May 2026

Brandon Waselnuk of Unblocked argues that coding agents are failing less because they lack access to tools than because they lack organizational context. In his account, MCP connections, larger context windows and naive RAG give agents more material, but not the judgment to know which code patterns, Slack decisions, ownership signals or backwards-compatibility rules matter. His proposed answer is a runtime context engine that reasons across code, PRs, documents, conversations and social structure before the agent writes code, so its output is closer to something a long-tenured engineer could merge.

Jiequn Han’s talk argues that transport-based generative models should be treated not only as tools for sampling clean data distributions, but as machinery for recovering and adapting those distributions when the usual clean training set is absent. His main proposal, Self-Consistent Stochastic Interpolants, learns a clean prior from corrupted observations by iterating a transport map until the learned distribution, passed through a trusted forward simulator, reproduces the observed data. Han presents the method as a black-box alternative to EM-style inverse generative modeling, with the caveat that simulator mismatch remains a central unresolved risk.

UC Berkeley PhD student Qiyang “Colin” Li argues that the flow-matching and diffusion policies now effective for robotic manipulation expose a weakness in standard Q-learning: they model complex, multimodal action chunks well, but are hard to optimize with the reparameterized actor gradients used in efficient continuous-control RL. He presents two approaches, Flow Q-learning and Q-learning with Adjoint Matching, as ways to make off-policy RL work with these policies while reusing prior robot data. The trade-off, in Li’s account, is between the stability gained by distilling flows into one-step actors and the expressivity preserved by keeping multistep flow policies.

Michael Plainer, Winfried Ripken and Gregor Lied argue that generative models can attack molecular dynamics’ central bottleneck: the gap between femtosecond integration steps and biological processes that unfold many orders of magnitude later. In the Microsoft Research seminar, they separate the problem by timescale, using diffusion models to sample equilibrium Boltzmann states and extract force information, while proposing Hamiltonian flow maps for the intermediate regime where simulations need large, stable steps without training on expensive future-state trajectories.

Lorenz Richter’s seminar argues for a non-Markovian route to diffusion-based sampling when the target distribution is known only through an unnormalized density rather than data. He presents existing Markovian path-space samplers as theoretically flexible but increasingly constrained by trajectory simulation and storage costs, then proposes building reciprocal bridge measures from endpoint couplings and learning their Markovian projection by fixed-point regression. The resulting Bridge Matching Sampler, Richter says, uses a single learned control, accommodates flexible priors and reference processes, and shows improved stability and mode preservation in high-dimensional synthetic and molecular benchmarks, especially with damping.

Stanford Ph.D. candidate Yinuo Ren argues that diffusion, discrete diffusion, and broader jump-based generative models can be treated as instances of the same problem: choose a forward Markov process that carries data toward a simple reference law, then learn its reverse-time generator. His framework gives conditions under which that reverse generator is explicit up to unknown densities and turns the resulting approximation problem into a path-space KL objective via Doob’s h-transform. The payoff, Ren says, is a principled way to design denoising models beyond Gaussian diffusion, including discrete and Lévy-type dynamics.

Aimee Maurais argues that dynamic measure transport, now central to diffusion models and flow matching, needs different design principles when the target distribution is specified by densities, likelihoods, or prior samples rather than training data. In a Microsoft Research seminar, she presents three lines of work toward that goal: gradient-free particle dynamics using likelihood evaluations, PDE-constrained path design to avoid unstable interpolations, and localized transport velocities that exploit conditional-independence structure in high-dimensional Bayesian and data-assimilation problems.

Aram-Alexandre Pooladian argues that blind denoising diffusion models can dispense with an explicit noise schedule because the noisy sample can reveal its own noise level when the data are low-dimensional inside a high-dimensional ambient space. In work with Zahra Kadkhodaie, Sinho Chewi, and Eero Simoncelli, he presents theory and experiments showing that such models can track an implicit reverse-process schedule and sample accurately in polynomially many steps as a function of intrinsic dimension. The empirical comparison suggests a further advantage: blind models may avoid finite-step mismatch between a prescribed schedule and the actual residual noise in the sample.

Mujin Kwun and Carles Domingo-Enrich present energy-based fine-tuning as a post-training method that replaces next-token imitation or task-specific rewards with sequence-level feature matching. Their argument is that supervised fine-tuning remains efficient but is trained under teacher forcing, while RL with verifiable rewards can improve accuracy without preserving the target completion distribution. EBFT instead samples model rollouts, compares their frozen-model feature embeddings with reference completions, and uses that signal for policy-gradient updates; in the reported coding and translation experiments, it matched or exceeded RLVR accuracy while producing lower validation cross-entropy than both RLVR and SFT.

Tristan Bereau presents Split-Flows, a flow-based method for connecting atomistic and coarse-grained molecular representations by adding explicit noise variables for the degrees of freedom lost under coarse-graining. The argument is that this augmentation turns a many-to-one mapping into a tractable coordinate transform, enabling both generative backmapping and computation of configuration-dependent mapping entropy. Bereau says the approach makes information loss measurable for complex molecular systems, though it depends on a differentiable bijective construction and still faces scaling costs.

Peter Potaptchik presents Meta Flow Maps as an amortized way to remove a costly inner loop in reward-aligning generative models: repeatedly simulating trajectories to estimate expected future reward from a noisy state. The method trains stochastic flow maps to produce differentiable, one-step samples from the clean-data posterior conditioned on any time and noisy state, enabling value-gradient estimates for inference-time steering and an off-policy objective for fine-tuning. In ImageNet experiments, Potaptchik argues, this lets a single-particle steered sampler outperform Best-of-1000 baselines across several rewards with far less compute.

Luca Ambrogioni argues that trained diffusion models generate images through brief instability windows rather than uniform step-by-step denoising. In a Microsoft Research generative modeling seminar, he links score dynamics, conditional entropy and statistical-physics phase transitions to show how low-frequency spatial modes soften at critical times, allowing noise to organize into coherent structure. Experiments on patch models, Fashion-MNIST and ImageNet models are presented as evidence that these critical windows govern both pattern formation and the timing of effective guidance.

Bloomberg Investigates follows Portland-based Innovation Law Lab as it challenges the Trump administration’s immigration enforcement in court and tries to free detainees before transfer or fast-track deportation makes relief harder to obtain. The lawyers’ central argument is that due process is being eroded not only by arrests and detention, but by speed: immigrants may have formal rights, they say, yet be unable to exercise them without timely access to counsel. DHS disputes that account, saying ICE does not deny counsel as a matter of policy and that any denials are isolated or justified.

David Layden, a staff research scientist at IBM Research, argues that trained continuous flow models can be recast as quantum simulation problems rather than merely classical samplers. In his account, the velocity field learned by a flow or diffusion-style model defines a Schrödinger equation whose solution is a quantum state encoding the model’s learned distribution. The result leaves training classical and theoretical, but claims that future quantum computers could provide coherent access to those distributions for downstream tasks such as Monte Carlo estimation, not just ordinary sampling.

Harvard postdoctoral fellow Lingkai Kong argues that generative AI can address three recurring failures in high-stakes One Health decision-making: scarce deployment data, hard-to-represent constrained policies, and shifting human priorities. In a Microsoft Research seminar, he presents flow matching, diffusion models and LLM agents as tools for patrol planning, poaching prediction, HIV testing policy and reward design, with collaborations involving conservation partners, the WHO, the Gates Foundation and South African health researchers.

Caltech applied math PhD candidate Yixuan Wang argues that high-precision computation can make singularity questions in nonlinear PDEs tractable only when it is tied to stability analysis and rigorous verification. In a Microsoft Research seminar on Navier-Stokes blowup and weak-solution nonuniqueness, Wang presents machine-learning tools such as PINNs, neural operators, and Kolmogorov–Arnold Networks as ways to discover candidate singular structures, not as substitutes for proof. His broader case is that numerics, analytical a posteriori estimates, and interval-certified computation must work together if singularities in systems such as Navier-Stokes are to be identified and verified.

Emma Finn argues that the memorization puzzle in diffusion models can be probed by replacing a black-box score network with an analytically solvable wavelet parameterization. In her Microsoft Research New England seminar, Finn presents the method as a way to isolate which data moments and dependency structures matter across noise scales. Her reported experiments on MNIST suggest that local same-scale wavelet interactions improve denoising more consistently than independent coefficient models or orientation-only coupling, while the larger question of whether the framework explains generative novelty remains unresolved.

Former Pentagon official Darren Farber argues to Patrick O’Shaughnessy that the United States’ military advantage depends less on battlefield skill than on whether its politics, industrial base, and technology pipeline can sustain force before a crisis becomes existential. Farber portrays China and Iran as powerful but brittle authoritarian systems, while warning that democracies face a harder test: defining victory, maintaining public consent, and converting commercial innovation into usable military depth. His case links Ukraine’s drone war, Taiwan, the Strait of Hormuz, defense startups, and military AI to a single constraint — whether America can turn legitimacy and markets into durable strategic capacity.

Cursor’s Federico Cassano and Fireworks’ Dmytro Dzhulgakov argue that Composer’s advantage comes from specializing a model for software engineering inside Cursor rather than spending capacity on general-purpose behavior. Starting from an open-source base, Cursor used mid-training and reinforcement learning against its own product environment, while Fireworks supplied the distributed infrastructure needed to make agent rollouts, weight synchronization, and inference efficient enough to run at scale. Their case is that application companies with enough product-specific usage, tools, and feedback can build models that are better, faster, and cheaper for their own workflows than larger general models.

OpenAI, Anthropic, and SpaceX are trying to finance larger AI bets as losses, infrastructure needs, and public tolerance become harder to separate from the growth story. Across Macrocosmos, Kaggle, OLIVER, Braintrust, Google, and DeepMind, the same pressure shows up in different forms: cost, evaluation, deployment fit, organizational ownership, and proof in the physical world.

Ben Todd, co-founder of 80,000 Hours, argues that AI has shortened the useful career-planning horizon but has not made preparation pointless. In a conversation with Nathan Labenz, Todd says people who want to improve the odds that AI benefits humanity should choose paths by problem importance, neglectedness, solvability and personal fit, with priority on loss of control, concentrated power and engineered pandemics. His case is broader than joining frontier labs: policy, biosecurity, communications and institution-building may be as important as technical safety research.

Alex Kantrowitz and Ranjan Roy argue on Big Technology that OpenAI’s potential IPO is less a sign of financial readiness than a race to define the AI market before Anthropic does. They say OpenAI’s huge revenue and deep losses, Anthropic’s reported acceleration and possible profitability, and SpaceX’s AI-heavy IPO pitch all point to companies trying to sell public investors on future infrastructure demand before the current growth story weakens. The discussion also frames rising public hostility to AI as a practical risk: the industry needs capital to build, but it may also need permission.

Demis Hassabis told Two Minute Papers’ Károly Zsolnai-Fehér that AI could help produce cures for most diseases on a 10- to 20-year horizon, but he framed the claim as a platform problem rather than a countdown. The DeepMind chief argued that AlphaFold is only one component of a broader drug-discovery system, with Isomorphic Labs and DeepMind building multiple specialized models to predict biological behavior, design molecules and eventually accelerate validation. He stressed that clinical testing and regulatory trust remain separate bottlenecks, and that evidence from working AI-designed drugs would have to come before any process change.

Nicholas Kang and Michael Aaron of Google DeepMind’s Kaggle team argue that AI evaluation is failing less because of a shortage of benchmarks than because benchmark results are hard to reproduce, easy to distort through hidden harness choices, and shaped by too narrow a group of authors. Their case is that agentic evals need shared infrastructure: transparent execution, community-created tests, model-versus-model arenas, and low-friction exams for builders who are not research labs. The recurring example is a wastewater treatment engineer in Turkey whose field experience produced a safety benchmark no lab was likely to create on its own.