The paper defines agentic cognitive depth as a trajectory-level profile across five operational criteria.
Source: arXiv
https://arxiv.org/abs/2610.04168
#MachineLearning
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Postingan yang diterbitkan di instalasi FPDP ini.
Studies examined generative Agentic AI for spatial rotation.
Source: arXiv
https://arxiv.org/abs/2610.04188
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04188
#MachineLearning
Language-model systems can separate contextual computation, persistent storage, and exact execution instead of updating all capabilities through one shared parameter system.
Source: arXiv
https://arxiv.org/abs/2610.04012
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04012
#MachineLearning
SGAnalog introduces an end-to-end circuit benchmark from open-source silicon tapeouts. It contains 273 topologically distinct top-level designs. Across seven models on 66 transcription tasks, the strongest model reaches 56.1 percent exact graph isomorphism.
Source: arXiv
https://arxiv.org/abs/2610.03934
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.03934
#MachineLearning
Nigeria LNG Limited presented GenScan AI. The innovation can reduce MRI scan times by up to 90 per cent without requiring new hardware.
Source: The Punch
https://punchng.com/nlng-unveils-ai-tool-to-cut-mri-scan-time/?utm_source=rss.punchng.com&utm_medium=web
#AI
Source: The Punch
https://punchng.com/nlng-unveils-ai-tool-to-cut-mri-scan-time/?utm_source=rss.punchng.com&utm_medium=web
#AI
Two Democratic senators wrote to Treasury Secretary Scott Bessent and White House officials asking for details on industry influence over the still-unreleased AI regulatory framework.
Source: Semafor
https://www.semafor.com/article/10/06/2026/democrats-press-white-house-on-still-secret-ai-framework
#TechPolicy #WhiteHouse
Source: Semafor
https://www.semafor.com/article/10/06/2026/democrats-press-white-house-on-still-secret-ai-framework
#TechPolicy #WhiteHouse
Fine-tuning Vision-Language Models enhances spatial intelligence and improves rotation detection.
Source: arXiv
https://arxiv.org/abs/2610.04206
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04206
#MachineLearning
We study continual harness learning for corpus-level spreadsheet question answering and introduce VIGIL.
Source: arXiv
https://arxiv.org/abs/2610.04287
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04287
#MachineLearning
Researchers proposed SHarP, a saliency-based pruning strategy for agent harnesses. Evaluation reveals that most studied harnesses are highly redundant.
Source: arXiv
https://arxiv.org/abs/2610.04178
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04178
#MachineLearning
OpenAI will show invisible watermarks for text generated by ChatGPT and Codex. The feature will be initially available in the European Union.
Source: Gadgets 360
https://www.gadgets360.com/ai/news/openai-invisible-watermarks-chatgpt-text-ad-format-testing-timeline-regions-12144879#rss-gadgets-all
#AI #OpenAI #ChatGPT
Source: Gadgets 360
https://www.gadgets360.com/ai/news/openai-invisible-watermarks-chatgpt-text-ad-format-testing-timeline-regions-12144879#rss-gadgets-all
#AI #OpenAI #ChatGPT
TikTok launched an AI shopping assistant and one-click checkout.
Source: TechJuice
https://www.techjuice.pk/tiktok-ai-shopping-assistant-one-click-checkout/
#AIAgents
Source: TechJuice
https://www.techjuice.pk/tiktok-ai-shopping-assistant-one-click-checkout/
#AIAgents
AMD CEO says the company plans to substantially increase supply in 2027.
Source: The Economic Times
https://economictimes.indiatimes.com/tech/technology/amd-plans-to-substantially-increase-supply-in-2027-ceo-says/articleshow/134729173.cms
#Semiconductors #AMD
Source: The Economic Times
https://economictimes.indiatimes.com/tech/technology/amd-plans-to-substantially-increase-supply-in-2027-ceo-says/articleshow/134729173.cms
#Semiconductors #AMD
Reinforcement Learning with Comparative Evidence (RLCE) learns social understanding from unlabeled training data without constructing rewards from ground-truth annotations.
Source: arXiv
https://arxiv.org/abs/2610.04072
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04072
#MachineLearning
DigitalMate is a mobile project that works as a task center on HarmonyOS devices. A user describes a goal in one sentence, and DigitalMate generates a native interface for that task.
Source: Pandaily
https://pandaily.com/openjiuwen-digitalmate-harmonyos-task-center-agent-a2ui
#AIAgents
Source: Pandaily
https://pandaily.com/openjiuwen-digitalmate-harmonyos-task-center-agent-a2ui
#AIAgents
Personal AI agents in enterprise multi-tenant deployments share a common vector store for long-term memory. Shared embedding spaces create a surface for cross-user memory leakage. Non-adversarial incidental leakage reaches 70 to 100 percent under pooled retrieval.
Source: arXiv
https://arxiv.org/abs/2610.04195
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04195
#MachineLearning
ALoDLM replaces uniform computation with token-adaptive latent recurrence, allocating more processing to difficult tokens while allowing easy tokens to commit early.
Source: arXiv
https://arxiv.org/abs/2610.04198
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04198
#MachineLearning
Researchers introduced CUAWright, a minimal terminal harness that uses bash commands and a file system to dynamically create tools and manage context.
Source: arXiv
https://arxiv.org/abs/2610.04116
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04116
#MachineLearning
Researchers study whether a misaligned agent can write a goal to persistent memory for a future aligned agent to carry out. The study investigates self-propagation of misalignment across 20 scenarios without an external adversary.
Source: arXiv
https://arxiv.org/abs/2610.04083
#MachineLearning
Source: arXiv
https://arxiv.org/abs/2610.04083
#MachineLearning
Aggressively GGUF-quantized small models can keep generating fluent text while no longer knowing what it means.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.04403
#MachineLearning
Source: arXiv cs.CL
https://arxiv.org/abs/2610.04403
#MachineLearning
SynLat aligns compression boundaries with syntactic structure through non-overlapping Syntax-Aligned Units.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.03839
#MachineLearning
Source: arXiv cs.CL
https://arxiv.org/abs/2610.03839
#MachineLearning