2026-09-28

Scientific discovery begins where known problems end. There, AI systems must engage in exploration: framing hypotheses, designing experiments, and iterating on the results. However, evaluating this ability is difficult: (1) how to verify whether a genuinely new hypothesis holds, and (2) how to determine whether a system has discovered it through exploration or merely recalled related knowledge from pre-training data. To this end, we introduce ExplorationBench, which turns the wicked problem of e

Supersonic Labs has released Julia 1, a 144.3M-parameter decision model built on mmBERT-small. It takes context, a question, and 2 to 20 options, then returns one choice with probabilities. The model runs on a CPU and ships under Apache 2.0. It beat Jev reference values on 3 of 4 pilots but trailed on the 72-label Banking77 test. The post Supersonic Labs Releases Julia 1: A 144.3M-Parameter Open Decision Model That Runs on a CPU appeared first on MarkTechPost .

A comprehensive coding tutorial on Google Research's Massive Sound Embedding Benchmark (MSEB), demonstrating how to implement custom sound encoders, drive classification, clustering, retrieval, and segmentation evaluators, and analyze multi-task benchmark performance. The post A Coding Guide to Google Research’s MSEB: Writing Sound Encoders to the Benchmark Contract and Scoring Them Across Classification, Clustering, Retrieval and Segmentation appeared first on MarkTechPost .

Sarvam AI's Saaras V4 is a speech-to-text model covering all 22 Indian languages plus global English. It pairs an audio encoder with a 3B hybrid state-space decoder. It adds keyterm prompting for up to 50 terms, 5 output modes from 1 model, and streaming with first-token latency under 150 ms. It is available today through Sarvam's API at ₹30 per hour. The post Sarvam AI Releases Saaras V4: A Speech-to-Text Model for All 22 Indian Languages and Global English appeared first on MarkTechPost .

Modern embodied agents achieve impressive success rates, yet their actual instruction-following ability is far weaker than these numbers suggest. We trace this illusion to a structural property we term low scene entropy: when a visual scene admits only one valid task, language becomes redundant and a policy can score highly while barely using it. We introduce RoboFollow, a diagnostic benchmark with three principles: (1) High Scene Entropy: each training scene supports multiple kinematically dist

Robotic bin packing requires long-horizon sequential decision-making, as each object placement affects the available space for subsequent packing. Existing methods primarily rely on hand-crafted geometric heuristics that optimize predefined objectives or reinforcement learning policies learned through trial and error over predefined training configurations. Despite recent advances in multimodal large language models (MLLMs) for this task, their potential for closed-loop sequential decisions acro

I was reading a paper that surveyed the field of neural architecture search , where it said within 5 years, around 3000+ new models were proposed. The amount of compute and resources spent on this is absolutely astronomical. However, the transformer was notably not one of the models that was found through NAS and then the field of NAS just quietly went away afterwards. In my mind this really raises question if any research in NAS should be continued. Then I recently found a talk by Nicholas Carl

Early this year when I was first looking at building up my inference capability you could get the 16GB Tesla P100s for between $60 and $80. Asked claude about it, told me absolutely not worth it. No tensor cores, bad int4/int8, no BF16, not worth it. Needs special power accommodations, Above 4G decoding option in the bios (it made it out like it was some rare option), and a semi-exotic cooling solution. Optimized the shit out of my RX6600XT in llama.cpp as a result. Got pretty far. Decided to sa