AI Agent Papers2026
Defines recursive self-improvement (RSI) with an evaluation metric (Headroom-Closed Index) and a five-level autonomy roadmap—from executing human-specified improvements to recursively improving improvement mechanisms—while analyzing scenarios, verification challenges, and governance risks.
An 8B native unified multimodal model that understands, reasons about, and generates high-fidelity visual content without a separate visual encoder or VAE—focused on spatially coherent patch reconstruction, native 4K generation, multi-expert distillation, and improved text rendering and image editing.
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.
Reframes Policy Mirror Descent for autoregressive LLM generation into a critic-free, trajectory-level objective using the Bellman equations to avoid per-token value estimation. Derives a practical token-level loss with a smoothed mismatch-correction weight and shows improved math-reasoning accuracy on benchmarks.
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.
MLOps2026
Provides a programming model and distributed runtime that preserves parent–child lineage and ordered variable-cardinality expansions for 1→M→1 dataflow pipelines, enabling completion-driven cross-input GPU batching and ordered gathers for foundation-model data preparation. Demonstrates multi-GPU scaling speedups and lower end-to-end time versus Ray Data and Daft.
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.
Analyzes a failure mode in PPO critics called “Value Flattening” and proposes SP³O (Sparse PPO), which supervises a small set of well-separated states per response to mitigate flattened value estimates and improve policy learning for LLM alignment and reasoning tasks.
Enables specifying exact 24-bit hex colors for object-level image generation and editing via a unified prompt interface, using a Paint-500K dataset and timestep-gated pure-color anchors to improve color fidelity.