Millimeter-scale particles of nuclear-reactor fuel are encased in four layers of different materials that act as a “miniature containment system”.
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June 8, 20267 min readFour approaches can dramatically improve the performance and trustworthiness of AI agents in operational environments. -
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May 27, 20264 min readMachine learning
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Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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ICML 2026 Workshop on Generative and Agentic AI for Biology2026Protein design requires extrapolating beyond training data to achieve higher fitness. State-of-the-art methods typically fine-tune billion-parameter language models end-to-end, often combined with external scorers, data distillation, and multiple rounds of iterative refinement. We introduce a residual latent adapter, a 5M parameter MLP inserted between the encoder and decoder of a frozen ProtT5-3B model
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2026As efficient alternatives to softmax Attention, linear statespace models (SSMs) achieve constant memory and linear compute, but maintain only a lossy, fading summary of the past, often leading to inferior performance in recall oriented settings. We propose Gated KalmaNet (GKA), a layer that reduces this gap by accounting for the full past when predicting the next token, while maintaining SSM-style efficiency
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2026Test-time scaling via sequential revision has emerged as a powerful paradigm for enhancing Large Language Model (LLM) reasoning. However, standard post-training methods primarily optimize single-shot objectives, creating a fundamental misalignment with multi-step inference dynamics. While recent work treats this as multi-turn reinforcement learning (RL), conventional approaches optimize over the multi-step
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KDD 20262026Deploying LLM-based analytics agents in enterprise settings requires evaluation frameworks that can reliably detect failures across complex, multi-tool workflows. We present a three-phase comparative study of three evaluation frameworks (Strands Evals, PromptFoo, and Agenta) applied to two analytics agents in a controlled research setting using frozen execution traces. Phase 1 quantifies evaluation harness
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ECML-PKDD 20262026Next-basket recommendation (NBR) in online grocery must capture both habitual repeat purchases and explore behavior. We propose BasketFormer, a Transformer encoder trained with a contrastive masked language modeling (C-MLM) objective that unifies three innovations: (1) an InfoNCE-based MLM loss replacing the full-vocabulary softmax with in-batch contrastive scoring; (2) a bit-level temporal encoding that
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