This work employs encoder-decoder networks built around long-short term memory (LSTM) cells in a distributed optimization algorithm that allows unprecedented reconstruction accuracy or the usage of different prediction-horizon lengths without the necessity to retrain.
Abstract
The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time step. To semantically reduce communication demands, this work employs encoder-decoder networks built around long-short term memory (LSTM) cells in a distributed optimization algorithm. Agents publish a reduced representation of a message and receivers reconstruct the original message upon reception. In tests with reduced communication using formations of mobile robots, trained networks retain satisfactory performance and work reliably under conditions overwhelming full communication. As the results show, the usage of LSTMs either allows unprecedented reconstruction accuracy or the usage of different prediction-horizon lengths without the necessity to retrain.
Structured Experience Compression (SEC), a lightweight method that distills each trajectory into a four-field unit encoding the task type, key decisions, failure reason, and repair action, is proposed, suggesting potential suitability for long-lived deployment settings.
Zhizhen Zhu, Ruochen Fang, Rong Hu· 2026 7th International Confe...· 2 citations
Deploying large language models in resource-constrained environments such as mobile devices requires effective compression. Pruning and quantization are largely used, yet applying pruning to a new model typically demands manual inspection of its architecture to identify layer names, MLP structure, and projection patterns, a process that is tedious, error-prone, and must be repeated for every new model design. We present ShrinkLLM, an end-to-end compression system that eliminates the manual inspection of model structure during pruning by delegating architectural analysis to an AI agent. Given a causal LLM, the agent automatically identifies MLP layer organization, expansion layers, gate projections for Gated Linear Unit architectures, and down-projection layers, structural information that is then passed directly to a compression engine to perform neuron pruning, quantization, or a combination of both. We demonstrate the system on llama-3.1-8b-instruct, Qwen2.5-3b-instruct and Gemma-2-2b-it showcasing how the agent correctly resolves architecturespecific structural details and drives compression across all MLP layers automatically.
Gyanko Issah Yussif, Tasnia Sultana, Mohamed Ali et al.· International Conference on...· 0 citations
Large Language Models (LLMs) trained using Chain-of-Thought (CoT) supervision have achieved state-of-the-art performance on complex reasoning tasks. However, the generation of long reasoning chains introduces substantial computational overhead during inference, limiting their deployment in low-latency and resource-constrained environments. This paper proposes AdaptiReason, a novel framework that dynamically compresses intermediate reasoning steps based on task difficulty and model confidence without requiring retraining of the underlying base model. AdaptiReason employs a lightweight difficulty estimator to determine the appropriate reasoning depth for each input, followed by a learned token-pruning policy that eliminates redundant or low-information reasoning steps. Experimental evaluation on the MATH, GSM8K, and ARC-Challenge benchmarks demonstrates that AdaptiReason reduces the average number of generated tokens by 3.7× while preserving 98.2% of the baseline reasoning accuracy. Furthermore, the proposed framework is model-agnostic and can be seamlessly integrated with instruction-tuned LLMs without requiring access to model parameters, relying solely on output logits for adaptive reasoning compression. The results demonstrate that AdaptiReason significantly improves inference efficiency while maintaining high reasoning performance, making it suitable for real-time and resource-constrained LLM applications.
V. A, Mithaguru, Amrita Kundu et al.· 2026 4th International Confe...· 0 citations
This study introduces CodeGrep, a 14B retrieval agent trained end-to-end with GRPO to issue multi-turn parallel grep, glob, and read tool calls and return candidate files to a frozen downstream coding agent, and applies the efficiency signal at the advantage layer rather than the reward layer to reduce KL drift and translates cleanly into downstream efficiency.
Wu-Ya Chen, Yihao Yang, Yang Cao et al.· 0 citations
This work studies value-controllable alignment through discrete condition vectors and proposes Verifiable-reward-Routed LoRA—a parameter-efficient mixture-of-experts LoRA framework enhanced with conditioned gating, which consistently out-performs prompt-based steering and multi-task PEFT baselines.
Jing Wang, Yaomin Wu, Yinglin Wang et al.· Annual Meeting of the Associ...· 0 citations
CoACT is built on next-action preservation (NAP), which requires a compressed observation to induce the same next action as the raw observation, and uses an action-preservation reward based on NAP to filter out candidates that would change the agent's next action.
Hao Chen, Yuancheng Zhu, Yitong Zhang et al.· 1 citation
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
