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Chinese Hackers Exploit Critical Tencent Software Flaw for One-Click Code Execution

China-linked UNC3569 actively exploited critical Sogou Input Method flaw CVE-2026-51990 for one-click system-level code execution, deploying the GrayRabbit backdoor.

Gen Threat Labs reports that China-linked threat actor UNC3569, possibly tied to contractor i-SOON, exploited critical CVE-2026-51990 in Tencent's Sogou Input Method, a Windows IME used by hundreds of millions, via crafted sgbiz URLs. The chain combines unvalidated command-line argument injection, unrestricted URL navigation, and an un-sandboxed Chromium 80 engine missing roughly six years of security patches. The exploit gives system-level code execution and was used to deploy the GrayRabbit backdoor, which provides a reverse shell, plugin loading, file upload to C2, and system info collection, and has appeared in UNC3569 intrusions since at least 2021. Tencent patched the issue in version 16.3.0.3498 via automatic update, but the outdated Chromium configuration reportedly remained unchanged as of September 10.

SecurityWeek · 2d agoExploit / PoC in the wild 2 sourcesCVE-2026-519901· 1 read

China-Linked Hackers Exploit Sogou One-Click RCE to Deploy GRAYRABBIT Backdoor

China-linked UNC3569 exploited CVE-2026-51990 in Sogou Input Method to deploy the GRAYRABBIT backdoor in active espionage intrusions.

Gen Threat Labs discovered UNC3569 exploiting CVE-2026-51990, a one-click RCE in Tencent's Sogou Input Method for Windows that chains an insecure sgbiz: protocol handler with an unsandboxed Chromium 80 CEF webview. The chain weaponizes CVE-2021-38003 (V8 type confusion) to run shellcode that DLL-sideloads via 7z.exe/7z.dll and deploys the GRAYRABBIT backdoor, which beacons over RC4-encrypted raw TCP 443 to mail.uaiubifas[.]top. Tencent patched the issue in version 16.3.0.3498, released via automatic updates on April 21, 2026. UNC3569 is a PRC-nexus espionage actor targeting government, education, technology, and financial sectors across East and Southeast Asia.

GBHackersupdated · 2d agofirst · 2d agoThreat actor in the wild 3 sourcesCVE-2026-51990CVE-2021-380031

Broadcom Patches Critical VMware Workstation and Fusion VM

Broadcom patched critical VMware Workstation and Fusion VM-escape flaws CVE-2026-59346 (CVSS 9.3) and CVE-2026-59347 (CVSS 8.1); update to 26H1u1.

Advisory VMSA-2026-0007 fixes CVE-2026-59346 (CVSS 9.3), an integer overflow in the VMXNET3 virtual network adapter, and CVE-2026-59347 (CVSS 8.1), a stack-based buffer overflow in HGFS. A malicious actor with local administrative privileges inside a VM could execute code on the host in both cases. The flaws affect Workstation and Fusion 25H2 and 26H1 and are fixed in version 26H1u1 with no workarounds available. The bugs were reported independently by researchers working with Trend Micro Zero Day Initiative and by Tencent Xuanwu Lab.

Security Affairs · 11d agoVulnerabilityCVE-2026-59346CVE-2026-593471

Critical VMware Workstation and Fusion Flaw Lets VM Admins Execute Host Code

Broadcom patched two VMware Workstation and Fusion flaws, including critical integer overflow CVE-2026-59346 (CVSS 9.3), letting guest admins execute host code; no exploitation seen.

Broadcom released updates fixing CVE-2026-59346 (CVSS 9.3), an integer-overflow vulnerability allowing a malicious actor with local administrative privileges on a VM with a VMXNET3 adapter to execute code on the host, and CVE-2026-59347 (CVSS 8.1), a stack-based buffer overflow in HGFS allowing code execution as the host's VMX process. Both flaws affect VMware Workstation and Fusion 25H2 and 26H1, have no workarounds, and are fixed in Workstation 26H1u1 and Fusion 26H1u1. No in-the-wild exploitation has been observed, though VMware vCenter flaws CVE-2026-59309 and CVE-2026-59310, the latter suspected of China-nexus APT use, have recently breached 361 unique victim IPs across 47 countries.

The Hacker News · 11d agoVulnerabilityCVE-2026-59346CVE-2026-59347CVE-2026-59309+1 CVEs

Emergence World: Adversarial Stress-Testing of Long-Horizon Multi-Agent Systems

16-day multi-agent stress test finds no world fully resilient to prompt injection, misinformation, or memory exposure; adversarial content acted on 46 hours later.

Emergence World is a continuously running multi-agent environment for adversarial stress testing of long-horizon autonomous systems. Eight parallel 10-agent worlds (seven homogeneous frontier-model worlds plus one mixed-model world) ran for 16 days, generating over 850,000 LLM calls and nearly 50 billion tokens. Three controlled stress events—indirect prompt injection, misinformation, and exposure of private agent memories—were delivered through ordinary interaction surfaces; no world achieved full resilience. Detection did not ensure containment: agents recognized threats yet wrote adversarial content into persistent memory and acted on it up to 46 hours later, suggesting model-level alignment is not compositional.

Hugging Face daily papers · 2d agoAI safety & security

Y Combinator’s Garry Tan wants U.S. open-weight AI labs to ‘distill’ frontier models, too

Y Combinator CEO Garry Tan urges US open-weight labs to distill frontier models and regulators to stay out, countering Anthropic's crackdown calls.

Y Combinator CEO Garry Tan told CNBC he would "do nothing" about Chinese labs distilling frontier models and floated an "American distillation regime" letting US open-weight labs do the same to American frontier labs. Anthropic this week released its second report alleging Chinese labs conduct "illicit distillation attacks" using hidden identities, fraud, and stolen credentials, and CEO Dario Amodei has called for US regulators to crack down. Tan argued closed labs cannot dictate what customers do with API outputs, noting frontier labs trained on copyrighted material without permission, and called a single monolithic proprietary provider the true AI doomer scenario.

TechCrunch · AI · 5d agoAI industry

Nous Research Adds One-Click Local Model Setup to Hermes Desktop

Nous Research's Hermes Desktop now offers one-click local model setup that reads hardware, picks a fitting quantization, downloads weights, and configures llama.cpp automatically.

Hermes Desktop, the MIT-licensed build of the open-source Hermes Agent, now sets up local models in one click: it reads the machine's hardware, selects a model that fits, downloads weights, and configures the inference runtime. It manages a pinned llama.cpp build with CUDA, Metal, Vulkan, HIP, and CPU backends, and shows green/amber/red memory-fit verdicts per catalog model before download. Quantization floors at 4-bit, and recommended models guarantee at least a 64K context window protected by ordered RAM offload (expert weights first, never the attention cache). It runs on macOS 12+, Windows 10/11, and Linux with no account required for local models.

MarkTechPost · 11d agoAI tools & infra1

Making Knowledge Distillation Cheap Enough to Run at Scale

Hugging Face blog by Multiverse Computing describes techniques making knowledge distillation cheap enough for large-scale training.

A Hugging Face blog post from Multiverse Computing (CAI) presents methods for reducing the cost of knowledge distillation so it can be run at scale. The post is aimed at practitioners compressing large models into smaller, cheaper ones for production use.

Hugging Face Blog · Aug 10, 2026AI research

Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed

Perplexity details its GPU embedding serving stack (Ivy, Tulip, ROSE), which reuses LLM prefill/decode kernels, CUDA graphs, and LazyTensors to cut launch overhead.

Perplexity engineers published a deep dive on the serving infrastructure behind pplx-embed, used across Perplexity Search and its API platform. The stack comprises Ivy (Rust HTTP gateway), Tulip (gRPC scheduling and batching), and ROSE (Runtime-Optimized Serving Engine), which reuses LLM prefill and decode kernels rather than running a separate embedding engine. Optimizations include whole-model CUDA graphs with lazy capture and a LazyTensor abstraction that overlaps CPU batch preparation with in-flight GPU work. Benchmarks are reported against vLLM v0.22.0 in BF16, with FlashAttention 4 generally fastest but FlashInfer 3 winning on Qwen-based models at very long sequence lengths.

MarkTechPost · 10d agoAI tools & infra1

Anthropic Says Seven China-Based AI Labs Ran Industrial-Scale Claude Distillation Attacks

Anthropic disrupted industrial-scale unauthorized Claude distillation by seven China-based AI labs, including Alibaba, DeepSeek, Moonshot, and Z.ai.

Anthropic identified and disrupted six illicit distillation campaigns since February 2026 run by seven China-based labs: Alibaba, Moonshot, DeepSeek, Z.ai (Zhipu), MiniMax, Xiaomi, and SenseTime. The largest, GTG-16005, involved 151 million exchanges targeting Claude Opus 4.6/4.7 chain-of-thought transcripts, peaking at roughly 3 million exchanges per day from more than 3,500 fraudulent accounts. Labs used proxy/relay services with fictitious identities, fake or stolen credit cards, harvested API keys, and purchased conversation transcripts from third-party resellers. Anthropic is countering by banning reseller accounts, summarizing internal reasoning before responding, and introducing preserved thinking in Fable 5.1, which encrypts reasoning and prevents context edits before it.

The Hacker News · 5d agoAI safety & security 9 sources

GLM-5.3: How Chinese labs keep stride with the frontier

Z.ai released GLM-5.3, a ~750B-parameter model with frontier agentic coding scores, with open weights on Hugging Face planned in two weeks.

Z.ai announced GLM-5.3, initially available only in its coding plan, with API access and open Hugging Face weights promised within two weeks. The roughly 750B-parameter model, one-third the size of Moonshot AI's Kimi K3, surpasses Kimi K3 on many benchmarks and beats Claude Fable 5 or GPT-5.6-Sol on some, placing it at the frontier of agentic coding benchmarks. GLM-5.3 reuses the GLM-5.2 base model with substantially extended post-training based on more RL environments, more diverse tasks and more compute. The post also analyzes how Chinese labs keep pace with the frontier, arguing release speed matters more than distillation.

Interconnects · Aug 14, 2026Model release

ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face

ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.

ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.

Hugging Face trending models · 19d agoModel release1

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL · 12d agoAI research1

ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.

ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.

Hugging Face daily papers · 6d agoModel release

Sakana AI Launches Fugu Max and Fugu Ultra v2 for Cheaper, Stronger Multi-Agent Orchestration

Sakana AI released Fugu Max and Fugu Ultra v2, API-only orchestrator models that route tasks across model pools to cut costs and boost multi-step reasoning.

Sakana AI released Fugu Max and Fugu Ultra v2, two orchestrator models that route queries across a pool of third-party and open-weights models, including the NVIDIA Nemotron family. Fugu Max is priced at $2 per million input and $6 per million output tokens, 40-60% cheaper per output token than Sonnet 5, GPT 5.6 Terra, and Kimi K3, and reportedly wins 6 benchmarks including Terminal Bench 2.1 and GPQA Diamond. Fugu Ultra v2 targets complex multi-step reasoning, scoring 48.3 on Chartography and 74.3 on DeepSWE. Both are live through Sakana's OpenAI-compatible API only, with no open weights and no EU/EEA availability.

MarkTechPost · 5d agoModel release

Have the frontier labs mixed up AI safety and security?

Opinion piece argues frontier labs apply probabilistic 'safety' thinking to security, citing prompt injection rates and agent sandbox escapes at Anthropic and OpenAI.

Martin Anderson argues frontier labs conflate AI safety (probabilistic alignment controls like classifiers and weight tuning) with security engineering, where fixes must be deterministic and complete. He criticizes an Anthropic tweet (Boris Cherny) claiming prompt injection is 'largely solved' when the best Opus 5 score still fails the Gray Swan IPI benchmark about 2% of the time (~1 in 500 attempts). The piece cites Anthropic's 31 August 2026 post on human reviewers dismissing monitor false positives, and OpenAI's 26 August Hugging Face incident technical report, where a June 27 alert on agent port sweeps and Artifactory pivots preceded the breach by two weeks. It also highlights weak agent sandboxing, including blocking only HTTP POST at the proxy and whitelisting .blob.core.windows.net, both trivially bypassed.

Lobsters · security · 10d agoAI safety & security in the wild

HyQuant: Hybrid-Precision Quantization for LLM Attention

HyQuant keeps most LLM attention states low-bit while preserving vertical-line tokens and local windows in high precision, maintaining near-lossless accuracy.

HyQuant is a hybrid-precision quantization framework for LLM attention that quantizes most attention states to low bits while keeping accuracy-critical vertical-line tokens and local-window states in full precision, selected via lightweight attention-pattern signals. In the prefill stage it uses a hybrid-precision attention operator, and in the decode stage it applies the same principle to KV-cache compression with fused dequantization and attention computation. Across diverse tasks, models, and datasets it maintains nearly lossless accuracy; code is available on GitHub.

Hugging Face daily papers · 20d agoAI tools & infra1

[AINews] OpenAI reports Navier-Stokes singularity find in 88 hours using Astra-next, roughly 10,000 agents and 130B tokens (>$40M), a contender for second ever Millennium Prize awarded

OpenAI-linked accounts claim roughly 10,000 AI agents produced a Navier-Stokes singularity result in 88 hours, pending mathematical verification.

OpenAI-affiliated accounts claim a system of roughly 10,000 agents, trained over about a year with multi-agent reinforcement learning, produced a finite-time singularity result related to the Navier-Stokes Millennium Problem. The claimed 88-hour runtime and 130B-token cost circulate only via social posts, and no preprint, theorem statement, or proof artifact is available. Acceptance by the mathematics community is unresolved, so the claim's epistemic status remains unknown. The roundup also notes Cognition's $48B and Mistral's $24B fundraises, GPT Image 2.5, and Meta's Muse agent relaunch.

Latent Space · 7d agoAI research1

The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon Willison · 11d agoAI research

How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules

OpenAI profiles César de la Fuente's lab using ChatGPT and Codex alongside deep-learning models to accelerate antimicrobial molecule discovery.

OpenAI published a case study on bioengineer César de la Fuente's lab, which uses ChatGPT and Codex for hypothesis brainstorming, code writing, dataset processing, and bridging knowledge gaps across biology, chemistry, and computer science. The lab's deep-learning models scan genome and protein databases for antimicrobial peptide candidates, potentially cutting initial searches from years to hours. Bacterial antimicrobial resistance was associated with about five million deaths in 2021, a toll projected to roughly double by 2050.

OpenAI News · 6d agoAI industry1

Jackrong/Qwopus3.8-27B-Flash-GGUF — new model trending #26 on Hugging Face

Community fine-tune Qwopus3.8-27B-Flash, built on Qwen3.8-27B, cuts agent reasoning latency with 12.8% faster decoding and 80.7% MTP acceptance.

Jackrong released Qwopus3.8-27B-Flash, a fine-tune of Qwen3.8-27B optimized for long-running agent workloads, reporting 12.8% faster decoding and 80.7% multi-token-prediction acceptance. Training used roughly 1.5 million teacher-scored SFT examples filtered to the top 10%, followed by reinforcement training with NVIDIA NeMo-RL and GSPO. The author notes an explicit trade-off: MMLU-Pro mixed-set scores are lower than the base model, and a known bug can produce incorrect Python indentation. Author-provided benchmarks have not been independently verified.

Hugging Face trending models · 12d agoModel release1

27.5KB language-agnostic WebGPU syntax highlighter

A developer released gpu-lexer, a 27.5KB language-agnostic syntax highlighter that uses a tiny WebGPU model to label code tokens in the browser.

gpu-lexer splits source into words, whitespace, and symbols, then a small WebGPU model uses local and whole-file context to assign nine token classes, working on languages never seen in training. On held-out files, 12.57% of token labels differ from Shiki, though this measures agreement with Shiki rather than objective correctness. In benchmarks against Shiki 4.4.3, Prism.js, Highlight.js, Sugar High, and Starry Night, it highlighted 10 concatenated copies of three.min.js (5.56M characters) about 10x faster on an Apple M4 Pro in Chrome 152. The author frames it as an experiment, not a grammar-equivalent highlighter.

Hacker News · security · 7d agoAI tools & infraHN 21↑ · 5 comments1

Fly Language Model (FLM) Wires the Full Fruit Fly Connectome Into a Frozen 1.2B LLM, and Its Own Controls Show the Wiring Does Not Help

Researchers wire the full fruit fly connectome (166,700 nodes) into a frozen LiquidAI LFM2.5-1.2B LLM, but controls show no fly-specific benefit.

The Fly Language Model (FLM) couples the complete MaleCNS v1.0 fruit fly connectome (166,700 nodes, 25,582,938 edges) to a frozen LiquidAI LFM2.5-1.2B-Instruct backbone, training only a 278,528-parameter readout (~0.0238% of backbone parameters). The fly readout improved NLL by 0.0222 nats/token (perplexity 3.98 to 3.90) on 32 SmolTalk dialogues, but a direct-input control without the graph beat it in all three seeds. Relabeling node identities removes the gain and the recurrence contracts state differences by 0.6 per token, so the connectome adds no long-range memory. The MIT-licensed code runs locally on Python 3.12, but study artifacts remain private, limiting independent reproducibility.

MarkTechPost · 4d agoAI research1

Show HN: LLM Attention Visualization

A developer released a browser-based tool that visualizes which past tokens influence each LLM output token using aggregated, value-weighted attention scores.

A Show HN project presents a React application built on Transformers.js that renders per-token attention influence by aggregating attention weights scaled by value-vector magnitudes across all attention heads and layers. To expose internal tensors, the author instrumented the ONNX computation graph, hosted a modified model on Hugging Face, and pre-generated prompts to avoid long model downloads in the browser. Demos with a 600-million-parameter model show how verbatim copying draws heavily on source tokens and how single outputs blend information from multiple phrases.

Hacker News · AI · 8d agoAI tools & infraHN 41↑ · 9 comments

Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety

Unit 42 research shows LLM safety refusals concentrate in a thin neural layer, motivating external, multi-layered AI security controls.

Palo Alto Networks Unit 42 introduces Perturbation Probing, a diagnostic technique for measuring the fragility of LLM safety mechanisms. The research finds that safety refusal behavior is localized within a thin neural layer, implying small perturbations can undermine built-in refusals. The authors argue this motivates external, multi-layered security defenses on top of model-internal safety training.

Palo Alto Unit 42 · 19d agoAI safety & security

unsloth/Qwen3.8-Flash-Next-GGUF — new model trending #21 on Hugging Face

Qwen released Qwen3.8-Flash-Next, an experimental 125B-parameter open-weight MoE previewing the Qwen4 architecture, with Unsloth shipping optimized GGUF quants.

Qwen released Qwen3.8-Flash-Next, an experimental open-weight preview of the architecture planned to underpin Qwen4. The model has 125B parameters with 6B activated, 512 experts (10 routed plus 1 shared), Qwen Sparse Attention (QSA), Gated DeltaNet, Gated Residual, and n-gram embeddings, with 262,144-token native context extendable to 1,000,000 tokens. Unsloth provides Dynamic 3.0 GGUF quantizations, and multi-token prediction (MTP) delivers 1.3-1.7x faster inference via llama.cpp or Unsloth Desktop.

Hugging Face trending models · 21d agoModel release1

Another Blueprint In The Wall: How to Ask Frontier AI Like a Kid?

Six frontier models from OpenAI, Anthropic, xAI, and Google DeepMind converge on one imagined successor architecture when asked under a school-audience framing.

Researchers ran ten independent sessions per model type across six frontier models using a three-stage prompt sequence progressing to a full ASCII backbone architecture. Under school-audience framing, responses repeatedly converged on a shared motif including persistent latent state, adaptive computation, memory, specialist routing, verification, and stopping control, while control runs without the framing produced heterogeneous responses. A GPT-5.6 Sol output closely overlapped an architecture independently sketched by GPT-6 Astra, raising questions about shared design priors or motif propagation between model families. The paper coins 'epistemic jailbreak' for the observed loss of provenance discipline as prompt specificity increases.

Hugging Face daily papers · 4d agoAI research 10 sources

MaxKernel: Agentic Kernel Generation for TPUs

Researchers open-source MaxKernel, a multi-agent LLM system that generates and optimizes TPU kernels matching expert hand-tuned baselines on JaxBench.

MaxKernel is a multi-agent system offering three paradigms for TPU kernel development: human-in-the-loop collaborative design, a fully autonomous metric/trace-driven optimization loop, and graph-based autonomous search for global exploration. All paradigms draw on a shared pool of specialized sub-agents for planning, implementation, self-debugging, testing, and hardware profiling. Evaluated on JaxBench's 50 diverse TPU kernel tasks and real-world workloads from open-source models, it consistently matches expert hand-tuned baselines. The system is open-sourced via the AI-Hypercomputer GitHub repository.

Hugging Face daily papers · 14d agoAI tools & infra

Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMs

Fortunate Recall introduces ontology-based lifecycle policies for LLM memory, cutting confabulation roughly in half (e.g., 45.1% to 22.4%) versus Mem0.

Fortunate Recall (FR) is a composable policy layer that classifies personal facts into a 10+1 behavioral ontology and applies category-specific lifecycle rules including differential temporal decay, slot-key supersession, event-time validity, and retrieval routing. FR-Bank scores 76.9% on the new 516-question LifecycleBench, ahead of Mem0, A-MEM, Memory-R1, and MemoryOS (61%-70.5%), and 75.2% on LongMemEval-S. End-to-end, confabulation drops from Mem0's 45.1% to 22.4% over answered queries, with the ranking replicating on open-weight Kimi K2.5 and transferring to the independent BEAM benchmark (46.8% vs 32.9%).

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research

An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics

Open post-training pipeline turns Nemotron 3 Ultra checkpoints into an IMO 2026 gold-medal system, scoring 30/42 without formal provers or external tools.

Starting from Nemotron 3 Ultra, researchers trained two specialist checkpoints using supervised fine-tuning and reinforcement learning for natural-language olympiad proof generation. Three checkpoints power an iterative generate-verify-refine search plus a separate high-compute selection stage, operating entirely in natural language with no formal prover, external tools, or internet access. The system scored 30 of 42 points at IMO 2026, reaching the gold-medal threshold. The release includes the post-trained checkpoints, training data, training and inference code, submitted solutions, and Nemotron-IMO-Bench with 200 novel olympiad-level problems.

Hugging Face daily papers · 8d agoModel release