Search papers, labs, and topics across Lattice.
14
0
12
28
Distilling operational knowledge from GitHub repositories boosts AI research agents' performance by over 134% on key benchmarks.
Search-oriented rubrics redefine how we evaluate document sets, leading to a 2.6-point performance boost in deep research tasks.
DME achieves state-of-the-art multimodal representation while maintaining efficiency, outperforming existing models in both retrieval and recommendation tasks.
AREX achieves superior performance in deep research tasks by recursively refining answers through a novel self-improvement mechanism that outpaces traditional search methods.
SearchOS turns fragile search progress into a robust, shared state, enabling agents to avoid repetitive failures and significantly improve search efficiency.
WebSwarm's innovative recursive delegation allows agents to not only search but also adaptively collaborate, leading to superior performance in complex web search tasks.
Explicit evidence graphs in VeriGraph enable LLMs to achieve 87.61% claim grounding, transforming how we verify AI-generated conclusions.
Arbor's innovative approach to autonomous research enables a cumulative learning process that outperforms existing models by over 2.5 times in real-world tasks.
LLM agents are shockingly vulnerable to multi-stage "trojan" attacks that inject malicious instructions into their workspace, achieving near-perfect success rates where standard prompt injection defenses fail.
Scaling out peer agents with a shared reasoning hub, AgentFugue, unlocks a new dimension of capability gains in long-horizon tasks, proving that collective reasoning is more than just parallel compute.
LLMs can now recall relevant information from long interaction histories without retraining, thanks to a state-adaptive memory framework that uses lightweight cues to reconstruct distant information on demand.
Key contribution not extracted.
Current multimodal models are stuck in bi-modal interactions, but OmniGAIA and OmniAtlas offer a path towards truly omni-modal AI assistants capable of reasoning and tool use across video, audio, and images.
Current multimodal retrieval systems fall flat when faced with realistic visual streams where context is distributed across time, motivating a new agentic paradigm for context-aware image retrieval.
