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باز کردنTool augmented LLM agents discover relevant APIs from large registries before planning execution, yet finite context windows prohibit injecting all tool definitions into a single prompt. The dominant approach, which we term Retrieve then Plan (RtP), embeds the user query and retrieves similar tools via approximate…
Multimodal agents commonly generate freeform reasoning before each action. For small models, limited model capacity can result in lengthy reasoning that provides little useful guidance for action generation while incurring substantial inference cost. To address this challenge, we introduce Selection based Structured…
Explore local and distributed inference for frontier LLMs like Muse Spark Open using MLX and RDMA over Thunderbolt 5 on Mac Studio with M5 Ultra
Discrete diffusion models have emerged as strong alternatives to autoregressive language models, with recent work initializing and finetuning a base unimodal model for bi modal generation. Diverging from previous approaches, we introduce the first tri modal Masked Diffusion Models (MDM) pretrained from scratch on…
Code switching (CS), the alternation between languages within an utterance, is common in multilingual speech but underrepresented in pretraining corpora. Increasing CS coverage requires finding relevant utterances in large speech collections so that they can be included in training data. One way to find such…
For most languages of the world, language model pre training operates in a data constrained regime where models must repeat their training data many times, degrading generalization. Two remedies exist: aggressive hyperparameter tuning such as high weight decay, and mixing in data from a high resource auxiliary…
Long agent traces composed of chains of thought and tool calls accumulate stale content that anchor subsequent generations, and eventually outgrow the context window. Existing scaffolds mitigate it with fixed interval compaction triggered at a token threshold. Such triggers pay no heed to trajectory structure,…
The increasing demand for GPU accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost, limited elasticity, and long wait times under resource constraints. At the same time, modern Apple Silicon–based macOS devices are equipped with powerful GPUs that…
Large Language Models (LLMs) are considered to be a more efficient and cost effective alternative to human judgment for Machine Translation (MT) evaluation. With MT evaluation spanning a large number of language pairs, domains and levels of annotation granularity, LLMs must be thoroughly evaluated across these…
Diffusion based models decompose sampling into many small Gaussian denoising steps, an assumption that breaks down when generation is compressed to a few coarse transitions. Existing few step methods address this through distillation, consistency training, or adversarial objectives, but sacrifice the likelihood…
Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing curriculum and data selection strategies often allocate training at the environment level or prioritize local reward based signals, without explicitly considering…
Designed artifacts are ontological, shaping, and at times limiting, what becomes possible or imaginable. One path toward mitigating such foreclosures is giving people power over how systems are designed and built. Despite decades of scholarship around systems that enable such authorship, these systems are often…
