AI / ML

The EXAONE Finance model addresses limitations in general-domain time series foundation models that struggle with financial data's specific dynamics. Unlike prior work relying on self-attention backbones with quadratic computational costs, this new model is designed for long, many-channel, and intermittently observed panels. It moves beyond the assumption of fully observed inputs to better capture the unique characteristics of financial markets.

• Moves beyond self-attention backbones to avoid quadratic scaling with sequence length and variate count.
• Designed for intermittently observed data, a common reality in financial time series.
• Trained on corpora that better reflect financial market dynamics versus general domains.
• Targets long, many-channel panels often found in institutional finance workflows.

A recent audit of 600 HybridQA questions reveals that identity mismatches between selected objects and retrieved passages break grounded language-model pipelines. While exact matching works perfectly for resolved records, body-only BM25 retrieval fails to return the object 26.6% of the time at cutoff five. In contrast, hybrid retrieval with reranking reduces this omission rate to just 1.0%, highlighting the critical need for robust identity verification in the retrieval stage.

• Identity mismatches between selectors and retrievers silently break pipeline accuracy.
• Body-only BM25 omits key objects in 26.6% of cases at cutoff five.
• Hybrid retrieval with reranking reduces omission to 1.0%, significantly improving recall.
• Benchmark recall checks dataset-linked objects, which may differ from selected identities.
• Audit identity handoffs to ensure the selected object actually reaches the reader.

MedProb challenges the assumption that medical VQA requires large models or fine-tuning by using a lightweight probing framework on frozen vision-language representations. It predicts multiple-choice answers directly from internal embeddings, bypassing text generation entirely. The method recovers more answer-relevant signal than standard prompting and outperforms specialized medical VLMs and agentic systems across several benchmarks. This suggests smaller models contain significantly more recoverable medical knowledge than generation-based metrics typically reveal.

• No fine-tuning or generation needed; answers derived from frozen VLM embeddings.
• Outperforms medical VLMs and agentic systems on PATH-VQA, SLAKE, and VQA-RAD.
• Smaller VLMs hold more recoverable medical signal than generation metrics show.
• Probing reveals capabilities that standard prompting misses in general models.

SageMaker AI now allows G6e instances for offline inference via Batch Transform. These instances feature up to eight NVIDIA L40S GPUs and third-generation AMD EPYC processors. This update targets GPU-intensive workloads like large language models and diffusion models.

• G6e instances support up to eight NVIDIA L40S GPUs with 48 GB memory each
• Optimized for GPU-intensive offline inference such as LLMs and diffusion models
• Batch Transform processes S3 datasets without requiring persistent endpoints
• Configure via AWS SDKs, CLI, or CreateTransformJob API