▲ Google DeepMind just introduced Gemini 4 Carbon, a frontier model built to find, validate and patch real software vulnerabilities, and run long agentic coding jobs. The key idea is sustained, long-horizon agent work. Google says internal teams watched Carbon run for days, pushing through dead ends to root-cause bugs in large codebases. It also covers the full cyber defense loop: find the flaw, prove it is exploitable, then fix the root cause. It ranks #1 on the Artificial Analysis Intelligence Index v5.0 at 59, just ahead of Claude Opus 5.5 at 58 and GPT-6 Astra at 57. On ExploitGym it scores 54.8% vs 42.4% for GPT-6 Astra. SEC-bench Pro hits 88.2%. It is a trade-off though: Opus 5.5 still wins OSWorld-2.1 computer use, 87.2% vs Carbon's 81.5%, and most rival scores are self-reported. Access starts this week with a subset of cyber defenders, then trusted partners via Google's Fairwind Program. No public API date yet. Parameters and context window are not disclosed. Introductory price: $2 / $10 per 1M input / output tokens, rising to $4 / $20 later...... Full analysis: marktechpost.com/2026/10/28/… Blog: blog.google/innovation-and-a… Evals: storage.googleapis.com/deepm… Access: deepmind.google/fairwind-pro… @GoogleDeepMind @sundarpichai @GoogleAI

▲ MiniMax just open-sourced MCode Stack, the agent skills its team actually uses to build MiniMax Code. The key idea is turning engineering essays into runnable checks. Each MCode Engineering Notes article ships as a SKILL.md skill or checklist. Your agent loads it with /skill-name and walks your project item by item. The numbers: 17 skills across 2 stacks, plus 5 checklists. The independent-review skill gives a reviewer only 3 inputs: the diff, the requirement and test results. The long-command audit has 10 items, from re-runs to cleanup after compaction. It is a trade-off though: 0 eval results are published, even though one skill is all about measuring harness changes. It is MIT licensed, with 6 skills under CC-BY 3.0 (adapted from Google Engineering Practices). It installs in MiniMax Code or Claude Code via a plugin marketplace...... Full analysis: marktechpost.com/2026/10/28/… Repo: github.com/MiniMax-AI/mcode-… MiniMax Code: github.com/MiniMax-AI/minima… @Ronny_MiniMax @MiniMaxAgent @MiniMax__AI

FlashMap, a speculative decoding drafter that writes a whole block of tokens jointly, in 1 forward pass. The key idea is to swap discrete diffusion for a continuous flow map. Diffusion drafters like DFlash predict each slot independently, so drafts can mix "new" with "diego". FlashMap maps 1 noise sample to 1 full block, so the 16 tokens stay consistent. On Qwen3-8B, 1-step FlashMap reaches up to 6.14x lossless speedup and beats a retrained DFlash on all 9 benchmarks, about 7% faster on average. It does this with a 0.5B drafter vs DFlash's 1B. It is a trade-off though: 1-step drafts lose acceptance at late block slots, and on Qwen3-4B it trails DFlash on 4 of 9 tasks at temperature 0. Drafters for Qwen3-8B and Qwen3-4B are out now, Apache-2.0 weights and MIT code, with vLLM 0.31 and SGLang 0.5.20 plugins...... Full analysis: marktechpost.com/2026/10/28/… Paper: arxiv.org/abs/2610.24365 Repo: github.com/al-jshen/flashmap Model: huggingface.co/j-shen/FlashM… Project: jshen.net/flashmap @jeff_shen_ @Jay41969851 @_ryantlee @jonathanmay @cosmo_shirley

▲ Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB The key idea is simple: graph search only touches a few nodes per query. So LEANN stores a pruned graph plus small PQ codes. It re-embeds only the nodes on the search path, in GPU batches. On a 76 GB Wikipedia corpus, LEANN needs 4 GB. HNSW needs 188 GB. Both hit 25.5% NQ accuracy at 90% recall. It is a trade-off though: retrieval takes 2.48 s vs 0.05 s for HNSW, and 44.80 s on HotpotQA on an M1 Ultra Mac. MIT licensed, pip install leann, and it won Best Research Paper at MLSys 2026. Numbers are self-reported...... Full analysis: marktechpost.com/2026/10/27/… Repo: github.com/StarTrail-org/LEA… Paper: arxiv.org/abs/2506.08276 PyPI: pypi.org/project/leann

▲ Liquid AI just released LFM3, 6 open-weight 2B-class models that put text, vision, audio, omni and decision tasks on 1 shared backbone. The key idea is a tri-hybrid architecture. Its 24 layers mix 13 gated short convolutions, 5 sliding-window attention layers and 6 global attention layers. Liquid tuned the mix with hardware-in-the-loop search across 14 device and backend combinations. On Liquid's own evals, LFM3-1.7B averages 49.37 across 29 benchmarks, vs 47.24 for Qwen3.5-9B. It scores 71.39 on BFCL V4 vs 62.83, and 38.42 on WebArena-Verified vs 28.94. It is a trade-off though: GPQA Diamond is 63.37 vs 80.40, and RULER 128K is 65.56 vs 87.11. LFM3-1.7B has 1.73B parameters, a 256K context window and takes about 1 GB at 4-bit. Weights ship under the LFM Open License v1.0, free for commercial use below $10M annual revenue...... Full analysis: marktechpost.com/2026/10/27/… Model: huggingface.co/collections/L… Blog: liquid.ai/blog/lfm3 Docs: docs.liquid.ai/lfm/models/co… @liquidai

▲ Mirendil just released Fern 0.1, its first model, and it can hold 10M tokens of code in one context window. The key idea is that context is a second way to spend compute. Fern was pretrained from scratch with architecture, training and serving built for 10M tokens. That is about 23 MB of code, roughly half a million lines. On Mirendil's own repos, loss keeps falling as the window grows: 0.246 bits per byte at 32K bytes down to 0.155 at 23M bytes. RULER single-needle retrieval stays at 100% from 32K to 10M tokens. It is a trade-off though: multi-hop HotpotQA drops to 45% at 10M, and Fern 0.1 scores 6.1% on Terminal Bench 4.0 versus 66.4% for Claude Opus 5.5. Fern sits on Mirendil-Base, a 0.1 to 0.5T model that 1 researcher built with agents in 3.5 months for under $10M. Access is early access only for now...... Full analysis: marktechpost.com/2026/10/27/… Announcement: mirendil.com/news/introducin… Pretraining post: mirendil.com/news/pretrainin… Early access: mirendil.com/early-access @mirendil @bneyshabur @tararezaeikh

▲ vLLM Semantic Router just open-sourced d3-ultra, a 27B decision model that answers questions with probabilities instead of text. The key idea is "state once." The model reads your input a single time, then answers every question on it. Those questions can be choices, yes/no, scores, boxes in an image, or a track across a video. On the team's own SR-Text test, d3-ultra scores 87.2, ahead of Fastino's GLiDE V1.2 at 85.1 and Liquid AI's d1-ultra at 83.4. On 1 AMD Instinct MI325X, 255 questions about 1 photo take 2.16 s. It is a trade-off though: on Box grounding it trails its sibling d3-turbo, 58.2% vs 60.0%, and both test sets are private. Weights are on Hugging Face under Apache-2.0: 26.09B parameters, 131,072 tokens per question, text, JSON, images and video in...... Full analysis: marktechpost.com/2026/10/26/… Model: huggingface.co/vllm-sr/d3-ul… Repo: github.com/vllm-project/sema… Free API: decision.vllm-sr.ai @XunzhuoLiu

▲ Baidu just open-sourced Unlimited OCR 2, a 3B OCR model that parses books and reports of hundreds of pages in a single request. The key innovation is SR-SWA (Sparse Reference Sliding Window Attention). Instead of scanning an ever-growing KV cache, the decoder attends to a fixed set of working references, 512 tokens in Baidu's example. Per-step decoding latency and memory stay nearly constant as the document grows. On MarathonDocBench (hundreds of pages) it scores 77.1 overall, against 33.7 for Kimi-K3 and 22.9 for Qwen3.8-Max. On MPDocBench (dozens of pages) it leads Qwen3.8-Max 84.6 to 78.3. It is a trade-off though: heading hierarchy is its weakest metric at 48.2 on long documents, the reference pool is lossy, and every score is self-reported. 3.34B parameters, MIT license, about 6.7 GB of BF16 weights. Runs on Transformers, with a customized SGLang build for serving...... Full analysis: marktechpost.com/2026/10/26/… Model: huggingface.co/baidu/Unlimit… Repo: github.com/baidu/Unlimited-O… Report: huggingface.co/baidu/Unlimit… @Baidu_Inc

▲ OpenAI just released GPT-6.1 Terra, a fast model that does Astra-level computer use at a fraction of the cost. The key idea is cost per task, not peak score. Terra looks at screens, drawings and photos, reasons at a dial-able effort level, then clicks and types through the computer-use tool. OpenAI tuned it for high-volume work like forms, invoices and technical drawings. On Agents' Last Exam v1 at medium effort, Terra scores 57.6% at $0.29 per task. GPT-6 Astra scores the same 57.6% at $4.82 per task. It also hits 85.5% on MMMU-Pro, close to Astra's 86.7%. It is a trade-off though: on Terminal-Bench 4.0 it solves 50.3%, behind GPT-6.1 Sol (55.9%) and Claude Sonnet 5.5 (70.6%). Available now in the API as gpt-6.1-terra, plus ChatGPT Work and Codex. 1.05M context, closed weights, $1 / $5 per million tokens, half of Sol...... Full analysis: marktechpost.com/2026/10/26/… Announcement: openai.com/index/introducing… Docs: developers.openai.com/api/do… System card: deploymentsafety.openai.com/… @OpenAI @OpenAIDevs

▲ Nace AI just released Drex for Search, which brings its Drex decision models to document search, so AI agents get cited pages instead of top-k chunks. The key idea is that search is a series of decisions. Drex first classifies the question as a quick lookup or a deeper agent search. It then narrows the workspace from folder to file, section and page, down to table and column inside a sheet. Only the kept pages reach the LLM, and the answer cites the file and page. The numbers, per Nace: 92.05% on BrowseComp-Plus with GPT-5, at 12.61 search calls per question, 16.6x fewer than the only entry ahead. 77.5% R@1 on VisDocAgentBench, 10 points above Claude Opus 5. 94.7% on MADQA with Gemini 3.5 Flash. On OfficeQA Pro, all 9 agent setups Nace tested needed fewer turns, with Claude Code on Opus 5.5 going from 18.5 to 9.1 turns. Claude Code, Codex, Cursor and OpenCode can call it through the nace-mcp server or the CLI. There are also Python and TypeScript SDKs and a REST API...... Full analysis: marktechpost.com/2026/10/26/… Product: aicampaign.live/NC-A1-1 Console: aicampaign.live/nc-a1-2 Docs: console.nace.ai/docs @NaceAI

▲ Tsinghua University researchers just open-sourced AKV, a serving system that lets LLM agents manage their own KV cache instead of summarizing old history. The key idea is "cache engineering". Agents drop selected KV entries from attention but keep the text history unchanged. Retained KV is reused as-is, so nothing gets re-prefilled. A Repos step re-rotates RoPE keys to compact positions. Across 3 models and 4 agent benchmarks, average cost falls 27.7% and average score rises 1.7 points versus threshold summarization. On SWE-bench Verified, GPT-OSS-120B's cost per task drops from 20.0 to 9.7 cents while its score rises from 40.0 to 43.2. It is a trade-off though: MiniMax-M2.7 on BrowseComp-Plus scores 58.9, below Text Engineering's 62.3. AKV extends SGLang with an OpenAI-compatible API and ships under Apache-2.0. Drop and Repos currently support Qwen, GPT-OSS and MiniMax-M2 text models...... Full analysis: marktechpost.com/2026/10/26/… Paper: arxiv.org/abs/2610.28577 Repo: github.com/thu-nics/AKV Project page: fuvty.github.io/thinking_yar…