightweight 149M text model to capture 95% of VLM teacher performance at 26x query speed using queries alone.
Extra test-time "thinking" cannot recover missing parametric facts, but teaching small reasoning models to recognize their own knowledge boundaries and selectively query larger models lets a 4B model outperform a 14B baseline at nearly a third of the serving cost.
Personalized alignment is fundamentally a candidate-matching problem rather than a generator bottleneck: million-parameter MLPs tapping internal LLM embeddings beat monolithic, billion-parameter reward models across the board at 10,000× lower latency.
Lazy-loading agent skills on demand doesn't just slash context window costs—it actively sharpens tool retrieval quality, all while incurring only a negligible hit to latency.
Contextual bandits don't actually need reward estimation—exploiting the elicitation geometry of shared monotone links lets algorithms target the decision boundary directly to secure logarithmic regret under severe functional misspecification.
A 13-parameter statistical count model with Kneser-Ney smoothing outperforms deep temporal graph networks across multiple standard benchmarks without learning a single node embedding.
Standard PDF text search fails to locate over half of extracted scientific claims due to layout and encoding artifacts, but decoupling normalized sequence alignment from character provenance drives evidence grounding accuracy to 92.6%.
Nearly a quarter of dense retrieval "false positives" turned out to be genuine, uncatalogued literary references, revealing how standard information retrieval benchmarks systematically undervalue semantic search under extreme allusion and paraphrase.
Personal GUI agents waste most of their retrieved history; this framework activates experience only when it's actually applicable, boosting performance and efficiency.
ZooWork-ShopRanker, a family of e-commerce rerankers aligned to judge-labeled shopping preference, and ShopRank-Bench, a contamination-limited benchmark of ~10,000 private-traffic preference pairs in both text formats, tiered by how many judge families committed to each label.
