Wednesday / AI 101 series: Neuro-symbolic AI

Friday / AI Literacy: co-create with AI

Visualize and curate multimodal data, including LiDAR and radar 

Automate 3D segmentation of obstacles with single-shot labeling and object tracking

Create robust, scalable pipelines for model training and evaluation

The Master Algorithm (book)

Pedro Domingos (Twitter)

Tensor Logic: The Language of AI (paper and website with slides)

Talk with Jensen Huang on AI & the Next Frontier of Growth (video) where he says: “We went from 95% market share to 0%. I can’t imagine any policymaker thinking that’s a good idea… whatever policy we implemented caused America to lose one of the largest markets in the world to 0%”

“How China Built a Parallel AI Chip Universe in 18 Months” by AI Supremacy

Ringing Black Hole Confirms Einstein and Hawking’s Predictions by Simons Foundation

Hugging Face launches its Omni Chat. The initial version of HF Chat was somewhat disappointing – when I tried it, it didn’t work as expected. Since then, they’ve completely overhauled the design and functionality, turning it into a powerful router that intelligently selects the most suitable open-source model for each prompt. The new iteration looks great and performs impressively.

Anthropic has introduced Agent Skills, a modular way to enhance Claude’s capabilities for specific tasks like Excel work or adhering to brand guidelines. Skills are lightweight, portable folders containing code, resources, and instructions that Claude loads only when needed. Developers and teams can now create and manage custom skills across Claude apps, API, and Claude Code—bringing more structure and precision to AI workflows. Related blog by Simon Willison: Claude Skills are awesome, maybe a bigger deal than MCP

DeepSeek-OCR: Contexts Optical Compression
Researchers from DeepSeek AI released DeepSeek-OCR, a vision-text model designed to integrate document understanding into LLMs efficiently. It introduces Contexts Optical Compression, supporting native resolutions from 512×512 to 1280×1280 and a dynamic "Gundam" mode. Prompts enable markdown conversion, layout parsing, OCR, and visual grounding. Running at ~2500 tokens/sec on A100-40G GPUs, it supports both vLLM and Transformers. The model emphasizes efficient visual-token compression and supports flash attention 2.0 for acceleration. It is open-sourced under MIT license and optimized for OCR-rich tasks across diverse visual layouts →GitHub

Fantastic (small) retrievers and how to train them: Mxbai-edge-colbert-v0 tech report
Researchers from Mixedbread AI and Waseda University introduced mxbai-edge-colbert-v0, two late-interaction ColBERT models with 17M and 32M parameters. These outperform ColBERTv2 on BEIR despite lower embedding dimensions (48/64). Using ModernBERT backbones, multi-stage training (contrastive pre-training, fine-tuning, distillation), and optimized ablations, the 17M model supports 32k contexts, runs efficiently on CPU, and stores vectors with 2.5× less memory. It achieves 0.6405 NDCG@10 on NanoBEIR →read the paper

Qwen3Guard technical report
Researchers from Qwen introduced Qwen3Guard, a multilingual safety moderation model available in 0.6B, 4B, and 8B sizes, supporting 119 languages. It includes Generative Qwen3Guard for tri-class safety classification (safe, controversial, unsafe) and Stream Qwen3Guard for token-level real-time moderation. Qwen3Guard-Gen achieves state-of-the-art F1 scores on 8 of 14 English benchmarks, surpasses larger models on multilingual tasks, and supports response refusal detection. Stream Qwen3Guard achieves near real-time latency with only ~2-point performance drop, enabling efficient streaming safety interventions →read the paper

A2fm: An adaptive agent foundation model for tool-aware hybrid reasoning
Researchers from OPPO developed A2FM, a 32B model integrating three execution modes—agentic (tool-using), reasoning (chain-of-thought), and instant (direct answers). It uses a route-then-align strategy and introduces Adaptive Policy Optimization (APO) for efficiency-accuracy trade-offs. A2FM achieves 13.4% on BrowseComp, 70.4% on AIME25, and 16.7% on HLE. It surpasses 32B peers in cost efficiency—$0.00487 per correct answer—cutting cost by 45.2% vs reasoning mode. It ranks top across agentic, reasoning, and general benchmarks →read the paper

🌟 🌟 🌟 The Art of Scaling Reinforcement Learning Compute for LLMs (by Meta et al.) – model compute–performance curves, isolate which choices shift asymptotes vs. efficiency, and propose a predictable, scalable RL recipe. This paper marks the turning point where reinforcement learning becomes an engineering science – transforming opaque, trial-and-error reward tuning into a predictable, scalable process that can guide the next generation of reasoning and alignment in large models →read the paper

QeRL: Beyond Efficiency – Quantization-enhanced Reinforcement Learning for LLMs – combine low-precision NVFP4 + LoRA with adaptive quantization noise to speed rollouts, raise exploration entropy, and match full-tune reasoning accuracy at far lower compute →read the paper

Agentic Entropy-Balanced Policy Optimization – balance entropy during rollouts and updates with pre-monitoring and entropy-aware advantages to stabilize long-horizon tool use and improve pass rates on web-agent tasks →read the paper

Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimization – identify attention patterns that mark critical tokens and assign targeted RL credit to them for consistent reasoning gains →read the paper

LaSeR: Reinforcement Learning with Last-Token Self-Rewarding – replace costly verifier passes by aligning a last-token self-reward with reasoning rewards, improving RLVR training and test-time scaling at minimal extra inference cost →read the paper

Information Gain-based Policy Optimization – compute dense, intrinsic turn-level rewards from belief updates to overcome reward sparsity in multi-turn agents and improve sample efficiency →read the paper

Demystifying Reinforcement Learning in Agentic Reasoning – distill practical recipes across data, algorithms, and reasoning modes that let small models rival larger ones on agentic benchmarks →read the paper

Stronger Together: On-Policy Reinforcement Learning for Collaborative LLMs – adapt grouped RL to multi-agent roles and turns, scaling cooperative planning, coding, and math accuracy →read the paper

Diffusion Transformers with Representation Autoencoders – replace VAEs with pretrained representation encoders plus trained decoders to yield richer latents, faster convergence, and state-of-the-art DiT image generation →read the paper

Attention Is All You Need for KV Cache in Diffusion LLMs – refresh KV caches adaptively by attention-aware drift tests and depth-aware schedules to accelerate diffusion decoding without quality loss →read the paper

Dr.LLM: Dynamic Layer Routing in LLMs – learn per-layer routers (skip/execute/repeat) from MCTS-discovered paths to cut compute while improving or preserving accuracy across tasks →read the paper

🌟 BitNet Distillation (by Microsoft) – distill full-precision LLMs into ternary-weight task models with SubLN, attention distillation, and warm-start pretraining to deliver large memory savings and faster CPU inference →read the paper

🌟RAG-Anything: All-in-One RAG Framework (by Hong Kong University) – unify multimodal documents via dual-graph structure and cross-modal hybrid retrieval to reason over long, heterogeneous evidence →read the paper

LLM-guided Hierarchical Retrieval – impose a semantic tree over large corpora and traverse with calibrated relevance to achieve logarithmic-complexity, zero-shot retrieval on reasoning-heavy datasets →read the paper

Scaling Language-Centric Omnimodal Representation Learning – leverage generative pretraining’s latent cross-modal alignment and refine with contrastive learning, revealing a generation-representation scaling law →read the paper

🌟 OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM (by NVIDIA) – align audio–vision embeddings and curate omni-modal conversations to outperform larger omni models with far fewer tokens →read the paper

A Theoretical Study on Bridging Internal Probability and Self-Consistency for LLM Reasoning – analyze sampling-based test-time scaling and propose a hybrid that improves confidence reliability while halving sampling cost →read the paper

🌟 The Role of Computing Resources in Publishing Foundation Model Research (by UT Austin, UCLA, Google) – quantify how compute access correlates with citations and advocate shared infrastructure to broaden participation →read the paper

AutoPR: Let’s Automate Your Academic Promotion! – transform papers into platform-optimized promotional content with a multi-agent pipeline, dramatically boosting real-world engagement →read the paper

Language Models Model Language – reframe evaluation with an empiricist lens where usage frequency governs language, offering guidance for designing and interpreting LLMs →read the paper