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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.
Agents should understand more than your codebase—they should understand how your team works. Your preferences, repository conventions, skills, and custom instructions guide how changes should be made, while code in… The post GitHub intelligence puts your code, context, and history to work appeared first on The GitHub Blog.
NVIDIA RTX Spark powers thin and light laptops built for everything that developers, creators and gamers do. RTX Spark laptops are now available from ASUS, Dell, HP, Lenovo, Microsoft and MSI. These powerful systems combine an NVIDIA Blackwell RTX GPU and an NVIDIA Grace CPU in a single superchip with up to 128GB of unified […]
British circuit board design startup Circuit Mind Ltd. today announced it has raised $25 million in new funding to extend its artificial intelligence platform across more of the hardware design process. A verification tool that puts AI agents to work hunting for errors in schematics launched at the same time. Circuit Mind offers software that […] The post Circuit Mind lands $25M and puts AI agents to work checking circuit board schematics appeared first on SiliconANGLE.
Since coding is solved, why code for the job?
FlashMap is a speculative decoding drafter from Polymathic AI, Princeton and USC ISI researchers. It drafts 16 tokens jointly in one pass and reaches up to 6.14x lossless speedup on Qwen3-8B. The post Polymathic AI and Princeton Researchers Release FlashMap, a Flow Map Drafter for 6.14x Faster LLM Decoding appeared first on MarkTechPost.
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.
Make every layer of your hardware PDN-aware and reach your maximum compute density. The post Foundations of Power Management in the Age of AI appeared first on Semiconductor Engineering.
arXiv:2610.36339v1 Announce Type: new Abstract: In expert-parallel mixture-of-experts (MoE) serving, dispatch moves tokens to the accelerators hosting their selected experts, and combine returns expert outputs to each token's owner. Both operations lie on the critical path and can dominate MoE layer latency in multi-node deployments. We present INC-MoE, an expert-parallel MoE layer that offloads dispatch and combine to the network switch and overlaps both collectives with each other and with expert computation. Dispatch uses in-network AllGather through switch-side multicast, while combine uses in-network ReduceScatter through switch-side reduction. Both collectives use message semantics between the end hosts and the switch. Because the in-network collectives load opposite directions of ea
arXiv:2610.36191v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate fluent yet factually incorrect responses, raising concerns about their reliability. Recent hallucination detectors increasingly exploit internal representations, but extracting informative signals from high-dimensional hidden states remains challenging. Existing approaches often rely on selected activations or pooling-based compression, potentially overlooking discriminative information distributed across Transformer layers, feature modes, and tokens. We propose LOTUS (Low-rank Order-statistic TUcker-based Subspace learning), a multilinear low-rank decomposition framework for hallucination detection that extracts compact representations from LLM hidden states while reducing dependence on labele
arXiv:2610.36524v1 Announce Type: new Abstract: Can controlled symbolic inputs reveal attention heads that influence a language model's decisions from natural language evidence? We introduce controlled evidence aggregation (CEA), which varies the counts and order of two symbols in otherwise fixed prompts and measures changes in a frozen Transformer's preference between two answer tokens. The preference is measured by their logit difference; the prompts prescribe no correct answer. Balanced utility causal selection (BUC) identifies compact head sets by jointly evaluating how much ablating their outputs removes the count effect and how closely patching mean outputs between count conditions recovers the effect in both directions. We derive conditional bounds linking head attribution to interv
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
arXiv:2610.35869v1 Announce Type: new Abstract: AI applications often invoke a model before they determine whether inference is necessary. That default is useful for ambiguous generative tasks, but it is poorly matched to work that is structured, deterministic, policy-bound, lookup-oriented, or directly verifiable. KORA introduces deterministic-first execution control: a pre-inference control layer that represents requests as task paths, attempts deterministic or structured routes first, validates route outcomes, records telemetry, and escalates to a model-candidate path only when deterministic handling is insufficient. This paper presents KORA as a deterministic-first execution-control architecture and reports bounded evidence from reproducible benchmark and validation workloads. In a rep
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
arXiv:2610.36668v1 Announce Type: new Abstract: The Asch conformity experiment has recently gained considerable attention as a way of measuring conformity in large language models (LLMs). While previous studies have focused on demonstrating conformity in LLMs, our work adds a further quantitative layer to this approach. Instead of sampling a frontier LLM on a suite of questions, our approach samples a single question repeatedly, leveraging the probabilistic nature of LLMs to produce a distribution of answers. In doing so, we can compare the distributions of answers before and after the model is presented with the manufactured consensus. Our experiments confirm that frontier LLMs do conform, reinforcing prior literature, and, through our distribution-to-distribution comparisons, provide add
Fake faces, cheap product clips, and rule-skirting story ads are pouring into short-video commerce. Platforms are answering with automated review systems. Honest advertisers are stuck paying for the cleanup.
Jev ranks warehouse tables and labels agent failures in under a second, but adding retrieval hints leaves accuracy flat across 20 models on the Agentic Analytics Benchmark.
Restoring data after a cyberattack isn’t enough if the identity systems that control access are compromised. Dell Technologies Inc. is extending its cyber recovery tools to cover those systems, putting identity resilience alongside data protection. That gap is widening as AI ties data, identity, infrastructure and applications into interdependent systems, making data protection a core […] The post Dell expands cyber recovery tools to protect identity appeared first on SiliconANGLE.
Liquid AI released LFM3, 6 open-weight 2B-class models for text, vision, audio, omni and decisions. LFM3-1.7B uses a tri-hybrid backbone, a 256K context window and fits in about 1 GB at 4-bit. The post Liquid AI Releases LFM3: 6 Open-Weight 2B-Class Multimodal Models appeared first on MarkTechPost.
llama : build the k-pool sparse selection independently of the kq_mask shape (#31243) * llama : build the k-pool sparse selection independently of the kq_mask shape qwen4exp (QSA) and glm5-next pick cache cells by index and no longer read the kq_mask values, but they still shaped the selection like the dense kq_mask. Build the selection as [n_kv, n_tokens] over the cache cells instead, and split it into the streams of the K view right before the attention. This lets the dense mask change its layout (e.g. per-sequence row lists) without touching the sparse layers. * improve comment Co-authored-by: Georgi Gerganov * llama : drop the unused build_fused_top_k_mask args, review nits --------- Co-authored-by: Georgi Gerganov **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silico
