Adds a Next Concept Prediction objective that predicts discrete, product-quantized latent concepts spanning multiple tokens alongside next-token prediction, yielding faster pretraining convergence and measurable downstream gains with an 8.9B-parameter latent-space LLM.
AI Video Papers2026
Translates natural-language instructions into executable programs that maintain an explicit, persistent global world state and compiles state-augmented 3D oriented bounding boxes into pixel-aligned conditioning signals for pretrained video generators. The approach decouples state evolution from rendering, enabling programmable entity control, off-screen state, and long-horizon interactive scenarios.
AI Dataset2026
Snapshot delivery of arXiv metadata, submission files and rendered documents in multiple Parquet configs (metadata, paper_text, latex, source, pdf, ps). Includes ~3.15M papers, full-text TeX assemblies and indexes to fetch large assets for training, retrieval and analysis.
Jiayin Chen, Yicheng Xu +1Miyang Technology (Shanghai) Co., Ltd., Shanghai, China, Key Laboratory of System Software (Chinese Academy of Sciences), Beijing, China +4
Suppresses grid-like and granular “digital ripple” artifacts introduced by iterative reference-conditioned image editing using selective spectral notching, structure-aware smoothing, and cleaned-reference regeneration. Prioritizes low-distortion filtering when artifacts are spectrally separable and visual reconstruction when artifacts overlap content.
Haojun Zhang, Yi Zou +8Affiliation: Xini Ding, Hao Li, Shuchang Zhou, Xianming Liu, Shiyu Huang‡, Affiliation: XPeng Inc.
Progressively prunes and distills audio encoders for speech LLMs to cut inference cost while preserving decoder-facing embeddings, using behavioral probes, representation alignment, cross-scale distillation and LoRA finetuning; reports reduced macro-error on Chinese–English benchmarks.
AI Agent Papers2026
Provides a self-evolving ontology layer that enables LLM-based data agents to query and interact with heterogeneous data via an MCP server; it auto-builds and iteratively refines schema, content, and tool layers based on agent interactions.
AI Agent Papers2026
Presents Atria Dawn Preview, an agentic foundation LLM trained with a Verifiable Experience Pipeline to connect tool-mediated interactions to executable environments; evaluated across 16 real-world research and engineering benchmarks with top scores on five, and a human–AI collaboration analysis of 769 task records.
AI Agent Papers2026
Turns past discovery traces into replayable simulators so alternative exploration policies can be evaluated offline ('dreaming'), enabling fast, low-cost meta-level policy improvement for agent-driven discovery across coding, optimization, and GPU-kernel tasks.
Surveys how foundation models reshape AI across the game lifecycle, organizing literature into six roles (play & act; model players & games; design; build & maintain; runtime generation & adaptation; test & evaluate). Emphasizes cross-role links, limits to transfer, and gaps in evaluation and persistent state.
AI Agent Papers2026
Estimates a model's probability of correctness by leveraging its past graded episodes: XConf recalls similar past cases and prompts the model to reflect and restate confidence, improving calibration and discrimination across reasoning, coding, multimodal QA, and agent tasks at much lower generation cost.
AI Video Papers2026
Haoyu Zhao, Zihao Zhao +11Affiliation: National University of Singapore, Affiliation: Fudan University +1
Evaluates whether MiniMax-H3 can infer latent physical events from incomplete multimodal evidence and produce videos consistent with those inferences. Builds a 517-instance benchmark across four reasoning scenarios (multi-view, audio disambiguation, video continuation, audiovisual integration) and quantifies modality-specific failure modes.
Introduces physical rank consistency (PRC) and ActionPiece, a learned action tokenizer that preserves local physical relationships via joint supervision of representation learning and quantization, improving autoregressive vision-language-action policy performance across multiple benchmarks.