Search papers, labs, and topics across Lattice.
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Selected features from sparse autoencoders can causally steer language models toward desired behaviors, like refusal, revealing new avenues for interpretability and control.
Language-action pretraining can lead to VLA policies that are not only more robust but also less dependent on visual cues, achieving up to 45% higher success rates in real-world tasks.
Leveraging historical solving traces transforms software engineering agents into self-evolving entities, achieving a 50.40% success rate on SWE-bench Verified after just three iterations.
Existing text-to-image benchmarks miss the mark on real-world artistic creation, but Qwen-Image-Bench finally provides a creator-centric evaluation that reliably distinguishes state-of-the-art models.
Architectural patterns in AI agent systems reveal that deeper coordination enhances context services, challenging assumptions about system complexity and performance.
LLM agents are stuck in chaotic "Agent Loops" – this paper offers a structured graph-based escape route for more controllable and verifiable execution.
