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QwenLM has released Qwen Code, an open-source AI coding agent whose entry points span the terminal, IDE plugins, a desktop app, a Web UI, SDKs and chat integrations including Telegram, DingTalk, WeChat, WeCom and Feishu. It ships with Auto-Memory, Skills, SubAgents and MCP, plus Agent Teams, Agent Arena and Dynamic Workflows, some marked experimental or opt-in. A daemon mode lets browser, editor, SDK and automation clients share one agent session, and clients can reconnect to active sessions.

QwenLM has released Qwen Code, an open-source AI coding agent whose entry points span the terminal, IDE plugins, a desktop app, a Web UI, SDKs and chat integrations including Telegram, DingTalk, WeChat, WeCom and Feishu. It ships with Auto-Memory, Skills, SubAgents and MCP, plus Agent Teams, Agent Arena and Dynamic Workflows, some marked experimental or opt-in. A daemon mode lets browser, editor, SDK and automation clients share one agent session, and clients can reconnect to active sessions.

GridPSF adds an inspectable forward constraint to the MambaTM restoration network's training graph: from the observed clip and the host's latent field it predicts an 8×8 grid of 35 piston-free Zernike coefficients per cell, then blends unit-mass point-spread functions rendered by a Fraunhofer model to fit the turbulent observation. On held-out ATSyn-dynamic videos, forward consistency reaches 29.49 dB PSNR and 0.8882 SSIM, 3.95 dB above the 25.54 dB identity floor of using the clean target as the prediction. One validation-selected matched-host checkpoint pair gives +0.419 dB PSNR and +0.0022 SSIM, but LPIPS rises by 0.0017, and a five-seed control detects no average restoration gain.

GridPSF adds an inspectable forward constraint to the MambaTM restoration network's training graph: from the observed clip and the host's latent field it predicts an 8×8 grid of 35 piston-free Zernike coefficients per cell, then blends unit-mass point-spread functions rendered by a Fraunhofer model to fit the turbulent observation. On held-out ATSyn-dynamic videos, forward consistency reaches 29.49 dB PSNR and 0.8882 SSIM, 3.95 dB above the 25.54 dB identity floor of using the clean target as the prediction. One validation-selected matched-host checkpoint pair gives +0.419 dB PSNR and +0.0022 SSIM, but LPIPS rises by 0.0017, and a five-seed control detects no average restoration gain.

Cloudflare has open-sourced Kitesurf, its stateless browser for AI agents, on GitHub under the Apache 2.0 license, so developers can read the code, run it locally, modify it, and deploy their own instance to their own Cloudflare Workers account. Kitesurf runs entirely on Workers, parsing HTML, applying CSS, executing page JavaScript and rendering screenshots without a browser container, and it supports the Chrome DevTools Protocol so clients such as Puppeteer can connect. Cloudflare says the goal is a lightweight browser for agents that uses less CPU and memory than a full browser like Chromium and scales on demand, while noting the project is under heavy development.

Cloudflare has open-sourced Kitesurf, its stateless browser for AI agents, on GitHub under the Apache 2.0 license, so developers can read the code, run it locally, modify it, and deploy their own instance to their own Cloudflare Workers account. Kitesurf runs entirely on Workers, parsing HTML, applying CSS, executing page JavaScript and rendering screenshots without a browser container, and it supports the Chrome DevTools Protocol so clients such as Puppeteer can connect. Cloudflare says the goal is a lightweight browser for agents that uses less CPU and memory than a full browser like Chromium and scales on demand, while noting the project is under heavy development.

NVIDIA released the cuda.bindings.cudla module in cuda-bindings 13.3.0, a thin Cython-generated Python layer over the cuDLA C++ runtime that lets Jetson AGX Orin applications create DLA devices, load networks, register buffers and submit inference without writing C++. The module targets hybrid mode, where DLA tasks are enqueued on a CUDA stream alongside GPU preprocessing and postprocessing, and the same API applies on RTX Spark. The release notes that standalone mode, which needs NvSci buffers and sync primitives, is not supported in this version.

NVIDIA released the cuda.bindings.cudla module in cuda-bindings 13.3.0, a thin Cython-generated Python layer over the cuDLA C++ runtime that lets Jetson AGX Orin applications create DLA devices, load networks, register buffers and submit inference without writing C++. The module targets hybrid mode, where DLA tasks are enqueued on a CUDA stream alongside GPU preprocessing and postprocessing, and the same API applies on RTX Spark. The release notes that standalone mode, which needs NvSci buffers and sync primitives, is not supported in this version.

Google has added Google Wallet to Gemini's list of connected apps under Personal Intelligence, so all Gemini app users in the U.S. can now tag @Google Wallet in a chat to ask about personal finance, budgeting and subscriptions, and Gemini generates dynamic charts of cash flow, asset allocation and budgets from that context. Users can optionally link credit cards and bank accounts to Wallet through Plaid for deeper analysis. Gemini can only read account information; it cannot move, manage or invest money, and the connection can be turned on or off at any time.

Google has added Google Wallet to Gemini's list of connected apps under Personal Intelligence, so all Gemini app users in the U.S. can now tag @Google Wallet in a chat to ask about personal finance, budgeting and subscriptions, and Gemini generates dynamic charts of cash flow, asset allocation and budgets from that context. Users can optionally link credit cards and bank accounts to Wallet through Plaid for deeper analysis. Gemini can only read account information; it cannot move, manage or invest money, and the connection can be turned on or off at any time.

Buzz is a self-hostable workspace where humans and AI agents share the same rooms, and each room is fixed by its URL. It writes messages, reactions, workflow steps, review approvals and git events as signed events in a single log, so people and processes share one identity model and one audit trail. Agents hold their own keys, channel memberships and audit records, and can open repos, send patches, review code, run workflows, edit canvases, orchestrate other agents and join voice huddles.

Buzz is a self-hostable workspace where humans and AI agents share the same rooms, and each room is fixed by its URL. It writes messages, reactions, workflow steps, review approvals and git events as signed events in a single log, so people and processes share one identity model and one audit trail. Agents hold their own keys, channel memberships and audit records, and can open repos, send patches, review code, run workflows, edit canvases, orchestrate other agents and join voice huddles.

Tencent has open-sourced LoopForge, a self-hosted workspace where a team describes a requirement to Main Bot in chat and it opens a Docker sandbox, checks out repositories, delegates to project roles and opens the PR. Previously, asking a coding agent to fix code left the result dependent on that run, which might skip the plan, forget tests or review its own code. LoopForge saves a proven way of working as an SOP, a standard workflow of agents, scripts and human gates, so later requirements follow the same stages and checks, with approvals, node status and tool traces kept in one place.

Tencent has open-sourced LoopForge, a self-hosted workspace where a team describes a requirement to Main Bot in chat and it opens a Docker sandbox, checks out repositories, delegates to project roles and opens the PR. Previously, asking a coding agent to fix code left the result dependent on that run, which might skip the plan, forget tests or review its own code. LoopForge saves a proven way of working as an SOP, a standard workflow of agents, scripts and human gates, so later requirements follow the same stages and checks, with approvals, node status and tool traces kept in one place.

AG-UI is an open, lightweight, event-based protocol that standardizes how AI agents connect to user-facing applications. Previously there was no shared convention for wiring an agent into an interface; AG-UI has agent backends emit events matching one of about 16 standard event types during execution and accept a few simple inputs, while a middleware layer adapts to any event transport such as SSE, WebSockets or webhooks. It grew out of CopilotKit's early partnership with LangChain and CrewAI, ships under the MIT license, and comes with a reference HTTP implementation and a default connector.

AG-UI is an open, lightweight, event-based protocol that standardizes how AI agents connect to user-facing applications. Previously there was no shared convention for wiring an agent into an interface; AG-UI has agent backends emit events matching one of about 16 standard event types during execution and accept a few simple inputs, while a middleware layer adapts to any event transport such as SSE, WebSockets or webhooks. It grew out of CopilotKit's early partnership with LangChain and CrewAI, ships under the MIT license, and comes with a reference HTTP implementation and a default connector.

LangChain announced a $300M Series C at a $3B valuation, led by existing investor CapitalG with new investors GIC and DFJ Growth participating. The company ties the funding to its goal of letting every company own its intelligence, anchored in LangSmith, a framework-agnostic platform for running, improving and governing agents. LangChain says more than 7,000 organizations already rely on LangSmith, and that Workday cut the time to deploy a new agent from five months to under a day.

LangChain announced a $300M Series C at a $3B valuation, led by existing investor CapitalG with new investors GIC and DFJ Growth participating. The company ties the funding to its goal of letting every company own its intelligence, anchored in LangSmith, a framework-agnostic platform for running, improving and governing agents. LangChain says more than 7,000 organizations already rely on LangSmith, and that Workday cut the time to deploy a new agent from five months to under a day.

Cohere said it has completed its acquisition of AISLE, a company building autonomous systems for vulnerability discovery and remediation, and has welcomed the team behind cubic, an AI-powered code review platform; the two are to form the foundation of Cohere Cyber, its cybersecurity business, and of a secure coding vertical. Cohere had already acquired Aleph Alpha and Reliant AI, and says these two additions bring its strategic M&A transactions this year to four. AISLE's co-founders take leadership roles in cybersecurity, corporate security and AI security research, and Cohere plans a substantial long-term investment in Prague as its global center for AI cybersecurity and autonomous security systems.

Cohere said it has completed its acquisition of AISLE, a company building autonomous systems for vulnerability discovery and remediation, and has welcomed the team behind cubic, an AI-powered code review platform; the two are to form the foundation of Cohere Cyber, its cybersecurity business, and of a secure coding vertical. Cohere had already acquired Aleph Alpha and Reliant AI, and says these two additions bring its strategic M&A transactions this year to four. AISLE's co-founders take leadership roles in cybersecurity, corporate security and AI security research, and Cohere plans a substantial long-term investment in Prague as its global center for AI cybersecurity and autonomous security systems.

Science Prediction Universe (SPU) places AI agents in a real wet-lab prediction setting: given only experimental conditions and candidates, an agent has eight hours to search the literature, run calculations and rank candidates, which are then scored against measurements by Spearman correlation. The 100 public tasks come from recent Nature Portfolio papers and the 16 private tasks from unpublished experiments, so retrieval cannot help. The best of thirteen frontier-model configurations beats chance on only about half its scored tasks, and more reasoning effort or more tool families brought no detectable score gain.

Science Prediction Universe (SPU) places AI agents in a real wet-lab prediction setting: given only experimental conditions and candidates, an agent has eight hours to search the literature, run calculations and rank candidates, which are then scored against measurements by Spearman correlation. The 100 public tasks come from recent Nature Portfolio papers and the 16 private tasks from unpublished experiments, so retrieval cannot help. The best of thirteen frontier-model configurations beats chance on only about half its scored tasks, and more reasoning effort or more tool families brought no detectable score gain.

Regional air-quality forecasting has relied on online-coupled numerical chemistry-weather models whose skill is sensitive to meteorological initial conditions, emission inventories and lateral boundary conditions. CWAI treats meteorology, particles and reactive gases as one joint atmospheric state, driven by two consecutive states, emissions and static surface descriptors through a hierarchical Swin Transformer, and rolls out 40 six-hour steps to a 10-day forecast. Against WRF-Chem over 62 January and July 2020 starts, CWAI had lower initialization-weighted RMSE for all six surface pollutants, from 15.8% for PM2.5 to 25.8% for NO2.

Regional air-quality forecasting has relied on online-coupled numerical chemistry-weather models whose skill is sensitive to meteorological initial conditions, emission inventories and lateral boundary conditions. CWAI treats meteorology, particles and reactive gases as one joint atmospheric state, driven by two consecutive states, emissions and static surface descriptors through a hierarchical Swin Transformer, and rolls out 40 six-hour steps to a 10-day forecast. Against WRF-Chem over 62 January and July 2020 starts, CWAI had lower initialization-weighted RMSE for all six surface pollutants, from 15.8% for PM2.5 to 25.8% for NO2.

DoorDash's autonomous delivery device Dot has begun making deliveries in Fremont, California, where it is manufactured by Sonic Manufacturing Technologies. DoorDash had already run autonomous delivery in other cities and built a standardized operating plan from that experience; in Fremont it signed an encroachment permit and is proceeding in phases, with Phase 1A capped at 10 robots for community engagement, testing and demonstration, chaperoned by local operators, and later phases gradually adding robots that can operate autonomously and be driven remotely as needed.

DoorDash's autonomous delivery device Dot has begun making deliveries in Fremont, California, where it is manufactured by Sonic Manufacturing Technologies. DoorDash had already run autonomous delivery in other cities and built a standardized operating plan from that experience; in Fremont it signed an encroachment permit and is proceeding in phases, with Phase 1A capped at 10 robots for community engagement, testing and demonstration, chaperoned by local operators, and later phases gradually adding robots that can operate autonomously and be driven remotely as needed.

The Allen Institute for Brain Science and AWS have released Bloom, a single-cell multimodal model that encodes gene expression and chromatin accessibility into one shared cell representation while training on roughly 100-fold fewer cells than comparable pretrained models. On adult mouse whole-brain data, linear classifiers on Bloom embeddings reach 91% cross-validated adjusted balanced accuracy for 315 subclasses (multiome), 94% for 310 subclasses (scRNA-seq) and 94% for 297 subclasses (snRNA-seq), although the model saw only mouse multiome data.

The Allen Institute for Brain Science and AWS have released Bloom, a single-cell multimodal model that encodes gene expression and chromatin accessibility into one shared cell representation while training on roughly 100-fold fewer cells than comparable pretrained models. On adult mouse whole-brain data, linear classifiers on Bloom embeddings reach 91% cross-validated adjusted balanced accuracy for 315 subclasses (multiome), 94% for 310 subclasses (scRNA-seq) and 94% for 297 subclasses (snRNA-seq), although the model saw only mouse multiome data.

Neuralign places neural recordings from different sessions and animals, with different sampled neurons, into one tensor decomposition in which each component shares a single temporal curve while trial strength and neuron weights are estimated per session, so no matched neurons or matched trials are needed. On 93 brain-wide recordings from 19 mice, an unsupervised fit yielded a single component that separated the three anterior lateral motor cortex (ALM) optogenetic inhibition conditions, with balanced accuracy of 0.647 on held-out sessions against a chance level of 1/3. That component's change showed a modest association with the drop in behavioural accuracy (ρ = 0.266 over 180 session–inhibition observations).

Neuralign places neural recordings from different sessions and animals, with different sampled neurons, into one tensor decomposition in which each component shares a single temporal curve while trial strength and neuron weights are estimated per session, so no matched neurons or matched trials are needed. On 93 brain-wide recordings from 19 mice, an unsupervised fit yielded a single component that separated the three anterior lateral motor cortex (ALM) optogenetic inhibition conditions, with balanced accuracy of 0.647 on held-out sessions against a chance level of 1/3. That component's change showed a modest association with the drop in behavioural accuracy (ρ = 0.266 over 180 session–inhibition observations).

RGB-X tracking pairs visible frames with thermal, depth or event data, but paired annotations are far scarcer than RGB pretraining data, so only a small set of parameters is usually tuned. Earlier bidirectional cross-modal corrections computed both directions side by side from uncorrected input states, so the second correction could not use the first one's result. CCLT inserts low-rank exchange modules at blocks 6 and 10 of a frozen backbone, letting one direction correct first and the other read the freshly corrected state; against a parameter-matched parallel version, mean LasHeR SR rises by 1.980 points (95% interval [0.360, 2.690]), with 196.6K trainable parameters, 0.212% of the 92.7M-parameter tracker.

RGB-X tracking pairs visible frames with thermal, depth or event data, but paired annotations are far scarcer than RGB pretraining data, so only a small set of parameters is usually tuned. Earlier bidirectional cross-modal corrections computed both directions side by side from uncorrected input states, so the second correction could not use the first one's result. CCLT inserts low-rank exchange modules at blocks 6 and 10 of a frozen backbone, letting one direction correct first and the other read the freshly corrected state; against a parameter-matched parallel version, mean LasHeR SR rises by 1.980 points (95% interval [0.360, 2.690]), with 196.6K trainable parameters, 0.212% of the 92.7M-parameter tracker.