…This coupling takes place in initialization, optimization and factor graph connections to improve the consistency of the map from multiple modalities. The optimization is wrapped within adaptive robust kernels , designed to handle…
…LLM coding performance on benchmarks through diverse problem variants and enhanced agent interactions. AI-generated summary Many real-world coding challenges are open-ended and admit no known optimal solution. Yet, recent…
…Existing KV eviction methods reduce this cost but typically degrade performance relative to full-cache inference. Our key insight is that full-cache attention is not always optimal: in long contexts, irrelevant…
…Abstract Multi-turn clinical reasoning training using reinforcement learning reveals degradation into verbose single-turn interactions, which is addressed through a novel self-distillation framework improving training stability and performance. AI-generated…
…Moreover, we scale training to billions of images and incorporate a synthetic rendering engine to improve performance in text-rich scenarios. To tackle the convergence challenges of high-dimensional latent space, we…
…We establish that under maximum-entropy optimality , the score function learned via denoising score matching recovers the gradient of the expert's soft Q-function , enabling reward extraction without adversarial training. Formally…
…Policy Optimization (GRPO) to AR-Diffusion Unified Multimodal Models (UMMs) to enhance multimodal generation capabilities without an additional cold-start stage. Our approach unlocks the model's intrinsic potential to perform advanced…
…Agentic harnesses must be specifically built, evaluated and optimized for markets in order to perform reliably. The paper reports concrete failure modes, fabricated rules, fee paralysis, numeric anchoring, cadence trading, and tokenomics…
…gradual architecture transitions lead to better optimization trajectories. Putting it all together, we compress Qwen3-Next-80A3B to a 23A2B model that retains competitive performance. These results offer practical guidance for efficient…
…In this work, we introduce Continuous-Time Distribution Matching (CDM), migrating the DMD framework from discrete anchoring to continuous optimization for the first time. CDM achieves this through two continuous-time designs…