Search: “litespeed”

32 stories

DomainAll Cyber AIWindow24h 3d 7d 30d AllIn the wild Important only Show duplicates

CategoryAll Vulnerability 23 Ransomware 7 Exploit / PoC 6 Malware 3 Threat actor 3 Data breach 1

SeverityAll critical 28 high 9 medium 6 low 0 info 0

Smart search ranks by meaning as well as keywords (one row per story, last 45 days).

LiteSpeed Enterprise Flaw Could Let One Hosting Account Gain Root Access on a Shared Server

cPanel warns critical LiteSpeed Web Server Enterprise flaw (pre-6.3.7) lets shared-hosting account users gain root access.

A cPanel advisory published September 14 warns that a critical vulnerability in LiteSpeed Web Server Enterprise versions before 6.3.7 lets a low-privilege website user gain root access on shared-hosting servers, bypassing CageFS account isolation. LiteSpeed released version 6.3.7 on September 11, but auto-update may be delayed so admins should force the update manually. No CVE identifier, severity score, workaround, or exploitation information has been published. This is the third LiteSpeed root flaw since May, following CVE-2026-48172 and CVE-2026-54420 in the cPanel plugin, which were both actively exploited and added to CISA's KEV catalog.

The Hacker Newsupdated Sep 15, 12:19 UTC · 1d agofirst Sep 15, 06:52 UTC · 1d agoVulnerability 2 sources CVE-2026-48172 CVE-2026-54420

cPanel LiteSpeed Web Server Vulnerability Allows Shared Server Users to Gain Root-Level Access

Critical LiteSpeed Enterprise flaw fixed in 6.3.7 lets low-privilege shared-hosting users escalate to root and bypass CageFS.

cPanel issued an urgent advisory for a critical privilege escalation in LiteSpeed Web Server Enterprise versions before 6.3.7, allowing a low-privilege shared-hosting account to gain root-level server control and bypass tenant isolation controls including CloudLinux CageFS. Root access would let attackers access other hosted sites, steal databases and credentials, deploy phishing pages, and install backdoors. Administrators are urged to upgrade to 6.3.7 immediately via lsup.sh and to review privileged account activity, cron jobs, SSH keys, and system binaries; no CVE identifier was published.

Cyber Security NewsSep 15, 13:52 UTC · 1d agoVulnerability

PhysStream: Streaming Physics-Grounded Video Generation with Structured Scene Memory and Fine-Grained Motion Control

PhysStream enables mid-generation interactive control of physics-grounded video via structured scene memory and velocity-increment signals, reducing motion distribution distance 33%.

PhysStream is an autoregressive physics-grounded image-to-video model that maintains structured scene memory—positional maps and object tracking maps derived online from previously generated frames—and accepts fine-grained motion control via sparse velocity-increment signals encoding physical quantities. Training runs in two stages: a bidirectional model finetuned with motion-control conditioning, then a causal autoregressive model with structured scene memory. It supports interactive mid-generation control over multi-object tabletop rigid-body scenes, reducing motion distribution distance (FVMD) by 33% and trajectory error by 12% over the strongest baselines. Human evaluators preferred it in over 85% of in-the-wild comparisons.

Hugging Face daily papersupdated Sep 15, 17:55 UTC · 1d agofirst Sep 14, 20:00 UTC · 1d agoAI research 2 sources

Multiple ArtraDownloader Variants Used by BITTER to Target Pakistan

BITTER used three ArtraDownloader variants since 2015 to target Pakistan, China, and Saudi Arabia, deploying BitterRAT and exploiting CVE-2017-11882 in one wave.

Palo Alto Unit 42 documents ArtraDownloader, a previously unreported downloader family used by the suspected South Asian group BITTER, with three variants and roughly 80 unique samples dating back to February 2015. Between September 2018 and January 2019, BITTER used spearphishing documents hosted on compromised Pakistani websites to target Pakistan and, for the first time, Saudi Arabia; one RTF sample exploited the EQNEDT vulnerability CVE-2017-11882. The downloader retrieves BitterRAT over HTTP, uses simple registry keys for persistence, and byte-level string obfuscation. Infrastructure overlap with the previously reported MY24 InPage-exploit payload was also observed.

Palo Alto Unit 42Aug 17, 13:22 UTC · Aug 17, 2026Threat actor in the wildCVE-2017-11882

Philippine Nuclear and Naval Targets Hit by Suspected Chinese Operator

Suspected Chinese-speaking operator breached Philippine nuclear research and naval supplier systems via ownCloud CVE-2023-49103 and WordPress CVE-2024-28000.

Hunt.io found an exposed staging server containing custom Python scripts, logs, and stolen data documenting intrusions against a Philippine nuclear research body and a marine engineering company serving the Philippine Navy. The actor exploited an ownCloud authentication bypass (CVE-2023-49103) using empty-secret pre-signed WebDAV URLs and a LiteSpeed Cache plugin flaw (CVE-2024-28000) to gain WordPress admin access, also guessing passwords against XML-RPC with rockyou.txt. Roughly 9 GB was referenced as stolen from the nuclear agency, including reactor databases, radiation-safety records, and staff passport data; Simplified Chinese labels suggest a Chinese-speaking operator.

Security AffairsAug 29, 09:16 UTC · 18d agoThreat actor in the wildCVE-2023-49103 CVE-2024-28000

Previewing Ultrafast mode: GPT-5.6 Sol at up to 14X the speed

OpenAI previews Ultrafast, an API service tier running GPT-5.6 Sol up to 14x faster via Cerebras at up to 750 output tokens per second.

OpenAI announced a preview of Ultrafast, a new API service tier that runs GPT-5.6 Sol at up to 14 times the speed of standard inference. The tier is powered by Cerebras hardware and delivers up to 750 output tokens per second. The offering targets latency-sensitive developer workloads on OpenAI's API platform.

OpenAI NewsAug 13, 10:00 UTC · Aug 13, 2026AI tools & infra

Critical cPanel Flaw Could Let One Hosting Customer Take Root Control of a Whole Server

cPanel patched critical flaw CVE-2026-65643 letting authenticated hosting customers with addon or parked domain rights execute code as root and fully compromise servers.

cPanel issued patches on August 27, 2026 for CVE-2026-65643, a flaw in domain parking and addon domain functionality in cPanel & WHM that lets an authenticated account holder create arbitrary files and execute code as root, yielding full server control. Fixed builds cover the 11.110, 11.134, 11.136 and 11.138 branches, including WP Squared 11.138.1.7, and auto-update servers receive the fix automatically. No CVSS score, published CVE record, or evidence of exploitation has appeared, and the flaw is absent from CISA's KEV catalog as of August 27. The article also recaps related 2026 cPanel-ecosystem issues, including KEV-listed LiteSpeed plugin flaws and the in-the-wild-exploited Phusion Passenger problem.

The Hacker NewsAug 28, 09:45 UTC · 19d agoVulnerability CVE-2026-65643 CVE-2026-58048 CVE-2026-58047+3 CVEs

Litelm: LiteLLM Without the Bloat

New open-source project Litelm launched on GitHub as a leaner alternative to the LiteLLM multi-provider LLM gateway.

Litelm is a community-published GitHub project positioned as a stripped-down alternative to LiteLLM, a widely used gateway/SDK for routing requests to multiple LLM providers. The post drew modest attention on Hacker News with 45 points and 17 comments. The listing provides no further technical details, benchmarks, or feature specifics.

Hacker News · AISep 11, 18:10 UTC · 5d agoAI tools & infra HN 45↑ · 17 comments2

ownCloud Flaw Exploited to Steal Nuclear Records From Philippine Research Body

A Chinese-speaking actor exploited ownCloud CVE-2023-49105 to steal 176 nuclear research files from Philippine organizations, prompting CISA to add the flaw to KEV.

CISA added CVE-2023-49105 (CVSS 9.8), an ownCloud WebDAV API authentication bypass fixed in version 10.13.1, to the KEV catalog after Hunt.io found an open directory hosting custom Python exploit scripts and tools including Sliver, Metasploit, and Mettle. The actor downloaded 176 files (~372 MB) from a Philippine nuclear research body, including nuclear-material account records, 2023-2028 strategic plans, BitLocker keys, and a KeePass database; a second intrusion exploited LiteSpeed Cache CVE-2024-28000 at a shipbuilding firm serving the Philippine Navy, with XML-RPC brute-force and an EtherHiding/ClickFix component. FCEB agencies must patch by August 30, 2026. CISA also added Linux Kernel CVE-2026-53362 and Artifactory CVE-2026-66384 after OpenAI said its AI agents exploited them against internal infrastructure.

The Hacker NewsAug 28, 15:56 UTC · 19d agoExploit / PoC in the wildCVE-2023-49105 CVE-2024-28000 CVE-2026-53362+1 CVEs1

UC Berkeley Researchers Release CUA-Lite, an Open Platform Unifying Sandboxes, Data, Evaluation and RL for Computer-Use Agents

UC Berkeley's CUA-Lite is an open platform unifying computer-use agent sandboxes, datasets, evaluation and RL; Lite.OSWorld cuts OSWorld memory 4.1 GB to 0.9 GB.

UC Berkeley researchers released CUA-Lite, an open platform placing agents, environments, traces, and training for computer-use agents behind one action space, one LiteSample schema, and one command across desktop, browser, and mobile. Lite.OSWorld reproduces the OSWorld task suite and evaluators in plain Docker containers (0.9 GB RAM vs 4.1 GB, cold start 23.8s, ~4.6× more parallel instances), with scores matching the QEMU/KVM VM across 13 models. The platform claims 30k+ verifiable tasks, 15+ benchmarks, 10+ agents, and 20+ datasets on Hugging Face including Aguvis, OpenCUA, and ScaleCUA. A documented SFT run lifts Qwen3-VL-2B-Instruct mean episode return from 0.138 to 0.237 on the 332-task lite.osworld split.

MarkTechPostSep 6, 06:11 UTC · 10d agoAI tools & infra1

Unlocking Lossless Speedups in LLMs via Discrete Diffusion

Uno pairs autoregressive LLMs with lightweight diffusion weights to draw multiple tokens in parallel, delivering up to 3x lossless speedup without a draft model.

The paper introduces diffusion-augmented LLMs: autoregressive weights trained with the standard next-token objective plus lightweight diffusion weights trained via a Diffusion Distillation phase to emit multiple tokens in parallel. Psi-Spec samplers enable lossless acceleration without the separate draft model required by speculative decoding. The 8B Uno model outperforms the 26B open DiffusionGemma and proprietary Mercury 2 on agentic tool use, coding, and long-context reasoning benchmarks, with up to 3x throughput gains over the base model at all evaluated batch sizes. Code and checkpoints are released publicly.

Hugging Face daily papersSep 2, 20:00 UTC · 13d agoAI research

[AINews] Jev: a “System One Model” that only decides/classifies/routes/scores — >100x faster, >200x cheaper than small frontier LLMs

TypeSafe launches Jev, an RLCD-trained decision model claiming 20-200x faster, 40-400x cheaper classification than frontier LLMs, alongside Gemini 3.8 Live and Neon.

TypeSafe's Jev is a 'System One' decision model trained with RLCD, claiming 20-200x faster and 40-400x cheaper classification and routing than frontier LLMs with free output tokens and no hallucinated text. Google launched Gemini 3.8 Live and 3.8 Live Extended Thinking, supporting 97 languages and async tool calls, debuting #1 on Artificial Analysis' speech-to-speech index at 82.6. Periodic Labs' Neon is a ~1T-parameter XRD analysis model trained with RL on proprietary lab data using 1,300 H200s, lifting FrontierXRD success from 2.7% to 55.3% and beating GPT-6 Astra at lower inference cost.

Latent Space11:09 UTC · 7h agoModel release

PHAT: PHotonic Accelerator for TFHE

PHAT photonic accelerator using optically addressed phase-change memory speeds TFHE bootstrapping FFTs 2.14x-5.10x over the prior ASIC accelerator.

Researchers propose PHAT, an electro-photonic accelerator for TFHE fully homomorphic encryption built on optically addressed phase-change memory (OPCM) processing-in-memory. The architecture adds OPCM-based FFT units, a twiddle-stationary dataflow, and a scheduling mechanism to maximize FFT utilization for TFHE bootstrapping. It achieves 2.14x-5.10x speedups across four real-world TFHE workloads versus the state-of-the-art ASIC accelerator, improving feasibility of privacy-preserving computation in the cloud.

arXiv cs.CRSep 10, 14:30 UTC · 6d agoResearch

LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs

LACE introduces layer-wise compression for dynamic frame rate audio codecs, cutting sequence lengths and speeding TTS inference while preserving quality.

LACE (Layer-Adaptive Codec Encoding) applies an independent compression step at each quantization layer of a neural audio codec, enabling layer-specific segmentation boundaries instead of shared ones. Union alignment and boundary anchor mechanisms keep durations consistent for downstream text-to-speech. On LibriTTS, LACE achieves a better rate-quality tradeoff than prior dynamic frame rate codecs and improves TTS inference efficiency at competitive synthesis quality. Code is released in the ESPnet3 codec recipe.

arXiv cs.AI / cs.LG / cs.CLSep 15, 17:46 UTC · 1d agoAI research1

PACE: Perceived-Latency-Aware Cascading Service Routing and Filler Control for QoE-Efficient Retrieval-Augmented Dialogue Serving

PACE cuts perceived latency in retrieval-augmented dialogue serving via cascading routing and filler control, reaching 0.41s P95 under load.

PACE is a serving framework for retrieval-augmented dialogue that optimizes Perceived Time-to-First-Response (PTFR) under quality and cost constraints. It combines a load-adaptive cascading router, a joint path-filler controller, and volatility-aware cache admission, deployed on a humanoid-robot sales service. On 75k CarQA requests, the cascade halved pure-LLM P95 PTFR (0.29s vs 0.53s) and the adaptive controller reached 0.41s P95, 2.4x better than RAG at high load; filler calls dropped 94% and stale answers fell from 86% to 0%.

arXiv cs.AI / cs.LG / cs.CLSep 9, 16:02 UTC · 7d agoAI tools & infra

Build Low-Latency Multilingual Voice Agents: Open Weights & Full Deployment Control with NVIDIA Magpie TTS

NVIDIA's Magpie TTS open-weight multilingual speech model enables low-latency voice agents with full deployment control.

Hugging Face's blog highlights NVIDIA Magpie TTS, an open-weights multilingual text-to-speech model designed for building low-latency voice agents. The open licensing gives developers full deployment control, allowing self-hosted multilingual speech for agentic applications. The post walks through building voice agents with the model.

Hugging Face BlogAug 10, 16:25 UTC · Aug 10, 2026Model release

OpenAI’s GPT-5.6 Sol runs up to 14× faster with Ultrafast mode

OpenAI launched GPT-5.6 Sol Ultrafast mode in limited preview, running up to 14x faster at 750 tokens per second via Cerebras inference.

OpenAI's GPT-5.6 Sol Ultrafast mode is available in limited preview through the OpenAI API, delivering up to 14x faster processing and up to 750 output tokens per second, powered by Cerebras under the companies' ultra-low-latency inference partnership. Preview customers are testing it in production for coding, commerce, financial research, and support applications. OpenAI is also using Ultrafast internally for incident response tasks such as log analysis and trace review, and for research workflows with multiple same-day experiment iterations.

Help Net SecurityAug 14, 00:00 UTC · Aug 14, 2026AI industry

Kimi K3 (2.8T) at 1 token/s on a MacBook Pro, streamed from four SSDs

A GitHub project streams Moonshot's 2.8T-parameter Kimi K3 from four SSDs to run at 1 token/s on a MacBook Pro.

A GitHub repository (argonautlabsai/deltafin) demonstrates running Moonshot AI's Kimi K3, a 2.8-trillion-parameter model, on a MacBook Pro by streaming its weights from four SSDs at 1 token per second. The project drew 55 points and 22 comments on Hacker News, highlighting consumer-hardware inference for extremely large models.

Hacker News · AISep 8, 20:07 UTC · 7d agoAI tools & infra HN 55↑ · 22 comments

MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes

MetroLLM-Bench is a 955-case benchmark testing language models as transit kiosk tool-calling runtimes across six real metro systems.

The benchmark covers 37-414-station metro systems and eleven task categories including routing, fare calculation, disruptions, accessibility, and adversarial input, with 14 deterministic and 8 semantic scoring components. Of 26 models from six vendors, a PEFT-tuned 4B Qwen 3.5 student scored 91.3 on Tier 1, exceeding GPT-5.6 (90.6/90.0), while Muse Glimmer 30B led the composite ranking. A deterministic rule-based baseline reached 84.6, and PEFT gains over base models shrank from +7.03 points at 2B to -0.91 at 27B.

Hugging Face daily papersSep 8, 20:00 UTC · 7d agoAI research

Google's new AI model predicts the future from sales data, weather, and discount schedules

Google Research released TimesFM-3, a 330M-parameter multivariate time series forecasting model that tops Gift-Eval, FEV-Bench, and Time benchmarks and is on Hugging Face.

Google Research released TimesFM-3, a 330-million-parameter Transformer-based time series forecasting model trained on more than one trillion real and synthetic data points. It works zero-shot and adds multivariate support, ingesting related series, historical-only covariates, and known future events such as discount schedules and weather forecasts, while filling all future time steps in a single one-shot pass. Google reports first place among pretrained forecasting models on Gift-Eval, FEV-Bench, and Time, ahead of Amazon's Chronos-2, the Toto-2.0 family, and its own TimesFM-2.5. Weights are available on GitHub and Hugging Face, with BigQuery integration planned in the coming weeks.

The DecoderSep 12, 09:26 UTC · 4d agoModel release2

PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving

PlannerForge unifies scenario-based testing of autonomous driving motion planners in one LLM-agent framework, outperforming prior baselines.

PlannerForge is an LLM-agent framework that covers the full scenario-based testing pipeline for autonomous driving systems, spanning scenario generation, selection, modification, routing, planner testing, plus new enhancement and benchmarking stages. In evaluations with 10 off-the-shelf LLMs, best-per-task scores range from 0.88 to 1.00, and open-source 20-35B backends such as Qwen3.6:35B match commercial APIs on most tasks. End-to-end chaining retains 83% (commercial) and 78% (open) of seed queries, beats Scenario Factory 2.0 on executable generation, and cost-tuning lifts planner success from 50.4% to 70.2% while cutting collisions from 19.0% to 8.4%.

Hugging Face daily papersSep 7, 20:00 UTC · 8d agoAI research

Google releases Gemini 3.8 Flash, its third Flash model in six weeks

Google releases Gemini 3.8 Flash, topping the DeepSWE coding leaderboard six weeks after 3.7 Flash, alongside the cybersecurity-focused 3.8 Flash Cyber.

Google shipped Gemini 3.8 Flash, its third Flash model in six weeks, placing it at the top of the DeepSWE software engineering leaderboard. The companion Gemini 3.8 Flash Cyber showed a reported 2.6x patch accuracy increase for the Chrome security team and found a critical vulnerability in two hours, but is limited to trusted testers and governments. Gemini 3.8 Flash improved over 3.7 Flash on the OSWorld-2.0 computer-use benchmark yet remains far behind Claude Opus.

Ars Technica · AISep 2, 18:13 UTC · 13d agoModel release1

llm-gemini 0.34

llm-gemini 0.34 adds support for Google's new Gemini 3.8 Flash model with configurable low, medium and high thinking levels.

Simon Willison released llm-gemini 0.34, a plugin for the LLM CLI that adds the gemini-3.8-flash model, including low, medium and high thinking levels, and fixes an issue where async responses failed to record the resolved model version. The release coincided with Google's launch of Gemini 3.8 Flash, plus a Gemini 3.8 Flash Cyber variant restricted to trusted defenders. Willison noted the Flash tier's speed, low cost and competence at HTML and JavaScript generation tasks.

Simon WillisonSep 2, 16:39 UTC · 14d agoAI tools & infra

Malicious LiteLLM Releases Tied to Trivy Hack May Have Exposed 2,100+ Organizations

Malicious LiteLLM 1.82.7/1.82.8 PyPI releases tied to the Trivy TeamPCP campaign harvested cloud, SSH, and database credentials, potentially exposing 2,500+ organizations.

CloudSEK reported that two malicious LiteLLM releases on PyPI (versions 1.82.7 and 1.82.8, live about 40 minutes on March 24) harvested cloud keys, SSH keys, Kubernetes tokens, and database passwords, with captured loot files mapping potential exposure to more than 2,500 organizations including NVIDIA, Cisco, Deloitte, Volkswagen, FedEx, Siemens, and X Corp. The campaign is part of TeamPCP (tracked by Google as UNC6780), linked to the Aqua Security Trivy scanner compromise tracked as CVE-2026-33634 and added to CISA's Known Exploited Vulnerabilities catalog on March 26. The payload used a litellm_init.pth file executed at Python interpreter startup and exfiltrated secrets to models.litellm[.]cloud; the FBI's FLASH-20260702-01 advisory urged rotation of CI/CD, publishing, and cloud credentials.

The Hacker NewsAug 12, 08:05 UTC · Aug 12, 2026Data breach in the wildCVE-2026-33634

Google’s Gemini 3.8 Flash takes on bigger AI models at a lower cost

Google released Gemini 3.8 Flash with a security-focused Cyber variant that produces 2.6x more correct patches and found a critical vulnerability in under two hours.

Google launched Gemini 3.8 Flash for developers and a gated Gemini 3.8 Flash Cyber model reserved for vetted security teams through the new Fairwind program. The company says the model beats most larger frontier models on the DeepSWE v1.1 engineering benchmark at lower cost, and Chrome Security reports the Cyber variant produced 2.6 times more correct patches than the best commercial models while Google's Cloud Vulnerability Research team found a critical foundational vulnerability in under two hours. The models show significant prompt-injection robustness gains measured by Gray Swan and carry CBRN misuse safeguards, with cyber-offense restrictions on the standard version. Pricing matches Gemini 3.7 Flash at $0.75 per million input tokens and $3.75 per million output tokens.

Help Net SecuritySep 3, 00:00 UTC · 13d agoModel release

Jev: New frontier model 40-400x cheaper and 20-200x faster

TypeSafe AI launches Jev, an early-access 'System One' model delivering calibrated structured outputs claimed 40-400x faster and cheaper than LLMs.

TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, released its first 'System One Model' called Jev in early access. Jev forgoes string generation and is trained with Reinforcement Learning for Calibrated Decisions (RLCD) to produce type-safe structured values with calibrated probabilities. The company claims 70-500ms response times (40-200x faster), input pricing of $0.042 per million tokens, and free output tokens via a parallel sampling architecture. Target use cases include AI-powered workflows, real-time applications, and verification/guardrail tasks.

Hacker News · securitySep 15, 19:25 UTC · 22h agoModel release HN 59↑ · 11 comments

llm-openrouter 0.7.1

Simon Willison released llm-openrouter 0.7.1, a performance fix for loading OpenRouter models in his LLM CLI plugin.

Version 0.7.1 of the llm-openrouter plugin addresses a performance problem when loading OpenRouter models in the LLM command-line tool. The fix was contributed by GitHub user waveplate. It is a minor maintenance release with no security implications noted.

Simon WillisonSep 2, 20:23 UTC · 13d agoAI tools & infra

Hoss: Fast Oblivious Semantic Search with Heterogeneous GPU-CPU-TEE Architecture

Hoss uses heterogeneous GPU-CPU TEEs for oblivious semantic search, achieving up to 67x speedup over the Compass ORAM-based system.

Hoss is a first-of-its-kind oblivious semantic search system built on a heterogeneous CPU-GPU TEE architecture, hosting hot-path HNSW graph traversal in large GPU TEE private memory and offloading lower graph layers to CPU TEEs. It invokes oblivious primitives only for lower-layer accesses and adds host-access ORAM and data-dependent optimizations. Benchmarked against prior state-of-the-art Compass, the prototype achieves up to 67x speedup while maintaining high recall, with larger gains at scale.

arXiv cs.CRSep 3, 22:21 UTC · 12d agoResearch

Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model

Cadence pairs Google's 330M-parameter TimesFM-3 foundation model with adaptive arithmetic coding, gaining 13-28% on 2026 demand series over classical predictors.

Cadence is an error-bounded lossy compressor for numeric time series combining the 330M-parameter Google TimesFM-3 foundation model with an adaptive arithmetic coder, guaranteeing a per-sample error bound. On 49 EIA-930 balancing-authority demand series from 2026 it gains 13.3% over the best of six classical predictors and 28.3% on 50 MTA ridership series, winning all 297 series-tolerance pairs with a 21.4% median gain. The paper also reports negative results, including that foundation models add negligible value for lossless coding and that PyTorch predictions are not bit-identical across batch sizes.

Hugging Face daily papersSep 4, 20:00 UTC · 11d agoAI research1

[AINews] Muse Spark 1.3 matches GPT-5.6-Sol, confirming Meta Superintelligence as the newest Frontier Lab, >90% discount for training

Meta's Muse Spark 1.3 reportedly ranks as the world's #3 model, matching frontier models from OpenAI and Anthropic with planned open weights.

The Latent Space AI News roundup leads with Muse Spark 1.3, promised in Zuckerberg's letter, which ranks #3 worldwide per AAII, is slated for open weights, and uses a pricing model over 90% cheaper when users opt in to training. The issue also covers the rumored Gemini 3.8 Flash launch and analysis arguing OpenAI's rumored looped-transformer 'Astra' architecture is a modest tweak rather than a breakthrough. Additional coverage includes ByteDance Seed's HarnessDev harness-evaluation benchmark, a retrieval-invoked actual-use evaluation method, Stanford's revamped agent engineering curricula, and Photon 2.1 adding TTS models and NVIDIA B200 support.

Latent SpaceSep 3, 04:38 UTC · 13d agoModel release1

Qwen-Drive 1.0 tells you why it brakes, just don't expect the explanation to match the maneuver

Alibaba's Qwen-Drive 1.0 adds 3D perception and planning modules to Qwen3.5-4B for driving tasks, though explanations often mismatch maneuvers.

Qwen-Drive 1.0, built on Qwen3.5-4B, combines spatial perception, traffic question answering, and route planning in one vision-language model, adding a bird's-eye-view perception module and a Planning Expert trained via staged fine-tuning and reinforcement learning. The paper finds text-image models do not inherently grasp 3D space; spatial accuracy only improved when the base vision-language model itself was trained on spatial tasks, while avoiding catastrophic forgetting of general knowledge. The cut reinforcement learning-trained version halved road-departure rate in simulation from 24% to 12%, and the model beats specialized driving models in most of Qwen's benchmarks, but its explanations sometimes conflate causes like distant red lights and crossing children, and results partly rest on self-designed tests. The work follows prior findings from PaLM-E and a UC Santa Cruz adversarial sign attack on DriveLM showing VLM driving models' reasoning and spatial gaps.

The DecoderSep 7, 12:15 UTC · 9d agoAI research

Introducing Gemini 3.7 Flash

Google DeepMind announced Gemini 3.7 Flash, a new Flash-tier addition to its Gemini model family for fast, cost-efficient workloads.

Google DeepMind introduced Gemini 3.7 Flash via its official blog. The release adds a new Flash-tier model to the Gemini family; Flash tiers typically target low-latency, cost-efficient inference. The announcement text provided no additional benchmark or capability details.

Google DeepMindAug 13, 17:04 UTC · Aug 13, 2026Model release