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