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Anthropic@AnthropicAIΒ·9h ago
πŸ“Š Benchmark

Anthropic Frontier Red Team: Claude, working with Theorem, formally proved no-crash properties at the RISC-V assembly level for 239 entry points across 123 OSS-Fuzz libraries, about 180M lines of Rocq

On 2026-10-22 Anthropic’s Frontier Red Team, working with Theorem, published research in which Claude turned OSS-Fuzz fuzz tests into formal specs and proved on the compiled RISC-V binaries that no input under 64 MB causes a crash. Over a few weeks it proved 239 entry points across 123 libraries, about 180 million lines of Rocq checked by the Rocq kernel. Along the way it found and patched hundreds of bugs, including an out-of-bounds write that needed a roughly 20 MB input with a rare layout. Anthropic says Opus 5.5 needs about 40% fewer tokens than Opus 5 to finish proofs.

Anthropic Frontier Red Team: Claude, working with Theorem, formally proved no-crash properties at the RISC-V assembly level for 239 entry points across 123 OSS-Fuzz libraries, about 180M lines of Rocq
⚑ Key Takeaways
  • β€’Scale: 239 entry points across 123 OSS-Fuzz libraries, about 180M lines of Rocq, a typical proof about 600,000 lines for binaries with a median of 18,000 RISC-V instructions
  • β€’Scope: no crash (including ASan memory errors and failed assertions) for any input under 64 MB, assuming a single-threaded static RISC-V binary on Linux with musl
  • β€’Hundreds of bugs found while proving, including an out-of-bounds write that needed about a 20 MB input, plus a rediscovery of CVE-2026-56132 in libexpat
  • β€’On 36 small verification tasks Opus 5.5 proves half with about 40% fewer tokens than Opus 5; a shared proof workspace cut tokens a further 17% (Anthropic figures)
  • β€’A subset of proofs and the RISC-V and Linux semantics are public (theorem-labs/oss-verify-proofs), and Theorem published a Claude skill to help developers start
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ClaudeDevs@ClaudeDevsΒ·13h ago
πŸ› οΈ Tooling

Anthropic donates the Agent Skills open standard to the Linux Foundation’s Agentic AI Foundation, after 70+ agent platforms adopted it with 1M skills published and nearly 280M installs

On 2026-10-22 Anthropic said it is donating Agent Skills, the open standard that teaches agents a task through a folder with a SKILL.md file, to the Agentic AI Foundation (AAIF) under the Linux Foundation, the same neutral home it gave MCP. The donation covers the Agent Skills name and logo, the GitHub organization and the agentskills.io domain. Anthropic says more than 70 agent platforms, including ChatGPT, Cursor, GitHub Copilot and VS Code, have integrated the standard since December 2025, with 1 million skills published and nearly 280 million installs. Engineers from Anthropic, Google, OpenAI and others will maintain it as individuals.

Anthropic donates the Agent Skills open standard to the Linux Foundation’s Agentic AI Foundation, after 70+ agent platforms adopted it with 1M skills published and nearly 280M installs
⚑ Key Takeaways
  • β€’Official figure: 70+ agent platforms have integrated Agent Skills, including ChatGPT, Cursor, GitHub Copilot and VS Code
  • β€’1 million skills published and nearly 280 million installs so far (Anthropic figures)
  • β€’Donated: the Agent Skills name and logo, the GitHub organization and the agentskills.io domain; you can keep using or contributing without joining AAIF
  • β€’Lead Maintainers are David Soria Parra and Mahesh Murag, with engineers from Anthropic, Google, OpenAI and others maintaining it as individuals
  • β€’Proposed first roadmap: versioned releases, a formal proposal process and security guidance for people who write and install skills
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Dmitry Lyalin@LyalinDotComΒ·16h ago
πŸ› οΈ Tooling

Google previews Gemini API CLI (gemini-api), a zero-dependency Go binary that lets Claude Code, Codex, Cursor and other coding agents generate media, transcribe and run managed agents from the shell

The Google AI Studio team released an experimental preview of the Gemini API CLI (command name gemini-api). It is a stateless, zero-dependency Go binary for macOS, Linux and Windows, open source under Apache 2.0. Any coding agent with shell access and a GEMINI_API_KEY can call text reasoning, image, video, music and speech generation, file and YouTube analysis, audio and video transcription, and managed agents such as Deep Research with one command. Media is written straight to disk and only the file path goes to stdout, so base64 payloads never land in the context window. Google labels it experimental and not a supported Google product.

Google previews Gemini API CLI (gemini-api), a zero-dependency Go binary that lets Claude Code, Codex, Cursor and other coding agents generate media, transcribe and run managed agents from the shell
⚑ Key Takeaways
  • β€’Default models (official README): gemini-3.8-flash for generate, analyze and transcribe; gemini-nano-banana-2.1 for image; gemini-omni-1.1-flash for video; lyria-3.5 for music; gemini-3.8-flash-tts for speech
  • β€’Install with a one-line curl script on macOS/Linux, a PowerShell script on Windows, or go install; the latest GitHub release is v0.4.3 (published 2026-10-19)
  • β€’Built for agents: --usage prints the command tree as KDL, --schema prints JSON Schema, --dry-run validates offline with credentials redacted, plus --output-format json/toon and inline --jq
  • β€’Exit codes separate 4 cases: 0 success, 1 runtime/API failure, 2 usage or validation error, 3 auth failure; --agent-mode emits structured JSON errors
  • β€’It also manages sandbox environments, server-side credentials, webhooks and cron triggers for managed agents; files uploaded via the Files API are kept for 48 hours
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SlowMist@SlowMist_TeamΒ·17h ago
πŸ”₯ Trending

LiteLLM Gateway Compromised in v1.107.0 Supply-Chain Attack: Emergency Rollback to 1.106.2 and Credential Rotation Issued

LiteLLM, a widely deployed unified proxy gateway powering millions of LLM and autonomous agent pipelines worldwide (including LangChain and OpenHands), suffered a critical supply-chain compromise. Attackers breached its automated CI/CD release pipeline, pushing a poisoned v1.107.0 release to PyPI and Docker Hub tags ('latest' and 'main-stable') equipped with a credential-harvesting payload targeting system environment variables and cloud API keys. Coordinated with security firms including SlowMist, the maintainers quickly contained the incident, yanked v1.107.0, restored PyPI to 1.106.2, and urged all operators to pin dependency versions and rotate API credentials immediately.

LiteLLM Gateway Compromised in v1.107.0 Supply-Chain Attack: Emergency Rollback to 1.106.2 and Credential Rotation Issued
⚑ Key Takeaways
  • β€’LiteLLM release pipeline compromised, publishing trojanized v1.107.0 packages across PyPI and Docker Hub tags
  • β€’Payload secretly exfiltrated host environment variables, harvesting model API keys, database credentials, and cloud secrets
  • β€’Maintainers yanked the malicious package within hours, rolling PyPI and official Docker containers back to stable 1.106.2
  • β€’Urges all agent and LLM platform operators to verify pinned package versions and execute immediate credential rotation
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Artificial Analysis@ArtificialAnlysΒ·17h ago
πŸ”₯ Trending

Artificial Analysis Benchmarks DeepSeek Search API: $0.75/1K Searches Delivers +36 Point Score Lift

Independent AI evaluation platform Artificial Analysis (@ArtificialAnlys) officially benchmarked DeepSeek's web search API (DeepSeek Search). Augmenting the base model with DeepSeek Search boosted its Search Index score from 33 to 69β€”an impressive +36 point leap (ranking 17th among 27 tested search APIs). DeepSeek Search established a market-leading price-performance ratio, priced at just $0.75 per 1,000 searches (~$0.032 per completed task), making it the most cost-effective paid search API evaluated. It recorded 70% accuracy on AA-Omniscience and 63% on BrowseComp, highlighting an optimal balance between factual grounding and operational economics.

Artificial Analysis Benchmarks DeepSeek Search API: $0.75/1K Searches Delivers +36 Point Score Lift
⚑ Key Takeaways
  • β€’Artificial Analysis independently benchmarked DeepSeek Search API, lifting Search Index score to 69 (+36 pts over base model)
  • β€’Industry-leading cost efficiency: priced at $0.75 per 1,000 searches (~$0.032 per task), the cheapest paid search API on the market
  • β€’Scores 70% accuracy on cross-domain factual QA benchmark AA-Omniscience (ranking 10th out of 27 configurations)
  • β€’Achieves 63% accuracy on long-horizon web navigation benchmark BrowseComp, offering high-ROI grounding for agent developers
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Hugging Face Daily Papers@huggingfaceΒ·17h ago
πŸ”₯ Trending

Microsoft Research Open-Sources WildAWM: Generalist Agent World Model in the Wild for GUI & Web Simulation

Microsoft Research (MSRA) researchers published and open-sourced WildAWM (arXiv:2610.26828, GitHub: MSR-Orchard/WildAWM), a generalist agent world model designed for open-ended 'in the wild' digital environments. Traditional computer-use agents rely on slow, brittle, and expensive live web/OS sandboxes for training and evaluation. WildAWM establishes a scalable multimodal world model that accurately predicts visual screen transitions and DOM mutations conditioned on agent actions (click, type, scroll). By replacing live execution environments with a high-fidelity neural simulator, WildAWM unlocks safe, accelerated offline reinforcement learning for GUI agents.

Microsoft Research Open-Sources WildAWM: Generalist Agent World Model in the Wild for GUI & Web Simulation
⚑ Key Takeaways
  • β€’Microsoft Research open-sources WildAWM (arXiv:2610.26828), establishing the first generalist world model for wild web and GUI domains
  • β€’Eliminates dependency on fragile, expensive live web sandboxes that risk side-effects during agent trial-and-error training
  • β€’Employs an action-conditioned multimodal autoregressive design predicting high-fidelity visual rollouts following clicks, typing, and scrolling
  • β€’Decisively outperforms prior state-transition baselines across e-commerce, enterprise software, and desktop GUI simulations
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Hugging Face Daily Papers@huggingfaceΒ·17h ago
πŸ”₯ Trending

Tencent ARC & Tsinghua Open-Source Looking Ahead: Lookahead Attention Eradicates Autoregressive Video Drift

Tencent ARC in collaboration with Tsinghua University released and open-sourced Looking Ahead (arXiv:2610.29989, GitHub: TencentARC/Looking-Ahead), establishing a breakthrough paradigm for autoregressive video synthesis. Overcoming the chronic pathology of traditional autoregressive video modelsβ€”where unidirectional sequential extrapolation causes exponential error accumulation and severe visual collapse over extended rolloutsβ€”Looking Ahead introduces Lookahead Attention. By coupling casual autoregressive decoding with a forward-looking planning stream that anchors future spatiotemporal keypoints, the framework eliminates cumulative drift and visual jitter in long-form generation.

Tencent ARC & Tsinghua Open-Source Looking Ahead: Lookahead Attention Eradicates Autoregressive Video Drift
⚑ Key Takeaways
  • β€’Tencent ARC and Tsinghua University introduce Looking Ahead (arXiv:2610.29989), open-sourcing a long-horizon video framework
  • β€’Eliminates exponential error propagation and visual degeneration inherent to unidirectional token-by-token video rollouts
  • β€’Pioneers Lookahead Attention, leveraging global temporal keypoint anchors to constrain local micro-texture rollouts
  • β€’Establishes a new state-of-the-art across physical consistency and dynamic motion metrics for open-weight autoregressive models
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LMSYS Org@lmsysorgΒ·23h ago
πŸ”₯ Trending

SGLang Introduces Elastic EP with NIXL: Dynamic Live MoE Scaling Without Restarts or Traffic Draining

LMSYS Org in collaboration with the NVIDIA Dynamo team officially unveiled Elastic Expert Parallelism (Elastic EP) with NIXL backend integration in SGLang (@lmsysorg). Addressing the critical vulnerability of large-scale MoE deployments (such as DeepSeek-V3/V4) where static EP causes entire cluster crashes upon single-node failures and prevents live capacity resizing, Elastic EP decouples rigid expert-to-GPU bindings. Leveraging NVIDIA NIXL device APIs and Mooncake orchestrators, SGLang enables live scaling of active MoE serving deployments (e.g. dynamically expanding from 20 to 24 GPUs) without draining traffic or restarting clusters, while delivering resilient fault tolerance.

SGLang Introduces Elastic EP with NIXL: Dynamic Live MoE Scaling Without Restarts or Traffic Draining
⚑ Key Takeaways
  • β€’LMSYS Org and NVIDIA Dynamo team roll out Elastic EP in SGLang, enabling live scaling of production MoE clusters
  • β€’Eliminates the single-point-of-failure bottleneck in static EP deployments, enabling instantaneous fault-tolerant degradation
  • β€’Integrates NVIDIA NIXL device primitives (--moe-a2a-backend nixl) with native RDMA and NVLink zero-copy token routing
  • β€’Demonstrates zero-downtime live expansion, seamlessly growing active deployments (e.g. from 20 to 24 GPUs) under live query load
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Hugging Face Daily Papers@huggingfaceΒ·23h ago
πŸ”₯ Trending

Cache Engineering for Agents: Tsinghua and CUHK Introduce AKV Paradigm Cutting Inference Costs by 27.7%

Researchers from Tsinghua University and CUHK introduced a breakthrough paper titled 'Cache Engineering for Agents' (arXiv:2610.28577) alongside the open-source AKV serving engine (thu-nics/AKV). While multi-turn agents rely on traditional 'context engineering' (text trimming and summarization) that invalidates prefix KV-caches and incurs punishing re-prefilling overhead, cache engineering empowers agents to directly manipulate KV-cache states via 'drop' and 'repos' primitives. Coupled with AKV's drop-aware radix-tree engine, the framework slashes agent inference costs by 27.7%, accelerates P95 latency by 1.5x, and boosts benchmark solve scores by 1.7 points.

Cache Engineering for Agents: Tsinghua and CUHK Introduce AKV Paradigm Cutting Inference Costs by 27.7%
⚑ Key Takeaways
  • β€’Tsinghua and CUHK researchers formalize 'Cache Engineering' in arXiv:2610.28577 for multi-turn autonomous agents
  • β€’Demonstrates how traditional prompt trimming destroys prefix cache reuse, causing massive latency and redundant re-prefilling
  • β€’Introduces 'drop' (selective cache eviction) and 'repos' (RoPE position alignment) primitives directly on GPU KV-caches
  • β€’Open-sources the AKV stateful serving engine (thu-nics/AKV), slashing agent serving costs by 27.7% while accelerating P95 latency by 1.5x
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Artificial Analysis@ArtificialAnlysΒ·23h ago
πŸ”₯ Trending

Artificial Analysis Releases HealthBench & MLCR-AA: Exposing Severe Verbosity Bias in Medical AI Rankings

Independent AI evaluation platform Artificial Analysis officially released HealthBench Professional alongside the length-normalized MLCR-AA index (@ArtificialAnlys). The benchmark revealed a pervasive industry blindspot: LLM-as-a-Judge evaluations suffer from acute 'verbosity bias', disproportionately rewarding longer generations regardless of medical conciseness. In raw HealthBench scoring where Anthropic outputs were 4–6x longer, Claude Opus 5.5 led at 71.4%. However, once length bias was scientifically normalized via MLCR-AA, GPT-6 Astra (max) tied Claude Sonnet 5.5 for #1, while Claude Opus 5.5 plummeted from #1 down to #6, setting a new rigorous standard for medical model evaluation.

Artificial Analysis Releases HealthBench & MLCR-AA: Exposing Severe Verbosity Bias in Medical AI Rankings
⚑ Key Takeaways
  • β€’Artificial Analysis unveils HealthBench Professional and the length-calibrated MLCR-AA benchmark
  • β€’Exposes pervasive verbosity bias where evaluation judges disproportionately favor verbose outputs (Anthropic outputs averaged 4–6x longer)
  • β€’Raw HealthBench placed Claude Opus 5.5 at #1 (71.4%) ahead of Sonnet 5.5 (70.8%) and GPT-6 Astra (69.7%)
  • β€’Length-adjusted MLCR-AA shows GPT-6 Astra (max) tying Sonnet 5.5 for #1, while Opus 5.5 plummets from #1 to #6
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Artificial Analysis@ArtificialAnlysΒ·23h ago
πŸ”₯ Trending

Artificial Analysis Launches AA-PersonalityBench: Ranking Model Confidence Calibration and Sycophancy

Artificial Analysis launched AA-PersonalityBench (@ArtificialAnlys), an innovative behavioral benchmark quantifying psychological traits in frontier LLMs, with initial rankings focusing on Confidence (uncertainty calibration) and Sycophancy (tendency to agree with user misconceptions). The data revealed that Google's Gemini 3.8 Flash (100) and Gemini 4 Argon (97) displayed the highest assertiveness while being the least likely to admit uncertainty. Conversely, on sycophancy tests, Llama 4 Maverick scored 97 (frequently validating flawed user prompts), whereas Claude Fable 5.1 scored a near-flawless 4, refusing to flatter and rigorously holding factual ground.

Artificial Analysis Launches AA-PersonalityBench: Ranking Model Confidence Calibration and Sycophancy
⚑ Key Takeaways
  • β€’Artificial Analysis rolls out AA-PersonalityBench, evaluating behavioral alignment across Confidence and Sycophancy
  • β€’Gemini 3.8 Flash (100) and Gemini 4 Argon (97) top confidence scores, yet exhibit minimal willingness to express epistemic uncertainty
  • β€’Llama 4 Maverick recorded a high sycophancy score of 97, regularly echoing invalid user premises
  • β€’Claude Fable 5.1 (4) and Claude Opus 5.5 (46) demonstrated elite resistance to user gaslighting, strictly preserving factual neutrality
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Claude@claudeaiΒ·1d ago
πŸ”₯ Trending

Anthropic Officially Releases Claude Duet Research Preview: Proactive Autonomous Workflow Agent Application

Anthropic (@claudeai) officially announced Claude Duet, a research-preview application that represents a generational leap in agentic user interfaces. Accumulating over 40K likes and 4.7M views within hours, Claude Duet shifts the AI paradigm from passive query-response chatbots to a proactive autonomous partner: it surfaces what requires user attention, autonomously executes delegated long-horizon tasks, works uninterrupted in the background while users step away, and explicitly requests confirmation before executing high-impact actions on the user's behalf.

Anthropic Officially Releases Claude Duet Research Preview: Proactive Autonomous Workflow Agent Application
⚑ Key Takeaways
  • β€’@claudeai officially launched Claude Duet as a research preview app, rapidly amassing 40K+ likes and 4.7M views
  • β€’Transcends reactive chat interfaces via a proactive architecture that continuously surfaces priorities requiring human attention
  • β€’Executes multi-step background tasks asynchronously through persistent 'keeps working when you step away' autonomy
  • β€’Integrates rigorous safety guardrails, mandating explicit confirmation before executing irreversible external actions
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OpenAI@OpenAIΒ·1d ago
πŸ”₯ Trending

OpenAI Discloses PHP Zero-Day Critical Flaws Found via GPT-5.6 Sol, Open-Sources Scanner and Pledges $1B Defense Fund

OpenAI announced a critical coordinated vulnerability disclosure in the core PHP runtime discovered by cybersecurity firm Calif utilizing GPT-5.6 Sol, alongside independent verification by Wiz. Because PHP powers roughly 70% of all websites globally, the vulnerability represented an existential risk to internet infrastructure. Following coordinated patching, OpenAI committed $1B in subsidized Daybreak security access and open-sourced openai/php2shell-checker, an automated Codex inspection tool enabling site administrators worldwide to audit their deployments.

OpenAI Discloses PHP Zero-Day Critical Flaws Found via GPT-5.6 Sol, Open-Sources Scanner and Pledges $1B Defense Fund
⚑ Key Takeaways
  • β€’Cybersecurity firm Calif used GPT-5.6 Sol to identify critical remote code execution (RCE) vulnerabilities in the core PHP interpreter
  • β€’PHP powers an estimated 70% of active web properties globally; patches have been coordinated and safely distributed
  • β€’OpenAI commits $1 billion in subsidized Daybreak program access to support security research across foundational open-source projects
  • β€’Open-sources openai/php2shell-checker on GitHub, providing an automated Codex-powered scanner for site vulnerability assessments
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Google Cloud@googlecloudΒ·1d ago
πŸ”₯ Trending

Google Cloud Open-Sources TorchTPU: Native PyTorch on Google TPU Hardware with Day-Zero vLLM and SGLang Support

Google Cloud officially open-sourced TorchTPU, enabling native, zero-compilation-barrier PyTorch execution directly on Google Cloud TPU hardware (TPU v5p and TPU v6e). Overcoming the legacy friction of PyTorch/XLA's rigid graph compilation and dynamic-shape stalls, TorchTPU supports native PyTorch eager execution and custom Triton/C++ kernels. Leading high-throughput serving engines vLLM and SGLang announced immediate Day-0 support. Concurrently, Google Cloud introduced OpenAI models and Codex into its Vertex Model Garden, becoming the first hyperscaler to offer all three top frontier closed models.

Google Cloud Open-Sources TorchTPU: Native PyTorch on Google TPU Hardware with Day-Zero vLLM and SGLang Support
⚑ Key Takeaways
  • β€’Google Cloud open-sources TorchTPU, delivering native, compilation-free PyTorch execution on TPU v5p and TPU v6e hardware
  • β€’Eliminates PyTorch/XLA graph-compilation overhead, natively supporting PyTorch eager mode and custom Triton/C++ kernels
  • β€’vLLM and SGLang announce immediate Day-0 TorchTPU releases, unlocking substantial throughput boosts on TPU infrastructure
  • β€’OpenAI frontier models and Codex join Google Cloud's Model Garden, solidifying Google Cloud as the premier multi-model hyperscaler
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GitHub Next@GitHubNextΒ·1d ago
πŸ”₯ Trending

GitHub Next Releases PDD Kit: Provable Specification Enforcement for Coding Agents to Eliminate Edge-Case Regressions

GitHub Next officially released PDD Kit (Property-Driven Development Kit), establishing a formal verification toolkit tailored for autonomous coding agents. Addressing the pervasive reliability gap where agent-generated code passes isolated unit tests but crashes on subtle edge cases, PDD Kit combines property-driven specifications with automated formal solvers. Instead of testing arbitrary sample inputs, PDD Kit mathematically proves that agent code adheres to rigorous invariant specifications across every possible input, preventing regressions and elevating coding agents from probabilistic generation to provable correctness.

GitHub Next Releases PDD Kit: Provable Specification Enforcement for Coding Agents to Eliminate Edge-Case Regressions
⚑ Key Takeaways
  • β€’GitHub Next releases PDD Kit to eliminate edge-case flaws in agent-generated code via automated mathematical proofs
  • β€’Pioneers 'proofs for every input', replacing fragile, underspecified unit test samples with comprehensive invariant verification
  • β€’Eliminates deadlocks, boundary index out-of-bounds, and state desynchronization in agentic refactoring workflows
  • β€’Provides an indispensable verification foundation for deploying autonomous coding agents into mission-critical production systems
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Ollama@ollamaΒ·1d ago
πŸ”₯ Trending

Ollama Natively Supports Recursive Flint 40B: 2.5B Active MoE Coding Agent Delivering 125+ tok/s on MacBook Pro

Recursive, founded by Richard Socher, has officially open-sourced Flint (RecursiveSI/flint), a 39.8B Mixture-of-Experts (MoE) coding agent model under Apache 2.0 with day-zero Ollama support (ollama run flint). Activating just 2.5B parameters per token across 47 MoE layers with a 262K context window, Flint hits 125+ tokens/sec on an M4 Pro MacBook Pro. It sets new benchmarks for sub-50B local models, scoring 74.2% on SWE-bench Verified and 89.7% on SWE-bench Pro v2.

Ollama Natively Supports Recursive Flint 40B: 2.5B Active MoE Coding Agent Delivering 125+ tok/s on MacBook Pro
⚑ Key Takeaways
  • β€’Flint employs a 49-layer architecture (2 dense + 47 MoE layers) with 39.8B total and 2.5B active parameters per token
  • β€’Native 262,144-token (256K) context window supports end-to-end repository indexing and cross-file debugging
  • β€’Blazing local inference speed: 125+ tokens/sec on M4 Pro MacBook Pro and 150+ tokens/sec on RTX Spark
  • β€’Scores 74.2% on SWE-bench Verified, 89.7% on SWE-bench Pro v2, and 80.3% on LiveCodeBench v6 with instant 'ollama run flint' execution
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Alibaba Qwen@Alibaba_QwenΒ·1d ago
πŸ”₯ Trending

Alibaba Tongyi Labs Open-Sources OpenGCUA: Full-Stack Generalist Computer-Use Agent Cutting OSWorld Steps by 53%

Alibaba Tongyi Labs (Tongyi-MAI) has published and open-sourced OpenGCUA (arXiv:2610.22200), establishing a fully open software stack for Generalist Computer Use Agents. Moving beyond brittle single-click GUI paradigms, OpenGCUA introduces a 'Code-Execution-as-Action' framework combining visual screen perception, accessibility trees, bash scripts, and Playwright APIs into unified programmatic execution blocks. Evaluated across eight frontier models on OSWorld-v2, OpenGCUA boosted mean scores by +19.6 points while cutting task steps by 53.0%, releasing an Apache-2.0 macOS menu-bar harness and Cross-Device SDK.

Alibaba Tongyi Labs Open-Sources OpenGCUA: Full-Stack Generalist Computer-Use Agent Cutting OSWorld Steps by 53%
⚑ Key Takeaways
  • β€’OpenGCUA published in arXiv:2610.22200 and released under Apache 2.0 as an open generalist computer-use agent stack
  • β€’Pioneers 'Code-Execution-as-Action', synthesizing loops, conditionals, and programmatic APIs into unified Python/Bash blocks
  • β€’On OSWorld-v2, boosts mean score by +19.6 points across eight frontier models while reducing steps per task by 53.0%
  • β€’Releases a native macOS menu-bar harness for physical execution alongside a Cross-Device SDK spanning PC, web, and mobile
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FrontierRefactor Research@huggingfaceΒ·1d ago
πŸ”₯ Trending

FrontierRefactor Introduces First Code Performance Benchmark: 41% of Agent Patches Pass Tests with Zero Gain or Regressions

Published in arXiv:2610.25834, FrontierRefactor establishes the industry's first evaluation benchmark targeting autonomous coding agents on multi-scale, performance-oriented codebase refactoring. Unlike traditional benchmarks like SWE-bench that measure only functional pass rates, FrontierRefactor spans seven programming languages and 60 Amdahl-qualified tasks ranging from kernels to full repositories (10^1 to 10^4+ LOC). Initial evaluations reveal that over 41% of agent patches pass functional equivalence suites yet yield zero verified speedupβ€”with 19.3% triggering severe runtime performance regressions, highlighting the vital need for multi-turn profiling feedback.

FrontierRefactor Introduces First Code Performance Benchmark: 41% of Agent Patches Pass Tests with Zero Gain or Regressions
⚑ Key Takeaways
  • β€’FrontierRefactor (arXiv:2610.25834) introduces the first multi-scale performance-oriented codebase refactoring benchmark across 7 languages and 60 tasks
  • β€’Reveals acute blindspot: over 41% of agent submissions pass functional tests but deliver zero verified acceleration (19.3% regress, 21.9% noise)
  • β€’Cross-language rewrites offer high theoretical speedups but suffer contract collapse; in-place optimizations preserve contracts but fail without profiling
  • β€’Multi-turn execution-timing feedback serves as the decisive lever, multiplying strict success rates across complex codebases
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SolarRenderer Research@huggingfaceΒ·1d ago
πŸ”₯ Trending

SolarRenderer Breaks Generative Video: 4-Step Denoising on Greyboxes and Depths for Real-Time 768p World Synthesis

Researchers from NUS and CUHKSZ have introduced SolarRenderer (arXiv:2610.28630), a structure-conditioned generative reference rendering framework. Converting game engine grey-boxes, pose skeletons, semantic maps, and depth passes into photorealistic video, SolarRenderer adapts a frozen MiniMax-H3 33B backbone via lightweight control and 4-step generation LoRAs. Using structure-guided distribution matching, it renders stylized video in just 4 denoising steps (~12.3x faster than 50-step diffusion) and introduces a block-causal variant for real-time 768p streaming synthesis in generative game engines.

SolarRenderer Breaks Generative Video: 4-Step Denoising on Greyboxes and Depths for Real-Time 768p World Synthesis
⚑ Key Takeaways
  • β€’SolarRenderer (arXiv:2610.28630) enables high-fidelity video rendering directly from coarse 3D geometry (grey-boxes, skeletons, depth, meshes)
  • β€’Adapts a frozen MiniMax-H3 33B backbone with lightweight Control and 4-step LoRA modules, preserving visual priors
  • β€’Requires only 4 denoising steps via structure-guided distribution matching, achieving a 12.3x acceleration over 50-step baselines
  • β€’Block-causal streaming variant achieves real-time continuous 768p video generation for next-generation generative gaming engines
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ClaudeDevsClaudeDevsΒ·1d ago
πŸ› οΈ Tooling

Claude API ships version 2026-10-21, its first since 2023: sampling parameters removed, new major SDKs across languages

On Oct 21 Anthropic released Claude API version 2026-10-21, its first official version since 2023. It removes the temperature, top_p and top_k sampling parameters and drops old Admin API field names, while new SDK majors (Python 2.0, TypeScript 1.0, Go 2.0, Java 3.0 and more) default to it. Version 2023-06-01 is not deprecated.

⚑ Key Takeaways
  • β€’Version: 2026-10-21 is the Claude API's first official version since 2023; 2023-06-01 is not deprecated and more frequent versions are planned
  • β€’Breaking: sending temperature, top_p or top_k returns a 400 on every model; Claude 4.7+ models behave as before
  • β€’SDKs: Python 2.0.0, TypeScript 1.0.0, Go 2.0.0 (/v2 path), Java 3.0.0, C# 13.0.0, Ruby 2.0.0, PHP 1.0.0, ant CLI 2.0.0
  • β€’Auth: SDKs send the API key as Authorization: Bearer and hold 1 credential per client; SDK license moves to Apache 2.0
  • β€’Platforms: Claude API, Claude Platform on AWS, Microsoft Foundry, Google Cloud; Bedrock only for Claude 4.7+, see the migration guide
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