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When you purchase through links in our articles, we may earn a small commission. This is where the magic happens. The Full Nerd podcast has been running for over a decade here at PCWorld, and this month marks a big shift for the show as we transition from broadcasting in the studio to doing it all online. Our own Adam Patrick Murray has left California and decamped to North Carolina, so getting him back in the studio every week would get expensive fast. We just don't have that kind of budget for plane tickets, so instead we're doing what most podcast crews do these days and switching to a fully remote arrangement.

Colin Kaepernick accuses NFL of'colluding' against Macklemore amid'Free Palestine' controversy Virginia Governor Abigail Spanberger quietly rolls back Youngkin's plans to protect girls' sports Human vs. ChatGPT NFL Week 2 betting picks: AI looks to rebound after disastrous 0-5 debut NBA star Kyrie Irving jumps into NFL drama, offers to pay player's fine for Gaza-themed eye black NFL's highest-paid TE George Kittle reveals favorite purchase: A 50-foot flagpole for a massive American flag Bill Belichick would like you to know that his 25-year-old girlfriend isn't running things at UNC Bijan Robinson believes Texas' comeback over Ohio State can change entire season trajectory Barstool chief Dave Portnoy blasts Macklemore's shady past, strongly supports Patriots owner Robert Kraft Lane Kiffin's return to Ole Miss comes with betrayal, lawsuits and memories of him trying to burn it all down Paige Spiranac and her'cans' go to bat for Sydney Sweeney, Ole Miss-LSU gets nasty & UF alumni ...

Outward Could ChatGPT Be the New Gender-Affirming Care? A growing number of transgender people are crediting A.I. with helping them discover and explore their identities. But should tech mired in controversy be trusted with something so profound? Become a member to share 10 free articles a month. Become a member to share 10 free articles a month.

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The death of physical media is getting closer--and we may miss it when it's gone. When I was 16, I did something I'm embarrassed to admit: I waited in a long line to buy a video game called Assassin's Creed III . Over the past few days, though, that experience has become ever so slightly tinged with nostalgia. Last week, Sony announced that, starting in 2028, new PlayStation games will be available only as digital downloads rather than physical discs. Will kids ever get to embarrass themselves like this again?

We study policy optimization for online episodic tabular Markov decision processes with unknown transition kernels, aiming for best-of-both-worlds guarantees together with data-dependent regret bounds. Recent work (Dann et al., 2023; Li et al., 2026) has shown that policy optimization can adapt to both adversarial and stochastic losses with first-order, second-order, and path-length bounds, but only under known transitions, leaving open whether such data-dependent guarantees are achievable by policy optimization when the transition kernel is unknown. We resolve this by developing a new algorithm based on optimistic follow-the-regularized-leader that attains these guarantees under unknown transitions. The key ingredient is a new design of optimistic $Q$-function estimators together with a data-dependent transition bonus that controls estimator bias through the loss-prediction error. Our analysis further identifies an unavoidable transition-dependent complexity term that captures the intrinsic cost of estimating the transition kernel. As a result, we obtain first-order, second-order, and path-length bounds with the transition-dependent complexity term while simultaneously achieving gap-dependent $\mathrm{polylog}(T)$ regret in the stochastic regime.

Inference-time scaling is a promising paradigm to improve generative models, especially when outputs must satisfy structural constraints or optimize downstream rewards. We consider Masked Diffusion Model (MDM) and introduce MDM-VGB, a discrete diffusion sampler that augments unmasking generation with theoretically principled reward-guided remasking. Inspired by the recent success of the classical Jerrum-Sinclair backtracking Markov chain in reward-tilted generation, MDM-VGB extends the backtracking random walk from a fixed prefix tree to a masked-state graph, allowing tokens to be unmasked and remasked at arbitrary positions. The resulting sampler favors unmasking and remasking moves that lead to higher-value partial configurations, enabling both effective high-reward generation and efficient repair of low-reward samples. We prove that MDM-VGB is robust to process-verifier noise and achieves quadratic complexity, while popular test-time heuristics such as best-of-$N$ can incur exponential complexity due to error accumulation. Our theoretical findings are corroborated by strong empirical performance, particularly on popular constraint-satisfaction and scientific benchmarks such as Sudoku and QM9.

We study PAC learning in tabular discounted Markov decision processes with exogenous i.i.d. contexts, with discount factor $γ$, finite state space $\mathcal X$, action space $\mathcal A$, and context space $\mathcal Z$. At each time step, a context is drawn independently from an unknown distribution $μ$ and revealed before the agent acts. This context may affect both rewards and transitions, while remaining uncontrolled by the agent. Depending on the regime, the learner has access either to a sampling oracle for $μ$, to a sampling oracle for the transition kernel conditioned on state-context-action tuples, or to both. Oracles can be accessed before and during policy execution. The sample complexity is measured by a couple $(n,m)$, where $n$ is the number of calls to the sampling oracles before execution and $m$ is the number of calls to the sampling oracles during execution. When rewards and transitions are known and only the context distribution $μ$ is sampled, we give a variance-reduced algorithm that solves policy evaluation (PE), best-value estimation (BVE), and best-policy extraction (BPE) with $\left(\widetilde O\left(1/((1-γ)^3\varepsilon^2)\right), 0 \right) $ sample complexity. The rate is independent of $|\mathcal Z|$ and minimax optimal up to logarithmic factors. As a corollary, we also obtain tight rates in the case of one-step perfect look-ahead, improving upon the existing guarantees. In the fully unknown regime, where both $μ$ and P must be learned, we show that PE remains $|\mathcal Z|$-free, with matching upper and lower bounds $\bigl(\widetilde O(|\mathcal X|/((1-γ)^3\varepsilon^2)),\, \widetilde O(1/((1-γ)^2\varepsilon^2))\bigr)$.

The function of biomolecules such as proteins depends on their ability to interconvert between a wide range of structures or "conformations." Researchers have endeavored for decades to develop computational methods to predict the distribution of conformations, which is far harder to determine experimentally than a static folded structure. We present ConforMix, an inference-time algorithm that enhances sampling of conformational distributions using a combination of classifier guidance, filtering, and free energy estimation. Our approach upgrades diffusion models--whether trained for static structure prediction or conformational generation--to enable more efficient discovery of conformational variability without requiring prior knowledge of major degrees of freedom. ConforMix is orthogonal to improvements in model pretraining and would benefit even a hypothetical model that perfectly reproduced the Boltzmann distribution. Remarkably, when applied to a diffusion model trained for static structure prediction, ConforMix captures structural changes including domain motion, cryptic pocket flexibility, and transporter cycling, while avoiding unphysical states. Case studies of biologically critical proteins demonstrate the scalability, accuracy, and utility of this method.