Discover the Best AI Resources

LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes

Builds high-fidelity image generators by pairing a 6B Diffusion Transformer with a frozen LLaDA2.0-Mini vision-language module, relying on extensive image-only pretraining and mid-training; model weights, training code, and recipes are released.

Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning

Compresses KV cache for long-chain reasoning by keeping prompt tokens and evicting remaining entries uniformly at random per attention head; across four models and six reasoning tasks it matches the strongest prior evictor while delivering 32–43% higher vLLM throughput. Relies on prompt protection and redundancy across heads/text to retain reasoning traces; suitable when static memory budgets and higher serving throughput are priorities.

Qwopus3.8-27B-Flash-GGUF

Fine-tuned variant of Qwen3.8-27B optimized to reduce reasoning cost and wall-clock latency for long-running agent workloads. Delivers ~12.8% faster decoding and higher MTP draft acceptance while cutting runaway reasoning, at a small MMLU-Pro accuracy trade-off (−1.45 pp).

Ask Before You Optimize: Dynamic Pre-Formulation Clarification for Interactive Optimization

Studies when and how an assistant should ask clarification questions before converting natural-language problem descriptions into optimization models. Introduces OR-Clarify, a benchmark for pre-formulation clarification, and InterOPT, a two-stage framework that diagnoses formulation-critical gaps and guides targeted questioning and stopping decisions.

WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data

Provides a diagnostic benchmark of 4,084 multiple-choice questions derived from longitudinal wearable time series, blood biomarkers, and demographics of 200 users to evaluate LLM reasoning on real-world wearable data. Uses 16 question types across complementary axes and a dual-grounding framework combining literature findings with population statistics.

Grounded Skill Synthesis from Code at Scale for Agentic Intelligence

Transforms source code into verifiable, reusable agent skills by extracting atomic operations, workflows, and recurring patterns and validating them via source-body-blind reconstruction. Produces CodeSkillBank (1,006,822 accepted records from 19,769 GitHub repos) and yields ~11.7% average downstream improvement.

GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation

Trains a world-action model that predicts future visual states to guide zero-shot robotic manipulation; introduces CoAE, SVP, IDM and KASO to pretrain generative and action components from scratch on manipulation data, scaling up to 30,000 hours and improving zero-shot success.

WorldSculpt: Generating Compositional Worlds from Grounded Videos

Generates compositional 3D scenes as collections of individual object meshes by conditioning a single-object 3D generative prior on multi-view posed observations. Key features include anchor-aligned canonical frames, multi-view DINOv3 feature lifting with an IBR-style fusion, and LoRA adaptation to complete heavily occluded objects; includes a large UE-MeshyScene benchmark.

Don't Drop Dropout: Optimizing Layer Sparsity for Efficient LLM Training and Inference

Evaluates layer dropout (stochastic depth) in LLM pretraining, finds optimal layer distributions and time schedules that save ~20–25% training FLOPs at comparable or lower validation loss, and enables zero-shot layer skipping and self-speculative decoding for up to ~1.4–1.5× inference speedup.

ZDTaichu5.0-9B

Multimodal foundation model for visual understanding, spatial reasoning and multi-step agent tool use — accepts text, multiple images and video at any resolution and supports long contexts (up to 128K tokens). Emphasizes fine-grained 2D/3D relations, affordance reasoning and embodied-AI planning.

NeoHorse-1-9B

Post‑trained 9B causal language model optimized for agentic workflows, tool use, coding, and long‑context instruction following. Uses a routing‑guided agentic post‑training pipeline that converts harness executions into training signal, improving agentic and coding benchmarks. Text‑only weights (safetensors/BF16), Apache‑2.0.

UltraData-Code

Provides layered code pretraining corpora (L2 ~400B tokens, L3 ~150B tokens) across 11 languages by filtering ~192M public GitHub repositories into standardized files, algorithmically relevant selections, and implementation-grounded programming exercises. Includes per-file metadata (role, algo relevance, quality) and serialized task records; released under Apache-2.0.