Agents

Google split Gemini Deep Research into Deep Research and Deep Research Max, with public preview starting today in paid Gemini API tiers. Both run on Gemini 3.1 Pro; one targets speed and cost, while Max runs longer with more compute and repeated search and reasoning. The update adds MCP support for sources such as FactSet, S&P, and PitchBook, plus files, code execution, and File Search; the post does not disclose pricing.

Ant Group's Inclusion AI team is identified as the maker of Elephant, a 100B-parameter model with 256K context and 32K output shown on OpenRouter. The post reports tests on bug fixing, summarizing a 3,000-word meeting note, and a light agent loop, plus AI BENCHY figures of about 2,500 output tokens, about 1 second average latency, and 9.6/10 consistency; the post does not disclose training details, pricing, or an official model card.

Brex open-sourced CrabTrap, an HTTP proxy that intercepts every agent request and allows or blocks it against a policy in real time. The page shows a dual path of static rules plus an LLM judge, and logs whether each decision came from rule matching or model judgment; the post does not disclose the model, latency overhead, or error rates.

Google has formed an AI coding strike team led by Sebastian Borgeaud, with Sergey Brin and Koray Kavukcuoglu directly involved, to improve long-context coding and internal code automation. The pressure signal cited is that Google said about 50% of its code is written by coding agents and reviewed by engineers, while Anthropic staff claimed 100% code use by Claude Code and Opus 4.5; the post does not disclose team size, launch timing, or the exact Google model version. The key issue is whether Google can turn private codebase training into stronger public models.

Researchers from Emory University, the University of Oxford, and Griffith University propose a 3D framework for large-scale agent networks, classifying 8 system types by topology, memory scope, and update behavior. The survey says the core scaling bottleneck is not only communication protocols but inconsistent world models across agents; it also says current benchmarks stay small while real deployments may involve thousands to millions of agents.

OpenAI launched Chronicle research preview for Codex on April 21. It is limited to ChatGPT Pro users on Mac and reads recent screen context to reduce repeated background prompts. OpenAI says data is “primarily processed locally,” but the post says some cases use cloud help; The Next Web reports screenshots are uploaded and local memories are unencrypted, while upload share and retention time are not disclosed.

Huawei launched Pura X Max on April 20 and debuted Xiaoyi companion AI on HarmonyOS 6.1. The post says it can be invoked by double-tapping the nav bar or voice, read screen content with consent, collect tasks across apps into Calendar, and connect with Amap and Didi. The key point is system-level cross-app access and persistent side-panel UX; the post does not disclose price, model specs, or coverage.

Moonshot released Kimi K2.6, a 1T-parameter MoE with 32B active and 256K context. The post cites 58.6 on SWE-Bench Pro, 4,000+ tool calls, 12+ hour runs, and 300 parallel sub-agents. The key signal is long-horizon agent execution, not only open-model scores.

OpenAI added Chronicle to Codex and is rolling it out to ChatGPT Pro users on macOS; it uses periodic screenshots, OCR, and tool detection to turn recent screen activity into memory. The memory is stored as plain Markdown in ~/.codex/memories_extensions/chronicle, and the EU, UK, and Switzerland are excluded; OpenAI says screenshots are uploaded for processing, deleted afterward, and not used for training. The part to watch is risk: the background agent can burn rate limits, local plain-text files widen exposure, and OpenAI warns it amplifies prompt-injection from malicious webpages.

Epic Games is rolling out a “conversations” tool for Fortnite creators, turning island NPCs into AI characters that can talk with players in unscripted ways. The snippet says creators define persona, knowledge, behavior, and voice with prompts; the title says don’t try to date them, but the post does not disclose the exact guardrails or moderation system.

Import AI 454 covers HiFloat4, Anthropic automated alignment R&D, and a Chinese model safety study. HiFloat4 reached about 1.0% relative BF16 loss on Ascend NPUs, versus MXFP4's about 1.5%. Anthropic's Claude Opus 4.6 AARs used 800 hours and about $18,000 to raise PGR from a 0.23 human baseline to 0.97.

Anthropic researcher Erik Schluntz said his team merged a 22,000-line production change, mostly written by Claude, cutting work from two weeks to one day. His workflow spends 15-20 minutes on repo exploration and planning, limits edits to leaf nodes, keeps humans on core logic, and validates with long stress tests plus a few E2E tests. The key issue is boundary control, not handing AI the system core; he also said task length AI can handle doubles about every seven months.

AgiBot said at its April 17 Shanghai event that it released 4 robots, 6 AI models, and 7 standardized deployment solutions, and framed 2026 as the first year of embodied AI “deployment mode.” The post cites concrete metrics: Expedition A3 runs 8-10 hours, WITA Omni 1.0 targets sub-500ms interaction latency, and BFM was trained on 100 million-plus frames and 700 hours of motion-capture data; it also claims 5,100-plus shipments and 39% share in 2025, with the 10,000th robot rolling off in March 2026. The real point for practitioners is repeatable delivery rather than launch volume: the post lists 7 scenarios from 3C line loading to patrol, but independent validation details are not disclosed.

A Renmin University of China team released AiScientist, which ran 23 hours and 74 experiment loops on MLE-Bench Lite Detecting Insults, raising validation AUC from 0.903 to 0.982 with 18 best-so-far updates. The paper says its core is File-as-Bus, which persists analysis, code, logs, and results in the workspace; removing it drops PaperBench by 6.41 points and MLE-Bench Lite Any Medal by 31.82 points. The real lever here is state continuity, not simply adding more agents.

A Reddit user routed Qwen3.6 through LM Studio as a Claude Code subagent and reported about 30x lower Opus marginal tokens on two audit tasks. In the examples, a 23-file route audit dropped from 13k to 0.4k marginal tokens, and an 18-file Astro site inventory fell from 89k to 3k; the setup used unsloth’s Qwen3.6-35B-A3B-MXFP4_MOE gguf on a 64GB M4 Max with a 64k context window. The key mechanism is offloading extraction and audit work to a local OpenAI-compatible server, while the post also says quality was mixed rather than strictly better than Opus.

Using the same Qwen3.5-9B Q4 weights on the 225-task Aider Polyglot benchmark, the author changed only the scaffold and raised mean pass@2 from 19.11% to 45.56%. The little-coder setup is not a new model; it uses bounded reasoning, a write guard, explicit workspace discovery, and small per-turn skill injections. The key claim is scaffold-model fit, but the post reports only two full runs and does not disclose ablations, cross-model replications, or a second benchmark.

A Shanghai Institute for Advanced Learning and ECNU team released MIA, a memory agent framework, and said it achieved the best results on 7 datasets. MIA uses a Manager-Planner-Executor design, dual parametric and non-parametric memory, alternating RL, and test-time continual learning; the post does not disclose exact benchmark scores. The key point is memory as capability internalization, not just retrieval, for open-world agents.

Amap showed its quadruped robot Tutu at the 2026 Yizhuang humanoid half marathon, claiming it completed a guide-assistance obstacle task in an open environment without preset routes or teleoperation. The post says its ABot stack includes ABot-N0, which reached SOTA on 7 navigation benchmarks with 88.3% on SocNav, and ABot-M0, which scored 80.5% on Libero-Plus. The key point is the integrated stack across navigation, manipulation, world modeling, and closed-loop correction; the post does not disclose guide-task test scope, commercialization timing, or safety incident data.

Amap unveiled embodied AI stack ABot and claimed SOTA on 15 metrics. The post says ABot-3DGS builds 10k-scale 3D scenes from centimeter-level map data, while ABot-PhysWorld uses a 14B DiT and 3M real manipulation videos. What matters is the interactive world model and VLA loop; the post does not disclose the 15 benchmarks, exact metrics, or the open-source timeline and scope.

Berkeley RDI used a roughly 10-line conftest.py exploit to score 100% on all 500 SWE-bench tasks while fixing 0 bugs. The post says its agent broke 8 major agent benchmarks with scores from 73% to 100%, via pytest hook tampering, file:// answer reads, and faulty validators. The real issue is benchmark isolation failure, not stronger models.