Crypto & Web3

Modern agent systems often suffer from fragmented runtime state: transcripts, tool effects, memory events, workspace placement, branch provenance, and replay evidence are recorded separately and become difficult to inspect or reproduce. OpenRath addresses this issue with a PyTorch-like programming model for multi-agent, multi-session systems. The analogy concerns the role of a central first-class runtime abstraction, not tensor computation. Its core abstraction is Session, the runtime value passed between agents and workflows. A Session is branchable, inspectable, replayable, backend-aware, and composable. It records conversation chunks, sandbox placement, lineage metadata, token usage, pending work, and tool evidence, while defining where memory interactions enter the runtime record. Since this state is carried by the same value used in program execution, fork, merge, and replay become explicit runtime operations rather than states reconstructed from external traces. OpenRath further defines Sandbox, Tool, Agent, Memory, Workflow, and Selector, with Selector turning control flow into runtime-routed decisions. This report presents the programming model, architecture, audited milestones, and evidence protocol. Its claims are limited to controlled runtime properties, while broad quantitative comparisons, live-provider quality, optional-backend availability, and memory quality are left for follow-on evaluation. The central thesis is that Session provides agent systems with a first-class runtime value for auditable composition.

Conditional diffusion and flow models routinely fail to satisfy the very constraints that define their task. For instance, a depth-conditioned model often produces images whose re-extracted depth disagrees with the input, even though the forward operator--the depth predictor defining the constraint--is available during both training and inference. Existing approaches generally fall into two categories: supervised models that treat the conditioning signal as a static cue and ignore alignment information at inference, and guidance-based methods that consult it through hand-tuned linear updates, typically trading fidelity to the condition against the plausibility of the generated sample. We argue that the fundamental gap in both paradigms is that the model is never trained to utilize its own alignment error. We introduce FlowBender, a closed-loop framework that treats this error as a first-class input, training the network to learn a correction policy conditioned on inference-time feedback. At each step, an unguided look-ahead pass estimates the clean signal, a task-specific deviation is computed via the forward operator, and a refinement pass consumes this signal to produce a corrected velocity. We propose several variants of FlowBender, including a gradient-based formulation for differentiable operators and a zero-order variant for non-differentiable settings such as JPEG compression. For efficient sampling, we introduce a prior-step shortcut that enables closed-loop correction at a minimal additional computational cost. Across image-to-image translation, restoration, and 3D mesh texturing, FlowBender consistently outperforms standard supervised baselines, alignment-loss-augmented training, and state-of-the-art inference-time guidance, improving fidelity and plausibility simultaneously rather than trading them against each other. Project page: https://flow-bender.github.io/

Wall Street Journal : Investigation: Polymarket is paying creators to make deceptive videos about winning bets, targeting users in the US, where its primary crypto platform is banned —  The prediction market has flooded social media with deceptive videos by paid creators, according to a Wall Street Journal investigation

Naga Avan-Nomayo / The Block : DOJ says two brothers pleaded guilty to robbing a Minnesota family of $8M+ in cryptocurrency after holding them at gunpoint for over eight hours in 2025 —  Quick Take  — Two brothers pleaded guilty to robbing a Minnesota family of more than $8 million in cryptocurrency after holding …

Ben Weiss / Fortune : Sources: APEC, a derivatives exchange founded by the 22-year-old son of pro-crypto Senator Kirsten Gillibrand, raised $30M led by Lux at a $300M valuation —  The 22-year-old son of a crypto-friendly senator plans to launch his own exchange for a type of derivative popularized by digital asset traders.

Michael Peel / Financial Times : After years of false dawns, Big Tech, startups, and governments are betting on commercially useful quantum computers by 2030, as skeptics worry about hype —  Companies are betting on big implications for pharmaceuticals, financial services and crypto.  But sceptics worry about hype.

Bill Toulas / BleepingComputer : Europol says it has dismantled the AudiA6 crypto mixing service, which allegedly laundered $380M+ for ransomware actors and others between 2022 and 2025 —  Law enforcement has dismantled the “AudiA6” cryptocurrency service allegedly used by ransomware actors and other cybercriminals to launder more than $380 million.