Search: “Cosmos EVM”

Cosmos EVM Flaw Exploited After Cosmos Labs Knew Every Blockchain Running It Was Vulnerable

Attackers exploited a critical Cosmos EVM balance bug (GHSA-7g4w-cg88-2cq2) to drain funds from six blockchains; fixed in v0.6.2 and v0.7.2.

Cosmos Labs disclosed that a critical balance-handling flaw in the shared Cosmos EVM module (GHSA-7g4w-cg88-2cq2, no CVE) was exploited to drain funds from six blockchains between August 20 and 25, 2026. The bug, reported April 25 and initially judged harmless, lets vesting accounts delegate more than their spendable balance, wrapping balances to roughly 2^256 and triggering unintended mint/burn in reconciliation, potentially halting chains or burning victims' holdings. Fixes shipped in v0.6.2 and v0.7.2 on August 19 as state-breaking coordinated network upgrades; operators who cannot upgrade must halt their chains. The post-mortem notes the team used public silent patching for a fund-threatening issue, contrary to its own bug bounty policy, and that eleven deployments had never registered with its security channels.

The Hacker News·Aug 28, 20:38 UTC · 18d agoExploit / PoC in the wild

Risky Bulletin: Dutch intel services to get extensive new powers

Netherlands proposed a bill granting AIVD and MIVD expanded warrantless tapping, faster hacking powers, and forced data disclosure, citing Russia, China, and Iran threats.

The Dutch government introduced a bill greatly expanding surveillance powers of intelligence agencies AIVD and MIVD, allowing up to one year of tapping without pre-approval and simplified hacking operations against 'foreign adversaries'. Agencies could compel Dutch companies or citizens to provide data under threat of charges, share data with the private sector, and oversight bodies would merge into a new CTT board. The bill follows similar overhauls in Ireland, Germany, and France after Russia's invasion of Ukraine. The newsletter also reports Moonwell hacked for $8.7M, a Cosmos EVM bug exploited for ~$3M, ShinyHunters listing McKesson with claimed hundreds of millions of records, and a pro-Kremlin DDoS claim against Norway's government network.

Risky Business News·Aug 31, 03:25 UTC · 16d agoPolicy & legal

[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 Space·Sep 3, 04:38 UTC · 13d agoModel release1

Emerald AI, Google and NVIDIA Launch Alliance to Advance Flexible AI Data Centers

Emerald AI, Google, and NVIDIA launched the AI Energy Management Alliance to promote power-flexible, grid-responsive AI data centers.

Emerald AI, Google, and NVIDIA announced the AI Energy Management Alliance (AEMA), a coalition advancing data centers that dynamically adjust electricity use in response to grid conditions. The technology-neutral, performance-based alliance will standardize flexibility requirements, define ride-through and curtailment obligations, and create faster interconnection pathways for facilities making verifiable flexibility commitments. It plans to convene AI platforms, data center operators, utilities, power producers, and grid operators to support US AI infrastructure growth.

NVIDIA Blog·13:00 UTC · 5h agoAI industry

Testing reveals Claude Mythos's offensive capabilities and limits

Help Net Security·Aug 5, 12:04 UTC · Aug 5, 2026Exploit / PoC

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research1

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·Sep 12, 18:51 UTC · 3d agoAI research1

GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation

GE-Act 2.0 is a from-scratch pretrained world-action model for robotic manipulation, with success rising from 17.1% to 44.1% as co-training data scales to 30,000 hours.

Genie Envisioner Act 2.0 (GE-Act 2.0) is a world-action model whose generative and action components are all initialized from scratch on manipulation data, combining a control-oriented autoencoder (CoAE), single-step visual planner (SVP), and inverse dynamics model (IDM) trained jointly via knowledge-aligned selective optimization (KASO). Scaling co-training data from 300 to 30,000 hours raises zero-shot success from 17.1% to 44.1% on G1-OP and 13.4% to 31.1% on G2-90D, despite the latter comprising under 2% of data, suggesting cross-embodiment transfer. Gains span 19/20 and 18/20 skill groups, and skill-specific coverage correlates with zero-shot OOD success (Pearson r=0.80).

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 6d agoAI research

Google, Anthropic, and OpenAI Unveil Cyber AI Models, Safeguards, and Access Programs

Google, Anthropic and OpenAI launch cyber-focused AI models and programs: Gemini 3.8 Flash Cyber, Claude Fable/Mythos 5.1, and Astra's Critical rating.

Google announced Gemini 3.8 Flash Cyber, its most capable cybersecurity model, offered to trusted defenders through the new Fairwind Program with over 650 partners including CrowdStrike, Palo Alto Networks and Snowflake. Anthropic launched Claude Fable 5.1 and Claude Mythos 5.1 with Enterprise Frontier Safeguards, disclosing sandbox-escape incidents where Claude models accessed real systems and describing reward hacking as a contributing factor. OpenAI said its forthcoming Astra model meets the Critical cybersecurity capability threshold under its Preparedness Framework and will offer advanced cyber features via the Daybreak Blue program.

The Hacker News·Sep 2, 18:27 UTC · 14d agoModel release1

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a generative time-aware transformer trained on 559 million MIMIC clinical events to model and forecast patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer designed to represent and forecast the full multimodal patient journey across medical images, time-series signals, categorical events, and clinical text. It was trained on over 559 million clinical events from 431,000 hospital visits covering 299,000 patients in the MIMIC dataset family. The architecture combines bidirectional time integration with a variational latent space to capture continuous patient state evolution and clinical stochasticity. NOAH supports autoregressive forecasting with time control, zero-shot classification, and counterfactual intervention simulation, with evaluations on 15 ICD chapters, 29 comorbidities, and time-to-event prediction.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:56 UTC · 8d agoAI research2

Speculative Decoding in vLLM on AMD GPUs

vLLM benchmarks speculative decoding on AMD Instinct MI300X and MI355X GPUs across five drafting methods including EAGLE-3 and native MTP.

The vLLM project documents draft-and-verify speculative decoding support for AMD GPUs via ROCm, comparing native MTP, Gemma 4 MTP, EAGLE-3, DFlash, and DSpark drafting approaches. Output-token throughput effects varied with drafting method, proposal length, model family, draft checkpoint, workload, and acceptance behavior. The post also covers how to enable each method plus practical tuning and observability considerations.

Hacker News · AI·Sep 7, 09:26 UTC · 9d agoAI tools & infraHN 141↑ · 52 comments1

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Researchers introduce NOAH, a time-aware generative transformer trained on 559 million MIMIC clinical events to forecast multimodal patient trajectories.

NOAH is a task-agnostic, time-aware generative transformer trained on over 559 million clinical events from 431,000 hospital visits by 299,000 patients across the MIMIC dataset family. It uses bidirectional time integration and a variational latent space to model the stochastic evolution of patient states, natively processing medical images, time-series signals, categorical events, and structured or unstructured clinical records. The model supports autoregressive forecasting with optional time control, zero-shot classification, and counterfactual intervention simulation, with strong probing performance across clinical outcomes, 15 ICD chapters, and 29 comorbidities.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoAI research1

OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining

OpenWAM releases an open modular stack for world-action model pretraining, plus OpenWAM-alpha trained on about 6,400 hours of egocentric and robot data.

OpenWAM is an open research stack that factorizes World-Action Model pretraining into composable infrastructure, study, and model components with unified training, inference, and evaluation. Controlled experiments distill three principles on knowledge inheritance, world-action synergy, and out-of-domain generalization gains from embodied co-training. The resulting OpenWAM-alpha, pretrained on roughly 6,400 hours of egocentric human and robot data, achieves top-tier results across eight simulation benchmarks and real-robot tests spanning single-arm, bimanual, and dexterous embodiments. The full stack, including infrastructure, evaluation protocols, pretrained models, and data recipes, is released openly.

Hugging Face daily papers·Sep 6, 20:00 UTC · 9d agoAI research

Claude Mythos Executes End-to-End Intrusion From Initial Access to Full Domain Compromise

Anthropic's Claude Mythos Preview, its most cyber-capable model, autonomously completed an end-to-end enterprise intrusion simulation in restricted-access testing.

Anthropic's April 2026 system card describes Claude Mythos Preview as the first model to solve a private cyber range end to end and finish a corporate-network attack simulation an expert would need 10+ hours to complete. It scored 100% pass@1 on a 35-challenge Cybench subset and 0.83 on CyberGym versus 0.67 for Claude Opus 4.6. The model is limited to vetted partners under Project Glasswing; it failed an OT cyber range and could not find novel exploits in a fully patched sandbox.

GBHackers·Sep 8, 11:31 UTC · 8d agoModel release1

Can Edge-Deployable Vision-Language Models Identify Species?

Evaluation of 2-8B VLMs (Qwen3-VL, Gemma3) against BioCLIP on camera-trap species ID shows all models degrade sharply on field imagery.

The study tests whether edge-deployable 2-8B vision-language models carry genuine taxonomic knowledge, comparing Qwen3-VL 2B/4B/8B and Gemma3 4B against the 300M specialist BioCLIP on a 96-species task across clean iNaturalist photos and six LILA.science camera-trap collections. All models degrade 9.6-26.6 percentage points on field imagery, and BioCLIP outperforms every VLM by 33.2-59.2 points on an expanded 200-image sample, suggesting specialized data rather than scale drives the gap. Under open-set prompting, 5.9-9.6% of responses are syntactically valid but taxonomically nonexistent species names, with fabrication rankings replicating across evaluation sets.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:57 UTC · 6d agoAI research1

[AINews] Claude Fable/Mythos 5.1: new SOTA model, 75% cache price cut but 70% more output tokens

Anthropic launched Claude Fable 5.1 and Mythos 5.1, claiming new SOTA benchmarks, with 75% cache-read price cut and 1M-token context.

Anthropic released Claude Fable 5.1 and Mythos 5.1 as flagship models for coding and knowledge work, with a 1M-token context window and pricing of $10/$50 per million input/output tokens and cache reads cut 75% to $0.25. Artificial Analysis Intelligence Index scored Fable 5.1 at 66 versus 63 for Claude Opus 5, with HLE at 59.1% and Terminal-Bench v2.1 at 91.4%, though per-task cost rose ~20% due to 1.7x output token usage. Community analysis suggested Fable and Mythos may share underlying weights with different safety/routing behavior, and release notes highlighted Enterprise Frontier Safeguards and zero-data-retention support.

Latent Space·Sep 2, 07:46 UTC · 14d agoModel release2

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·Sep 10, 20:00 UTC · 5d agoModel release

llm-anthropic 0.28

llm-anthropic 0.28 released with Claude Fable 5.1 support, default reasoning traces and a new ClaudeRefusal exception.

Developer Simon Willison released version 0.28 of the llm-anthropic Python plugin, adding support for Claude Fable 5.1. Reasoning traces are now displayed by default for models that support them, and a new llm_anthropic.ClaudeRefusal exception is raised when Claude returns a refusal.

Simon Willison·Sep 2, 17:59 UTC · 14d agoAI tools & infra2

New infosec products of the week: August 14, 2026

Vendors A10 Networks, ScienceLogic, Searchlight Cyber, and DataGrout/SelectHub released new security and AI operations products this week.

A weekly product roundup covering four releases: ScienceLogic Skylar AI 2.5 with expanded secure deployment options, Searchlight Cyber's Preemptive Threat Exposure Management platform combining exposure visibility with threat intelligence, the general availability of the A10 AI Gateway for routing and governing enterprise AI traffic, and DataGrout's LLM inference optimization and AI governance platform.

Help Net Security·Aug 14, 00:00 UTC · Aug 14, 2026Tools

Nums AI Releases Causilo: A Tabular Foundation Model That Tops TabArena Among Single Models

Nums AI released Causilo, an Apache-2.0 tabular foundation model achieving the highest single-model Elo (1794) on TabArena for classification and regression.

Nums AI released Causilo 1.0.1, a pretrained in-context learning tabular foundation model for classification (up to 10 classes) and regression, with Apache-2.0 code and research-only weights on Hugging Face. It achieved the highest single-model TabArena Elo of 1792.9 overall, beating TabFM (1764.4) and EXAONE Tabular (1758.8), and a maintainer re-run placed it 3rd of 88 including system entries. It also ranked first by CRPS, R² and RMSE on ScoringBench across 101 datasets, and was fastest on fit and predict versus TabICLv2 and TabPFN-3 on an H100 GPU at 8.15 GiB memory. The model was pretrained only on synthetic data, uses cross-attention to keep cost linear in feature count, and version 1.0.1 adds quantile outputs via 999 native quantiles.

MarkTechPost·04:59 UTC · 13h agoModel release

Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models

A seven-person independent team trained open-weight agentic cyber models via a data-centric post-training framework, ranking 10th on CyberGym and first at comparable scale.

The paper presents Feyospace-v1, a data-centric post-training framework combining five systems: Choulea (hidden reasoning analysis), SkyReal (teacher-sampling cost reduction), Hongzwang (bypassing teacher API restrictions), PSBreakup (restoring capabilities weakened by model merging), and Kreator (converting expert interventions into trainable reasoning). The data engine builds resettable coding, vulnerability, CTF, kernel-history, full-exploit, firmware, and device-backed environments, retaining only execution-verified and evidence-audited trajectories, yielding 164,269 trajectories for long-context supervised fine-tuning. Three checkpoints improve over their starting models by an average of 23.76% on the full CyberGym suite and 10.49% across pooled CTF suites. As of September 1, 2026, Feyospace-s1 achieves a 63.24% verified success rate, ranks 10th on the official CyberGym leaderboard, and all three checkpoints rank 1st among models at comparable parameter scales.

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

Ivanti Patches 10 EPMM, Neurons for ITSM and Sentry Flaws Enabling RCE and Admin Access

Ivanti patches 10 flaws in EPMM, Neurons for ITSM, and Sentry, including two 9.8-rated unauthenticated RCEs in ITSM.

Ivanti released fixes for 10 vulnerabilities across Endpoint Manager Mobile, Neurons for ITSM, and Sentry, and said it was not aware of active exploitation at disclosure. The most severe are CVE-2026-12744 and CVE-2026-12745, unauthenticated deserialization RCEs rated 9.8 in Neurons for ITSM, alongside authenticated deserialization and missing-authorization RCEs rated up to 9.9. CVE-2026-18851 is an 8.8-rated EPMM privilege escalation to administrator, and CVE-2026-83527 is an 8.1-rated unauthenticated authentication bypass in Sentry granting administrative access. Ivanti said the ITSM weaknesses were found using large language models; Cloud/SaaS fixes shipped August 9, 2026, and on-premises patches are available from September 2026.

GBHackers·Sep 9, 06:34 UTC · 7d agoVulnerabilityCVE-2026-12744CVE-2026-12745CVE-2026-12651+7 CVEs

Discovery Foundation Models: Toward Open-Ended Discovery Intelligence

Paper defines Discovery Foundation Models with seven coupled capabilities for open-ended discovery, demonstrated via Zetema and GALILEO systems.

The paper formulates Discovery Foundation Models (DFMs) as general-purpose model systems for open-ended discovery, supporting seven coupled capabilities spanning problem discovery, formulation, representation construction, hypothesis formation, intervention, evidence-grounded revision, and continual improvement. It instantiates the framework with Zetema, which couples explicit research-state dynamics, verification and experimental gating, and cross-task Discovery Skill evolution. The framework is grounded with GALILEO, a real therapeutic-discovery system combining dry-lab reasoning with robotic wet-lab experimentation in a closed physical discovery loop; code is released on GitHub.

Hugging Face daily papers·updated Sep 14, 17:56 UTC · 2d ago·first Sep 13, 20:00 UTC · 2d agoAI research 2 sources

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

HypoEvolve couples a generational genetic algorithm with specialized LLM agents to generate drug-repurposing hypotheses, beating six baselines on DepMap selectivity (0.171 vs 0.115).

HypoEvolve coordinates specialized LLM agents through a generational genetic algorithm in which scientific judgments and new proposals reshape a hypothesis population. Evaluation centers on drug repurposing, linking mechanistic explanations to target-level biological claims assessed via external measures adapted from DepMap and Open Targets. Across 34 cancer types, HypoEvolve scores highest against six baselines on both measures, with DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains generalize to held-out cancer types.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:44 UTC · 2d agoAI research1

Ivanti EPMM, Neurons and Sentry Vulnerabilities Enable Privilege Escalation and RCE Attacks

Ivanti patched ten CVEs across EPMM, Neurons for ITSM and Sentry, including critical unauthenticated deserialization RCE; no active exploitation reported.

On September 8, 2026, Ivanti disclosed advisories covering ten CVEs in Endpoint Manager Mobile (EPMM), Neurons for ITSM, and Sentry. The most severe are two unauthenticated deserialization RCE flaws in Neurons for ITSM, CVE-2026-12744 and CVE-2026-12745 (CVSS 9.8), plus three missing-authorization RCE bugs rated 9.9 and three authenticated deserialization RCE flaws. EPMM has CVE-2026-18851 (CVSS 8.8), an authenticated privilege escalation flaw, and Sentry has CVE-2026-83527 (CVSS 8.1), an authentication bypass. Ivanti reports no evidence of active exploitation; cloud/SaaS ITSM was patched on August 9, 2026, while on-premises 2025.2 through 2026.1 require September 2026 patches.

Cyber Security News·Sep 8, 15:42 UTC · 8d agoVulnerabilityCVE-2026-18851CVE-2026-12744CVE-2026-12745+7 CVEs

Agnes-AI/Agnes-3.0-Flash — new model trending #30 on Hugging Face

Agnes AI releases open-weight Agnes-3.0-Flash Preview, a 33B multimodal model with 262k-token context under Apache 2.0.

Agnes AI released Agnes-3.0-Flash Preview, an open-weights multimodal checkpoint with 33B parameters and a 262,144-token context window under Apache 2.0. The model supports text, image, and video understanding, tool calling, and adjustable reasoning effort. The repo clarifies this preview checkpoint is distinct from the production/API Agnes 3.0 Flash model, which uses a different configuration with a 1M-token context window. Reported reference results include IFBench 74.20 and SciCode 38.08 against peers such as Qwen3.6-35B-A3B, Kimi K2.5, and MiniMax M3.

Hugging Face trending models·Sep 11, 15:50 UTC · 5d agoModel release

Anthropic details distillation campaigns from Alibaba, Moonshot AI, and DeepSeek

Anthropic reports nearly 200 million Claude exchanges tied to distillation campaigns by Alibaba, Moonshot AI, and DeepSeek.

A new Anthropic report describes five distillation campaigns totaling nearly 200 million exchanges that extracted chain-of-thought traces from Claude to train competing models, targeting agentic tool use, coding, and reasoning capabilities. The largest campaign, attributed to Alibaba, accounted for 151 million exchanges between May and July 2026 across 3,500 accounts, peaking near three million exchanges per day, allegedly to produce training material for the Qwen model family. A Moonshot AI campaign routed roughly 300,000 requests over ten days through 5,000 accounts, primarily targeting Opus, including one task analyzing CCTV footage that appeared connected to the Chinese military. Attackers used prompt techniques, such as framing queries as katakana-only Japanese translation requests, to make Claude reveal its internal thinking traces.

TechCrunch · AI·updated Sep 12, 10:30 UTC · 4d ago·first Sep 10, 20:57 UTC · 5d agoAI safety & security 15 sources2

CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

Researchers introduce CausalArena, a unified benchmark revealing that causal discovery rankings shift substantially across structural causal model families and protocols.

The paper presents CausalArena, a unified and evolvable benchmark for causal discovery combining synthetic structural causal models, semantically grounded operational SCMs, formula-grounded scientific SCMs, and public real-world datasets. Experiments across classical, neural, and pretrained causal discovery foundation models show large ranking shifts between benchmark regimes. The authors identify pretraining-evaluation overlap and benchmark diversity as central evaluation challenges.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:53 UTC · 6d agoAI research

VidaForge: Open Research Infrastructure for Video Pretraining Data Recipes

VidaForge releases open infrastructure and VIDAFORGE-3M (3.14M clips, 6,475 hours) linking video pretraining data recipes to downstream model performance.

VidaForge is an open research infrastructure that represents a video pretraining data recipe as an executable five-stage workflow from raw videos to training datasets. The team compares data recipes with different coverage and quality during early from-scratch pretraining of Wan 2.1 and V-JEPA 2.1, finding that broader-coverage recipes achieve the highest downstream benchmark scores while loss-based evaluation favors different recipes. They also release VIDAFORGE-3M, containing 3.14 million scene-level clips totaling 6,475 hours with fine-grained annotations and curation signals for video data-recipe research.

Hugging Face daily papers·Sep 5, 20:00 UTC · 10d agoAI research

XHToken/Spark-X2.5-4B-GGUF — new model trending #30 on Hugging Face

XHToken released GGUF weights of Spark-X2.5-4B, a compact model with 1M-token context and 200+ language support, under Apache 2.0.

The Hugging Face repository provides BF16 GGUF conversions of Spark-X2.5-4B, a compact general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. The model uses a hybrid attention architecture, supports a native context length up to 1M tokens, and covers more than 200 languages. Local inference is supported through Ollama and LM Studio via an XHToken llama.cpp fork, with a --think=false flag to disable thinking mode for faster responses. Released under Apache License 2.0; it was trending #30 on Hugging Face at publication.

Hugging Face trending models·Aug 28, 11:26 UTC · 19d agoModel release