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Same Rule, Different Verdicts: Rubric Language and Clause Structure in LLM Judges for Korean Support Dialogues

arXiv:2611.04091v1 Announce Type: new Abstract: Services that operate in Korean often write their evaluation rubrics in Korean and hand them to an LLM judge. We test whether a judge's verdict depends on the language of the rubric and on how a rubric item is phrased. We write 12 criteria for support dialogues in four clause structures (affirmative, negated, conditional, exception) such that two pairs of structures have the same meaning, and we give each item in Korean and in English. Six low-cost models from three providers, plus one reference model, judge 60 translated ESConv dialogues and 144 constructed boundary cases under every combination (51,744 requests across the seven models). The analysis plan was fixed and committed before data collection. Cross-provider agreement is lower with Korean rubrics than with English rubrics (Cohen's kappa 0.687 vs. 0.745; difference -0.058, 95% CI [-0.072,-0.045]). Disagreement concentrates in negated, conditional, and exception items (odds ratio 5.6 relative to affirmative items). A single model changes its verdict between two items with the same meaning in 11-15% of cases. Two hypotheses are not supported: models from the same provider do not agree more than models from different providers, and adding paired examples does not reduce the language gap. We measure consistency only; no human labels are used, and all materials were drafted by an LLM.

vLLM Delivers Day-0 Support for Kimi K3.1 Pro

vLLM supports Kimi K3.1 Pro on day 0: sparse attention on a KDA hybrid, a packed KV cache with FP8 or NVFP4 latents and MXFP4 index keys, token-sharded prefill, decode context parallelism, and fused low-latency decode.

Liquid AI unveils the LFM3 multimodal model family, emphasizing efficient, specialized intelligence

Recently, Liquid AI officially launched its next-generation foundation model family, LFM3. The first release includes six models with approximately 2 billion parameters, covering text, vision, audio, omni-modal, and decision-making capabilities—all built on the LFM3-1.7B-Base foundation and achieving a pre-training throughput of 22 trillion tokens. This series adopts a novel tri-hybrid layer architecture, integrating gated short convolutions, sliding windows, and other mechanisms, prioritizing fewer parameters and higher efficiency.

Liquid AI重磅发布LFM3 多模态模型家族,主打高效专用智能
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AK_QMUL at ArGuard Shared Tasks: A Single Fine-Tuned JAIS for Arabic Harmful-Prompt Detection

arXiv:2610.36360v1 Announce Type: new Abstract: We present AK_QMUL's submissions to the ArGuard-2026 shared tasks on Arabic harmful-prompt detection: a binary safe/unsafe task (B1) and a seven-way harm-domain task (B2). Both submissions use one backbone -- JAIS-6.7B fine-tuned with 4-bit QLoRA and a linear classification head over its last-token representation. For B1 we train a four-way head that factors the label by dialect register {safe, unsafe} x {MSA, other}, we augment the training fold with MSA prompts from AraSafe, and decode with a plain argmax to score safe/unsafe. For B2 we fine-tune the same backbone as a seven-way classifier and average the softmax outputs of five random seeds. Both submissions ranked first on their track, with macro-F1 0.984 on B1 and 0.790 on B2. We report

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Multi-Agent Retrieval-Augmented Causality Assessment (MARCA) of Individual Case Safety Reports in Pharmacovigilance

arXiv:2610.33279v1 Announce Type: new Abstract: Introduction: Increasing volumes of individual case safety reports (ICSRs) challenge expert-led causality assessment in pharmacovigilance. We developed MARCA (Multi-Agent Retrieval-Augmented Causality Assessment), a four-agent framework for Naranjo-based assessment, and evaluated agreement with human experts and exploratory differences between non-RAG and RAG-labelled scores. Methods: MARCA combined scientific literature retrieval, drug-label assessment, objective-evidence classification, and clinical causality reasoning using Gemini 2.5 Pro and DeepSeek-V3.2. Agent-generated rationales informed human-assisted scoring. A consensus expert reference standard covered 215 ICSRs and 1,562 drug-event combinations (DECs) from FAERS (Q1-Q2 2025). Agr

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When to Stop Penalizing Actions? Martingale GRPO for VLA Reinforcement Learning

arXiv:2610.32431v1 Announce Type: new Abstract: A failed vision-language-action (VLA) rollout does not imply that every action contributed to failure. Yet in group-relative policy optimization (GRPO), a single trajectory-level advantage is assigned to all actions in a rollout. Potentially useful recovery attempts can thus be penalized alongside the errors they aim to correct. Ideal stopping conditions for failure feedback are characterized using success-probability and policy-score martingales. At these stops, the removed gradient contribution has zero conditional expectation given the retained information. Truncation therefore preserves the expectation of a reference GRPO group-gradient estimator and can reduce its covariance. One such stop occurs when the conditional probability of succe

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v0.43.0

## What's Changed * model_prefetch: syncrhonize all graph cleanups by @rattus128 in https://github.com/Comfy-Org/ComfyUI/pull/17178 * Add rope scaling support to MOSS-image by @kijai in https://github.com/Comfy-Org/ComfyUI/pull/17172 * [Partner Nodes] Renew partner-node sign-in tokens in API nodes by @dante01yoon in https://github.com/Comfy-Org/ComfyUI/pull/16943 * [Partner Nodes] feat(HeyGen): add HeyGen Voice instant clone and text-to-speech by @bigcat88 in https://github.com/Comfy-Org/ComfyUI/pull/17043 * [Partner Nodes] feat(ClarityAI): add Crystal Video Upscaler node by @bigcat88 in https://github.com/Comfy-Org/ComfyUI/pull/17192 * [Partner Nodes] feat(BFL): add 2K/4K resolutions to FLUX 3 Video nodes by @bigcat88 in https://github.com/Comfy-Org/ComfyUI/pull/17200 * Support MOSS-image

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Cutting both ways: AI sharpens systems protection while opening new exploits for malicious actors

The cybersecurity industry is discovering that powerful frontier AI models are a double-edged sword. Companies can use models to identify vulnerabilities in source code and system configurations faster than ever before. As it turns out, malicious actors can do this as well. A prime example of how AI models have shifted cybersecurity into a new […] The post Cutting both ways: AI sharpens systems protection while opening new exploits for malicious actors appeared first on SiliconANGLE.

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Edge AI Daily Morning Briefing (October 27)

Today’s core intelligence focuses on a structural turning point in AI capital and governance. Anthropic publicly filed its S‑1, listing on Nasdaq under the ticker ANT; H1 revenue reached $16.3 billion, up roughly 13.5 times year over year, with a net loss of $80.7 billion (about $72 billion of which was non‑cash fair value expense). The company has secured at least $518 billion in irrevocable compute commitments, targeting a valuation of up to $2 trillion. NVIDIA is in talks to invest up to $10 billion as a cornerstone stakeholder, marking the first stress test of AI valuation narratives in the public markets. The prospectus also warns, across 80 pages of risk factors, of models’ tendency toward self‑preservation. OpenAI unveiled GPT‑6.1 Terra, slashing standard API pricing by half and reducing per‑task costs by roughly ninefold, shifting the unit of measurement from tokens to “completed tasks,” and transferring pricing authority from the lab to corporate CFOs. Meta plans to raise up to $95 billion through equity financing for AI infrastructure, with NVIDIA, AMD, and Broadcom reportedly interested in collectively subscribing to $5 billion—reshaping the supply chain from a buyer‑seller dynamic to one of capital interlocking. Meanwhile, Microsoft has issued buyout offers to approximately 60,000 employees. On the regulatory and governance front, Texas has sued OpenAI and Altman; Delaware has drafted the nation’s first legislation governing AI companies; 39 data center ballot initiatives are slated for the U.S. midterm elections; and Claude bypassed Georgia’s paywall—all contributing to an ongoing debate over accountability and authorization boundaries. Domestically, general‑purpose large models are accelerating their integration into robotic motion control, while strong sales of Apple’s foldable iPhone Duo have pushed Lens Technology’s production lines to full capacity. On the open‑source side, NVIDIA’s open‑source robotics data engine Flora has reduced annotation costs to about 1/125th, while a Linux Foundation report reveals that roughly 25% of pull requests constitute low‑quality “AI slop,” raising concerns about inefficiencies in resource reallocation.

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Morning Briefing | Apple responds to cracks in the iPhone Duo’s inner screen / Anthropic files for a public IPO, aiming for the largest IPO in history / Xpeng’s Robotaxi receives approval for Level 4 demonstration operations

· OpenAI releases GPT-6.1 Terra, with API prices cut in half and faster computer performance · Apple officially rolls out iOS 27.2: Apple Music gets a new black-and-white design, and the iPhone Duo now supports the Apple Pencil · Huawei’s Enjoy series has cumulatively shipped over 160 million units #Follow iFanr’s official WeChat account: iFanr (WeChat ID: ifanr) for more exciting content delivered to you第一时间.

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Anthropic files S-1 paperwork for proposed IPO

Artificial intelligence giant Anthropic PBC filed papers today for an initial public offering with the United States Securities and Exchange Commission, putting it on track for what will likely be the biggest stock market debut of an AI developer to come, amid warnings about advanced AI. In its filing, Anthropic said it plans to list […] The post Anthropic files S-1 paperwork for proposed IPO appeared first on SiliconANGLE.

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