Devang Acharya

Yilin Guan, Qingfeng Lan, Sun Fei et al.

Despite their remarkable success in complex tasks propelling widespread adoption, large language-model-based agents still face critical deployment challenges due to prohibitive latency and inference costs. While recent work has explored various methods to accelerate inference, existing approaches suffer from significant limitations: they either fail to preserve performance fidelity, require extensive offline training of router modules, or incur excessive operational costs. Moreover, they provide minimal user control over the tradeoff between acceleration and other performance metrics. To address these gaps, we introduce Dynamic Speculative Planning (DSP), an asynchronous online reinforcement learning framework that provides lossless acceleration with substantially reduced costs without requiring additional pre-deployment preparation. DSP explicitly optimizes a joint objective balancing end-to-end latency against dollar cost, allowing practitioners to adjust a single parameter that steers the system toward faster responses, cheaper operation, or any point along this continuum. Experiments on two standard agent benchmarks demonstrate that DSP achieves comparable efficiency to the fastest lossless acceleration method while reducing total cost by 30% and unnecessary cost up to 60%. Our code and data are available through https://github.com/guanyilin428/Dynamic-Speculative-Planning.

Devang Acharya, Mohammad Hammoud

Compact pretrained bidirectional encoders remain the backbone of industrial NLP under tight compute and memory budgets. Their effectiveness stems from self-attention's ability to deliver high-quality bidirectional contextualization with sequence-level parallelism, as popularized by BERT-style architectures. Recently, Avey was introduced as an autoregressive, attention-free alternative that naturally admits an encoder-only adaptation. In this paper, we reformulate Avey for the encoder-only paradigm and propose several innovations to its architecture, including decoupled static and dynamic parameterizations, stability-oriented normalization, and neural compression. Results show that this reformulated architecture compares favorably to four widely used Transformer-based encoders, consistently outperforming them on standard token-classification and information-retrieval benchmarks while scaling more efficiently to long contexts.

Mohammad Hammoud, Devang Acharya

The Transformer has become the de facto standard for modern language models owing to its parallelizable training and effective autoregressive decoding. However, its fixed context window and the quadratic time and memory costs of its self-attention mechanism remain central bottlenecks. These constraints have revived interest in recurrent architectures that scale linearly with sequence length, but at the cost of reduced parallelism. In this paper, we introduce Avey, a new foundational architecture that breaks away from both attention and recurrence. Avey pairs a ranker with an autoregressive neural processor to select and contextualize only the most relevant tokens for any given token. Specifically, it decouples sequence length from context width, thus enabling effective and efficient processing of arbitrarily long sequences. Results show that Avey compares favorably to the Transformer across a variety of standard short-range NLP benchmarks, while significantly outperforming it on tasks requiring long-range dependency modeling.