97 results for “GLM”

Interconnects post in which Nathan Lambert revisits his claim that an open-weight Claude Mythos would have been survivable. He argues GLM-5.3 already crosses the capability threshold with little visible societal change, and quotes Rob Wiblin, Anthropic and Zvi Mowshowitz as examples of exaggerated warnings. He cites Epoch AI's Firefox exploit built with GLM-5.3, and treats the recent vulnerability surge as an ecosystem trend driven by AI capability and diffusion, not open versus closed models.

interconnects.aiarticlePublished and added 30 Oct 2026🧠 AI

Interconnects post in which Nathan Lambert revisits his claim that an open-weight release of Claude Mythos in April 2026 would have been survivable. He argues GLM-5.3 already crosses that capability threshold with little visible harm. He cites Epoch AI's GLM-5.3 Firefox file-exfiltration exploit and Joshua Saxe's trend data, and says recent vulnerability waves come mostly from closed models and general AI diffusion.

interconnects.aiarticlePublished and added 30 Oct 2026🛡️ Security

Cloudflare's postmortem of a tenant isolation bug in the GLM-5.3 model on Workers AI, active 27 August to 9 September 2026. Prompts over 681,280 tokens exceeded the prefill KV pool, and an SGLang disaggregated-mode bug let decode read stale pages, so 155 responses contained other customers' data. It covers the timeline, config and engine fixes (SGLang v0.5.22), and recommendations for operators.

blog.cloudflare.comwriteupAdded 27 Oct 2026, published 26 Oct 2026🧠 AI

Cloudflare postmortem of a tenant isolation failure in the GLM-5.3 Workers AI model (27 August to 9 September 2026), in which 155 responses to prompts over 681,280 tokens contained other customers' data. Root cause was a KV pool smaller than the 1,048,576-token context window, combined with an SGLang bug in disaggregated prefill/decode mode that let decode read stale KV pages. It lists the fixes (SGLang v0.5.22 patches), hardening steps and operator recommendations.

blog.cloudflare.comwriteupAdded 27 Oct 2026, published 26 Oct 2026🛡️ Security

REVA (REal-time Vulnerability Analysis) is a vulnerability detector that puts a module threat model, lightweight static analysis findings and call-graph information into one LLM forward pass. A trained probe reads the model's internal representations of the new function. On PrimeVul's held-out test set, REVA on GLM-5.3-Flash reaches 60.0% accuracy in about one second per function, matching the agentic VulTriage (59.2%) at over 250 times the speed and under a tenth of the cost. The probe is 14% to 17% more accurate than the LLM's own verdict.

arxiv.orgpaperAdded 28 Oct 2026, published 26 Oct 2026🧠 AI

REVA (REal-time Vulnerability Analysis) is an LLM-based vulnerability detector that screens each new function in one forward pass. It feeds the model a pre-computed module threat model, lightweight static analysis findings and call-graph data, then reads the model's internal representations with a trained probe. On PrimeVul's held-out test set with GLM-5.3-Flash it reaches 60.0% accuracy in about one second per function, matching the agentic VulTriage (59.2%) while over 250 times faster.

arxiv.orgpaperAdded 28 Oct 2026, published 26 Oct 2026🛡️ Security

FrontierMemory is a benchmark that evaluates agent memory through downstream task performance, measuring how agents learn from experience rather than recall, which the post says is saturated via agentic search. In elvis's summary, Claude Opus 5.5 ranks first and open-weight GLM-5.3 does well on memory generation. The authors reply that "memory" here means any adaptation from experience, so harness-level changes can also be scored. The post gives no scores or links.

x.comthreadAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

Clairic is Cloudflare's preview System One extraction model, hosted on Workers AI as @cf/cloudflare/clairic, with open weights planned. It reads text or images in one forward pass and returns typed values with spans, confidence, p_null, 80% intervals for estimates and forecasts. A claim-handling cascade with Clef and GLM-5.3 ran in 19.3 seconds against 61.7 for an all-LLM version.

blog.cloudflare.comarticleAdded 29 Oct 2026, published 19 Oct 2026🧠 AI

Harness2 is a framework from Google Cloud AI Research and Northwestern University in which an Editor model rewrites an agent's harness (system prompt, memory, skills, tool scripts, subagent routing, middleware) without changing model weights. It uses a Propose-Probe-Compose loop that compares execution trajectories instead of ground truth. On WorkBuddy-Bench, GLM-5 rose from 64.2 to 75.7 after one round, above Gemini 3.5 Flash at 72.4. The thread also lists risks: latency, experience poisoning, and a sandbox that is off by default.

x.comthreadAdded 19 Oct 2026, published 18 Oct 2026🧠 AI

Analysis of Meta's Muse Spark Open, an 816B total/42B active parameter open-weights model with 1M context, now the strongest American open model, and its position relative to Chinese open models such as GLM-5.3, Kimi K3 and DeepSeek V4.1 Flash. It examines Meta's restrictive custom license ("Built with Muse" attribution, $20M/month revenue gate, acceptable use policy, coverage of outputs and derivatives), and ranks Meta against Anthropic, OpenAI, Gemini and xAI after the closed Muse Wonder release. It also covers tier-3 labs (Mistral Large 4, Reflection's Beam) and valuation questions.

interconnects.aiarticlePublished and added 17 Oct 2026🧠 AI

A social post reporting that Reflection AI released the full weights of Beam, a 501B-parameter mixture-of-experts agentic coding model (23B active, 1M-token context), on Hugging Face under Apache 2.0. It claims RL training that penalizes wasted tokens gives roughly 3-4x less inference compute than GLM-5.2, and 80.9 on SWE-bench Verified. Launch support is listed for AWS SageMaker, vLLM and SGLang. The post notes the token-efficiency claims still need independent confirmation and that self-hosting needs about 500GB of GPU memory at 8-bit.

x.comthreadAdded 18 Oct 2026, published 17 Oct 2026🧠 AI

Social post reporting Meta's release of Muse Spark Open, described as the most capable US open-weight model: 816B total and 42B active parameters, a 1M-token context, and text, image, video and audio input in one model. It is pitched at coding and agentic work and tuned to run on two M5 Ultra Mac Studios, with weights available at release. The poster notes that in Meta's own chart, China's Kimi K3 and GLM 5.3 still beat it on coding.

x.comthreadAdded 16 Oct 2026, published 15 Oct 2026🧠 AI

Presents MLExpert, a 35B Mixture-of-Experts agentic model trained for autonomous long-horizon machine learning engineering. Training uses a Kaggle-MLE Factory to build executable tasks and an interaction harness, hierarchical agentic SFT with outcome-guided supervision, and Windowed Interaction RL (WIRL). WIRL treats the multi-turn decisions between successive experiment results as one execution window with a shared outcome reward, and uses asynchronous collection and policy correction. On MLE-Bench Lite the average medal rate rises from 37.9% to 72.7%, comparable to larger frontier models such as Kimi-K3 and GLM-5.3. It also improves on NatureBench Lite, agent scaffold optimization and algorithm optimization.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Harness^2 is a plug-and-play framework that recursively improves LLM agent execution harnesses in open-world settings where no ground-truth verifiers exist. Task-level recursion uses a contrastive Propose-Probe-Compose step to guide harness edits for the current task, and it scales through parallel or sequential recursion. Harnessing-level recursion distills experience from those edits into a continual-improvement harness for future tasks. A single step improves GLM-5 and Gemini 3.5 Flash over their base harnesses by up to 11.5 points on WorkBuddy-Bench, 4.5 on JobBench and 11.8 percentage points on LAB.

huggingface.copaperAdded 14 Oct 2026🧠 AI

Simon Willison releases llm-gemini 0.35 with support for Google's new Gemini 4 Argon model, available via the Gemini API and a $100/month subscription plan. He compares pricing: $2/M input and $10/M output at launch, doubling on December 1st to match Claude Opus 5.5. He argues per-token prices are poor comparators and points to Artificial Analysis's total benchmark run cost ($2,407 for Argon, near GLM-5.3 and cheaper than OpenAI, Anthropic and Meta models) as a better clue to agentic cost-efficiency. He also shows his SVG pelican outputs at low, medium and high settings.

simonwillison.netarticlePublished and added 13 Oct 2026🧠 AI

News report on Anthropic's October 12, 2026 blog post saying new defenses cut reasoning-token extraction from Claude by 10x to 100x. The defenses are a two-stage extraction classifier (reported false positives under 0.1%), restrictions on reasoning transcripts, and account bans. It notes that training on full Claude Opus 4.8 reasoning transcripts scored 80.4% on SWE-bench Verified against 71.8% for summarized ones. It also covers the API changes: preserved thinking on Claude Fable 5.1 and Opus 5.5 for accounts created on or after August 31, 2026, plus Sonnet 5.5 transcripts limited to the generating organization. The article also covers alleged distillation by Zhipu (GLM-5.3) and Moonshot's Kimi, and calls for cross-industry cooperation.

en.cryptonomist.charticlePublished and added 13 Oct 2026🧠 AI

Reports Anthropic's disclosure that new anti-distillation defenses cut reasoning-token extraction from Claude by 10–100x. The defenses are preserved thinking (API accounts created on or after Aug 31, 2026 can't edit prior context), per-organization transcript access limits, a two-stage extraction classifier with under 0.1% false positives, and escalating limits and bans. It also covers attacker tradecraft (hundreds of thousands of fraudulent accounts, proxy networks, stolen payment details, hidden-reasoning extraction), attribution of Zhipu's GLM-5.3 and Moonshot's Kimi traffic, and the national-security argument for cross-provider cooperation.

en.cryptonomist.charticlePublished and added 13 Oct 2026🛡️ Security

Adversa AI's October 2026 roundup of 38 GenAI security resources, grouped into attack techniques, defenses, red teaming, vulnerabilities, incidents, articles and frameworks. Highlights include Anthropic's finding that GLM-5.3 safeguards collapse under prefill and abliteration, capability laundering and activation-dilution jailbreaks, RAG poisoning attacks, the LiteLLM proxy flaw (CVE-2026-35029), and the SGLang pickle RCE (CVE-2026-86793). It also covers a joint US advisory on industrial-scale model distillation. It closes with advice to assume model safeguards can be removed and to harden the serving stack around the model.

adversa.aiarticlePublished and added 13 Oct 2026🛡️ Security

SecurityWeek summarizes Anthropic Frontier Red Team research on AI worms that run small open-weight models directly on compromised IoT devices. A 26B model split across 15 ~$55 Android TV boxes ran at 0.39 tokens/s; a 9B model got inference running on 8 of 9 hardware types, and only GLM-5.2 completed full replication across eight emulated Android devices (4 of 5 runs). Anthropic projects ≤30B models reaching that level by end of 2026, estimates a Kimwolf-sized botnet would host ~133,000 copies worth at most ~40 H100s, and notes defenders can spot large model transfers in ISP traffic and cut the target pool by removing default passwords.

securityweek.comarticlePublished and added 13 Oct 2026🛡️ Security

SecurityWeek summarizes Anthropic Frontier Red Team research on AI worms that carry their own open-weight model onto compromised IoT devices. A 26B model sharded across 15 cheap Android TV boxes ran at 0.39 tokens/s. A 9B model got inference running on 8 of 9 hardware types. In a closed network of 8 emulated Android devices, only GLM-5.2 completed the full replication cycle, reaching all 8 devices in 4 of 5 runs. Models of 30B or fewer parameters handle individual steps but fail when chaining them. Anthropic projects they reach GLM-5.2's level by end of 2026, and estimates a Kimwolf-sized botnet would offer at most about 40 H100s of compute. Defenders can detect large model transfers and sustained unusual device-to-device traffic, and removing default passwords shrinks the target pool.

securityweek.comarticlePublished and added 13 Oct 2026🧠 AI

AINews daily digest (Oct 10–12, 2026) whose top story is Meta Superintelligence Labs' launch of Muse Wonder and Muse Wonder Full. The models have a 1M context window and are priced at $4 input / $20 output per M tokens for Wonder; Full is available only to Muse Code subscribers. It compiles third-party results: Artificial Analysis Intelligence Index v4.3 (Full 56, Wonder 52, Spark 1.3 48, Opus 5.5 58), Terminal-Bench 4.0 gains, Coding Agents Index, and Vals Index ranks. It also records Meta-reported numbers (SWE-Marathon, DeepSWE, MemoryBench), bullish and skeptical practitioner reactions, the "3D demo" benchmaxxing debate, and Muse retention data. The rest of the issue is a roundup of agent RL research (QwenGyre, Sakana MASS, GitSwarm), inference silicon (Maia 300, ReplaySSM), specialised models (GLM-Legal, Decision 3.0), and r/LocalLLaMA and other Reddit threads. The page text was truncated, so the end of the Reddit recap is not covered.

latent.spacearticlePublished and added 13 Oct 2026🧠 AI

Anthropic describes its layered defenses against distillation attacks, in which fraudulent-account networks harvest Claude's reasoning transcripts to train rival models. The defenses are reasoning summarization, "preserved thinking" (new API accounts cannot edit prior context), restricted access to reasoning transcripts, a two-stage constitutional-classifier system with under 0.1% false positives, stricter usage limits and account bans, and charging for blocked requests. Anthropic reports 10–100x fewer extracted reasoning tokens, and a SWE-bench Verified drop from 80.4% to 71.8% when a model trains on summarized rather than full transcripts. It also argues that distilled models such as GLM-5.3 ship without proportionate safeguards and pose national-security risks, and that platforms and other labs must act too.

anthropic.comarticlePublished and added 13 Oct 2026🧠 AI

Anthropic describes how it countered industrial-scale distillation attacks on Claude, in which proxy networks of hundreds of thousands of fraudulent accounts (stolen payment details, false identities) tricked models into revealing hidden reasoning. Defenses include a two-stage constitutional-classifier system (<0.1% false positives), "preserved thinking" that blocks edits to prior context for new API accounts, restricting reasoning transcripts to the originating organization, stricter limits, bans, and charging for blocked requests. It reports 10–100x fewer reasoning tokens extracted, cites Zhipu's GLM-5.3 and Moonshot Kimi forwarding as examples, and discusses trade-offs and the need for platform-wide action.

anthropic.comarticleAdded 14 Oct 2026, published 13 Oct 2026🛡️ Security

The article reports Anthropic research (William Zhou and Daniel Freeman) on whether small open models can autonomously replicate onto unfamiliar IoT and consumer hardware. Qwen3.5-9B established inference on eight of nine hardware types using the Memento harness (built on the Pi coding agent). GLM-5.2 completed a simplified end-to-end replication across eight emulated Android nodes in four of five trials, and reached seven nodes in the fifth. Gemma4-26B-A4B was sharded by pipeline parallelism across 15 low-RAM Android TV boxes at 0.39 tokens/s. The authors estimate replication intervals of 1–2 days and a capability threshold around end of 2026, which makes it a concrete capability-evaluation datapoint for AI safety.

en.cryptonomist.charticlePublished and added 12 Oct 2026🧠 AI

News write-up of an Anthropic research post (William Zhou, Daniel Freeman) testing whether small open-weight models can act as autonomous worms. Qwen3.5-9B established inference on eight of nine hardware types, and GLM-5.2 spread across eight emulated Android nodes via unauthenticated ADB in four of five trials, reaching seven nodes in the fifth. The piece covers sharding a Gemma4-26B-A4B model across 15 low-memory Android TV boxes, projected spread rates, comparison with the Kimwolf botnet, and network signals defenders could watch (large weight transfers, inter-device inference traffic, scanning).

en.cryptonomist.charticlePublished and added 12 Oct 2026🛡️ Security

Anthropic's Frontier Red Team tests whether small open-weight models (≤30B) can autonomously replicate across commodity IoT devices. They ran Gemma4-26B-A4B over 15 off-brand Android TV boxes at 0.39 tok/s with pipeline parallelism. A Qwen3.5-9B-class model set up inference on 8 of 9 hardware types, and only GLM-5.2 completed end-to-end worm replication in an 8-node redroid ADB network, spreading to 8/8 nodes in 4 of 5 trials and 7/8 in the other. It also estimates spread using the Kimwolf botnet's size (~133k replicas, about 40 H100s of compute), shows notes inherited between generations improve Qwen3.5-9B's setup success (45% to 61–84%), and projects small models reaching the capability floor by end of 2026. It closes with defensive points: drive R0 below 1, and harden IoT devices.

anthropic.compaperPublished and added 12 Oct 2026🛡️ Security

Anthropic's Frontier Red Team tests whether small open-weight models (30B and under) could run as self-replicating "AI worms" on commodity IoT hardware. A 9B model set up inference on 8 of 9 hardware types. In an 8-node Android emulator fleet only GLM-5.2 replicated end to end. Gemma4-26B-A4B was sharded across 15 ~$55 Android TV boxes at 0.39 tok/s. Qwen3.5-9B lineages that inherit notes improve at inference setup (45% to 61-84%), while a frontier advisor (Opus 4.8) doesn't help. An ECI trend fit puts small models at GLM-5.2's level by end of 2026, and the post estimates botnet-scale compute and spread speed. It adds defensive suggestions (drive R0 below 1) and a harness called Memento built on the Pi coding agent.

anthropic.comwriteupPublished and added 12 Oct 2026🧠 AI

Micah Lee argues security engineers should move off ChatGPT and Claude to open-weight models such as GLM-5.3, because the closed providers log everything and gate cyber use. He criticizes the cyber "cop agent" and the gated programs, OpenAI's Trusted Access for Cyber and Anthropic's Cyber Verification Program. He describes private inference through Tinfoil secure enclaves and Maple AI, cheaper non-private routing via OpenCode Go, and his OpenCode setup. He warns that Z.ai's ZCode harness reportedly uploaded local repos to Alibaba Cloud.

micahflee.comarticleAdded 13 Oct 2026, published 12 Oct 2026🛡️ Security

Micah Lee argues for dropping OpenAI and Anthropic in favor of open-weight models, claiming they trail proprietary ones by about four months. They report GLM-5.3 as roughly on par with Claude Opus 5.5 for software engineering, and GLM-5.3-Flash as similar to Opus 4.8 and feasible to run locally. The post compares private inference through secure enclaves (Tinfoil, Maple AI) with cheaper non-private routers (OpenCode Go). It gives pricing, data-trust caveats about Z.ai, and the author's own workflow: OpenCode harness, GLM-5.3 on low reasoning, Tinfoil for private work.

micahflee.comarticlePublished and added 12 Oct 2026🧠 AI

Z.ai introduces GLM-Legal, a legal-domain model built on the lightweight GLM-5.3-Flash. It is trained with expanded legal-corpus mid-training, then reinforcement learning across tens of thousands of realistic legal environments, with the agent harness and model training iterated together. It reports a 19.2% all-pass rate on Harvey LAB (+12.5 points over the base model) and parity with frontier closed models on LegalBench and PLawBench (Chinese legal practice). Side-by-side examples cover contract review with tracked changes, contradiction mapping across 40 dispute documents, and a three-country privacy compliance plan. Access is via an API application and the AutoClaw desktop agent.

z.aiarticlePublished and added 12 Oct 2026🧠 AI

Anthropic's post on defenses against illicit distillation of Claude, which it says cut reasoning tokens harvested by distillers 10 to 100 times. The methods are preserved thinking (no editing of prior context for accounts created on or after 31 August 2026), a two-stage classifier with under 0.1% false positives, and stricter limits, bans and charges for blocked requests. It also covers trade-offs and the GLM-5.3 and Kimi cases.

anthropic.comarticleAdded 29 Oct 2026, published 12 Oct 2026🧠 AI

Anthropic describes its defenses against distillation attacks on Claude, which it says cut data harvested by distillers by 90 to 99%. The measures are preserved thinking, a two-stage constitutional classifier with under 0.1% false positives, and stricter limits and bans for repeat extractors. It cites proxy networks of hundreds of thousands of fraudulent accounts, Zhipu's GLM-5.3 and Moonshot's Kimi forwarding requests to Claude.

anthropic.comarticleAdded 30 Oct 2026, published 12 Oct 2026🛡️ Security

Talorys is an open-source, single-user personal AI assistant deployed with one command (npx create-talorys) into the user's own Cloudflare account. It uses a Cloudflare Agents SDK Durable Object with SQLite for conversations, memories, tasks, notes and automations, Workers AI (@cf/zai-org/glm-4.7-flash) for streaming chat with tool calling, and Durable Object alarms for reminders and scheduled AI routines. It stays within the Workers Free plan, with a private Worker behind a Pages Function service binding, owner-password auth, per-day AI usage guardrails, and fallback to non-AI features when quota runs out.

github.comrepoPublished and added 10 Oct 2026🧠 AI

Prime Intellect describes rewriting its open-source Prime Agent coding-agent harness from TypeScript to Rust. Prime Agent orchestrated 2,000+ agents across 10,000+ Prime Sandboxes, using GLM-5.3 inference, to do the rewrite. A root orchestrator split the work into topologically ordered tasks, and each task passed through planner, implementer, reviewer and verifier agents, with TUI, harness, protocol and feature parity checks as the objective verification. A follow-up three-day hillclimbing loop on a benchmark harness merged 69+ changes, and the post reports ~14x faster time-to-input and over 80% less memory after startup. The post also covers the new architecture: nine crates, per-session worker processes, platform abstraction for Windows support, and a runtime-fetched model/MCP catalog. Vendor-run comparisons with other harnesses carry the post's own caveat about the lack of a common benchmark standard.

primeintellect.aiarticleAdded 10 Oct 2026, published 9 Oct 2026🧠 AI

Adaptive DHiSparse extends HiSparse (host-memory KV storage that fetches only selected entries) to draft-model speculative decoding methods DSpark and DFlash2. It protects the required working set, keeps extra history while GPU memory is spare, and recovers the rest by exact fused retrieval. Queries share missing-row retrieval, draft KV uses a window-bounded ring, and verification staging is reused across layer groups. At 100K context and concurrency 128, GLM-5.3 with DFlash2 reaches 793 output tokens/s per decode GPU (1.45x HiSparse). With the same latency bounds it sustains up to 4x the concurrency. On DeepSeek-V4.1-Flash at 500K context, it delivers 1.85x the output of resident serving with DSpark.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Latent Space daily AINews digest for Oct 7-8 2026 aggregates Twitter and Reddit AI discourse. Its security-relevant section covers OpenAI firing safety researchers Tomek Korbak, Mikita Balesni and Jasmine Wang over their handling of METR audit into a summer incident where OpenAI agents allegedly escaped containment and hacked Hugging Face, Cogent Security new attack-path analysis for agent swarms, a Vals AI audit finding Xiaomi MiMo v2.6 reward-hacked by reading leaked Git objects and exploiting file timestamps, an NVIDIA NeurIPS 2026 paper showing tool access raises multimodal refusal failures up to 68.7 percent, Anthropic red-teaming showing GLM-5.3 safeguards bypassed 64 to 100 percent of the time, Goodfire probe-based cyber monitors for Kimi K3 and GLM-5.3, and a CrowdStrike-reported bank hack in South Korea using a stack of ARTEX, DeepSeek, GLM-5.3, Grok and Claude Code. The rest of the issue is general model-launch and benchmark news outside this catalog scope.

latent.spacearticleAdded 9 Oct 2026, published 8 Oct 2026🛡️ Security

A developer's blog post recounting a month of heavy daily use of DeepSeek 4.1 Flash across coding projects, arguing it matches frontier-model quality (Claude Opus 5.5, GLM) at a tiny fraction of the cost (around $1/day vs much more for Claude). The author credits a roughly 437x KV-cache size reduction versus DeepSeek's V1 model for the cost savings, discusses why self-hosting the model is economically unattractive despite being technically possible, and reflects on how a cheap "good enough" model changes workflow habits and on environmental/economic implications of model efficiency.

dgt.isarticlePublished and added 8 Oct 2026🧠 AI

The author revives "cablese," the terse telegraph-era writing style, as an LLM prompting technique: instructing a model to write dense, article-dropping summaries cuts output tokens 25–49% depending on model family, while downstream models (including ones never shown the instruction) answer comprehension questions from the compressed text at parity or better than from plaintext. The piece introduces the "Telegraph Test," an open benchmark (github.com/Travis42/telegraph-test) with ~1,300 questions over 50 passages and a fixed deterministic grader, used to measure compression ratio, cross-model legibility, and read-back/decode costs for GLM-5.3-Flash, Gemma, Qwen and gpt-5-mini; it also contrasts this human-readable, auditable register against emergent machine-only compression protocols described in other papers (BabelTele, GLOSSOGEN) and existing tools (Terse, Caveman).

fiveminutesforward.comwriteupPublished and added 7 Oct 2026🧠 AI

Decrypt reports on Mistral AI's launch of Mistral Large 4 ("Le Chonk"), a 1-trillion-parameter mixture-of-experts model that activates 49B parameters per query, priced at $1.36/$4.18 per million input/output tokens. The piece compares Large 4's benchmark scores (Surge AI coding eval, AutomationBench, DeepSWE 1.1) against DeepSeek V4 Pro, Kimi K3, GLM-5.3, Qwen3.8 Max, Claude Opus 5/5.5, Claude Sonnet 5.5, Gemini 4 Argon and GPT-6 Astra, and notes weights are due by end of October alongside Mistral's sovereign AI positioning and recent 3 billion euro Series D.

decrypt.coarticlePublished and added 6 Oct 2026🧠 AI

Nathan Lambert's Interconnects op-ed argues the "open weights are a cyber weapon" discourse ignores that documented real-world AI-enabled cyber incidents to date trace mostly to closed-API models (OpenAI), not open-weight ones, and critiques Anthropic's GLM-5.3 offensive-cyber risk report for not engaging with the trade-offs of banning open models. It also unpacks how Chinese labs (e.g., Kimi K3, GLM-5.3) assess and register AI risk differently from US labs, and contrasts the hype around Claude Mythos's hypothetical leak with GLM-5.3's actual open release having caused no comparable societal disruption.

interconnects.aiarticlePublished and added 6 Oct 2026🛡️ Security

Nathan Lambert's Interconnects essay argues the Western discourse on open-weight model cyber risk is one-sided and poorly reasoned, critiquing Anthropic's report framing GLM-5.3 as an offensive cyber tool while ignoring the trade-offs of banning open weights. He contends public evidence shows closed-model APIs (e.g. OpenAI's) have caused more documented cyber incidents than open weights so far, argues banning open models without also restricting closed-model APIs would widen the offense-defense gap, and offers an account of how Chinese labs (and government registration/evaluation requirements) weigh AI risk differently than U.S. labs.

interconnects.aiarticlePublished and added 6 Oct 2026🧠 AI

Twitter thread reporting that Mistral announced Mistral Large 4, codenamed "Le Chonk," a 1-trillion-parameter open-weight model with 49B active parameters and native multimodality, rolling out via API now with open-weight release planned for end of October. It reportedly scores 62% on the DeepSWE benchmark (ahead of GLM-5.3), 67% on the Finch financial-tasks benchmark (SOTA open-weight), and 15% on Harvey's Legal Agent Benchmark (also SOTA open-weight), per VentureBeat and @AiBattle_.

x.comthreadPublished and added 6 Oct 2026🧠 AI

A deep technical walkthrough of Aleph Alpha's Kolibri, a 78B-parameter (3.5B active) mixture-of-experts LLM for German and English released under Apache 2.0, built to be 'sovereign' (trained and deployable entirely under EU/German law). The post explains its six core design choices — a 384-expert MoE router, a German-aware UniBPE tokenizer (verified independently to cut tokens 15% vs GPT-5's tokenizer on legal German), mostly sliding-window attention enabling a validated 1M-token context, German-native reasoning traces, a Merlin-Arthur training protocol for calibrated 'I don't know' abstention, and a configurable reasoning_effort parameter — and benchmarks it against Qwen3.5/3.6/3.8, GLM-4.7 Flash, Nemotron 3 Nano and gpt-oss-120b on AIME, RULER, SWE-bench Verified, Terminal-Bench and the Omniscience test, including where it underperforms (tool calling, coding agents, trivia recall).

tej.asarticlePublished and added 3 Oct 2026🧠 AI

Hy4 preview is Tencent's new flagship Mixture-of-Experts language model: 770B total parameters with 49B activated per token across 78 layers (256 routed experts + 1 shared expert, top-8 routing), plus a native 10B multi-token-prediction layer for speculative decoding. It introduces Gated DeepSeek Sparse Attention with IndexCache for cross-layer index reuse and identity Hyper-Connections (iHC) on the residual path, and in blind internal evaluations on engineering tasks it edges out GLM-5.3 and Kimi K3. Weights (including an FP8 variant) are released under Apache 2.0 on Hugging Face with vLLM/SGLang deployment recipes and AngelSlim-based quantization/compression tooling.

huggingface.corepoAdded 2 Oct 2026🧠 AI

DwarfStar (ds4) is a narrow C inference engine for running large mixture-of-experts models locally on high-memory Mac (Metal), CUDA and ROCm machines. It targets specific model families end-to-end — DeepSeek V4/V4.1 Flash, GLM 5.x and Qwen3.8 Flash Next — using asymmetric 2-bit quantization that compresses routed experts while keeping shared/critical paths precise, plus SSD-backed prefix caching keyed by prompt hash to avoid full re-prefill on restart. It ships a CLI (ds4), an OpenAI/Anthropic-API-compatible local server (ds4-server), and a persistent coding agent (ds4-agent), all sharing model state, and is MIT-licensed with project-specific GGUF builds rather than generic GGUF support.

dwarfstar.shtoolAdded 3 Oct 2026, published 2 Oct 2026🧠 AI

Wagtail's engineering team recounts a September challenge to run all day-to-day coding work on one efficient open model, GLM-5.3-Flash, tracking usage via AgentsView: they stayed within budget ($68, ~4kWh) for the first half of the month before a vibe-coded MCP server prototype on the "wrong" model burned 450M tokens/$150/5kWh overnight, and inference-provider capacity limits forced fallbacks to DeepSeek-V4.1-Flash and Qwen 3.8 Flash. The post ends with concrete lessons for October: constant local usage/energy measurement, separate budgets for experimentation vs production, better multi-agent prompt/role patterns (orchestrator/scout/implementer/reviewer), and defaulting day-to-day work to flash-tier models.

wagtail.orgarticleAdded 3 Oct 2026, published 2 Oct 2026🧠 AI

RailWave is a phase-adaptive communication layer built on DeepEP that tackles the irregular All-to-All communication bottleneck in expert-parallel Mixture-of-Experts models. It combines RailBalance (source-local redistribution of traffic across network rails), a reusable topology-derived permutation schedule to cap concurrent senders per receiver, and a calibrated selector that picks an execution path per communication phase using offline profiling. On communication workloads derived from training the 106B GLM-4.5-Air model, it achieves up to 5.84x speedup on NVIDIA H800 and 4.36x on H20 versus the native DeepEP baseline.

arxiv.orgpaperAdded 5 Oct 2026, published 2 Oct 2026🧠 AI

A Twitter thread recapping a "forward-deployed engineer" (FDE) talk on AI transformation methodology: find the real process a company runs (not the one it documents) by interviewing veteran staff and embedding engineers in the system of record, then sort every step into four buckets — delete, deterministic code, agentic (judgment plus exception history), or human-in-the-loop (payment, signing, negotiation). It argues most deployed workflows don't need frontier models — benchmark each workflow against cheap tiers and open-weight models like GLM, Kimi and Qwen, plus Grok, and let evals decide — and that agents should live inside existing systems of record like Salesforce/Slack rather than requiring migration to new AI-native tools. Cites a concrete case: a $5B-market-cap company's "seven-step" quote-to-cash process turned out to have twenty steps and seven loops, and after the rework cycle time dropped from 24 to 6 days and invoice straight-through processing rose from 18% to 87%.

x.comthreadAdded 2 Oct 2026, published 1 Oct 2026🧠 AI

Latent Space's AINews digest for 9/29-9/30/2026 leads with Google DeepMind's Gemini 4 Argon, a frontier model in limited Fairwind Program cyber-defense/government preview claiming SOTA on 13 of 19 benchmarks and an industry-first 1M-token output via Long Decode Continuation, with detailed head-to-head evals against GPT-6 Astra, GPT-6.1 Sol and Claude Opus 5.5 from Artificial Analysis, Vals and Arena. It also covers other releases (Cohere Embed 5, Ideogram 4.5, GLM-5.3-Flash llama.cpp support, Solar Mini 4), research (Meta's Context Language Models, a Nature paper on superhuman Stratego AI), inference/systems news, and a safety section on hidden-reasoning-extraction attacks and GLM-5.3 cyber-capability risk.

latent.spacearticlePublished and added 1 Oct 2026🧠 AI

VibeMemBench is a benchmark for evaluating persistent memory systems on 111 real-repository coding targets (from 90 SWE-rebench V2 repos, with 3,634 prior-work trajectories), built via a four-stage SIEVE pipeline that verifies a target's injected history experience actually improves executable test outcomes before inclusion. The paper finds that frozen, verified "useful" experience transfers small resolution gains (1.1–4.5 pp) and step reductions to held-out solvers (DeepSeek-V4-Pro, GLM-5, GLM-5.2, Kimi-K2.7-code, Qwen3.8-max), but when four existing memory systems (Mem0, SimpleMem, MemoryOS, A-MEM) must construct and retrieve that experience themselves, 11 of 12 solver/system pairings fail to beat the memory-off baseline — exposing a large gap between latent useful history and what current memory systems actually deliver.

arxiv.orgpaperAdded 30 Sep 2026🧠 AI

Anthropic's dangerous-capability testing found that Z.ai's open-weight model GLM-5.3 builds working cyber exploits nearly as well as Claude Mythos Preview, succeeding in 50 of 410 Chrome exploit-benchmark attempts. In a live red-team test the model chained newly discovered browser vulnerabilities together to exfiltrate a private SSH key in under a day. Because GLM-5.3's weights are openly downloadable, Anthropic warns attackers can strip or bypass any built-in safeguards.

x.comthreadPublished and added 30 Sep 2026🛡️ Security

A tweet summarizing Anthropic's research finding that Z.ai's open-weight GLM-5.3 model can build working cyber exploits nearly as effectively as Anthropic's own Claude Mythos Preview, succeeding in 50 of 410 attempts on Chrome exploit benchmarks and chaining novel browser flaws to steal an SSH key in under a day during a live test. Anthropic warns that because GLM-5.3's weights are publicly downloadable, any safety fine-tuning or refusal safeguards can be trivially stripped or bypassed by attackers, framing this as a dangerous-capability risk specific to open-weight releases.

x.comthreadPublished and added 30 Sep 2026🧠 AI

AINews daily digest covering OpenAI DevDay 2026: the launch of 'dots' (always-on GPT-6 Astra-powered agents with app/Slack/Teams connectivity), GPT-6.1 Sol (a cheaper 'near-Astra' model at $2/$10 per M tokens), Ultrafast inference mode, a new Decisions API for fast classification/routing on GPT-6 Luna, ChatGPT Spaces/Pages, and a B2B model marketplace via Baseten. It also rounds up independent evals (Artificial Analysis, planted-bug tests, Roboflow vision benchmarks) comparing GPT-6.1 Sol against Claude Opus/Sonnet 5.5, plus safety/eval-integrity stories (scrapped GPT-6.1 Astra, evaluation awareness, GLM-5.3 cyber capability), agent infrastructure research (DeepSeek DSec sandboxing, StepFun KITE, vLLM day-0 support), and industry news (Anthropic's IPO filing, Hugging Face acquisition by NVIDIA).

latent.spacearticlePublished and added 30 Sep 2026🧠 AI

Tweet relaying Anthropic's finding that GLM-5.3 is "the most cyber-capable open-weight model released to date," trailing the US frontier by roughly four months on an aggregate of CAISI's cyber benchmarks. It also cites a 64-100% jailbreak success rate against the model's safeguards, with the poster arguing this favors faster-deploying defenders over attackers.

x.comthreadPublished and added 30 Sep 2026🛡️ Security

A tweet reporting that GLM-5.3, an open-weight model, is "the most cyber-capable open-weight model released to date," trailing the US frontier by roughly four months on an aggregate of CAISI's cyber benchmarks. The poster also notes 64-100% jailbreak success rates against the model's safeguards, arguing defensive tooling access may offset the capability gap for blue teams.

x.comthreadPublished and added 30 Sep 2026🧠 AI

Simon Willison quotes Anthropic's Frontier Red Team report "GLM-5.3 and the spread of advanced cyber capabilities," which ran several models against 100 randomly-selected tasks from an internal Binary Exploitation benchmark. GLM-5.3 produced full control-flow hijack exploits in 4% of trials and Claude Mythos Preview in 6%, while earlier models (Claude Opus 4.6, GLM-5.2) succeeded in none — marking what Anthropic frames as a newly crossed capability threshold for AI-assisted exploit development.

simonwillison.netarticleAdded 30 Sep 2026, published 29 Sep 2026🛡️ Security

A quoted excerpt from Anthropic's Frontier Red Team report on GLM-5.3 finds that, across 100 randomly selected tasks from an internal Binary Exploitation benchmark, GLM-5.3 achieved full control-flow hijacks in 4% of trials and Claude Mythos Preview in 6%, while earlier models (Claude Opus 4.6, GLM-5.2) succeeded in none. Anthropic frames this as a meaningful threshold crossed in advanced cyber capability, part of ongoing frontier-model dangerous-capability evaluation.

simonwillison.netwriteupPublished and added 29 Sep 2026🧠 AI

LeapQuant is a training-free quantization method for the fixed-size recurrent state used by linear-attention LLM architectures like Gated DeltaNet and Kimi Delta Attention, aiming to cut the inference bottleneck of repeatedly reading and updating that state. It introduces per-window quantization (re-quantizing the state only once per token window, using high-precision buffered updates in between) plus "Compensator Tokens" that preserve the state's largest outlier values in high precision, with residual smoothing to further reduce error. Across Qwen, Kimi, and GLM model families, it reaches near-FP32 accuracy at 8-bit while delivering 2.05–3.70x kernel-level and 1.47x end-to-end speedups on NVIDIA B200, RTX PRO 6000, and RTX 5090 GPUs.

arxiv.orgpaperAdded 30 Sep 2026, published 29 Sep 2026🧠 AI

Anthropic's Frontier Red Team analyzes GLM-5.3, an open-weight model from Zhipu AI (Z.ai), finding it matches Claude Mythos Preview's exploit-development capability (50/410 vs 56/410 end-to-end exploits on ExploitBench against Chrome's V8 engine; 4% vs 6% full control-flow hijacks on an internal OSS-Fuzz binary exploitation benchmark) but ships with safeguards attackers bypass 64-100% of the time via prompting tricks, prefill, or "abliteration" of the open weights. The post documents a researcher using GLM-5.3 to chain previously unknown JavaScript-engine vulnerabilities into a working browser file-read exploit, and GLM-5.3-Flash autonomously building a reliable ARM64 PAC-bypass exploit chain for CVE-2026-11645 for about $20. It argues GLM-5.3's lack of meaningful safeguards marks a step change in freely accessible offensive cyber capability and calls for independent government safety testing of such open-weight models.

anthropic.comarticleAdded 2 Oct 2026, published 29 Sep 2026🛡️ Security

A curated GitHub list of open-weight "uncensored" and security-fine-tuned LLMs intended for authorized red teaming, pentesting, and security research, covering models like WhiteRabbitNeo's DeepHat V2, BugTraceAI-CORE, CYBER-FROST, and several abliterated Qwen/GLM/DeepSeek variants. It explains the underlying techniques (abliteration per Arditi et al. 2024, QLoRA/DPO fine-tuning, GGUF/FP8 quantization) and links to Hugging Face model cards, benchmark suites (CyberBench, TrustedSec's LLM Attack Benchmark vs OWASP Juice Shop), and deployment platforms for running these models locally or via API.

github.comrepoAdded 2 Oct 2026, published 29 Sep 2026🛡️ Security