Gen-Searcher trains a search-augmented image generation agent via supervised and reinforcement learning, yielding about 16-point gains on knowledge-intensive benchmarks.
SnapMLA improves long-context MLA decoding throughput up to 1.91x via hardware-aware FP8 quantization and pipeline optimization while preserving benchmark quality.
TGRL turns temperature-induced rollout diversity into an explicit RLVR training signal via reward contrast and Jensen-Shannon divergence, accelerating convergence up to 36% while improving math, code, and agent benchmarks.
Structured Defect Grounding models text-to-image failures as structured tuples for diagnosis and alignment, outperforming proprietary vision-language models and improving generation via importance-weighted rewards.
StableVQ decouples encoder-decoder and codebook training via Dynamic STE, Region VQ Loss, and independent schedules to stabilize VQ tokenizers and boost utilization and reconstruction.
Code2World uses renderable code generation for GUI world modeling, achieving top next-UI prediction and boosting Android navigation success by up to 9.5%.
RTPurbo converts full-attention LLMs into sparse models within hundreds of steps via retrieval heads and dynamic indexing, achieving near-lossless accuracy with 9.36x prefill and 2.01x decode speedups.
TransitLM provides 13 million transit records to train LLMs for map-free route generation, yielding accurate routes that implicitly ground GPS coordinates without maps.
A finite-horizon stochastic online allocation planner maximizes workflow completion probability under strict budget and deadline constraints via simulated replanning.
GenEvolve is a self-evolving image-generation agent that uses tool-orchestrated visual experience distillation to improve tool use and achieve state-of-the-art results.