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arXiv:2610.37684v1 Announce Type: new Abstract: Large Language Models (LLMs) have rapidly become important tools for science, education, medicine, and professional work. Despite their apparent simplicity from the user's perspective, effective and responsible use requires an understanding of how these systems work, what their limitations are, and how their outputs should be evaluated. I wrote this document as a basis for my lecture on LLM in the master in artificial intelligence in medicine at th

arXiv:2610.37685v1 Announce Type: new Abstract: Language models do not merely memorize facts; they absorb rhetorical strategies from pretraining data, including the human tendency to reason by analogy. We ask: is this absorbed capability geometrically accessible before any instruction tuning, and what determines whether it can be activated at inference time? We extract an "Analogy Vector" from the residual stream via difference-in-means and show it enables steerable explanation style across seve

arXiv:2610.37692v1 Announce Type: new Abstract: Users engage with large language models on tasks that are purely objective ("When was Netflix founded?"), purely subjective ("What should I name my child?") or anywhere in-between. We have rich tools for evaluating model accuracy with respect to objective criteria. However, when it comes to subjective aspects of a task, we do not have similarly developed methodology at hand. In this paper, we take some early steps towards framing the question of su

arXiv:2610.37778v1 Announce Type: new Abstract: Speculative decoding is used to reduce the inference cost of language models. One-step diffusion drafters propose a block of multiple tokens in parallel, reducing the sequential cost of speculative decoding. On-policy distillation trains these drafters using verifier feedback on their own proposals. However, parallel drafting makes this feedback difficult to use: the drafter predicts each token without observing the preceding tokens sampled within

arXiv:2610.37788v1 Announce Type: new Abstract: Language model assistants sometimes change a correct answer when the user suggests a wrong one. The behavior, called sycophancy, is usually attributed to preference-based post-training, yet early measurements found it in pretrained models. We ask where it comes from by measuring it at every available stage of training: 13 open checkpoints from 5 lineages, from base model through supervised fine-tuning and preference optimization to release. We sepa

arXiv:2610.37826v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) lets a large language model (LLM) answer questions by retrieving relevant passages from a large corpus. However, RAG struggles with multi-hop questions, which require chaining facts spread across several documents through intermediate entities that the question never mentions. Graph-based RAG methods retrieve once from the entities a question names and so rarely recover the full evidence chain. Iterative RAG met

arXiv:2610.37868v1 Announce Type: new Abstract: Large language models (LLMs) are widely used as automated judges, yet most alignment methods target aggregated human judgments and overlook variation among individual annotators. Existing approaches to matching the full distribution of human ratings mainly rely on output-level calibration or parameter fine-tuning. We propose Human-Residual Steering (HRS), to our knowledge the first method to align frozen LLM judges with human judgment distributions

arXiv:2610.37913v1 Announce Type: new Abstract: We propose a multimodal transformer framework for efficiently modeling long1 financial documents that combine structured tabular data with noisy, unstructured2 textual disclosures. The framework represents financial tables as semantically3 enriched token sequences and integrates them with long-form text through a cross-4 attention mechanism that explicitly captures cross-modal dependencies. In par-5 ticular, we augment each numerical variable with

arXiv:2610.37921v1 Announce Type: new Abstract: Key-value (KV) caching is a core mechanism in large language models, where intermediate key and value representations are stored to accelerate autoregressive decoding. As generation length increases, KV cache memory and computation grow linearly, making compression critical for efficient long-output reasoning. Dictionary-based sparse KV compression is appealing because it represents each token's KV states as a sparse combination of learned basis el

arXiv:2610.37995v1 Announce Type: new Abstract: We describe the QMUL_AK submission to AISA-ArabicFC 2026. The system adapts the Arabic-English ALLaM-7B-Instruct model to function calling with low-rank adaptation (LoRA). It combines an Arabic schema-aware prompt, dialect and no-call resampling, a token-weighted training objective, and deterministic validation of generated calls against the candidate tool schema. The same output supports Track A function calling and Track B reasoning-augmented cal

arXiv:2610.38009v1 Announce Type: new Abstract: Medical chart abstraction transforms unstructured clinical narratives into structured variables for research and quality monitoring. We evaluated a prompting-based large language model (LLM) framework for extracting eight clinical variables from 2,416 neurointerventional acute stroke intervention reports across three hospitals. Twenty-two instruction-tuned open-source LLMs were evaluated across architectures, model sizes, and biomedical adaptations

arXiv:2610.38016v1 Announce Type: new Abstract: Automatic speech recognition (ASR) increasingly supplies transcripts for educational NLP, but aggregate word error rate (WER) can conceal errors concentrated in learner speech, multilingual turns, and speaker attribution. We compare platform-integrated, open-weight, adapted, and commercial ASR configurations on held-out sessions of online L2 Spanish interaction between L1 English learners and their instructors and tutors. Because English is the lea

arXiv:2610.38076v1 Announce Type: new Abstract: LLM-as-a-Judge has become a standard for evaluating large language models, yet most approaches implicitly assume that a single judge prompt functions as a stable evaluation instrument. Prior work has primarily explored deliberate prompt modifications such as scoring rubrics, role descriptions, or chain-of-thought reasoning in order to optimize accuracy. In contrast, we investigate whether validated prompt variants intended to preserve evaluation se

arXiv:2610.38095v1 Announce Type: new Abstract: We introduce Nemotron 3.5 Super and Nemotron 3.5 Lightning, open and efficient Mixture-of-Experts Hybrid Mamba-Transformer models designed for reasoning, long-context understanding, and agentic workloads. Super is a 120B-parameter, 12B-active multimodal model, while Lightning is a smaller 30B-parameter, 3B-active text model; both designed for efficient deployment. Both models combine large-scale distillation, long-context training, and native specu

Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research .

This article is brought to you by Qualcomm Technologies . For many consumers, much of the promise and appeal of AI is personal. Consumers are looking past generic tools and want to know how these technologies will help them in their daily lives. These trends coincide with the advent of new classes of wearable devices that, in addition to being able to leverage powerful frontier models, feature built-in intelligence that understand the context of a user. Where are they? What are they seeing and h

Prime Intellect released Prime Mega MoE, a backward pass for DeepSeek’s fused Mega MoE kernel. It makes the kernel trainable and speeds Qwen3 MoE training steps by up to 34% in prime-rl. The post Prime Intellect Releases Prime Mega MoE: Trainable Fused MoE Kernel, 3.11x Faster Forward Pass appeared first on MarkTechPost .

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Opinion: We urgently need to monitor AI agents’ actions in real time —Madhulika Srikumar and Claire Leibowicz from the Partnership on AI, a nonprofit advancing best practices in AI governance. AI…

This summer, hundreds of OpenAI agents tried to cheat on a cybersecurity test. They broke out of their test environment and successfully hacked Hugging Face, a site that they believed held clues about how they’d be graded. Around the same time, another OpenAI agent accessed private files from an Australian government website. It’s a problem…

Infini-AI-Lab released Hermit, an Apache-2.0 harness adapter framework that trains an external layer of instructions, skills, memory and tools from task feedback, leaving the agent’s model and harness unchanged. The post Infini-AI-Lab Releases Hermit, an Open-Source Harness Adapter Framework for AI Agents appeared first on MarkTechPost .

Block’s AI lab released Dessa, a full-duplex speech-to-speech model trained only on human conversation audio. It runs locally on a MacBook Pro M4 Max, but Block has published no size, benchmarks or weights. The post Block Unveils Dessa, a Full-Duplex Speech Model Trained Only on Speech appeared first on MarkTechPost .

AssemblyAI has released AssemblyAI-TTS-1, its first text-to-speech model for voice agents. It costs $15 per 1M characters, offers unlimited concurrency without a contract, and supports 6 languages at launch. The post AssemblyAI Ships AssemblyAI-TTS-1: A $15 per 1M Character Text-to-Speech API for Voice Agents appeared first on MarkTechPost .

NVIDIA released Nemotron 3.5 Transcribe, an open-weights speech LLM with 4.3B active parameters. It transcribes 40 languages, adds speaker labels and word timestamps, and posts a 4.71% mean WER on the English Open ASR Leaderboard. The post NVIDIA Releases Nemotron 3.5 Transcribe: An Open 30B MoE Model for 40-Language Transcription with ~4B Active Parameters appeared first on MarkTechPost .

Across a business, any number of AI platforms, systems, and tools may be at work simultaneously—for example, AI-powered customer platforms, conversational assistants, and workflow and process automation tools. Each one is sophisticated, advanced, and quick to respond. But, when set to work, the outcomes and actions that emerge may be wrong, confused, or lacking a…

Modern engineering projects increasingly combine human judgment with machine capability. An engineer can define a problem, then ask an artificial intelligence tool to search relevant research and technical standards, generate design options, test them in simulation, and compare the results. The engineer can then bring the results to specialists who understand the parts AI cannot judge on its own, such as whether a design can be built safely, whether it makes sense in a particular setting, or how

<!-- SC_OFF --><div class="md"><p>We study hindsight self-distillation for multi-step financial reasoning on GPT-OSS-20B. The student finds its own first wrong step and writes a critique. A frozen copy, conditioned on the prefix plus the critique, is distilled back into the student under reverse KL. Notably, replacing the critique with a fixed generic feedback line <strong>matches</strong> the full gain of about 3 points, and critiques from a 6x larger model do not materially improve performance

Google DeepMind introduced Gemini 4 Carbon, a frontier model that scores 59 on the Artificial Analysis Intelligence Index and 88.2% on SEC-bench Pro. Access starts with vetted cyber defenders, at $2/$10 per million tokens. The post Google DeepMind Introduces Gemini 4 Carbon, Its Top Model for Cyber Defense and Coding appeared first on MarkTechPost .

MiniMax has open-sourced MCode Stack, an MIT-licensed repo of 17 SKILL.md skills and 5 checklists that its MiniMax Code team uses daily for code review, subagent splits, context budgets and long-running commands. The post MiniMax Open-Sources MCode Stack: 15 Agent Skills Its Coding Team Uses Daily appeared first on MarkTechPost .

<!-- SC_OFF --><div class="md"><p>In theoretical computer science, we have the halting problem, which proves that certain problems cannot be solved by any general algorithm.<br/> This made me wonder about something similar in machine learning.<br/> Today, we can often distinguish AI-generated content from human-created content, at least statistically. But as AI models improve, could we eventually reach a point where the two become completely indistinguishable?<br/> Not just because our detectors

<!-- SC_OFF --><div class="md"><p>Hello all!</p> <p>I'm a PhD student in a niche field and the next plan is to apply agentic AI to automate a rather boring yet important workflow. I'm very well aware of LLMs but not very much into agentic AI and how do you set up?</p> <p>I've heard terms like harness and others, but I'd like to know how to get started with this (details below). Using AI for this proved to be pretty much useless.</p> <p>I'll present an input and a testing scenario and my model, a

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 […]

<!-- SC_OFF --><div class="md"><p><a href="https://www.reddit.com/r/ArtificialInteligence/comments/1xfuacm/ama_this_wednesday_stephen_cas_casper_harvard/">https://www.reddit.com/r/ArtificialInteligence/comments/1xfuacm/ama_this_wednesday_stephen_cas_casper_harvard/</a></p> <p><a href="/r/ArtificialInteligence">r/ArtificialInteligence</a> We have an AMA today Wednesday 28 October from 3 to 4pm ET (19:00 UTC, 20:00 CET) with Stephen “Cas” Casper, Assistant Professor of Public Policy at the Harvard

<!-- SC_OFF --><div class="md"><p>Model & weights: <a href="https://huggingface.co/BrunoHays/embeddinggemma-2-nullspace-audio">BrunoHays/embeddinggemma-2-nullspace-audio</a></p> <p>I wanted to share an experiment on <code>google/embeddinggemma-2</code> (577M) for MAEB, the 30-task audio benchmark on the <a href="https://huggingface.co/spaces/mteb/leaderboard">MTEB leaderboard</a>. By projecting intermediate Conformer layers into the unused null space of the model's final projection matrix using

<!-- SC_OFF --><div class="md"><p>I've been thinking about AI agent safety, and I built a test harness to check my own results. I think it may be useful to people who study models too.</p> <p>It records where each tool argument came from before the tool executes, measures whether the same input gives the same decision, and tests whether a model takes arguments from someone else's text instead of its user's. </p> <p>If you modify the harness, it can probably be used for much more.</p> <p><a href=

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI labs are buying data from dead startups to train their models When Keelan Miskell began winding down his crypto startup, he didn’t think its data was worth much. But he’s…

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1xhkli0/pandora_m1_a_training_monitor_box_64_nodes_x_8/"> <img src="https://preview.redd.it/yvlvz5hws6yh1.jpeg?width=640&crop=smart&auto=webp&s=0a8b1661ea4f8d369e3fa41743498712fe389b45" alt="Pandora M1, a training monitor box (64 nodes x 8 GPUs support) [P]" title="Pandora M1, a training monitor box (64 nodes x 8 GPUs support) [P]" /> </a> </td><td> <!-- SC_OFF --><div class="md"><p>Hi, just <a href="https://blog.christi

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 .

Google DeepMind released Genie 4 as a research preview. The real-time world model adds native audio, live dialogue and text steering, with Gemini 3.8 Flash planning events inside the simulation loop. The post Meet Google DeepMind’s Genie 4: Real-Time World Model With Native Audio appeared first on MarkTechPost .

arXiv:2610.35942v1 Announce Type: new Abstract: I consider what it means for AI assistance to save time. I contrast a stopwatch view, on which time saved is whatever the clock records, with a ledger view, on which it is a counterfactual that has to be read next to the errors the work lets through, and I argue for the latter. The argument has four parts. I define a model-free pair of quantities, time saved and excess escaped defects, and one decision number built from them: the break-even defect