Decision Model Factory: Building Specialized Sovereign Decision Models

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1. Proche Pas pertinent

   Foundation Models for Decision Making: Problems, Methods, and Opportunities

   Sherry Yang, Ofir Nachum, Yilun Du et al. · 2023 · Artificial Intelligence · preprint, non relu par les pairs · cité 262 fois · Résumé IA disponible

   Résumé scientifique

   Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. When such models are deployed in real world environments, they inevitably interface with other entities and agents. For example, language models are often used to interact with human beings through dialogue, and visual perception models are used to autonomously navigate neighborhood streets. In response to these developments, new paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. These paradigms leverage the existence of ever-larger datasets curated for multimodal, multitask, and gene…

2. Apparenté Cité par cet article Pas pertinent

   LLM-as-Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them

   Yin-Heng Li, Justin Wagle · 2026 · cité 19 fois · Pas encore résumé

   Résumé scientifique

   Jev-style decision models have gained surging traction by making decision-making dramatically faster and more efficient, returning categorical probability distributions over predefined options without generating free-form text. However, because Jev's architecture and training methodologies remain proprietary, how to replicate these capabilities effectively remains an open question. In this work, we investigate the extent to which general-purpose LLMs already possess this capability out of the box, and when fine-tuning is actually necessary. We present LLM-as-Jev, an architecture-preserving framework offering both a training-free inference recipe and a fine-tuning objective with KL-divergence…

3. Apparenté* Pas pertinent

   Large Language Model as Autonomous Decision Maker

   Yining Ye, Xin Cong, Yujia Qin et al. · 2023 · arXiv.org · preprint, non relu par les pairs · cité 11 fois · Pas encore résumé
4. Apparenté Pas pertinent

   Unified Deployment-Aware Evaluation of Open Reasoning Language Models

   M. Manik, Wang Ge · 2026 · cité 3 fois · Pas encore résumé

   Résumé scientifique

   Open reasoning language models are often compared under mixed sample sizes, partially standardized prompts, and accuracy-centered summaries, which makes practical model selection difficult to interpret. We present a unified evaluation of seven open reasoning language model configurations across four benchmarks: ARC-Challenge, GSM8K, MATH levels 1 to 3, and TruthfulQA MC1. We test zero-shot, chain-of-thought (CoT), and few-shot CoT prompting on the same 238-example subset for every model--dataset--strategy condition, yielding a complete 7 x 4 x 3 design with 84 conditions and 19,992 evaluated examples. Beyond accuracy, we report Wilson confidence intervals, latency, peak video random access m…

5. Apparenté Cité par cet article Pas pertinent

   Releasing the Inner Jev: A Simple Recipe for Open Decision Models

   Jonathan Geuter, Andrew Cheng, David Alvarez-Melis · 2026 · cité 6 fois · Pas encore résumé

   Résumé scientifique

   LLM-based decision-making systems, also called System One models, have recently gained popularity, most notably through Jev (Almeida, 2026). Given a typed input state, a question, and a set of options, they return a probability for each option in a single forward pass, without text generation. We show that existing open instruction-tuned language models can achieve comparable performance \textit{without any additional training}, by reading out answers from letters (A, B, C, etc.) in the model's output logits at the last token position. Furthermore, we show how a simple supervised fine-tuning recipe can improve their performance further. We evaluate four open instruction-tuned language models…

6. Apparenté Pas pertinent

   DeLLMa: Decision Making Under Uncertainty with Large Language Models

   Ollie Liu, Deqing Fu, Dani Yogatama et al. · 2024 · Artificial Intelligence · cité 33 fois · Résumé IA disponible

   Résumé scientifique

   The potential of large language models (LLMs) as decision support tools is increasingly being explored in fields such as business, engineering, and medicine, which often face challenging tasks of decision-making under uncertainty. In this paper, we show that directly prompting LLMs on these types of decision-making problems can yield poor results, especially as the problem complexity increases. To aid in these tasks, we propose DeLLMa (Decision-making Large Language Model assistant), a framework designed to enhance decision-making accuracy in uncertain environments. DeLLMa involves a multi-step reasoning procedure that integrates recent best practices in scaling inference-time reasoning, dra…

7. Apparenté Pas pertinent

   Demand-Driven Context: A Methodology for Building Enterprise Knowledge Bases Through Agent Failure

   Raj Navakoti, Saideep Navakoti · 2026 · Artificial Intelligence · preprint, non relu par les pairs · Résumé IA disponible

   Résumé scientifique

   Large language model agents demonstrate expert-level reasoning, yet consistently fail on enterprise-specific tasks due to missing domain knowledge -- terminology, operational procedures, system interdependencies, and institutional decisions that exist largely as tribal knowledge. Current approaches fall into two categories: top-down knowledge engineering, which documents domain knowledge before agents use it, and bottom-up automation, where agents learn from task experience. Both have fundamental limitations: top-down efforts produce bloated, untested knowledge bases; bottom-up approaches cannot acquire knowledge that exists only in human heads. We present Demand-Driven Context (DDC), a prob…

8. Apparenté Pas pertinent

   Sakana Fugu Technical Report

   Yujin Tang, Edoardo Cetin, Jinglue Xu et al. · 2026 · Machine Learning · preprint, non relu par les pairs · cité 4 fois · Résumé IA disponible

   Résumé scientifique

   The capabilities of frontier Large Language Models (LLMs) continue to advance, with different providers increasingly specializing in distinct domains. This raises a natural next objective: how to combine the individual specializations of various LLMs into a collectively intelligent system. To this end, we report the development of Sakana Fugu, a family of orchestrator models that harness and amplify the capabilities of an LLM agent team. Fugu models are themselves language models trained to understand user queries and dynamically devise agentic scaffolds to solve them. Through these adaptive scaffolds, Fugu accesses performance beyond any individual LLM agent, achieving state-of-the-art resu…

Les travaux antérieurs sur lesquels il s’appuie

1. Apparenté Pas pertinent

   Language Models are Few-Shot Learners

   Tom B. Brown, Benjamin Mann, Nick Ryder et al. · 2020 · Neural Information Processing Systems · cité 66 023 fois · Résumé IA disponible

   Résumé scientifique

   Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Spec…

2. Apparenté Pas pertinent

   PHOTON: Hierarchical Autoregressive Modeling for Lightspeed and Memory-Efficient Language Generation

   Yuma Ichikawa, N. Takagi, Takumi Nakagawa et al. · 2025 · Annual Meeting of the Association for Computational Linguistics · cité 1 fois · Pas encore résumé

   Résumé scientifique

   Transformers operate as horizontal token-by-token scanners; at each generation step, attending to an ever-growing sequence of token-level states. This access pattern increases prefill latency and makes long-context decoding more memory-bound, as KV-cache reads and writes dominate inference time over arithmetic operations. We propose Parallel Hierarchical Operation for TOp-down Networks (PHOTON), a hierarchical autoregressive model that replaces horizontal scanning with vertical, multi-resolution context scanning. PHOTON maintains a hierarchy of latent streams: a bottom-up encoder compresses tokens into low-rate contextual states, while lightweight top-down decoders reconstruct fine-grained t…

3. Apparenté Pas pertinent

   Distilling the Knowledge in a Neural Network

   Geoffrey E. Hinton, O. Vinyals, Jeffrey Dean · 2015 · arXiv.org · preprint, non relu par les pairs · cité 27 520 fois · Résumé IA disponible

   Résumé scientifique

   A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we …

4. Apparenté Pas pertinent

   Lost in the Middle: How Language Models Use Long Contexts

   Nelson F. Liu, Kevin Lin, John Hewitt et al. · 2023 · Transactions of the Association for Computational Linguistics · cité 5 819 fois · Résumé IA disponible

   Résumé scientifique

   While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context. We analyze the performance of language models on two tasks that require identifying relevant information in their input contexts: multi-document question answering and key-value retrieval. We find that performance can degrade significantly when changing the position of relevant information, indicating that current language models do not robustly make use of information in long input contexts. In particular, we observe that performance is often highest when relevant information occurs at the beginning or end of the input context, and significantly de…

5. Apparenté Pas pertinent

   Document Ranking with a Pretrained Sequence-to-Sequence Model

   Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep et al. · 2020 · Findings · cité 938 fois · Pas encore résumé

   Résumé scientifique

   This work proposes the use of a pretrained sequence-to-sequence model for document ranking. Our approach is fundamentally different from a commonly adopted classification-based formulation based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-to-sequence model can be trained to generate relevance labels as “target tokens”, and how the underlying logits of these target tokens can be interpreted as relevance probabilities for ranking. Experimental results on the MS MARCO passage ranking task show that our ranking approach is superior to strong encoder-only models. On three other document retrieval test collections, we demonstrate a zero-shot transfer-b…