Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials
Atomistic simulations help researchers understand how molecules and materials behave, from chemical reactions and phase transitions to mechanical failure. These...
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n8n@1.123.90
1.123.90 (2026-10-29) core: Require a signed token to accept an invitation (#42292) (4580f22)
n8n@2.45.6
core: Record dynamic time saved for executions in queue mode (#42482) (f2f0c2f) editor: Match compact sidebar icon sizes (#42378) (56eec49)
How to Build Proactive, Always-On Agents with the Schedules SDK in Managed Deep Agents
Most proactive agents run on a simple cron. Learn what scheduled runs unlock, from memory to smart triage, and how to add them to Managed Deep Agents.
How to build evidence-grounded agent memory with OpenWiki and Graphs
LangChain used OpenWiki, its open-source wiki compiler, and an evidence graph to build a persistent memory layer that preserves knowledge for agents to reuse across tasks. Learn how.
Route and orchestrate models with GitHub Copilot Fusion
GitHub Copilot Fusion is generally available. Instead of picking a model for each task, choose Copilot Fusion and let Copilot intelligently route and orchestrate which models it uses for you.… The post Route and orchestrate models with GitHub Copilot Fusion appeared first on The GitHub Blog.
South Korea’s financial industry hit by fresh wave of cyberattacks
South Korea’s financial institutions have been hit by another wave of cyberattacks, highlighting the growing risk of AI-driven hacking in Asia.
Jedify formally launches Data Agents for building AI analysts in plain language
Enterprise artificial intelligence context startup Jedify Inc. today formally launched Data Agents, a product that lets business teams set up autonomous AI analysts by describing the work they want done in plain language. Data Agents takes on a longstanding limit of business intelligence software, where analysts have to build dashboards ahead of time around the […] The post Jedify formally launches Data Agents for building AI analysts in plain language appeared first on SiliconANGLE.
Artificial Analysis v5 and the benchmarker's dilemma
A sort of review of the benchmarking industry and what's missing.
Macmillan Staff Say the Publisher Threw a Party for a Workplace AI Assistant That Was Later Killed
Workers tell WIRED that NOA—or “No Ordinary Assistant”—was unhelpful. Bestselling authors also say AI is ruining their relationships with Big Five publishers.
[AINews] Anthropic ends Coding Interviews: "Hunger beats tenure"
Since coding is solved, why code for the job?
n8n@3.0.1
## [3.0.1](https://github.com/n8n-io/n8n/compare/n8n@3.0.0...n8n@3.0.1) (2026-10-28) ### Bug Fixes core: Keep the ExpressionError name in minified builds (https://github.com/n8n-io/n8n/issues/42309) ([b3192c6](https://github.com/n8n-io/n8n/commit/b3192c6d151125c76c88fe8a421e027b4242a3f9)) core: Keep undefined values in QuickJS expression results (https://github.com/n8n-io/n8n/issues/42264) ([532f645](https://github.com/n8n-io/n8n/commit/532f645f21c99e178a09448caf37ebd77d24b56a)) editor: Align carets within split buttons (https://github.com/n8n-io/n8n/issues/42203) ([61cbfea](https://github.com/n8n-io/n8n/commit/61cbfeafa267b5c769b345c50ccbd4fc504b0e69)) editor: Improve first load speed (https://github.com/n8n-io/n8n/issues/41987) ([8b6bdbb](https://github.com/n8n-io/n8n/commit/8b6bdbbe874b
n8n@2.45.5
## [2.45.5](https://github.com/n8n-io/n8n/compare/n8n@2.45.4...n8n@2.45.5) (2026-10-28) ### Bug Fixes * **core:** Keep the ExpressionError name in minified builds ([#42309](https://github.com/n8n-io/n8n/issues/42309)) ([b3192c6](https://github.com/n8n-io/n8n/commit/b3192c6d151125c76c88fe8a421e027b4242a3f9)) * **core:** Keep undefined values in QuickJS expression results ([#42264](https://github.com/n8n-io/n8n/issues/42264)) ([532f645](https://github.com/n8n-io/n8n/commit/532f645f21c99e178a09448caf37ebd77d24b56a)) * **editor:** Align carets within split buttons ([#42203](https://github.com/n8n-io/n8n/issues/42203)) ([61cbfea](https://github.com/n8n-io/n8n/commit/61cbfeafa267b5c769b345c50ccbd4fc504b0e69)) * **editor:** Improve first load speed ([#41987](https://github.com/n8n-io/n8n/issues/4
Introducing Build-A-Chip-Bench: From Workload Specification to GDS
Evaluate AI agents from workload specification through GDS layout, using open-source tools and an open-source PDK to assess their ability to complete the chip-design workflow. The post Introducing Build-A-Chip-Bench: From Workload Specification to GDS appeared first on Semiconductor Engineering.
FinLLM-KG: Knowledge Graph-Enhanced LLMs for Financial Sentiment Analysis in Algorithmic Trading
arXiv:2610.36083v1 Announce Type: new Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs). However, existing approaches remain confined to predicting the sentiment of each article in isolation and operate as black boxes, which limits contextual understanding and hinders deployment in high-stakes financial decision-making. To this end, we introduce FinLLM-KG, a model-agnostic framework that combines causal LLMs with a financial knowledge graph, in order to inject temporally grounded, company-level knowledge from SEC 10-K filings at inference time, without updating model weights. Within FinLLM-KG, context is supplied either as a natural-language company profile or as a small set of rel
Benchmarking Task Takeover Across Agent Handoffs
arXiv:2610.36523v1 Announce Type: new Abstract: As agents collaborate on longer tasks, responsibility may pass between models before the work is complete. The receiving model must build on prior decisions, observations, and actions while addressing the remaining requirements. Evaluating this transition requires connecting the information preserved at handoff to how the receiver continues the work, a relationship that task-success and memory-retention scores alone do not capture. We introduce the Handoff State Continuity Benchmark (HSC-Bench), comprising 1,039 tasks that evaluate two complementary aspects of task takeover: recovering relevant repository evidence and continuing from established conversational, tool, and file-work state. Comparisons across six receiving models show that struc
3, 7, 14, 17, 42: Instruction Tuning Installs and Sharpens Number-Choice Heuristics in Language Models
arXiv:2610.36178v1 Announce Type: new Abstract: We measure, without sampling error, the probability a language model assigns to each integer from 1 to N when asked for a random number. For a fixed prompt the model defines one probability vector, which we compute by summing over each integer's scored spellings rather than estimate by sampling, removing the noise that limits earlier studies [1, 4]. We evaluate 18 open-weight model lines at five ranges up to N = 100, under 29 decoding configurations, on two inference stacks; 15 of them have matched base and instruction-tuned checkpoints that both give usable answers. Instruction tuning makes the distribution less uniform in all 15 pairs. It installs one heuristic, round-number avoidance, and sharpens another, endpoint avoidance. Base checkpoi
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
CONCUR: Concordant Auxiliary Rewards for Multi-Task, Multi-Reward Reinforcement Learning
arXiv:2610.36437v1 Announce Type: new Abstract: When a Large Language Model (LLM) is trained with Reinforcement Learning (RL) on multiple tasks, a task's primary reward can gate auxiliary rewards such as a length reward: on a failing response, an auxiliary cannot exceed the primary. However, group-relative advantage estimators treat a gated auxiliary like any other component, so it can reverse the primary's order, and at the primary's ceiling only auxiliaries order responses. Keeping gated auxiliaries hurts some tasks, while dropping them hurts others. We propose CONCUR, an advantage estimator that replaces the standardized scores of each gated auxiliary with the closest scores that satisfy two conditions: responses with higher primary rewards cannot receive lower scores, and responses at
Beyond IceBERT: Open-Data ModernBERT Encoders for Icelandic from 12M to 1B Parameters
arXiv:2610.36230v1 Announce Type: new Abstract: IceBERT, the reference encoder for Icelandic, found that a cleaned web crawl is enough to train a strong model and that starting from a multilingual model can help on some tasks. We revisit both findings with the ModernBERT architecture. We train Icelandic encoders from 12M to 1B parameters on 9B tokens of open data, and rerun IceBERT's benchmarks and five newer tasks for 30 encoders under one protocol with confidence intervals. Our 1B model trained from scratch, to our knowledge the first monolingual Icelandic encoder of that size, is significantly better than IceBERT on all eleven tasks; warm-started from the English Ettin encoder of the same shape, it has the best score of all encoders on every task we rank. Because our model shapes match