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ACEA: An Adversarial Co-Evolution Arena for Head-to-Head Red-Team and Blue-Team LLM Testing

ACEA is a pluggable arena scoring LLM red-team attackers and blue-team defenses head-to-head with an LLM judge and verifiable leakage ground truth.

ACEA connects pluggable red- and blue-team adapters to a shared target LLM through the model-agnostic ASAP HTTP protocol and scores attack and defense rates per adversarial round. Canonical seeded secrets provide verifiable ground truth that separates real leakage from hallucination, and attacks are delivered to the target even when blocked to measure raw potency. The platform adds real-time battle visualization, failure-localizing end-of-battle reports, and an optional in-context improvement loop that feeds advisory hints between rounds.

arXiv cs.CR · 8d agoAI safety & security1

Thinking of ACE? We Can Do It with Fewer Tokens

IBM Research's ALTK-EVOLVE-SLDD blog post claims ACE-style LLM analysis using substantially fewer tokens.

IBM Research published a Hugging Face blog post titled 'Thinking of ACE? We Can Do It with Fewer Tokens' describing ALTK-EVOLVE-SLDD. The method targets reproducing ACE (attribution-based confidence estimation) capabilities in LLMs while consuming far fewer tokens. No article text was available, so details beyond the title are limited.

Hugging Face Blog · Aug 11, 2026AI research

Salesforce and Nvidia’s new reasoning model is everything the AI labs should fear

Salesforce and Nvidia launch Koa, Salesforce's first reasoning model, built on Nvidia's open-weight Nemotron and post-trained on synthetic sales and support data.

Salesforce announced Koa at Dreamforce, its first reasoning model, built on Nvidia's open-weight Nemotron and post-trained with synthetic data mimicking sales and customer-support scenarios rather than real customer data. Koa will be offered through the Agentforce platform's AI gateway as a cheaper, token-efficient alternative to closed frontier models like Claude and ChatGPT for enterprise tasks. Salesforce simultaneously announced a ClaudeForce partnership with Anthropic keeping customer data inside Salesforce's infrastructure.

TechCrunch · AI · 1d agoModel release1

‘Now We Can Know Everything and Do Anything,’ Jensen Huang Says at Dreamforce

Salesforce unveiled Koa, its first CRM reasoning model post-trained on NVIDIA Nemotron 3 Super, announced during Jensen Huang's Dreamforce keynote.

At Salesforce Dreamforce, NVIDIA CEO Jensen Huang joined Marc Benioff onstage as Salesforce announced Koa, its first CRM reasoning model, post-trained from NVIDIA Nemotron 3 Super using NeMo RL, NeMo Gym, and NeMo AutoModel. Koa was fine-tuned on a proprietary synthetic dataset drawn from nearly three decades of enterprise CRM deployments across 14+ industries, with no customer data used in training or inference. On Salesforce's CRM Bench of real-world tasks, Koa matches or exceeds leading model performance on CRM actions with 3x fewer errors. Koa already powers an employee agent in Slack, enters customer pilots in October with Formula 1, UChicago Medicine, Baxter Credit Union, 1-800Accountant, Engine, and Xero, and reaches general availability in Winter 2026 in U.S. regions.

NVIDIA Blog · 21h agoAI industry

Edge0/Edge0-35B-A3B-preview — new model trending #30 on Hugging Face

Edge0 released a 35B sparse MoE model running in under 3 GiB of memory at 15 tok/s via SSD expert offload and int4 quantization.

Edge0-35b-a3b-preview is a 35B-parameter MoE (256 experts, 4 active per token) built on Qwen3.5-MoE 35B-A3B, shipped as a 4-bit checkpoint with LoRA and prerouter adapters under Apache 2.0. The edge0 framework streams expert weights from SSD on demand, bounding peak active memory at 2.9 GiB and achieving 14.9-17.7 tok/s decode on a Mac mini M4 Pro (MLX backend). Recover-LoRA distillation keeps the int4 model within 3.9 points of its fp16 base (79.2 vs 83.2 average on OpenCompass benchmarks including AIME 2026, HumanEval, GPQA-Diamond, MMLU-Pro, and IFBench).

Hugging Face trending models · 8d agoModel release

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 · 5d agoModel release

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 · 7h agoAI industry

CARDEA: Auditable Reasoning Grounded in Spatial Evidence for End-to-End Coronary Angiography Interpretation

CARDEA, a vision-language model trained only on public data, matches cardiologists on coronary angiography complexity assessment while exposing auditable bounding-box evidence.

CARDEA is a unified large vision-language model serving as the inference core of an end-to-end coronary angiography pipeline from multi-view videos to study-level diagnosis. It was trained on public datasets through visual alignment, self-distilled Chain-of-Box cold start, and reinforcement learning with verifiable rewards encouraging bounding-box reasoning. It reached 0.91 accuracy on dominance classification under domain shift and 0.90 on complexity assessment, comparable to two interventional cardiologists. RLVR raised zero-shot report generation vessel-severity macro-F1 from 0.513 to 0.686, while supervised imitation alone did not.

Hugging Face daily papers · 10d agoAI research

CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

CoRA-NAS combines zero-cost proxy ranking with low-cost learning-curve refinement, achieving the best worst-space Spearman correlation across NAS benchmarks.

The paper proposes CoRA-NAS, a two-stage neural architecture search framework pairing a static ranking prior (CoRA-Rank) with learning-curve refinement (CoRA-Refine) that extrapolates early validation curves for sampled anchors and propagates residual corrections with an ExtraTrees model at about 1% of full training cost. Across NAS-Bench-201, NAS-Bench-101, TransNAS-Bench-101, and NATS-SSS it achieves mean Spearman correlations of 0.946, 0.715, 0.786, and 0.894 respectively, with the best worst-space correlation of 0.715 among compared methods. On NAS-Bench-201/CIFAR-100 its selected architecture reaches 73.32% accuracy versus a 73.37% ground-truth best.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

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.

MarkTechPost · 10d agoAI tools & infra1

IFM/K2-Horizon-MoVA-36B-A4B — new model trending #15 on Hugging Face

IFM released K2-Horizon-MoVA-36B-A4B, an open-weights 36B-parameter MoE model with 4B active parameters and native 512K context.

IFM released the final checkpoint of K2-Horizon-MoVA-36B-A4B, a sparse Mixture-of-Experts model using Mixture-of-Values (MoVA) attention with 36B total and 4B active parameters. The model supports native 524,288-token context and reportedly outscores open-weight dense and MoE models up to 15x its size on agentic and reasoning benchmarks, while competing against closed frontier models. Intermediate checkpoints, training data, the training recipe, and training code are slated for public release.

Hugging Face trending models · 14d agoModel release

Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

Rosetta ranks 4th and 5th in AlexandriaX-2026 dialectal Arabic dialogue translation using a LoRA adapter on NileChat-3B, finding limited pretraining benefit.

The Rosetta system for the AlexandriaX-2026 shared task fine-tunes a LoRA adapter on NileChat-3B for context-aware English-to-dialectal Arabic dialogue translation. The adapter was additionally pretrained on MADAR and PADIC dialect corpora for the unconstrained track. It achieved spBLEU 26.10 (4th, constrained) and 25.09 (5th, unconstrained). External dialect pretraining improved only two of thirteen dialects while slightly degrading overall performance, indicating negative transfer.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research1

Rebuilding AUTOMATIC1111 with Gradio Workflow

Hugging Face demonstrates rebuilding the AUTOMATIC1111 Stable Diffusion web interface using its Gradio Workflow framework.

Hugging Face published a post showing how to rebuild the AUTOMATIC1111 Stable Diffusion WebUI experience with the Gradio Workflow framework. The article body was unavailable, so details beyond the title are limited, but the piece appears to be a tutorial on composing interactive AI interfaces with Gradio Workflow components.

Hugging Face Blog · 6d 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

CISA's logging guidance works beyond government

CISA released its Logging Reference Architecture in August 2026 to help federal agencies meet OMB M-26-14 logging requirements, usable as a benchmark by critical infrastructure operators.

CISA's Logging Reference Architecture (LRA), released in August 2026, helps US federal civilian agencies satisfy logging requirements in OMB Memorandum M-26-14 and explicitly encourages critical infrastructure operators to use it as a benchmark. The framework is organized around continuous event monitoring and threat hunting, investigation, response, and forensics, with a federal baseline of six months searchable and one year retrievable logs. Agencies must submit Agency Logging Plans within 90 days and work toward Advanced maturity within 320 days; the guidance also treats AI outputs as derived data requiring human review and preserved metadata.

Help Net Security · 23d agoAdvisory

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 · 15h agoModel release

Jalapeño’s first results show industry-leading speed and efficiency in AI inference

OpenAI's Jalapeño custom inference chip shows faster, more power-efficient inference with higher throughput and lower latency.

OpenAI announced first results for Jalapeño, its custom AI inference chip. The company claims industry-leading speed and efficiency, delivering higher throughput and lower latency for modern models compared with existing options. The chip targets more power-efficient serving of large models at scale.

OpenAI News · 22d agoAI industry

Knowing What Not to Answer: Selective Non-Compliance in Vision-Language Models

Researchers introduce KoNA, a benchmark exposing vision-language models' failures at selective non-compliance, plus fine-tuning that improves refusal and abstention accuracy.

KoNA is a benchmark for evaluating selective non-compliance in vision-language models across five categories: False Premise, Visual Inaccessibility, Universal Unknown, Task Feasibility and Safety. It tests both query-level and component-level non-compliance using paired single and compound queries, and evaluations across diverse VLMs show models often fail to refuse, correct or abstain appropriately, with failures worsening on compound queries. Fine-tuning VLMs on KoNA examples substantially improves non-compliance accuracy while largely maintaining performance on fully answerable tasks.

Hugging Face daily papers · 13d agoAI research1

Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.

DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.

Latent Space · 4d agoModel release 4 sources1

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Hugging Face details building and using multi-vector late-interaction embedding models with Sentence Transformers for retrieval workloads.

Hugging Face published a guide on multi-vector, late-interaction embedding models (ColBERT-style) supported through Sentence Transformers. The post covers how practitioners can build and use these models for retrieval and RAG pipelines. It is a developer tooling and technique write-up, not a security advisory.

Hugging Face Blog · 29d agoAI tools & infra1

ENISA launched the CRA Single Reporting Platform for actively exploited vulnerabilities

ENISA launched the CRA Single Reporting Platform, making EU manufacturers report actively exploited vulnerabilities and severe incidents through one portal.

ENISA switched on the Cyber Resilience Act's Single Reporting Platform on 11 September 2026, the same day CRA reporting obligations became binding on manufacturers. Reports require an early warning within 24 hours, a fuller notification within 72 hours, and a final report within 14 days (one month after notification for severe incidents). Filings go through an EU Login account with MFA, are routed to a coordinating CSIRT chosen by the manufacturer, and no API is available in the first release. Open-source software stewards fall under the same obligations from 11 December 2027.

Help Net Security · 2d agoPolicy & legal

Instacart launches an AI grocery shopping assistant called Clementine

Instacart launches Clementine, a conversational AI grocery assistant that turns chats, lists, or recipes into purchasable carts in the US and Canada.

Instacart launched Clementine, an AI assistant that converts conversations, grocery lists, photos of handwritten lists, or recipes into ready-to-buy carts, now available in the US and Canada. It generates personalized recipes, tailors recommendations to dietary needs such as gluten-free and vegetarian, and surfaces deals and lower-cost alternatives. Uber Eats and DoorDash have launched similar AI assistants as delivery apps compete to become households' default meal-planning tools.

TechCrunch · AI · 7d agoAI industry

Sakana AI Researchers Introduce PC-ALM, a Layer-Local Alternative to Backpropagation That Trains 1000-Layer Networks

Sakana AI's PC-ALM adds per-layer Lagrange multipliers to predictive coding, matching backprop on networks up to 1000 layers with layer-local updates.

Sakana AI researchers propose Augmented Lagrangian Predictive Coding (PC-ALM), a training method that keeps every update layer-local while recovering backprop-aligned credit signals. The team proves multipliers converge to exact backprop adjoints in linear networks and trains 1000-layer residual MLPs on MNIST within about 2 points of backprop accuracy. PC-ALM matched backprop across a width/depth grid from 8 to 128 on MNIST and Fashion-MNIST where standard predictive coding failed in deep, narrow networks, and improved over PC on ResNet-18 with CIFAR-10 and Tiny ImageNet. An MIT-licensed JAX reference implementation reproduces the results on CPU.

MarkTechPost · 1d agoAI research1

Atria Dawn: The Dawn of Agentic Superintelligence

Atria Dawn Preview, an agentic foundation model trained on verifiable experiences, tops five of 16 research and engineering benchmarks.

Atria Dawn Preview is a foundation agentic language model for scientific research and engineering workflows, trained via a Verifiable Experience Pipeline connecting tool-mediated interactions to executable environments and externally verified outcomes. Across 16 benchmarks spanning research, engineering, and digital work it is competitive with frontier agents and achieves the highest reported score on five of them. The release includes a human-AI collaboration case study analyzing 769 task records from 56 participants, where about one-third of completed AI-assisted tasks were rated infeasible without AI and agents frequently proposed methods and implemented revisions while humans retained final decisions.

Hugging Face daily papers · 3d agoModel release

CISA Adds One Known Exploited Vulnerability to Catalog

CISA added CVE-2026-76461, an actively exploited SQL injection in Cisco Secure Email Gateway, to the KEV catalog.

CISA added CVE-2026-76461, a SQL injection vulnerability in Cisco Secure Email Gateway, to its Known Exploited Vulnerabilities (KEV) Catalog based on evidence of active exploitation. BOD 26-04 requires Federal Civilian Executive Branch agencies to prioritize rapid remediation of KEV vulnerabilities on publicly exposed assets and to check whether systems were compromised before patching. CISA encourages all organizations to adopt risk-based vulnerability management and prioritize KEV remediation.

CISA Advisoriesupdated · 6h agofirst · 2d agoExploit / PoC in the wild 17 sourcesCVE-2026-76461

E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning

E2A-Bench, a 969-query financial chart reasoning benchmark, finds VLMs fail evidence-to-action consistency, with fine-tuning amplifying BUY:SELL bias 4-6x.

E2A-Bench is a 969-query benchmark built from 323 HS300 constituents across three input modalities with deterministic OHLCV-derived evidence anchors, evaluating grounding, reasoning-action consistency, evidence-confidence calibration, and directional coverage via UCR, RCI, ECI, and NDR metrics. Testing 20 VLMs showed the lowest-hallucination model ranked near the bottom on coverage with only 6.4% directional coverage, and oracle-aided verification reduced unsupported claims but could collapse coverage. Financial fine-tuning amplified the BUY:SELL ratio by factors of 4.21 to 4.68 across base-fine-tuned pairs.

Hugging Face daily papers · 4d agoAI research

Your Agent Aced the Task. Will It Do It Again?

IBM Research Hugging Face post examines whether LLM agents that succeed at a task once will reliably succeed again.

Hugging Face published an IBM Research blog post titled 'Your Agent Aced the Task. Will It Do It Again?' with URL slug 'altk-evolve-consistency'. No article text was provided, but it appears to address agent consistency and reliability evaluation across repeated task runs. This is relevant to developers building or evaluating LLM agent systems.

Hugging Face Blog · 1d agoAI tools & infra

CUA-Universe: A Scalable and Dynamic Environment for Hybrid GUI+CLI Agents

Researchers introduce CUA-Universe, a pipeline turning real desktop software into hybrid GUI+CLI agent environments, lifting a 9B model's OSWorld success rate.

CUA-Universe is an environment-to-data pipeline that converts real desktop applications into hybrid GUI+CLI environments, scaling to 16 applications via App-Forge, Task-Weave, and Path-Steer. Training on its harvested trajectories shifted a 9B model toward effective GUI+CLI orchestration, yielding +39.3 points on CUA-Verse, +16.8 points success rate on OSWorld, and +7.84 points on OSWorld-MCP while cutting steps and tokens by up to 57% and 60%. The work addresses the scarcity of scalable hybrid environments for computer-use agents.

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