Research Papers Rankings
Large language model papers from the last 90 days, ranked by three independent signals: how fast the official implementation is gaining stars on GitHub, how often the paper is cited, and how much attention it drew on Hugging Face. A paper that scores on only one of the three does not make the list. Rebuilt every day.
Kimi K3 is an open Mixture-of-Experts model with 2.8T parameters (104B activated per token), native vision input and a 1-million-token context window, built on Kimi Delta Attention and post-trained with reinforcement learning across general, agentic and coding tasks.
VoiceMem gives duplex speech models a working memory that keeps facts and emotional context across a conversation, so a voice assistant can remember both what you said and how you said it, in real time.
Inspired by human working memory while copying, this technical report builds an end-to-end OCR model whose decoding cost stays flat as output text grows long, avoiding the KV-cache slowdown and memory creep of LLM-decoder OCR systems.
AuK unifies speech generation and editing in a single open-source model: you describe the desired change in natural language, and it either synthesizes new speech or modifies existing audio accordingly.
Xiaomi's robotics team presents a vision-language-action foundation model for mobile manipulation, pretrained on more than 100K hours of real-world UMI trajectories with auto-labeled scene-transition language, then post-trained to match embodiments and human imperative prompts. The paper claims clean scaling with more data and parameters, out-of-the-box performance in unseen environments and efficient fine-tuning for dexterous tasks.
This technical report upgrades LingBot-VLA with a revamped data pipeline of about 60,000 pretraining hours: 50K robot-trajectory hours across 20 embodiments plus 10K egocentric human-video hours. It extends control beyond dual arms to heads, waists, mobile bases and dexterous hands, aiming squarely at the gap between lab robotics and real deployment.
Frontis-MA1 ships with OpenMLE, an open full-stack system (task gyms, RL operator learning, evolutionary search) for studying recursive self-improvement. Its 35B meta-evolution agent, post-trained around Draft, Improve, Debug and Crossover program-evolution operators, claims MLE-Bench Lite results the authors say exceed GPT-5.5 plus Codex, with components transferring to held-out NatureBench Lite.
NeoHorse-1 records, for every user turn, the predicted capability demand, the selected service tier and how the interaction went, then converts these records into training examples admitted through structural validation and multi-dimensional semantic checks, feeding them back as the next round of agentic post-training.
OpenWAM breaks the tightly coupled world-action model into swappable modules with unified training, deployment and evaluation, turning pretraining into a controlled experimental program where each design choice can be isolated and tested.
RoboDojo is a unified sim-and-real benchmark for generalist robot manipulation policies, with 42 simulation tasks and 18 real-world tasks probing generalization, memory, precision and long-horizon control. It ships parallel Isaac Sim evaluation plus a cloud-accessible real-eval system and a leaderboard the authors populated with 30 policies.
This work revisits the Marigold recipe for turning DiT image models into depth estimators, targeting single-step inference distilled from pretrained flow-matching models, with optional quantization, aiming at sharp depth maps that hold up on out-of-distribution inputs.
This reusable skill suite covers the first mile of research ideation: it searches papers across multiple sources, checks whether a novelty claim has already been scooped, and composes end-to-end ideas grounded in evidence from the literature.
DreamX-World 1.0 is a general-purpose interactive world model that turns text or image prompts into steerable video: you can move the camera, revisit earlier regions, and trigger promptable events across photorealistic, game-style and stylized scenes. The authors report a few-step autoregressive design distilled for speed, running at up to 16 FPS on eight RTX 5090 GPUs.
Instead of waiting to be asked, this model continuously watches a live video stream, decides on its own whether to speak or stay silent, handles easy questions in real time and delegates hard ones to a background model, targeting scenarios like security monitoring and livestreams.
MonkeyOCRv2 pretrains on MonkeyDoc v2, a 113-million-image document corpus spanning 17 languages, combining image-to-text generation with pixel-level stroke reconstruction, so the visual encoder acquires the character-level perception that document tasks demand.
Large language model papers from the last 90 days, ranked by three independent signals: how fast the official implementation is gaining stars on GitHub, how often the paper is cited, and how much attention it drew on Hugging Face. A paper that scores on only one of the three does not make the list. Rebuilt every day.
A dash means the signal was unavailable, not zero.