Your briefing

Varick argues AI success requires implementation services: teams that analyze and refactor processes and build custom agents, not just selling tools. He touts Varick’s record (100% production, 100% repeat clients), notes OpenAI launched an OpenAI Deployment Company on 2026-05-11 backed by 19 partners, warns of vendor lock-in risks, and is hiring Engineers, FDEs, and Consultants.

Discriminative factorization is proposed to quantify and distinguish high- versus low-quality query sets for black-box model-level classification. The framework yields a theoretical result: the probability of chance-level classification decays exponentially with query budget. On three auditing tasks the authors show estimated parameters track empirical decay and enable query selection that mirrors oracle ordering.

CUTS-GPR targets the computational bottleneck of Gaussian process regression in high-dimensional problems by exploiting an additive kernel on an incomplete grid to reveal structure that yields an extremely fast kernel matrix–vector product. The method shows near-linear (or linear) scaling in N and low-order polynomial scaling in D, with benchmarks on billions of points and thousands of dimensions and a full GPR + hyperparameter run in hours for N=447,265, D=24, enabling Bayesian modeling of high-dimensional potential energy surfaces in computational chemistry.

Distributional treatment effects that leave means unchanged are targeted by DR-ME, a semiparametrically efficient finite-location test (Zenati & Gretton, arXiv:2605.08034v1, 2026-05-08). From observational data it derives orthogonal doubly robust kernel features whose centered oracle is the canonical gradient; for fixed locations the test is chi-square calibrated and has noncentral chi-square local power with covariance whitening optimizing local SNR. Sample splitting preserves post-selection validity; experiments show near-nominal Type-I error and competitive power, with learned locations that localize effects in a semi-synthetic medical-imaging study.

The paper investigates whether a single-DOF actuated sagittal spine improves learned agile locomotion for a quadruped. Using MuJoCo simulations of the Silver Badger robot, the authors evaluate high-speed running, stair and high-angle slope climbing, hurdling, and crawling. Results show the spine increases agility and enables traversing higher stairs, steeper slopes, taller obstacles, and narrower passages, suggesting spine actuation is a promising design extension for agile robots.

Yo Ehara proposes a Householder-aligned permutation test to more accurately compare word semantic breadth from contextualized token embeddings. By reflecting one token cloud to align mean directions before permutation testing, the method prevents directional differences from masquerading as dispersion effects. Empirically it cut Type‑I error by 32.5% and, with a GPU-batched implementation, ran 23× faster than a CPU baseline; accepted to ACL 2026.

Text-to-CAD evaluation is framed as automated testing in this work: Mallis et al. propose CADTestBench and CADTests, executable checks that validate geometric and topological constraints of generated CAD models. They benchmark recent Text-to-CAD systems on CADTestBench and demonstrate that using CADTests to guide generation yields simple baselines that outperform prior methods; code and datasets are open-sourced.

Vibe-Trading is an open-source AI + quant trading framework that hit 6k+ GitHub stars and has shipped daily updates for a month (published 2026-05-11). It connects to Tushare and AKShare, supports A‑shares/crypto/global markets, exports to TradingView/通达信/MetaTrader, and offers brokerage analytics, a Shadow Account, persistent-memory agents (13+ models), and Docker-ready security for AI+Human workflows.

Proxy3D proposes compact 3D proxy representations extracted from video frames via semantic and geometric encoders and semantic-aware clustering. The authors curate the SpaceSpan dataset and apply multi-stage training to align proxies with vision-language models; on 3D visual question answering, visual grounding, and spatial intelligence benchmarks the method achieves competitive or state-of-the-art results while using shorter vision sequences. (Abstract-only summary.)

Flow-OPD addresses reward sparsity and gradient interference in multi-task alignment for Flow Matching text-to-image models by combining single-reward GRPO teachers with a Flow-based Cold-Start and on-policy distillation into a single student, plus Manifold Anchor Regularization to prevent aesthetic degradation. Built on Stable Diffusion 3.5 Medium, the abstract reports GenEval 63→92 and OCR 59→94; full text was not available.

Online kernel regression: Li and Hiratani (2026) derive a closed-form expression showing online kernel regression is equivalent to offline kernel regression with shifted, inaccurate target outputs. They give a closed-form and an iterative target-correction that provably recovers the offline predictor. Experiments on CIFAR-10 and CORe50 show online SGD with corrected targets outperforms using true targets in continual learning.

A popular dining-focused credit card received an anniversary refresh (reported May 11, 2026) that adds 5X points on hotels, nearly $100 in limited-time travel and dining credits, and enrollable rental-car elite status; the promotion also advertises a welcome bonus up to 100,000 points and requires enrollment for some benefits.

Prop trading on Polymarket is now live, according to @0xd1namit (2026-05-11), who credits @BagCalls and his team for standout marketing and expects strong execution. The Funding Predicts Beta Competition (powered by Polymarket) launches with over 1.4m in funded accounts and 10K+ in cash prizes, giving traders 14 days to qualify at fundingpredicts.com.

SCOPE proposes a specification-guided framework that tracks semantic commitments across generation by decomposing intents into structured specifications and conditionally calling retrieval, reasoning, and repair skills to fix violations (the 'Conceptual Rift'). Evaluated on the new Gen-Arena benchmark with the EGIP metric, SCOPE yields 0.60 EGIP and also performs strongly on WISE-V (0.907) and MindBench (0.61). Summary based on the abstract (full text not provided).

The paper tackles inverse imaging with Vision–Language Latent Diffusion Models by introducing a unified Euclidean–Wasserstein-2 gradient-flow that jointly samples the posterior and optimizes prompts in latent space. By pairing this flow with few-step latent text-to-image models, the method achieves low-NFE inference and avoids backprop through autoencoders, yielding state-of-the-art results on canonical inverse problems with much lower compute; summary based on the abstract only.

Lin and Liu show that mechanistic interpretability work often uses causal vocabulary (circuits, mediators, causal abstraction) while omitting explicit identification assumptions. Via a purposive audit of 10 papers and a two-coder check on 30 items, they document widespread substitution of validation metrics for identification and offer a five-step disclosure norm to make causal claims explicit. Submitted to NeurIPS 2026 (Position Track).

It Just Takes Two introduces a simple, theoretically grounded strategy for amortized neural posterior estimation that decouples representation learning from posterior modeling. The authors train a mean-pool Deep Set on sets of size ≤2 to produce an encoder that generalizes to arbitrary set sizes, then finetune an inference head on aggregated embeddings; this makes training cost essentially independent of deployment set size N. Across diverse benchmarks (scalar, image, multi-view 3D, molecular, high-dimensional generation) with N in the thousands, the method matches or outperforms baselines while using much less compute.

The Visa Business Card Companion Fare Offer in Spencer Burleigh’s Alaska Airlines (Atmos Rewards) account is set to expire on August 8, 2026 (about three months after the 2026-05-10 AwardWallet notice). AwardWallet reports the account was last updated 101 days ago and urges logging into AwardWallet to verify the coupon using provided auto-login and account-restore links.

The authors apply deep learning to infer global asteroseismic parameters (Δν, νmax, and for K2 also ΔΠ1) from short, one-month lightcurves to enable scalable analysis of TESS/K2 red-giant samples. Their model recovers Δν and νmax for ~50% of one-month Kepler/K2 cases but only ~23% for single-sector TESS; it produces ~200 reliable ΔΠ1 measures that match the Kepler Δν–ΔΠ1 sequence. (Summary based on the abstract; full text was not provided.)

The paper shows that language models (270M–27B, including Gemma 3, Qwen, Llama 3.1) encode tool selection as a linearly readable vector: adding per-tool mean-difference vectors reliably flips name-only single-turn tool choices and causes downstream JSON arguments to match the new schema. Causal attribution concentrates on one output-row and a few mid/late attention heads; base-model representations already carry tool identity (69–82% recoverable), while instruction tuning wires it to generation. Measurements are for single-turn fixed-menu settings; multi-turn transfer is noted as more fragile.

123D unifies multi-modal driving datasets by representing each sensor or annotation as a timestamped event stream, allowing flexible synchronization across heterogeneous formats. The authors merge eight real-world datasets (3,300 hours, 90,000 km) and a synthetic generator, perform systematic analyses of annotations and pose/calibration, and demonstrate cross-dataset 3D detection transfer and RL planning; the framework and tools are released open-source.

Karpathy recommends appending 'structure your response as HTML' to LLM prompts to render outputs in a browser, argues audio will be humans' preferred input while vision becomes AI's preferred output, and outlines a progression from text→markdown→HTML→interactive neural videos (eventually diffusion‑generated), urging improved multimodal inputs before BCIs.

The Garry's List Action Voter Guide, published May 11, 2026 by Garry Tan, Shaudi Fulp, and Forrest Liu, aggregates endorsements from local housing groups, labor unions, and civic reform organizations ahead of the California June 2 primary. The resource is presented as transparent, sourced, and searchable at garrysguide.org/elections and accepts suggested additions via email or X.

The paper addresses zero-shot human motion generation under very challenging spatiotemporal constraints by augmenting training-free diffusion noise optimization with retrieval guidance. It parses task constraints into groups (relational task parsing, powered by an LLM), retrieves reference motions for the hardest constraints, and forms a reward-guided mask to blend retrieved and random noise for improved diffusion initialization. The authors report this approach successfully handles tasks that prior methods struggle with, enabling more reliable constrained motion synthesis; accepted to CVPR 2026 (arXiv 2026-05-08).

CMR-EXTR converts free-text cardiac magnetic resonance (CMR) reports into auditable structured data with per-field confidence. The method employs a teacher–student distillation pipeline for fully offline inference and reduced annotation effort, plus an uncertainty model that blends distribution plausibility, sampling stability, and cross-field consistency to prioritize human review. It achieves 99.65% variable-level accuracy; code on GitHub; accepted to ISBI 2026.

The note proves that finite Gaussian surface area Γ implies existence of non-negative degree-k polynomials that ε-approximate indicator functions in L1 under the standard Gaussian, with k = ~O(Γ^2/ε^2). This adds a pointwise non-negativity guarantee (range [0,∞)), sits between plain L1-approximation and sandwiching polynomials, matches prior degree bounds up to constants, and targets smoothed positive-only learning. Only the abstract was available for this summary.

Reinforcement-learning for exponential-utility optimization in discounted MDPs: the paper derives two Q-value–style Bellman extensions whose operators are contractions in L_infty and sup-log/Thompson metrics, proves fixed-point structure and optimality of the induced greedy stationary policy among stationary policies, and presents two model-free algorithms — a two-timescale Q-learning with a.s. convergence and finite-time rates, and a one-timescale power-law method whose convergence is established via delicate local arguments. Full text on arXiv (abstract used).

Memelord’s May 11, 2026 newsletter from founder Jason “The Memelord” Levin claims the Memelord app has in‑app 'AGI for memes' and corrects a prior wrong download/link. The note links to the app, promotes a free trial at Memelord.com, and offers promo code MYTHOS for 50% off signups.

CA-SQL is a complexity-aware Text-to-SQL inference pipeline that scales exploration breadth by estimated task difficulty, employs evolutionary-search-inspired prompt seeding to elicit diverse candidates, and uses a novel voting method to pick final queries. On the Bird-Bench development set it reports 51.72% on the "challenging" tier (SOTA among in-context approaches with GPT-4o-mini), plus 61.06% execution accuracy and 68.77% Soft F1.

Imagined speech decoding: the authors recorded paired listened and imagined MEG from trained musicians and built a three-stage pipeline that maps imagined MEG to listened MEG (six mapping models), decodes words with a contrastive listened-only decoder (four embedding strategies), and applies the decoder to mapped imagined data from held-out subjects. Proof-of-concept results show significant above-chance decoding and scalability with more training data; only the abstract was available for this summary.

GRAPHLCP tackles conformal prediction for graph neural networks by addressing failures of embedding-space localization on graphs. It combines feature-aware densification to reduce locality bias in sparse graphs with a Personalized PageRank kernel to capture structural proximity for anchor sampling and calibration weighting. The approach yields finite-sample marginal coverage and empirically better test conditional coverage and smaller prediction sets on several regression and classification benchmarks compared to prior embedding-only localization methods.

The paper 'The Memory Curse' (Liu et al., 2026) shows that expanding LLMs' context windows often erodes cooperation in multi-agent social dilemmas: across 7 models and 4 games over 500 rounds cooperation fell in 18 of 28 model–game settings. Analyses of 378,000 reasoning traces implicate loss of forward-looking intent; targeted LoRA fine-tuning, memory sanitization, and CoT ablations partly restore cooperation. (Based on the abstract.)

NoiseGate reframes per-latent timestep selection in joint video–action world-action models as a learnable information-gating policy: by adjusting each predicted latent frame's noise level, a Gating Policy Network controls its Key/Value reliability for action generation. The approach (independent per-latent timestep sampling plus task‑reward optimization) improves RoboTwin random-scene manipulation performance. Summary based on the paper's abstract; full text not available here.