New paper by Philip Resnik asks a simple question: are today’s language models (like ChatGPT) actually models of how the human mind works? * Implementation level: No. Brains and LMs are built and operate very differently. * Algorithmic/representational level: Weak support. We lack good evidence that LMs use the same
LLMs turbocharge ontology development Ontological Knowledge Bases (OKBs) organize domain know-how, but hand-crafting them is slow and brittle. This research shows how Large Language Models can help experts build and refine OKBs faster—and with more consistency. The team proposes a structured, iterative workflow that uses LLMs to:
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AI agents can be powerful—and unpredictable. AgentGuardian is a new security framework that keeps them on-task by learning and enforcing access rules tailored to each agent. How it works: * Staging phase: It watches the agent run in a controlled setting, logging execution traces and typical inputs to learn
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Most top video AIs are locked up. Molmo2 opens the door: open weights and open datasets, built to understand videos and ground that understanding by pointing to and tracking objects in the pixels. * Data you can build on: 7 new video datasets and 2 multi-image sets, including rich video
Can AI make architecture students better designers? A controlled study of 36 students compared solo work to a second round with either Generative AI or a repository of past projects. Experts rated designs; students reported confidence and mental effort. What they found: * No overall performance boost from GenAI versus the
AI that transcribes drums—no paired audio required Most drum-transcription AIs need huge, matched audio–MIDI datasets. Those are scarce. Synthetic stand‑ins often sound cheap, creating a domain gap. We flip the script. With a semi‑supervised pipeline, we automatically curate a large, diverse library of high‑quality
Tiny pixels, big clues for Chinese AI Chinese characters pack meaning and sound into their shapes. This study asks: can AI learn better by seeing characters instead of just looking up IDs? * Using tiny 8x8 grayscale images of single characters, the model hits 39.2% accuracy—on par with 39.
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Smaller, smarter music AI Foundation models are powerful—but huge and pricey to run. This study shows how to keep music understanding strong while trimming model size and compute. * What’s new: A lean architecture that swaps heavy self‑attention for Branchformer + SummaryMixing, plus a simple random quantizer to learn
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New AI that keeps creativity: DPWriter LLMs trained with reinforcement learning often play it safe, shrinking the variety of their stories. DPWriter flips the script by planning before writing. It breaks generation into semi-structured steps, then uses Diverse Planning Branching to explore multiple, intentionally different routes. A group-aware
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Large language models can ace math benchmarks yet still stumble on simple number sense because they treat numbers like ordinary words. This work fixes that by giving models a value-aware way to read numbers. How it works: whenever a number appears, the input is augmented with a tiny prefix
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What if AI could think with pictures? Omni-R1 is a new multimodal AI that doesn’t just “talk through” problems—it draws its intermediate steps. Instead of relying on one fixed reasoning style, it unifies many skills (like zooming into regions, pointing to objects, or marking paths) by generating
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Big business decisions often rely on complex optimization models, but building them is slow and manual. Meet LEAN-LLM-OPT, a lightweight, multi-agent AI that auto-formulates large-scale optimization models from a plain-English problem description and datasets. How it works: two planner agents design a step-by-