Wednesday / AI 101 series: From vibe coding to Spec-driven development

Friday / GenAI Unicorn profile or a Reflection on a private event in SF

Google DeepMind – Aletheia Takes FirstProof for a Spin
Google DeepMind reports that its autonomous math agent, Aletheia (powered by Gemini 3 Deep Think), solved 6 out of 10 research-level problems in the inaugural FirstProof challenge—within deadline and without human mathematical intervention. Experts were unanimous on five, split on one. Transparency was emphasized, raw prompts published, and inference costs disclosed. Peer review may remain human, but the co-author list is getting crowded.

AWS in Dubai – not resting
Amazon’s cloud arm reported outages across its UAE and Bahrain regions after unidentified “objects” struck a UAE data center, sparking a fire and cutting power to two clusters. Recovery may take at least a day, with banks among those affected.

Extra hot week for OpenAI
OpenAI locked in a reported $110 billion raise – valuing it at $730 billion and securing vast Nvidia capacity – while signing a classified deployment deal with the Pentagon, hours after Anthropic was labeled a supply chain risk. Cloud-only safeguards and embedded engineers aim to keep red lines intact, yet backlash is brewing. In one week: capital secured, critics mobilized, and the AI power map redrawn.

Implicit intelligence – Evaluating agents on what users don’t say

Google DeepMind Nano Banana 2

Qwen 3.5 medium model series (open source)

Researchers from Alibaba introduced Qwen3.5-Flash, 27B, 35B-A3B, and 122B-A10B models, improving intelligence with lower compute. The 35B-A3B surpasses Qwen3-235B-A22B-2507 and Qwen3-VL-235B-A22B through architectural, data, and RL advances. The 122B-A10B and 27B narrow gaps with frontier systems in complex agent tasks. Flash offers 1M default context and built-in tools, and 35B-A3B runs locally on 24GB devices via GGUF. Series released via Hugging Face, ModelScope, and dedicated API endpoints for production deployment →read their post

Efficient reasoning is turning “thinking” into a budgeted resource.

Agentic RL stability is becoming the main gate on scaling long-horizon agents.

Decoding is being treated as an optimization layer, not a superstition.

Search-heavy agents are shifting from deep chains of thought to parallel evidence.

Memory design is resurfacing as the bottleneck for long-context behavior.

World models are being defined by consistency constraints, not prettier videos.

Inference-time steering is expanding as a practical alternative to retraining.

🌟 Does Your Reasoning Model Implicitly Know When to Stop Thinking? (Beihang University and Bytedance) Uncovers that reasoning models often contain an implicit “stop” signal that current sampling hides, then introduces a sampling recipe that surfaces shorter correct chains and can be baked into RL to improve pass@1 efficiency →read the paper

The Art of Efficient Reasoning: Data, Reward, and Optimization Maps efficient reasoning into a practical training playbook by dissecting prompts, rollouts, reward shaping, and optimization, and by treating length control as something you can train deliberately instead of hoping the model “figures it out” →read the paper

Decoding as Optimisation on the Probability Simplex: From Top-K to Top-P (Nucleus) to Best-of-K Samplers Reframes decoding as solving a regularized optimization problem over the token probability simplex, then uses that lens to derive new samplers aimed at multi-sample pipelines (self-consistency, reranking, verifier selection) →read the paper

🌟 ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning (University of California, University of Wisconsin) Builds a clean testbed plus a “design-dimensions” analysis of policy-gradient choices for agentic RL, then distills a training recipe and an optimizer variant intended to prevent the classic ARL faceplant →read the paper

🌟 Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization (Microsoft and KAIST) Targets the “agents don’t explore” problem by mixing on- and off-policy updates and using memory as an exploration aid while training robustness to operate without memory at test time →read the paper

VESPO: Variational Sequence-Level Soft Policy Optimization for Stable Off-Policy LLM Training Stabilizes off-policy RL for LLMs under policy staleness and asynchronous training by deriving a sequence-level importance-weight reshaping rule with a variational justification, reducing variance without hacky length normalization →read the paper

DSDR: Dual-Scale Diversity Regularization for Exploration in LLM Reasoning Forces exploration to stay meaningfully diverse by regularizing both trajectory-level variety and token-level entropy (only on correct paths), reducing mode collapse where the policy learns one beloved reasoning rut and refuses to leave it →read the paper

Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization Shifts the compute budget from serial chain-of-thought to parallel evidence gathering, aiming to cut latency and improve cross-setting generalization for research agents that live and die by retrieval →read the paper

TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents Improves reliability in brittle environments by assembling multiple candidate plans into a graph, solving for a feasible path, and executing with constrained decoding plus replanning on feedback drift →read the paper

Revisiting Text Ranking in Deep Research Dissects what actually matters in deep-research pipelines by testing retrieval units, reranking depth, and query mismatch, then shows how “agent-style” queries interact with classic IR components (and how to translate them into something rankers like) →read the paper

🌟 On Data Engineering for Scaling LLM Terminal Capabilities (NVIDIA) Builds a synthetic task generation pipeline and an open terminal-task corpus, then demonstrates that data filtering, curricula, and long-context training can move terminal-agent performance by a lot without magical new architectures →read the paper

🌟 The Trinity of Consistency as a Defining Principle for General World Models Proposes a clean conceptual checklist (modal, spatial, temporal consistency) for what a general world model should satisfy, then pairs it with a benchmark aimed at multi-frame reasoning and generation →read the paper

Imagination Helps Visual Reasoning, But Not Yet in Latent Space Tests whether latent “imagination tokens” causally drive visual reasoning, finds weak causal influence, and argues for teaching explicit textual imagination as a simpler, stronger alternative →read the paper

See and Fix the Flaws: Enabling VLMs and Diffusion Models to Comprehend Visual Artifacts via Agentic Data Synthesis Automates artifact dataset creation by using agents to detect entities, inject controlled artifacts during diffusion, and curate explanations, turning “artifact handling” into something you can scale rather than hand-label forever. →read the paper

TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics Extracts task-progress rewards from a pretrained video model’s token logits instead of asking it to output numbers, making reward signals more robust and portable across tasks and robot setups. →read the paper

World Guidance: World Modeling in Condition Space for Action Generation Compresses predicted future observations into a condition representation that is injected into the action inference path, aiming to keep action generation both fine-grained and generalizable. →read the paper

Test-Time Training with KV Binding Is Secretly Linear Attention Reinterprets a broad slice of test-time-training-with-binding architectures as learned linear attention, yielding simplifications and parallel formulations while explaining behaviors that the “it’s memorizing KV pairs” story cannot. →read the paper

Memory Caching: RNNs with Growing Memory Extends recurrent models with cached hidden-state checkpoints so effective memory can grow with sequence length, offering a knob between RNN linear cost and transformer quadratic cost while narrowing the recall gap. →read the paper

InfoNCE Induces Gaussian Distribution Analyzes contrastive learning and argues that InfoNCE-trained representations tend toward Gaussian structure under plausible assumptions, giving a more principled handle on a phenomenon people often treat as an empirical quirk. →read the paper

The Design Space of Tri-Modal Masked Diffusion Models Pretrains masked diffusion from scratch across text, image, and audio modalities, then studies scaling and training knobs systematically, pushing diffusion beyond “language only” into a unified multimodal recipe space. →read the paper

The Diffusion Duality, Chapter II: Ψ-Samplers and Efficient Curriculum Improves discrete diffusion sampling with predictor-corrector samplers that keep getting better as steps increase (instead of plateauing), and adds a more memory-efficient training curriculum for the Gaussian relaxation phase. →read the paper

No One Size Fits All: QueryBandits for LLM Hallucination Mitigation Learns an online policy that chooses query-rewrite strategies per prompt using contextual features, reducing hallucinations in black-box settings where you cannot fine-tune or edit weights. →read the paper